From cbfd966555540272734e67bf9968d421c06e8da8 Mon Sep 17 00:00:00 2001 From: Hao Date: Thu, 20 May 2021 13:20:48 -0700 Subject: [PATCH] init --- .github/image/detections.png | Bin 0 -> 364422 bytes .gitignore | 141 + LICENSE | 21 + README.md | 86 + analysis/figures.R | 252 ++ analysis/results.Rmd | 280 ++ data/image/.keepme | 0 data/info.yaml | 22 + data/meta.csv | 3156 +++++++++++++++++ detection/data/__init__.py | 13 + detection/data/base.py | 77 + detection/data/constants.py | 1 + detection/data/detection.py | 79 + detection/data/info.py | 178 + detection/data/util.py | 6 + detection/data/version.py | 99 + detection/eval/__init__.py | 2 + detection/eval/detection/__init__.py | 4 + detection/eval/detection/bbox.py | 232 ++ detection/eval/detection/evaluator.py | 359 ++ detection/eval/detection/utils.py | 126 + detection/eval/evaluator.py | 158 + detection/eval/loss.py | 16 + detection/lightning/__init__.py | 13 + detection/lightning/detection.py | 175 + detection/lightning/logger.py | 61 + detection/lightning/util.py | 34 + detection/main.py | 115 + detection/models/__init__.py | 14 + detection/models/classification.py | 238 ++ detection/models/detection/__init__.py | 3 + detection/models/detection/detectron.py | 98 + .../models/detection/efficientdet/__init__.py | 103 + .../models/detection/efficientdet/backbone.py | 119 + .../models/detection/efficientdet/config.py | 26 + .../efficientdet/efficientnet/__init__.py | 10 + .../efficientdet/efficientnet/model.py | 237 ++ .../efficientdet/efficientnet/utils.py | 313 ++ .../efficientdet/efficientnet/utils_extra.py | 86 + .../models/detection/efficientdet/loss.py | 148 + .../models/detection/efficientdet/model.py | 465 +++ .../models/detection/efficientdet/process.py | 314 ++ .../efficientdet/sync_batchnorm/__init__.py | 13 + .../efficientdet/sync_batchnorm/batchnorm.py | 394 ++ .../sync_batchnorm/batchnorm_reimpl.py | 74 + .../efficientdet/sync_batchnorm/comm.py | 137 + .../efficientdet/sync_batchnorm/replicate.py | 94 + .../efficientdet/sync_batchnorm/unittest.py | 29 + .../models/detection/efficientdet/utils.py | 139 + detection/models/detection/yolo/__init__.py | 96 + detection/models/detection/yolo/backbone.py | 408 +++ detection/models/detection/yolo/constants.py | 7 + detection/models/detection/yolo/utils.py | 292 ++ .../models/detection/yolo/yolov3-tiny.cfg | 206 ++ detection/models/detection/yolo/yolov3.cfg | 788 ++++ detection/plots/.keepme | 0 detection/sandbox/.keepme | 0 detection/util/.keepme | 0 detection/util/__init__.py | 2 + detection/util/constants.py | 25 + detection/util/nni.py | 45 + detection/util/sbatch_template.sh | 31 + detection/util/util.py | 70 + main.py | 11 + plot/__init__.py | 11 + plot/coverage.py | 42 + plot/precision_recall_curve.py | 29 + plot/spatial_distribution.py | 125 + requirements.txt | 17 + streetview/__init__.py | 7 + streetview/coverage.py | 75 + streetview/download.py | 130 + streetview/evaluate.py | 71 + streetview/plot.py | 39 + streetview/road.py | 28 + streetview/sample.py | 173 + streetview/util.py | 8 + streetview/zoning.py | 78 + util/constants.py | 22 + 79 files changed, 11596 insertions(+) create mode 100644 .github/image/detections.png create mode 100644 .gitignore create mode 100644 LICENSE create mode 100644 README.md create mode 100644 analysis/figures.R create mode 100644 analysis/results.Rmd create mode 100644 data/image/.keepme create mode 100644 data/info.yaml create mode 100644 data/meta.csv create mode 100644 detection/data/__init__.py create mode 100644 detection/data/base.py create mode 100644 detection/data/constants.py create mode 100644 detection/data/detection.py create mode 100644 detection/data/info.py create mode 100644 detection/data/util.py create mode 100644 detection/data/version.py create mode 100644 detection/eval/__init__.py create mode 100644 detection/eval/detection/__init__.py create mode 100644 detection/eval/detection/bbox.py create mode 100644 detection/eval/detection/evaluator.py create mode 100644 detection/eval/detection/utils.py create mode 100644 detection/eval/evaluator.py create mode 100644 detection/eval/loss.py create mode 100644 detection/lightning/__init__.py create mode 100644 detection/lightning/detection.py create mode 100644 detection/lightning/logger.py create mode 100644 detection/lightning/util.py create mode 100644 detection/main.py create mode 100644 detection/models/__init__.py create mode 100644 detection/models/classification.py create mode 100644 detection/models/detection/__init__.py create mode 100644 detection/models/detection/detectron.py create mode 100644 detection/models/detection/efficientdet/__init__.py create mode 100644 detection/models/detection/efficientdet/backbone.py create mode 100644 detection/models/detection/efficientdet/config.py create mode 100644 detection/models/detection/efficientdet/efficientnet/__init__.py create mode 100644 detection/models/detection/efficientdet/efficientnet/model.py create mode 100644 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zYBsJ9fj=ibN9PU!HEgRXqy%dIPVBK%`;HkvWjyY$P}8iW&-Y-WMEQ>z+N0!WISqHn zg0yQFjwbL1Vx{tstD_tCe!yD)Y0qiEgE6=6>N-CMDHBY`afU;YKKIfvUyWo8n`y53 z7`d}rwj0JCHRj!K%N&zp{zoJ&TWUQ^nRIGlAG25V*=n_de2x0eahh48TanP-!ZU}%OIP+{BP_h^aOC9{su+H+ z*7*}=u12d+BT4AtSwH>$y^GtboEAiIo|!sMs~B+;Hm9Y*)m^ZZAP+xNF8)%JOI`r< zh|2u^Uicz+Z&H2q=C_zHArfVy)u1f7rc-yAn<2420z?6OnVlLPLjsr3adVf4?+2m|XCCt%&> z12ZX}rTHZpxZszCRxt{#h|HKMj(k;oFPU+rIm2-_Qqy|knptrX$ZzL=aqB;kI}pKm zW*%~3e$Tq7?5b#o+wbBU?G#mVe$iQIMRXKLaqJu8wbhL)q2Z%3;+Q^nMu|!dD5d7xVD8H$PmU|L4q)PYvwQx|nd|kb8w7H^Q5gP?#MJ<;v@z}; z!x*;esLb>()V@&r*?(Dog8M% + left_join(city_data) %>% + transmute( + city, + type, + period, + road_network_length_km , + m_per_pano, + pop_pano = 2 * road_network_length_km * 1000 / m_per_pano, # N + n_pano, + n_detection, + # detection rate (unadjusted detections per pano) + p_hat = n_detection / n_pano, + # infinite population sd: + p_hat_sd = sqrt(p_hat * (1 - p_hat) / n_pano), + # for finite population sd: + # p_hat_sd = sqrt((p_hat * (1 - p_hat) / n_pano) * ((pop_pano - n_pano) / (pop_pano - 1))), + # detection rate (detections per km counting both sides of the road per km) + est_detections_per_km = p_hat * (1000 / m_per_pano) * (2 / recall), + se_detections_per_km = p_hat_sd * (1000 / m_per_pano) * (2 / recall), + # detection count + est_detections = est_detections_per_km * road_network_length_km, + se_detections = se_detections_per_km * road_network_length_km + ) %>% + ungroup() %>% + select(-p_hat, -p_hat_sd) +} + +plot_camera_density <- function(df, legend = TRUE) { + if (legend) { + legend_position = "bottom" + } else { + legend_position = "none" + } + + df %>% + ggplot(aes(x = city, y = est_detections_per_km, fill = type)) + + geom_col() + + geom_linerange(aes( + ymin = est_detections_per_km - 1.96*se_detections_per_km, + ymax = est_detections_per_km + 1.96*se_detections_per_km + )) + + scale_x_discrete(name = "") + + scale_y_continuous( + name = "Estimated cameras per km", + position = "right", + expand = expansion(mult = c(0, 0.1)) + ) + + scale_fill_discrete(name = "") + + coord_flip() + + theme( + panel.border = element_blank(), + axis.line = element_line(size = 1, color = "black"), + axis.title.x = element_text(family = "Helvetica", color = "black"), + axis.text = element_text(family = "Helvetica", color = "black"), + legend.position = legend_position, + panel.grid.major.x = element_blank(), + panel.grid.major.y = element_blank(), + panel.grid.minor = element_blank() + ) +} + +load_road_network <- function(city_name){ + stopifnot(city_name %in% city_data$city) + + path <- here::here("data", "road_network", city_name, "edges.shp") + read_sf(path) +} + +get_max_points <- function(df){ + df %>% + select(geometry) %>% + st_cast("POINT") %>% + st_coordinates() %>% + as_tibble() %>% + summarize( + x_max = max(X), + x_min = min(X), + y_max = max(Y), + y_min = min(Y) + ) +} + +generate_sampled_point_map <- function(df, city_name){ + # load road network + road_network <- load_road_network(city_name) + + # get crs + road_network_crs <- st_crs(road_network) %>% + as.integer() + road_network_crs <- road_network_crs[1] + + # find bounding coordinates of road network + bbox <- st_bbox(road_network) + + # plot points + road_network %>% + ggplot() + + geom_sf(fill = "white", color = "gray", alpha = 0.6) + + geom_sf( + data = df %>% + filter(city == city_name) %>% + st_as_sf(coords = c("lon", "lat"), + # ensure same crs as road network + crs = road_network_crs, + agr = "constant"), + color = "blue", size = 0.2, + shape = 16, alpha = 1 + ) + + scale_x_continuous(expand = expansion(mult = c(0.02, 0.02))) + + scale_y_continuous(expand = expansion(mult = c(0, 0.02))) + + coord_sf(xlim = c(bbox$xmin, bbox$xmax), ylim = c(bbox$ymin, bbox$ymax)) + + theme( + axis.text = element_blank(), + axis.ticks = element_blank(), + panel.grid = element_blank(), + panel.border = element_blank(), + legend.position = "bottom", + legend.text = element_text(size = 20) + ) +} + +generate_detected_point_map <- function(df, city_name){ + # load road network + road_network <- load_road_network(city_name) + + # get crs + road_network_crs <- st_crs(road_network) %>% + as.integer() + road_network_crs <- road_network_crs[1] + + # find bounding coordinates of road network + bbox <- st_bbox(road_network) + + # plot points + road_network %>% + ggplot() + + geom_sf(fill = "white", color = "gray", alpha = 0.6) + + geom_sf( + data = df %>% + filter( + city == city_name, + camera_count > 0 + ) %>% + st_as_sf(coords = c("lon", "lat"), + # ensure same crs as road network + crs = road_network_crs, + agr = "constant"), + color = "red", size = 0.5, + shape = 16, alpha = 1 + ) + + scale_x_continuous(expand = expansion(mult = c(0.02, 0.02))) + + scale_y_continuous(expand = expansion(mult = c(0, 0.02))) + + coord_sf(xlim = c(bbox$xmin, bbox$xmax), ylim = c(bbox$ymin, bbox$ymax)) + + theme( + axis.text = element_blank(), + axis.ticks = element_blank(), + panel.grid = element_blank(), + panel.border = element_blank(), + legend.position = "bottom", + legend.text = element_text(size = 20) + ) +} + +annotate_points_with_census <- function(df, city_name, census_var){ + stopifnot(census_var %in% c("income", "race")) + + # define state, county using `city_data` + state <- city_data %>% + filter(city == city_name) %>% + pull(state) + county <- city_data %>% + filter(city == city_name) %>% + pull(county) + + + # specify variables + summary_vars <- "B03002_001" # total population + if (census_var == "income") { + vars <- c(Income = "B19113_001") + } else if (census_var == "race") { + vars <- c(White = "B03002_003") #non-Hispanic white + } + + # get census data + if (city_name == "New York") { + state = "NY" + counties <- c("New York County", "Kings County", "Queens County", + "Bronx County", "Richmond County") + + new_york <- purrr::map( + counties, + ~ get_acs( + state = state, + county = .x, + geography = "block group", + variables = vars, + summary_var = summary_vars, + geometry = TRUE + ) + ) + + df_census_block_group <- bind_rows(new_york) + + } else{ + + if (city_name == "Washington") { + county <- NULL + } + + df_census_block_group <- get_acs( + state = state, + county = county, + geography = "block group", + variables = vars, + summary_var = summary_vars, + geometry = TRUE + ) + } + + + # add GIS features + df <- df %>% + filter(city == city_name) %>% + # ensure same coords as tidycensus + st_as_sf( + coords = c("lon", "lat"), + crs = 4269, + agr = "constant" + ) + + # annotate points with census data + if (census_var == "income") { + df <- st_join( + df, + df_census_block_group %>% + select(GEOID, NAME, median_household_income = estimate, geometry) + ) + } else if (census_var == "race") { + df <- st_join( + df, + df_census_block_group %>% + transmute( + GEOID, NAME, + percentage_minority = (summary_est - estimate) / summary_est, geometry + ) + ) + } + + df +} diff --git a/analysis/results.Rmd b/analysis/results.Rmd new file mode 100644 index 0000000..4118e50 --- /dev/null +++ b/analysis/results.Rmd @@ -0,0 +1,280 @@ +--- +title: "results" +author: "Keniel Yao" +date: "4/26/2021" +output: html_document +--- + +```{r setup, include=FALSE} +knitr::opts_chunk$set(echo = TRUE) +``` + +```{r load-functions} +library(tidyverse) +library(sf) +library(glue) +library(tidycensus) +library(broom) + +source(here::here('analysis', 'figures.R')) + +theme_set(theme_bw(base_size = 14)) +``` + + +# Load data + +```{r data} +df_pre <- read_csv(here::here("data", "cameras_2011-2015.csv")) %>% + mutate(period = "2011-2015") +df_post <- read_csv(here::here("data", "cameras_2015-2021.csv")) %>% + mutate(period = "2015-2021") +city_data <- read_csv(here::here("data", "city_metadata.csv")) + +recall <- 0.63 +``` + +# Figures + +## Table 1: City metadata + +```{r metadata} +city_data %>% + arrange(desc(type), desc(road_network_length_km)) %>% + transmute( + City = case_when( + city == "New York" ~ "New York City", + city == "Washington" ~ "Washington, D.C.", + TRUE ~ city + ), + Population = formatC(round(population_census2010, -3), format = "d", big.mark=","), + `Area (sq. km)` = formatC(area_sqkm_census2010, format = "d", big.mark=","), + `Road length (km)` = formatC(road_network_length_km, format = "d", big.mark=",") + ) +``` + +## Figure 5: Spatial distribution of sampled points + +```{r sampled-points} +generate_sampled_point_map(df_post, "San Francisco") +generate_sampled_point_map(df_post, "Chicago") +generate_sampled_point_map(df_post, "New York") +``` + +## Table 3: Detection count, density and total camera estimates + +```{r main-table} +bind_rows( + df_pre, + df_post +) %>% + group_by(city, period) %>% + summarize( + n_pano = n(), + n_detection = sum(camera_count) + ) %>% + ungroup() %>% + estimate_detection_metrics(recall = recall) %>% + transmute( + rank = if_else(period == "2015-2021", est_detections_per_km, 0), + city = fct_reorder(city, - rank), + type, + period = if_else(period == "2015-2021", "2016-2020", period), + road_network_length_km = formatC(road_network_length_km, format = "d", big.mark=","), + m_per_pano = round(m_per_pano, 0), + n_detection, + est_detections_per_km = round(est_detections_per_km, 2), + se_detections_per_km = glue("({ round(se_detections_per_km, 2) })"), + est_detections = formatC(round(est_detections, -2), format = "d", big.mark=","), + se_detections = glue('({ formatC(round(se_detections, -2), format = "d", big.mark=",") })') + ) %>% + pivot_wider( + id_cols = c(city, type, road_network_length_km, m_per_pano), + names_from = period, + values_from = c(n_detection, est_detections_per_km, se_detections_per_km, est_detections, se_detections) + ) %>% + arrange(desc(type), city) %>% + mutate( + across(ends_with("2011-2015"), ~ str_replace_na(.x, "-")), + city = as.character(city) + ) %>% + select( + city, road_network_length_km, m_per_pano, + `n_detection_2011-2015`, `n_detection_2016-2020`, + `est_detections_per_km_2011-2015`, `se_detections_per_km_2011-2015`, + `est_detections_per_km_2016-2020`, `se_detections_per_km_2016-2020`, + `est_detections_2011-2015`, `se_detections_2011-2015`, + `est_detections_2016-2020`, `se_detections_2016-2020` + ) +``` + + +## Figure 9: Maps of detected points + +```{r detected-points} +generate_detected_point_map(df_post, "San Francisco") +generate_detected_point_map(df_post, "Chicago") +generate_detected_point_map(df_post, "New York") +``` + +## Figure 10: Pre-post estimated camera density + +```{r density-plot} +df_post %>% + group_by(city, period) %>% + summarize( + n_pano = n(), + n_detection = sum(camera_count) + ) %>% + ungroup() %>% + estimate_detection_metrics(recall = recall) %>% + mutate( + city = case_when( + city == "New York" ~ "New York City", + city == "Washington" ~ "Washington, D.C.", + T ~ city + ), + type = factor(type, c("Global", "US")), + city = fct_reorder(city, est_detections_per_km) + ) %>% + plot_camera_density(legend = FALSE) +``` + +## Figure 11: Zone identification rate + +```{r annotate-race-data} +us_cities <- city_data %>% + filter(type == "US") %>% + pull(city) + +df_post_w_race <- us_cities %>% + map_dfr(~ annotate_points_with_census(df_post, .x, "race")) %>% + st_drop_geometry() %>% + mutate( + city = case_when( + city == "New York" ~ "New York City", + city == "Washington" ~ "Washington D.C.", + TRUE ~ city + ), + city = factor( + city, + c("New York City", "San Francisco", "Boston", "Chicago", "Philadelphia", + "Washington D.C.", "Los Angeles", "Baltimore", "Seattle", "Milwaukee") + ), + zone_type = str_to_title(zone_type), + zone_type = factor( + zone_type, + c("Public", "Residential", "Industrial", "Commercial", "Mixed"), + exclude = NULL + ), + zone_type = fct_explicit_na(zone_type, na_level = "Unknown"), + camera_count = as.integer(camera_count) + ) +``` + + +```{r zone-all} +df_post_w_race %>% + filter(zone_type != "Unknown") %>% + group_by(zone_type) %>% + summarize( + total = n(), + total_identified = sum(camera_count, na.rm=T), + perc_detected = sum(total_identified) / total + ) %>% + mutate(se = sqrt(perc_detected * (1 - perc_detected) / total)) %>% + ungroup() %>% + mutate( + zone_type = fct_relevel( + zone_type, + c("Mixed", "Commercial", "Industrial", "Public", "Residential", "Unknown") + ), + zone_type = fct_rev(zone_type) + ) %>% + ggplot(aes(x = zone_type, y = perc_detected)) + + geom_point() + + geom_pointrange(aes( + ymin = perc_detected - 1.96 * se, + ymax = perc_detected + 1.96 * se + )) + + scale_x_discrete(name = "") + + scale_y_continuous( + name = "Identification rate", + position = "right", + labels = scales::percent_format(accuracy = 0.01), + expand = expansion(mult = c(0, 0.1)), + limits = c(0, NA) + ) + + coord_flip() + + theme( + panel.grid = element_blank(), + panel.border = element_blank(), + axis.text = element_text(family = "Helvetica", color = "black"), + axis.title.x = element_text(family = "Helvetica", color = "black"), + axis.line = element_line(size = 0.5, color = "black"), + axis.ticks = element_line(size = 0.5, color = "black") + ) + +``` + +## Figure 12: Race identification rate + +```{r race-all} +df_post_w_race %>% + ggplot(aes(x = percentage_minority, y = camera_count)) + + geom_smooth( + method = "lm", + formula = y ~ poly(x, degree = 2), + se = TRUE + ) + + scale_x_continuous( + name = "Minority share of population (census block group)", + expand = expansion(mult = c(0, 0.05)), + labels = scales::percent_format(accuracy = 1) + ) + + scale_y_continuous( + name = "Identification rate", + limits = c(0, NA), + oob = scales::squish, + expand = expansion(mult = c(0, 0.1)), + labels = scales::percent_format(accuracy = 0.1) + ) + + theme( + panel.grid = element_blank(), + panel.border = element_blank(), + axis.text = element_text(family = "Helvetica", color = "black"), + axis.title = element_text(family = "Helvetica", color = "black"), + axis.line = element_line(size = 0.5, color = "black"), + axis.ticks.x = element_line(size = 0.5, color = "black"), + axis.ticks.y = element_line(size = 0.5, color = "black") + ) +``` + +## Table 4: Regression output + +```{r regression-model} +# reference level: +# - city: None (interceptless) +# - zone_type: residential +model_lm_poly <- df_post_w_race %>% + filter(zone_type != "Unknown") %>% + mutate( + detected = if_else(camera_count > 0, 1, 0), + zone_type = fct_relevel( + zone_type, + c("Residential", "Public", "Commercial", "Industrial", "Mixed", "Unknown") + ) + ) %>% + lm(detected ~ city-1 + zone_type + percentage_minority + I(percentage_minority^2), data = .) + +tidy(model_lm_poly) %>% + filter(!str_detect(term, "^city")) %>% + transmute( + term, + estimate = formatC(estimate, format = "f"), + std.error = formatC(std.error, format = "f") + ) +``` + + + diff --git a/data/image/.keepme b/data/image/.keepme new file mode 100644 index 0000000..e69de29 diff --git a/data/info.yaml b/data/info.yaml new file mode 100644 index 0000000..3db06b9 --- /dev/null +++ b/data/info.yaml @@ -0,0 +1,22 @@ +author: Hao 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277, 533, 277, 533, 277, 533, 276, 532, 276, 532, 276, 533, 275, 535, 274, 539, 271, 541, 271, 542, 271, 542, 271, 542, 272, 542, 273, 542, 273, 540, 273, 539, 274, 539, 275, 539, 275, 539, 276, 539, 277, 539, 277, 538, 277, 538, 277, 538, 277, 538, 276, 537, 276]}]",valid,2014,ZPbZkF2SOfunktiXIAzA_g,16,../data/image/ZPbZkF2SOfunktiXIAzA_g_16.jpg +[],valid,2015,Lo5DS5t_PWiRG5dMEuQgYQ,130,../data/image/Lo5DS5t_PWiRG5dMEuQgYQ_130.jpg +"[{'category_id': 0, 'bbox': [121, 225, 131, 245], 'bbox_mode': 0, 'segmentation': [121, 237, 121, 237, 121, 238, 121, 239, 121, 240, 121, 241, 121, 241, 121, 243, 122, 243, 122, 244, 123, 244, 124, 245, 125, 245, 128, 245, 129, 245, 130, 244, 130, 242, 131, 241, 131, 240, 131, 239, 131, 237, 130, 236, 129, 235, 129, 234, 128, 233, 128, 232, 128, 231, 128, 229, 128, 227, 128, 227, 128, 226, 127, 225, 127, 225, 126, 225, 125, 225, 125, 226, 125, 227, 125, 228, 125, 229, 125, 230, 125, 231, 125, 231, 125, 232, 124, 233, 123, 234, 123, 234, 122, 234, 122, 235, 122, 236, 121, 236]}]",valid,2016,S-GW671xFNwpaxD3BgSRlw,132,../data/image/S-GW671xFNwpaxD3BgSRlw_132.jpg +"[{'category_id': 1, 'bbox': [611, 171, 626, 179], 'bbox_mode': 0, 'segmentation': [615, 173, 619, 172, 622, 171, 625, 171, 626, 171, 626, 173, 625, 174, 624, 175, 622, 175, 619, 176, 619, 177, 618, 178, 616, 179, 616, 176, 614, 177, 612, 177, 611, 176, 611, 175, 611, 173, 612, 173]}]",valid,2017,cDkOGfc5mz8nUyC8jO6kjA,104,../data/image/cDkOGfc5mz8nUyC8jO6kjA_104.jpg +"[{'category_id': 1, 'bbox': [143, 165, 154, 170], 'bbox_mode': 0, 'segmentation': [143, 167, 143, 166, 143, 166, 144, 165, 145, 165, 146, 165, 147, 165, 149, 165, 151, 165, 152, 165, 153, 165, 153, 165, 154, 166, 154, 167, 154, 167, 153, 168, 152, 168, 149, 168, 149, 169, 149, 169, 148, 170, 147, 169, 147, 169, 146, 168, 146, 168, 145, 168]}, {'category_id': 0, 'bbox': [167, 223, 173, 230], 'bbox_mode': 0, 'segmentation': [173, 224, 172, 224, 171, 223, 170, 223, 169, 223, 168, 223, 168, 223, 167, 224, 167, 225, 167, 226, 167, 227, 167, 229, 168, 229, 169, 230, 170, 230, 171, 230, 171, 230, 172, 229, 172, 228, 173, 228, 173, 227, 173, 226, 173, 225]}]",valid,2018,gB7i4Ocouy4mnKpyKMcdDA,88,../data/image/gB7i4Ocouy4mnKpyKMcdDA_88.jpg +"[{'category_id': 1, 'bbox': [100, 289, 108, 297], 'bbox_mode': 0, 'segmentation': [102, 297, 103, 296, 104, 295, 105, 294, 107, 294, 108, 293, 108, 292, 107, 291, 106, 291, 105, 290, 104, 289, 103, 289, 102, 289, 101, 289, 101, 290, 102, 291, 102, 291, 103, 292, 101, 295, 100, 296, 101, 297]}]",valid,2019,GmkoBTKIN-JDEUN-wnujSA,291,../data/image/GmkoBTKIN-JDEUN-wnujSA_291.jpg +[],valid,2020,KgjsPUheHZuAPAeBkKX7Kg,137,../data/image/KgjsPUheHZuAPAeBkKX7Kg_137.jpg +"[{'category_id': 1, 'bbox': [516, 291, 523, 297], 'bbox_mode': 0, 'segmentation': [521, 291, 522, 291, 523, 292, 523, 293, 522, 294, 520, 297, 519, 297, 518, 296, 517, 295, 516, 295, 516, 294, 517, 293, 518, 293]}, {'category_id': 0, 'bbox': [261, 280, 265, 285], 'bbox_mode': 0, 'segmentation': [262, 280, 264, 280, 265, 281, 265, 282, 265, 282, 265, 283, 265, 284, 264, 285, 262, 285, 262, 284, 261, 283, 261, 281]}]",valid,2021,00zTRyjhu3jkgRWVUOuwRw,267,../data/image/00zTRyjhu3jkgRWVUOuwRw_267.jpg +"[{'category_id': 0, 'bbox': [528, 207, 535, 219], 'bbox_mode': 0, 'segmentation': [531, 209, 531, 210, 531, 211, 531, 211, 531, 212, 530, 212, 530, 213, 529, 213, 529, 214, 528, 215, 528, 216, 529, 216, 529, 217, 529, 218, 530, 218, 531, 219, 531, 219, 532, 219, 533, 219, 534, 218, 534, 218, 535, 217, 535, 216, 535, 215, 535, 214, 535, 214, 535, 213, 534, 212, 534, 211, 534, 210, 534, 209, 533, 208, 533, 207, 533, 207, 532, 207, 532, 208]}]",valid,2022,iTsP8UVoD4SwPO-G44VIsg,15,../data/image/iTsP8UVoD4SwPO-G44VIsg_15.jpg +"[{'category_id': 0, 'bbox': [623, 245, 636, 254], 'bbox_mode': 0, 'segmentation': [625, 245, 624, 246, 623, 247, 623, 249, 624, 250, 624, 250, 625, 251, 626, 252, 627, 253, 627, 253, 627, 254, 628, 254, 629, 254, 630, 254, 631, 254, 631, 254, 632, 253, 633, 252, 633, 252, 634, 251, 635, 250, 636, 248, 636, 246, 635, 245, 634, 245, 632, 245, 631, 245, 631, 245, 630, 245, 629, 245, 628, 245, 627, 245]}]",valid,2023,5IF5MsctiLzqmLekvx9qAw,272,../data/image/5IF5MsctiLzqmLekvx9qAw_272.jpg +[],valid,2024,YfAxh9pMPyd60mvKxIPgoA,58,../data/image/YfAxh9pMPyd60mvKxIPgoA_58.jpg +[],valid,2025,NVz8wQQJ-3zcgU7GUZ5ThA,9,../data/image/NVz8wQQJ-3zcgU7GUZ5ThA_9.jpg +"[{'category_id': 0, 'bbox': [480, 291, 484, 295], 'bbox_mode': 0, 'segmentation': [480, 291, 483, 291, 484, 292, 484, 294, 483, 295, 480, 295, 480, 293, 480, 292]}]",valid,2026,B0VQOPahWbOofah_zmclHA,86,../data/image/B0VQOPahWbOofah_zmclHA_86.jpg +"[{'category_id': 1, 'bbox': [218, 310, 225, 319], 'bbox_mode': 0, 'segmentation': [225, 317, 222, 312, 221, 310, 220, 310, 219, 310, 219, 310, 218, 310, 218, 311, 218, 311, 218, 311, 218, 313, 218, 313, 218, 315, 219, 317, 220, 318, 221, 319, 222, 319, 223, 319, 224, 319, 225, 319, 225, 319, 225, 318]}]",valid,2027,HnORFBvZ5nxZhGr_71wS9w,80,../data/image/HnORFBvZ5nxZhGr_71wS9w_80.jpg +"[{'category_id': 0, 'bbox': [497, 277, 502, 282], 'bbox_mode': 0, 'segmentation': [498, 277, 500, 277, 501, 278, 502, 279, 502, 280, 501, 280, 501, 281, 501, 282, 500, 282, 499, 282, 498, 282, 498, 280, 497, 280, 497, 279, 497, 278, 498, 277]}]",valid,2028,1Kgr4611UZ_5TKLCCJNLFg,114,../data/image/1Kgr4611UZ_5TKLCCJNLFg_114.jpg +"[{'category_id': 1, 'bbox': [273, 253, 283, 263], 'bbox_mode': 0, 'segmentation': [278, 253, 273, 260, 273, 261, 273, 262, 275, 263, 277, 263, 279, 263, 281, 261, 282, 256, 283, 255, 283, 253, 281, 253]}]",valid,2029,9z3ZxXBkaOSB_xsiKKfDrg,98,../data/image/9z3ZxXBkaOSB_xsiKKfDrg_98.jpg +"[{'category_id': 1, 'bbox': [108, 261, 129, 272], 'bbox_mode': 0, 'segmentation': [109, 266, 113, 268, 113, 272, 115, 272, 116, 270, 118, 271, 121, 272, 125, 272, 127, 271, 129, 271, 128, 269, 126, 267, 113, 261, 111, 261, 110, 261, 109, 261, 108, 264]}]",valid,2030,6rbcBMcy7W6hjWk4tvwDGQ,359,../data/image/6rbcBMcy7W6hjWk4tvwDGQ_359.jpg +"[{'category_id': 1, 'bbox': [123, 166, 139, 176], 'bbox_mode': 0, 'segmentation': [123, 169, 123, 167, 124, 167, 125, 167, 125, 166, 126, 166, 128, 166, 130, 166, 132, 167, 134, 168, 137, 169, 138, 170, 139, 173, 138, 174, 137, 174, 136, 174, 134, 172, 133, 172, 132, 171, 131, 171, 131, 172, 131, 174, 130, 175, 129, 176, 127, 176, 125, 176, 124, 175, 123, 174, 123, 172, 123, 171]}]",valid,2031,nX26nlUNeKO6aJ9QWsSgIA,302,../data/image/nX26nlUNeKO6aJ9QWsSgIA_302.jpg +"[{'category_id': 0, 'bbox': [163, 295, 171, 307], 'bbox_mode': 0, 'segmentation': [166, 296, 166, 296, 167, 295, 168, 295, 168, 295, 168, 296, 169, 297, 169, 297, 170, 299, 171, 299, 171, 301, 171, 302, 171, 304, 170, 306, 169, 307, 167, 307, 165, 306, 164, 305, 163, 304, 163, 302, 163, 300, 164, 299, 164, 298, 165, 297, 166, 297]}]",valid,2032,gf2jbghjcteEsY0nbve2jQ,29,../data/image/gf2jbghjcteEsY0nbve2jQ_29.jpg +"[{'category_id': 1, 'bbox': [477, 308, 491, 317], 'bbox_mode': 0, 'segmentation': [482, 309, 482, 309, 483, 309, 483, 309, 484, 310, 485, 310, 485, 310, 486, 310, 487, 310, 488, 311, 488, 311, 488, 311, 490, 312, 491, 312, 491, 313, 491, 313, 490, 314, 489, 315, 488, 315, 487, 315, 486, 315, 485, 315, 484, 315, 484, 315, 484, 315, 484, 315, 484, 316, 484, 317, 483, 317, 482, 317, 482, 317, 482, 317, 481, 316, 481, 315, 481, 314, 481, 314, 481, 314, 481, 313, 479, 312, 478, 312, 478, 311, 477, 310, 477, 309, 478, 308, 478, 308, 480, 308]}]",valid,2033,qt4b1eibiRwYInBKlIGFBA,359,../data/image/qt4b1eibiRwYInBKlIGFBA_359.jpg +"[{'category_id': 1, 'bbox': [531, 255, 544, 267], 'bbox_mode': 0, 'segmentation': [534, 260, 533, 260, 533, 260, 532, 261, 532, 261, 532, 262, 531, 263, 531, 263, 531, 264, 531, 264, 531, 265, 532, 266, 532, 266, 533, 266, 534, 266, 535, 266, 536, 265, 537, 265, 538, 265, 538, 265, 538, 265, 539, 265, 539, 266, 539, 266, 539, 267, 539, 267, 540, 267, 541, 267, 541, 266, 541, 265, 541, 264, 541, 263, 541, 263, 541, 262, 541, 262, 542, 261, 542, 261, 543, 260, 544, 259, 544, 258, 544, 257, 543, 256, 543, 256, 542, 255, 541, 255, 540, 255, 541, 255, 539, 256, 539, 256, 538, 257, 537, 258, 536, 258, 535, 259, 535, 259]}]",valid,2034,LlowI8j7guaF7lWLzBl8fw,209,../data/image/LlowI8j7guaF7lWLzBl8fw_209.jpg +"[{'category_id': 0, 'bbox': [252, 179, 258, 186], 'bbox_mode': 0, 'segmentation': [254, 179, 256, 179, 257, 180, 258, 181, 258, 183, 257, 184, 256, 186, 255, 186, 254, 185, 253, 183, 252, 184, 252, 182, 253, 180]}]",valid,2035,E-yd_WAHZsJfIXX4Ok39vw,137,../data/image/E-yd_WAHZsJfIXX4Ok39vw_137.jpg +"[{'category_id': 0, 'bbox': [391, 203, 399, 214], 'bbox_mode': 0, 'segmentation': [399, 206, 398, 206, 397, 205, 397, 204, 396, 203, 395, 203, 394, 204, 394, 205, 394, 207, 393, 207, 392, 208, 391, 209, 391, 210, 392, 212, 393, 213, 394, 214, 396, 214, 397, 213, 398, 212, 398, 211, 398, 210, 397, 209, 397, 208, 396, 207, 396, 206, 396, 206, 397, 206, 397, 207, 398, 207, 399, 207, 399, 207, 399, 207]}]",valid,2036,OUMmUaV1HyhPSF1SrnE6ig,145,../data/image/OUMmUaV1HyhPSF1SrnE6ig_145.jpg +"[{'category_id': 1, 'bbox': [88, 168, 99, 175], 'bbox_mode': 0, 'segmentation': [94, 170, 93, 171, 92, 172, 91, 172, 91, 172, 90, 173, 90, 173, 89, 173, 88, 173, 88, 174, 88, 174, 89, 175, 90, 175, 90, 175, 91, 175, 92, 175, 92, 175, 92, 175, 92, 175, 93, 175, 94, 175, 94, 175, 95, 175, 96, 175, 96, 175, 97, 175, 97, 175, 97, 174, 98, 175, 98, 175, 99, 174, 99, 174, 99, 173, 99, 172, 99, 172, 99, 172, 98, 171, 98, 171, 98, 171, 97, 171, 97, 170, 96, 170, 97, 169, 96, 169, 96, 169, 96, 169, 95, 168, 95, 169, 94, 169]}]",valid,2037,Z_flicjlFRDOApBmh11r6g,0,../data/image/Z_flicjlFRDOApBmh11r6g_0.jpg +"[{'category_id': 0, 'bbox': [430, 243, 438, 248], 'bbox_mode': 0, 'segmentation': [436, 247, 436, 246, 436, 246, 436, 245, 435, 245, 435, 245, 435, 245, 435, 244, 435, 244, 435, 244, 435, 245, 435, 245, 435, 245, 435, 246, 435, 246, 435, 246, 435, 247, 435, 247, 434, 247, 434, 247, 434, 248, 433, 248, 433, 248, 433, 248, 432, 248, 432, 248, 432, 247, 431, 247, 431, 247, 431, 247, 431, 246, 431, 246, 431, 246, 430, 245, 430, 245, 431, 244, 431, 244, 431, 243, 432, 243, 432, 243, 432, 243, 433, 243, 433, 243, 434, 243, 434, 243, 435, 243, 435, 243, 435, 243, 436, 244, 436, 244, 437, 244, 437, 245, 437, 245, 437, 245, 437, 246, 437, 246, 438, 247, 437, 247, 437, 247, 437, 247, 436, 247]}, {'category_id': 1, 'bbox': [437, 199, 443, 204], 'bbox_mode': 0, 'segmentation': [439, 202, 438, 202, 438, 202, 438, 202, 438, 202, 438, 202, 438, 202, 438, 202, 438, 202, 438, 202, 438, 201, 438, 201, 438, 201, 438, 201, 437, 201, 437, 201, 437, 201, 437, 201, 437, 201, 437, 201, 437, 201, 437, 201, 437, 200, 437, 200, 437, 200, 437, 200, 437, 200, 437, 200, 437, 200, 437, 199, 437, 199, 438, 199, 438, 199, 438, 199, 439, 199, 439, 199, 439, 199, 440, 199, 440, 199, 441, 199, 441, 199, 441, 199, 442, 199, 442, 199, 442, 199, 443, 200, 443, 200, 443, 200, 443, 200, 443, 201, 443, 201, 443, 201, 443, 202, 443, 202, 443, 202, 443, 202, 442, 202, 442, 202, 442, 202, 442, 202, 442, 203, 442, 203, 442, 203, 442, 204, 442, 204, 441, 204, 441, 204, 441, 204, 441, 204, 440, 204, 440, 204, 440, 204, 440, 204, 440, 204, 439, 204, 439, 204, 439, 204, 439, 203, 439, 203, 439, 203, 439, 202]}, {'category_id': 1, 'bbox': [420, 214, 439, 219], 'bbox_mode': 0, 'segmentation': [438, 217, 436, 218, 434, 218, 434, 218, 432, 218, 431, 218, 431, 218, 430, 218, 429, 218, 428, 219, 427, 219, 426, 219, 425, 219, 424, 219, 423, 219, 422, 219, 421, 219, 420, 219, 420, 218, 420, 218, 420, 217, 420, 216, 421, 215, 421, 215, 422, 214, 423, 214, 424, 214, 425, 214, 426, 214, 428, 214, 430, 214, 433, 214, 435, 214, 437, 214, 438, 215, 439, 215, 439, 217]}]",valid,2038,mRIcgWWRJQnJm9ur8m0U3A,302,../data/image/mRIcgWWRJQnJm9ur8m0U3A_302.jpg +[],valid,2039,kUjZ4kd_hw7f3e4EI4Rh4A,59,../data/image/kUjZ4kd_hw7f3e4EI4Rh4A_59.jpg +"[{'category_id': 1, 'bbox': [470, 189, 482, 193], 'bbox_mode': 0, 'segmentation': [474, 192, 477, 193, 479, 193, 481, 193, 482, 193, 482, 192, 482, 192, 482, 191, 480, 191, 472, 189, 471, 189, 470, 189, 470, 190, 470, 191, 471, 192]}]",valid,2040,aGFAEEjKuOtt-jn0xYkSPA,176,../data/image/aGFAEEjKuOtt-jn0xYkSPA_176.jpg +"[{'category_id': 1, 'bbox': [404, 167, 435, 192], 'bbox_mode': 0, 'segmentation': [424, 181, 435, 172, 435, 169, 433, 167, 430, 167, 428, 168, 404, 187, 404, 189, 406, 191, 409, 192, 419, 186, 422, 189, 426, 187, 423, 183]}]",valid,2041,73LUKVDUVQftVuqEwTkaJg,99,../data/image/73LUKVDUVQftVuqEwTkaJg_99.jpg +"[{'category_id': 0, 'bbox': [171, 97, 181, 110], 'bbox_mode': 0, 'segmentation': [175, 97, 176, 97, 177, 97, 179, 98, 180, 100, 181, 102, 181, 105, 181, 108, 179, 109, 177, 110, 175, 110, 173, 109, 172, 108, 172, 106, 171, 105, 171, 103, 172, 101, 173, 99, 174, 98]}]",valid,2042,qnbi4ui3Vr2vSVINQ8o5_g,135,../data/image/qnbi4ui3Vr2vSVINQ8o5_g_135.jpg +[],valid,2043,gcLQNEpcPWHBxGbVq3BC2A,35,../data/image/gcLQNEpcPWHBxGbVq3BC2A_35.jpg +"[{'category_id': 0, 'bbox': [352, 223, 363, 235], 'bbox_mode': 0, 'segmentation': [355, 223, 361, 223, 363, 224, 363, 232, 360, 232, 360, 235, 357, 235, 355, 231, 352, 231, 353, 227]}]",valid,2044,DXqlGO2dd8O9tECWnHPu1w,186,../data/image/DXqlGO2dd8O9tECWnHPu1w_186.jpg +"[{'category_id': 0, 'bbox': [564, 173, 582, 191], 'bbox_mode': 0, 'segmentation': [568, 188, 568, 188, 568, 189, 569, 190, 570, 190, 572, 191, 573, 191, 575, 191, 576, 191, 578, 191, 578, 191, 579, 191, 581, 190, 581, 189, 582, 187, 582, 186, 582, 184, 582, 182, 582, 180, 582, 178, 582, 176, 582, 175, 580, 173, 578, 173, 577, 173, 575, 173, 572, 173, 570, 174, 569, 175, 568, 175, 567, 176, 566, 177, 565, 178, 564, 179, 564, 180, 564, 182, 564, 183, 564, 184, 564, 185, 564, 187, 564, 188, 564, 189, 564, 189, 564, 190, 565, 189, 566, 189, 567, 188]}]",valid,2045,ZtS6WQ87e96c53BxA64P1w,274,../data/image/ZtS6WQ87e96c53BxA64P1w_274.jpg +"[{'category_id': 1, 'bbox': [257, 244, 266, 250], 'bbox_mode': 0, 'segmentation': [259, 250, 264, 248, 266, 247, 266, 246, 265, 244, 263, 244, 257, 246, 257, 249, 257, 250]}]",valid,2046,6lsWdoaC97s6tIHggvmqEw,180,../data/image/6lsWdoaC97s6tIHggvmqEw_180.jpg +"[{'category_id': 0, 'bbox': [292, 203, 298, 209], 'bbox_mode': 0, 'segmentation': [295, 203, 293, 204, 292, 205, 293, 207, 293, 208, 295, 209, 297, 208, 298, 205, 298, 204, 297, 203]}]",valid,2047,5_Gxitv_RW1_9IzivBMKzA,152,../data/image/5_Gxitv_RW1_9IzivBMKzA_152.jpg +[],valid,2048,NUeoITjTVtPqp0irKuiIAQ,150,../data/image/NUeoITjTVtPqp0irKuiIAQ_150.jpg +"[{'category_id': 0, 'bbox': [422, 225, 429, 234], 'bbox_mode': 0, 'segmentation': [425, 225, 425, 225, 426, 225, 426, 225, 427, 226, 427, 227, 428, 228, 428, 228, 429, 228, 429, 230, 429, 231, 428, 231, 428, 232, 427, 233, 426, 234, 425, 234, 424, 234, 423, 233, 422, 232, 422, 231, 422, 231, 423, 230, 423, 229, 424, 228, 424, 228, 425, 227, 425, 226, 425, 226]}]",valid,2049,yK51qI4vZrrnlNdP9vAgqQ,236,../data/image/yK51qI4vZrrnlNdP9vAgqQ_236.jpg +"[{'category_id': 1, 'bbox': [103, 279, 116, 293], 'bbox_mode': 0, 'segmentation': [104, 286, 104, 290, 106, 292, 106, 291, 106, 288, 109, 290, 113, 293, 115, 291, 116, 288, 115, 286, 110, 283, 104, 279, 103, 279, 103, 282, 103, 284]}, {'category_id': 1, 'bbox': [414, 274, 428, 288], 'bbox_mode': 0, 'segmentation': [414, 284, 416, 286, 417, 286, 418, 285, 421, 284, 422, 283, 423, 282, 424, 282, 424, 284, 422, 284, 422, 284, 422, 286, 422, 288, 422, 288, 423, 287, 424, 286, 424, 285, 426, 284, 426, 284, 426, 282, 426, 282, 426, 279, 428, 279, 428, 275, 427, 275, 425, 274, 424, 276, 420, 279, 418, 280, 415, 283, 414, 283]}]",valid,2050,FXz-tXEN7--MW2EnHFw1Uw,194,../data/image/FXz-tXEN7--MW2EnHFw1Uw_194.jpg +"[{'category_id': 1, 'bbox': [23, 199, 33, 206], 'bbox_mode': 0, 'segmentation': [27, 206, 30, 205, 31, 203, 32, 202, 33, 201, 33, 200, 32, 199, 30, 199, 27, 201, 25, 202, 24, 203, 23, 204, 24, 204, 25, 205]}]",valid,2051,5KxzlWIGgg-jz1Pf3CanJA,130,../data/image/5KxzlWIGgg-jz1Pf3CanJA_130.jpg +"[{'category_id': 0, 'bbox': [254, 228, 261, 236], 'bbox_mode': 0, 'segmentation': [256, 228, 254, 229, 254, 231, 254, 232, 254, 233, 255, 235, 256, 235, 257, 236, 259, 235, 260, 234, 261, 231, 260, 230, 260, 229, 259, 228, 257, 228]}]",valid,2052,FPcYaAPC1d8cicZZPg3K8w,34,../data/image/FPcYaAPC1d8cicZZPg3K8w_34.jpg +"[{'category_id': 1, 'bbox': [262, 212, 275, 222], 'bbox_mode': 0, 'segmentation': [267, 219, 266, 219, 265, 219, 265, 219, 264, 219, 263, 218, 262, 217, 262, 217, 262, 216, 263, 216, 264, 215, 266, 215, 269, 214, 270, 214, 272, 213, 273, 212, 274, 212, 275, 212, 275, 214, 275, 216, 275, 216, 275, 217, 273, 218, 271, 218, 270, 218, 271, 219, 271, 220, 271, 222, 271, 222, 270, 222, 269, 222, 269, 221, 269, 220, 269, 218]}]",valid,2053,sG_r7OsXP-KTbuMwUmJK_A,180,../data/image/sG_r7OsXP-KTbuMwUmJK_A_180.jpg +"[{'category_id': 0, 'bbox': [238, 242, 247, 254], 'bbox_mode': 0, 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311]}]",valid,2056,4bZX2d-q7H-NA5qhVV-CXg,328,../data/image/4bZX2d-q7H-NA5qhVV-CXg_328.jpg +"[{'category_id': 0, 'bbox': [281, 178, 286, 185], 'bbox_mode': 0, 'segmentation': [283, 178, 281, 178, 281, 181, 281, 184, 282, 185, 285, 185, 286, 183, 286, 181, 286, 179, 285, 178]}]",valid,2057,AhHnrDcyqAr3Wt5zl52XBw,137,../data/image/AhHnrDcyqAr3Wt5zl52XBw_137.jpg +"[{'category_id': 1, 'bbox': [165, 109, 173, 116], 'bbox_mode': 0, 'segmentation': [168, 113, 169, 115, 170, 115, 172, 116, 172, 116, 173, 115, 173, 113, 173, 112, 173, 111, 172, 111, 171, 111, 170, 111, 169, 110, 168, 110, 167, 109, 166, 110, 165, 110, 165, 111, 166, 112, 166, 113]}, {'category_id': 1, 'bbox': [133, 113, 138, 118], 'bbox_mode': 0, 'segmentation': [137, 114, 137, 113, 137, 113, 136, 113, 135, 113, 134, 113, 134, 113, 133, 114, 133, 114, 133, 116, 133, 117, 133, 117, 134, 118, 134, 118, 135, 118, 136, 118, 137, 118, 137, 118, 137, 118, 138, 117, 138, 117, 138, 116]}]",valid,2058,X0eu8icu572KzZ7whOtlZw,178,../data/image/X0eu8icu572KzZ7whOtlZw_178.jpg +[],valid,2059,L8YheBgbYm5ktpRGb-QSUA,208,../data/image/L8YheBgbYm5ktpRGb-QSUA_208.jpg +[],valid,2060,VyKlPBX4J-dqRKrDOYmppg,302,../data/image/VyKlPBX4J-dqRKrDOYmppg_302.jpg +[],valid,2061,UPKJffdkSMzAt3RfmVaolg,189,../data/image/UPKJffdkSMzAt3RfmVaolg_189.jpg +"[{'category_id': 1, 'bbox': [388, 203, 405, 209], 'bbox_mode': 0, 'segmentation': [388, 208, 396, 208, 402, 209, 403, 207, 404, 206, 405, 205, 405, 204, 401, 204, 390, 203, 389, 203, 388, 205]}, {'category_id': 1, 'bbox': [122, 239, 135, 248], 'bbox_mode': 0, 'segmentation': [122, 242, 126, 245, 131, 248, 132, 248, 133, 248, 135, 247, 135, 246, 134, 244, 130, 241, 128, 240, 126, 239, 124, 239, 122, 241]}, {'category_id': 1, 'bbox': [633, 234, 640, 247], 'bbox_mode': 0, 'segmentation': [640, 234, 638, 238, 634, 242, 633, 243, 634, 245, 635, 245, 638, 246, 640, 247, 640, 243]}]",valid,2062,1P7ksiTEmo33hnfCNQpRHw,251,../data/image/1P7ksiTEmo33hnfCNQpRHw_251.jpg +"[{'category_id': 0, 'bbox': [526, 221, 539, 233], 'bbox_mode': 0, 'segmentation': [532, 221, 530, 223, 528, 224, 528, 225, 527, 226, 526, 228, 526, 229, 527, 230, 528, 231, 529, 231, 529, 231, 530, 232, 530, 233, 531, 233, 532, 233, 533, 233, 534, 232, 534, 232, 535, 231, 535, 230, 536, 229, 537, 229, 538, 229, 538, 228, 539, 227, 539, 226, 539, 225, 538, 224, 537, 222, 537, 222, 536, 221, 535, 221, 534, 221]}]",valid,2063,31gNYL3tTOvkjZVdgalF1Q,87,../data/image/31gNYL3tTOvkjZVdgalF1Q_87.jpg +[],valid,2064,L4Biy4MbfqBi0QsP1R0DtQ,286,../data/image/L4Biy4MbfqBi0QsP1R0DtQ_286.jpg +"[{'category_id': 1, 'bbox': [358, 292, 363, 295], 'bbox_mode': 0, 'segmentation': [358, 294, 361, 292, 361, 292, 361, 292, 362, 292, 362, 292, 363, 292, 363, 293, 363, 293, 362, 294, 362, 294, 361, 295, 361, 295, 361, 295, 360, 295, 360, 295, 359, 295, 359, 295, 358, 295, 358, 295, 358, 294, 358, 294]}]",valid,2065,vTnqnFi5LC4wqeAWymJNGQ,286,../data/image/vTnqnFi5LC4wqeAWymJNGQ_286.jpg +[],valid,2066,-xGtL8IqaD7ZSMJG2mbB_g,131,../data/image/-xGtL8IqaD7ZSMJG2mbB_g_131.jpg +"[{'category_id': 1, 'bbox': [151, 241, 164, 247], 'bbox_mode': 0, 'segmentation': [162, 243, 154, 241, 153, 241, 152, 241, 151, 242, 151, 243, 152, 244, 152, 245, 158, 247, 159, 247, 161, 247, 163, 247, 163, 246, 164, 244]}]",valid,2067,g3xZIHlo2K4PSJaMoi8jLw,251,../data/image/g3xZIHlo2K4PSJaMoi8jLw_251.jpg +"[{'category_id': 1, 'bbox': [321, 101, 338, 116], 'bbox_mode': 0, 'segmentation': [321, 101, 325, 103, 331, 106, 336, 107, 338, 108, 338, 109, 337, 111, 337, 113, 336, 114, 333, 113, 331, 113, 328, 112, 327, 116, 325, 115, 322, 113, 321, 110]}]",valid,2068,Au_ws7fMWKW959I3QqOmyA,86,../data/image/Au_ws7fMWKW959I3QqOmyA_86.jpg +[],valid,2069,Mi8IyMrv0HYRKoDzDwIKDA,88,../data/image/Mi8IyMrv0HYRKoDzDwIKDA_88.jpg +"[{'category_id': 0, 'bbox': [500, 181, 516, 195], 'bbox_mode': 0, 'segmentation': [509, 181, 508, 181, 507, 182, 505, 182, 504, 183, 504, 183, 502, 184, 501, 185, 500, 186, 500, 187, 500, 188, 501, 190, 502, 190, 502, 191, 503, 192, 504, 193, 505, 193, 506, 195, 506, 195, 507, 195, 508, 195, 509, 195, 510, 195, 510, 195, 511, 194, 512, 193, 513, 193, 513, 192, 515, 191, 515, 191, 516, 190, 516, 189, 516, 188, 515, 186, 515, 185, 515, 184, 514, 184, 513, 182, 512, 182, 512, 181, 511, 181, 510, 181]}]",valid,2070,Xty2CJ62pYM05k0q-aQ6GQ,336,../data/image/Xty2CJ62pYM05k0q-aQ6GQ_336.jpg +[],valid,2071,L95XIbzJ9Ga1GRg8GUnOzA,80,../data/image/L95XIbzJ9Ga1GRg8GUnOzA_80.jpg +"[{'category_id': 1, 'bbox': [518, 176, 531, 181], 'bbox_mode': 0, 'segmentation': [529, 177, 527, 176, 526, 177, 525, 177, 524, 177, 523, 177, 522, 177, 521, 178, 520, 178, 519, 178, 518, 179, 519, 180, 519, 180, 520, 181, 521, 181, 522, 181, 524, 181, 525, 181, 526, 181, 527, 180, 529, 180, 530, 180, 530, 179, 531, 178, 531, 177, 530, 177, 530, 176]}]",valid,2072,C35IAliKyY6YyewJouWAHA,359,../data/image/C35IAliKyY6YyewJouWAHA_359.jpg +"[{'category_id': 0, 'bbox': [605, 222, 616, 230], 'bbox_mode': 0, 'segmentation': [605, 226, 605, 228, 606, 230, 607, 230, 609, 228, 609, 226, 609, 224, 616, 225, 615, 222, 607, 222, 606, 223, 605, 224]}]",valid,2073,fVHoyZG63FkXH4oTgiX58w,268,../data/image/fVHoyZG63FkXH4oTgiX58w_268.jpg +"[{'category_id': 0, 'bbox': [549, 235, 558, 244], 'bbox_mode': 0, 'segmentation': [550, 239, 550, 240, 550, 241, 550, 242, 550, 242, 550, 243, 551, 243, 551, 244, 552, 244, 552, 244, 553, 244, 554, 244, 556, 244, 557, 244, 557, 243, 558, 241, 558, 241, 558, 240, 558, 239, 558, 238, 558, 238, 557, 237, 557, 236, 556, 236, 556, 236, 555, 236, 555, 235, 554, 235, 553, 235, 553, 235, 552, 236, 552, 236, 552, 236, 551, 236, 551, 237, 551, 237, 551, 237, 550, 237, 550, 237, 549, 237, 549, 237, 549, 237, 550, 237, 550, 238, 550, 237, 551, 238, 551, 238, 551, 238, 551, 239, 550, 239]}]",valid,2074,poVeJeBm7rs9cpx1okTLYQ,41,../data/image/poVeJeBm7rs9cpx1okTLYQ_41.jpg +"[{'category_id': 0, 'bbox': [119, 284, 127, 294], 'bbox_mode': 0, 'segmentation': [122, 285, 119, 287, 119, 288, 119, 290, 120, 291, 121, 293, 123, 294, 125, 294, 126, 293, 127, 291, 127, 287, 127, 285, 125, 284]}]",valid,2075,ENghkd8LyIb76tkKMS-Gaw,29,../data/image/ENghkd8LyIb76tkKMS-Gaw_29.jpg +"[{'category_id': 1, 'bbox': [533, 258, 547, 271], 'bbox_mode': 0, 'segmentation': [534, 265, 542, 259, 544, 258, 547, 260, 547, 261, 543, 264, 543, 267, 541, 268, 540, 266, 538, 269, 537, 271, 536, 271, 534, 270, 533, 269, 533, 267]}]",valid,2076,7OM0Q9EFRAh64MEyphAj0A,218,../data/image/7OM0Q9EFRAh64MEyphAj0A_218.jpg +"[{'category_id': 0, 'bbox': [433, 92, 452, 121], 'bbox_mode': 0, 'segmentation': [434, 104, 433, 106, 433, 108, 433, 110, 433, 111, 433, 113, 433, 116, 434, 116, 435, 118, 437, 119, 440, 120, 442, 121, 445, 120, 447, 119, 448, 118, 449, 116, 450, 113, 451, 111, 452, 108, 452, 108, 452, 105, 452, 103, 452, 101, 451, 99, 448, 98, 446, 97, 445, 96, 445, 94, 445, 93, 444, 93, 443, 92, 442, 92, 440, 93, 439, 94, 439, 95, 439, 97, 438, 98, 437, 98, 436, 98, 435, 99, 434, 100, 434, 101]}]",valid,2077,ZumiSoCm5JYynwbmQtWtGQ,88,../data/image/ZumiSoCm5JYynwbmQtWtGQ_88.jpg +"[{'category_id': 0, 'bbox': [185, 160, 198, 170], 'bbox_mode': 0, 'segmentation': [185, 160, 189, 160, 192, 160, 193, 160, 194, 161, 195, 161, 196, 162, 197, 163, 197, 163, 198, 165, 198, 166, 198, 168, 197, 170, 195, 170, 194, 170, 192, 170, 191, 168, 190, 166, 190, 163, 190, 162, 189, 162, 187, 162, 185, 161, 185, 161]}]",valid,2078,UjDAM9XU3quBZ80P1kODkw,178,../data/image/UjDAM9XU3quBZ80P1kODkw_178.jpg +[],valid,2079,Wjo5qdN8Xu_K0AhxjFTxMw,355,../data/image/Wjo5qdN8Xu_K0AhxjFTxMw_355.jpg +"[{'category_id': 0, 'bbox': [268, 283, 274, 290], 'bbox_mode': 0, 'segmentation': [268, 286, 269, 285, 269, 284, 270, 284, 271, 283, 273, 284, 273, 284, 274, 285, 274, 287, 274, 288, 273, 289, 272, 290, 271, 290, 270, 290, 269, 289, 268, 288]}]",valid,2080,W6eypma97VmyFp4RokdySA,208,../data/image/W6eypma97VmyFp4RokdySA_208.jpg +"[{'category_id': 0, 'bbox': [105, 237, 111, 243], 'bbox_mode': 0, 'segmentation': [105, 241, 105, 238, 105, 237, 107, 237, 110, 237, 111, 237, 111, 239, 111, 241, 110, 242, 109, 243, 108, 243, 106, 242, 106, 242]}]",valid,2081,96KvtZgg4SYZLRI9YrO2Pg,351,../data/image/96KvtZgg4SYZLRI9YrO2Pg_351.jpg +[],valid,2082,8MT4xgGA6ajLQ1oFGUZVsw,359,../data/image/8MT4xgGA6ajLQ1oFGUZVsw_359.jpg +"[{'category_id': 0, 'bbox': [78, 155, 87, 165], 'bbox_mode': 0, 'segmentation': [85, 157, 84, 157, 82, 157, 81, 158, 81, 158, 81, 159, 80, 160, 80, 160, 79, 161, 79, 161, 78, 162, 78, 162, 78, 162, 78, 163, 78, 163, 79, 163, 79, 163, 79, 164, 80, 164, 80, 164, 80, 165, 81, 165, 81, 165, 81, 165, 82, 165, 82, 165, 83, 165, 83, 165, 83, 164, 83, 164, 84, 164, 84, 164, 85, 163, 85, 163, 85, 162, 84, 161, 83, 160, 83, 160, 83, 159, 83, 159, 84, 159, 85, 159, 85, 159, 86, 160, 86, 161, 86, 161, 87, 161, 87, 160, 87, 160, 87, 159, 87, 157, 87, 156, 87, 156, 87, 155, 87, 155, 86, 155, 86, 155, 86, 156, 86, 157]}, {'category_id': 1, 'bbox': [92, 138, 106, 147], 'bbox_mode': 0, 'segmentation': [106, 145, 95, 139, 95, 138, 94, 138, 93, 139, 93, 139, 93, 139, 92, 139, 92, 140, 93, 140, 93, 141, 94, 141, 97, 143, 97, 143, 97, 144, 97, 144, 96, 144, 96, 145, 97, 145, 97, 145, 97, 145, 99, 145, 99, 145, 100, 145, 100, 145, 101, 146, 102, 146, 103, 147, 104, 147, 105, 147, 106, 147, 106, 146, 106, 146]}]",valid,2083,IoQvyav_iAt_sKFCJq2Z7g,267,../data/image/IoQvyav_iAt_sKFCJq2Z7g_267.jpg +"[{'category_id': 0, 'bbox': [242, 263, 252, 273], 'bbox_mode': 0, 'segmentation': [244, 263, 250, 263, 251, 265, 252, 267, 252, 270, 251, 270, 250, 272, 249, 273, 247, 273, 245, 272, 243, 270, 242, 270, 242, 267]}]",valid,2084,dU-Z9DDSPgy45xsV7n329g,80,../data/image/dU-Z9DDSPgy45xsV7n329g_80.jpg +[],valid,2085,SHL7GjhyubqyV5zADSVxCg,208,../data/image/SHL7GjhyubqyV5zADSVxCg_208.jpg +"[{'category_id': 0, 'bbox': [429, 227, 434, 234], 'bbox_mode': 0, 'segmentation': [431, 228, 431, 227, 432, 227, 433, 228, 433, 228, 434, 229, 434, 230, 434, 231, 434, 233, 433, 233, 432, 234, 431, 234, 430, 234, 429, 232, 429, 230, 429, 229, 430, 228]}]",valid,2086,n7Sml7rP9v_02AOtNZUMEQ,222,../data/image/n7Sml7rP9v_02AOtNZUMEQ_222.jpg +[],valid,2087,Me9oNhuc8-gR6xLe0EclqA,85,../data/image/Me9oNhuc8-gR6xLe0EclqA_85.jpg +[],valid,2088,UOI9DTeHXJ0i5GPiWP8n1Q,355,../data/image/UOI9DTeHXJ0i5GPiWP8n1Q_355.jpg +"[{'category_id': 1, 'bbox': [22, 195, 49, 204], 'bbox_mode': 0, 'segmentation': [46, 198, 25, 195, 22, 195, 22, 195, 22, 196, 22, 197, 23, 199, 24, 200, 25, 201, 27, 202, 28, 202, 48, 204, 49, 204, 49, 203, 49, 202, 49, 200, 48, 199]}]",valid,2089,HSmzgfIjJnbGqG6bdryozg,356,../data/image/HSmzgfIjJnbGqG6bdryozg_356.jpg +"[{'category_id': 1, 'bbox': [292, 303, 300, 310], 'bbox_mode': 0, 'segmentation': [292, 308, 293, 307, 293, 306, 295, 305, 296, 304, 298, 303, 299, 303, 300, 304, 300, 306, 299, 307, 298, 308, 299, 309, 298, 310, 297, 310, 297, 310, 295, 309, 294, 309, 292, 309, 292, 309]}]",valid,2090,J_nmcS1ldseOxPfGzWsefg,270,../data/image/J_nmcS1ldseOxPfGzWsefg_270.jpg +"[{'category_id': 1, 'bbox': [417, 196, 429, 201], 'bbox_mode': 0, 'segmentation': [425, 199, 427, 199, 428, 199, 429, 199, 429, 198, 429, 197, 428, 197, 426, 198, 424, 197, 423, 197, 421, 197, 420, 197, 419, 196, 417, 197, 417, 198, 417, 198, 418, 199, 421, 199, 421, 200, 422, 201, 423, 201, 424, 201, 424, 200, 425, 200]}]",valid,2091,PvrGD2P7Q9wFllfyVUlpkA,176,../data/image/PvrGD2P7Q9wFllfyVUlpkA_176.jpg +"[{'category_id': 1, 'bbox': [630, 163, 639, 171], 'bbox_mode': 0, 'segmentation': [630, 165, 630, 167, 631, 167, 634, 167, 635, 171, 637, 170, 638, 169, 638, 166, 639, 166, 639, 163, 630, 164]}, {'category_id': 1, 'bbox': [207, 208, 210, 212], 'bbox_mode': 0, 'segmentation': [207, 209, 207, 210, 209, 212, 210, 212, 210, 209, 210, 208]}, {'category_id': 1, 'bbox': [56, 173, 69, 180], 'bbox_mode': 0, 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374, 269, 374, 269, 375, 269, 375, 270, 376, 270, 376, 270, 377, 270, 377, 269, 378, 269, 378, 269, 378, 268, 378, 267, 378, 266, 378, 266, 378, 265, 378, 264, 378, 264, 377, 263, 377, 263]}]",valid,2099,TtmbQgMKZckLVCWnuqgpsg,268,../data/image/TtmbQgMKZckLVCWnuqgpsg_268.jpg +"[{'category_id': 0, 'bbox': [376, 225, 386, 240], 'bbox_mode': 0, 'segmentation': [378, 226, 379, 226, 380, 225, 382, 225, 382, 226, 383, 226, 383, 227, 383, 228, 383, 228, 383, 229, 383, 229, 383, 229, 384, 229, 384, 230, 385, 230, 385, 231, 385, 232, 385, 233, 386, 233, 386, 234, 386, 235, 386, 236, 386, 237, 385, 237, 385, 238, 384, 238, 383, 239, 383, 239, 383, 240, 382, 240, 381, 240, 381, 240, 380, 240, 380, 239, 379, 239, 379, 238, 379, 237, 378, 237, 378, 237, 377, 237, 377, 236, 377, 235, 377, 235, 378, 234, 378, 233, 378, 232, 378, 231, 378, 230, 379, 230, 379, 230, 380, 230, 381, 230, 381, 229, 381, 228, 381, 227, 381, 227, 380, 227, 379, 227, 378, 228, 378, 228, 378, 228, 377, 228, 376, 227, 376, 227, 376, 227, 377, 226]}]",valid,2100,zCtH9XL8azXIgHIF4qfc1g,186,../data/image/zCtH9XL8azXIgHIF4qfc1g_186.jpg +"[{'category_id': 1, 'bbox': [59, 248, 67, 253], 'bbox_mode': 0, 'segmentation': [65, 253, 65, 252, 65, 252, 66, 251, 66, 250, 67, 250, 66, 249, 66, 249, 65, 249, 64, 249, 63, 248, 62, 248, 61, 249, 60, 249, 60, 249, 59, 249, 59, 250, 59, 250, 59, 251, 59, 251, 60, 251, 60, 251, 60, 251, 61, 251, 61, 251, 62, 251, 62, 252, 63, 252, 63, 252, 63, 253, 64, 253, 64, 253, 64, 253]}]",valid,2101,reBgGMph4qCAHLbqtZ4Dhw,33,../data/image/reBgGMph4qCAHLbqtZ4Dhw_33.jpg +[],valid,2102,_Q6EjT0hR9Nw3pCVPhPclg,269,../data/image/_Q6EjT0hR9Nw3pCVPhPclg_269.jpg +"[{'category_id': 1, 'bbox': [366, 160, 378, 170], 'bbox_mode': 0, 'segmentation': [366, 163, 370, 166, 368, 169, 370, 170, 371, 167, 375, 170, 377, 169, 378, 168, 378, 166, 368, 160, 367, 160, 366, 161]}]",valid,2103,9mA40oJLht_f4Jk5RCemZQ,269,../data/image/9mA40oJLht_f4Jk5RCemZQ_269.jpg +"[{'category_id': 1, 'bbox': [40, 241, 53, 253], 'bbox_mode': 0, 'segmentation': [40, 250, 44, 245, 47, 242, 50, 241, 52, 242, 53, 243, 53, 245, 50, 248, 53, 250, 53, 251, 52, 252, 48, 250, 45, 253, 44, 253, 42, 253, 40, 252]}]",valid,2104,8IYZl0YwUa9PQJ2ViXff4g,80,../data/image/8IYZl0YwUa9PQJ2ViXff4g_80.jpg +"[{'category_id': 1, 'bbox': [168, 210, 190, 226], 'bbox_mode': 0, 'segmentation': [170, 218, 169, 216, 168, 215, 168, 214, 168, 212, 168, 211, 170, 210, 171, 210, 172, 211, 174, 211, 177, 213, 178, 214, 181, 214, 184, 216, 187, 217, 189, 219, 190, 220, 190, 221, 189, 222, 189, 223, 188, 224, 186, 225, 185, 225, 184, 226, 183, 226, 181, 226, 179, 226, 178, 224, 175, 222, 173, 221, 173, 222, 172, 223, 172, 224, 172, 225, 172, 225, 171, 225, 171, 225, 170, 223, 170, 223, 170, 221]}]",valid,2105,MYJyES74_1QfyKMWxMz3pQ,9,../data/image/MYJyES74_1QfyKMWxMz3pQ_9.jpg +"[{'category_id': 1, 'bbox': [148, 199, 157, 204], 'bbox_mode': 0, 'segmentation': [149, 199, 152, 199, 153, 199, 154, 200, 154, 200, 155, 201, 156, 201, 156, 201, 157, 201, 157, 202, 157, 203, 157, 203, 156, 204, 156, 204, 155, 204, 154, 204, 154, 204, 153, 204, 152, 203, 151, 203, 150, 203, 150, 203, 149, 203, 148, 203, 148, 203, 148, 202, 148, 202, 148, 201, 148, 200, 148, 199]}, {'category_id': 0, 'bbox': [153, 210, 161, 222], 'bbox_mode': 0, 'segmentation': [156, 210, 156, 211, 156, 211, 156, 213, 156, 213, 156, 213, 155, 214, 154, 214, 154, 214, 154, 214, 153, 215, 153, 215, 153, 216, 153, 217, 153, 218, 153, 218, 153, 219, 154, 219, 155, 220, 155, 221, 155, 221, 156, 222, 157, 222, 157, 222, 158, 222, 158, 222, 159, 221, 159, 220, 159, 220, 160, 219, 160, 219, 160, 219, 161, 219, 161, 219, 161, 218, 161, 218, 161, 217, 161, 217, 161, 216, 160, 215, 160, 215, 160, 215, 159, 214, 159, 214, 158, 213, 158, 212, 158, 211, 157, 210, 157, 210, 156, 210]}]",valid,2106,MFtMK6ZIGi0odEs602Ko4A,179,../data/image/MFtMK6ZIGi0odEs602Ko4A_179.jpg +[],valid,2107,unvsoHW1KVYpkHF3-GoLzg,28,../data/image/unvsoHW1KVYpkHF3-GoLzg_28.jpg +[],valid,2108,WDPsvDMMTGm903m_8UfqZw,33,../data/image/WDPsvDMMTGm903m_8UfqZw_33.jpg +"[{'category_id': 1, 'bbox': [595, 147, 613, 159], 'bbox_mode': 0, 'segmentation': [603, 148, 595, 155, 595, 158, 600, 159, 603, 158, 610, 151, 613, 149, 611, 147, 608, 148, 606, 147]}, {'category_id': 1, 'bbox': [627, 143, 638, 151], 'bbox_mode': 0, 'segmentation': [628, 147, 631, 149, 633, 151, 634, 151, 636, 149, 638, 146, 636, 144, 633, 143, 629, 143, 627, 144]}]",valid,2109,5igd2DA09w_JC71CCQF95A,278,../data/image/5igd2DA09w_JC71CCQF95A_278.jpg +"[{'category_id': 0, 'bbox': [442, 259, 448, 265], 'bbox_mode': 0, 'segmentation': [444, 259, 447, 259, 447, 260, 448, 262, 448, 263, 448, 264, 447, 265, 444, 265, 443, 264, 442, 263, 442, 262, 442, 260]}]",valid,2110,8FXZxAt_UARqajiwoO2O3Q,36,../data/image/8FXZxAt_UARqajiwoO2O3Q_36.jpg +"[{'category_id': 0, 'bbox': [373, 249, 376, 253], 'bbox_mode': 0, 'segmentation': [374, 249, 375, 249, 375, 250, 375, 250, 376, 250, 376, 251, 376, 251, 376, 252, 376, 253, 375, 253, 374, 253, 374, 253, 373, 253, 373, 252, 373, 251, 373, 251, 374, 250, 374, 250, 374, 250]}]",valid,2111,Pn4M68XpO1T4chz_xs9v4w,99,../data/image/Pn4M68XpO1T4chz_xs9v4w_99.jpg +"[{'category_id': 0, 'bbox': [417, 170, 424, 178], 'bbox_mode': 0, 'segmentation': [420, 170, 419, 171, 418, 172, 417, 173, 417, 174, 418, 175, 418, 176, 419, 177, 419, 177, 420, 178, 420, 178, 421, 178, 422, 178, 422, 178, 422, 177, 423, 177, 423, 176, 423, 175, 424, 174, 424, 173, 424, 171, 423, 170, 422, 170]}, {'category_id': 0, 'bbox': [425, 175, 430, 182], 'bbox_mode': 0, 'segmentation': [426, 175, 425, 176, 425, 177, 425, 177, 425, 178, 425, 179, 425, 180, 426, 181, 426, 181, 427, 182, 427, 182, 428, 182, 429, 182, 429, 181, 429, 181, 430, 180, 430, 179, 430, 178, 430, 177, 430, 176, 430, 175, 429, 175]}]",valid,2112,N4J9CKglOynNZ2W82nE_tg,68,../data/image/N4J9CKglOynNZ2W82nE_tg_68.jpg +"[{'category_id': 1, 'bbox': [484, 249, 497, 255], 'bbox_mode': 0, 'segmentation': [488, 249, 488, 249, 490, 249, 490, 249, 492, 249, 493, 249, 494, 249, 495, 249, 496, 250, 496, 250, 497, 251, 497, 251, 496, 252, 496, 252, 494, 252, 494, 253, 493, 253, 493, 253, 493, 254, 492, 255, 492, 255, 491, 255, 490, 255, 489, 255, 489, 255, 488, 254, 487, 253, 487, 252, 487, 252, 485, 252, 484, 252, 484, 251, 484, 250, 484, 250, 484, 249]}, {'category_id': 0, 'bbox': [497, 205, 508, 216], 'bbox_mode': 0, 'segmentation': [500, 208, 500, 209, 499, 209, 499, 210, 498, 210, 498, 210, 498, 211, 497, 211, 497, 212, 497, 213, 497, 213, 497, 214, 498, 214, 499, 214, 499, 214, 499, 215, 499, 215, 499, 215, 500, 216, 500, 216, 501, 216, 501, 216, 501, 215, 501, 214, 502, 214, 503, 214, 504, 214, 505, 214, 506, 213, 507, 213, 507, 213, 508, 213, 508, 212, 508, 211, 508, 209, 508, 208, 508, 207, 508, 207, 507, 206, 506, 206, 506, 206, 505, 205, 504, 205, 503, 206, 502, 206, 502, 206]}]",valid,2113,IP8dLHZnh2W9p8bsN2e70A,356,../data/image/IP8dLHZnh2W9p8bsN2e70A_356.jpg +[],valid,2114,WaL3bsqqbTUdB5W1W6dnuA,352,../data/image/WaL3bsqqbTUdB5W1W6dnuA_352.jpg +"[{'category_id': 1, 'bbox': [403, 179, 420, 189], 'bbox_mode': 0, 'segmentation': [405, 180, 406, 179, 407, 179, 407, 179, 408, 179, 408, 179, 408, 179, 409, 179, 409, 179, 410, 179, 410, 180, 410, 180, 411, 180, 411, 180, 412, 180, 412, 180, 413, 180, 414, 180, 414, 180, 415, 180, 416, 180, 416, 180, 417, 180, 417, 180, 418, 180, 418, 181, 418, 181, 419, 181, 419, 181, 419, 181, 420, 182, 420, 182, 420, 182, 420, 183, 420, 183, 420, 183, 420, 183, 419, 184, 419, 184, 419, 184, 419, 185, 419, 185, 418, 185, 418, 185, 417, 185, 417, 185, 416, 185, 415, 185, 414, 185, 414, 185, 413, 185, 412, 186, 412, 186, 412, 186, 412, 186, 412, 187, 412, 187, 412, 188, 412, 188, 412, 188, 412, 189, 412, 189, 412, 189, 411, 189, 411, 189, 410, 189, 410, 189, 410, 189, 410, 189, 410, 188, 410, 188, 410, 188, 409, 187, 409, 187, 409, 186, 409, 185, 409, 185, 409, 185, 409, 185, 408, 185, 408, 185, 407, 185, 406, 185, 406, 185, 405, 185, 405, 185, 404, 185, 404, 185, 403, 184, 403, 184, 403, 184, 403, 183, 403, 183, 403, 182, 403, 182, 404, 182, 404, 181, 404, 181, 404, 181, 405, 180]}]",valid,2115,KLMAPgk7NvX5k6YivZ_R2g,269,../data/image/KLMAPgk7NvX5k6YivZ_R2g_269.jpg +"[{'category_id': 0, 'bbox': [78, 234, 82, 242], 'bbox_mode': 0, 'segmentation': [80, 234, 80, 234, 80, 234, 81, 235, 82, 236, 82, 237, 82, 238, 82, 239, 82, 240, 82, 241, 81, 242, 81, 242, 80, 242, 79, 242, 79, 241, 78, 240, 78, 239, 78, 238, 78, 238, 78, 238, 78, 237, 78, 236, 79, 235, 79, 234, 79, 234]}]",valid,2116,InOw5kZJnr2U4mQyMRB_UA,0,../data/image/InOw5kZJnr2U4mQyMRB_UA_0.jpg +[],valid,2117,MMTCF5VMvAP2poI8Pn_POg,224,../data/image/MMTCF5VMvAP2poI8Pn_POg_224.jpg +[],valid,2118,JI9N21B8s1mW1lQYjOGIgQ,224,../data/image/JI9N21B8s1mW1lQYjOGIgQ_224.jpg +[],valid,2119,B8_OpKWgkJOddgAm9CBNbw,277,../data/image/B8_OpKWgkJOddgAm9CBNbw_277.jpg +[],valid,2120,UvLjZQEQEuO4g-4u21yISA,0,../data/image/UvLjZQEQEuO4g-4u21yISA_0.jpg 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258]}]",valid,2122,dC3qXPgC1HAts4bfVJV_ow,272,../data/image/dC3qXPgC1HAts4bfVJV_ow_272.jpg +[],valid,2123,Jw5hQBv6gjPdZbOP6680Rw,206,../data/image/Jw5hQBv6gjPdZbOP6680Rw_206.jpg +[],valid,2124,Xxuqp_Xhv0b65wuSH3vAfA,166,../data/image/Xxuqp_Xhv0b65wuSH3vAfA_166.jpg +"[{'category_id': 1, 'bbox': [522, 178, 529, 183], 'bbox_mode': 0, 'segmentation': [525, 181, 525, 182, 525, 182, 525, 182, 524, 183, 524, 183, 524, 182, 524, 182, 524, 181, 524, 181, 523, 180, 523, 180, 523, 180, 522, 180, 522, 180, 522, 180, 522, 180, 522, 179, 522, 179, 522, 178, 522, 178, 523, 178, 524, 178, 525, 178, 525, 178, 527, 178, 528, 178, 528, 178, 529, 179, 529, 179, 529, 180, 529, 180, 529, 180, 528, 180, 527, 180, 526, 180, 525, 180]}]",valid,2125,os4NKpKyxz9b11FXwbQ9KQ,302,../data/image/os4NKpKyxz9b11FXwbQ9KQ_302.jpg +[],valid,2126,Kx-S2ZidMpPxzrr5lKfvxw,80,../data/image/Kx-S2ZidMpPxzrr5lKfvxw_80.jpg +[],valid,2127,OnQL3agxpicQg1lL6aE1UQ,124,../data/image/OnQL3agxpicQg1lL6aE1UQ_124.jpg +"[{'category_id': 0, 'bbox': [497, 286, 518, 303], 'bbox_mode': 0, 'segmentation': [501, 289, 503, 288, 505, 288, 505, 288, 507, 288, 508, 288, 508, 288, 510, 288, 510, 288, 510, 290, 509, 291, 507, 292, 506, 294, 505, 296, 506, 298, 506, 299, 507, 300, 508, 301, 509, 303, 511, 303, 512, 303, 513, 303, 515, 302, 516, 301, 517, 299, 518, 298, 518, 295, 518, 293, 516, 291, 515, 291, 514, 289, 512, 287, 510, 286, 509, 286, 506, 286, 502, 286, 500, 286, 498, 287, 497, 289, 497, 290, 499, 290]}]",valid,2128,Yu3feiB2JdB1vd8iawp20g,87,../data/image/Yu3feiB2JdB1vd8iawp20g_87.jpg +"[{'category_id': 0, 'bbox': [505, 311, 512, 319], 'bbox_mode': 0, 'segmentation': [507, 311, 509, 311, 510, 312, 511, 313, 512, 315, 512, 316, 512, 317, 512, 318, 511, 319, 511, 319, 509, 319, 508, 319, 506, 319, 505, 318, 505, 317, 505, 316, 505, 315, 505, 313, 505, 313, 506, 311]}]",valid,2129,RtQRJeD83cuZM7FS6zHcJQ,351,../data/image/RtQRJeD83cuZM7FS6zHcJQ_351.jpg +"[{'category_id': 1, 'bbox': [447, 234, 455, 244], 'bbox_mode': 0, 'segmentation': [450, 234, 449, 235, 449, 236, 448, 237, 447, 239, 447, 240, 447, 241, 447, 242, 448, 242, 449, 243, 450, 244, 452, 244, 452, 243, 453, 243, 454, 242, 454, 242, 455, 241, 455, 240, 455, 238, 455, 237, 453, 235, 452, 235, 451, 234]}]",valid,2130,i3O2JYHlhIsDYP-5ANC1Xw,219,../data/image/i3O2JYHlhIsDYP-5ANC1Xw_219.jpg +"[{'category_id': 1, 'bbox': [139, 296, 152, 309], 'bbox_mode': 0, 'segmentation': [145, 296, 144, 296, 143, 297, 143, 298, 143, 299, 140, 300, 140, 301, 140, 302, 139, 303, 139, 305, 139, 306, 141, 307, 142, 308, 144, 309, 145, 309, 147, 309, 149, 308, 151, 307, 152, 306, 152, 305, 152, 303, 151, 301, 151, 300, 150, 300, 149, 299, 149, 298, 149, 298, 148, 297, 148, 296]}, {'category_id': 1, 'bbox': [354, 296, 362, 310], 'bbox_mode': 0, 'segmentation': [356, 296, 356, 297, 356, 298, 355, 299, 355, 300, 354, 301, 354, 301, 354, 302, 354, 304, 354, 305, 354, 306, 354, 306, 355, 307, 356, 308, 356, 309, 357, 309, 357, 310, 358, 310, 359, 310, 360, 309, 361, 308, 361, 307, 362, 306, 362, 305, 362, 304, 362, 302, 362, 301, 362, 300, 361, 300, 360, 299, 360, 299, 359, 297, 359, 297, 359, 296]}]",valid,2131,UNgRvv8TiPqsEpOaZAX9Vw,135,../data/image/UNgRvv8TiPqsEpOaZAX9Vw_135.jpg +"[{'category_id': 1, 'bbox': [165, 12, 178, 29], 'bbox_mode': 0, 'segmentation': [168, 12, 165, 15, 166, 18, 168, 21, 170, 24, 171, 29, 175, 29, 177, 28, 177, 28, 178, 26, 175, 20, 171, 13]}]",valid,2132,0L_Hb--59X2O0gZx2kGISQ,269,../data/image/0L_Hb--59X2O0gZx2kGISQ_269.jpg +[],valid,2133,Us_z1gQrRpdPRODCUHM_ww,177,../data/image/Us_z1gQrRpdPRODCUHM_ww_177.jpg +"[{'category_id': 0, 'bbox': [542, 233, 551, 241], 'bbox_mode': 0, 'segmentation': [545, 233, 549, 233, 551, 234, 551, 236, 551, 238, 551, 238, 550, 239, 550, 240, 549, 241, 546, 241, 543, 241, 543, 239, 542, 238, 542, 237, 542, 234, 542, 234]}, {'category_id': 0, 'bbox': [303, 184, 311, 193], 'bbox_mode': 0, 'segmentation': [305, 184, 309, 184, 311, 186, 311, 188, 311, 190, 310, 190, 308, 193, 306, 193, 306, 192, 304, 189, 303, 189, 303, 187, 304, 186]}]",valid,2134,C7P1ZnHih9k2EA33gDc6VQ,145,../data/image/C7P1ZnHih9k2EA33gDc6VQ_145.jpg +"[{'category_id': 1, 'bbox': [229, 211, 243, 237], 'bbox_mode': 0, 'segmentation': [229, 213, 229, 228, 229, 228, 230, 228, 230, 230, 231, 230, 232, 231, 234, 232, 234, 233, 234, 234, 234, 235, 235, 236, 236, 236, 237, 237, 238, 237, 239, 237, 239, 236, 240, 234, 240, 233, 242, 233, 242, 232, 243, 232, 243, 228, 243, 222, 243, 219, 243, 218, 242, 217, 240, 215, 239, 213, 238, 212, 236, 211, 233, 212, 231, 212]}]",valid,2135,gtIg3ITMmYtu3Co5j4oJag,179,../data/image/gtIg3ITMmYtu3Co5j4oJag_179.jpg +"[{'category_id': 0, 'bbox': [285, 302, 291, 309], 'bbox_mode': 0, 'segmentation': [287, 303, 288, 303, 288, 302, 289, 302, 290, 302, 290, 303, 290, 304, 290, 305, 291, 306, 291, 307, 290, 308, 289, 308, 288, 309, 287, 309, 286, 308, 285, 307, 286, 305, 287, 304]}, {'category_id': 1, 'bbox': [322, 298, 329, 305], 'bbox_mode': 0, 'segmentation': [323, 302, 323, 301, 322, 301, 322, 300, 322, 300, 322, 299, 323, 298, 324, 298, 325, 299, 326, 300, 327, 300, 329, 301, 329, 301, 329, 302, 329, 302, 328, 303, 327, 303, 326, 303, 325, 303, 325, 304, 324, 305, 324, 304, 324, 303]}]",valid,2136,owNbYmycw0kohZ64hNOqtg,0,../data/image/owNbYmycw0kohZ64hNOqtg_0.jpg +"[{'category_id': 0, 'bbox': [479, 293, 492, 309], 'bbox_mode': 0, 'segmentation': [479, 300, 479, 301, 479, 302, 479, 303, 479, 304, 479, 305, 479, 305, 480, 307, 480, 307, 481, 308, 482, 308, 483, 309, 484, 309, 486, 309, 488, 309, 489, 309, 491, 307, 491, 307, 492, 306, 492, 305, 492, 304, 492, 302, 492, 300, 491, 299, 491, 297, 490, 296, 489, 296, 489, 295, 489, 295, 488, 295, 488, 293, 487, 293, 486, 293, 486, 293, 485, 293, 484, 293, 483, 294, 482, 295, 482, 296, 482, 297, 482, 297, 481, 298, 480, 298, 479, 299]}]",valid,2137,N8SDtN8gOcy8UnqlJLJFNA,261,../data/image/N8SDtN8gOcy8UnqlJLJFNA_261.jpg +[],valid,2138,LCe96-7x7fCS9dn2JF67qA,61,../data/image/LCe96-7x7fCS9dn2JF67qA_61.jpg 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'segmentation': [231, 268, 234, 268, 234, 269, 234, 271, 234, 272, 233, 274, 231, 274, 230, 273, 230, 271, 230, 269]}]",valid,2145,1cmZ7cC3POlDEJJeJ40oBg,359,../data/image/1cmZ7cC3POlDEJJeJ40oBg_359.jpg +[],valid,2146,JIgLBN7lQt4cdJ2MsicWeA,248,../data/image/JIgLBN7lQt4cdJ2MsicWeA_248.jpg +[],valid,2147,_9m15_dnHPvWZ7HLmqBLIw,159,../data/image/_9m15_dnHPvWZ7HLmqBLIw_159.jpg +"[{'category_id': 1, 'bbox': [62, 247, 70, 252], 'bbox_mode': 0, 'segmentation': [69, 250, 70, 249, 70, 249, 70, 248, 70, 248, 69, 248, 68, 248, 67, 248, 66, 248, 65, 247, 64, 247, 63, 248, 62, 248, 62, 249, 63, 250, 64, 250, 64, 250, 65, 250, 66, 250, 66, 250, 67, 251, 67, 252, 68, 252, 68, 251, 68, 250, 69, 250]}]",valid,2148,bAdqlTvKFU2VCoMtFdNeQw,33,../data/image/bAdqlTvKFU2VCoMtFdNeQw_33.jpg +"[{'category_id': 0, 'bbox': [630, 83, 640, 108], 'bbox_mode': 0, 'segmentation': [637, 84, 637, 85, 637, 87, 635, 88, 634, 89, 632, 90, 631, 91, 630, 95, 630, 99, 630, 100, 631, 102, 633, 105, 635, 106, 637, 107, 639, 108, 639, 105, 640, 99, 639, 93, 639, 89, 639, 85, 639, 83, 639, 83, 638, 83]}]",valid,2149,YPF8SpXye_EiDwAYPwzQNw,65,../data/image/YPF8SpXye_EiDwAYPwzQNw_65.jpg +"[{'category_id': 0, 'bbox': [497, 186, 512, 205], 'bbox_mode': 0, 'segmentation': [498, 200, 499, 202, 499, 202, 499, 203, 500, 204, 500, 204, 501, 204, 502, 205, 502, 205, 503, 205, 504, 205, 505, 205, 507, 205, 507, 205, 509, 205, 509, 204, 510, 204, 511, 203, 511, 201, 511, 200, 512, 199, 512, 198, 512, 197, 512, 196, 512, 194, 511, 193, 510, 193, 508, 192, 507, 191, 507, 190, 507, 190, 507, 189, 507, 188, 507, 188, 507, 187, 506, 186, 506, 186, 505, 186, 504, 186, 504, 187, 504, 188, 504, 188, 504, 189, 504, 190, 504, 191, 504, 191, 503, 191, 502, 192, 502, 192, 501, 192, 501, 192, 501, 192, 500, 192, 500, 192, 499, 192, 499, 193, 498, 193, 497, 194, 497, 196, 497, 198]}]",valid,2150,IcKlS849A5RU5VEU76ubfA,281,../data/image/IcKlS849A5RU5VEU76ubfA_281.jpg +"[{'category_id': 1, 'bbox': [256, 152, 266, 170], 'bbox_mode': 0, 'segmentation': [260, 170, 260, 169, 259, 168, 256, 155, 256, 154, 256, 153, 257, 153, 258, 152, 259, 152, 260, 152, 261, 153, 262, 154, 266, 167, 266, 168, 266, 169, 266, 170, 265, 170, 263, 170, 261, 170]}, {'category_id': 1, 'bbox': [229, 152, 237, 170], 'bbox_mode': 0, 'segmentation': [234, 170, 233, 170, 233, 170, 232, 170, 231, 169, 231, 168, 230, 167, 230, 167, 230, 165, 230, 163, 230, 162, 230, 160, 230, 159, 229, 157, 229, 155, 230, 154, 230, 153, 231, 152, 233, 152, 235, 152, 236, 153, 236, 155, 236, 156, 236, 159, 236, 162, 237, 168, 237, 169, 237, 169, 236, 170]}, {'category_id': 1, 'bbox': [242, 152, 247, 166], 'bbox_mode': 0, 'segmentation': [242, 163, 242, 154, 242, 153, 243, 152, 244, 152, 246, 152, 247, 153, 247, 153, 247, 157, 247, 159, 247, 161, 247, 164, 247, 165, 246, 166, 245, 166, 244, 166, 243, 165, 243, 165, 242, 164, 242, 164]}]",valid,2151,K439bl97R3FJ9KM1WzMHwQ,270,../data/image/K439bl97R3FJ9KM1WzMHwQ_270.jpg +"[{'category_id': 1, 'bbox': [472, 298, 481, 304], 'bbox_mode': 0, 'segmentation': [473, 301, 474, 301, 472, 303, 473, 304, 475, 304, 477, 303, 479, 304, 480, 304, 481, 303, 481, 302, 479, 300, 475, 298, 474, 298, 473, 298]}]",valid,2152,dmOMbmAN21Hyop2rXhnhhA,0,../data/image/dmOMbmAN21Hyop2rXhnhhA_0.jpg +"[{'category_id': 1, 'bbox': [108, 276, 119, 287], 'bbox_mode': 0, 'segmentation': [118, 279, 113, 276, 112, 276, 110, 276, 109, 276, 108, 277, 108, 278, 108, 279, 109, 280, 109, 281, 110, 282, 110, 282, 110, 283, 110, 285, 110, 285, 110, 286, 111, 286, 111, 285, 112, 285, 112, 284, 112, 284, 113, 285, 114, 286, 115, 287, 115, 287, 118, 287, 118, 287, 119, 286, 119, 285, 119, 284, 119, 283, 119, 283, 119, 282, 119, 281, 119, 280, 119, 280]}]",valid,2153,jIFHaniWuCbhT9Sv99jp6g,345,../data/image/jIFHaniWuCbhT9Sv99jp6g_345.jpg +"[{'category_id': 0, 'bbox': [450, 290, 455, 295], 'bbox_mode': 0, 'segmentation': [451, 290, 451, 291, 450, 291, 450, 292, 450, 293, 450, 294, 451, 295, 451, 295, 452, 295, 453, 295, 454, 295, 454, 295, 455, 294, 455, 293, 455, 292, 455, 292, 455, 291, 454, 290, 453, 290, 452, 290]}]",valid,2154,NxLFXL_7JOgAwtk-td4qHg,86,../data/image/NxLFXL_7JOgAwtk-td4qHg_86.jpg +[],valid,2155,POE_qvSpM6zdHRnRStRCVQ,110,../data/image/POE_qvSpM6zdHRnRStRCVQ_110.jpg +[],valid,2156,zGN9gBowmSywGsfdxW2hcg,324,../data/image/zGN9gBowmSywGsfdxW2hcg_324.jpg +"[{'category_id': 0, 'bbox': [353, 229, 359, 234], 'bbox_mode': 0, 'segmentation': [354, 229, 359, 229, 359, 229, 359, 231, 359, 233, 358, 234, 356, 234, 354, 233, 354, 232, 353, 230]}]",valid,2157,eC7ajm5-Nm9D4D0PTsqIgA,260,../data/image/eC7ajm5-Nm9D4D0PTsqIgA_260.jpg +"[{'category_id': 1, 'bbox': [332, 266, 344, 274], 'bbox_mode': 0, 'segmentation': [332, 270, 335, 271, 335, 273, 335, 273, 337, 274, 339, 272, 341, 273, 343, 272, 344, 272, 344, 271, 343, 270, 334, 266, 333, 266, 333, 267, 332, 268]}, {'category_id': 1, 'bbox': [297, 267, 307, 275], 'bbox_mode': 0, 'segmentation': [299, 273, 301, 272, 301, 273, 302, 275, 303, 275, 304, 274, 305, 271, 307, 270, 307, 269, 307, 268, 306, 268, 305, 267, 303, 268, 299, 270, 297, 271]}]",valid,2158,A-iwN4oZyvTPf4GJyO64Zg,351,../data/image/A-iwN4oZyvTPf4GJyO64Zg_351.jpg +"[{'category_id': 0, 'bbox': [18, 154, 33, 168], 'bbox_mode': 0, 'segmentation': [23, 154, 26, 155, 28, 156, 30, 158, 32, 161, 33, 163, 32, 166, 29, 166, 29, 167, 28, 168, 26, 168, 23, 167, 22, 166, 21, 164, 19, 164, 18, 160, 18, 158, 19, 155, 21, 155]}]",valid,2159,ce0y1Co053zNWxtwlXA-bA,34,../data/image/ce0y1Co053zNWxtwlXA-bA_34.jpg +"[{'category_id': 1, 'bbox': [529, 214, 539, 218], 'bbox_mode': 0, 'segmentation': [532, 214, 532, 214, 533, 214, 534, 214, 534, 214, 535, 214, 536, 214, 537, 214, 538, 214, 538, 214, 539, 214, 539, 215, 539, 215, 539, 216, 538, 216, 537, 216, 536, 216, 536, 216, 535, 216, 535, 216, 535, 216, 534, 216, 534, 216, 534, 217, 534, 217, 534, 217, 534, 218, 533, 218, 533, 218, 532, 217, 532, 217, 532, 216, 532, 216, 531, 216, 531, 216, 530, 216, 529, 216, 529, 215, 529, 215, 530, 214, 530, 214, 531, 214, 531, 214]}]",valid,2160,7c3UjxEojqocJqdEnMNvNw,104,../data/image/7c3UjxEojqocJqdEnMNvNw_104.jpg +[],valid,2161,UiF2WsKMA3k69j2de97P0w,260,../data/image/UiF2WsKMA3k69j2de97P0w_260.jpg +"[{'category_id': 1, 'bbox': [505, 283, 520, 290], 'bbox_mode': 0, 'segmentation': [517, 283, 506, 286, 505, 286, 506, 287, 506, 288, 507, 289, 507, 289, 508, 289, 509, 290, 510, 290, 512, 289, 519, 287, 520, 287, 520, 286, 520, 285, 520, 284, 519, 283]}]",valid,2162,tvJIqX5yN1V2CUloQ2zQgA,208,../data/image/tvJIqX5yN1V2CUloQ2zQgA_208.jpg +"[{'category_id': 0, 'bbox': [552, 275, 558, 281], 'bbox_mode': 0, 'segmentation': [555, 275, 555, 276, 556, 276, 556, 277, 557, 277, 558, 278, 558, 278, 558, 279, 558, 280, 558, 280, 557, 280, 557, 281, 557, 281, 556, 281, 555, 281, 555, 281, 554, 281, 554, 281, 553, 281, 553, 280, 552, 279, 552, 279, 552, 278, 552, 277, 553, 277, 553, 277, 553, 277, 553, 276, 553, 275, 553, 275, 554, 275, 554, 275, 555, 275]}]",valid,2163,tVdJaJyn9OsqTe3onSsMKw,114,../data/image/tVdJaJyn9OsqTe3onSsMKw_114.jpg +"[{'category_id': 1, 'bbox': [72, 227, 84, 234], 'bbox_mode': 0, 'segmentation': [77, 233, 76, 231, 75, 231, 73, 231, 72, 231, 72, 230, 73, 228, 82, 227, 83, 227, 84, 227, 84, 229, 84, 230, 83, 231, 80, 231, 81, 232, 81, 232, 82, 234, 82, 234, 80, 234, 78, 234]}]",valid,2164,BKp3469b10T2aMuvigXEdg,222,../data/image/BKp3469b10T2aMuvigXEdg_222.jpg +"[{'category_id': 0, 'bbox': [234, 161, 241, 170], 'bbox_mode': 0, 'segmentation': [236, 162, 237, 161, 238, 162, 240, 163, 241, 165, 241, 167, 240, 168, 239, 170, 237, 170, 236, 170, 235, 168, 234, 167, 234, 166, 234, 164, 234, 163]}]",valid,2165,p3-dPNYj_J9evQthCmIw7A,356,../data/image/p3-dPNYj_J9evQthCmIw7A_356.jpg +[],valid,2166,Q9PEWTmL3qKDuc2i4Dg9OA,356,../data/image/Q9PEWTmL3qKDuc2i4Dg9OA_356.jpg +"[{'category_id': 1, 'bbox': [618, 53, 640, 78], 'bbox_mode': 0, 'segmentation': [619, 78, 619, 77, 618, 76, 621, 73, 625, 69, 628, 66, 633, 61, 636, 57, 640, 54, 640, 53, 640, 54, 640, 57, 640, 61, 640, 64, 639, 65, 639, 67, 639, 69, 639, 72, 637, 74, 636, 77, 632, 78, 629, 78, 627, 78, 625, 78, 623, 78, 622, 78, 620, 78]}]",valid,2167,wIG8zpTzoFjoPqy-TaKkgw,110,../data/image/wIG8zpTzoFjoPqy-TaKkgw_110.jpg +"[{'category_id': 1, 'bbox': [289, 203, 299, 211], 'bbox_mode': 0, 'segmentation': [289, 205, 293, 208, 291, 210, 292, 211, 294, 209, 297, 211, 299, 209, 291, 203]}]",valid,2168,4le5raBJ0J2D3cQqiQbPEA,269,../data/image/4le5raBJ0J2D3cQqiQbPEA_269.jpg +"[{'category_id': 0, 'bbox': [592, 41, 606, 59], 'bbox_mode': 0, 'segmentation': [603, 45, 604, 46, 604, 47, 604, 48, 604, 49, 604, 50, 605, 50, 605, 50, 606, 51, 606, 51, 605, 53, 604, 53, 603, 54, 603, 55, 602, 55, 602, 56, 601, 57, 600, 58, 599, 58, 599, 59, 597, 59, 597, 59, 596, 59, 595, 59, 595, 59, 594, 59, 593, 59, 593, 59, 592, 58, 592, 58, 592, 57, 592, 56, 592, 54, 592, 53, 593, 53, 593, 52, 593, 51, 593, 50, 593, 50, 593, 49, 593, 48, 594, 47, 595, 46, 596, 46, 597, 45, 598, 45, 599, 45, 599, 44, 600, 43, 600, 43, 601, 42, 602, 41, 603, 41, 604, 41, 604, 41, 605, 42, 605, 43, 605, 43]}]",valid,2169,HYgWHsbFUFZjTIi1Go9Olw,310,../data/image/HYgWHsbFUFZjTIi1Go9Olw_310.jpg +"[{'category_id': 1, 'bbox': [443, 221, 447, 229], 'bbox_mode': 0, 'segmentation': [444, 221, 444, 223, 444, 224, 443, 224, 443, 225, 443, 226, 443, 227, 443, 228, 444, 229, 444, 229, 445, 229, 445, 229, 446, 229, 446, 229, 447, 229, 447, 228, 447, 228, 447, 227, 447, 226, 447, 225, 447, 224, 446, 223, 446, 221, 445, 221]}]",valid,2170,LwmO-C-svfRf1lLRvI81VQ,232,../data/image/LwmO-C-svfRf1lLRvI81VQ_232.jpg +"[{'category_id': 0, 'bbox': [425, 39, 442, 62], 'bbox_mode': 0, 'segmentation': [427, 43, 430, 40, 431, 40, 433, 39, 434, 40, 434, 40, 435, 41, 435, 42, 437, 42, 439, 43, 440, 45, 441, 46, 441, 47, 442, 48, 442, 50, 442, 52, 442, 53, 441, 55, 440, 56, 439, 57, 438, 59, 437, 60, 437, 60, 436, 61, 434, 62, 432, 62, 431, 61, 431, 61, 430, 60, 429, 59, 428, 59, 427, 58, 427, 57, 426, 56, 426, 55, 427, 51, 427, 49, 428, 48, 429, 47, 430, 46, 431, 45, 432, 44, 432, 43, 431, 42, 429, 43, 428, 44, 427, 45, 426, 45, 426, 45, 425, 45, 425, 44, 426, 43]}]",valid,2171,JjKw9fsMzZ0oXndstTHhUQ,268,../data/image/JjKw9fsMzZ0oXndstTHhUQ_268.jpg +"[{'category_id': 0, 'bbox': [523, 273, 527, 277], 'bbox_mode': 0, 'segmentation': [525, 273, 524, 273, 524, 273, 524, 273, 523, 274, 523, 274, 523, 275, 523, 275, 523, 276, 523, 276, 523, 276, 524, 276, 524, 277, 524, 277, 525, 277, 525, 277, 525, 277, 526, 276, 526, 276, 526, 275, 526, 275, 527, 275, 527, 275, 527, 275, 527, 274, 527, 274, 527, 273, 527, 273]}]",valid,2172,MSb1vrCXj5JclrB_xShlMQ,358,../data/image/MSb1vrCXj5JclrB_xShlMQ_358.jpg +"[{'category_id': 1, 'bbox': [344, 301, 360, 308], 'bbox_mode': 0, 'segmentation': [360, 303, 360, 303, 360, 302, 358, 301, 357, 301, 355, 301, 354, 302, 353, 302, 351, 303, 350, 303, 348, 304, 346, 305, 345, 305, 344, 307, 345, 308, 346, 308, 348, 308, 350, 308, 351, 307, 353, 307, 355, 306, 357, 306, 358, 305, 359, 304, 360, 304]}]",valid,2173,UQqVPBgTsGFs_d70q3rfKQ,80,../data/image/UQqVPBgTsGFs_d70q3rfKQ_80.jpg +"[{'category_id': 1, 'bbox': [82, 148, 97, 160], 'bbox_mode': 0, 'segmentation': [82, 150, 82, 149, 82, 148, 84, 148, 85, 148, 87, 148, 89, 150, 90, 150, 93, 152, 95, 152, 97, 154, 97, 155, 97, 157, 96, 158, 94, 157, 93, 157, 92, 157, 92, 158, 92, 160, 90, 160, 88, 160, 88, 158, 88, 156, 88, 155, 87, 154, 86, 153, 84, 153, 83, 152, 82, 150]}]",valid,2174,pk_7cWwpi5sTHTHwChrbfQ,359,../data/image/pk_7cWwpi5sTHTHwChrbfQ_359.jpg +[],valid,2175,J8rPftvdvL8lRrrtCUS48Q,117,../data/image/J8rPftvdvL8lRrrtCUS48Q_117.jpg +"[{'category_id': 1, 'bbox': [95, 268, 104, 272], 'bbox_mode': 0, 'segmentation': [102, 269, 96, 268, 95, 268, 95, 269, 95, 270, 95, 271, 95, 271, 96, 271, 101, 272, 102, 272, 102, 272, 103, 271, 104, 271, 103, 270, 103, 270]}]",valid,2176,h-quEpuH7INPZA77A8znKw,71,../data/image/h-quEpuH7INPZA77A8znKw_71.jpg +"[{'category_id': 0, 'bbox': [409, 146, 419, 159], 'bbox_mode': 0, 'segmentation': [410, 152, 410, 153, 409, 154, 409, 155, 409, 155, 409, 157, 410, 157, 410, 158, 411, 159, 412, 159, 413, 159, 414, 159, 415, 159, 416, 158, 417, 157, 418, 156, 419, 154, 419, 152, 418, 151, 418, 150, 418, 149, 418, 148, 418, 147, 417, 146, 417, 146, 416, 146, 415, 146, 414, 146, 413, 147, 412, 149, 411, 150, 411, 150, 410, 151]}]",valid,2177,GoXIZlo-rSB5VjDYaUkBfQ,235,../data/image/GoXIZlo-rSB5VjDYaUkBfQ_235.jpg +"[{'category_id': 1, 'bbox': [404, 251, 418, 259], 'bbox_mode': 0, 'segmentation': [415, 251, 411, 253, 405, 257, 404, 258, 405, 258, 406, 259, 407, 259, 408, 259, 410, 259, 412, 259, 418, 255, 418, 254, 417, 252, 416, 251]}, {'category_id': 1, 'bbox': [551, 235, 561, 250], 'bbox_mode': 0, 'segmentation': [556, 236, 551, 247, 551, 248, 551, 249, 552, 250, 554, 250, 556, 250, 557, 250, 557, 248, 558, 245, 561, 236, 560, 236, 556, 235]}]",valid,2178,cW-OHZQDIhpuKYMkC8Veyw,215,../data/image/cW-OHZQDIhpuKYMkC8Veyw_215.jpg +"[{'category_id': 0, 'bbox': [444, 129, 456, 146], 'bbox_mode': 0, 'segmentation': [448, 133, 449, 132, 449, 131, 449, 130, 449, 129, 450, 129, 451, 129, 451, 130, 452, 131, 453, 132, 454, 132, 455, 132, 455, 133, 456, 137, 456, 139, 455, 140, 454, 141, 453, 142, 453, 144, 453, 145, 452, 145, 451, 146, 449, 146, 448, 146, 447, 145, 447, 144, 447, 144, 446, 143, 445, 143, 444, 142, 444, 141, 444, 140, 444, 138, 444, 137, 445, 136, 445, 135, 446, 134, 447, 133]}]",valid,2179,h9eWdZeg8kC36cMD-GpRnA,63,../data/image/h9eWdZeg8kC36cMD-GpRnA_63.jpg +"[{'category_id': 1, 'bbox': [551, 188, 569, 203], 'bbox_mode': 0, 'segmentation': [553, 194, 558, 196, 556, 199, 551, 202, 551, 203, 553, 203, 557, 202, 561, 197, 563, 198, 565, 198, 568, 197, 569, 196, 568, 195, 558, 188, 555, 189, 554, 191, 553, 192]}]",valid,2180,aOzBkOKrtuB3ORcE-yAMKQ,315,../data/image/aOzBkOKrtuB3ORcE-yAMKQ_315.jpg +[],valid,2181,ifqIN2rUgv0QJ9ujINOTIQ,9,../data/image/ifqIN2rUgv0QJ9ujINOTIQ_9.jpg 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236]}]",valid,2197,3IA-VODYqAdhmZYojvAjug,278,../data/image/3IA-VODYqAdhmZYojvAjug_278.jpg +[],valid,2198,ISdLNJI_Nxs0IwMRY3GDzQ,166,../data/image/ISdLNJI_Nxs0IwMRY3GDzQ_166.jpg +[],valid,2199,J_IMtolKM2RcLfHpUvVCZA,350,../data/image/J_IMtolKM2RcLfHpUvVCZA_350.jpg +"[{'category_id': 1, 'bbox': [390, 214, 409, 226], 'bbox_mode': 0, 'segmentation': [402, 214, 394, 218, 391, 220, 391, 221, 390, 223, 390, 224, 392, 225, 394, 226, 396, 225, 398, 225, 399, 224, 401, 223, 403, 221, 404, 220, 406, 218, 409, 218, 409, 216, 406, 216, 405, 215, 404, 214]}]",valid,2200,0E3WnKRrC_oywmAn-E2WrQ,99,../data/image/0E3WnKRrC_oywmAn-E2WrQ_99.jpg +"[{'category_id': 1, 'bbox': [311, 243, 315, 249], 'bbox_mode': 0, 'segmentation': [315, 244, 315, 245, 315, 245, 315, 246, 314, 247, 314, 247, 314, 248, 314, 249, 313, 249, 312, 249, 312, 248, 311, 247, 311, 246, 311, 246, 311, 245, 311, 244, 311, 244, 311, 243, 312, 243, 313, 243, 314, 243, 314, 244]}]",valid,2201,Hd3W5c2cQqeRhr_M_f2Zig,323,../data/image/Hd3W5c2cQqeRhr_M_f2Zig_323.jpg +[],valid,2202,AJlytPOdCY-bXc14BoPQoA,178,../data/image/AJlytPOdCY-bXc14BoPQoA_178.jpg +"[{'category_id': 0, 'bbox': [412, 275, 417, 281], 'bbox_mode': 0, 'segmentation': [413, 280, 414, 281, 415, 281, 416, 281, 416, 281, 417, 280, 417, 279, 416, 278, 416, 278, 416, 277, 416, 276, 415, 275, 415, 275, 414, 275, 414, 275, 414, 276, 413, 276, 413, 277, 413, 277, 412, 278, 412, 278, 412, 279, 412, 279, 412, 280, 413, 280]}]",valid,2203,xznsYfwxXAog4GzRHyg5Xg,36,../data/image/xznsYfwxXAog4GzRHyg5Xg_36.jpg +"[{'category_id': 0, 'bbox': [485, 124, 498, 140], 'bbox_mode': 0, 'segmentation': [488, 125, 494, 124, 495, 124, 497, 125, 498, 127, 498, 130, 498, 132, 498, 133, 497, 135, 496, 139, 491, 140, 489, 139, 487, 139, 485, 136, 485, 134, 485, 131, 485, 129, 486, 127]}]",valid,2204,d_OtCzeiRtu-G5sXcy8pvA,359,../data/image/d_OtCzeiRtu-G5sXcy8pvA_359.jpg 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304, 160, 304, 158, 302, 156]}]",valid,2208,aHg60rhzKPRXwJakTywgow,357,../data/image/aHg60rhzKPRXwJakTywgow_357.jpg +[],valid,2209,GubXlxuyKHlJGv2ZKgOnCA,348,../data/image/GubXlxuyKHlJGv2ZKgOnCA_348.jpg +"[{'category_id': 1, 'bbox': [149, 249, 154, 254], 'bbox_mode': 0, 'segmentation': [150, 249, 149, 249, 149, 250, 149, 250, 149, 251, 149, 251, 149, 252, 149, 253, 150, 253, 150, 253, 151, 254, 152, 254, 152, 254, 153, 254, 153, 254, 153, 253, 154, 252, 154, 251, 153, 250, 153, 250, 152, 250, 151, 249, 150, 249]}]",valid,2210,0SEVAmA6BRUqBG_PX20Kgw,179,../data/image/0SEVAmA6BRUqBG_PX20Kgw_179.jpg +"[{'category_id': 0, 'bbox': [344, 178, 349, 185], 'bbox_mode': 0, 'segmentation': [349, 182, 349, 181, 349, 181, 348, 180, 348, 179, 348, 178, 347, 178, 347, 178, 346, 178, 346, 178, 345, 179, 344, 180, 344, 181, 344, 182, 344, 183, 344, 184, 345, 185, 346, 185, 347, 185, 348, 185, 348, 184, 349, 183]}]",valid,2211,E5zAhNMrTdHMP-f9UjSlBw,137,../data/image/E5zAhNMrTdHMP-f9UjSlBw_137.jpg 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278, 410, 279, 405, 282, 406, 285, 404, 286, 403, 282, 400, 284, 398, 284, 397, 283, 396, 282, 395, 281, 394, 280]}]",valid,2215,flk02sgYeUP1WB0SP-ydBQ,266,../data/image/flk02sgYeUP1WB0SP-ydBQ_266.jpg +[],valid,2216,3EzqwRp11xqlJ0uEn4Dkrw,85,../data/image/3EzqwRp11xqlJ0uEn4Dkrw_85.jpg +"[{'category_id': 1, 'bbox': [193, 293, 199, 297], 'bbox_mode': 0, 'segmentation': [194, 295, 193, 296, 194, 297, 196, 297, 197, 296, 199, 295, 198, 294, 197, 293, 196, 294, 195, 294]}]",valid,2217,5hLlHaRnp1GXokICC72tSQ,260,../data/image/5hLlHaRnp1GXokICC72tSQ_260.jpg +"[{'category_id': 0, 'bbox': [338, 307, 345, 313], 'bbox_mode': 0, 'segmentation': [340, 307, 344, 307, 345, 308, 345, 310, 345, 311, 344, 313, 342, 313, 341, 312, 339, 311, 338, 309, 338, 308]}]",valid,2218,fpSCo11F1dibzEMLERD6Bg,80,../data/image/fpSCo11F1dibzEMLERD6Bg_80.jpg +[],valid,2219,MP3AeHzTxSzY2Sy9_gdb8A,80,../data/image/MP3AeHzTxSzY2Sy9_gdb8A_80.jpg +"[{'category_id': 0, 'bbox': [188, 245, 191, 250], 'bbox_mode': 0, 'segmentation': [189, 246, 189, 245, 189, 245, 190, 245, 190, 245, 191, 246, 191, 246, 191, 247, 191, 248, 191, 248, 191, 249, 191, 249, 191, 250, 191, 250, 190, 250, 190, 250, 189, 250, 189, 250, 188, 250, 188, 250, 188, 249, 188, 249, 188, 248, 188, 248, 188, 247, 188, 246, 188, 246]}]",valid,2220,qJAX9POGygrhVYZdcdL-7g,143,../data/image/qJAX9POGygrhVYZdcdL-7g_143.jpg +"[{'category_id': 0, 'bbox': [128, 48, 138, 62], 'bbox_mode': 0, 'segmentation': [128, 53, 128, 54, 128, 56, 128, 57, 128, 59, 129, 60, 130, 60, 131, 61, 132, 61, 134, 61, 134, 60, 135, 60, 135, 60, 136, 60, 136, 61, 137, 62, 138, 61, 138, 61, 138, 60, 138, 58, 137, 56, 136, 54, 136, 52, 135, 50, 133, 49, 132, 48, 130, 48, 129, 49, 128, 50, 128, 51]}]",valid,2221,KFKSS7MroXJnGqT8LSj8dQ,180,../data/image/KFKSS7MroXJnGqT8LSj8dQ_180.jpg +[],valid,2222,IDChgmLpkaNCp17W5TiMuA,142,../data/image/IDChgmLpkaNCp17W5TiMuA_142.jpg +"[{'category_id': 1, 'bbox': [222, 289, 231, 294], 'bbox_mode': 0, 'segmentation': [229, 292, 229, 291, 230, 290, 230, 290, 231, 290, 231, 289, 231, 289, 230, 289, 229, 289, 228, 289, 227, 289, 226, 289, 226, 289, 225, 289, 225, 290, 224, 290, 224, 290, 223, 291, 222, 291, 222, 291, 222, 292, 222, 292, 222, 292, 222, 293, 222, 293, 223, 293, 224, 293, 224, 293, 224, 293, 225, 293, 226, 293, 226, 293, 227, 293, 227, 292, 228, 292, 228, 292, 228, 292, 228, 293, 228, 293, 229, 293, 229, 294, 229, 294, 229, 294, 230, 293, 230, 293, 230, 293, 230, 292, 229, 292]}]",valid,2223,hh8eGPAklYCWnrdOT0DFBg,267,../data/image/hh8eGPAklYCWnrdOT0DFBg_267.jpg +[],valid,2224,01lsdUwANTqPacdm2zAXSw,268,../data/image/01lsdUwANTqPacdm2zAXSw_268.jpg +"[{'category_id': 1, 'bbox': [502, 201, 514, 207], 'bbox_mode': 0, 'segmentation': [505, 201, 513, 201, 514, 202, 514, 203, 513, 204, 512, 205, 511, 205, 510, 205, 509, 205, 509, 206, 508, 207, 507, 207, 506, 207, 505, 206, 504, 206, 504, 206, 503, 206, 502, 205, 502, 205, 502, 205, 502, 204, 502, 204, 503, 203, 503, 202, 504, 201, 504, 201]}]",valid,2225,sPQid7Q2vhx5WHlv-QDYFQ,359,../data/image/sPQid7Q2vhx5WHlv-QDYFQ_359.jpg +"[{'category_id': 0, 'bbox': [358, 129, 364, 135], 'bbox_mode': 0, 'segmentation': [361, 129, 359, 130, 358, 132, 359, 135, 361, 135, 364, 135, 364, 132, 363, 131, 362, 129]}, {'category_id': 0, 'bbox': [546, 257, 560, 267], 'bbox_mode': 0, 'segmentation': [547, 263, 557, 257, 559, 258, 560, 260, 557, 263, 558, 267, 554, 267, 553, 264, 550, 266, 548, 266, 546, 264]}]",valid,2226,7S98uT76HePoJ5XZ6UkXrw,209,../data/image/7S98uT76HePoJ5XZ6UkXrw_209.jpg +"[{'category_id': 1, 'bbox': [81, 283, 94, 291], 'bbox_mode': 0, 'segmentation': [82, 286, 85, 288, 85, 290, 86, 291, 88, 290, 90, 290, 92, 290, 93, 290, 94, 288, 84, 283, 82, 283, 81, 284]}]",valid,2227,e2qfOolCvwcwS1Va6Odq2A,270,../data/image/e2qfOolCvwcwS1Va6Odq2A_270.jpg +[],valid,2228,V0OIocm52EZ7y2d0Ns1Ymw,268,../data/image/V0OIocm52EZ7y2d0Ns1Ymw_268.jpg +[],valid,2229,IMdNhsU06Scon9t5bA6KfA,9,../data/image/IMdNhsU06Scon9t5bA6KfA_9.jpg 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104, 245, 103, 246, 97, 246, 97, 249, 94, 249, 93, 246, 91, 246, 87, 246, 87, 245, 86, 244, 86, 243, 85, 243, 85, 242]}]",valid,2235,1Yj6oTRD6ZZKTgWnngGZVQ,324,../data/image/1Yj6oTRD6ZZKTgWnngGZVQ_324.jpg +"[{'category_id': 1, 'bbox': [408, 266, 416, 271], 'bbox_mode': 0, 'segmentation': [409, 266, 415, 268, 416, 269, 416, 270, 415, 271, 411, 270, 410, 271, 409, 270, 410, 269, 408, 269, 408, 268, 408, 267]}]",valid,2236,abB2s-sC9RgRdupWT-pAog,128,../data/image/abB2s-sC9RgRdupWT-pAog_128.jpg +[],valid,2237,M39JlTo_EMkQ1XNGQskw1g,248,../data/image/M39JlTo_EMkQ1XNGQskw1g_248.jpg +"[{'category_id': 0, 'bbox': [535, 176, 545, 186], 'bbox_mode': 0, 'segmentation': [539, 176, 536, 177, 535, 178, 535, 180, 535, 182, 535, 183, 536, 184, 536, 184, 537, 185, 539, 186, 540, 186, 541, 186, 543, 185, 544, 184, 545, 182, 545, 180, 545, 178, 545, 176, 544, 176]}]",valid,2238,UVD5V9xd6212uX9wcmpIfg,260,../data/image/UVD5V9xd6212uX9wcmpIfg_260.jpg +"[{'category_id': 0, 'bbox': [350, 243, 356, 251], 'bbox_mode': 0, 'segmentation': [351, 243, 354, 243, 355, 244, 355, 245, 355, 247, 356, 249, 355, 249, 354, 250, 353, 251, 352, 251, 352, 251, 351, 249, 350, 249, 350, 248, 350, 245, 351, 244]}]",valid,2239,aWu1bOKSLhQ2GHMaw56q-A,329,../data/image/aWu1bOKSLhQ2GHMaw56q-A_329.jpg +"[{'category_id': 0, 'bbox': [10, 189, 21, 199], 'bbox_mode': 0, 'segmentation': [12, 189, 18, 189, 20, 190, 21, 191, 21, 193, 21, 197, 19, 197, 18, 199, 15, 199, 14, 198, 13, 195, 10, 195, 10, 193, 10, 191]}]",valid,2240,a0F0pWdlEGipx9DgisNBfQ,351,../data/image/a0F0pWdlEGipx9DgisNBfQ_351.jpg +"[{'category_id': 1, 'bbox': [59, 285, 70, 290], 'bbox_mode': 0, 'segmentation': [60, 289, 64, 290, 66, 290, 70, 290, 70, 289, 70, 288, 70, 286, 69, 285, 66, 285, 59, 285, 59, 286, 59, 287, 59, 289]}]",valid,2241,8YrBQqh9JD1kDrcIUrRtUQ,245,../data/image/8YrBQqh9JD1kDrcIUrRtUQ_245.jpg +"[{'category_id': 1, 'bbox': [629, 229, 640, 237], 'bbox_mode': 0, 'segmentation': [629, 235, 630, 233, 631, 231, 633, 229, 636, 229, 638, 230, 640, 231, 640, 234, 640, 237, 638, 237, 634, 236, 630, 236]}, {'category_id': 1, 'bbox': [597, 230, 619, 242], 'bbox_mode': 0, 'segmentation': [619, 232, 618, 231, 617, 230, 615, 230, 614, 231, 612, 232, 610, 233, 608, 233, 605, 234, 603, 235, 600, 236, 598, 237, 597, 238, 597, 240, 599, 241, 601, 242, 603, 242, 605, 242, 606, 241, 608, 241, 610, 240, 612, 239, 614, 238, 616, 238, 616, 240, 618, 240, 618, 238, 618, 236, 619, 234, 619, 233]}]",valid,2242,krO7wwaABCZOR0uk4TrXMA,310,../data/image/krO7wwaABCZOR0uk4TrXMA_310.jpg +"[{'category_id': 1, 'bbox': [548, 275, 553, 282], 'bbox_mode': 0, 'segmentation': [553, 276, 553, 275, 553, 275, 553, 275, 553, 275, 553, 275, 552, 275, 551, 275, 550, 275, 550, 275, 549, 276, 549, 277, 549, 277, 549, 278, 549, 278, 548, 278, 548, 279, 548, 279, 548, 280, 548, 280, 548, 281, 548, 281, 549, 282, 550, 282, 550, 282, 551, 282, 551, 282, 552, 282, 552, 281, 552, 281, 553, 280, 553, 280, 553, 279, 553, 279, 553, 278, 553, 278, 553, 278, 552, 278, 552, 277, 552, 276, 552, 276, 552, 276, 552, 276, 552, 276, 553, 276]}]",valid,2243,lwXq2LayMq7AVl26IxULdQ,300,../data/image/lwXq2LayMq7AVl26IxULdQ_300.jpg +"[{'category_id': 1, 'bbox': [233, 221, 248, 231], 'bbox_mode': 0, 'segmentation': [234, 226, 234, 225, 235, 225, 236, 224, 237, 224, 238, 223, 240, 222, 241, 222, 243, 221, 245, 221, 247, 222, 248, 224, 247, 226, 244, 227, 244, 228, 244, 229, 245, 230, 244, 231, 243, 231, 242, 230, 240, 229, 238, 230, 237, 229, 235, 228, 234, 227, 233, 227]}]",valid,2244,o4P_CCcEqkx7yLQz9FZzXg,90,../data/image/o4P_CCcEqkx7yLQz9FZzXg_90.jpg +"[{'category_id': 0, 'bbox': [105, 105, 115, 115], 'bbox_mode': 0, 'segmentation': [107, 105, 112, 105, 115, 108, 115, 112, 115, 114, 113, 113, 112, 115, 110, 115, 109, 115, 107, 114, 107, 111, 105, 110, 105, 109, 105, 106]}]",valid,2245,9q0qEO9zTEKckcyoHr5vmg,243,../data/image/9q0qEO9zTEKckcyoHr5vmg_243.jpg +[],valid,2246,6rv9QmfMUz088MePin-qnw,269,../data/image/6rv9QmfMUz088MePin-qnw_269.jpg 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303, 182, 304, 179, 304, 178, 304, 177, 304, 175, 304, 174, 303, 173, 302, 172, 302, 171, 301, 170, 301, 169, 301, 168, 301, 167, 301, 167, 301, 166, 301, 166, 301, 165, 300, 165, 299, 165, 299, 165, 299, 166, 299, 166, 299, 166, 299, 167, 298, 168, 298, 168, 297, 171, 297, 171, 297, 172, 297, 173, 296, 174, 296, 174, 296, 175, 295, 175, 295, 176, 295, 177, 295, 178]}]",valid,2250,-Gbw36DViOdECDDhMSVhiw,222,../data/image/-Gbw36DViOdECDDhMSVhiw_222.jpg +"[{'category_id': 1, 'bbox': [233, 292, 242, 297], 'bbox_mode': 0, 'segmentation': [235, 293, 238, 292, 240, 292, 241, 292, 242, 293, 242, 294, 242, 296, 239, 297, 238, 297, 236, 297, 235, 297, 234, 297, 233, 296, 233, 295, 233, 294]}]",valid,2251,0uvvQDzGjNWwGZ8bOOATbg,260,../data/image/0uvvQDzGjNWwGZ8bOOATbg_260.jpg +"[{'category_id': 1, 'bbox': [455, 293, 468, 306], 'bbox_mode': 0, 'segmentation': [459, 305, 463, 302, 464, 303, 464, 305, 466, 305, 467, 303, 466, 299, 467, 298, 468, 296, 467, 294, 466, 293, 465, 294, 457, 300, 455, 303, 455, 304, 457, 306]}, {'category_id': 1, 'bbox': [145, 299, 160, 310], 'bbox_mode': 0, 'segmentation': [148, 308, 151, 307, 153, 309, 154, 310, 156, 310, 157, 310, 159, 308, 160, 306, 149, 299, 146, 299, 146, 301, 145, 302, 148, 305, 147, 307]}]",valid,2252,6n_YKO8g-kP2Zw21-MlRIg,194,../data/image/6n_YKO8g-kP2Zw21-MlRIg_194.jpg +"[{'category_id': 0, 'bbox': [548, 265, 560, 279], 'bbox_mode': 0, 'segmentation': [560, 275, 560, 266, 560, 266, 559, 266, 557, 266, 556, 266, 555, 266, 555, 265, 554, 265, 553, 265, 553, 265, 552, 265, 552, 266, 551, 266, 551, 267, 551, 267, 551, 267, 551, 268, 551, 268, 551, 268, 550, 269, 550, 269, 550, 270, 550, 270, 550, 270, 549, 270, 549, 271, 549, 271, 548, 271, 548, 272, 548, 272, 548, 272, 548, 273, 548, 274, 548, 274, 548, 275, 548, 275, 548, 275, 549, 276, 549, 276, 549, 277, 550, 277, 550, 278, 550, 278, 550, 278, 552, 278, 552, 278, 553, 279, 554, 279, 555, 279, 555, 279, 556, 279, 556, 278, 557, 278, 558, 277, 558, 276, 558, 276, 558, 276, 558, 275, 558, 275, 558, 274, 558, 274, 558, 274]}]",valid,2253,poiSt7Hk9dDdDAuDMbrb4g,68,../data/image/poiSt7Hk9dDdDAuDMbrb4g_68.jpg +"[{'category_id': 1, 'bbox': [415, 256, 443, 275], 'bbox_mode': 0, 'segmentation': [424, 269, 423, 269, 421, 269, 420, 269, 419, 269, 417, 269, 416, 268, 416, 268, 416, 266, 415, 264, 415, 263, 415, 262, 416, 261, 416, 261, 417, 261, 419, 260, 421, 259, 424, 259, 426, 259, 431, 258, 433, 258, 436, 257, 437, 257, 439, 257, 440, 256, 441, 256, 442, 257, 442, 257, 443, 258, 443, 260, 443, 262, 443, 263, 443, 264, 442, 265, 441, 265, 440, 266, 439, 266, 436, 266, 434, 266, 433, 267, 433, 267, 433, 269, 433, 270, 433, 270, 433, 272, 432, 274, 431, 275, 430, 275, 429, 274, 428, 273, 428, 270, 428, 268, 428, 268, 428, 268, 426, 268, 425, 268, 424, 268]}]",valid,2254,Y31KUmc26--acRo6HUa-7A,215,../data/image/Y31KUmc26--acRo6HUa-7A_215.jpg +"[{'category_id': 1, 'bbox': [105, 270, 114, 279], 'bbox_mode': 0, 'segmentation': [105, 271, 105, 272, 105, 273, 105, 274, 106, 275, 106, 276, 107, 277, 108, 278, 109, 279, 110, 278, 112, 278, 113, 277, 113, 276, 114, 276, 114, 274, 114, 273, 114, 271, 113, 270, 113, 270, 112, 270, 107, 270, 106, 270]}, {'category_id': 1, 'bbox': [261, 280, 268, 287], 'bbox_mode': 0, 'segmentation': [264, 280, 263, 281, 262, 281, 262, 282, 261, 284, 261, 285, 262, 285, 262, 286, 263, 286, 264, 287, 264, 287, 265, 287, 265, 287, 266, 286, 267, 286, 268, 286, 268, 285, 268, 284, 268, 283, 268, 282, 267, 281, 266, 280]}]",valid,2255,mLAXQ69JrlGyfsGtLlTNeg,29,../data/image/mLAXQ69JrlGyfsGtLlTNeg_29.jpg +[],valid,2256,h5HB2hTqjVHdvaZnDKC3AA,282,../data/image/h5HB2hTqjVHdvaZnDKC3AA_282.jpg +"[{'category_id': 1, 'bbox': [427, 278, 436, 284], 'bbox_mode': 0, 'segmentation': [436, 278, 435, 278, 434, 278, 433, 278, 431, 279, 430, 279, 429, 280, 427, 281, 427, 281, 427, 282, 428, 283, 429, 283, 430, 283, 431, 283, 431, 283, 432, 284, 433, 284, 433, 283, 433, 282, 433, 281, 434, 281, 435, 280, 436, 280, 436, 279, 436, 279]}, 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410, 297, 410, 296, 410, 296]}]",valid,2258,jPiOh3jUYOsftQ9gdKU4EQ,128,../data/image/jPiOh3jUYOsftQ9gdKU4EQ_128.jpg +[],valid,2259,L0s1jw0-95bjbhA5vusw_g,248,../data/image/L0s1jw0-95bjbhA5vusw_g_248.jpg +"[{'category_id': 1, 'bbox': [450, 286, 460, 294], 'bbox_mode': 0, 'segmentation': [456, 292, 456, 293, 456, 293, 455, 293, 455, 293, 454, 293, 454, 294, 454, 294, 453, 294, 453, 294, 453, 294, 452, 294, 452, 294, 452, 294, 452, 293, 451, 293, 451, 292, 451, 292, 451, 292, 450, 292, 450, 291, 450, 291, 451, 290, 453, 289, 454, 288, 455, 288, 456, 287, 456, 287, 457, 287, 457, 287, 458, 287, 459, 286, 459, 286, 460, 286, 460, 287, 460, 287, 460, 288, 460, 288, 460, 289, 460, 289, 459, 290]}]",valid,2260,sYfTH1ZBZf27iHmm5ARseg,267,../data/image/sYfTH1ZBZf27iHmm5ARseg_267.jpg +[],valid,2261,dBNtb78oe-mTGJ6NPYNR_A,99,../data/image/dBNtb78oe-mTGJ6NPYNR_A_99.jpg +[],valid,2262,5yeowdL6rCaIVH5BFVB9kA,176,../data/image/5yeowdL6rCaIVH5BFVB9kA_176.jpg +[],valid,2263,O8zJnv6xg6fR9jx4H4Ik0A,161,../data/image/O8zJnv6xg6fR9jx4H4Ik0A_161.jpg +[],valid,2264,YRaXp4J2sHlHqQ_zdIGY_A,298,../data/image/YRaXp4J2sHlHqQ_zdIGY_A_298.jpg +"[{'category_id': 1, 'bbox': [571, 210, 576, 215], 'bbox_mode': 0, 'segmentation': [571, 211, 571, 215, 572, 215, 575, 215, 575, 211, 575, 211, 576, 210, 575, 210, 573, 210, 572, 210]}]",valid,2265,5ZkTySVjp5giak-F5bi7qQ,224,../data/image/5ZkTySVjp5giak-F5bi7qQ_224.jpg +"[{'category_id': 0, 'bbox': [507, 236, 512, 244], 'bbox_mode': 0, 'segmentation': [507, 237, 507, 236, 508, 236, 509, 236, 510, 237, 510, 237, 510, 239, 511, 240, 512, 240, 512, 241, 512, 243, 511, 243, 511, 244, 509, 244, 508, 244, 508, 243, 508, 242, 508, 240, 508, 240, 510, 239, 510, 237, 508, 237, 507, 237, 507, 237]}, {'category_id': 0, 'bbox': [107, 235, 113, 242], 'bbox_mode': 0, 'segmentation': [109, 238, 109, 236, 109, 235, 110, 235, 112, 235, 113, 235, 113, 236, 112, 236, 111, 236, 110, 237, 110, 238, 111, 238, 111, 239, 111, 240, 111, 241, 111, 242, 109, 242, 108, 242, 108, 241, 107, 240, 107, 239, 107, 238, 108, 238]}]",valid,2266,j8MbEDaEn15juPGPJbG9EQ,236,../data/image/j8MbEDaEn15juPGPJbG9EQ_236.jpg +"[{'category_id': 0, 'bbox': [130, 175, 137, 183], 'bbox_mode': 0, 'segmentation': [132, 175, 135, 175, 136, 175, 137, 177, 137, 179, 137, 182, 136, 183, 135, 183, 132, 183, 132, 182, 131, 181, 130, 180, 130, 178, 130, 177]}]",valid,2267,f3YXkhIUWTT1iQBBZiVy1A,0,../data/image/f3YXkhIUWTT1iQBBZiVy1A_0.jpg +"[{'category_id': 0, 'bbox': [292, 173, 302, 194], 'bbox_mode': 0, 'segmentation': [296, 174, 296, 175, 296, 176, 296, 177, 296, 178, 296, 180, 295, 181, 293, 184, 293, 184, 293, 186, 292, 187, 292, 190, 293, 191, 294, 193, 296, 194, 295, 193, 296, 194, 297, 194, 299, 194, 301, 193, 302, 191, 302, 189, 302, 187, 302, 184, 301, 183, 300, 181, 299, 180, 298, 179, 297, 178, 297, 177, 297, 175, 297, 175, 297, 174, 296, 173]}]",valid,2268,Zylyryo6h2vO-i56hBU-GA,222,../data/image/Zylyryo6h2vO-i56hBU-GA_222.jpg 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322, 214, 324, 213, 324, 210]}]",valid,2271,wv-tXpxNtJg1bMvqve17vA,89,../data/image/wv-tXpxNtJg1bMvqve17vA_89.jpg +[],valid,2272,KMhSIApSzauhxpLWhqK5jg,267,../data/image/KMhSIApSzauhxpLWhqK5jg_267.jpg +"[{'category_id': 0, 'bbox': [211, 294, 219, 308], 'bbox_mode': 0, 'segmentation': [211, 303, 212, 302, 212, 301, 213, 300, 213, 299, 213, 299, 214, 298, 214, 296, 214, 295, 214, 294, 214, 294, 215, 294, 215, 294, 216, 294, 216, 294, 217, 294, 217, 295, 217, 296, 217, 297, 217, 298, 217, 298, 217, 299, 217, 299, 218, 300, 218, 300, 218, 301, 218, 302, 219, 303, 219, 304, 219, 304, 219, 305, 218, 306, 218, 307, 218, 307, 217, 308, 217, 308, 216, 308, 215, 308, 214, 308, 214, 308, 213, 308, 213, 308, 213, 307, 212, 307, 212, 306, 212, 305, 212, 305, 212, 304, 211, 303]}]",valid,2273,xQOmgVnBwYYWSBqKuSuy8A,29,../data/image/xQOmgVnBwYYWSBqKuSuy8A_29.jpg +[],valid,2274,SQUGuc_CwRj2KtWgazGMmQ,271,../data/image/SQUGuc_CwRj2KtWgazGMmQ_271.jpg +"[{'category_id': 0, 'bbox': [65, 245, 74, 256], 'bbox_mode': 0, 'segmentation': [68, 246, 69, 245, 70, 245, 71, 246, 73, 246, 74, 248, 74, 250, 74, 251, 73, 254, 71, 256, 69, 256, 68, 256, 67, 254, 66, 253, 65, 251, 65, 249, 65, 247, 66, 246, 67, 246]}, {'category_id': 0, 'bbox': [599, 243, 610, 255], 'bbox_mode': 0, 'segmentation': [603, 243, 604, 243, 605, 243, 607, 243, 609, 243, 610, 244, 610, 245, 610, 247, 610, 249, 610, 250, 608, 252, 607, 253, 605, 255, 603, 255, 601, 255, 600, 253, 599, 251, 599, 250, 599, 248, 599, 246, 600, 245, 600, 244, 602, 244]}]",valid,2275,vJrXB3eLDTcGCQE72EcxZw,154,../data/image/vJrXB3eLDTcGCQE72EcxZw_154.jpg +"[{'category_id': 0, 'bbox': [361, 291, 369, 305], 'bbox_mode': 0, 'segmentation': [366, 292, 366, 293, 366, 294, 366, 295, 366, 296, 367, 296, 367, 296, 367, 297, 368, 297, 368, 298, 368, 298, 368, 299, 368, 299, 368, 300, 369, 300, 369, 300, 369, 301, 368, 301, 368, 302, 368, 302, 368, 302, 368, 303, 368, 303, 368, 304, 367, 304, 367, 305, 366, 305, 366, 305, 365, 305, 364, 305, 364, 305, 363, 305, 363, 305, 363, 305, 363, 304, 362, 304, 362, 303, 362, 303, 362, 302, 362, 301, 361, 301, 361, 301, 361, 301, 361, 301, 361, 301, 361, 300, 361, 300, 361, 299, 361, 299, 361, 299, 362, 298, 362, 297, 362, 297, 362, 297, 362, 296, 363, 296, 363, 296, 363, 296, 363, 296, 363, 295, 364, 294, 364, 294, 364, 294, 364, 294, 364, 293, 364, 292, 364, 292, 364, 292, 364, 292, 364, 291, 364, 291, 364, 291, 364, 291, 364, 291, 365, 291, 365, 291, 365, 291, 365, 291]}]",valid,2276,pYYTwhbWs4aOf6yKbP-9og,29,../data/image/pYYTwhbWs4aOf6yKbP-9og_29.jpg +[],valid,2277,I4OP_i4chXeyE9350P6Qhw,350,../data/image/I4OP_i4chXeyE9350P6Qhw_350.jpg +"[{'category_id': 0, 'bbox': [91, 167, 105, 181], 'bbox_mode': 0, 'segmentation': [94, 167, 101, 167, 103, 169, 105, 173, 105, 178, 102, 178, 101, 180, 100, 181, 97, 181, 96, 180, 93, 177, 91, 176, 91, 173, 92, 170]}]",valid,2278,aphPa8RJBEwJywbqCQPqzA,34,../data/image/aphPa8RJBEwJywbqCQPqzA_34.jpg +"[{'category_id': 0, 'bbox': [361, 277, 373, 290], 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101]}]",valid,2284,ZwMiGAs6BOvhMLvnn9reUQ,179,../data/image/ZwMiGAs6BOvhMLvnn9reUQ_179.jpg +[],valid,2285,4G8vmBy_uZru_Sz-4FmHUw,350,../data/image/4G8vmBy_uZru_Sz-4FmHUw_350.jpg +"[{'category_id': 1, 'bbox': [494, 283, 502, 290], 'bbox_mode': 0, 'segmentation': [495, 287, 495, 287, 496, 287, 496, 288, 496, 288, 496, 289, 496, 289, 496, 290, 496, 290, 497, 290, 497, 290, 498, 290, 498, 290, 498, 290, 498, 290, 499, 290, 499, 290, 499, 290, 500, 290, 500, 290, 501, 290, 501, 290, 502, 290, 502, 290, 502, 290, 502, 289, 501, 288, 500, 287, 499, 286, 498, 285, 498, 284, 497, 284, 496, 283, 496, 283, 495, 284, 495, 284, 494, 285, 494, 285, 494, 286, 494, 286, 494, 286]}]",valid,2286,z3ngef5tCwDITUiuVxVSMg,189,../data/image/z3ngef5tCwDITUiuVxVSMg_189.jpg +[],valid,2287,6SpWyC_5Bg8W6kVdzEokEQ,279,../data/image/6SpWyC_5Bg8W6kVdzEokEQ_279.jpg +[],valid,2288,PAWVLj1OAQOj4RxUtO3QfQ,270,../data/image/PAWVLj1OAQOj4RxUtO3QfQ_270.jpg +"[{'category_id': 0, 'bbox': [166, 68, 172, 78], 'bbox_mode': 0, 'segmentation': [167, 78, 167, 77, 166, 76, 166, 74, 166, 73, 167, 72, 167, 71, 168, 70, 168, 69, 169, 68, 170, 68, 170, 68, 171, 70, 171, 71, 172, 72, 172, 74, 172, 75, 172, 76, 172, 77, 171, 78, 170, 78, 169, 78]}]",valid,2289,oPjkgCfR6skLZiCWfRmn4g,135,../data/image/oPjkgCfR6skLZiCWfRmn4g_135.jpg +"[{'category_id': 1, 'bbox': [428, 211, 445, 222], 'bbox_mode': 0, 'segmentation': [428, 216, 438, 211, 440, 211, 441, 211, 443, 211, 444, 211, 445, 211, 445, 212, 444, 213, 443, 213, 443, 214, 443, 215, 442, 216, 442, 217, 440, 218, 437, 220, 437, 221, 435, 221, 434, 222, 432, 222, 430, 221, 429, 221, 428, 220, 428, 219, 428, 218]}]",valid,2290,HM0oFEIbPcRpfXv36dceyw,99,../data/image/HM0oFEIbPcRpfXv36dceyw_99.jpg +"[{'category_id': 0, 'bbox': [55, 177, 74, 202], 'bbox_mode': 0, 'segmentation': [61, 178, 63, 177, 65, 178, 67, 179, 68, 181, 69, 184, 70, 187, 72, 189, 74, 192, 74, 196, 73, 198, 71, 200, 70, 202, 67, 202, 65, 202, 63, 202, 61, 201, 59, 198, 57, 197, 55, 194, 55, 191, 56, 187, 57, 184, 58, 182, 59, 180, 60, 178]}, {'category_id': 1, 'bbox': [0, 31, 44, 80], 'bbox_mode': 0, 'segmentation': [0, 31, 3, 31, 7, 31, 11, 31, 17, 32, 23, 34, 30, 37, 37, 39, 41, 44, 43, 50, 44, 55, 43, 57, 39, 57, 36, 57, 32, 57, 28, 56, 23, 56, 27, 60, 44, 74, 42, 80, 29, 69, 21, 60, 18, 56, 15, 53, 9, 51, 5, 50, 0, 48, 0, 44, 0, 37, 0, 32]}]",valid,2291,hOBzPL3EWm7YXP0iaGS0FQ,292,../data/image/hOBzPL3EWm7YXP0iaGS0FQ_292.jpg +"[{'category_id': 1, 'bbox': [318, 235, 330, 243], 'bbox_mode': 0, 'segmentation': [319, 241, 327, 236, 328, 235, 329, 235, 329, 235, 329, 236, 329, 236, 330, 236, 330, 237, 330, 237, 330, 238, 330, 238, 330, 239, 328, 240, 327, 240, 327, 240, 327, 241, 327, 241, 327, 242, 327, 242, 327, 243, 326, 243, 326, 243, 325, 242, 325, 242, 324, 242, 323, 242, 322, 243, 322, 243, 321, 243, 321, 243, 320, 243, 320, 243, 319, 243, 318, 243, 318, 242, 318, 242]}, {'category_id': 1, 'bbox': [363, 246, 369, 251], 'bbox_mode': 0, 'segmentation': [365, 251, 365, 250, 364, 250, 364, 249, 363, 247, 363, 246, 364, 246, 365, 246, 367, 246, 367, 246, 368, 246, 369, 247, 369, 248, 369, 249, 369, 250, 368, 251, 367, 251, 367, 251, 366, 251]}]",valid,2292,hvJm77L3A_7kDZrnCaJjmg,179,../data/image/hvJm77L3A_7kDZrnCaJjmg_179.jpg +"[{'category_id': 1, 'bbox': [491, 293, 498, 298], 'bbox_mode': 0, 'segmentation': [493, 298, 496, 297, 498, 296, 498, 295, 498, 294, 498, 294, 497, 293, 496, 293, 495, 293, 491, 295, 491, 296, 491, 296, 491, 297, 492, 298]}]",valid,2293,A5wTv2tW0UchPTfSWUu_5Q,86,../data/image/A5wTv2tW0UchPTfSWUu_5Q_86.jpg +"[{'category_id': 0, 'bbox': [336, 201, 346, 211], 'bbox_mode': 0, 'segmentation': [337, 208, 337, 208, 337, 207, 336, 206, 337, 206, 337, 205, 337, 204, 338, 203, 338, 203, 339, 202, 339, 201, 340, 201, 341, 201, 342, 201, 343, 201, 343, 202, 344, 202, 344, 203, 345, 204, 345, 204, 345, 205, 345, 206, 346, 207, 346, 208, 346, 208, 345, 209, 345, 209, 344, 210, 343, 211, 343, 211, 342, 211, 341, 211, 341, 211, 340, 211, 340, 211, 339, 211, 339, 211, 338, 210, 338, 209, 337, 209, 337, 208]}]",valid,2294,pToqwppz_1UtovEpWeH9SQ,196,../data/image/pToqwppz_1UtovEpWeH9SQ_196.jpg +[],valid,2295,K9D-qYYlA9E33ztfbiPJkA,19,../data/image/K9D-qYYlA9E33ztfbiPJkA_19.jpg +"[{'category_id': 0, 'bbox': [205, 298, 211, 303], 'bbox_mode': 0, 'segmentation': [206, 298, 209, 298, 210, 299, 211, 300, 211, 302, 210, 303, 208, 303, 207, 303, 206, 303, 205, 302, 205, 300]}]",valid,2296,5oGsFsYVip0OBi64msQqKw,332,../data/image/5oGsFsYVip0OBi64msQqKw_332.jpg +"[{'category_id': 1, 'bbox': [443, 267, 459, 272], 'bbox_mode': 0, 'segmentation': [449, 267, 447, 267, 445, 267, 444, 267, 443, 267, 443, 268, 443, 268, 444, 269, 444, 269, 446, 269, 446, 270, 446, 270, 446, 271, 446, 271, 445, 271, 445, 271, 445, 272, 446, 272, 446, 272, 447, 272, 449, 271, 451, 271, 452, 271, 453, 271, 455, 271, 456, 271, 457, 270, 458, 270, 458, 270, 459, 270, 459, 269, 459, 268, 459, 268, 458, 267, 457, 267, 456, 267, 454, 267]}]",valid,2297,KMzUeBrClNSG60UB_pWT9g,265,../data/image/KMzUeBrClNSG60UB_pWT9g_265.jpg +"[{'category_id': 1, 'bbox': [229, 229, 243, 234], 'bbox_mode': 0, 'segmentation': [229, 229, 233, 229, 240, 229, 241, 229, 242, 229, 243, 229, 243, 230, 243, 230, 243, 231, 242, 232, 242, 232, 241, 233, 241, 233, 240, 234, 239, 234, 238, 234, 237, 234, 236, 234, 235, 234, 234, 234, 234, 233, 233, 233, 233, 233, 232, 233, 232, 233, 231, 233, 231, 233, 230, 233, 230, 232, 230, 232, 229, 231, 229, 231, 229, 230, 229, 230, 229, 229]}, {'category_id': 1, 'bbox': [228, 209, 244, 215], 'bbox_mode': 0, 'segmentation': [229, 209, 242, 209, 242, 209, 243, 209, 244, 210, 244, 210, 244, 211, 244, 211, 244, 212, 244, 213, 244, 214, 244, 214, 243, 214, 242, 215, 242, 215, 241, 215, 241, 215, 241, 215, 240, 215, 240, 215, 239, 215, 238, 215, 238, 215, 237, 215, 237, 215, 236, 215, 235, 215, 235, 214, 234, 214, 234, 214, 233, 214, 232, 214, 231, 214, 231, 214, 230, 214, 230, 214, 229, 213, 229, 213, 229, 212, 229, 212, 229, 212, 228, 212, 228, 211, 228, 211, 228, 210, 228, 210, 228, 210, 228, 209, 229, 209]}]",valid,2298,sUj1BcKlOTYuTv6JMvbw_Q,124,../data/image/sUj1BcKlOTYuTv6JMvbw_Q_124.jpg +"[{'category_id': 0, 'bbox': [95, 234, 99, 242], 'bbox_mode': 0, 'segmentation': [96, 237, 96, 237, 95, 238, 95, 238, 95, 239, 95, 239, 95, 240, 96, 241, 96, 241, 97, 241, 98, 242, 98, 242, 99, 241, 99, 240, 99, 240, 99, 239, 99, 238, 99, 238, 99, 237, 99, 236, 99, 236, 98, 236, 98, 236, 98, 235, 98, 235, 98, 234, 98, 234, 97, 234, 97, 234, 97, 234, 97, 235, 97, 235, 97, 235, 97, 236, 96, 236, 96, 236, 96, 236]}]",valid,2299,HqcLUBUU_xUTdvjtPPqpJg,130,../data/image/HqcLUBUU_xUTdvjtPPqpJg_130.jpg +[],valid,2300,TjCkyheBUdoP8hCPCLHFMg,355,../data/image/TjCkyheBUdoP8hCPCLHFMg_355.jpg +"[{'category_id': 0, 'bbox': [584, 211, 594, 222], 'bbox_mode': 0, 'segmentation': [587, 211, 585, 214, 584, 216, 585, 219, 586, 221, 588, 222, 591, 221, 593, 218, 594, 215, 594, 213, 591, 211]}]",valid,2301,48XHRZBIs4grH1e0bKNLcw,111,../data/image/48XHRZBIs4grH1e0bKNLcw_111.jpg +[],valid,2302,OYruh4mQEsn9Q6pRsvEQ5g,359,../data/image/OYruh4mQEsn9Q6pRsvEQ5g_359.jpg +"[{'category_id': 1, 'bbox': [575, 276, 586, 286], 'bbox_mode': 0, 'segmentation': [575, 278, 575, 277, 576, 276, 576, 276, 577, 276, 579, 276, 580, 277, 582, 278, 583, 279, 585, 280, 586, 280, 586, 282, 586, 284, 585, 285, 584, 286, 583, 285, 581, 284, 580, 284, 578, 284, 578, 283, 578, 282, 577, 281, 576, 280, 575, 279, 575, 278]}]",valid,2303,tskTnKbIQYjDZI9_IgJwdw,326,../data/image/tskTnKbIQYjDZI9_IgJwdw_326.jpg +"[{'category_id': 0, 'bbox': [589, 274, 597, 279], 'bbox_mode': 0, 'segmentation': [590, 278, 589, 278, 589, 277, 589, 277, 589, 276, 590, 274, 591, 274, 592, 274, 592, 274, 593, 274, 593, 274, 594, 274, 594, 274, 596, 274, 597, 275, 597, 276, 597, 277, 597, 277, 597, 278, 597, 278, 597, 278, 597, 279, 597, 279, 596, 279, 596, 279, 595, 279, 595, 279, 594, 279, 593, 279, 593, 279, 592, 279, 592, 278, 592, 277, 592, 277]}]",valid,2304,xh4eoaOp-TVOWg-_URAG2A,0,../data/image/xh4eoaOp-TVOWg-_URAG2A_0.jpg +"[{'category_id': 1, 'bbox': [314, 272, 320, 279], 'bbox_mode': 0, 'segmentation': [317, 272, 318, 272, 318, 272, 319, 272, 319, 272, 320, 273, 320, 273, 320, 274, 320, 275, 320, 275, 319, 275, 319, 276, 319, 276, 319, 277, 318, 278, 318, 278, 317, 279, 317, 279, 316, 279, 316, 278, 316, 277, 316, 276, 315, 276, 315, 275, 314, 275, 314, 274, 314, 273, 314, 273, 315, 272, 315, 272, 316, 272]}]",valid,2305,GKJhFRzIL06fcKaHTv73rQ,331,../data/image/GKJhFRzIL06fcKaHTv73rQ_331.jpg +"[{'category_id': 1, 'bbox': [116, 212, 124, 219], 'bbox_mode': 0, 'segmentation': [118, 213, 119, 213, 119, 213, 120, 213, 120, 213, 121, 213, 122, 213, 122, 213, 122, 213, 123, 213, 123, 213, 123, 213, 124, 214, 124, 214, 124, 214, 124, 215, 124, 215, 124, 215, 124, 215, 124, 216, 124, 216, 124, 216, 123, 216, 123, 216, 123, 217, 123, 217, 123, 218, 122, 218, 122, 218, 122, 219, 122, 219, 121, 219, 121, 219, 121, 219, 121, 219, 121, 219, 121, 219, 121, 219, 120, 218, 120, 218, 120, 218, 120, 218, 120, 218, 120, 217, 119, 217, 119, 217, 119, 217, 119, 216, 119, 216, 118, 216, 118, 215, 118, 215, 118, 215, 117, 215, 117, 215, 117, 214, 117, 214, 116, 213, 116, 213, 117, 213, 117, 213, 117, 212]}]",valid,2306,VTAKy3d1QoLVQya6HqaAZw,357,../data/image/VTAKy3d1QoLVQya6HqaAZw_357.jpg +"[{'category_id': 1, 'bbox': [82, 285, 95, 293], 'bbox_mode': 0, 'segmentation': [84, 285, 95, 289, 95, 290, 95, 291, 93, 292, 92, 292, 89, 291, 89, 293, 87, 293, 87, 290, 83, 289, 82, 287, 82, 286]}, {'category_id': 1, 'bbox': [54, 284, 69, 293], 'bbox_mode': 0, 'segmentation': [57, 287, 66, 284, 68, 285, 69, 286, 69, 287, 68, 288, 66, 288, 63, 289, 65, 291, 64, 292, 64, 293, 61, 290, 60, 290, 57, 291, 56, 290, 55, 289, 54, 288, 54, 287, 55, 286]}]",valid,2307,aFfAkMdojgc7GUcpm_FanA,81,../data/image/aFfAkMdojgc7GUcpm_FanA_81.jpg +"[{'category_id': 1, 'bbox': [55, 315, 62, 320], 'bbox_mode': 0, 'segmentation': [62, 319, 62, 319, 62, 318, 62, 317, 62, 317, 61, 316, 61, 316, 60, 315, 59, 315, 58, 315, 57, 315, 56, 315, 55, 316, 55, 317, 55, 318, 55, 318, 56, 319, 57, 319, 58, 319, 59, 319, 59, 319, 59, 319, 60, 320, 60, 320, 61, 320, 62, 320, 62, 320, 62, 319, 62, 319]}]",valid,2308,LJGRp8ThvI-eHdVNATMo1Q,248,../data/image/LJGRp8ThvI-eHdVNATMo1Q_248.jpg +[],valid,2309,Ow9iIw4F9P6zB28dSFT1gg,31,../data/image/Ow9iIw4F9P6zB28dSFT1gg_31.jpg +"[{'category_id': 1, 'bbox': [204, 102, 211, 127], 'bbox_mode': 0, 'segmentation': [209, 102, 210, 103, 211, 104, 211, 106, 211, 109, 211, 111, 211, 114, 211, 116, 211, 119, 211, 122, 210, 125, 209, 126, 206, 127, 205, 125, 204, 121, 204, 118, 204, 116, 204, 113, 204, 110, 205, 108, 205, 106, 205, 104, 205, 103, 206, 102, 207, 102, 208, 102]}]",valid,2310,QCi0B2Wc6HJ_fwr0O-4H7A,358,../data/image/QCi0B2Wc6HJ_fwr0O-4H7A_358.jpg +[],valid,2311,Vx0XDtAXN2PNpBLC9He57g,358,../data/image/Vx0XDtAXN2PNpBLC9He57g_358.jpg +"[{'category_id': 1, 'bbox': [35, 242, 48, 248], 'bbox_mode': 0, 'segmentation': [44, 246, 45, 246, 47, 246, 48, 245, 48, 244, 48, 243, 47, 242, 38, 242, 37, 243, 36, 243, 35, 243, 35, 243, 35, 244, 35, 244, 35, 244, 36, 244, 36, 245, 37, 245, 38, 245, 39, 245, 40, 245, 40, 245, 40, 245, 40, 246, 40, 247, 40, 247, 40, 247, 41, 248, 41, 248, 42, 248, 42, 248, 43, 248, 43, 248, 43, 247, 43, 246, 43, 246, 43, 246]}]",valid,2312,CyX7UQSM3JKtlAEWIt8NcQ,268,../data/image/CyX7UQSM3JKtlAEWIt8NcQ_268.jpg +"[{'category_id': 1, 'bbox': [450, 261, 457, 268], 'bbox_mode': 0, 'segmentation': [451, 263, 456, 261, 457, 263, 456, 265, 457, 267, 456, 268, 454, 266, 452, 266, 451, 266, 450, 264]}, {'category_id': 0, 'bbox': [496, 292, 504, 298], 'bbox_mode': 0, 'segmentation': [497, 292, 501, 292, 503, 292, 504, 293, 504, 295, 503, 298, 499, 298, 498, 298, 496, 296, 496, 294]}]",valid,2313,dJsbGpoZlJh--r4qbbHc6g,279,../data/image/dJsbGpoZlJh--r4qbbHc6g_279.jpg +[],valid,2314,YGWLIwQuuq3fEfrG-CZ-Wg,0,../data/image/YGWLIwQuuq3fEfrG-CZ-Wg_0.jpg +[],valid,2315,RjLachJoy4AHdm74X34gsw,279,../data/image/RjLachJoy4AHdm74X34gsw_279.jpg +"[{'category_id': 0, 'bbox': [596, 0, 616, 21], 'bbox_mode': 0, 'segmentation': [597, 10, 596, 11, 596, 14, 596, 16, 597, 18, 598, 20, 601, 21, 604, 21, 607, 20, 609, 19, 612, 14, 613, 12, 614, 10, 615, 7, 616, 4, 616, 4, 616, 2, 615, 1, 614, 0, 613, 0, 613, 0, 613, 0, 612, 0, 610, 0, 607, 0, 604, 0, 603, 0, 603, 0, 603, 0, 603, 1, 603, 2, 601, 3, 600, 4, 599, 5, 598, 6, 597, 8]}]",valid,2316,vgxldcCboB-ROXgZgh_-CA,158,../data/image/vgxldcCboB-ROXgZgh_-CA_158.jpg +"[{'category_id': 1, 'bbox': [497, 238, 516, 250], 'bbox_mode': 0, 'segmentation': [511, 238, 497, 246, 498, 248, 499, 250, 502, 250, 506, 247, 508, 249, 510, 248, 509, 246, 515, 242, 516, 240, 514, 238]}]",valid,2317,AflSTpgA2Mx7Nrgl3-Blew,68,../data/image/AflSTpgA2Mx7Nrgl3-Blew_68.jpg +"[{'category_id': 1, 'bbox': [626, 255, 640, 266], 'bbox_mode': 0, 'segmentation': [638, 259, 631, 255, 630, 255, 628, 256, 627, 257, 627, 257, 626, 259, 627, 260, 628, 261, 629, 262, 630, 263, 633, 264, 634, 265, 635, 266, 636, 266, 637, 266, 638, 265, 639, 265, 640, 265, 640, 260]}]",valid,2318,GMUt2oiUuDeNkfdzRs5q2A,20,../data/image/GMUt2oiUuDeNkfdzRs5q2A_20.jpg +"[{'category_id': 0, 'bbox': [566, 149, 586, 164], 'bbox_mode': 0, 'segmentation': [585, 151, 582, 152, 579, 152, 577, 152, 574, 153, 572, 154, 572, 155, 574, 156, 574, 158, 574, 159, 574, 161, 572, 163, 571, 164, 568, 164, 566, 162, 566, 160, 567, 158, 567, 157, 568, 156, 568, 154, 568, 153, 571, 151, 575, 150, 579, 149, 581, 149, 584, 149, 585, 149, 586, 150]}]",valid,2319,-pNBn_8yNI7SMC2JPu5v3w,86,../data/image/-pNBn_8yNI7SMC2JPu5v3w_86.jpg +"[{'category_id': 0, 'bbox': [483, 211, 490, 218], 'bbox_mode': 0, 'segmentation': [485, 211, 487, 211, 488, 212, 489, 213, 490, 215, 489, 215, 488, 217, 487, 218, 485, 218, 485, 217, 484, 216, 484, 216, 483, 214, 484, 212]}, {'category_id': 1, 'bbox': [489, 152, 498, 155], 'bbox_mode': 0, 'segmentation': [493, 155, 498, 154, 498, 153, 498, 152, 497, 152, 492, 152, 491, 152, 490, 152, 489, 153, 489, 153, 490, 154, 491, 154, 492, 155]}]",valid,2320,FiBxDv5CyRvDUJZM_XS9_g,302,../data/image/FiBxDv5CyRvDUJZM_XS9_g_302.jpg +[],valid,2321,X8TNpKmxBvBqsEvqY46Ylw,99,../data/image/X8TNpKmxBvBqsEvqY46Ylw_99.jpg +[],valid,2322,Nu1OD0QYC9eCFMUez6xS9Q,114,../data/image/Nu1OD0QYC9eCFMUez6xS9Q_114.jpg +"[{'category_id': 0, 'bbox': [221, 130, 234, 145], 'bbox_mode': 0, 'segmentation': [224, 131, 230, 130, 232, 132, 233, 134, 234, 142, 231, 143, 230, 145, 226, 145, 224, 144, 223, 140, 221, 140, 221, 138, 221, 133, 222, 132]}]",valid,2323,1SLFSQY8onCINYfUUa-EBQ,90,../data/image/1SLFSQY8onCINYfUUa-EBQ_90.jpg +[],valid,2324,NCzj7Tk5FxUKU_eM5CiJsg,356,../data/image/NCzj7Tk5FxUKU_eM5CiJsg_356.jpg +"[{'category_id': 1, 'bbox': [127, 103, 142, 112], 'bbox_mode': 0, 'segmentation': [142, 108, 142, 107, 142, 106, 142, 105, 141, 104, 141, 104, 140, 103, 140, 103, 129, 105, 128, 105, 127, 105, 127, 106, 127, 106, 128, 108, 128, 108, 130, 108, 131, 109, 132, 109, 133, 109, 134, 110, 134, 111, 134, 111, 135, 111, 136, 112, 136, 111, 137, 111, 137, 110, 137, 109, 138, 109, 141, 108, 142, 108]}]",valid,2325,nI648LZ1yC6l8xMvlvyosA,95,../data/image/nI648LZ1yC6l8xMvlvyosA_95.jpg +"[{'category_id': 1, 'bbox': [254, 227, 257, 229], 'bbox_mode': 0, 'segmentation': [254, 227, 255, 227, 255, 227, 256, 227, 256, 227, 257, 227, 257, 227, 257, 227, 257, 228, 257, 228, 257, 229, 257, 229, 257, 229, 256, 229, 256, 229, 255, 229, 255, 229, 255, 229, 254, 229, 254, 229, 254, 228, 254, 228, 254, 228]}]",valid,2326,SWbUS-3lP-2RdVXNCCc4Hw,282,../data/image/SWbUS-3lP-2RdVXNCCc4Hw_282.jpg +"[{'category_id': 0, 'bbox': [111, 214, 118, 219], 'bbox_mode': 0, 'segmentation': [116, 219, 116, 219, 116, 219, 115, 219, 114, 219, 114, 219, 113, 219, 113, 219, 113, 219, 112, 219, 112, 218, 112, 218, 111, 217, 111, 217, 111, 217, 111, 216, 111, 215, 111, 215, 112, 214, 112, 214, 113, 214, 113, 214, 114, 214, 115, 214, 115, 214, 116, 214, 116, 214, 117, 214, 117, 214, 118, 215, 118, 215, 117, 216, 117, 216, 117, 216, 117, 217, 117, 218]}, {'category_id': 1, 'bbox': [71, 145, 88, 151], 'bbox_mode': 0, 'segmentation': [85, 145, 75, 146, 74, 146, 73, 146, 72, 146, 72, 146, 71, 146, 71, 147, 72, 148, 72, 148, 73, 149, 74, 149, 77, 149, 77, 149, 78, 149, 78, 150, 78, 150, 79, 151, 79, 151, 80, 151, 80, 151, 81, 151, 81, 150, 81, 150, 81, 149, 82, 149, 82, 149, 83, 149, 84, 149, 85, 149, 85, 149, 86, 149, 86, 149, 87, 149, 87, 148, 87, 148, 88, 147, 87, 146, 87, 146, 86, 145]}, {'category_id': 1, 'bbox': [238, 192, 241, 198], 'bbox_mode': 0, 'segmentation': [238, 195, 239, 193, 239, 192, 239, 192, 240, 192, 240, 192, 241, 193, 241, 193, 241, 193, 241, 194, 241, 196, 241, 197, 241, 197, 240, 198, 240, 198, 239, 198, 239, 197, 238, 197]}]",valid,2327,mvV_pExbSB9ElgfeIjr-nQ,88,../data/image/mvV_pExbSB9ElgfeIjr-nQ_88.jpg +[],valid,2328,YbEOXMa6zAvJCZmb7l-c3g,0,../data/image/YbEOXMa6zAvJCZmb7l-c3g_0.jpg +[],valid,2329,JD7lgzS5UIb5J3a0U6qroQ,42,../data/image/JD7lgzS5UIb5J3a0U6qroQ_42.jpg +"[{'category_id': 0, 'bbox': [582, 274, 586, 279], 'bbox_mode': 0, 'segmentation': [583, 274, 582, 275, 582, 277, 582, 278, 585, 279, 586, 277, 586, 274, 584, 274]}]",valid,2330,dLbeRXjudCLJtD_6_ira-Q,114,../data/image/dLbeRXjudCLJtD_6_ira-Q_114.jpg +[],valid,2331,UPD6kLyGVWroOI31hvrBUw,115,../data/image/UPD6kLyGVWroOI31hvrBUw_115.jpg +[],valid,2332,XOcbPeA1sGBy9l2BsWj8Ww,80,../data/image/XOcbPeA1sGBy9l2BsWj8Ww_80.jpg +"[{'category_id': 1, 'bbox': [507, 271, 518, 279], 'bbox_mode': 0, 'segmentation': [515, 271, 514, 271, 508, 276, 508, 277, 508, 277, 507, 277, 507, 278, 508, 278, 508, 279, 508, 279, 509, 279, 509, 279, 510, 279, 510, 279, 511, 278, 511, 278, 512, 278, 512, 278, 513, 279, 513, 279, 514, 279, 514, 279, 514, 278, 515, 278, 515, 277, 515, 276, 515, 276, 515, 275, 516, 275, 517, 274, 517, 274, 518, 273, 518, 273, 518, 273, 518, 272, 517, 272, 517, 271, 516, 271]}]",valid,2333,z5-7qq2zZOB6fo2AtWqPgQ,16,../data/image/z5-7qq2zZOB6fo2AtWqPgQ_16.jpg +"[{'category_id': 0, 'bbox': [491, 137, 498, 150], 'bbox_mode': 0, 'segmentation': [497, 137, 496, 138, 494, 139, 493, 140, 492, 141, 492, 143, 491, 144, 491, 146, 491, 148, 493, 149, 493, 150, 495, 150, 496, 149, 497, 149, 498, 148, 498, 145, 498, 142, 498, 140, 498, 138]}]",valid,2334,POge-LX6IdkET_JMYFH6Ag,310,../data/image/POge-LX6IdkET_JMYFH6Ag_310.jpg +"[{'category_id': 0, 'bbox': [187, 295, 193, 300], 'bbox_mode': 0, 'segmentation': [187, 295, 193, 295, 193, 297, 193, 298, 191, 300, 189, 300, 188, 299, 187, 297, 187, 296]}]",valid,2335,4CjQMWpRoFohBJRDG4yRkw,152,../data/image/4CjQMWpRoFohBJRDG4yRkw_152.jpg +"[{'category_id': 0, 'bbox': [517, 138, 532, 157], 'bbox_mode': 0, 'segmentation': [517, 146, 517, 147, 517, 148, 517, 148, 517, 149, 517, 149, 517, 150, 517, 150, 517, 151, 517, 151, 517, 152, 517, 152, 517, 153, 518, 153, 518, 154, 518, 155, 518, 155, 519, 155, 519, 156, 520, 156, 520, 156, 521, 157, 521, 157, 522, 157, 522, 157, 523, 157, 523, 157, 524, 157, 525, 157, 525, 157, 526, 157, 526, 156, 527, 156, 528, 155, 529, 154, 530, 153, 530, 153, 531, 152, 531, 151, 532, 150, 532, 148, 532, 147, 532, 146, 531, 145, 531, 144, 531, 143, 530, 142, 529, 142, 527, 141, 527, 141, 526, 140, 526, 140, 526, 139, 526, 138, 526, 138, 525, 138, 524, 138, 524, 138, 524, 139, 524, 139, 524, 140, 524, 140, 523, 141, 523, 141, 523, 142, 523, 142, 523, 142, 522, 142, 522, 142, 521, 142, 520, 142, 520, 143, 519, 143, 518, 143, 518, 144, 518, 144]}]",valid,2336,w5_KcZbxe-oZ2mZaiIhN-Q,111,../data/image/w5_KcZbxe-oZ2mZaiIhN-Q_111.jpg +"[{'category_id': 1, 'bbox': [380, 260, 396, 271], 'bbox_mode': 0, 'segmentation': [385, 264, 385, 264, 384, 264, 383, 265, 382, 265, 381, 266, 380, 266, 380, 267, 380, 268, 380, 270, 381, 271, 382, 271, 384, 271, 385, 270, 386, 270, 387, 269, 388, 269, 388, 269, 389, 270, 389, 270, 389, 271, 390, 271, 390, 271, 391, 270, 391, 269, 391, 268, 391, 267, 391, 267, 392, 267, 394, 266, 395, 265, 396, 264, 396, 262, 396, 261, 396, 260, 395, 260, 394, 260, 393, 260, 391, 261, 389, 262, 389, 262, 388, 262, 387, 263, 387, 263, 386, 263]}]",valid,2337,K9aavgFJwcihJZwSPbPLZQ,68,../data/image/K9aavgFJwcihJZwSPbPLZQ_68.jpg +[],valid,2338,Hxg26dh4Frrw-6_0Oh9B-A,189,../data/image/Hxg26dh4Frrw-6_0Oh9B-A_189.jpg +"[{'category_id': 1, 'bbox': [141, 88, 174, 99], 'bbox_mode': 0, 'segmentation': [144, 91, 161, 90, 172, 88, 174, 89, 174, 92, 174, 95, 171, 96, 150, 99, 148, 98, 144, 96, 142, 95, 141, 93, 141, 91, 141, 91]}, {'category_id': 1, 'bbox': [142, 123, 178, 137], 'bbox_mode': 0, 'segmentation': [155, 126, 175, 127, 176, 128, 177, 129, 178, 130, 178, 133, 178, 135, 175, 137, 172, 137, 158, 133, 153, 132, 147, 129, 145, 128, 144, 127, 142, 124, 144, 123, 146, 123, 151, 124]}, {'category_id': 0, 'bbox': [176, 221, 188, 234], 'bbox_mode': 0, 'segmentation': [182, 221, 184, 222, 185, 223, 187, 225, 188, 227, 188, 229, 188, 231, 187, 231, 186, 234, 182, 234, 179, 231, 177, 231, 176, 230, 176, 227, 177, 224, 180, 222]}]",valid,2339,eWwyO3munzjbYj1mQt_WHQ,292,../data/image/eWwyO3munzjbYj1mQt_WHQ_292.jpg +"[{'category_id': 1, 'bbox': [251, 203, 278, 230], 'bbox_mode': 0, 'segmentation': [259, 230, 265, 225, 267, 225, 269, 225, 271, 224, 271, 222, 269, 218, 278, 209, 278, 207, 276, 206, 274, 203, 266, 209, 251, 225, 251, 227, 253, 228, 257, 230]}]",valid,2340,3HAgE5v8fsncNlUhJWHtbA,99,../data/image/3HAgE5v8fsncNlUhJWHtbA_99.jpg +"[{'category_id': 0, 'bbox': [371, 210, 382, 222], 'bbox_mode': 0, 'segmentation': [374, 216, 374, 217, 374, 218, 374, 218, 374, 219, 374, 219, 374, 220, 375, 221, 375, 221, 375, 221, 376, 222, 376, 222, 377, 222, 379, 222, 380, 222, 381, 221, 381, 221, 382, 219, 382, 218, 382, 217, 382, 216, 382, 215, 381, 214, 381, 213, 380, 212, 379, 211, 379, 211, 378, 211, 378, 210, 377, 210, 377, 210, 375, 210, 375, 211, 374, 211, 373, 212, 373, 212, 372, 213, 372, 214, 372, 214, 371, 215, 371, 216, 371, 216, 372, 217, 372, 217, 373, 216, 373, 215, 374, 215, 374, 215, 374, 216]}]",valid,2341,Xpdnkf7XQyAwKJmTZwuivQ,196,../data/image/Xpdnkf7XQyAwKJmTZwuivQ_196.jpg +[],valid,2342,J3pEzzlBFIWRk_u6XFGv4g,169,../data/image/J3pEzzlBFIWRk_u6XFGv4g_169.jpg +"[{'category_id': 0, 'bbox': [332, 178, 337, 186], 'bbox_mode': 0, 'segmentation': [333, 178, 335, 179, 336, 180, 337, 181, 337, 183, 337, 184, 335, 185, 335, 186, 334, 186, 333, 186, 333, 185, 332, 185, 332, 184, 332, 181]}]",valid,2343,Cx9qfunPwFDbN6eSXSVJrg,137,../data/image/Cx9qfunPwFDbN6eSXSVJrg_137.jpg +"[{'category_id': 0, 'bbox': [465, 198, 476, 210], 'bbox_mode': 0, 'segmentation': [470, 198, 467, 199, 465, 203, 466, 206, 468, 209, 470, 210, 474, 209, 476, 205, 476, 200, 475, 199, 472, 198]}]",valid,2344,A0Vh9Z3SfChnyUbFQmAzPA,177,../data/image/A0Vh9Z3SfChnyUbFQmAzPA_177.jpg +"[{'category_id': 1, 'bbox': [190, 171, 202, 181], 'bbox_mode': 0, 'segmentation': [193, 176, 193, 176, 193, 175, 192, 175, 191, 175, 190, 174, 190, 173, 190, 172, 190, 171, 191, 171, 193, 171, 195, 171, 195, 171, 196, 172, 196, 173, 198, 174, 200, 175, 200, 175, 201, 176, 201, 177, 202, 178, 202, 179, 202, 180, 201, 181, 200, 181, 199, 181, 198, 180, 197, 179, 197, 178, 196, 178, 195, 178, 194, 179, 194, 180, 194, 181, 193, 181, 193, 180, 192, 179, 193, 178, 193, 177, 193, 177, 193, 176]}]",valid,2345,TtPKwmpeTWKqMVTBiUGtmw,269,../data/image/TtPKwmpeTWKqMVTBiUGtmw_269.jpg +[],valid,2346,HF1mIDBBZtFuXbMeaVrMqA,285,../data/image/HF1mIDBBZtFuXbMeaVrMqA_285.jpg +"[{'category_id': 0, 'bbox': [213, 143, 218, 150], 'bbox_mode': 0, 'segmentation': [214, 143, 217, 143, 218, 145, 218, 147, 218, 149, 217, 150, 215, 150, 214, 149, 214, 148, 213, 146]}]",valid,2347,f0dr5gQkd_CJTe2aZ6PnXA,179,../data/image/f0dr5gQkd_CJTe2aZ6PnXA_179.jpg +[],valid,2348,ROlkA0XtmyvzyYQpMHMzAA,189,../data/image/ROlkA0XtmyvzyYQpMHMzAA_189.jpg +"[{'category_id': 0, 'bbox': [553, 167, 564, 178], 'bbox_mode': 0, 'segmentation': [555, 167, 561, 167, 563, 169, 564, 171, 564, 174, 563, 175, 562, 176, 561, 177, 558, 178, 556, 178, 555, 176, 553, 175, 553, 173, 553, 171]}]",valid,2349,dTTCOSEXzdqxvT5oP7LKaA,236,../data/image/dTTCOSEXzdqxvT5oP7LKaA_236.jpg +"[{'category_id': 1, 'bbox': [108, 279, 117, 285], 'bbox_mode': 0, 'segmentation': [110, 279, 117, 283, 117, 284, 116, 285, 115, 285, 112, 285, 108, 281, 108, 280, 109, 279, 109, 279]}]",valid,2350,7g50JQg3pZVLaIv3WvSUaA,270,../data/image/7g50JQg3pZVLaIv3WvSUaA_270.jpg +[],valid,2351,IY4P0sPu7vaT5Rukn0UQAg,237,../data/image/IY4P0sPu7vaT5Rukn0UQAg_237.jpg +[],valid,2352,YYNCsBHoyrMlPWjL7fuM8Q,58,../data/image/YYNCsBHoyrMlPWjL7fuM8Q_58.jpg +[],valid,2353,GmCYIQD3uOKqgJO0z_-Ghg,260,../data/image/GmCYIQD3uOKqgJO0z_-Ghg_260.jpg +[],valid,2354,R0UTmKezDJsk_JyApw6ZhA,99,../data/image/R0UTmKezDJsk_JyApw6ZhA_99.jpg +[],valid,2355,NHwBwPyKBr11o9zKbwopIw,326,../data/image/NHwBwPyKBr11o9zKbwopIw_326.jpg +[],valid,2356,J7u4xplOB876o6w9c9gqBQ,272,../data/image/J7u4xplOB876o6w9c9gqBQ_272.jpg +[],valid,2357,7HTqNmrRYvSxSQECBnXLDw,180,../data/image/7HTqNmrRYvSxSQECBnXLDw_180.jpg +"[{'category_id': 1, 'bbox': [473, 183, 483, 188], 'bbox_mode': 0, 'segmentation': [474, 188, 480, 187, 482, 186, 483, 185, 483, 184, 482, 183, 474, 184, 473, 184, 473, 185, 473, 186]}]",valid,2358,b6sohpdhp1_lk913dTQB0w,104,../data/image/b6sohpdhp1_lk913dTQB0w_104.jpg +[],valid,2359,OmWgNIsvMYs5KMMyjjFrIg,340,../data/image/OmWgNIsvMYs5KMMyjjFrIg_340.jpg +"[{'category_id': 0, 'bbox': [530, 119, 553, 138], 'bbox_mode': 0, 'segmentation': [542, 119, 547, 120, 549, 121, 551, 122, 552, 124, 553, 127, 552, 129, 551, 130, 550, 132, 547, 134, 543, 136, 539, 138, 535, 138, 533, 137, 531, 135, 531, 135, 530, 133, 530, 131, 531, 128, 533, 125, 536, 122, 539, 121]}]",valid,2360,TB0vFxr_PJqHdUoNM_FBrw,281,../data/image/TB0vFxr_PJqHdUoNM_FBrw_281.jpg +"[{'category_id': 0, 'bbox': [339, 274, 346, 282], 'bbox_mode': 0, 'segmentation': [341, 274, 340, 275, 339, 277, 339, 278, 340, 280, 341, 281, 342, 282, 344, 282, 345, 281, 346, 280, 346, 278, 345, 276, 345, 275, 344, 274, 342, 274]}]",valid,2361,ZFQSGnOEMaKbZxpZTnfjEQ,106,../data/image/ZFQSGnOEMaKbZxpZTnfjEQ_106.jpg +[],valid,2362,ZDoVbIgd8AS5daRubGdmIw,237,../data/image/ZDoVbIgd8AS5daRubGdmIw_237.jpg +[],valid,2363,RNrtSrKhyO7NHhYnSYQA5A,308,../data/image/RNrtSrKhyO7NHhYnSYQA5A_308.jpg +[],valid,2364,P89sqiX4S6ovUjOa8noyMA,350,../data/image/P89sqiX4S6ovUjOa8noyMA_350.jpg +"[{'category_id': 1, 'bbox': [285, 242, 314, 254], 'bbox_mode': 0, 'segmentation': [312, 242, 313, 243, 314, 246, 314, 247, 312, 249, 304, 250, 296, 252, 289, 254, 288, 252, 287, 251, 285, 250, 285, 248, 289, 247]}]",valid,2365,5X09jAiPaNvacMRknTDUNg,177,../data/image/5X09jAiPaNvacMRknTDUNg_177.jpg +"[{'category_id': 1, 'bbox': [555, 275, 582, 289], 'bbox_mode': 0, 'segmentation': [568, 289, 570, 289, 572, 287, 573, 286, 573, 285, 575, 284, 577, 284, 578, 284, 579, 284, 580, 284, 581, 283, 582, 282, 582, 280, 581, 279, 577, 278, 572, 277, 568, 277, 561, 276, 557, 275, 555, 276, 555, 278, 557, 280, 560, 282, 563, 283, 565, 283, 565, 286, 566, 288]}]",valid,2366,X9y4a_uma0L69obiJfHTbA,177,../data/image/X9y4a_uma0L69obiJfHTbA_177.jpg +"[{'category_id': 1, 'bbox': [165, 165, 181, 178], 'bbox_mode': 0, 'segmentation': [174, 174, 176, 175, 177, 176, 178, 177, 180, 177, 181, 176, 181, 174, 181, 172, 178, 171, 174, 168, 170, 165, 167, 165, 166, 167, 165, 168, 166, 169, 171, 173, 168, 175, 168, 177, 170, 178, 172, 176]}]",valid,2367,bWmQkUTAs9Rh6T1veXAMAw,226,../data/image/bWmQkUTAs9Rh6T1veXAMAw_226.jpg +"[{'category_id': 1, 'bbox': [350, 203, 358, 207], 'bbox_mode': 0, 'segmentation': [350, 205, 355, 207, 357, 207, 358, 206, 358, 205, 357, 204, 352, 203, 350, 203]}, {'category_id': 1, 'bbox': [361, 203, 366, 208], 'bbox_mode': 0, 'segmentation': [361, 207, 365, 203, 366, 204, 366, 205, 365, 206, 364, 208, 363, 208, 362, 208]}]",valid,2368,70g6mnLp9DoDdTzhmrpH5w,135,../data/image/70g6mnLp9DoDdTzhmrpH5w_135.jpg 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129, 249, 129, 248, 130, 247, 130, 247, 132, 247, 133, 246, 134, 247, 136, 247, 138, 248, 139, 250, 139, 251, 139, 253, 138, 254, 136, 253, 133, 253, 131, 252, 130, 252, 129]}]",valid,2375,YMvZ1MA1nIo5zL0HCos4NQ,137,../data/image/YMvZ1MA1nIo5zL0HCos4NQ_137.jpg +[],valid,2376,TcvwPPO-0PeHS82gWzlrQg,115,../data/image/TcvwPPO-0PeHS82gWzlrQg_115.jpg +"[{'category_id': 0, 'bbox': [586, 12, 596, 23], 'bbox_mode': 0, 'segmentation': [589, 12, 587, 14, 586, 16, 586, 17, 586, 18, 586, 19, 586, 21, 586, 22, 587, 23, 588, 23, 590, 23, 592, 23, 594, 22, 595, 21, 596, 19, 596, 17, 596, 16, 596, 15, 596, 13, 595, 12, 594, 12, 593, 12]}]",valid,2377,oOCJ80Xz72vW_4uHBc2ckQ,268,../data/image/oOCJ80Xz72vW_4uHBc2ckQ_268.jpg +[],valid,2378,LzQD_9hce2PA2x8JbXz6Hg,208,../data/image/LzQD_9hce2PA2x8JbXz6Hg_208.jpg +[],valid,2379,Sy2PSCU3nVWYL995uS0lyw,179,../data/image/Sy2PSCU3nVWYL995uS0lyw_179.jpg +"[{'category_id': 1, 'bbox': [441, 197, 445, 201], 'bbox_mode': 0, 'segmentation': [441, 200, 443, 201, 445, 199, 442, 197, 441, 199]}]",valid,2380,4Vj3X2g2gSUwwbskDJ5Nfw,0,../data/image/4Vj3X2g2gSUwwbskDJ5Nfw_0.jpg +[],valid,2381,QbzXIZztOlJE-6ShS9X9OQ,0,../data/image/QbzXIZztOlJE-6ShS9X9OQ_0.jpg +"[{'category_id': 0, 'bbox': [541, 220, 557, 238], 'bbox_mode': 0, 'segmentation': [541, 230, 542, 227, 544, 225, 545, 225, 546, 224, 547, 223, 547, 222, 547, 221, 548, 220, 549, 220, 549, 220, 551, 220, 551, 220, 552, 221, 552, 222, 552, 223, 553, 223, 555, 224, 556, 224, 556, 225, 556, 226, 557, 227, 557, 230, 557, 231, 557, 232, 556, 234, 555, 235, 553, 237, 552, 237, 550, 238, 548, 238, 547, 238, 546, 238, 545, 237, 544, 237, 544, 236, 543, 236, 542, 235, 542, 234, 541, 233, 541, 232]}]",valid,2382,T3CrW4pU__pokI6R0fk5PA,125,../data/image/T3CrW4pU__pokI6R0fk5PA_125.jpg +[],valid,2383,MQlGntFYfbGi_5Ex7YgGlw,135,../data/image/MQlGntFYfbGi_5Ex7YgGlw_135.jpg +"[{'category_id': 0, 'bbox': [117, 157, 133, 175], 'bbox_mode': 0, 'segmentation': [121, 158, 119, 158, 119, 160, 118, 161, 117, 162, 117, 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275, 472, 276, 472, 278, 472, 280, 471, 281, 470, 282, 469, 282, 468, 281, 467, 279, 467, 277]}]",valid,2390,38iXnoOHStBz6rkuqanhvQ,209,../data/image/38iXnoOHStBz6rkuqanhvQ_209.jpg +[],valid,2391,piad2m1q5n4ma98ql8V_Xw,350,../data/image/piad2m1q5n4ma98ql8V_Xw_350.jpg +[],valid,2392,OG0wVXgi_0tMfM8Sg3Xkjg,107,../data/image/OG0wVXgi_0tMfM8Sg3Xkjg_107.jpg +[],valid,2393,IOKNVS6GZ_Q94DlpbruLZA,274,../data/image/IOKNVS6GZ_Q94DlpbruLZA_274.jpg +[],valid,2394,ZNfiwxshxN-R63NDz10D0w,177,../data/image/ZNfiwxshxN-R63NDz10D0w_177.jpg +[],valid,2395,Mmq-2tx0Z_m0rbVhThilrA,98,../data/image/Mmq-2tx0Z_m0rbVhThilrA_98.jpg +"[{'category_id': 0, 'bbox': [477, 191, 483, 204], 'bbox_mode': 0, 'segmentation': [477, 200, 477, 200, 477, 200, 477, 200, 477, 201, 477, 201, 477, 202, 478, 203, 478, 203, 478, 204, 478, 204, 479, 204, 480, 204, 481, 204, 482, 204, 482, 203, 483, 202, 483, 201, 483, 200, 483, 199, 483, 198, 483, 197, 482, 197, 482, 196, 482, 195, 482, 194, 482, 194, 482, 193, 482, 192, 481, 191, 481, 191, 480, 192, 480, 192, 480, 192, 480, 193, 480, 193, 480, 194, 479, 195, 479, 196, 479, 196, 479, 197, 478, 198, 478, 198, 478, 199, 477, 199]}, {'category_id': 0, 'bbox': [237, 250, 241, 260], 'bbox_mode': 0, 'segmentation': [238, 253, 238, 254, 238, 254, 238, 255, 238, 255, 237, 256, 237, 256, 237, 256, 237, 257, 237, 257, 237, 258, 237, 259, 238, 260, 238, 260, 239, 260, 240, 260, 241, 260, 241, 259, 241, 258, 241, 257, 241, 256, 240, 255, 240, 255, 240, 253, 240, 252, 240, 251, 240, 251, 239, 250, 239, 250, 238, 250, 238, 250, 237, 251, 237, 252, 237, 252, 237, 252]}, {'category_id': 0, 'bbox': [250, 247, 255, 254], 'bbox_mode': 0, 'segmentation': [250, 249, 250, 249, 250, 250, 250, 250, 250, 251, 250, 252, 251, 252, 251, 252, 251, 253, 251, 253, 252, 254, 252, 254, 253, 254, 254, 254, 255, 254, 255, 253, 255, 253, 255, 252, 255, 251, 255, 250, 255, 249, 255, 249, 255, 248, 255, 248, 255, 247, 254, 247, 254, 247, 253, 247, 252, 247, 251, 247, 251, 248, 251, 248]}]",valid,2396,Q3JUh2qkQJ_onBCiC_uCMw,232,../data/image/Q3JUh2qkQJ_onBCiC_uCMw_232.jpg +"[{'category_id': 0, 'bbox': [35, 298, 50, 312], 'bbox_mode': 0, 'segmentation': [37, 300, 36, 302, 36, 302, 35, 303, 35, 305, 36, 307, 36, 309, 37, 310, 39, 311, 40, 312, 42, 312, 44, 312, 46, 312, 47, 311, 48, 310, 49, 309, 50, 307, 50, 305, 49, 303, 48, 302, 47, 300, 44, 299, 43, 298, 41, 299, 40, 299, 38, 299]}, {'category_id': 0, 'bbox': [116, 301, 126, 312], 'bbox_mode': 0, 'segmentation': [117, 302, 116, 304, 116, 306, 116, 308, 116, 309, 117, 311, 118, 311, 119, 312, 120, 312, 122, 312, 123, 312, 124, 311, 125, 310, 125, 309, 126, 308, 126, 307, 126, 305, 126, 304, 125, 302, 123, 302, 122, 301, 121, 301, 120, 301, 118, 302]}]",valid,2397,Gri3-BdAQk0UoLMs2WNxdg,159,../data/image/Gri3-BdAQk0UoLMs2WNxdg_159.jpg +"[{'category_id': 1, 'bbox': [503, 258, 517, 268], 'bbox_mode': 0, 'segmentation': [507, 261, 505, 262, 504, 263, 504, 263, 503, 264, 503, 264, 504, 265, 504, 265, 505, 266, 506, 267, 506, 268, 507, 268, 508, 268, 508, 268, 510, 268, 510, 268, 511, 267, 511, 267, 512, 266, 512, 266, 512, 265, 513, 266, 513, 266, 513, 267, 513, 267, 514, 267, 514, 266, 514, 266, 514, 265, 514, 264, 513, 264, 513, 263, 514, 263, 516, 261, 517, 261, 517, 260, 517, 259, 517, 258, 516, 258, 516, 258, 515, 258, 514, 258, 514, 258, 513, 258]}]",valid,2398,YYMX9PbvK7Tq2qieNU49QQ,265,../data/image/YYMX9PbvK7Tq2qieNU49QQ_265.jpg +[],valid,2399,ROjM5EKWTBRGoEwcKnUBRA,135,../data/image/ROjM5EKWTBRGoEwcKnUBRA_135.jpg +"[{'category_id': 0, 'bbox': [535, 287, 540, 292], 'bbox_mode': 0, 'segmentation': [536, 287, 535, 288, 535, 289, 535, 291, 536, 292, 537, 292, 538, 292, 540, 292, 540, 290, 540, 289, 540, 288, 539, 287, 538, 287, 537, 287]}]",valid,2400,fVFFq-Dyutx6GVaDdrXG1g,7,../data/image/fVFFq-Dyutx6GVaDdrXG1g_7.jpg +"[{'category_id': 1, 'bbox': [74, 165, 82, 172], 'bbox_mode': 0, 'segmentation': [75, 170, 81, 172, 82, 171, 78, 167, 76, 165, 75, 165, 74, 167]}, {'category_id': 0, 'bbox': [166, 205, 175, 215], 'bbox_mode': 0, 'segmentation': [167, 205, 174, 206, 175, 208, 175, 212, 174, 212, 173, 214, 171, 215, 169, 215, 169, 212, 166, 212, 166, 210, 167, 208]}]",valid,2401,EO6WpJruMwLPPXi9fEbV6g,351,../data/image/EO6WpJruMwLPPXi9fEbV6g_351.jpg +[],valid,2402,XDEj6doStUaQ2-FDQZnMvg,114,../data/image/XDEj6doStUaQ2-FDQZnMvg_114.jpg +[],valid,2403,2UKt1oDF5_V0mvlmtQVujw,267,../data/image/2UKt1oDF5_V0mvlmtQVujw_267.jpg +"[{'category_id': 1, 'bbox': [558, 181, 569, 187], 'bbox_mode': 0, 'segmentation': [558, 183, 561, 182, 565, 182, 568, 181, 569, 181, 569, 182, 569, 183, 568, 184, 567, 185, 565, 185, 565, 186, 564, 187, 562, 187, 561, 187, 561, 186, 559, 185, 558, 184, 558, 184]}]",valid,2404,9HBoaC_igiZbGB-uEHtr_Q,104,../data/image/9HBoaC_igiZbGB-uEHtr_Q_104.jpg +[],valid,2405,QDT7s-L3FKH8fEV9hFOTDg,5,../data/image/QDT7s-L3FKH8fEV9hFOTDg_5.jpg +"[{'category_id': 0, 'bbox': [540, 203, 547, 213], 'bbox_mode': 0, 'segmentation': [542, 203, 545, 203, 546, 204, 547, 205, 547, 207, 547, 208, 547, 210, 547, 211, 546, 212, 544, 213, 542, 212, 541, 211, 540, 210, 540, 209, 540, 207, 540, 205, 541, 204]}]",valid,2406,bP0MFskmsb5svtNLXBCOvQ,227,../data/image/bP0MFskmsb5svtNLXBCOvQ_227.jpg +"[{'category_id': 1, 'bbox': [587, 77, 595, 85], 'bbox_mode': 0, 'segmentation': [595, 81, 594, 78, 591, 77, 588, 79, 587, 80, 587, 83, 590, 85, 591, 84, 593, 84, 594, 82]}, {'category_id': 1, 'bbox': [460, 161, 467, 167], 'bbox_mode': 0, 'segmentation': [460, 162, 463, 161, 466, 162, 467, 164, 467, 166, 466, 167, 464, 167, 462, 166, 461, 165, 460, 163, 460, 162]}]",valid,2407,vLrbnucgDK0Fyg7j53QZoQ,189,../data/image/vLrbnucgDK0Fyg7j53QZoQ_189.jpg +"[{'category_id': 0, 'bbox': [347, 189, 355, 198], 'bbox_mode': 0, 'segmentation': [347, 195, 348, 192, 350, 190, 352, 189, 353, 190, 355, 194, 353, 196, 352, 198, 350, 198, 349, 198, 348, 196]}, {'category_id': 0, 'bbox': [357, 194, 364, 204], 'bbox_mode': 0, 'segmentation': [357, 199, 357, 197, 358, 194, 361, 194, 363, 196, 364, 198, 364, 201, 362, 202, 361, 204, 360, 204, 358, 203, 358, 201]}]",valid,2408,2gnhJ_hvTE3xhy0JPRWVuA,68,../data/image/2gnhJ_hvTE3xhy0JPRWVuA_68.jpg +"[{'category_id': 1, 'bbox': [357, 197, 370, 209], 'bbox_mode': 0, 'segmentation': [358, 197, 359, 197, 361, 198, 362, 198, 363, 199, 364, 200, 365, 200, 367, 202, 368, 202, 369, 203, 370, 204, 370, 205, 370, 206, 369, 207, 368, 207, 367, 208, 366, 208, 365, 207, 364, 206, 363, 205, 363, 207, 363, 207, 363, 208, 362, 209, 362, 209, 361, 209, 361, 209, 360, 209, 360, 208, 359, 207, 360, 206, 360, 204, 360, 204, 359, 203, 358, 203, 357, 202, 357, 201, 357, 200, 357, 198, 357, 198, 358, 197]}]",valid,2409,HU4s0kjJMFsEod7mFip-ag,226,../data/image/HU4s0kjJMFsEod7mFip-ag_226.jpg +"[{'category_id': 0, 'bbox': [469, 226, 474, 235], 'bbox_mode': 0, 'segmentation': [472, 226, 470, 227, 470, 228, 470, 228, 469, 229, 469, 231, 469, 232, 469, 232, 470, 233, 470, 234, 470, 234, 471, 234, 472, 235, 473, 234, 473, 234, 474, 234, 474, 233, 474, 232, 474, 231, 474, 230, 474, 229, 474, 228, 474, 227, 474, 226, 473, 226, 473, 226]}]",valid,2410,He9UP4eazdbecZUExKdsXA,260,../data/image/He9UP4eazdbecZUExKdsXA_260.jpg +[],valid,2411,YykkE-cvLePzVdrJbui68Q,260,../data/image/YykkE-cvLePzVdrJbui68Q_260.jpg +[],valid,2412,IcSoSFWq2g3szhfZM6q7tg,142,../data/image/IcSoSFWq2g3szhfZM6q7tg_142.jpg +[],valid,2413,-gnPAaVZrhG18vYDr-nLlg,42,../data/image/-gnPAaVZrhG18vYDr-nLlg_42.jpg +[],valid,2414,Pctg54cpVlqplXEwugKe7Q,359,../data/image/Pctg54cpVlqplXEwugKe7Q_359.jpg +[],valid,2415,JtOXI0A5AjrmXgW2RN7nGw,270,../data/image/JtOXI0A5AjrmXgW2RN7nGw_270.jpg +"[{'category_id': 1, 'bbox': [134, 221, 140, 226], 'bbox_mode': 0, 'segmentation': [134, 222, 135, 221, 135, 221, 136, 222, 136, 222, 137, 222, 137, 222, 138, 222, 139, 222, 139, 222, 140, 223, 140, 223, 140, 224, 140, 225, 139, 225, 139, 226, 139, 226, 138, 226, 138, 226, 137, 226, 136, 225, 136, 225, 135, 225, 135, 225, 134, 224, 134, 224, 134, 224, 134, 223, 134, 222, 134, 222, 134, 222]}]",valid,2416,lt11i_Q7r6yYkNkF2MxMOA,0,../data/image/lt11i_Q7r6yYkNkF2MxMOA_0.jpg +[],valid,2417,iBtiWeG7HaQkrryU1FPzJQ,0,../data/image/iBtiWeG7HaQkrryU1FPzJQ_0.jpg +"[{'category_id': 1, 'bbox': [204, 223, 220, 235], 'bbox_mode': 0, 'segmentation': [220, 228, 219, 226, 219, 226, 219, 225, 218, 224, 217, 223, 216, 223, 214, 224, 213, 225, 211, 225, 210, 226, 208, 227, 206, 228, 205, 229, 204, 229, 205, 231, 206, 231, 206, 232, 207, 232, 208, 233, 209, 234, 210, 233, 211, 233, 213, 233, 213, 234, 214, 234, 215, 235, 216, 235, 216, 234, 217, 233, 217, 232, 216, 231, 216, 230, 217, 230, 218, 229, 219, 229, 220, 228]}]",valid,2418,JmUqFeVC4Jqk-8AMrfCOrg,90,../data/image/JmUqFeVC4Jqk-8AMrfCOrg_90.jpg +"[{'category_id': 0, 'bbox': [363, 258, 368, 263], 'bbox_mode': 0, 'segmentation': [364, 258, 366, 258, 367, 258, 368, 260, 368, 261, 367, 262, 366, 263, 364, 263, 363, 263, 363, 261, 363, 260, 363, 259, 363, 258]}]",valid,2419,CYxrM2YGPRU_WrnKqJfWfA,298,../data/image/CYxrM2YGPRU_WrnKqJfWfA_298.jpg +"[{'category_id': 0, 'bbox': [587, 154, 604, 168], 'bbox_mode': 0, 'segmentation': [593, 155, 590, 157, 588, 159, 587, 162, 587, 163, 587, 165, 588, 166, 589, 167, 590, 167, 592, 168, 593, 168, 594, 168, 595, 168, 597, 168, 598, 168, 599, 167, 601, 165, 603, 164, 603, 163, 604, 161, 603, 159, 603, 157, 601, 156, 600, 155, 598, 154, 597, 154, 596, 154]}]",valid,2420,UhO-l7K9_aPyqwfn6q_j4Q,281,../data/image/UhO-l7K9_aPyqwfn6q_j4Q_281.jpg +[],valid,2421,HJ3EL7fkA1Bg2jfS3ULbRQ,350,../data/image/HJ3EL7fkA1Bg2jfS3ULbRQ_350.jpg +"[{'category_id': 1, 'bbox': [546, 257, 569, 271], 'bbox_mode': 0, 'segmentation': [546, 261, 548, 260, 550, 260, 552, 259, 554, 258, 556, 258, 558, 257, 561, 258, 563, 258, 566, 257, 568, 258, 569, 260, 569, 262, 568, 265, 567, 266, 565, 267, 564, 268, 564, 270, 563, 271, 561, 271, 560, 271, 560, 268, 559, 267, 558, 267, 555, 268, 552, 268, 551, 268, 548, 268, 547, 266, 546, 264, 546, 262]}]",valid,2422,i8_O-zqQmpYdmQZr3OXQww,236,../data/image/i8_O-zqQmpYdmQZr3OXQww_236.jpg +"[{'category_id': 1, 'bbox': [74, 80, 91, 92], 'bbox_mode': 0, 'segmentation': [82, 81, 82, 81, 83, 81, 83, 82, 84, 82, 85, 82, 86, 83, 86, 83, 87, 83, 88, 84, 89, 84, 89, 84, 90, 85, 90, 85, 91, 86, 90, 87, 90, 88, 89, 89, 87, 89, 87, 89, 86, 89, 86, 89, 85, 88, 84, 88, 84, 88, 84, 88, 84, 89, 84, 90, 84, 91, 84, 91, 83, 92, 82, 91, 82, 91, 82, 90, 82, 88, 82, 87, 82, 87, 81, 87, 80, 87, 79, 86, 78, 85, 77, 85, 76, 84, 74, 83, 74, 83, 74, 81, 75, 81, 75, 80, 76, 80, 77, 80, 78, 80, 78, 80, 79, 80]}]",valid,2423,ZGuD69ZXT8nReZqV3swuJg,266,../data/image/ZGuD69ZXT8nReZqV3swuJg_266.jpg +"[{'category_id': 0, 'bbox': [241, 281, 248, 290], 'bbox_mode': 0, 'segmentation': [245, 282, 245, 281, 246, 282, 247, 282, 247, 284, 248, 285, 248, 287, 248, 289, 246, 290, 243, 290, 242, 289, 241, 287, 242, 285, 242, 284, 243, 283, 244, 282]}]",valid,2424,yzp3HjTYcaBeBnkXBtkSkg,106,../data/image/yzp3HjTYcaBeBnkXBtkSkg_106.jpg +[],valid,2425,XxWvILwd8FpAOEa_RVr1rw,242,../data/image/XxWvILwd8FpAOEa_RVr1rw_242.jpg +"[{'category_id': 1, 'bbox': [298, 71, 305, 84], 'bbox_mode': 0, 'segmentation': [300, 72, 300, 72, 300, 71, 300, 71, 301, 71, 302, 71, 302, 72, 303, 72, 303, 73, 304, 74, 304, 75, 304, 76, 305, 77, 305, 78, 305, 80, 305, 82, 304, 83, 303, 84, 302, 84, 301, 84, 300, 83, 299, 82, 299, 82, 299, 80, 298, 80, 298, 78, 298, 78, 298, 77, 298, 76, 299, 75, 299, 74, 299, 74, 299, 73, 299, 72]}]",valid,2426,Ouo0vVTnarpnhRs_0jCf0g,85,../data/image/Ouo0vVTnarpnhRs_0jCf0g_85.jpg +"[{'category_id': 0, 'bbox': [4, 230, 13, 236], 'bbox_mode': 0, 'segmentation': [5, 231, 6, 230, 8, 230, 10, 230, 11, 231, 12, 231, 13, 233, 13, 235, 12, 236, 10, 236, 8, 236, 6, 236, 5, 235, 4, 234, 4, 232, 5, 231]}]",valid,2427,p2GUm0kzhUsDRWfoDQduVg,64,../data/image/p2GUm0kzhUsDRWfoDQduVg_64.jpg +[],valid,2428,hNT4ghuKog3-8Nz31eofQw,229,../data/image/hNT4ghuKog3-8Nz31eofQw_229.jpg +"[{'category_id': 1, 'bbox': [264, 255, 272, 264], 'bbox_mode': 0, 'segmentation': [264, 262, 268, 255, 270, 255, 272, 256, 272, 258, 270, 263, 268, 264, 266, 264, 265, 264, 264, 263]}]",valid,2429,FfbPiVbjRRTW1dwuhyWcbQ,179,../data/image/FfbPiVbjRRTW1dwuhyWcbQ_179.jpg +"[{'category_id': 0, 'bbox': [405, 252, 414, 263], 'bbox_mode': 0, 'segmentation': [408, 253, 408, 254, 408, 255, 407, 255, 406, 256, 406, 257, 405, 258, 405, 259, 406, 260, 406, 261, 407, 262, 408, 262, 409, 263, 410, 263, 411, 262, 411, 262, 412, 261, 413, 260, 413, 259, 414, 258, 414, 257, 413, 256, 413, 255, 412, 254, 411, 253, 410, 252, 410, 252, 409, 252, 409, 252]}]",valid,2430,IZXqv8lvtYUn1SOfZQ7N9w,269,../data/image/IZXqv8lvtYUn1SOfZQ7N9w_269.jpg +"[{'category_id': 0, 'bbox': [120, 289, 126, 297], 'bbox_mode': 0, 'segmentation': [121, 290, 121, 289, 122, 289, 123, 289, 124, 289, 125, 290, 126, 292, 126, 293, 126, 295, 125, 297, 123, 297, 121, 297, 121, 296, 121, 294, 121, 293, 121, 291, 120, 291, 120, 290]}]",valid,2431,gWHTycyKRCloaqQaPC2Hkg,115,../data/image/gWHTycyKRCloaqQaPC2Hkg_115.jpg +"[{'category_id': 1, 'bbox': [623, 180, 634, 192], 'bbox_mode': 0, 'segmentation': [623, 184, 626, 188, 625, 189, 625, 191, 626, 192, 629, 191, 631, 190, 633, 191, 634, 190, 634, 189, 633, 185, 628, 180, 626, 180, 624, 182]}]",valid,2432,EkIMOwSRrHThvFq8M9NqRA,315,../data/image/EkIMOwSRrHThvFq8M9NqRA_315.jpg +"[{'category_id': 1, 'bbox': [276, 172, 289, 180], 'bbox_mode': 0, 'segmentation': [276, 172, 282, 174, 286, 175, 289, 176, 289, 178, 289, 179, 289, 180, 287, 180, 285, 180, 281, 180, 279, 180, 277, 180, 276, 180]}]",valid,2433,AdbadEV3ZM0mGj4AkHyNjQ,86,../data/image/AdbadEV3ZM0mGj4AkHyNjQ_86.jpg +"[{'category_id': 0, 'bbox': [377, 183, 382, 188], 'bbox_mode': 0, 'segmentation': [378, 183, 381, 183, 381, 184, 382, 186, 381, 188, 380, 188, 379, 187, 378, 186, 377, 184]}]",valid,2434,dXECvmhu8Yn2y6sZB-mqyQ,177,../data/image/dXECvmhu8Yn2y6sZB-mqyQ_177.jpg +[],valid,2435,Tna0Dmwxcrxx-M9Zqie71w,218,../data/image/Tna0Dmwxcrxx-M9Zqie71w_218.jpg +"[{'category_id': 1, 'bbox': [407, 209, 417, 216], 'bbox_mode': 0, 'segmentation': [407, 209, 416, 209, 417, 211, 417, 212, 416, 212, 415, 213, 414, 213, 413, 213, 413, 215, 413, 216, 411, 216, 410, 214, 410, 213, 408, 212, 407, 212, 407, 211, 407, 210]}]",valid,2436,802c7ZkdwVt5P7Czj6Vptg,323,../data/image/802c7ZkdwVt5P7Czj6Vptg_323.jpg +"[{'category_id': 0, 'bbox': [208, 245, 213, 251], 'bbox_mode': 0, 'segmentation': [210, 246, 209, 246, 208, 247, 209, 248, 209, 249, 210, 250, 211, 251, 212, 250, 213, 249, 213, 248, 213, 247, 213, 246, 212, 246, 211, 245]}]",valid,2437,2BOn3NLr1Or__FnDUiydmA,179,../data/image/2BOn3NLr1Or__FnDUiydmA_179.jpg +[],valid,2438,W6iB1b-wYiu0EZIF9N9UuQ,115,../data/image/W6iB1b-wYiu0EZIF9N9UuQ_115.jpg +"[{'category_id': 0, 'bbox': [61, 256, 66, 261], 'bbox_mode': 0, 'segmentation': [62, 256, 62, 257, 61, 257, 61, 257, 61, 258, 61, 258, 61, 259, 61, 259, 61, 260, 61, 260, 62, 260, 62, 260, 63, 261, 63, 261, 64, 261, 64, 261, 64, 261, 65, 261, 65, 260, 66, 260, 66, 260, 66, 259, 66, 259, 66, 258, 66, 258, 66, 258, 66, 257, 66, 257, 65, 257, 65, 256, 64, 256, 63, 256, 63, 256]}]",valid,2439,mqQqF7syTtc2gaGlyz01-w,50,../data/image/mqQqF7syTtc2gaGlyz01-w_50.jpg +"[{'category_id': 1, 'bbox': [411, 200, 411, 200], 'bbox_mode': 0, 'segmentation': [411, 200]}]",valid,2440,I1IZyc6W7SJ2nmzX4IP0hg,128,../data/image/I1IZyc6W7SJ2nmzX4IP0hg_128.jpg +"[{'category_id': 1, 'bbox': [69, 257, 85, 269], 'bbox_mode': 0, 'segmentation': [71, 265, 72, 264, 77, 257, 79, 257, 80, 257, 81, 257, 81, 257, 82, 257, 83, 257, 84, 258, 85, 259, 85, 260, 85, 261, 85, 263, 85, 264, 84, 264, 83, 265, 82, 266, 81, 267, 80, 269, 79, 269, 78, 269, 76, 269, 75, 269, 73, 269, 72, 268, 72, 268, 71, 268, 70, 268, 70, 268, 69, 267, 69, 267, 69, 266, 70, 266, 70, 266]}]",valid,2441,NgN93wEqRjD9LzeT8leFBA,80,../data/image/NgN93wEqRjD9LzeT8leFBA_80.jpg +[],valid,2442,XESrVFQhcTOBVdE9NtExBw,180,../data/image/XESrVFQhcTOBVdE9NtExBw_180.jpg +"[{'category_id': 1, 'bbox': [124, 300, 132, 303], 'bbox_mode': 0, 'segmentation': [132, 301, 132, 301, 132, 301, 131, 300, 130, 300, 129, 300, 129, 300, 128, 300, 127, 300, 126, 300, 126, 300, 125, 300, 125, 301, 125, 301, 124, 301, 124, 302, 124, 302, 125, 303, 126, 303, 126, 303, 127, 303, 128, 303, 129, 303, 130, 303, 130, 303, 131, 303, 132, 303, 132, 303, 132, 302, 132, 302, 132, 302, 132, 302]}]",valid,2443,IFWwgLoCdMEOZ0_37ZDVnQ,305,../data/image/IFWwgLoCdMEOZ0_37ZDVnQ_305.jpg +"[{'category_id': 1, 'bbox': [614, 248, 637, 254], 'bbox_mode': 0, 'segmentation': [616, 254, 626, 254, 631, 254, 633, 254, 635, 253, 636, 252, 637, 250, 637, 249, 634, 248, 620, 248, 617, 248, 615, 249, 614, 250, 614, 251, 614, 253]}]",valid,2444,fv9-vRblf6HKpt33PGE4KQ,38,../data/image/fv9-vRblf6HKpt33PGE4KQ_38.jpg +[],valid,2445,JoBEVHr0D52p6gZhwAD5Rg,80,../data/image/JoBEVHr0D52p6gZhwAD5Rg_80.jpg +"[{'category_id': 0, 'bbox': [556, 281, 566, 287], 'bbox_mode': 0, 'segmentation': [560, 281, 559, 281, 558, 281, 557, 282, 556, 282, 556, 282, 556, 283, 556, 284, 556, 285, 556, 286, 556, 287, 557, 287, 557, 287, 558, 287, 558, 287, 559, 287, 560, 287, 560, 287, 561, 287, 562, 287, 563, 287, 563, 287, 564, 287, 565, 287, 566, 287, 566, 286, 566, 285, 566, 284, 566, 284, 565, 284, 565, 283, 564, 283, 564, 282, 564, 281, 563, 281, 562, 281, 561, 281]}]",valid,2446,G-QgN-_Xw7qRrAcRE6vk2A,152,../data/image/G-QgN-_Xw7qRrAcRE6vk2A_152.jpg +"[{'category_id': 1, 'bbox': [331, 184, 356, 201], 'bbox_mode': 0, 'segmentation': [332, 194, 333, 192, 335, 191, 336, 190, 340, 188, 342, 186, 344, 185, 346, 184, 349, 184, 351, 185, 354, 186, 356, 189, 356, 191, 355, 195, 354, 198, 351, 199, 349, 199, 347, 198, 344, 197, 343, 198, 341, 200, 337, 201, 334, 201, 332, 198, 331, 196]}, {'category_id': 1, 'bbox': [612, 198, 632, 207], 'bbox_mode': 0, 'segmentation': [612, 200, 613, 199, 614, 198, 616, 199, 618, 199, 621, 200, 624, 200, 627, 201, 629, 201, 631, 202, 632, 203, 632, 205, 631, 206, 628, 207, 626, 207, 624, 206, 622, 206, 619, 205, 617, 204, 614, 204, 613, 203, 612, 201]}]",valid,2447,wGkORE51cXVw9aEd6rxx1A,279,../data/image/wGkORE51cXVw9aEd6rxx1A_279.jpg +"[{'category_id': 0, 'bbox': [309, 227, 314, 236], 'bbox_mode': 0, 'segmentation': [309, 233, 309, 233, 309, 234, 309, 234, 309, 235, 309, 235, 310, 236, 310, 236, 311, 236, 312, 236, 313, 236, 313, 235, 314, 234, 314, 233, 313, 232, 313, 231, 313, 231, 312, 230, 312, 229, 312, 228, 312, 228, 311, 227, 311, 227, 311, 227, 311, 228, 311, 228, 311, 228, 311, 229, 311, 229, 311, 230, 311, 231, 310, 231, 310, 231, 309, 231, 309, 231, 309, 232]}]",valid,2448,i46OS9c0IxgwyKCKg2z0bg,322,../data/image/i46OS9c0IxgwyKCKg2z0bg_322.jpg +[],valid,2449,RK394ejreI_biHApEBmENg,58,../data/image/RK394ejreI_biHApEBmENg_58.jpg +"[{'category_id': 0, 'bbox': [237, 18, 249, 45], 'bbox_mode': 0, 'segmentation': [241, 28, 241, 26, 241, 23, 241, 20, 241, 19, 242, 18, 243, 18, 243, 18, 244, 18, 244, 21, 244, 24, 244, 26, 245, 28, 246, 29, 247, 30, 248, 33, 249, 37, 248, 39, 249, 41, 247, 44, 245, 45, 242, 45, 239, 44, 238, 42, 237, 39, 237, 35, 238, 31]}]",valid,2450,-oCnWS4GQfgvqy44zou-ng,300,../data/image/-oCnWS4GQfgvqy44zou-ng_300.jpg +"[{'category_id': 0, 'bbox': [57, 196, 68, 210], 'bbox_mode': 0, 'segmentation': [60, 198, 60, 199, 60, 199, 59, 199, 59, 200, 58, 200, 58, 200, 57, 200, 57, 201, 57, 203, 57, 204, 57, 204, 57, 205, 57, 206, 58, 207, 59, 208, 61, 209, 62, 210, 63, 210, 63, 210, 64, 210, 64, 210, 65, 210, 65, 210, 65, 209, 66, 209, 66, 208, 66, 208, 67, 208, 67, 208, 68, 208, 68, 207, 68, 206, 68, 205, 68, 205, 68, 204, 68, 203, 67, 202, 67, 201, 66, 201, 65, 200, 65, 200, 64, 200, 64, 199, 63, 199, 63, 199, 63, 198, 63, 198, 63, 197, 62, 196, 62, 196, 62, 196, 62, 196, 61, 196, 61, 196, 61, 196, 60, 196, 60, 196]}]",valid,2451,gJKPE0GjTzxCOito6YADkQ,351,../data/image/gJKPE0GjTzxCOito6YADkQ_351.jpg +"[{'category_id': 1, 'bbox': [147, 18, 162, 28], 'bbox_mode': 0, 'segmentation': [148, 20, 150, 18, 152, 18, 154, 18, 156, 19, 159, 20, 162, 23, 162, 25, 162, 28, 160, 28, 158, 28, 156, 26, 155, 25, 153, 24, 152, 23, 150, 23, 148, 23, 147, 22, 147, 21]}]",valid,2452,LcKUS1DgcKyU8RSE6xtCQA,263,../data/image/LcKUS1DgcKyU8RSE6xtCQA_263.jpg +"[{'category_id': 0, 'bbox': [412, 224, 419, 233], 'bbox_mode': 0, 'segmentation': [415, 224, 415, 224, 415, 224, 415, 225, 415, 225, 414, 226, 414, 226, 414, 226, 414, 227, 414, 227, 413, 227, 413, 227, 413, 227, 412, 228, 412, 228, 412, 229, 412, 229, 412, 230, 412, 230, 413, 230, 413, 230, 413, 231, 413, 231, 413, 231, 414, 232, 414, 232, 414, 233, 415, 233, 415, 233, 416, 233, 416, 233, 417, 233, 417, 232, 417, 232, 418, 232, 418, 231, 418, 231, 418, 231, 418, 230, 419, 230, 419, 230, 419, 229, 419, 229, 419, 229, 419, 228, 419, 228, 419, 227, 419, 227, 419, 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336, 638, 334, 636, 333, 634, 332, 632, 331, 630, 331, 629, 331, 628, 331, 627, 332, 626, 333]}]",valid,2457,N2lqLuRHG_RJnjEN8WGBUA,159,../data/image/N2lqLuRHG_RJnjEN8WGBUA_159.jpg +[],valid,2458,VJmL5yED_nAukrwPC_YMbg,178,../data/image/VJmL5yED_nAukrwPC_YMbg_178.jpg +"[{'category_id': 0, 'bbox': [196, 262, 203, 271], 'bbox_mode': 0, 'segmentation': [199, 262, 199, 262, 198, 263, 197, 263, 197, 265, 196, 266, 196, 268, 197, 270, 198, 271, 199, 271, 201, 270, 202, 269, 203, 268, 203, 267, 203, 266, 202, 265, 201, 264, 201, 263, 200, 263]}]",valid,2459,gqxAIBI9idU0JNarEceMJA,268,../data/image/gqxAIBI9idU0JNarEceMJA_268.jpg +"[{'category_id': 0, 'bbox': [515, 205, 525, 216], 'bbox_mode': 0, 'segmentation': [519, 205, 520, 205, 521, 205, 521, 206, 522, 207, 524, 207, 525, 208, 525, 210, 525, 212, 524, 214, 522, 215, 520, 216, 518, 216, 517, 215, 516, 213, 515, 211, 515, 210, 516, 209, 517, 208, 518, 207, 518, 206, 519, 206]}]",valid,2460,Ie7pRxTCgq4Jt74UgToKcA,196,../data/image/Ie7pRxTCgq4Jt74UgToKcA_196.jpg +"[{'category_id': 1, 'bbox': [308, 252, 314, 256], 'bbox_mode': 0, 'segmentation': [308, 253, 313, 252, 314, 253, 314, 255, 314, 256, 309, 256]}]",valid,2461,7__HhnH42V18vSKFDOxPsg,175,../data/image/7__HhnH42V18vSKFDOxPsg_175.jpg +[],valid,2462,JhQ7lt7wLmaDbiryw5vP0Q,225,../data/image/JhQ7lt7wLmaDbiryw5vP0Q_225.jpg +"[{'category_id': 0, 'bbox': [189, 171, 198, 181], 'bbox_mode': 0, 'segmentation': [189, 171, 190, 171, 191, 171, 193, 171, 194, 171, 194, 171, 195, 172, 196, 173, 196, 173, 197, 174, 198, 175, 198, 176, 198, 177, 198, 178, 198, 179, 197, 180, 197, 180, 196, 180, 196, 180, 196, 181, 195, 181, 195, 181, 194, 181, 193, 181, 193, 180, 192, 180, 192, 179, 192, 179, 191, 179, 191, 179, 190, 178, 190, 177, 191, 175, 191, 174, 192, 173, 192, 173, 192, 173, 192, 172, 192, 172, 191, 172, 191, 172, 190, 173, 190, 173, 190, 173, 189, 173, 189, 173, 189, 172, 189, 172, 189, 172, 189, 172]}]",valid,2463,GaIa1EUS9BBeJlyjshSNoA,178,../data/image/GaIa1EUS9BBeJlyjshSNoA_178.jpg +"[{'category_id': 1, 'bbox': [184, 122, 189, 128], 'bbox_mode': 0, 'segmentation': [188, 126, 186, 122, 185, 122, 185, 122, 185, 122, 184, 122, 184, 122, 184, 123, 184, 123, 187, 127, 187, 128, 188, 128, 188, 128, 189, 128, 189, 128, 189, 128, 189, 128, 189, 127]}, {'category_id': 1, 'bbox': [569, 249, 576, 253], 'bbox_mode': 0, 'segmentation': [573, 249, 571, 249, 571, 249, 570, 250, 569, 251, 569, 251, 569, 251, 569, 252, 569, 252, 571, 252, 571, 253, 572, 252, 573, 252, 574, 252, 574, 252, 575, 252, 575, 251, 575, 250, 576, 249, 576, 249, 575, 249]}]",valid,2464,npjnJpakAs4l6rd9tq022Q,88,../data/image/npjnJpakAs4l6rd9tq022Q_88.jpg +"[{'category_id': 0, 'bbox': [562, 266, 570, 273], 'bbox_mode': 0, 'segmentation': [566, 266, 567, 266, 567, 266, 568, 267, 568, 268, 569, 268, 570, 268, 570, 269, 570, 270, 570, 271, 569, 272, 568, 273, 567, 273, 566, 273, 565, 273, 564, 272, 563, 272, 563, 271, 562, 270, 562, 269, 563, 268, 564, 268, 565, 268, 566, 268]}, {'category_id': 0, 'bbox': [541, 267, 548, 274], 'bbox_mode': 0, 'segmentation': [543, 268, 544, 267, 544, 267, 545, 267, 546, 268, 546, 269, 547, 269, 548, 270, 548, 271, 548, 273, 547, 273, 546, 274, 545, 274, 544, 274, 542, 274, 541, 274, 541, 273, 541, 271, 541, 270, 541, 269, 542, 269, 543, 269, 543, 268]}]",valid,2465,IE1bwCSovX7Xrq3cL0SX8w,128,../data/image/IE1bwCSovX7Xrq3cL0SX8w_128.jpg +"[{'category_id': 0, 'bbox': [623, 207, 634, 219], 'bbox_mode': 0, 'segmentation': [623, 213, 623, 213, 623, 215, 623, 216, 623, 217, 623, 218, 624, 218, 625, 219, 626, 219, 627, 219, 629, 219, 630, 219, 632, 219, 633, 218, 634, 216, 634, 214, 634, 213, 634, 211, 633, 210, 632, 209, 631, 209, 630, 209, 630, 207, 629, 207, 629, 207, 628, 207, 628, 207, 627, 207, 625, 207, 625, 208, 624, 208, 624, 209, 624, 210]}, {'category_id': 1, 'bbox': [145, 264, 150, 269], 'bbox_mode': 0, 'segmentation': [146, 264, 146, 264, 147, 264, 147, 264, 148, 264, 148, 264, 148, 264, 149, 265, 149, 265, 149, 266, 150, 266, 150, 267, 150, 268, 149, 268, 149, 269, 148, 269, 147, 269, 147, 269, 146, 268, 145, 268, 145, 268, 145, 267, 145, 267, 145, 266, 145, 265, 145, 265, 145, 265]}, {'category_id': 0, 'bbox': [24, 276, 31, 283], 'bbox_mode': 0, 'segmentation': [24, 280, 24, 280, 24, 280, 24, 281, 24, 281, 24, 282, 24, 282, 25, 283, 25, 283, 26, 283, 27, 283, 28, 283, 28, 283, 29, 283, 29, 282, 29, 281, 29, 281, 29, 280, 29, 280, 29, 279, 30, 279, 30, 279, 30, 280, 30, 280, 31, 279, 31, 279, 30, 278, 30, 278, 29, 277, 29, 277, 28, 277, 28, 277, 27, 277, 27, 276, 26, 276, 26, 277, 25, 277, 24, 278, 24, 278, 24, 279]}]",valid,2466,NTGoUGBWmn4tcjekAIYceg,197,../data/image/NTGoUGBWmn4tcjekAIYceg_197.jpg +"[{'category_id': 1, 'bbox': [185, 94, 194, 109], 'bbox_mode': 0, 'segmentation': [193, 99, 189, 95, 188, 94, 188, 94, 187, 94, 186, 94, 186, 95, 185, 96, 185, 97, 188, 102, 190, 105, 190, 108, 191, 109, 192, 109, 193, 109, 194, 108, 194, 108, 194, 106, 194, 105, 194, 104, 194, 102, 194, 101]}]",valid,2467,kVRMrSX6f4J_u4KFwyQNyQ,269,../data/image/kVRMrSX6f4J_u4KFwyQNyQ_269.jpg +[],valid,2468,TmAkTvnrQ_uPGsbgb-xjsw,195,../data/image/TmAkTvnrQ_uPGsbgb-xjsw_195.jpg +"[{'category_id': 0, 'bbox': [323, 228, 328, 235], 'bbox_mode': 0, 'segmentation': [325, 228, 325, 228, 325, 229, 325, 229, 324, 229, 324, 230, 324, 230, 324, 230, 323, 230, 323, 230, 323, 231, 323, 232, 323, 232, 323, 233, 323, 233, 323, 233, 324, 233, 324, 233, 324, 234, 324, 234, 325, 234, 325, 235, 325, 235, 325, 235, 325, 235, 326, 235, 326, 235, 326, 235, 327, 235, 327, 235, 327, 234, 327, 234, 328, 234, 328, 233, 328, 233, 328, 233, 328, 232, 328, 231, 328, 231, 328, 230, 328, 230, 328, 230, 327, 229, 327, 229, 326, 229, 326, 228, 326, 228, 326, 228]}]",valid,2469,y5sdISYUcNME4sOkSXOMag,33,../data/image/y5sdISYUcNME4sOkSXOMag_33.jpg +"[{'category_id': 1, 'bbox': [388, 250, 401, 257], 'bbox_mode': 0, 'segmentation': [388, 253, 392, 254, 392, 257, 394, 257, 394, 255, 396, 256, 398, 256, 401, 255, 401, 253, 400, 252, 389, 250, 388, 251]}, {'category_id': 0, 'bbox': [323, 116, 330, 126], 'bbox_mode': 0, 'segmentation': [324, 116, 328, 116, 329, 117, 330, 120, 330, 122, 330, 123, 327, 126, 325, 126, 324, 125, 323, 122, 323, 120]}]",valid,2470,dR6pwuRVVm3VC24p7NMjVw,179,../data/image/dR6pwuRVVm3VC24p7NMjVw_179.jpg +[],valid,2471,5xQ9SbZxAomnv2LSgvrLRQ,178,../data/image/5xQ9SbZxAomnv2LSgvrLRQ_178.jpg +"[{'category_id': 0, 'bbox': [407, 239, 414, 245], 'bbox_mode': 0, 'segmentation': [407, 240, 409, 241, 410, 243, 410, 244, 411, 245, 413, 245, 414, 244, 414, 244, 414, 243, 413, 242, 412, 241, 411, 239, 411, 239, 407, 239]}]",valid,2472,-VtSAMFgMLE7DlkS0b2GRQ,90,../data/image/-VtSAMFgMLE7DlkS0b2GRQ_90.jpg +[],valid,2473,U_2zXQBFN45WvgSfgLFDqQ,270,../data/image/U_2zXQBFN45WvgSfgLFDqQ_270.jpg +"[{'category_id': 0, 'bbox': [4, 108, 16, 118], 'bbox_mode': 0, 'segmentation': [8, 108, 13, 109, 15, 111, 16, 113, 16, 115, 16, 117, 11, 118, 8, 117, 7, 115, 5, 113, 4, 111, 4, 109, 6, 108]}]",valid,2474,cwQCQcdQo1Vw0MxGqDYOwg,357,../data/image/cwQCQcdQo1Vw0MxGqDYOwg_357.jpg +"[{'category_id': 0, 'bbox': [308, 249, 313, 257], 'bbox_mode': 0, 'segmentation': [309, 249, 312, 249, 313, 251, 313, 253, 313, 255, 312, 257, 310, 257, 309, 256, 308, 255, 308, 253, 308, 250]}]",valid,2475,F46vRi5Y4MYDcy0e80KlQg,298,../data/image/F46vRi5Y4MYDcy0e80KlQg_298.jpg +"[{'category_id': 1, 'bbox': [360, 263, 373, 273], 'bbox_mode': 0, 'segmentation': [368, 264, 361, 268, 360, 269, 360, 269, 360, 270, 361, 271, 362, 272, 362, 272, 363, 273, 364, 272, 364, 272, 365, 272, 366, 271, 366, 272, 366, 273, 367, 273, 368, 273, 369, 273, 369, 272, 369, 271, 369, 270, 369, 269, 370, 269, 372, 268, 372, 268, 373, 267, 373, 266, 373, 266, 373, 265, 372, 264, 372, 264, 371, 263, 371, 263]}]",valid,2476,vcJhd4X67UNOSASB_airgA,68,../data/image/vcJhd4X67UNOSASB_airgA_68.jpg +[],valid,2477,GOhL8BqvJGoYNHCgRQdWiA,269,../data/image/GOhL8BqvJGoYNHCgRQdWiA_269.jpg 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'bbox_mode': 0, 'segmentation': [515, 240, 524, 239, 526, 238, 526, 237, 527, 236, 527, 235, 527, 234, 526, 234, 521, 234, 514, 234, 513, 235, 511, 235, 510, 235, 509, 236, 509, 236, 509, 237, 509, 238, 509, 239, 510, 240, 511, 240]}]",valid,2480,noYTzYzx_FNXJ2lDg5zvMg,324,../data/image/noYTzYzx_FNXJ2lDg5zvMg_324.jpg +"[{'category_id': 1, 'bbox': [556, 298, 564, 302], 'bbox_mode': 0, 'segmentation': [561, 299, 559, 299, 558, 299, 557, 300, 557, 300, 557, 300, 556, 300, 557, 301, 557, 301, 558, 302, 559, 302, 559, 302, 560, 302, 560, 302, 561, 302, 561, 302, 561, 302, 562, 301, 562, 301, 563, 301, 563, 301, 563, 300, 563, 300, 564, 299, 564, 299, 563, 299, 563, 298]}]",valid,2481,QSB5m3XhlnsznOecfRUfuA,260,../data/image/QSB5m3XhlnsznOecfRUfuA_260.jpg +[],valid,2482,X4Rn3IdZU_cFRkU4IOUxDg,226,../data/image/X4Rn3IdZU_cFRkU4IOUxDg_226.jpg +"[{'category_id': 1, 'bbox': [467, 174, 478, 180], 'bbox_mode': 0, 'segmentation': [478, 175, 477, 174, 476, 174, 475, 174, 474, 174, 472, 175, 471, 175, 469, 176, 468, 176, 467, 176, 468, 177, 469, 178, 470, 178, 471, 179, 471, 180, 472, 180, 473, 180, 473, 179, 473, 178, 474, 177, 475, 177, 475, 177, 476, 177, 477, 176, 478, 176]}]",valid,2483,tjud_9vxhrNZjbOInLpzIA,359,../data/image/tjud_9vxhrNZjbOInLpzIA_359.jpg +"[{'category_id': 1, 'bbox': [116, 145, 129, 148], 'bbox_mode': 0, 'segmentation': [117, 145, 127, 145, 128, 145, 129, 145, 129, 145, 129, 146, 129, 147, 129, 147, 129, 148, 129, 148, 119, 148, 118, 148, 117, 148, 117, 148, 116, 147, 116, 147, 116, 146, 116, 146, 117, 145, 117, 145]}]",valid,2484,LyAEoCodcIzwpeS6iLg2ZQ,88,../data/image/LyAEoCodcIzwpeS6iLg2ZQ_88.jpg +[],valid,2485,c_98PE8oABNNm_g3hoVDAA,260,../data/image/c_98PE8oABNNm_g3hoVDAA_260.jpg +"[{'category_id': 1, 'bbox': [40, 200, 64, 211], 'bbox_mode': 0, 'segmentation': [42, 200, 60, 203, 63, 204, 64, 207, 63, 210, 59, 210, 55, 209, 51, 209, 50, 211, 47, 211, 47, 208, 41, 206, 40, 204, 40, 201]}]",valid,2486,232LdKq-0UpUqQw0gGfbug,38,../data/image/232LdKq-0UpUqQw0gGfbug_38.jpg +[],valid,2487,N1XNy5v1PBUAA7mfKkPQdA,265,../data/image/N1XNy5v1PBUAA7mfKkPQdA_265.jpg +[],valid,2488,VhZACZQOJnkSiuMx8aQwOQ,23,../data/image/VhZACZQOJnkSiuMx8aQwOQ_23.jpg +[],valid,2489,RkQ_BEjvCDVhk8P9HFuMsw,87,../data/image/RkQ_BEjvCDVhk8P9HFuMsw_87.jpg +"[{'category_id': 0, 'bbox': [409, 210, 417, 223], 'bbox_mode': 0, 'segmentation': [413, 210, 416, 210, 417, 211, 417, 213, 417, 219, 416, 223, 413, 223, 412, 222, 410, 221, 409, 219, 409, 217, 409, 215, 409, 213, 409, 212, 411, 211]}]",valid,2490,aOhYAbeWMx0EDrEZT77elg,274,../data/image/aOhYAbeWMx0EDrEZT77elg_274.jpg +[],valid,2491,GdCRQm6ToIJssb8po4ab-w,260,../data/image/GdCRQm6ToIJssb8po4ab-w_260.jpg +"[{'category_id': 0, 'bbox': [561, 58, 567, 65], 'bbox_mode': 0, 'segmentation': [563, 59, 564, 59, 565, 58, 566, 59, 566, 60, 567, 61, 567, 62, 567, 62, 567, 63, 567, 64, 565, 65, 564, 65, 563, 65, 562, 65, 561, 65, 561, 64, 561, 63, 561, 62, 561, 61, 562, 60, 562, 60, 563, 60]}]",valid,2492,OfZUU2FcSci0FfKac2vZ0g,171,../data/image/OfZUU2FcSci0FfKac2vZ0g_171.jpg +"[{'category_id': 1, 'bbox': [194, 220, 211, 230], 'bbox_mode': 0, 'segmentation': [194, 223, 200, 226, 199, 230, 201, 230, 202, 227, 207, 229, 209, 229, 210, 227, 211, 225, 210, 224, 196, 220, 195, 220, 194, 222]}]",valid,2493,flANxxI65CSY7EpxIkcouw,120,../data/image/flANxxI65CSY7EpxIkcouw_120.jpg +[],valid,2494,-uPZQc6ALDpBQcG5pqEVGQ,180,../data/image/-uPZQc6ALDpBQcG5pqEVGQ_180.jpg +[],valid,2495,HB4p1YRWbtRU9i-GOyIKgQ,53,../data/image/HB4p1YRWbtRU9i-GOyIKgQ_53.jpg +"[{'category_id': 0, 'bbox': [109, 255, 117, 267], 'bbox_mode': 0, 'segmentation': [112, 255, 114, 255, 116, 257, 117, 260, 117, 262, 117, 264, 116, 266, 115, 267, 112, 267, 111, 266, 110, 264, 109, 262, 109, 259, 110, 257]}]",valid,2496,eUVmuxJpMbw9vxGjPGlVSg,268,../data/image/eUVmuxJpMbw9vxGjPGlVSg_268.jpg +"[{'category_id': 1, 'bbox': [409, 232, 429, 239], 'bbox_mode': 0, 'segmentation': [411, 239, 416, 239, 425, 239, 427, 239, 428, 238, 428, 237, 429, 236, 428, 236, 428, 235, 428, 235, 427, 235, 427, 234, 420, 234, 419, 233, 419, 233, 418, 232, 416, 232, 414, 232, 411, 232, 410, 233, 410, 233, 410, 235, 409, 237, 409, 238, 410, 239]}]",valid,2497,YXcqlYtwbCzZZHFmlQXRPw,38,../data/image/YXcqlYtwbCzZZHFmlQXRPw_38.jpg +"[{'category_id': 1, 'bbox': [545, 176, 551, 183], 'bbox_mode': 0, 'segmentation': [548, 176, 545, 178, 545, 179, 545, 180, 546, 183, 548, 183, 550, 182, 551, 180, 551, 178, 550, 177, 549, 176]}]",valid,2498,5cFtXejdSmXnzveaURLHTA,90,../data/image/5cFtXejdSmXnzveaURLHTA_90.jpg +[],valid,2499,0ff3PeZu2Qym-BkMUe2AhA,135,../data/image/0ff3PeZu2Qym-BkMUe2AhA_135.jpg +"[{'category_id': 0, 'bbox': [398, 214, 405, 223], 'bbox_mode': 0, 'segmentation': [399, 214, 405, 214, 405, 217, 405, 219, 404, 221, 402, 223, 399, 223, 399, 222, 398, 220, 398, 218]}]",valid,2500,45VoG4Mw6BgPZO04bRX_Zg,196,../data/image/45VoG4Mw6BgPZO04bRX_Zg_196.jpg +"[{'category_id': 0, 'bbox': [151, 80, 158, 88], 'bbox_mode': 0, 'segmentation': [156, 80, 155, 80, 154, 80, 153, 81, 152, 82, 152, 83, 151, 84, 151, 85, 151, 86, 152, 87, 154, 88, 155, 88, 156, 87, 157, 87, 158, 86, 158, 84, 157, 83, 157, 82, 156, 81]}]",valid,2501,XwqbM3nsZ8o_zL5Wpshn4Q,80,../data/image/XwqbM3nsZ8o_zL5Wpshn4Q_80.jpg +"[{'category_id': 0, 'bbox': [384, 182, 393, 191], 'bbox_mode': 0, 'segmentation': [393, 183, 392, 182, 391, 182, 389, 182, 386, 182, 384, 182, 384, 185, 384, 187, 385, 189, 386, 190, 388, 191, 390, 190, 391, 189, 392, 188, 393, 186, 393, 185]}]",valid,2502,s2ssnQ6IzIulXQoXAb2Wgw,88,../data/image/s2ssnQ6IzIulXQoXAb2Wgw_88.jpg +[],valid,2503,hjMRhRJqzxG0XKen3-iE8A,313,../data/image/hjMRhRJqzxG0XKen3-iE8A_313.jpg +"[{'category_id': 0, 'bbox': [456, 155, 461, 165], 'bbox_mode': 0, 'segmentation': [459, 156, 459, 157, 459, 157, 460, 158, 460, 158, 461, 158, 461, 159, 461, 159, 461, 160, 461, 160, 461, 161, 461, 162, 461, 163, 461, 163, 461, 164, 461, 164, 460, 164, 460, 164, 459, 165, 459, 165, 458, 165, 458, 165, 458, 164, 457, 164, 457, 164, 457, 164, 456, 164, 456, 163, 456, 163, 456, 163, 456, 162, 456, 162, 456, 161, 456, 161, 456, 160, 457, 159, 457, 159, 458, 158, 458, 158, 458, 158, 458, 157, 458, 157, 458, 156, 458, 156, 458, 155, 458, 155, 458, 155, 458, 155, 458, 155, 458, 155, 459, 155, 459, 155, 459, 155, 459, 155]}]",valid,2504,KUrT7KIlWBRDijSOJXZ7-Q,355,../data/image/KUrT7KIlWBRDijSOJXZ7-Q_355.jpg +"[{'category_id': 0, 'bbox': [111, 304, 122, 314], 'bbox_mode': 0, 'segmentation': [113, 304, 112, 304, 112, 304, 112, 304, 111, 305, 111, 309, 111, 309, 111, 310, 111, 310, 112, 310, 112, 310, 113, 312, 113, 312, 114, 313, 115, 314, 116, 314, 117, 314, 118, 314, 120, 313, 120, 312, 120, 311, 121, 311, 121, 310, 121, 310, 122, 310, 122, 310, 122, 310, 122, 309, 122, 307, 120, 304, 119, 304, 118, 304]}]",valid,2505,JIFIjwvcdeO8IPVQk3s8Xw,130,../data/image/JIFIjwvcdeO8IPVQk3s8Xw_130.jpg +"[{'category_id': 0, 'bbox': [505, 226, 514, 236], 'bbox_mode': 0, 'segmentation': [508, 226, 512, 226, 513, 228, 513, 230, 514, 233, 513, 233, 512, 235, 511, 236, 509, 236, 507, 233, 505, 233, 505, 230, 506, 228]}]",valid,2506,4ZJ8szGvM75ZzBLDtEKA2w,219,../data/image/4ZJ8szGvM75ZzBLDtEKA2w_219.jpg +"[{'category_id': 0, 'bbox': [412, 130, 418, 139], 'bbox_mode': 0, 'segmentation': [413, 130, 417, 130, 417, 132, 418, 134, 418, 135, 417, 137, 416, 139, 413, 139, 413, 137, 412, 135, 412, 132]}]",valid,2507,fl3IXdnDFA_-BtWuXCfCbg,176,../data/image/fl3IXdnDFA_-BtWuXCfCbg_176.jpg +"[{'category_id': 1, 'bbox': [428, 274, 447, 283], 'bbox_mode': 0, 'segmentation': [432, 282, 436, 280, 443, 279, 447, 277, 447, 277, 447, 276, 447, 274, 445, 274, 430, 279, 429, 279, 428, 280, 428, 281, 428, 282, 430, 283]}]",valid,2508,9i8t-UCQP19wXZBLBtPx3A,215,../data/image/9i8t-UCQP19wXZBLBtPx3A_215.jpg +"[{'category_id': 0, 'bbox': [273, 245, 286, 250], 'bbox_mode': 0, 'segmentation': [285, 247, 283, 247, 281, 247, 279, 247, 279, 246, 278, 245, 277, 245, 276, 245, 275, 245, 274, 245, 273, 246, 273, 247, 273, 248, 273, 249, 275, 250, 276, 250, 278, 250, 279, 249, 280, 249, 282, 249, 284, 249, 285, 249, 286, 248, 286, 248, 286, 247]}]",valid,2509,g-VNJs8eJ7JHPxhxHrZh6A,268,../data/image/g-VNJs8eJ7JHPxhxHrZh6A_268.jpg +"[{'category_id': 1, 'bbox': [503, 174, 516, 182], 'bbox_mode': 0, 'segmentation': [505, 176, 503, 176, 503, 177, 504, 178, 506, 178, 508, 178, 508, 182, 510, 181, 510, 178, 512, 177, 514, 177, 516, 176, 516, 175, 515, 174, 511, 175]}]",valid,2510,d-LheB6A-DvM-hj34MwuSQ,359,../data/image/d-LheB6A-DvM-hj34MwuSQ_359.jpg +"[{'category_id': 0, 'bbox': [235, 226, 249, 243], 'bbox_mode': 0, 'segmentation': [240, 228, 240, 227, 241, 227, 241, 227, 242, 226, 243, 226, 244, 227, 244, 228, 245, 228, 245, 229, 246, 229, 247, 230, 248, 231, 249, 232, 249, 233, 249, 234, 249, 235, 249, 237, 249, 238, 248, 239, 247, 240, 247, 241, 245, 242, 244, 243, 243, 243, 242, 243, 241, 243, 239, 243, 237, 242, 237, 241, 236, 239, 235, 238, 235, 237, 235, 235, 235, 233, 235, 232, 235, 231, 235, 231, 236, 230, 237, 230, 238, 230, 238, 229, 239, 229, 240, 229]}]",valid,2511,UjLQ0avjFBrdCNl85Yu6VA,279,../data/image/UjLQ0avjFBrdCNl85Yu6VA_279.jpg +"[{'category_id': 0, 'bbox': [373, 179, 388, 197], 'bbox_mode': 0, 'segmentation': [373, 189, 373, 190, 373, 190, 373, 191, 373, 192, 373, 193, 373, 194, 374, 195, 374, 196, 375, 196, 375, 197, 377, 197, 378, 197, 379, 197, 381, 197, 382, 197, 383, 196, 384, 194, 384, 191, 384, 190, 384, 188, 384, 188, 384, 187, 384, 187, 384, 187, 385, 187, 386, 186, 387, 186, 387, 186, 388, 186, 388, 185, 388, 185, 388, 184, 386, 184, 385, 184, 384, 184, 383, 184, 382, 183, 381, 183, 381, 181, 381, 180, 381, 180, 381, 179, 380, 179, 379, 179, 378, 179, 377, 179, 377, 179, 376, 180, 376, 181, 376, 181, 376, 182, 376, 183, 376, 184, 376, 185, 376, 185, 375, 186, 375, 186, 374, 186, 374, 187, 373, 187, 373, 188]}]",valid,2512,Gbu2E0fkUvRXUXgakB9-Cw,0,../data/image/Gbu2E0fkUvRXUXgakB9-Cw_0.jpg +"[{'category_id': 0, 'bbox': [91, 238, 97, 244], 'bbox_mode': 0, 'segmentation': [92, 238, 91, 239, 91, 239, 91, 240, 91, 241, 91, 242, 92, 243, 93, 244, 94, 243, 95, 243, 96, 242, 97, 241, 97, 240, 96, 239, 96, 238, 95, 238, 94, 238, 93, 238]}, {'category_id': 0, 'bbox': [504, 240, 508, 244], 'bbox_mode': 0, 'segmentation': [505, 240, 505, 241, 505, 241, 504, 242, 504, 242, 504, 243, 504, 244, 505, 244, 505, 244, 506, 244, 506, 244, 506, 244, 507, 244, 507, 244, 508, 243, 508, 243, 508, 242, 508, 241, 507, 241, 507, 240, 506, 240]}]",valid,2513,NNS8DNvH-ACwb_XESwpLDw,236,../data/image/NNS8DNvH-ACwb_XESwpLDw_236.jpg +"[{'category_id': 1, 'bbox': [365, 232, 368, 240], 'bbox_mode': 0, 'segmentation': [365, 232, 365, 235, 365, 239, 366, 240, 367, 239, 367, 237, 368, 236, 368, 235, 368, 233, 368, 232]}]",valid,2514,bYMFhfcNVFhooyfLdrOuug,278,../data/image/bYMFhfcNVFhooyfLdrOuug_278.jpg +"[{'category_id': 0, 'bbox': [464, 216, 471, 225], 'bbox_mode': 0, 'segmentation': [466, 217, 469, 216, 470, 217, 471, 218, 471, 221, 471, 222, 470, 223, 468, 225, 467, 225, 465, 224, 464, 223, 464, 220, 465, 218, 465, 217]}]",valid,2515,2nOxrG5PMxHQ5afoswzUGQ,357,../data/image/2nOxrG5PMxHQ5afoswzUGQ_357.jpg +"[{'category_id': 0, 'bbox': [441, 118, 445, 125], 'bbox_mode': 0, 'segmentation': [441, 124, 441, 125, 442, 125, 443, 125, 443, 125, 444, 125, 444, 125, 444, 125, 445, 125, 445, 124, 445, 124, 445, 123, 445, 122, 445, 122, 445, 120, 444, 119, 444, 118, 443, 118, 443, 118, 442, 118, 442, 118, 442, 119, 441, 120, 441, 121, 441, 122, 441, 123, 441, 124]}]",valid,2516,K9qSA_ArrulkKsO4j1xrOg,2,../data/image/K9qSA_ArrulkKsO4j1xrOg_2.jpg +[],valid,2517,I_ZfgYFS63i8qA-L3aL6Dw,180,../data/image/I_ZfgYFS63i8qA-L3aL6Dw_180.jpg +"[{'category_id': 0, 'bbox': [229, 255, 234, 262], 'bbox_mode': 0, 'segmentation': [230, 256, 231, 255, 231, 255, 232, 255, 233, 255, 233, 256, 234, 257, 234, 258, 234, 259, 234, 260, 234, 261, 233, 262, 233, 262, 232, 262, 231, 262, 230, 262, 229, 261, 229, 260, 229, 259, 229, 258, 229, 257, 230, 256]}]",valid,2518,JzmXpZzq_xNDoKcFrJWPDw,358,../data/image/JzmXpZzq_xNDoKcFrJWPDw_358.jpg +"[{'category_id': 1, 'bbox': [48, 174, 57, 181], 'bbox_mode': 0, 'segmentation': [48, 178, 53, 181, 55, 181, 57, 180, 57, 179, 56, 178, 48, 174]}]",valid,2519,BxXz2jiCK_57uWq6cfwnkQ,266,../data/image/BxXz2jiCK_57uWq6cfwnkQ_266.jpg +[],valid,2520,No-uQWFZcMEG4OQ8Rr3w8A,222,../data/image/No-uQWFZcMEG4OQ8Rr3w8A_222.jpg +"[{'category_id': 1, 'bbox': [185, 276, 191, 282], 'bbox_mode': 0, 'segmentation': [186, 277, 187, 276, 188, 276, 190, 278, 191, 279, 191, 281, 190, 281, 189, 282, 187, 282, 186, 280, 186, 279, 185, 279]}, {'category_id': 1, 'bbox': [360, 285, 367, 290], 'bbox_mode': 0, 'segmentation': [360, 285, 362, 285, 364, 286, 365, 286, 367, 287, 367, 288, 366, 290, 365, 290, 364, 290, 363, 289, 362, 288, 361, 288, 361, 287, 360, 287, 360, 286, 360, 286]}, {'category_id': 1, 'bbox': [537, 269, 544, 274], 'bbox_mode': 0, 'segmentation': [544, 270, 543, 269, 542, 269, 541, 269, 540, 270, 538, 271, 537, 271, 537, 272, 538, 274, 539, 274, 540, 274, 541, 274, 543, 273, 543, 273, 544, 272, 544, 271, 544, 270]}, {'category_id': 0, 'bbox': [626, 278, 635, 287], 'bbox_mode': 0, 'segmentation': [630, 279, 630, 278, 631, 278, 632, 278, 633, 280, 634, 280, 634, 281, 635, 282, 635, 283, 635, 285, 634, 286, 632, 286, 630, 287, 629, 287, 627, 286, 627, 285, 626, 283, 627, 282, 627, 281, 627, 281, 628, 280, 629, 280, 630, 280]}]",valid,2521,lXUgUzBcSbqTo3jIh0e2oQ,208,../data/image/lXUgUzBcSbqTo3jIh0e2oQ_208.jpg +"[{'category_id': 1, 'bbox': [61, 228, 72, 238], 'bbox_mode': 0, 'segmentation': [61, 228, 63, 228, 68, 228, 71, 228, 72, 230, 72, 231, 71, 231, 68, 231, 68, 233, 68, 235, 68, 238, 65, 238, 65, 233, 65, 231, 63, 231, 62, 231, 61, 230, 61, 230]}]",valid,2522,AZItLW8TYE8u12XxGMAz0w,305,../data/image/AZItLW8TYE8u12XxGMAz0w_305.jpg +"[{'category_id': 1, 'bbox': [417, 239, 427, 244], 'bbox_mode': 0, 'segmentation': [421, 239, 421, 239, 422, 239, 422, 239, 424, 239, 424, 239, 425, 239, 426, 239, 426, 239, 427, 240, 427, 240, 427, 241, 427, 242, 426, 242, 425, 242, 425, 242, 425, 242, 424, 242, 424, 242, 424, 243, 424, 243, 424, 244, 423, 244, 422, 244, 422, 244, 421, 244, 421, 243, 421, 242, 421, 242, 420, 242, 419, 241, 418, 241, 418, 241, 417, 241, 417, 240, 418, 239, 418, 239, 418, 239]}]",valid,2523,ijApcy8pkIPLsD3QgD2U8Q,323,../data/image/ijApcy8pkIPLsD3QgD2U8Q_323.jpg +"[{'category_id': 1, 'bbox': [525, 146, 540, 166], 'bbox_mode': 0, 'segmentation': [525, 158, 525, 159, 525, 160, 525, 161, 525, 162, 526, 163, 527, 164, 528, 165, 529, 166, 531, 166, 533, 166, 535, 166, 537, 165, 539, 161, 540, 158, 540, 156, 540, 153, 539, 152, 536, 151, 535, 150, 535, 149, 535, 148, 535, 147, 534, 146, 534, 146, 533, 146, 532, 146, 532, 148, 532, 148, 532, 149, 531, 149, 530, 150, 529, 150, 528, 150, 527, 150, 526, 151, 525, 153, 525, 154, 525, 155]}]",valid,2524,l3FNra9Kskragd-K5b4DRQ,111,../data/image/l3FNra9Kskragd-K5b4DRQ_111.jpg +"[{'category_id': 1, 'bbox': [420, 72, 431, 80], 'bbox_mode': 0, 'segmentation': [429, 78, 430, 78, 430, 78, 431, 77, 431, 76, 431, 76, 431, 75, 431, 74, 431, 73, 431, 73, 430, 72, 430, 72, 429, 73, 428, 73, 423, 73, 420, 74, 420, 75, 420, 76, 420, 77, 420, 78, 421, 78, 421, 78, 421, 78, 422, 78, 422, 79, 423, 79, 423, 79, 423, 79, 423, 79, 424, 79, 425, 79, 426, 79, 426, 78, 427, 79, 427, 80, 428, 80, 428, 80, 429, 79, 429, 79, 429, 78, 428, 78, 429, 78]}]",valid,2525,wrRLmLimS2gzW2CAmr6Lcw,95,../data/image/wrRLmLimS2gzW2CAmr6Lcw_95.jpg +"[{'category_id': 1, 'bbox': [398, 265, 408, 271], 'bbox_mode': 0, 'segmentation': [400, 268, 405, 265, 406, 265, 407, 266, 408, 267, 408, 267, 408, 268, 407, 269, 403, 271, 401, 271, 400, 271, 399, 271, 399, 270, 398, 269]}, {'category_id': 1, 'bbox': [211, 283, 218, 289], 'bbox_mode': 0, 'segmentation': [212, 286, 214, 288, 215, 288, 216, 289, 218, 288, 218, 288, 218, 287, 218, 285, 217, 285, 216, 284, 212, 283, 211, 283, 211, 284, 211, 286]}, {'category_id': 0, 'bbox': [26, 274, 33, 279], 'bbox_mode': 0, 'segmentation': [28, 274, 31, 274, 32, 274, 33, 276, 33, 277, 33, 278, 32, 278, 32, 279, 30, 279, 28, 279, 28, 279, 27, 278, 27, 277, 27, 277, 26, 276, 26, 275, 27, 274]}, {'category_id': 0, 'bbox': [498, 277, 505, 284], 'bbox_mode': 0, 'segmentation': [500, 277, 503, 277, 505, 278, 505, 281, 505, 282, 505, 283, 503, 284, 500, 284, 499, 284, 498, 282, 498, 281, 498, 280, 498, 278]}]",valid,2526,ApgXbMpCT6-YYF-BrTrLQQ,208,../data/image/ApgXbMpCT6-YYF-BrTrLQQ_208.jpg +[],valid,2527,VKQLRrIbaWneJE2eSw5fnA,265,../data/image/VKQLRrIbaWneJE2eSw5fnA_265.jpg +"[{'category_id': 0, 'bbox': [627, 258, 637, 267], 'bbox_mode': 0, 'segmentation': [628, 260, 627, 259, 628, 258, 629, 258, 632, 258, 634, 258, 635, 259, 636, 262, 637, 263, 637, 264, 636, 265, 635, 267, 633, 266, 632, 265, 632, 263, 633, 262, 633, 260, 632, 260, 630, 260, 628, 259, 628, 260, 628, 260]}]",valid,2528,gFIHlYN6y97SX66UuIHP2Q,356,../data/image/gFIHlYN6y97SX66UuIHP2Q_356.jpg +"[{'category_id': 0, 'bbox': [86, 122, 95, 132], 'bbox_mode': 0, 'segmentation': [90, 122, 87, 123, 86, 125, 86, 127, 86, 129, 87, 131, 89, 132, 91, 132, 94, 132, 95, 130, 95, 127, 95, 125, 93, 123, 92, 122]}]",valid,2529,CWRk-TodlqRjf0Qlu0_YwQ,243,../data/image/CWRk-TodlqRjf0Qlu0_YwQ_243.jpg +"[{'category_id': 0, 'bbox': [184, 247, 190, 258], 'bbox_mode': 0, 'segmentation': [185, 252, 185, 253, 184, 253, 184, 254, 184, 255, 185, 256, 185, 257, 186, 257, 186, 258, 187, 258, 188, 258, 188, 258, 189, 257, 189, 257, 189, 256, 190, 256, 190, 255, 190, 254, 190, 253, 189, 252, 189, 251, 188, 251, 188, 250, 188, 249, 188, 248, 188, 247, 188, 247, 187, 247, 187, 247, 187, 248, 187, 248, 186, 249, 186, 250, 186, 250, 186, 251]}]",valid,2530,O8XJzyabeuaReoLTIN9_3g,132,../data/image/O8XJzyabeuaReoLTIN9_3g_132.jpg +"[{'category_id': 1, 'bbox': [607, 275, 618, 284], 'bbox_mode': 0, 'segmentation': [607, 277, 616, 275, 618, 276, 618, 276, 618, 278, 617, 280, 618, 283, 614, 284, 613, 284, 612, 280, 609, 282, 609, 282, 608, 280, 607, 280]}]",valid,2531,b0JqKDoJ9N_6HxIiLv5L1Q,170,../data/image/b0JqKDoJ9N_6HxIiLv5L1Q_170.jpg +"[{'category_id': 1, 'bbox': [506, 197, 519, 204], 'bbox_mode': 0, 'segmentation': [518, 198, 519, 198, 519, 198, 519, 198, 519, 199, 519, 200, 519, 200, 519, 201, 519, 201, 518, 202, 518, 202, 517, 203, 516, 203, 515, 203, 514, 203, 512, 203, 512, 203, 511, 203, 510, 203, 509, 203, 508, 204, 508, 204, 507, 204, 507, 204, 506, 203, 506, 203, 506, 202, 506, 201, 506, 200, 506, 200, 507, 199, 507, 198, 507, 198, 508, 197, 508, 197, 509, 197, 510, 197, 510, 197, 511, 197, 512, 197, 513, 197, 514, 197, 514, 197, 515, 197, 515, 197, 516, 197, 517, 197, 517, 197, 517, 197, 518, 197, 518, 197]}, {'category_id': 0, 'bbox': [491, 225, 496, 231], 'bbox_mode': 0, 'segmentation': [494, 225, 494, 225, 494, 225, 493, 225, 493, 225, 493, 225, 492, 225, 492, 225, 492, 226, 492, 226, 491, 226, 491, 226, 491, 227, 491, 227, 491, 228, 491, 228, 491, 229, 491, 230, 491, 230, 491, 231, 492, 231, 492, 231, 493, 231, 494, 231, 494, 231, 495, 231, 495, 230, 495, 230, 496, 229, 496, 228, 496, 227, 496, 227, 495, 226, 495, 226, 495, 225, 495, 225, 494, 225, 494, 225]}]",valid,2532,IMlZ7lqKqFdcRS-IY8TEfw,88,../data/image/IMlZ7lqKqFdcRS-IY8TEfw_88.jpg +[],valid,2533,-vjNB7E25pqMKJ7x74VwhQ,178,../data/image/-vjNB7E25pqMKJ7x74VwhQ_178.jpg +"[{'category_id': 1, 'bbox': [325, 221, 334, 236], 'bbox_mode': 0, 'segmentation': [325, 226, 325, 227, 325, 227, 325, 227, 325, 228, 325, 228, 325, 229, 325, 230, 325, 230, 326, 231, 326, 232, 326, 232, 326, 233, 327, 234, 327, 234, 327, 235, 327, 235, 328, 236, 329, 236, 331, 236, 332, 236, 333, 235, 333, 234, 334, 233, 334, 232, 334, 231, 334, 229, 334, 228, 334, 226, 334, 225, 334, 225, 333, 224, 332, 224, 332, 223, 332, 223, 331, 222, 331, 221, 331, 221, 331, 221, 330, 221, 330, 221, 329, 221, 329, 221, 329, 221, 329, 222, 329, 222, 329, 223, 328, 223, 328, 224, 327, 224, 326, 225, 326, 225]}]",valid,2534,p1KUSsti8BG4oSUheJxoMw,87,../data/image/p1KUSsti8BG4oSUheJxoMw_87.jpg +"[{'category_id': 1, 'bbox': [183, 245, 198, 254], 'bbox_mode': 0, 'segmentation': [188, 254, 189, 254, 190, 254, 190, 252, 191, 251, 192, 251, 194, 252, 196, 252, 198, 252, 198, 251, 198, 250, 196, 249, 194, 249, 193, 248, 190, 247, 189, 247, 187, 246, 186, 245, 185, 245, 184, 245, 183, 247, 184, 249, 185, 249, 187, 250, 188, 250, 187, 252, 187, 253]}]",valid,2535,vqVpuDkBVX5SHf4DtfGznw,359,../data/image/vqVpuDkBVX5SHf4DtfGznw_359.jpg +"[{'category_id': 1, 'bbox': [63, 247, 78, 254], 'bbox_mode': 0, 'segmentation': [63, 251, 74, 247, 76, 247, 77, 248, 78, 249, 77, 250, 73, 252, 73, 254, 72, 254, 71, 253, 66, 254, 64, 254, 63, 253]}, {'category_id': 1, 'bbox': [93, 249, 101, 256], 'bbox_mode': 0, 'segmentation': [95, 249, 101, 254, 101, 255, 100, 256, 99, 256, 97, 255, 94, 256, 93, 255, 96, 254, 94, 253, 93, 252, 93, 251, 94, 250]}]",valid,2536,_XFowVBvcEUqT6oD0HE3pA,215,../data/image/_XFowVBvcEUqT6oD0HE3pA_215.jpg +"[{'category_id': 0, 'bbox': [225, 277, 231, 287], 'bbox_mode': 0, 'segmentation': [227, 278, 227, 278, 227, 279, 227, 280, 226, 281, 225, 283, 225, 284, 226, 286, 227, 286, 229, 287, 230, 286, 230, 285, 231, 284, 231, 282, 230, 281, 230, 280, 229, 279, 229, 278, 229, 278, 228, 277, 228, 277, 227, 277]}, {'category_id': 0, 'bbox': [487, 298, 492, 309], 'bbox_mode': 0, 'segmentation': [488, 299, 488, 301, 488, 302, 487, 305, 487, 306, 488, 309, 490, 309, 490, 309, 491, 309, 492, 309, 492, 307, 492, 306, 492, 305, 492, 304, 492, 303, 491, 301, 491, 300, 490, 299, 490, 298, 489, 298]}, {'category_id': 0, 'bbox': [221, 193, 226, 200], 'bbox_mode': 0, 'segmentation': [224, 193, 225, 194, 225, 195, 225, 195, 226, 197, 226, 198, 226, 199, 225, 200, 223, 200, 223, 199, 222, 198, 222, 196, 221, 195, 222, 194, 222, 194, 223, 193, 223, 193, 223, 193]}]",valid,2537,RSV1U5lcbwgHDd4jiRu5Ig,170,../data/image/RSV1U5lcbwgHDd4jiRu5Ig_170.jpg +[],valid,2538,LZX4Yf7Z8tcaavBb5id6OQ,86,../data/image/LZX4Yf7Z8tcaavBb5id6OQ_86.jpg +"[{'category_id': 1, 'bbox': [52, 236, 64, 242], 'bbox_mode': 0, 'segmentation': [60, 240, 61, 240, 62, 240, 62, 240, 63, 239, 63, 239, 64, 239, 64, 239, 64, 238, 64, 238, 64, 237, 64, 237, 63, 237, 63, 237, 62, 237, 61, 236, 59, 237, 57, 237, 54, 237, 53, 237, 53, 236, 52, 237, 52, 237, 52, 237, 52, 238, 52, 238, 52, 238, 52, 239, 52, 239, 52, 239, 52, 239, 53, 239, 53, 240, 53, 240, 53, 240, 54, 240, 55, 240, 56, 240, 56, 240, 57, 240, 57, 240, 57, 241, 57, 241, 57, 241, 57, 241, 57, 242, 57, 242, 58, 242, 58, 242, 58, 242, 58, 242, 59, 242, 59, 242, 59, 242, 59, 242, 59, 242, 59, 241, 59, 241, 59, 240, 59, 240, 59, 240]}]",valid,2539,qQ3v3MEQgD0xqN-i3HwMHA,268,../data/image/qQ3v3MEQgD0xqN-i3HwMHA_268.jpg +"[{'category_id': 1, 'bbox': [205, 236, 210, 239], 'bbox_mode': 0, 'segmentation': [206, 236, 209, 236, 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438, 315, 438, 315, 439, 315, 439, 314, 439, 314, 439, 314, 439, 313, 439, 313, 439, 313, 440, 313, 440, 313, 440, 312, 440, 312, 440, 311, 440, 311, 440, 310, 440, 310, 439, 310, 439, 310, 438, 309, 438, 304, 438, 303, 438, 303, 437, 303, 437, 303]}]",valid,2554,rdoILq-Xq-3VqdQXtuzB7w,29,../data/image/rdoILq-Xq-3VqdQXtuzB7w_29.jpg +[],valid,2555,SAOZOYprPabp2aaQeU7Y1A,176,../data/image/SAOZOYprPabp2aaQeU7Y1A_176.jpg +[],valid,2556,GdnbMX5dj8b72RHJaIYs7Q,114,../data/image/GdnbMX5dj8b72RHJaIYs7Q_114.jpg +"[{'category_id': 0, 'bbox': [437, 300, 446, 312], 'bbox_mode': 0, 'segmentation': [441, 300, 440, 300, 439, 301, 439, 301, 438, 301, 438, 302, 437, 303, 437, 304, 437, 306, 437, 307, 437, 308, 437, 309, 438, 309, 439, 309, 439, 310, 440, 311, 440, 311, 441, 312, 442, 312, 443, 311, 443, 310, 444, 309, 444, 309, 445, 309, 446, 309, 446, 309, 446, 308, 446, 308, 446, 307, 446, 306, 446, 305, 446, 304, 446, 304, 446, 303, 445, 302, 445, 301, 445, 301, 444, 300, 443, 300, 442, 300, 442, 300, 442, 300]}]",valid,2557,VCsVKeVw_Dego9MpBqMOOA,261,../data/image/VCsVKeVw_Dego9MpBqMOOA_261.jpg +[],valid,2558,GibRa9LJFVwjqJlTeBADnw,356,../data/image/GibRa9LJFVwjqJlTeBADnw_356.jpg +"[{'category_id': 1, 'bbox': [585, 249, 606, 258], 'bbox_mode': 0, 'segmentation': [585, 254, 595, 257, 599, 258, 600, 258, 603, 258, 605, 257, 606, 255, 606, 253, 599, 252, 589, 249, 587, 249, 586, 250, 585, 251]}, {'category_id': 1, 'bbox': [494, 250, 509, 259], 'bbox_mode': 0, 'segmentation': [497, 254, 505, 250, 507, 250, 508, 250, 508, 251, 509, 252, 508, 254, 507, 255, 498, 258, 496, 259, 495, 259, 494, 259, 494, 258, 494, 257, 494, 256, 494, 256]}]",valid,2559,bUVSgqbz-y0GhTNyo_U_7Q,90,../data/image/bUVSgqbz-y0GhTNyo_U_7Q_90.jpg +"[{'category_id': 1, 'bbox': [276, 305, 284, 321], 'bbox_mode': 0, 'segmentation': [280, 308, 280, 308, 280, 309, 279, 309, 279, 310, 279, 311, 279, 311, 279, 312, 279, 312, 279, 312, 278, 312, 278, 313, 277, 313, 277, 313, 277, 314, 276, 315, 276, 315, 276, 316, 276, 317, 276, 317, 276, 318, 277, 318, 277, 319, 277, 319, 277, 320, 278, 320, 278, 320, 278, 320, 279, 320, 279, 321, 280, 321, 280, 321, 281, 321, 281, 321, 281, 321, 282, 321, 282, 321, 283, 321, 283, 320, 284, 320, 284, 319, 284, 319, 284, 318, 284, 317, 284, 317, 284, 316, 284, 316, 284, 315, 284, 314, 284, 314, 283, 313, 283, 313, 283, 313, 282, 312, 282, 312, 282, 311, 282, 310, 282, 309, 282, 309, 282, 309, 282, 308, 282, 307, 282, 307, 282, 306, 282, 306, 282, 305, 281, 305, 281, 305, 280, 305, 280, 305, 280, 306]}]",valid,2560,HaxrMHbe9PpWguyF4E87dg,80,../data/image/HaxrMHbe9PpWguyF4E87dg_80.jpg +"[{'category_id': 1, 'bbox': [25, 268, 45, 277], 'bbox_mode': 0, 'segmentation': [26, 269, 26, 268, 27, 268, 44, 268, 44, 268, 45, 269, 45, 272, 43, 274, 36, 274, 36, 276, 36, 277, 34, 277, 32, 277, 32, 276, 32, 274, 30, 274, 28, 274, 26, 274, 25, 273, 26, 270]}]",valid,2561,iyKYBPpFwDGzQar1s1l0Qw,245,../data/image/iyKYBPpFwDGzQar1s1l0Qw_245.jpg 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151, 482, 152, 482, 152, 482, 152]}]",valid,2568,l0Mk0P6qOmp5iUSmR25Lrw,279,../data/image/l0Mk0P6qOmp5iUSmR25Lrw_279.jpg +"[{'category_id': 1, 'bbox': [461, 172, 474, 183], 'bbox_mode': 0, 'segmentation': [471, 177, 473, 175, 474, 175, 474, 174, 474, 172, 472, 172, 471, 172, 470, 173, 468, 173, 466, 174, 464, 175, 462, 175, 461, 177, 461, 179, 462, 179, 464, 179, 466, 179, 467, 178, 468, 178, 468, 179, 468, 181, 468, 182, 470, 183, 471, 183, 472, 182, 472, 181, 473, 180, 472, 178, 471, 178]}]",valid,2569,hp5XpJz4KPEyUY3hpr6hcA,224,../data/image/hp5XpJz4KPEyUY3hpr6hcA_224.jpg +"[{'category_id': 0, 'bbox': [606, 308, 615, 315], 'bbox_mode': 0, 'segmentation': [608, 308, 612, 308, 614, 309, 615, 311, 614, 312, 612, 315, 608, 315, 607, 314, 606, 313, 606, 312, 606, 311, 606, 309]}]",valid,2570,6Bg3BEBGOOYDkRrzEf_zfA,86,../data/image/6Bg3BEBGOOYDkRrzEf_zfA_86.jpg +"[{'category_id': 1, 'bbox': [99, 289, 105, 294], 'bbox_mode': 0, 'segmentation': [105, 294, 105, 293, 104, 291, 102, 290, 101, 289, 99, 289, 99, 290, 100, 293, 102, 294, 103, 294, 104, 294]}]",valid,2571,WmGjaTSlxcr8nbxkh2leVQ,270,../data/image/WmGjaTSlxcr8nbxkh2leVQ_270.jpg +"[{'category_id': 0, 'bbox': [117, 263, 123, 271], 'bbox_mode': 0, 'segmentation': [118, 266, 118, 267, 117, 268, 118, 268, 118, 269, 118, 270, 119, 270, 120, 271, 121, 271, 122, 270, 123, 269, 123, 268, 123, 267, 123, 267, 122, 266, 122, 265, 121, 265, 121, 264, 121, 264, 121, 263, 120, 263, 120, 263, 120, 264, 119, 264, 119, 265, 119, 265, 119, 265, 119, 266, 118, 266, 118, 266]}]",valid,2572,-MevS5QyQIGalgJKrxSRCQ,245,../data/image/-MevS5QyQIGalgJKrxSRCQ_245.jpg +[],valid,2573,HRzNLVnoB1v7cyK5-VHNRA,262,../data/image/HRzNLVnoB1v7cyK5-VHNRA_262.jpg +[],valid,2574,-v3OYoIyAPwKea4b4FwGaA,66,../data/image/-v3OYoIyAPwKea4b4FwGaA_66.jpg +[],valid,2575,R-CnEOZnbjJtxqE6JhPS9A,135,../data/image/R-CnEOZnbjJtxqE6JhPS9A_135.jpg +"[{'category_id': 0, 'bbox': [425, 200, 432, 208], 'bbox_mode': 0, 'segmentation': [426, 201, 426, 202, 425, 203, 425, 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b/detection/data/__init__.py @@ -0,0 +1,13 @@ +import pandas as pd +import os + +from .version import Version +from .base import BaseDataset +from .info import DatasetInfo +from . import constants as C + + +def get_dataset(split="train"): + meta = pd.read_csv("../data/meta.csv") + info = DatasetInfo.load("../data/info.yaml") + return BaseDataset(info, meta)[split] diff --git a/detection/data/base.py b/detection/data/base.py new file mode 100644 index 0000000..261b086 --- /dev/null +++ b/detection/data/base.py @@ -0,0 +1,77 @@ +import numpy as np +import os +from PIL import Image +from tqdm import tqdm +from torch.utils.data import Dataset + +from .info import DatasetInfoMixin +from .detection import DetectionMixin +from .util import _is_path + + +class BaseDataset(Dataset, + DatasetInfoMixin, + DetectionMixin): + + def __init__(self, + info, + meta, + split=None, + ): + DatasetInfoMixin.__init__(self, + info=info, + meta=meta, + split=split) + + @staticmethod + def _load_image_file(file_path): + if not _is_path(file_path): + return None + image_pil = Image.open(file_path).convert('RGB') + image_np = np.array(image_pil) + return image_np + + @staticmethod + def _load_pickle_file(file_path): + with open(file_path, 'rb') as f: + data = pickle.load(f) + return data + + @staticmethod + def _load_numpy_file(file_path): + data = np.load(file_path) + return data + + @classmethod + def _load_single_image(cls, sample_dict): + new_sample_dict = {} + for k, v in sample_dict.items(): + if k.endswith("image_path"): + new_sample_dict[k.replace( + "_image_path", "_image")] = cls._load_image_file(v) + else: + new_sample_dict[k] = v + return new_sample_dict + + def __getitem__(self, index): + if isinstance(index, str): + return self.get_split(index) + elif isinstance(index, slice): + return self.slice(index) + + sample = self._meta.iloc[index].to_dict() + + # Replace Nan + # TODO + + # Load Images + sample = self._load_single_image(sample) + + # Apply Format + if isinstance(self._format, list): + sample = {k: v for k, v in sample.items() if k in self._format} + elif isinstance(self._format, dict): + sample = {self._format[k]: v for k, + v in sample.items() if k in self._format} + + return sample diff --git a/detection/data/constants.py b/detection/data/constants.py new file mode 100644 index 0000000..aaf2b1a --- /dev/null +++ b/detection/data/constants.py @@ -0,0 +1 @@ +ANNOTATION_COLUMN = "annotations" diff --git a/detection/data/detection.py b/detection/data/detection.py new file mode 100644 index 0000000..8adbede --- /dev/null +++ b/detection/data/detection.py @@ -0,0 +1,79 @@ +import torch +from torch.utils.data import Dataset + +from .info import DatasetInfoMixin +from . import constants as C + + +def trivial_batch_collator(batch): + return batch + + +class DetectionMixin: + def detection_dataloader(self, + augmentations=None, + is_train=True, + use_instance_mask=False, + image_path_col=None, + **kwargs): + from detectron2.data import DatasetMapper + if augmentations is None: + augmentations = [] + mapper = DatasetMapper(is_train=is_train, + image_format="RGB", + use_instance_mask=use_instance_mask, + instance_mask_format="bitmask", + augmentations=augmentations + ) + return DetectionDataset(info=self.info, + meta=self.meta, + split=self.split, + image_path_col=image_path_col, + mapper=mapper) \ + .dataloader(**kwargs) + + +class DetectionDataset(Dataset, DatasetInfoMixin): + """ + Dataset class that provides standard Detectron2 model input format: + https://detectron2.readthedocs.io/en/latest/tutorials/models.html?highlight=input%20format#model-input-format + Notice the annotation column in the meta file need to follow Detectron2's + standard dataset dict format: + https://detectron2.readthedocs.io/en/latest/tutorials/datasets.html#standard-dataset-dicts + """ + + def __init__(self, info, meta, mapper, split=None, image_path_col=None): + if C.ANNOTATION_COLUMN not in meta.columns: + raise ValueError(f"[{C.ANNOTATION_COLUMN}] column not found in the meta data.") + + if image_path_col is None: + image_path_cols = [ + c for c in meta.columns if c.endswith("image_path")] + if len(image_path_cols) == 0: + raise ValueError( + "No image path column found in the meta data. Please check meta data and use `image_path_col` argument to specify the column.") + elif len(image_path_cols) > 1: + raise ValueError( + "Multiple image path columns found in the meta data. Please use `image_path_col` argument to specify the column.") + else: + image_path_col = image_path_cols[0] + + meta = meta.rename(columns={image_path_col: "file_name"}) + + self.mapper = mapper + + DatasetInfoMixin.__init__(self, + info=info, + meta=meta, + split=split) + + def __getitem__(self, index): + sample = self._meta.iloc[index].to_dict() + sample[C.ANNOTATION_COLUMN] = eval(sample[C.ANNOTATION_COLUMN]) + return self.mapper(sample) + + def dataloader(self, **kwargs): + return torch.utils.data.DataLoader( + self, + collate_fn=trivial_batch_collator, + **kwargs) diff --git a/detection/data/info.py b/detection/data/info.py new file mode 100644 index 0000000..22dac1b --- /dev/null +++ b/detection/data/info.py @@ -0,0 +1,178 @@ +import yaml +import dataclasses +import pandas as pd +from copy import deepcopy +from dataclasses import asdict, dataclass, field +from typing import List, Optional, Union + +from .version import Version + + +class BaseInfo: + @classmethod + def from_dict(cls, dataset_info_dict: dict) -> "DatasetInfo": + field_names = set(f.name for f in dataclasses.fields(cls)) + return cls( + **{k: v for k, v in dataset_info_dict.items() if k in field_names}) + + +@dataclass +class ImageSourceInfo(BaseInfo): + # Required Fields + name: str = field(default_factory=str) + height: int = field(default_factory=int) + width: int = field(default_factory=int) + date: str = field(default_factory=str) + # Optional Fields + channels: Optional[list] = None + resolution: Optional[str] = field(default_factory=str) + + +@dataclass +class DatasetInfo(BaseInfo): + name: str = field(default_factory=str) + description: str = field(default_factory=str) + author: str = field(default_factory=str) + version: Union[str, Version] = field(default_factory=Version) + date: str = field(default_factory=str) + task: List[str] = field(default_factory=list) + class_names: List[str] = field(default_factory=list) + sources: List[ImageSourceInfo] = field(default_factory=ImageSourceInfo) + + def __post_init__(self): + if self.version is not None and not isinstance(self.version, Version): + if isinstance(self.version, str): + self.version = Version(self.version) + else: + self.version = Version.from_dict(self.version) + if self.sources is not None and not all( + [isinstance(s, ImageSourceInfo) for s in self.sources]): + sources = [] + for source in self.sources: + if isinstance(source, ImageSourceInfo): + pass + elif isinstance(source, dict): + source = ImageSourceInfo.from_dict(source) + else: + raise ValueError( + f"Unknown type for ImageSourceInfo: {type(source)}") + sources.append(source) + self.sources = sources + + @classmethod + def load(cls, path): + with open(path, "r") as f: + yaml_dict = yaml.load(f, Loader=yaml.SafeLoader) + return cls.from_dict(yaml_dict) + + def save(self, path): + with open(path, "w") as f: + yaml.dump(asdict(self), f) + + def dump(self, fileobj): + yaml.dump(asdict(self), fileobj) + + +class DatasetInfoMixin: + + def __init__(self, + info: DatasetInfo, + meta: pd.DataFrame, + split: Optional[str] = None): + self._info = info + self._meta = meta + self._split = split + self._format = None + + if self._split is not None and self._split != 'all': + self._meta.query(f"split == '{self._split}'", inplace=True) + + def __len__(self): + return len(self._meta) + + def __repr__(self): + features = self.features + if len(features) < 5: + features_repr = "[" + ", ".join(features) + "]" + else: + features_repr = "[" + \ + ", ".join(features[:3] + ["...", features[-1]]) + "]" + return f"{type(self).__name__}(split: {self.split}, version: {self.version}, features[{len(features)}]: {features_repr}, samples: {self.__len__()})" + + def get_split(self, split): + if split == "all": + return self + elif split in self.splits: + result = self.query(f"split == '{split}'") + result._split = split + return result + else: + raise ValueError( + f"Unknown split {split}. Split has to be one of {list(self.splits.keys())}") + + def slice(self, expr): + result = deepcopy(self) + result._meta = result._meta.iloc[expr] + return result + + def query(self, expr): + result = deepcopy(self) + result._meta = result._meta.query(expr) + return result + + def filter(self, func): + result = deepcopy(self) + result._meta = result._meta[result._meta.apply(func, 1)].reset_index() + return result + + def set_format(self, columns: Union[dict, list]): + self._format = columns + + def reset_format(self): + self.set_format(None) + + def value_counts(self, value): + return self._meta[value].value_counts().to_dict() + + @property + def info(self): + return self._info + + @property + def meta(self): + return self._meta.copy() + + @property + def name(self): + return self._info.name + + @property + def version(self): + return self._info.version + + @property + def description(self): + return self._info.description + + @property + def author(self): + return self._info.author + + @property + def sources(self): + return [s.name for s in self._info.sources] + + @property + def split(self): + if self._split is None: + return "all" + return self._split + + @property + def splits(self): + return self.value_counts("split") + + @property + def features(self): + features = list(self._meta.columns) + return features diff --git a/detection/data/util.py b/detection/data/util.py new file mode 100644 index 0000000..70c2f07 --- /dev/null +++ b/detection/data/util.py @@ -0,0 +1,6 @@ +from pathlib import Path, PosixPath + + +def _is_path(file_path): + return isinstance(file_path, (str, PosixPath)) + diff --git a/detection/data/version.py b/detection/data/version.py new file mode 100644 index 0000000..f9205de --- /dev/null +++ b/detection/data/version.py @@ -0,0 +1,99 @@ +""" Adapted from + https://github.com/huggingface/datasets/blob/master/src/datasets/utils/version.py +""" +import dataclasses +import re +from dataclasses import dataclass + + +_VERSION_TMPL = r"^(?P{v})" r"\.(?P{v})" r"\.(?P{v})$" +_VERSION_WILDCARD_REG = re.compile(_VERSION_TMPL.format(v=r"\d+|\*")) +_VERSION_RESOLVED_REG = re.compile(_VERSION_TMPL.format(v=r"\d+")) + + +@dataclass() +class Version: + """Dataset version MAJOR.MINOR.PATCH. + Args: + version_str: string. Eg: "1.2.3". + description: string, a description of what is new in this version. + """ + + version_str: str + description: str = None + major: str = None + minor: str = None + patch: str = None + + def __post_init__(self): + self.major, self.minor, self.patch = _str_to_version(self.version_str) + + def __repr__(self): + return "{}.{}.{}".format(*self.tuple) + + @property + def tuple(self): + return self.major, self.minor, self.patch + + def _validate_operand(self, other): + if isinstance(other, str): + return Version(other) + elif isinstance(other, Version): + return other + raise AssertionError("{} (type {}) cannot be compared to version.".format(other, type(other))) + + def __hash__(self): + return hash(self.tuple) + + def __eq__(self, other): + other = self._validate_operand(other) + return self.tuple == other.tuple + + def __ne__(self, other): + other = self._validate_operand(other) + return self.tuple != other.tuple + + def __lt__(self, other): + other = self._validate_operand(other) + return self.tuple < other.tuple + + def __le__(self, other): + other = self._validate_operand(other) + return self.tuple <= other.tuple + + def __gt__(self, other): + other = self._validate_operand(other) + return self.tuple > other.tuple + + def __ge__(self, other): + other = self._validate_operand(other) + return self.tuple >= other.tuple + + def match(self, other_version): + """Returns True if other_version matches. + + Args: + other_version: string, of the form "x[.y[.x]]" where {x,y,z} can be a + number or a wildcard. + """ + major, minor, patch = _str_to_version(other_version, allow_wildcard=True) + return major in [self.major, "*"] and minor in [self.minor, "*"] and patch in [self.patch, "*"] + + @classmethod + def from_dict(cls, dic): + field_names = set(f.name for f in dataclasses.fields(cls)) + return cls(**{k: v for k, v in dic.items() if k in field_names}) + + +def _str_to_version(version_str, allow_wildcard=False): + """Return the tuple (major, minor, patch) version extracted from the str.""" + reg = _VERSION_WILDCARD_REG if allow_wildcard else _VERSION_RESOLVED_REG + res = reg.match(version_str) + if not res: + msg = "Invalid version '{}'. Format should be x.y.z".format(version_str) + if allow_wildcard: + msg += " with {x,y,z} being digits or wildcard." + else: + msg += " with {x,y,z} being digits." + raise ValueError(msg) + return tuple(v if v == "*" else int(v) for v in [res.group("major"), res.group("minor"), res.group("patch")]) diff --git a/detection/eval/__init__.py b/detection/eval/__init__.py new file mode 100644 index 0000000..53a3e29 --- /dev/null +++ b/detection/eval/__init__.py @@ -0,0 +1,2 @@ +from .loss import get_loss_fn +from .evaluator import * diff --git a/detection/eval/detection/__init__.py b/detection/eval/detection/__init__.py new file mode 100644 index 0000000..dea7f4d --- /dev/null +++ b/detection/eval/detection/__init__.py @@ -0,0 +1,4 @@ +# Adapted from https://github.com/rafaelpadilla/Object-Detection-Metrics +from .evaluator import Evaluator +from .bbox import BoundingBox, BoundingBoxes +from .utils import BBType, BBFormat, CoordinatesType diff --git a/detection/eval/detection/bbox.py b/detection/eval/detection/bbox.py new file mode 100644 index 0000000..ca68b87 --- /dev/null +++ b/detection/eval/detection/bbox.py @@ -0,0 +1,232 @@ +from .utils import * + + +class BoundingBox: + def __init__(self, + imageName, + classId, + x, + y, + w, + h, + typeCoordinates=CoordinatesType.Absolute, + imgSize=None, + bbType=BBType.GroundTruth, + classConfidence=None, + format=BBFormat.XYWH): + """Constructor. + Args: + imageName: String representing the image name. + classId: String value representing class id. + x: Float value representing the X upper-left coordinate of the bounding box. + y: Float value representing the Y upper-left coordinate of the bounding box. + w: Float value representing the width bounding box. + h: Float value representing the height bounding box. + typeCoordinates: (optional) Enum (Relative or Absolute) represents if the bounding box + coordinates (x,y,w,h) are absolute or relative to size of the image. Default:'Absolute'. + imgSize: (optional) 2D vector (width, height)=>(int, int) represents the size of the + image of the bounding box. If typeCoordinates is 'Relative', imgSize is required. + bbType: (optional) Enum (Groundtruth or Detection) identifies if the bounding box + represents a ground truth or a detection. If it is a detection, the classConfidence has + to be informed. + classConfidence: (optional) Float value representing the confidence of the detected + class. If detectionType is Detection, classConfidence needs to be informed. + format: (optional) Enum (BBFormat.XYWH or BBFormat.XYX2Y2) indicating the format of the + coordinates of the bounding boxes. BBFormat.XYWH: + BBFormat.XYX2Y2: . + """ + self._imageName = imageName + self._typeCoordinates = typeCoordinates + if typeCoordinates == CoordinatesType.Relative and imgSize is None: + raise IOError( + 'Parameter \'imgSize\' is required. It is necessary to inform the image size.') + if bbType == BBType.Detected and classConfidence is None: + raise IOError( + 'For bbType=\'Detection\', it is necessary to inform the classConfidence value.') + # if classConfidence != None and (classConfidence < 0 or classConfidence > 1): + # raise IOError('classConfidence value must be a real value between 0 and 1. Value: %f' % + # classConfidence) + + self._classConfidence = classConfidence + self._bbType = bbType + self._classId = classId + self._format = format + + # If relative coordinates, convert to absolute values + # For relative coords: (x,y,w,h)=(X_center/img_width , + # Y_center/img_height) + if (typeCoordinates == CoordinatesType.Relative): + (self._x, self._y, self._w, self._h) = convertToAbsoluteValues( + imgSize, (x, y, w, h)) + self._width_img = imgSize[0] + self._height_img = imgSize[1] + if format == BBFormat.XYWH: + self._x2 = self._w + self._y2 = self._h + self._w = self._x2 - self._x + self._h = self._y2 - self._y + else: + raise IOError( + 'For relative coordinates, the format must be XYWH (x,y,width,height)') + # For absolute coords: (x,y,w,h)=real bb coords + else: + self._x = x + self._y = y + if format == BBFormat.XYWH: + self._w = w + self._h = h + self._x2 = self._x + self._w + self._y2 = self._y + self._h + else: # format == BBFormat.XYX2Y2: . + self._x2 = w + self._y2 = h + self._w = self._x2 - self._x + self._h = self._y2 - self._y + if imgSize is None: + self._width_img = None + self._height_img = None + else: + self._width_img = imgSize[0] + self._height_img = imgSize[1] + + def getAbsoluteBoundingBox(self, format=BBFormat.XYWH): + if format == BBFormat.XYWH: + return (self._x, self._y, self._w, self._h) + elif format == BBFormat.XYX2Y2: + return (self._x, self._y, self._x2, self._y2) + + def getRelativeBoundingBox(self, imgSize=None): + if imgSize is None and self._width_img is None and self._height_img is None: + raise IOError( + 'Parameter \'imgSize\' is required. It is necessary to inform the image size.') + if imgSize is not None: + return convertToRelativeValues( + (imgSize[0], imgSize[1]), (self._x, self._x2, self._y, self._y2)) + else: + return convertToRelativeValues( + (self._width_img, self._height_img), (self._x, self._x2, self._y, self._y2)) + + def getImageName(self): + return self._imageName + + def getConfidence(self): + return self._classConfidence + + def getFormat(self): + return self._format + + def getClassId(self): + return self._classId + + def getImageSize(self): + return (self._width_img, self._height_img) + + def getCoordinatesType(self): + return self._typeCoordinates + + def getBBType(self): + return self._bbType + + @staticmethod + def compare(det1, det2): + det1BB = det1.getAbsoluteBoundingBox() + det1ImgSize = det1.getImageSize() + det2BB = det2.getAbsoluteBoundingBox() + det2ImgSize = det2.getImageSize() + + if det1.getClassId() == det2.getClassId() and \ + det1.classConfidence == det2.classConfidenc() and \ + det1BB[0] == det2BB[0] and \ + det1BB[1] == det2BB[1] and \ + det1BB[2] == det2BB[2] and \ + det1BB[3] == det2BB[3] and \ + det1ImgSize[0] == det1ImgSize[0] and \ + det2ImgSize[1] == det2ImgSize[1]: + return True + return False + + @staticmethod + def clone(boundingBox): + absBB = boundingBox.getAbsoluteBoundingBox(format=BBFormat.XYWH) + # return (self._x,self._y,self._x2,self._y2) + newBoundingBox = BoundingBox( + boundingBox.getImageName(), + boundingBox.getClassId(), + absBB[0], + absBB[1], + absBB[2], + absBB[3], + typeCoordinates=boundingBox.getCoordinatesType(), + imgSize=boundingBox.getImageSize(), + bbType=boundingBox.getBBType(), + classConfidence=boundingBox.getConfidence(), + format=BBFormat.XYWH) + return newBoundingBox + + +class BoundingBoxes: + def __init__(self): + self._boundingBoxes = [] + + def addBoundingBox(self, bb): + self._boundingBoxes.append(bb) + + def removeBoundingBox(self, _boundingBox): + for d in self._boundingBoxes: + if BoundingBox.compare(d, _boundingBox): + del self._boundingBoxes[d] + return + + def removeAllBoundingBoxes(self): + self._boundingBoxes = [] + + def getBoundingBoxes(self): + return self._boundingBoxes + + def getBoundingBoxByClass(self, classId): + boundingBoxes = [] + for d in self._boundingBoxes: + if d.getClassId() == classId: # get only specified bounding box type + boundingBoxes.append(d) + return boundingBoxes + + def getClasses(self): + classes = [] + for d in self._boundingBoxes: + c = d.getClassId() + if c not in classes: + classes.append(c) + return classes + + def getBoundingBoxesByType(self, bbType): + # get only specified bb type + return [d for d in self._boundingBoxes if d.getBBType() == bbType] + + def getBoundingBoxesByImageName(self, imageName): + # get only specified bb type + return [d for d in self._boundingBoxes if d.getImageName() == imageName] + + def count(self, bbType=None): + if bbType is None: # Return all bounding boxes + return len(self._boundingBoxes) + count = 0 + for d in self._boundingBoxes: + if d.getBBType() == bbType: # get only specified bb type + count += 1 + return count + + def clone(self): + newBoundingBoxes = BoundingBoxes() + for d in self._boundingBoxes: + det = BoundingBox.clone(d) + newBoundingBoxes.addBoundingBox(det) + return newBoundingBoxes + + def drawAllBoundingBoxes(self, image, imageName): + bbxes = self.getBoundingBoxesByImageName(imageName) + for bb in bbxes: + if bb.getBBType() == BBType.GroundTruth: # if ground truth + image = add_bb_into_image(image, bb, color=(0, 255, 0)) # green + else: # if detection + image = add_bb_into_image(image, bb, color=(255, 0, 0)) # red + return image diff --git a/detection/eval/detection/evaluator.py b/detection/eval/detection/evaluator.py new file mode 100644 index 0000000..2164a43 --- /dev/null +++ b/detection/eval/detection/evaluator.py @@ -0,0 +1,359 @@ +import os +import sys +from collections import Counter + +import matplotlib.pyplot as plt +import numpy as np + +from .bbox import * +from .utils import * + + +class Evaluator: + def GetPascalVOCMetrics( + self, + boundingboxes, + IOUThreshold=0.5, + method=MethodAveragePrecision.EveryPointInterpolation): + """Get the metrics used by the VOC Pascal 2012 challenge. + Get + Args: + boundingboxes: Object of the class BoundingBoxes representing ground truth and detected + bounding boxes; + IOUThreshold: IOU threshold indicating which detections will be considered TP or FP + (default value = 0.5); + method (default = EveryPointInterpolation): It can be calculated as the implementation + in the official PASCAL VOC toolkit (EveryPointInterpolation), or applying the 11-point + interpolatio as described in the paper "The PASCAL Visual Object Classes(VOC) Challenge" + or EveryPointInterpolation" (ElevenPointInterpolation); + Returns: + A list of dictionaries. Each dictionary contains information and metrics of each class. + The keys of each dictionary are: + dict['class']: class representing the current dictionary; + dict['precision']: array with the precision values; + dict['recall']: array with the recall values; + dict['AP']: average precision; + dict['interpolated precision']: interpolated precision values; + dict['interpolated recall']: interpolated recall values; + dict['total positives']: total number of ground truth positives; + dict['total TP']: total number of True Positive detections; + dict['total FP']: total number of False Positive detections; + """ + ret = [ + ] # list containing metrics (precision, recall, average precision) of each class + # List with all ground truths (Ex: [imageName,class,confidence=1, (bb + # coordinates XYX2Y2)]) + groundTruths = [] + # List with all detections (Ex: [imageName,class,confidence,(bb + # coordinates XYX2Y2)]) + detections = [] + # Get all classes + classes = [] + # Loop through all bounding boxes and separate them into GTs and + # detections + for bb in boundingboxes.getBoundingBoxes(): + # [imageName, class, confidence, (bb coordinates XYX2Y2)] + if bb.getBBType() == BBType.GroundTruth: + groundTruths.append([ + bb.getImageName(), + bb.getClassId(), 1, + bb.getAbsoluteBoundingBox(BBFormat.XYX2Y2) + ]) + else: + detections.append([ + bb.getImageName(), + bb.getClassId(), + bb.getConfidence(), + bb.getAbsoluteBoundingBox(BBFormat.XYX2Y2) + ]) + # get class + if bb.getClassId() not in classes: + classes.append(bb.getClassId()) + classes = sorted(classes) + # Precision x Recall is obtained individually by each class + # Loop through by classes + for c in classes: + # Get only detection of class c + dects = [] + [dects.append(d) for d in detections if d[1] == c] + # Get only ground truths of class c, use filename as key + gts = {} + npos = 0 + for g in groundTruths: + if g[1] == c: + npos += 1 + gts[g[0]] = gts.get(g[0], []) + [g] + + # sort detections by decreasing confidence + dects = sorted(dects, key=lambda conf: conf[2], reverse=True) + TP = np.zeros(len(dects)) + FP = np.zeros(len(dects)) + # create dictionary with amount of gts for each image + det = {key: np.zeros(len(gts[key])) for key in gts} + + # Loop through detections + for d in range(len(dects)): + # Find ground truth image + gt = gts[dects[d][0]] if dects[d][0] in gts else [] + iouMax = sys.float_info.min + for j in range(len(gt)): + iou = Evaluator.iou(dects[d][3], gt[j][3]) + if iou > iouMax: + iouMax = iou + jmax = j + # Assign detection as true positive/don't care/false positive + if iouMax >= IOUThreshold: + if det[dects[d][0]][jmax] == 0: + TP[d] = 1 # count as true positive + det[dects[d][0]][jmax] = 1 # flag as already 'seen' + else: + FP[d] = 1 # count as false positive + # - A detected "cat" is overlaped with a GT "cat" with IOU >= IOUThreshold. + else: + FP[d] = 1 # count as false positive + # compute precision, recall and average precision + acc_FP = np.cumsum(FP) + acc_TP = np.cumsum(TP) + rec = acc_TP / npos + prec = np.divide(acc_TP, (acc_FP + acc_TP)) + # Depending on the method, call the right implementation + if method == MethodAveragePrecision.EveryPointInterpolation: + [ap, mpre, mrec, ii] = Evaluator.CalculateAveragePrecision( + rec, prec) + else: + [ap, mpre, mrec, _] = Evaluator.ElevenPointInterpolatedAP( + rec, prec) + # add class result in the dictionary to be returned + r = { + 'class': c, + 'precision': prec, + 'recall': rec, + 'AP': ap, + 'interpolated precision': mpre, + 'interpolated recall': mrec, + 'total positives': npos, + 'total TP': np.sum(TP), + 'total FP': np.sum(FP) + } + ret.append(r) + return ret + + def PlotPrecisionRecallCurve( + self, + boundingBoxes, + IOUThreshold=0.5, + method=MethodAveragePrecision.EveryPointInterpolation, + showAP=False, + showInterpolatedPrecision=False, + savePath=None, + showGraphic=True): + """PlotPrecisionRecallCurve + Plot the Precision x Recall curve for a given class. + Args: + boundingBoxes: Object of the class BoundingBoxes representing ground truth and detected + bounding boxes; + IOUThreshold (optional): IOU threshold indicating which detections will be considered + TP or FP (default value = 0.5); + method (default = EveryPointInterpolation): It can be calculated as the implementation + in the official PASCAL VOC toolkit (EveryPointInterpolation), or applying the 11-point + interpolatio as described in the paper "The PASCAL Visual Object Classes(VOC) Challenge" + or EveryPointInterpolation" (ElevenPointInterpolation). + showAP (optional): if True, the average precision value will be shown in the title of + the graph (default = False); + showInterpolatedPrecision (optional): if True, it will show in the plot the interpolated + precision (default = False); + savePath (optional): if informed, the plot will be saved as an image in this path + (ex: /home/mywork/ap.png) (default = None); + showGraphic (optional): if True, the plot will be shown (default = True) + Returns: + A list of dictionaries. Each dictionary contains information and metrics of each class. + The keys of each dictionary are: + dict['class']: class representing the current dictionary; + dict['precision']: array with the precision values; + dict['recall']: array with the recall values; + dict['AP']: average precision; + dict['interpolated precision']: interpolated precision values; + dict['interpolated recall']: interpolated recall values; + dict['total positives']: total number of ground truth positives; + dict['total TP']: total number of True Positive detections; + dict['total FP']: total number of False Negative detections; + """ + results = self.GetPascalVOCMetrics(boundingBoxes, IOUThreshold, method) + result = None + # Each resut represents a class + for result in results: + if result is None: + raise IOError('Error: Class %d could not be found.' % classId) + + classId = result['class'] + precision = result['precision'] + recall = result['recall'] + average_precision = result['AP'] + mpre = result['interpolated precision'] + mrec = result['interpolated recall'] + npos = result['total positives'] + total_tp = result['total TP'] + total_fp = result['total FP'] + + plt.close() + if showInterpolatedPrecision: + if method == MethodAveragePrecision.EveryPointInterpolation: + plt.plot( + mrec, + mpre, + '--r', + label='Interpolated precision (every point)') + elif method == MethodAveragePrecision.ElevenPointInterpolation: + nrec = [] + nprec = [] + for idx in range(len(mrec)): + r = mrec[idx] + if r not in nrec: + idxEq = np.argwhere(mrec == r) + nrec.append(r) + nprec.append(max([mpre[int(id)] for id in idxEq])) + plt.plot( + nrec, + nprec, + 'or', + label='11-point interpolated precision') + plt.plot(recall, precision, label='Precision') + plt.xlabel('recall') + plt.ylabel('precision') + if showAP: + ap_str = "{0:.2f}%".format(average_precision * 100) + # ap_str = "{0:.4f}%".format(average_precision * 100) + plt.title( + 'Precision x Recall curve \nClass: %s, AP: %s' % + (str(classId), ap_str)) + else: + plt.title('Precision x Recall curve \nClass: %s' % str(classId)) + plt.legend(shadow=True) + plt.grid() + if savePath is not None: + plt.savefig(os.path.join(savePath, str(classId) + '.png')) + if showGraphic is True: + plt.show() + # plt.waitforbuttonpress() + plt.pause(0.05) + return results + + @staticmethod + def CalculateAveragePrecision(rec, prec): + mrec = [] + mrec.append(0) + [mrec.append(e) for e in rec] + mrec.append(1) + mpre = [] + mpre.append(0) + [mpre.append(e) for e in prec] + mpre.append(0) + for i in range(len(mpre) - 1, 0, -1): + mpre[i - 1] = max(mpre[i - 1], mpre[i]) + ii = [] + for i in range(len(mrec) - 1): + if mrec[1 + i] != mrec[i]: + ii.append(i + 1) + ap = 0 + for i in ii: + ap = ap + np.sum((mrec[i] - mrec[i - 1]) * mpre[i]) + return [ap, mpre[0:len(mpre) - 1], mrec[0:len(mpre) - 1], ii] + + @staticmethod + # 11-point interpolated average precision + def ElevenPointInterpolatedAP(rec, prec): + # def CalculateAveragePrecision2(rec, prec): + mrec = [] + [mrec.append(e) for e in rec] + mpre = [] + [mpre.append(e) for e in prec] + recallValues = np.linspace(0, 1, 11) + recallValues = list(recallValues[::-1]) + rhoInterp = [] + recallValid = [] + # For each recallValues (0, 0.1, 0.2, ... , 1) + for r in recallValues: + # Obtain all recall values higher or equal than r + argGreaterRecalls = np.argwhere(mrec[:] >= r) + pmax = 0 + # If there are recalls above r + if argGreaterRecalls.size != 0: + pmax = max(mpre[argGreaterRecalls.min():]) + recallValid.append(r) + rhoInterp.append(pmax) + # By definition AP = sum(max(precision whose recall is above r))/11 + ap = sum(rhoInterp) / 11 + # Generating values for the plot + rvals = [] + rvals.append(recallValid[0]) + [rvals.append(e) for e in recallValid] + rvals.append(0) + pvals = [] + pvals.append(0) + [pvals.append(e) for e in rhoInterp] + pvals.append(0) + cc = [] + for i in range(len(rvals)): + p = (rvals[i], pvals[i - 1]) + if p not in cc: + cc.append(p) + p = (rvals[i], pvals[i]) + if p not in cc: + cc.append(p) + recallValues = [i[0] for i in cc] + rhoInterp = [i[1] for i in cc] + return [ap, rhoInterp, recallValues, None] + + # For each detections, calculate IOU with reference + @staticmethod + def _getAllIOUs(reference, detections): + ret = [] + bbReference = reference.getAbsoluteBoundingBox(BBFormat.XYX2Y2) + for d in detections: + bb = d.getAbsoluteBoundingBox(BBFormat.XYX2Y2) + iou = Evaluator.iou(bbReference, bb) + ret.append((iou, reference, d)) # iou, reference, detection + return sorted(ret, key=lambda i: i[0], reverse=True) + + @staticmethod + def iou(boxA, boxB): + if Evaluator._boxesIntersect(boxA, boxB) is False: + return 0 + interArea = Evaluator._getIntersectionArea(boxA, boxB) + union = Evaluator._getUnionAreas(boxA, boxB, interArea=interArea) + iou = interArea / union + assert iou >= 0 + return iou + + @staticmethod + def _boxesIntersect(boxA, boxB): + if boxA[0] > boxB[2]: + return False # boxA is right of boxB + if boxB[0] > boxA[2]: + return False # boxA is left of boxB + if boxA[3] < boxB[1]: + return False # boxA is above boxB + if boxA[1] > boxB[3]: + return False # boxA is below boxB + return True + + @staticmethod + def _getIntersectionArea(boxA, boxB): + xA = max(boxA[0], boxB[0]) + yA = max(boxA[1], boxB[1]) + xB = min(boxA[2], boxB[2]) + yB = min(boxA[3], boxB[3]) + # intersection area + return (xB - xA + 1) * (yB - yA + 1) + + @staticmethod + def _getUnionAreas(boxA, boxB, interArea=None): + area_A = Evaluator._getArea(boxA) + area_B = Evaluator._getArea(boxB) + if interArea is None: + interArea = Evaluator._getIntersectionArea(boxA, boxB) + return float(area_A + area_B - interArea) + + @staticmethod + def _getArea(box): + return (box[2] - box[0] + 1) * (box[3] - box[1] + 1) diff --git a/detection/eval/detection/utils.py b/detection/eval/detection/utils.py new file mode 100644 index 0000000..bf37c9a --- /dev/null +++ b/detection/eval/detection/utils.py @@ -0,0 +1,126 @@ +from enum import Enum + +import cv2 + + +class MethodAveragePrecision(Enum): + """ + Class representing if the coordinates are relative to the + image size or are absolute values. + + Developed by: Rafael Padilla + Last modification: Apr 28 2018 + """ + EveryPointInterpolation = 1 + ElevenPointInterpolation = 2 + + +class CoordinatesType(Enum): + """ + Class representing if the coordinates are relative to the + image size or are absolute values. + + Developed by: Rafael Padilla + Last modification: Apr 28 2018 + """ + Relative = 1 + Absolute = 2 + + +class BBType(Enum): + """ + Class representing if the bounding box is groundtruth or not. + + Developed by: Rafael Padilla + Last modification: May 24 2018 + """ + GroundTruth = 1 + Detected = 2 + + +class BBFormat(Enum): + """ + Class representing the format of a bounding box. + It can be (X,Y,width,height) => XYWH + or (X1,Y1,X2,Y2) => XYX2Y2 + + Developed by: Rafael Padilla + Last modification: May 24 2018 + """ + XYWH = 1 + XYX2Y2 = 2 + + +def convertToRelativeValues(size, box): + """Convert absolute box coordinates to relative ones. + + Args: + size (tuple of int): (width, height) of the image + box (tuple of int): (X1, X2, Y1, Y2) of the bounding box + """ + dw = 1. / (size[0]) + dh = 1. / (size[1]) + cx = (box[1] + box[0]) / 2.0 + cy = (box[3] + box[2]) / 2.0 + w = box[1] - box[0] + h = box[3] - box[2] + x = cx * dw + y = cy * dh + w = w * dw + h = h * dh + return (x, y, w, h) + + +def convertToAbsoluteValues(size, box): + """Convert relative box coordinates to absolute ones. + Args: + size (tuple of int): (width, height) of the image + box (tuple of int): (centerX, centerY, w, h) of the bounding box relative to the image + """ + xIn = round(((2 * float(box[0]) - float(box[2])) * size[0] / 2)) + yIn = round(((2 * float(box[1]) - float(box[3])) * size[1] / 2)) + xEnd = xIn + round(float(box[2]) * size[0]) + yEnd = yIn + round(float(box[3]) * size[1]) + if xIn < 0: + xIn = 0 + if yIn < 0: + yIn = 0 + if xEnd >= size[0]: + xEnd = size[0] - 1 + if yEnd >= size[1]: + yEnd = size[1] - 1 + return (xIn, yIn, xEnd, yEnd) + + +def add_bb_into_image(image, bb, color=(255, 0, 0), thickness=2, label=None): + r = int(color[0]) + g = int(color[1]) + b = int(color[2]) + + font = cv2.FONT_HERSHEY_SIMPLEX + fontScale = 0.5 + fontThickness = 1 + + x1, y1, x2, y2 = bb.getAbsoluteBoundingBox(BBFormat.XYX2Y2) + x1 = int(x1) + y1 = int(y1) + x2 = int(x2) + y2 = int(y2) + cv2.rectangle(image, (x1, y1), (x2, y2), (b, g, r), thickness) + # Add label + if label is not None: + # Get size of the text box + (tw, th) = cv2.getTextSize(label, font, fontScale, fontThickness)[0] + # Top-left coord of the textbox + (xin_bb, yin_bb) = (x1 + thickness, y1 - th + int(12.5 * fontScale)) + # Checking position of the text top-left (outside or inside the bb) + if yin_bb - th <= 0: # if outside the image + yin_bb = y1 + th # put it inside the bb + r_Xin = x1 - int(thickness / 2) + r_Yin = y1 - th - int(thickness / 2) + # Draw filled rectangle to put the text in it + cv2.rectangle(image, (r_Xin, r_Yin - thickness), (r_Xin + tw + \ + thickness * 3, r_Yin + th + int(12.5 * fontScale)), (b, g, r), -1) + cv2.putText(image, label, (xin_bb, yin_bb), font, fontScale, + (0, 0, 0), fontThickness, cv2.LINE_AA) + return image diff --git a/detection/eval/evaluator.py b/detection/eval/evaluator.py new file mode 100644 index 0000000..8dbcf2d --- /dev/null +++ b/detection/eval/evaluator.py @@ -0,0 +1,158 @@ +import numpy as np + +from . import detection + + +class DatasetEvaluator: + """ + Base class for a dataset evaluator. + + This class will accumulate information of the inputs/outputs (by :meth:`process`), + and produce evaluation results in the end (by :meth:`evaluate`). + """ + + def reset(self): + """ + Preparation for a new round of evaluation. + Should be called before starting a round of evaluation. + """ + raise NotImplementedError ("[reset] method need to be implemented in child class.") + + def process(self, inputs, outputs): + """ + Process the pair of inputs and outputs. + If they contain batches, the pairs can be consumed one-by-one using `zip`: + + Args: + inputs (list): the inputs that's used to call the model. + outputs (list): the return value of `model(inputs)` + """ + raise NotImplementedError ("[process] method need to be implemented in child class.") + + def evaluate(self): + """ + Evaluate/summarize the performance, after processing all input/output pairs. + + """ + raise NotImplementedError ("[evaluate] method need to be implemented in child class.") + + +class DetectionEvaluator(DatasetEvaluator): + """ + Evaluator for detection task. + This class will accumulate information of the inputs/outputs (by :meth:`process`), + and produce evaluation results in the end (by :meth:`evaluate`). + """ + + def __init__(self, iou_thresh=0.5): + self._evaluator = detection.Evaluator() + self._iou_thresh = iou_thresh + self.reset() + + def reset(self): + self._bbox = detection.BoundingBoxes() + + def process(self, groudtruths, predictions): + """ + Inputs format: + https://detectron2.readthedocs.io/en/latest/tutorials/models.html?highlight=input%20format#model-input-format + Outputs format: + https://detectron2.readthedocs.io/en/latest/tutorials/models.html?highlight=input%20format#model-output-format + """ + for sample_input, sample_output in zip(groudtruths, predictions): + image_id = sample_input['image_id'] + gt_instances = sample_input['instances'] + pred_instances = sample_output['instances'] + width = sample_input['width'] + height = sample_input['height'] + for i in range(len(gt_instances)): + instance = gt_instances[i] + class_id = instance.get( + 'gt_classes').cpu().detach().numpy().item() + boxes = instance.get('gt_boxes') + for box in boxes: + box_np = box.cpu().detach().numpy() + bb = detection.BoundingBox( + image_id, + class_id, + box_np[0], + box_np[1], + box_np[2], + box_np[3], + detection.CoordinatesType.Absolute, + (width, + height), + detection.BBType.GroundTruth, + format=detection.BBFormat.XYX2Y2) + self._bbox.addBoundingBox(bb) + for i in range(len(pred_instances)): + instance = pred_instances[i] + class_id = instance.get( + 'pred_classes').cpu().detach().numpy().item() + scores = instance.get('scores').cpu().detach().numpy().item() + boxes = instance.get('pred_boxes') + for box in boxes: + box_np = box.cpu().detach().numpy() + bb = detection.BoundingBox( + image_id, + class_id, + box_np[0], + box_np[1], + box_np[2], + box_np[3], + detection.CoordinatesType.Absolute, + (width, + height), + detection.BBType.Detected, + scores, + format=detection.BBFormat.XYX2Y2) + self._bbox.addBoundingBox(bb) + + def evaluate(self): + results = self._evaluator.GetPascalVOCMetrics(self._bbox, self._iou_thresh) + if isinstance(results, dict): + results = [results] + metrics = {} + APs = [] + for result in results: + metrics[f'AP_{result["class"]}'] = result['AP'] + APs.append(result['AP']) + metrics['mAP'] = np.nanmean(APs) + self._evaluator.PlotPrecisionRecallCurve(self._bbox, savePath="./plots/", showGraphic=False) + return metrics + +class DatasetEvaluators(DatasetEvaluator): + """ + Wrapper class to combine multiple :class:`DatasetEvaluator` instances. + + This class dispatches every evaluation call to + all of its :class:`DatasetEvaluator`. + """ + + def __init__(self, evaluators): + """ + Args: + evaluators (list): the evaluators to combine. + """ + super().__init__() + self._evaluators = evaluators + + def reset(self): + for evaluator in self._evaluators: + evaluator.reset() + + def process(self, inputs, outputs): + for evaluator in self._evaluators: + evaluator.process(inputs, outputs) + + def evaluate(self): + results = OrderedDict() + for evaluator in self._evaluators: + result = evaluator.evaluate() + if is_main_process() and result is not None: + for k, v in result.items(): + assert ( + k not in results + ), "Different evaluators produce results with the same key {}".format(k) + results[k] = v + return results diff --git a/detection/eval/loss.py b/detection/eval/loss.py new file mode 100644 index 0000000..f8bdb72 --- /dev/null +++ b/detection/eval/loss.py @@ -0,0 +1,16 @@ +import torch +import argparse + + +def get_loss_fn(loss_args): + loss_args_ = loss_args + if isinstance(loss_args, argparse.Namespace): + loss_args_ = vars(loss_args) + loss_fn = loss_args_.get("loss_fn") + + if loss_fn == "BCE": + return torch.nn.BCEWithLogitsLoss() + elif loss_fn == "CE": + return torch.nn.CrossEntropyLoss() + else: + raise ValueError(f"loss_fn {loss_args.loss_fn} not supported.") diff --git a/detection/lightning/__init__.py b/detection/lightning/__init__.py new file mode 100644 index 0000000..af71f81 --- /dev/null +++ b/detection/lightning/__init__.py @@ -0,0 +1,13 @@ +import torch + +from .detection import DetectionTask +from .util import get_ckpt_callback, get_early_stop_callback +from .util import get_logger + + +def get_task(args): + return DetectionTask(args) + +def load_task(ckpt_path, **kwargs): + args = torch.load(ckpt_path, map_location='cpu')['hyper_parameters'] + return DetectionTask.load_from_checkpoint(ckpt_path, **kwargs) diff --git a/detection/lightning/detection.py b/detection/lightning/detection.py new file mode 100644 index 0000000..43fc7d6 --- /dev/null +++ b/detection/lightning/detection.py @@ -0,0 +1,175 @@ +import nni +import pickle as pkl +import json +import pytorch_lightning as pl +import os +import numpy as np +import torch +import torchvision +from PIL import Image +import pandas as pd +from detectron2.data import transforms as T +from detectron2.structures import Instances, Boxes +from detectron2.utils.visualizer import Visualizer +from torch.utils.data import DataLoader +from ignite.metrics import Accuracy + +from models import get_model +from eval import DetectionEvaluator +from data import get_dataset +from util import constants as C +from util import get_concat_h_cut +from .logger import TFLogger + + +class DetectionTask(pl.LightningModule, TFLogger): + """Standard interface for the trainer to interact with the model.""" + + def __init__(self, params): + super().__init__() + self.save_hyperparameters(params) + self.model = get_model(params) + self.evaluator = DetectionEvaluator() + + def training_step(self, batch, batch_nb): + losses = self.model.forward(batch) + loss = torch.stack(list(losses.values())).mean() + return loss + + def validation_step(self, batch, batch_nb): + losses = self.model.forward(batch) + loss = torch.stack(list(losses.values())).mean() + preds = self.model.infer(batch) + self.evaluator.process(batch, preds) + return loss + + def validation_epoch_end(self, outputs): + avg_loss = torch.stack(outputs).mean() + self.log("val_loss", avg_loss) + metrics = self.evaluator.evaluate() + nni.report_intermediate_result(metrics['mAP']) + self.evaluator.reset() + self.log_dict(metrics, prog_bar=True) + + def test_step(self, batch, batch_nb): + preds = self.model.infer(batch) + conf_threshold = self.hparams.get("conf_threshold", 0) + iou_threshold = self.hparams.get("iou_threshold", 0.5) + padding = self.hparams.get("padding", 10) + if self.hparams.get('visualize', False) or self.hparams.get("deploy", False): + for i, (sample, pred) in enumerate(zip(batch, preds)): + instances = pred['instances'] + boxes = instances.get('pred_boxes').tensor + class_id = instances.get('pred_classes') + + # Filter by scores + scores = instances.scores + keep_id_conf = scores > conf_threshold + boxes_conf = boxes[keep_id_conf] + scores_conf = scores[keep_id_conf] + class_id_conf = class_id[keep_id_conf] + if boxes_conf.size(0) == 0: + continue + + # Filter by nms + keep_id_nms = torchvision.ops.nms(boxes_conf, + scores_conf, + iou_threshold) + + boxes_nms = boxes_conf[keep_id_nms] + scores_nms = scores_conf[keep_id_nms] + class_id_nms = class_id_conf[keep_id_nms] + + # Pad box size + boxes_nms[:, 0] -= padding + boxes_nms[:, 1] -= padding + boxes_nms[:, 2] += padding + boxes_nms[:, 3] += padding + boxes_nms = torch.clip(boxes_nms, 0, 640) + + for j in range(len(scores_nms)): + instances = Instances((640, 640)) + class_id_numpy = class_id_nms.to("cpu").numpy()[j] + box_numpy = boxes_nms.to("cpu").numpy()[j] + score_numpy = scores_nms.to("cpu").numpy()[j] + + instances.pred_classes = np.array([class_id_numpy]) + instances.pred_boxes = Boxes(box_numpy[np.newaxis,:]) + instances.scores = np.array([score_numpy]) + + v = Visualizer(np.transpose(sample['image'].to("cpu"), (1,2,0)), + instance_mode=1, + metadata=C.META) + out = v.draw_instance_predictions(instances) + img_box = Image.fromarray(out.get_image()) + + if self.hparams.get("deploy", False): + panoid = sample['panoid'] + heading = sample['heading'] + save_path = f".output/{panoid[:2]}/{panoid}_{heading}_{j}.jpg" + json_save_path = f".output/{panoid[:2]}/{panoid}_{heading}_{j}.json" + + os.makedirs(os.path.dirname(save_path), exist_ok=True) + + img_org = Image.open(sample['save_path']) + img_out = get_concat_h_cut(img_org, img_box) + img_out.save(save_path) + data = {"panoid": panoid, + "heaidng": int(heading), + "detection_id": int(j), + "class_id": int(class_id_numpy), + "box": [int(x) for x in box_numpy], + "score": float(score_numpy), + "save_path": save_path} + with open(json_save_path, 'w') as fp: + json.dump(data, fp) + else: + img_box.save(f"outputs/{batch_nb}_{i}.jpg") + + self.evaluator.process(batch, preds) + + def test_epoch_end(self, outputs): + metrics = self.evaluator.evaluate() + nni.report_final_result(metrics['mAP']) + self.log_dict(metrics) + + def configure_optimizers(self): + return [torch.optim.Adam(self.parameters(), lr=self.hparams['learning_rate'])] + + def train_dataloader(self): + dataset = get_dataset('train') + return dataset.detection_dataloader( + shuffle=True, + augmentations=[ + T.RandomBrightness(0.9, 1.1), + T.RandomFlip(prob=0.5), + ], + batch_size=self.hparams['batch_size'], + num_workers=8) + + def val_dataloader(self): + dataset = get_dataset('valid') + return dataset.detection_dataloader( + shuffle=False, + batch_size=1, + num_workers=8) + + def test_dataloader(self): + if self.hparams.get('deploy', False): + dataset = load_dataset(self.hparams['dataset_name']) + df = pd.read_csv(self.hparams['deploy_meta_path']).query("downloaded == True") + df["image_id"] = df['save_path'] + df["gsv_image_path"] = df['save_path'] + df['annotations'] = "[]" + dataset._meta = df + return dataset.detection_dataloader( + shuffle=False, + batch_size=self.hparams.get("test_batch_size", 1), + num_workers=8) + else: + test_split = self.hparams.get("test_split", "valid") + dataset = get_dataset(test_split) + return dataset.detection_dataloader( + shuffle=False, + batch_size=1, + num_workers=8) diff --git a/detection/lightning/logger.py b/detection/lightning/logger.py new file mode 100644 index 0000000..aa2c012 --- /dev/null +++ b/detection/lightning/logger.py @@ -0,0 +1,61 @@ +import torch +import torch.nn.functional as F + +from util.constants import IMAGENET_MEAN, IMAGENET_STD + + +class TFLogger: + def log_images(self, images, tag, size=125): + """ + Log images and optionally detection to tensorboard + :param logger: [Tensorboard Logger] Tensorboard logger object. + :param images: [tensor] batch of images indexed + [batch, channel, size1, size2] + TODO: Include an argument for image labels; + Print the labels on the images. + """ + images = prep_images_for_logging(images, + pretrained=self.args['pretrained'], + size=size) + self.logger.experiment.add_images(tag, images) + + +def prep_images_for_logging(images, pretrained=False, + size=125): + """ + Prepare images to be logged + :param images: [tensor] batch of images indexed + [channel, size1, size2] + :param mean: [list] mean values used to normalize images + :param std: [list] standard deviation values used to normalize images + :param size: [int] new size of the image to be rescaled + :return: images that are reversely normalized + """ + if pretrained: + mean = IMAGENET_MEAN + std = IMAGENET_STD + else: + mean = [0, 0, 0] + std = [1, 1, 1] + images = normalize_inverse(images, mean, std) + images = F.interpolate(images, size=size, + mode='bilinear', align_corners=True) + return images + + +def normalize_inverse(images, mean=IMAGENET_MEAN, std=IMAGENET_STD): + """ + Reverse Normalization of Pytorch Tensor + :param images: [tensor] batch of images indexed + [batch, channel, size1, size2] + :param mean: [list] mean values used to normalize images + :param std: [list] standard deviation values used to normalize images + :return: images that are reversely normalized + """ + mean_inv = torch.FloatTensor( + [-m/s for m, s in zip(mean, std)]).view(1, 3, 1, 1) + std_inv = torch.FloatTensor([1/s for s in std]).view(1, 3, 1, 1) + if torch.cuda.is_available(): + mean_inv = mean_inv.cuda() + std_inv = std_inv.cuda() + return (images - mean_inv) / std_inv diff --git a/detection/lightning/util.py b/detection/lightning/util.py new file mode 100644 index 0000000..fffdff4 --- /dev/null +++ b/detection/lightning/util.py @@ -0,0 +1,34 @@ +"""Define Logger class for logging information to stdout and disk.""" +import json +import os +from os.path import join +from pytorch_lightning.loggers.test_tube import TestTubeLogger +from pytorch_lightning.callbacks import ModelCheckpoint, EarlyStopping + + +def get_ckpt_dir(save_path, exp_name): + return os.path.join(save_path, exp_name, "ckpts") + + +def get_ckpt_callback(save_path, exp_name, monitor="val_loss", mode="min"): + ckpt_dir = os.path.join(save_path, exp_name, "ckpts") + return ModelCheckpoint(filepath=ckpt_dir, + save_top_k=1, + verbose=True, + monitor=monitor, + mode=mode, + prefix='') + + +def get_early_stop_callback(patience=10): + return EarlyStopping(monitor='val_loss', + patience=patience, + verbose=True, + mode='min') + + +def get_logger(save_path, exp_name): + exp_dir = os.path.join(save_path, exp_name) + return TestTubeLogger(save_dir=exp_dir, + name='lightning_logs', + version="0") diff --git a/detection/main.py b/detection/main.py new file mode 100644 index 0000000..a4b21b2 --- /dev/null +++ b/detection/main.py @@ -0,0 +1,115 @@ +import os +import fire +from pytorch_lightning import Trainer + +from util.nni import run_nni +from util import init_exp_folder, Args +from util import constants as C +from lightning import (get_task, + load_task, + get_ckpt_callback, + get_early_stop_callback, + get_logger) + + +def train(save_dir=C.SANDBOX_PATH, + tb_path=C.TB_PATH, + exp_name="DemoExperiment", + model="FasterRCNN", + task='detection', + gpus=1, + pretrained=True, + batch_size=8, + accelerator="ddp", + gradient_clip_val=0.5, + max_epochs=100, + learning_rate=1e-5, + patience=30, + limit_train_batches=1.0, + limit_val_batches=1.0, + limit_test_batches=1.0, + weights_summary=None, + ): + """ + Run the training experiment. + + Args: + save_dir: Path to save the checkpoints and logs + exp_name: Name of the experiment + model: Model name + gpus: int. (ie: 2 gpus) + OR list to specify which GPUs [0, 1] OR '0,1' + OR '-1' / -1 to use all available gpus + pretrained: Whether or not to use the pretrained model + num_classes: Number of classes + accelerator: Distributed computing mode + gradient_clip_val: Clip value of gradient norm + limit_train_batches: Proportion of training data to use + max_epochs: Max number of epochs + patience: number of epochs with no improvement after + which training will be stopped. + tb_path: Path to global tb folder + loss_fn: Loss function to use + weights_summary: Prints a summary of the weights when training begins. + + Returns: None + + """ + num_classes = 2 + dataset_name = "camera-detection-new" + + args = Args(locals()) + init_exp_folder(args) + task = get_task(args) + trainer = Trainer(gpus=gpus, + accelerator=accelerator, + logger=get_logger(save_dir, exp_name), + callbacks=[get_early_stop_callback(patience), + get_ckpt_callback(save_dir, exp_name, monitor="mAP", mode="max")], + weights_save_path=os.path.join(save_dir, exp_name), + gradient_clip_val=gradient_clip_val, + limit_train_batches=limit_train_batches, + limit_val_batches=limit_val_batches, + limit_test_batches=limit_test_batches, + weights_summary=weights_summary, + max_epochs=max_epochs) + trainer.fit(task) + return save_dir, exp_name + + +def test(ckpt_path, + visualize=False, + deploy=False, + limit_test_batches=1.0, + gpus=1, + deploy_meta_path="/home/haosheng/dataset/camera/deployment/16cityp1.csv", + test_batch_size=1, + **kwargs): + """ + Run the testing experiment. + + Args: + ckpt_path: Path for the experiment to load + gpus: int. (ie: 2 gpus) + OR list to specify which GPUs [0, 1] OR '0,1' + OR '-1' / -1 to use all available gpus + Returns: None + + """ + task = load_task(ckpt_path, + visualize=visualize, + deploy=deploy, + deploy_meta_path=deploy_meta_path, + test_batch_size=test_batch_size, + **kwargs) + trainer = Trainer(gpus=gpus, + limit_test_batches=limit_test_batches) + trainer.test(task) + + +def nni(): + run_nni(train, test) + + +if __name__ == "__main__": + fire.Fire() diff --git a/detection/models/__init__.py b/detection/models/__init__.py new file mode 100644 index 0000000..42d6347 --- /dev/null +++ b/detection/models/__init__.py @@ -0,0 +1,14 @@ +import argparse + +from util import Args +from .classification import * +from .detection import * + + +def get_model(model_args): + model_args_ = model_args + + if isinstance(model_args, argparse.Namespace): + model_args_ = Args(vars(model_args)) + + return globals().copy()[model_args_.get("model")](model_args_) diff --git a/detection/models/classification.py b/detection/models/classification.py new file mode 100644 index 0000000..3d79aa2 --- /dev/null +++ b/detection/models/classification.py @@ -0,0 +1,238 @@ +import pretrainedmodels +import torch.nn as nn +import torch.nn.functional as F +from torchvision import models +from efficientnet_pytorch import EfficientNet + + +class PretrainedModel(nn.Module): + """Pretrained model, either from Cadene or TorchVision.""" + + def __init__(self): + super(PretrainedModel, self).__init__() + + def forward(self, x): + raise NotImplementedError('Subclass of PretrainedModel ' + + 'must implement forward method.') + + def fine_tuning_parameters(self, boundary_layers, lrs): + """Get a list of parameter groups that can be passed to an optimizer. + + Args: + boundary_layers: List of names for the boundary layers. + lrs: List of learning rates for each parameter group, from earlier + to later layers. + + Returns: + param_groups: List of dictionaries, one per parameter group. + """ + + def gen_params(start_layer, end_layer): + saw_start_layer = False + for name, param in self.named_parameters(): + if end_layer is not None and name == end_layer: + # Saw the last layer -> done + return + if start_layer is None or name == start_layer: + # Saw the first layer -> Start returning layers + saw_start_layer = True + + if saw_start_layer: + yield param + + if len(lrs) != boundary_layers + 1: + raise ValueError(f'Got {boundary_layers + 1} param groups, ' + + f'but {lrs} learning rates') + + # Fine-tune the network's layers from encoder.2 onwards + boundary_layers = [None] + boundary_layers + [None] + param_groups = [] + for i in range(len(boundary_layers) - 1): + start, end = boundary_layers[i:i + 2] + param_groups.append({'params': gen_params(start, end), + 'lr': lrs[i]}) + return param_groups + + +class EfficientNetModel(PretrainedModel): + """EfficientNet models: + https://github.com/lukemelas/EfficientNet-PyTorch + """ + def __init__(self, model_name, model_args=None): + super().__init__() + num_classes = model_args.get("num_classes", None) + pretrained = model_args.get("pretrained", False) + + if pretrained: + self.model = EfficientNet.from_pretrained( + model_name, num_classes=num_classes) + else: + self.model = EfficientNet.from_name( + model_name, num_classes=num_classes) + + def forward(self, x): + x = self.model(x) + return x + + +class CadeneModel(PretrainedModel): + """Models from Cadene's GitHub page of pretrained networks: + https://github.com/Cadene/pretrained-models.pytorch + """ + + def __init__(self, model_name, model_args=None): + super(CadeneModel, self).__init__() + + model_class = pretrainedmodels.__dict__[model_name] + pretrained = "imagenet" if model_args['pretrained'] else None + self.model = model_class(num_classes=1000, + pretrained=pretrained) + self.pool = nn.AdaptiveAvgPool2d(1) + + num_ftrs = self.model.last_linear.in_features + self.fc = nn.Linear(num_ftrs, model_args['num_classes']) + + def forward(self, x): + x = self.model.features(x) + x = F.relu(x, inplace=False) + x = self.pool(x).view(x.size(0), -1) + x = self.fc(x) + return x + + +class TorchVisionModel(PretrainedModel): + """Models from TorchVision's GitHub page of pretrained neural networks: + https://github.com/pytorch/vision/tree/master/torchvision/models + """ + + def __init__(self, model_fn, model_args): + super(TorchVisionModel, self).__init__() + + self.model = model_fn(pretrained=model_args.pretrained) + self.pool = nn.AdaptiveAvgPool2d(1) + + num_outputs = model_args['num_classes'] + + if 'fc' in self.model.__dict__: + num_ftrs = self.model.classifier.in_features + self.model.fc = nn.Linear(num_ftrs, num_outputs) + elif 'classifier' in self.model.__dict__: + num_ftrs = self.model.classifier.in_features + self.model.classifier = nn.Linear(num_ftrs, num_outputs) + + def forward(self, x): + x = self.model.features(x) + x = F.relu(x, inplace=False) + x = self.pool(x).view(x.size(0), -1) + x = self.model.classifier(x) + return x + + +class EfficientNetB0(EfficientNetModel): + def __init__(self, model_args=None): + super().__init__('efficientnet-b0', model_args) + + +class EfficientNetB1(EfficientNetModel): + def __init__(self, model_args=None): + super().__init__('efficientnet-b1', model_args) + + +class EfficientNetB2(EfficientNetModel): + def __init__(self, model_args=None): + super().__init__('efficientnet-b2', model_args) + + +class EfficientNetB3(EfficientNetModel): + def __init__(self, model_args=None): + super().__init__('efficientnet-b3', model_args) + + +class EfficientNetB4(EfficientNetModel): + def __init__(self, model_args=None): + super().__init__('efficientnet-b4', model_args) + + +class EfficientNetB5(EfficientNetModel): + def __init__(self, model_args=None): + super().__init__('efficientnet-b5', model_args) + + +class EfficientNetB6(EfficientNetModel): + def __init__(self, model_args=None): + super().__init__('efficientnet-b6', model_args) + + +class EfficientNetB7(EfficientNetModel): + def __init__(self, model_args=None): + super().__init__('efficientnet-b7', model_args) + + +class DenseNet121(TorchVisionModel): + def __init__(self, model_args=None): + super(DenseNet121, self).__init__(models.densenet121, model_args) + + +class DenseNet161(TorchVisionModel): + def __init__(self, model_args=None): + super(DenseNet161, self).__init__(models.densenet161, model_args) + + +class DenseNet201(TorchVisionModel): + def __init__(self, model_args=None): + super(DenseNet201, self).__init__(models.densenet201, model_args) + + +class ResNet101(TorchVisionModel): + def __init__(self, model_args=None): + super(ResNet101, self).__init__(models.resnet101, model_args) + + +class ResNet152(TorchVisionModel): + def __init__(self, model_args=None): + super(ResNet152, self).__init__(models.resnet152, model_args) + + +class Inceptionv3(TorchVisionModel): + def __init__(self, model_args=None): + super(Inceptionv3, self).__init__(models.inception_v3, model_args) + + +class Inceptionv4(CadeneModel): + def __init__(self, model_args=None): + super(Inceptionv4, self).__init__('inceptionv4', model_args) + + +class ResNet18(CadeneModel): + def __init__(self, model_args=None): + super(ResNet18, self).__init__('resnet18', model_args) + + +class ResNet34(CadeneModel): + def __init__(self, model_args=None): + super(ResNet34, self).__init__('resnet34', model_args) + + +class ResNeXt101(CadeneModel): + def __init__(self, model_args=None): + super(ResNeXt101, self).__init__('resnext101_64x4d', model_args) + + +class NASNetA(CadeneModel): + def __init__(self, model_args=None): + super(NASNetA, self).__init__('nasnetalarge', model_args) + + +class MNASNet(CadeneModel): + def __init__(self, model_args=None): + super(MNASNet, self).__init__('nasnetamobile', model_args) + + +class SENet154(CadeneModel): + def __init__(self, model_args=None): + super(SENet154, self).__init__('senet154', model_args) + + +class SEResNeXt101(CadeneModel): + def __init__(self, model_args=None): + super(SEResNeXt101, self).__init__('se_resnext101_32x4d', model_args) diff --git a/detection/models/detection/__init__.py b/detection/models/detection/__init__.py new file mode 100644 index 0000000..d16445a --- /dev/null +++ b/detection/models/detection/__init__.py @@ -0,0 +1,3 @@ +from .detectron import * +from .efficientdet import * +from .yolo import * diff --git a/detection/models/detection/detectron.py b/detection/models/detection/detectron.py new file mode 100644 index 0000000..6297f8a --- /dev/null +++ b/detection/models/detection/detectron.py @@ -0,0 +1,98 @@ +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from detectron2.config import get_cfg +from detectron2 import model_zoo +from detectron2.modeling import build_model +from detectron2.utils.events import EventStorage +from detectron2.structures import Instances, Boxes +from detectron2.checkpoint import DetectionCheckpointer + + +class Detectron2Model(nn.Module): + """Detectron2 model: + https://github.com/facebookresearch/detectron2 + """ + MODEL_CONFIG = { + "mask_rcnn": "COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml", + "faster_rcnn": "COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml", + "retinanet": "COCO-Detection/retinanet_R_50_FPN_3x.yaml", + "rpn": "COCO-Detection/rpn_R_50_FPN_1x.yaml", + "fast_rcnn": "COCO-Detection/fast_rcnn_R_50_FPN_1x.yaml"} + + def __init__(self, model_name, model_args=None): + super().__init__() + num_classes = model_args.get("num_classes", None) + pretrained = model_args.get("pretrained", False) + nms_threshold = model_args.get("nms_threshold", 0.5) + if model_args.get("gpus", None) is None: + device = "cpu" + else: + device = "cuda" + + self.cfg = get_cfg() + config_path = self.MODEL_CONFIG[model_name] + self.cfg.merge_from_file(model_zoo.get_config_file(config_path)) + + # Update number of classes + self.cfg.MODEL.ROI_HEADS.NUM_CLASSES = num_classes + self.cfg.MODEL.RETINANET.NUM_CLASSES = num_classes + + # Segmentation + self.cfg.INPUT.MASK_FORMAT='bitmask' + + # NMS + self.cfg.MODEL.ROI_HEADS.NMS_THRESH_TEST = nms_threshold + self.cfg.MODEL.RPN.NMS_THRESH_TEST = nms_threshold + + self.cfg.MODEL.DEVICE = device + model = build_model(self.cfg) + + # Load pretrained model + if pretrained: + DetectionCheckpointer(model).load( + model_zoo.get_checkpoint_url(config_path)) + + self.model = model + + def forward(self, x): + if self.training: + with EventStorage() as storage: + out = self.model(x) + else: + self.model.train() + with torch.no_grad(), EventStorage() as storage: + out = self.model(x) + self.model.eval() + return out + + def infer(self, x): + with torch.no_grad(): + out = self.model(x) + return out + + +class FasterRCNN(Detectron2Model): + def __init__(self, model_args=None): + super().__init__('faster_rcnn', model_args) + + +class MaskRCNN(Detectron2Model): + def __init__(self, model_args=None): + super().__init__('mask_rcnn', model_args) + + +class FastRCNN(Detectron2Model): + def __init__(self, model_args=None): + super().__init__('fast_rcnn', model_args) + + +class RetinaNet(Detectron2Model): + def __init__(self, model_args=None): + super().__init__('retinanet', model_args) + + +class RPN(Detectron2Model): + def __init__(self, model_args=None): + super().__init__('rpn', model_args) diff --git a/detection/models/detection/efficientdet/__init__.py b/detection/models/detection/efficientdet/__init__.py new file mode 100644 index 0000000..edd3a81 --- /dev/null +++ b/detection/models/detection/efficientdet/__init__.py @@ -0,0 +1,103 @@ +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from detectron2.structures import Instances, Boxes + +from .backbone import EfficientDetWithLoss + + +class EfficientDetModel(nn.Module): + """Detectron2 model: + https://github.com/zylo117/Yet-Another-EfficientDet-Pytorch + """ + + def __init__(self, compound_coef, model_args=None): + super().__init__() + num_classes = model_args.get("num_classes", None) + pretrained = model_args.get("pretrained", False) + self.max_bbox = model_args.get("max_bbox", 30) + + self.model = EfficientDetWithLoss(num_classes=num_classes, + compound_coef=compound_coef, + load_weights=pretrained) + + @staticmethod + def to_numpy(v): + if isinstance(v, np.ndarray): + return v + else: + return v.detach().cpu().numpy() + + def forward(self, x): + N = len(x) + imgs = torch.stack([sample['image'].float() for sample in x]) + annotations = np.ones((N, self.max_bbox, 5)) * -1 + for i, sample in enumerate(x): + instances = sample['instances'] + boxes = self.to_numpy(instances.gt_boxes.tensor) + class_id = self.to_numpy(instances.gt_classes) + annotation = np.concatenate([boxes, class_id[:, np.newaxis]], 1) + if len(class_id) > self.max_bbox: + annotation = annotation[:self.max_bbox, :] + annotations[i, :len(class_id), :] = annotation + annotations = torch.from_numpy(annotations) + return self.model(imgs, annotations, is_train) + + def infer(self, x): + imgs = torch.stack([sample['image'].float() for sample in x]) + rois = self.model.infer(imgs) + outs = [] + for sample_input, sample_output in zip(x, rois): + instances = Instances( + (sample_input['height'], sample_input['width'])) + instances.pred_boxes = Boxes(sample_output['rois']) + instances.scores = torch.tensor(sample_output['scores']) + instances.pred_classes = torch.tensor(sample_output['class_ids']) + outs.append({"instances": instances}) + return outs + + +class EfficientDetD0(EfficientDetModel): + def __init__(self, model_args=None): + super().__init__(0, model_args) + + +class EfficientDetD1(EfficientDetModel): + def __init__(self, model_args=None): + super().__init__(1, model_args) + + +class EfficientDetD2(EfficientDetModel): + def __init__(self, model_args=None): + super().__init__(2, model_args) + + +class EfficientDetD3(EfficientDetModel): + def __init__(self, model_args=None): + super().__init__(3, model_args) + + +class EfficientDetD4(EfficientDetModel): + def __init__(self, model_args=None): + super().__init__(4, model_args) + + +class EfficientDetD5(EfficientDetModel): + def __init__(self, model_args=None): + super().__init__(5, model_args) + + +class EfficientDetD6(EfficientDetModel): + def __init__(self, model_args=None): + super().__init__(6, model_args) + + +class EfficientDetD7(EfficientDetModel): + def __init__(self, model_args=None): + super().__init__(7, model_args) + + +class EfficientDetD7X(EfficientDetModel): + def __init__(self, model_args=None): + super().__init__(8, model_args) diff --git a/detection/models/detection/efficientdet/backbone.py b/detection/models/detection/efficientdet/backbone.py new file mode 100644 index 0000000..9af32fc --- /dev/null +++ b/detection/models/detection/efficientdet/backbone.py @@ -0,0 +1,119 @@ +import torch +from torch import nn + +from .model import BiFPN, Regressor, Classifier, EfficientNet +from .utils import Anchors, BBoxTransform, ClipBoxes +from .process import postprocess +from .loss import FocalLoss + + +class EfficientDetWithLoss(nn.Module): + def __init__(self, **kwargs): + super().__init__() + self.model = EfficientDetBackbone(**kwargs) + self.criterion = FocalLoss() + self.threshold = kwargs.get("threshold", 0.2) + self.iou_threshold = kwargs.get("iou_threshold", 0.2) + + def forward(self, imgs, annotations): + if self.training: + features, regression, classification, anchors = self.model(imgs) + cls_loss, reg_loss = self.criterion(classification, regression, anchors, annotations) + else: + with torch.no_grad(): + features, regression, classification, anchors = self.model(imgs) + cls_loss, reg_loss = self.criterion(classification, regression, anchors, annotations) + losses = {"cls_loss": cls_loss, "reg_loss": reg_loss} + return losses + + def infer(self, imgs): + with torch.no_grad(): + features, regression, classification, anchors = self.model(imgs) + regressBoxes = BBoxTransform() + clipBoxes = ClipBoxes() + + out = postprocess(imgs, + anchors, regression, classification, + regressBoxes, clipBoxes, + self.threshold, self.iou_threshold) + return out + + +class EfficientDetBackbone(nn.Module): + def __init__(self, num_classes=80, compound_coef=0, load_weights=False, **kwargs): + super().__init__() + self.compound_coef = compound_coef + + self.backbone_compound_coef = [0, 1, 2, 3, 4, 5, 6, 6, 7] + self.fpn_num_filters = [64, 88, 112, 160, 224, 288, 384, 384, 384] + self.fpn_cell_repeats = [3, 4, 5, 6, 7, 7, 8, 8, 8] + self.input_sizes = [512, 640, 768, 896, 1024, 1280, 1280, 1536, 1536] + self.box_class_repeats = [3, 3, 3, 4, 4, 4, 5, 5, 5] + self.pyramid_levels = [5, 5, 5, 5, 5, 5, 5, 5, 6] + self.anchor_scale = [4., 4., 4., 4., 4., 4., 4., 5., 4.] + self.aspect_ratios = kwargs.get('ratios', [(1.0, 1.0), (1.4, 0.7), (0.7, 1.4)]) + self.num_scales = len(kwargs.get('scales', [2 ** 0, 2 ** (1.0 / 3.0), 2 ** (2.0 / 3.0)])) + conv_channel_coef = { + # the channels of P3/P4/P5. + 0: [40, 112, 320], + 1: [40, 112, 320], + 2: [48, 120, 352], + 3: [48, 136, 384], + 4: [56, 160, 448], + 5: [64, 176, 512], + 6: [72, 200, 576], + 7: [72, 200, 576], + 8: [80, 224, 640], + } + + num_anchors = len(self.aspect_ratios) * self.num_scales + + self.bifpn = nn.Sequential( + *[BiFPN(self.fpn_num_filters[self.compound_coef], + conv_channel_coef[compound_coef], + True if _ == 0 else False, + attention=True if compound_coef < 6 else False, + use_p8=compound_coef > 7) + for _ in range(self.fpn_cell_repeats[compound_coef])]) + + self.num_classes = num_classes + self.regressor = Regressor(in_channels=self.fpn_num_filters[self.compound_coef], num_anchors=num_anchors, + num_layers=self.box_class_repeats[self.compound_coef], + pyramid_levels=self.pyramid_levels[self.compound_coef]) + self.classifier = Classifier(in_channels=self.fpn_num_filters[self.compound_coef], num_anchors=num_anchors, + num_classes=num_classes, + num_layers=self.box_class_repeats[self.compound_coef], + pyramid_levels=self.pyramid_levels[self.compound_coef]) + + self.anchors = Anchors(anchor_scale=self.anchor_scale[compound_coef], + pyramid_levels=(torch.arange(self.pyramid_levels[self.compound_coef]) + 3).tolist(), + **kwargs) + + self.backbone_net = EfficientNet(self.backbone_compound_coef[compound_coef], load_weights) + + def freeze_bn(self): + for m in self.modules(): + if isinstance(m, nn.BatchNorm2d): + m.eval() + + def forward(self, inputs): + max_size = inputs.shape[-1] + + _, p3, p4, p5 = self.backbone_net(inputs) + + features = (p3, p4, p5) + features = self.bifpn(features) + + regression = self.regressor(features) + classification = self.classifier(features) + anchors = self.anchors(inputs, inputs.dtype) + + return features, regression, classification, anchors + + def init_backbone(self, path): + state_dict = torch.load(path) + try: + ret = self.load_state_dict(state_dict, strict=False) + print(ret) + except RuntimeError as e: + print('Ignoring ' + str(e) + '"') diff --git a/detection/models/detection/efficientdet/config.py b/detection/models/detection/efficientdet/config.py new file mode 100644 index 0000000..76f170f --- /dev/null +++ b/detection/models/detection/efficientdet/config.py @@ -0,0 +1,26 @@ +COCO_CLASSES = ["person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", + "traffic light", "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", + "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", + "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sports ball", "kite", + "baseball bat", "baseball glove", "skateboard", "surfboard", "tennis racket", "bottle", + "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple", "sandwich", "orange", + "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch", "potted plant", + "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone", + "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", + "teddy bear", "hair drier", "toothbrush"] + +colors = [(39, 129, 113), (164, 80, 133), (83, 122, 114), (99, 81, 172), (95, 56, 104), (37, 84, 86), (14, 89, 122), + (80, 7, 65), (10, 102, 25), (90, 185, 109), (106, 110, 132), (169, 158, 85), (188, 185, 26), (103, 1, 17), + (82, 144, 81), (92, 7, 184), (49, 81, 155), (179, 177, 69), (93, 187, 158), (13, 39, 73), (12, 50, 60), + (16, 179, 33), (112, 69, 165), (15, 139, 63), (33, 191, 159), (182, 173, 32), (34, 113, 133), (90, 135, 34), + (53, 34, 86), (141, 35, 190), (6, 171, 8), (118, 76, 112), (89, 60, 55), (15, 54, 88), (112, 75, 181), + (42, 147, 38), (138, 52, 63), (128, 65, 149), (106, 103, 24), (168, 33, 45), (28, 136, 135), (86, 91, 108), + (52, 11, 76), (142, 6, 189), (57, 81, 168), (55, 19, 148), (182, 101, 89), (44, 65, 179), (1, 33, 26), + (122, 164, 26), (70, 63, 134), (137, 106, 82), (120, 118, 52), (129, 74, 42), (182, 147, 112), (22, 157, 50), + (56, 50, 20), (2, 22, 177), (156, 100, 106), (21, 35, 42), (13, 8, 121), (142, 92, 28), (45, 118, 33), + (105, 118, 30), (7, 185, 124), (46, 34, 146), (105, 184, 169), (22, 18, 5), (147, 71, 73), (181, 64, 91), + (31, 39, 184), (164, 179, 33), (96, 50, 18), (95, 15, 106), (113, 68, 54), (136, 116, 112), (119, 139, 130), + (31, 139, 34), (66, 6, 127), (62, 39, 2), (49, 99, 180), (49, 119, 155), (153, 50, 183), (125, 38, 3), + (129, 87, 143), (49, 87, 40), (128, 62, 120), (73, 85, 148), (28, 144, 118), (29, 9, 24), (175, 45, 108), + (81, 175, 64), (178, 19, 157), (74, 188, 190), (18, 114, 2), (62, 128, 96), (21, 3, 150), (0, 6, 95), + (2, 20, 184), (122, 37, 185)] diff --git a/detection/models/detection/efficientdet/efficientnet/__init__.py b/detection/models/detection/efficientdet/efficientnet/__init__.py new file mode 100644 index 0000000..4c93560 --- /dev/null +++ b/detection/models/detection/efficientdet/efficientnet/__init__.py @@ -0,0 +1,10 @@ +__version__ = "0.6.1" +from .model import EfficientNet +from .utils import ( + GlobalParams, + BlockArgs, + BlockDecoder, + efficientnet, + get_model_params, +) + diff --git a/detection/models/detection/efficientdet/efficientnet/model.py b/detection/models/detection/efficientdet/efficientnet/model.py new file mode 100644 index 0000000..743ccbb --- /dev/null +++ b/detection/models/detection/efficientdet/efficientnet/model.py @@ -0,0 +1,237 @@ +import torch +from torch import nn +from torch.nn import functional as F + +from .utils import ( + round_filters, + round_repeats, + drop_connect, + get_same_padding_conv2d, + get_model_params, + efficientnet_params, + load_pretrained_weights, + Swish, + MemoryEfficientSwish, +) + +class MBConvBlock(nn.Module): + """ + Mobile Inverted Residual Bottleneck Block + + Args: + block_args (namedtuple): BlockArgs, see above + global_params (namedtuple): GlobalParam, see above + + Attributes: + has_se (bool): Whether the block contains a Squeeze and Excitation layer. + """ + + def __init__(self, block_args, global_params): + super().__init__() + self._block_args = block_args + self._bn_mom = 1 - global_params.batch_norm_momentum + self._bn_eps = global_params.batch_norm_epsilon + self.has_se = (self._block_args.se_ratio is not None) and (0 < self._block_args.se_ratio <= 1) + self.id_skip = block_args.id_skip # skip connection and drop connect + + # Get static or dynamic convolution depending on image size + Conv2d = get_same_padding_conv2d(image_size=global_params.image_size) + + # Expansion phase + inp = self._block_args.input_filters # number of input channels + oup = self._block_args.input_filters * self._block_args.expand_ratio # number of output channels + if self._block_args.expand_ratio != 1: + self._expand_conv = Conv2d(in_channels=inp, out_channels=oup, kernel_size=1, bias=False) + self._bn0 = nn.BatchNorm2d(num_features=oup, momentum=self._bn_mom, eps=self._bn_eps) + + # Depthwise convolution phase + k = self._block_args.kernel_size + s = self._block_args.stride + self._depthwise_conv = Conv2d( + in_channels=oup, out_channels=oup, groups=oup, # groups makes it depthwise + kernel_size=k, stride=s, bias=False) + self._bn1 = nn.BatchNorm2d(num_features=oup, momentum=self._bn_mom, eps=self._bn_eps) + + # Squeeze and Excitation layer, if desired + if self.has_se: + num_squeezed_channels = max(1, int(self._block_args.input_filters * self._block_args.se_ratio)) + self._se_reduce = Conv2d(in_channels=oup, out_channels=num_squeezed_channels, kernel_size=1) + self._se_expand = Conv2d(in_channels=num_squeezed_channels, out_channels=oup, kernel_size=1) + + # Output phase + final_oup = self._block_args.output_filters + self._project_conv = Conv2d(in_channels=oup, out_channels=final_oup, kernel_size=1, bias=False) + self._bn2 = nn.BatchNorm2d(num_features=final_oup, momentum=self._bn_mom, eps=self._bn_eps) + self._swish = MemoryEfficientSwish() + + def forward(self, inputs, drop_connect_rate=None): + """ + :param inputs: input tensor + :param drop_connect_rate: drop connect rate (float, between 0 and 1) + :return: output of block + """ + + # Expansion and Depthwise Convolution + x = inputs + if self._block_args.expand_ratio != 1: + x = self._expand_conv(inputs) + x = self._bn0(x) + x = self._swish(x) + + x = self._depthwise_conv(x) + x = self._bn1(x) + x = self._swish(x) + + # Squeeze and Excitation + if self.has_se: + x_squeezed = F.adaptive_avg_pool2d(x, 1) + x_squeezed = self._se_reduce(x_squeezed) + x_squeezed = self._swish(x_squeezed) + x_squeezed = self._se_expand(x_squeezed) + x = torch.sigmoid(x_squeezed) * x + + x = self._project_conv(x) + x = self._bn2(x) + + # Skip connection and drop connect + input_filters, output_filters = self._block_args.input_filters, self._block_args.output_filters + if self.id_skip and self._block_args.stride == 1 and input_filters == output_filters: + if drop_connect_rate: + x = drop_connect(x, p=drop_connect_rate, training=self.training) + x = x + inputs # skip connection + return x + + def set_swish(self, memory_efficient=True): + """Sets swish function as memory efficient (for training) or standard (for export)""" + self._swish = MemoryEfficientSwish() if memory_efficient else Swish() + + +class EfficientNet(nn.Module): + """ + An EfficientNet model. Most easily loaded with the .from_name or .from_pretrained methods + + Args: + blocks_args (list): A list of BlockArgs to construct blocks + global_params (namedtuple): A set of GlobalParams shared between blocks + + Example: + model = EfficientNet.from_pretrained('efficientnet-b0') + + """ + + def __init__(self, blocks_args=None, global_params=None): + super().__init__() + assert isinstance(blocks_args, list), 'blocks_args should be a list' + assert len(blocks_args) > 0, 'block args must be greater than 0' + self._global_params = global_params + self._blocks_args = blocks_args + + # Get static or dynamic convolution depending on image size + Conv2d = get_same_padding_conv2d(image_size=global_params.image_size) + + # Batch norm parameters + bn_mom = 1 - self._global_params.batch_norm_momentum + bn_eps = self._global_params.batch_norm_epsilon + + # Stem + in_channels = 3 # rgb + out_channels = round_filters(32, self._global_params) # number of output channels + self._conv_stem = Conv2d(in_channels, out_channels, kernel_size=3, stride=2, bias=False) + self._bn0 = nn.BatchNorm2d(num_features=out_channels, momentum=bn_mom, eps=bn_eps) + + # Build blocks + self._blocks = nn.ModuleList([]) + for block_args in self._blocks_args: + + # Update block input and output filters based on depth multiplier. + block_args = block_args._replace( + input_filters=round_filters(block_args.input_filters, self._global_params), + output_filters=round_filters(block_args.output_filters, self._global_params), + num_repeat=round_repeats(block_args.num_repeat, self._global_params) + ) + + # The first block needs to take care of stride and filter size increase. + self._blocks.append(MBConvBlock(block_args, self._global_params)) + if block_args.num_repeat > 1: + block_args = block_args._replace(input_filters=block_args.output_filters, stride=1) + for _ in range(block_args.num_repeat - 1): + self._blocks.append(MBConvBlock(block_args, self._global_params)) + + # Head + in_channels = block_args.output_filters # output of final block + out_channels = round_filters(1280, self._global_params) + self._conv_head = Conv2d(in_channels, out_channels, kernel_size=1, bias=False) + self._bn1 = nn.BatchNorm2d(num_features=out_channels, momentum=bn_mom, eps=bn_eps) + + # Final linear layer + self._avg_pooling = nn.AdaptiveAvgPool2d(1) + self._dropout = nn.Dropout(self._global_params.dropout_rate) + self._fc = nn.Linear(out_channels, self._global_params.num_classes) + self._swish = MemoryEfficientSwish() + + def set_swish(self, memory_efficient=True): + """Sets swish function as memory efficient (for training) or standard (for export)""" + self._swish = MemoryEfficientSwish() if memory_efficient else Swish() + for block in self._blocks: + block.set_swish(memory_efficient) + + + def extract_features(self, inputs): + """ Returns output of the final convolution layer """ + + # Stem + x = self._swish(self._bn0(self._conv_stem(inputs))) + + # Blocks + for idx, block in enumerate(self._blocks): + drop_connect_rate = self._global_params.drop_connect_rate + if drop_connect_rate: + drop_connect_rate *= float(idx) / len(self._blocks) + x = block(x, drop_connect_rate=drop_connect_rate) + # Head + x = self._swish(self._bn1(self._conv_head(x))) + + return x + + def forward(self, inputs): + """ Calls extract_features to extract features, applies final linear layer, and returns logits. """ + bs = inputs.size(0) + # Convolution layers + x = self.extract_features(inputs) + + # Pooling and final linear layer + x = self._avg_pooling(x) + x = x.view(bs, -1) + x = self._dropout(x) + x = self._fc(x) + return x + + @classmethod + def from_name(cls, model_name, override_params=None): + cls._check_model_name_is_valid(model_name) + blocks_args, global_params = get_model_params(model_name, override_params) + return cls(blocks_args, global_params) + + @classmethod + def from_pretrained(cls, model_name, load_weights=True, advprop=False, num_classes=1000, in_channels=3): + model = cls.from_name(model_name, override_params={'num_classes': num_classes}) + if load_weights: + load_pretrained_weights(model, model_name, load_fc=(num_classes == 1000), advprop=advprop) + if in_channels != 3: + Conv2d = get_same_padding_conv2d(image_size = model._global_params.image_size) + out_channels = round_filters(32, model._global_params) + model._conv_stem = Conv2d(in_channels, out_channels, kernel_size=3, stride=2, bias=False) + return model + + @classmethod + def get_image_size(cls, model_name): + cls._check_model_name_is_valid(model_name) + _, _, res, _ = efficientnet_params(model_name) + return res + + @classmethod + def _check_model_name_is_valid(cls, model_name): + """ Validates model name. """ + valid_models = ['efficientnet-b'+str(i) for i in range(9)] + if model_name not in valid_models: + raise ValueError('model_name should be one of: ' + ', '.join(valid_models)) diff --git a/detection/models/detection/efficientdet/efficientnet/utils.py b/detection/models/detection/efficientdet/efficientnet/utils.py new file mode 100644 index 0000000..d4bda20 --- /dev/null +++ b/detection/models/detection/efficientdet/efficientnet/utils.py @@ -0,0 +1,313 @@ +""" +This file contains helper functions for building the model and for loading model parameters. +These helper functions are built to mirror those in the official TensorFlow implementation. +""" + +import re +import math +import collections +from functools import partial +import torch +from torch import nn +from torch.nn import functional as F +from torch.utils import model_zoo +from .utils_extra import Conv2dStaticSamePadding + +######################################################################## +############### HELPERS FUNCTIONS FOR MODEL ARCHITECTURE ############### +######################################################################## + + +# Parameters for the entire model (stem, all blocks, and head) + +GlobalParams = collections.namedtuple('GlobalParams', [ + 'batch_norm_momentum', 'batch_norm_epsilon', 'dropout_rate', + 'num_classes', 'width_coefficient', 'depth_coefficient', + 'depth_divisor', 'min_depth', 'drop_connect_rate', 'image_size']) + +# Parameters for an individual model block +BlockArgs = collections.namedtuple('BlockArgs', [ + 'kernel_size', 'num_repeat', 'input_filters', 'output_filters', + 'expand_ratio', 'id_skip', 'stride', 'se_ratio']) + +# Change namedtuple defaults +GlobalParams.__new__.__defaults__ = (None,) * len(GlobalParams._fields) +BlockArgs.__new__.__defaults__ = (None,) * len(BlockArgs._fields) + + +class SwishImplementation(torch.autograd.Function): + @staticmethod + def forward(ctx, i): + result = i * torch.sigmoid(i) + ctx.save_for_backward(i) + return result + + @staticmethod + def backward(ctx, grad_output): + i = ctx.saved_variables[0] + sigmoid_i = torch.sigmoid(i) + return grad_output * (sigmoid_i * (1 + i * (1 - sigmoid_i))) + + +class MemoryEfficientSwish(nn.Module): + def forward(self, x): + return SwishImplementation.apply(x) + + +class Swish(nn.Module): + def forward(self, x): + return x * torch.sigmoid(x) + + +def round_filters(filters, global_params): + """ Calculate and round number of filters based on depth multiplier. """ + multiplier = global_params.width_coefficient + if not multiplier: + return filters + divisor = global_params.depth_divisor + min_depth = global_params.min_depth + filters *= multiplier + min_depth = min_depth or divisor + new_filters = max(min_depth, int(filters + divisor / 2) // divisor * divisor) + if new_filters < 0.9 * filters: # prevent rounding by more than 10% + new_filters += divisor + return int(new_filters) + + +def round_repeats(repeats, global_params): + """ Round number of filters based on depth multiplier. """ + multiplier = global_params.depth_coefficient + if not multiplier: + return repeats + return int(math.ceil(multiplier * repeats)) + + +def drop_connect(inputs, p, training): + """ Drop connect. """ + if not training: return inputs + batch_size = inputs.shape[0] + keep_prob = 1 - p + random_tensor = keep_prob + random_tensor += torch.rand([batch_size, 1, 1, 1], dtype=inputs.dtype, device=inputs.device) + binary_tensor = torch.floor(random_tensor) + output = inputs / keep_prob * binary_tensor + return output + + +def get_same_padding_conv2d(image_size=None): + """ Chooses static padding if you have specified an image size, and dynamic padding otherwise. + Static padding is necessary for ONNX exporting of models. """ + if image_size is None: + return Conv2dDynamicSamePadding + else: + return partial(Conv2dStaticSamePadding, image_size=image_size) + + +class Conv2dDynamicSamePadding(nn.Conv2d): + """ 2D Convolutions like TensorFlow, for a dynamic image size """ + + def __init__(self, in_channels, out_channels, kernel_size, stride=1, dilation=1, groups=1, bias=True): + super().__init__(in_channels, out_channels, kernel_size, stride, 0, dilation, groups, bias) + self.stride = self.stride if len(self.stride) == 2 else [self.stride[0]] * 2 + + def forward(self, x): + ih, iw = x.size()[-2:] + kh, kw = self.weight.size()[-2:] + sh, sw = self.stride + oh, ow = math.ceil(ih / sh), math.ceil(iw / sw) + pad_h = max((oh - 1) * self.stride[0] + (kh - 1) * self.dilation[0] + 1 - ih, 0) + pad_w = max((ow - 1) * self.stride[1] + (kw - 1) * self.dilation[1] + 1 - iw, 0) + if pad_h > 0 or pad_w > 0: + x = F.pad(x, [pad_w // 2, pad_w - pad_w // 2, pad_h // 2, pad_h - pad_h // 2]) + return F.conv2d(x, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups) + + +class Identity(nn.Module): + def __init__(self, ): + super(Identity, self).__init__() + + def forward(self, input): + return input + + +######################################################################## +############## HELPERS FUNCTIONS FOR LOADING MODEL PARAMS ############## +######################################################################## + + +def efficientnet_params(model_name): + """ Map EfficientNet model name to parameter coefficients. """ + params_dict = { + # Coefficients: width,depth,res,dropout + 'efficientnet-b0': (1.0, 1.0, 224, 0.2), + 'efficientnet-b1': (1.0, 1.1, 240, 0.2), + 'efficientnet-b2': (1.1, 1.2, 260, 0.3), + 'efficientnet-b3': (1.2, 1.4, 300, 0.3), + 'efficientnet-b4': (1.4, 1.8, 380, 0.4), + 'efficientnet-b5': (1.6, 2.2, 456, 0.4), + 'efficientnet-b6': (1.8, 2.6, 528, 0.5), + 'efficientnet-b7': (2.0, 3.1, 600, 0.5), + 'efficientnet-b8': (2.2, 3.6, 672, 0.5), + 'efficientnet-l2': (4.3, 5.3, 800, 0.5), + } + return params_dict[model_name] + + +class BlockDecoder(object): + """ Block Decoder for readability, straight from the official TensorFlow repository """ + + @staticmethod + def _decode_block_string(block_string): + """ Gets a block through a string notation of arguments. """ + assert isinstance(block_string, str) + + ops = block_string.split('_') + options = {} + for op in ops: + splits = re.split(r'(\d.*)', op) + if len(splits) >= 2: + key, value = splits[:2] + options[key] = value + + # Check stride + assert (('s' in options and len(options['s']) == 1) or + (len(options['s']) == 2 and options['s'][0] == options['s'][1])) + + return BlockArgs( + kernel_size=int(options['k']), + num_repeat=int(options['r']), + input_filters=int(options['i']), + output_filters=int(options['o']), + expand_ratio=int(options['e']), + id_skip=('noskip' not in block_string), + se_ratio=float(options['se']) if 'se' in options else None, + stride=[int(options['s'][0])]) + + @staticmethod + def _encode_block_string(block): + """Encodes a block to a string.""" + args = [ + 'r%d' % block.num_repeat, + 'k%d' % block.kernel_size, + 's%d%d' % (block.strides[0], block.strides[1]), + 'e%s' % block.expand_ratio, + 'i%d' % block.input_filters, + 'o%d' % block.output_filters + ] + if 0 < block.se_ratio <= 1: + args.append('se%s' % block.se_ratio) + if block.id_skip is False: + args.append('noskip') + return '_'.join(args) + + @staticmethod + def decode(string_list): + """ + Decodes a list of string notations to specify blocks inside the network. + + :param string_list: a list of strings, each string is a notation of block + :return: a list of BlockArgs namedtuples of block args + """ + assert isinstance(string_list, list) + blocks_args = [] + for block_string in string_list: + blocks_args.append(BlockDecoder._decode_block_string(block_string)) + return blocks_args + + @staticmethod + def encode(blocks_args): + """ + Encodes a list of BlockArgs to a list of strings. + + :param blocks_args: a list of BlockArgs namedtuples of block args + :return: a list of strings, each string is a notation of block + """ + block_strings = [] + for block in blocks_args: + block_strings.append(BlockDecoder._encode_block_string(block)) + return block_strings + + +def efficientnet(width_coefficient=None, depth_coefficient=None, dropout_rate=0.2, + drop_connect_rate=0.2, image_size=None, num_classes=1000): + """ Creates a efficientnet model. """ + + blocks_args = [ + 'r1_k3_s11_e1_i32_o16_se0.25', 'r2_k3_s22_e6_i16_o24_se0.25', + 'r2_k5_s22_e6_i24_o40_se0.25', 'r3_k3_s22_e6_i40_o80_se0.25', + 'r3_k5_s11_e6_i80_o112_se0.25', 'r4_k5_s22_e6_i112_o192_se0.25', + 'r1_k3_s11_e6_i192_o320_se0.25', + ] + blocks_args = BlockDecoder.decode(blocks_args) + + global_params = GlobalParams( + batch_norm_momentum=0.99, + batch_norm_epsilon=1e-3, + dropout_rate=dropout_rate, + drop_connect_rate=drop_connect_rate, + # data_format='channels_last', # removed, this is always true in PyTorch + num_classes=num_classes, + width_coefficient=width_coefficient, + depth_coefficient=depth_coefficient, + depth_divisor=8, + min_depth=None, + image_size=image_size, + ) + + return blocks_args, global_params + + +def get_model_params(model_name, override_params): + """ Get the block args and global params for a given model """ + if model_name.startswith('efficientnet'): + w, d, s, p = efficientnet_params(model_name) + # note: all models have drop connect rate = 0.2 + blocks_args, global_params = efficientnet( + width_coefficient=w, depth_coefficient=d, dropout_rate=p, image_size=s) + else: + raise NotImplementedError('model name is not pre-defined: %s' % model_name) + if override_params: + # ValueError will be raised here if override_params has fields not included in global_params. + global_params = global_params._replace(**override_params) + return blocks_args, global_params + + +url_map = { + 'efficientnet-b0': 'https://publicmodels.blob.core.windows.net/container/aa/efficientnet-b0-355c32eb.pth', + 'efficientnet-b1': 'https://publicmodels.blob.core.windows.net/container/aa/efficientnet-b1-f1951068.pth', + 'efficientnet-b2': 'https://publicmodels.blob.core.windows.net/container/aa/efficientnet-b2-8bb594d6.pth', + 'efficientnet-b3': 'https://publicmodels.blob.core.windows.net/container/aa/efficientnet-b3-5fb5a3c3.pth', + 'efficientnet-b4': 'https://publicmodels.blob.core.windows.net/container/aa/efficientnet-b4-6ed6700e.pth', + 'efficientnet-b5': 'https://publicmodels.blob.core.windows.net/container/aa/efficientnet-b5-b6417697.pth', + 'efficientnet-b6': 'https://publicmodels.blob.core.windows.net/container/aa/efficientnet-b6-c76e70fd.pth', + 'efficientnet-b7': 'https://publicmodels.blob.core.windows.net/container/aa/efficientnet-b7-dcc49843.pth', +} + +url_map_advprop = { + 'efficientnet-b0': 'https://publicmodels.blob.core.windows.net/container/advprop/efficientnet-b0-b64d5a18.pth', + 'efficientnet-b1': 'https://publicmodels.blob.core.windows.net/container/advprop/efficientnet-b1-0f3ce85a.pth', + 'efficientnet-b2': 'https://publicmodels.blob.core.windows.net/container/advprop/efficientnet-b2-6e9d97e5.pth', + 'efficientnet-b3': 'https://publicmodels.blob.core.windows.net/container/advprop/efficientnet-b3-cdd7c0f4.pth', + 'efficientnet-b4': 'https://publicmodels.blob.core.windows.net/container/advprop/efficientnet-b4-44fb3a87.pth', + 'efficientnet-b5': 'https://publicmodels.blob.core.windows.net/container/advprop/efficientnet-b5-86493f6b.pth', + 'efficientnet-b6': 'https://publicmodels.blob.core.windows.net/container/advprop/efficientnet-b6-ac80338e.pth', + 'efficientnet-b7': 'https://publicmodels.blob.core.windows.net/container/advprop/efficientnet-b7-4652b6dd.pth', + 'efficientnet-b8': 'https://publicmodels.blob.core.windows.net/container/advprop/efficientnet-b8-22a8fe65.pth', +} + + +def load_pretrained_weights(model, model_name, load_fc=True, advprop=False): + """ Loads pretrained weights, and downloads if loading for the first time. """ + # AutoAugment or Advprop (different preprocessing) + url_map_ = url_map_advprop if advprop else url_map + state_dict = model_zoo.load_url(url_map_[model_name], map_location=torch.device('cpu')) + # state_dict = torch.load('../../weights/backbone_efficientnetb0.pth') + if load_fc: + ret = model.load_state_dict(state_dict, strict=False) + print(ret) + else: + state_dict.pop('_fc.weight') + state_dict.pop('_fc.bias') + res = model.load_state_dict(state_dict, strict=False) + assert set(res.missing_keys) == set(['_fc.weight', '_fc.bias']), 'issue loading pretrained weights' + print('Loaded pretrained weights for {}'.format(model_name)) diff --git a/detection/models/detection/efficientdet/efficientnet/utils_extra.py b/detection/models/detection/efficientdet/efficientnet/utils_extra.py new file mode 100644 index 0000000..9b4227c --- /dev/null +++ b/detection/models/detection/efficientdet/efficientnet/utils_extra.py @@ -0,0 +1,86 @@ +# Author: Zylo117 + +import math + +from torch import nn +import torch.nn.functional as F + + +class Conv2dStaticSamePadding(nn.Module): + """ + created by Zylo117 + The real keras/tensorflow conv2d with same padding + """ + + def __init__(self, in_channels, out_channels, kernel_size, stride=1, bias=True, groups=1, dilation=1, **kwargs): + super().__init__() + self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride=stride, + bias=bias, groups=groups) + self.stride = self.conv.stride + self.kernel_size = self.conv.kernel_size + self.dilation = self.conv.dilation + + if isinstance(self.stride, int): + self.stride = [self.stride] * 2 + elif len(self.stride) == 1: + self.stride = [self.stride[0]] * 2 + + if isinstance(self.kernel_size, int): + self.kernel_size = [self.kernel_size] * 2 + elif len(self.kernel_size) == 1: + self.kernel_size = [self.kernel_size[0]] * 2 + + def forward(self, x): + h, w = x.shape[-2:] + + extra_h = (math.ceil(w / self.stride[1]) - 1) * self.stride[1] - w + self.kernel_size[1] + extra_v = (math.ceil(h / self.stride[0]) - 1) * self.stride[0] - h + self.kernel_size[0] + + left = extra_h // 2 + right = extra_h - left + top = extra_v // 2 + bottom = extra_v - top + + x = F.pad(x, [left, right, top, bottom]) + + x = self.conv(x) + return x + + +class MaxPool2dStaticSamePadding(nn.Module): + """ + created by Zylo117 + The real keras/tensorflow MaxPool2d with same padding + """ + + def __init__(self, *args, **kwargs): + super().__init__() + self.pool = nn.MaxPool2d(*args, **kwargs) + self.stride = self.pool.stride + self.kernel_size = self.pool.kernel_size + + if isinstance(self.stride, int): + self.stride = [self.stride] * 2 + elif len(self.stride) == 1: + self.stride = [self.stride[0]] * 2 + + if isinstance(self.kernel_size, int): + self.kernel_size = [self.kernel_size] * 2 + elif len(self.kernel_size) == 1: + self.kernel_size = [self.kernel_size[0]] * 2 + + def forward(self, x): + h, w = x.shape[-2:] + + extra_h = (math.ceil(w / self.stride[1]) - 1) * self.stride[1] - w + self.kernel_size[1] + extra_v = (math.ceil(h / self.stride[0]) - 1) * self.stride[0] - h + self.kernel_size[0] + + left = extra_h // 2 + right = extra_h - left + top = extra_v // 2 + bottom = extra_v - top + + x = F.pad(x, [left, right, top, bottom]) + + x = self.pool(x) + return x diff --git a/detection/models/detection/efficientdet/loss.py b/detection/models/detection/efficientdet/loss.py new file mode 100644 index 0000000..ac99b8c --- /dev/null +++ b/detection/models/detection/efficientdet/loss.py @@ -0,0 +1,148 @@ +import torch +import torch.nn as nn +import cv2 +import numpy as np + +from .utils import BBoxTransform, ClipBoxes +from .process import postprocess, invert_affine, display + + +def calc_iou(a, b): + # a(anchor) [boxes, (y1, x1, y2, x2)] + # b(gt, coco-style) [boxes, (x1, y1, x2, y2)] + + area = (b[:, 2] - b[:, 0]) * (b[:, 3] - b[:, 1]) + iw = torch.min(torch.unsqueeze(a[:, 3], dim=1), b[:, 2]) - torch.max(torch.unsqueeze(a[:, 1], 1), b[:, 0]) + ih = torch.min(torch.unsqueeze(a[:, 2], dim=1), b[:, 3]) - torch.max(torch.unsqueeze(a[:, 0], 1), b[:, 1]) + iw = torch.clamp(iw, min=0) + ih = torch.clamp(ih, min=0) + ua = torch.unsqueeze((a[:, 2] - a[:, 0]) * (a[:, 3] - a[:, 1]), dim=1) + area - iw * ih + ua = torch.clamp(ua, min=1e-8) + intersection = iw * ih + IoU = intersection / ua + + return IoU + + +class FocalLoss(nn.Module): + def __init__(self): + super(FocalLoss, self).__init__() + + def forward(self, classifications, regressions, anchors, annotations, **kwargs): + alpha = 0.25 + gamma = 2.0 + batch_size = classifications.shape[0] + device = classifications.device + annotations = annotations.to(device) + anchors = anchors.to(device) + classification_losses = [] + regression_losses = [] + + anchor = anchors[0, :, :] # assuming all image sizes are the same, which it is + dtype = anchors.dtype + + anchor_widths = anchor[:, 3] - anchor[:, 1] + anchor_heights = anchor[:, 2] - anchor[:, 0] + anchor_ctr_x = anchor[:, 1] + 0.5 * anchor_widths + anchor_ctr_y = anchor[:, 0] + 0.5 * anchor_heights + + for j in range(batch_size): + + classification = classifications[j, :, :] + regression = regressions[j, :, :] + + bbox_annotation = annotations[j] + bbox_annotation = bbox_annotation[bbox_annotation[:, 4] != -1] + + classification = torch.clamp(classification, 1e-4, 1.0 - 1e-4) + + if bbox_annotation.shape[0] == 0: + alpha_factor = torch.ones_like(classification) * alpha + alpha_factor = alpha_factor.to(device) + alpha_factor = 1. - alpha_factor + focal_weight = classification + focal_weight = alpha_factor * torch.pow(focal_weight, gamma) + + bce = -(torch.log(1.0 - classification)) + + cls_loss = focal_weight * bce + + regression_losses.append(torch.tensor(0).to(dtype).to(device)) + classification_losses.append(cls_loss.sum()) + + continue + + IoU = calc_iou(anchor[:, :], bbox_annotation[:, :4]) + + IoU_max, IoU_argmax = torch.max(IoU, dim=1) + + # compute the loss for classification + targets = torch.ones_like(classification) * -1 + targets = targets.to(device) + + targets[torch.lt(IoU_max, 0.4), :] = 0 + + positive_indices = torch.ge(IoU_max, 0.5) + + num_positive_anchors = positive_indices.sum() + + assigned_annotations = bbox_annotation[IoU_argmax, :] + + targets[positive_indices, :] = 0 + targets[positive_indices, assigned_annotations[positive_indices, 4].long()] = 1 + + alpha_factor = torch.ones_like(targets) * alpha + alpha_factor = alpha_factor.to(device) + + alpha_factor = torch.where(torch.eq(targets, 1.), alpha_factor, 1. - alpha_factor) + focal_weight = torch.where(torch.eq(targets, 1.), 1. - classification, classification) + focal_weight = alpha_factor * torch.pow(focal_weight, gamma) + + bce = -(targets * torch.log(classification) + (1.0 - targets) * torch.log(1.0 - classification)) + + cls_loss = focal_weight * bce + + zeros = torch.zeros_like(cls_loss) + zeros = zeros.to(device) + cls_loss = torch.where(torch.ne(targets, -1.0), cls_loss, zeros) + + classification_losses.append(cls_loss.sum() / torch.clamp(num_positive_anchors.to(dtype), min=1.0)) + + if positive_indices.sum() > 0: + assigned_annotations = assigned_annotations[positive_indices, :] + + anchor_widths_pi = anchor_widths[positive_indices] + anchor_heights_pi = anchor_heights[positive_indices] + anchor_ctr_x_pi = anchor_ctr_x[positive_indices] + anchor_ctr_y_pi = anchor_ctr_y[positive_indices] + + gt_widths = assigned_annotations[:, 2] - assigned_annotations[:, 0] + gt_heights = assigned_annotations[:, 3] - assigned_annotations[:, 1] + gt_ctr_x = assigned_annotations[:, 0] + 0.5 * gt_widths + gt_ctr_y = assigned_annotations[:, 1] + 0.5 * gt_heights + + # efficientdet style + gt_widths = torch.clamp(gt_widths, min=1) + gt_heights = torch.clamp(gt_heights, min=1) + + targets_dx = (gt_ctr_x - anchor_ctr_x_pi) / anchor_widths_pi + targets_dy = (gt_ctr_y - anchor_ctr_y_pi) / anchor_heights_pi + targets_dw = torch.log(gt_widths / anchor_widths_pi) + targets_dh = torch.log(gt_heights / anchor_heights_pi) + + targets = torch.stack((targets_dy, targets_dx, targets_dh, targets_dw)) + targets = targets.t() + + regression_diff = torch.abs(targets - regression[positive_indices, :]) + + regression_loss = torch.where( + torch.le(regression_diff, 1.0 / 9.0), + 0.5 * 9.0 * torch.pow(regression_diff, 2), + regression_diff - 0.5 / 9.0 + ) + regression_losses.append(regression_loss.mean()) + else: + regression_losses.append(torch.tensor(0).to(dtype).to(device)) + + return torch.stack(classification_losses).mean(dim=0, keepdim=True), \ + torch.stack(regression_losses).mean(dim=0, keepdim=True) * 50 # https://github.com/google/automl/blob/6fdd1de778408625c1faf368a327fe36ecd41bf7/efficientdet/hparams_config.py#L233 diff --git a/detection/models/detection/efficientdet/model.py b/detection/models/detection/efficientdet/model.py new file mode 100644 index 0000000..0be5f16 --- /dev/null +++ b/detection/models/detection/efficientdet/model.py @@ -0,0 +1,465 @@ +import torch.nn as nn +import torch +from torchvision.ops.boxes import nms as nms_torch + +from .efficientnet import EfficientNet as EffNet +from .efficientnet.utils import MemoryEfficientSwish, Swish +from .efficientnet.utils_extra import Conv2dStaticSamePadding, MaxPool2dStaticSamePadding + + +def nms(dets, thresh): + return nms_torch(dets[:, :4], dets[:, 4], thresh) + + +class SeparableConvBlock(nn.Module): + """ + created by Zylo117 + """ + + def __init__(self, in_channels, out_channels=None, norm=True, activation=False, onnx_export=False): + super(SeparableConvBlock, self).__init__() + if out_channels is None: + out_channels = in_channels + + # Q: whether separate conv + # share bias between depthwise_conv and pointwise_conv + # or just pointwise_conv apply bias. + # A: Confirmed, just pointwise_conv applies bias, depthwise_conv has no bias. + + self.depthwise_conv = Conv2dStaticSamePadding(in_channels, in_channels, + kernel_size=3, stride=1, groups=in_channels, bias=False) + self.pointwise_conv = Conv2dStaticSamePadding(in_channels, out_channels, kernel_size=1, stride=1) + + self.norm = norm + if self.norm: + # Warning: pytorch momentum is different from tensorflow's, momentum_pytorch = 1 - momentum_tensorflow + self.bn = nn.BatchNorm2d(num_features=out_channels, momentum=0.01, eps=1e-3) + + self.activation = activation + if self.activation: + self.swish = MemoryEfficientSwish() if not onnx_export else Swish() + + def forward(self, x): + x = self.depthwise_conv(x) + x = self.pointwise_conv(x) + + if self.norm: + x = self.bn(x) + + if self.activation: + x = self.swish(x) + + return x + + +class BiFPN(nn.Module): + """ + modified by Zylo117 + """ + + def __init__(self, num_channels, conv_channels, first_time=False, epsilon=1e-4, onnx_export=False, attention=True, + use_p8=False): + """ + + Args: + num_channels: + conv_channels: + first_time: whether the input comes directly from the efficientnet, + if True, downchannel it first, and downsample P5 to generate P6 then P7 + epsilon: epsilon of fast weighted attention sum of BiFPN, not the BN's epsilon + onnx_export: if True, use Swish instead of MemoryEfficientSwish + """ + super(BiFPN, self).__init__() + self.epsilon = epsilon + self.use_p8 = use_p8 + + # Conv layers + self.conv6_up = SeparableConvBlock(num_channels, onnx_export=onnx_export) + self.conv5_up = SeparableConvBlock(num_channels, onnx_export=onnx_export) + self.conv4_up = SeparableConvBlock(num_channels, onnx_export=onnx_export) + self.conv3_up = SeparableConvBlock(num_channels, onnx_export=onnx_export) + self.conv4_down = SeparableConvBlock(num_channels, onnx_export=onnx_export) + self.conv5_down = SeparableConvBlock(num_channels, onnx_export=onnx_export) + self.conv6_down = SeparableConvBlock(num_channels, onnx_export=onnx_export) + self.conv7_down = SeparableConvBlock(num_channels, onnx_export=onnx_export) + if use_p8: + self.conv7_up = SeparableConvBlock(num_channels, onnx_export=onnx_export) + self.conv8_down = SeparableConvBlock(num_channels, onnx_export=onnx_export) + + # Feature scaling layers + self.p6_upsample = nn.Upsample(scale_factor=2, mode='nearest') + self.p5_upsample = nn.Upsample(scale_factor=2, mode='nearest') + self.p4_upsample = nn.Upsample(scale_factor=2, mode='nearest') + self.p3_upsample = nn.Upsample(scale_factor=2, mode='nearest') + + self.p4_downsample = MaxPool2dStaticSamePadding(3, 2) + self.p5_downsample = MaxPool2dStaticSamePadding(3, 2) + self.p6_downsample = MaxPool2dStaticSamePadding(3, 2) + self.p7_downsample = MaxPool2dStaticSamePadding(3, 2) + if use_p8: + self.p7_upsample = nn.Upsample(scale_factor=2, mode='nearest') + self.p8_downsample = MaxPool2dStaticSamePadding(3, 2) + + self.swish = MemoryEfficientSwish() if not onnx_export else Swish() + + self.first_time = first_time + if self.first_time: + self.p5_down_channel = nn.Sequential( + Conv2dStaticSamePadding(conv_channels[2], num_channels, 1), + nn.BatchNorm2d(num_channels, momentum=0.01, eps=1e-3), + ) + self.p4_down_channel = nn.Sequential( + Conv2dStaticSamePadding(conv_channels[1], num_channels, 1), + nn.BatchNorm2d(num_channels, momentum=0.01, eps=1e-3), + ) + self.p3_down_channel = nn.Sequential( + Conv2dStaticSamePadding(conv_channels[0], num_channels, 1), + nn.BatchNorm2d(num_channels, momentum=0.01, eps=1e-3), + ) + + self.p5_to_p6 = nn.Sequential( + Conv2dStaticSamePadding(conv_channels[2], num_channels, 1), + nn.BatchNorm2d(num_channels, momentum=0.01, eps=1e-3), + MaxPool2dStaticSamePadding(3, 2) + ) + self.p6_to_p7 = nn.Sequential( + MaxPool2dStaticSamePadding(3, 2) + ) + if use_p8: + self.p7_to_p8 = nn.Sequential( + MaxPool2dStaticSamePadding(3, 2) + ) + + self.p4_down_channel_2 = nn.Sequential( + Conv2dStaticSamePadding(conv_channels[1], num_channels, 1), + nn.BatchNorm2d(num_channels, momentum=0.01, eps=1e-3), + ) + self.p5_down_channel_2 = nn.Sequential( + Conv2dStaticSamePadding(conv_channels[2], num_channels, 1), + nn.BatchNorm2d(num_channels, momentum=0.01, eps=1e-3), + ) + + # Weight + self.p6_w1 = nn.Parameter(torch.ones(2, dtype=torch.float32), requires_grad=True) + self.p6_w1_relu = nn.ReLU() + self.p5_w1 = nn.Parameter(torch.ones(2, dtype=torch.float32), requires_grad=True) + self.p5_w1_relu = nn.ReLU() + self.p4_w1 = nn.Parameter(torch.ones(2, dtype=torch.float32), requires_grad=True) + self.p4_w1_relu = nn.ReLU() + self.p3_w1 = nn.Parameter(torch.ones(2, dtype=torch.float32), requires_grad=True) + self.p3_w1_relu = nn.ReLU() + + self.p4_w2 = nn.Parameter(torch.ones(3, dtype=torch.float32), requires_grad=True) + self.p4_w2_relu = nn.ReLU() + self.p5_w2 = nn.Parameter(torch.ones(3, dtype=torch.float32), requires_grad=True) + self.p5_w2_relu = nn.ReLU() + self.p6_w2 = nn.Parameter(torch.ones(3, dtype=torch.float32), requires_grad=True) + self.p6_w2_relu = nn.ReLU() + self.p7_w2 = nn.Parameter(torch.ones(2, dtype=torch.float32), requires_grad=True) + self.p7_w2_relu = nn.ReLU() + + self.attention = attention + + def forward(self, inputs): + """ + illustration of a minimal bifpn unit + P7_0 -------------------------> P7_2 --------> + |-------------| ↑ + ↓ | + P6_0 ---------> P6_1 ---------> P6_2 --------> + |-------------|--------------↑ ↑ + ↓ | + P5_0 ---------> P5_1 ---------> P5_2 --------> + |-------------|--------------↑ ↑ + ↓ | + P4_0 ---------> P4_1 ---------> P4_2 --------> + |-------------|--------------↑ ↑ + |--------------↓ | + P3_0 -------------------------> P3_2 --------> + """ + + # downsample channels using same-padding conv2d to target phase's if not the same + # judge: same phase as target, + # if same, pass; + # elif earlier phase, downsample to target phase's by pooling + # elif later phase, upsample to target phase's by nearest interpolation + + if self.attention: + outs = self._forward_fast_attention(inputs) + else: + outs = self._forward(inputs) + + return outs + + def _forward_fast_attention(self, inputs): + if self.first_time: + p3, p4, p5 = inputs + + p6_in = self.p5_to_p6(p5) + p7_in = self.p6_to_p7(p6_in) + + p3_in = self.p3_down_channel(p3) + p4_in = self.p4_down_channel(p4) + p5_in = self.p5_down_channel(p5) + + else: + # P3_0, P4_0, P5_0, P6_0 and P7_0 + p3_in, p4_in, p5_in, p6_in, p7_in = inputs + + # P7_0 to P7_2 + + # Weights for P6_0 and P7_0 to P6_1 + p6_w1 = self.p6_w1_relu(self.p6_w1) + weight = p6_w1 / (torch.sum(p6_w1, dim=0) + self.epsilon) + # Connections for P6_0 and P7_0 to P6_1 respectively + p6_up = self.conv6_up(self.swish(weight[0] * p6_in + weight[1] * self.p6_upsample(p7_in))) + + # Weights for P5_0 and P6_1 to P5_1 + p5_w1 = self.p5_w1_relu(self.p5_w1) + weight = p5_w1 / (torch.sum(p5_w1, dim=0) + self.epsilon) + # Connections for P5_0 and P6_1 to P5_1 respectively + p5_up = self.conv5_up(self.swish(weight[0] * p5_in + weight[1] * self.p5_upsample(p6_up))) + + # Weights for P4_0 and P5_1 to P4_1 + p4_w1 = self.p4_w1_relu(self.p4_w1) + weight = p4_w1 / (torch.sum(p4_w1, dim=0) + self.epsilon) + # Connections for P4_0 and P5_1 to P4_1 respectively + p4_up = self.conv4_up(self.swish(weight[0] * p4_in + weight[1] * self.p4_upsample(p5_up))) + + # Weights for P3_0 and P4_1 to P3_2 + p3_w1 = self.p3_w1_relu(self.p3_w1) + weight = p3_w1 / (torch.sum(p3_w1, dim=0) + self.epsilon) + # Connections for P3_0 and P4_1 to P3_2 respectively + p3_out = self.conv3_up(self.swish(weight[0] * p3_in + weight[1] * self.p3_upsample(p4_up))) + + if self.first_time: + p4_in = self.p4_down_channel_2(p4) + p5_in = self.p5_down_channel_2(p5) + + # Weights for P4_0, P4_1 and P3_2 to P4_2 + p4_w2 = self.p4_w2_relu(self.p4_w2) + weight = p4_w2 / (torch.sum(p4_w2, dim=0) + self.epsilon) + # Connections for P4_0, P4_1 and P3_2 to P4_2 respectively + p4_out = self.conv4_down( + self.swish(weight[0] * p4_in + weight[1] * p4_up + weight[2] * self.p4_downsample(p3_out))) + + # Weights for P5_0, P5_1 and P4_2 to P5_2 + p5_w2 = self.p5_w2_relu(self.p5_w2) + weight = p5_w2 / (torch.sum(p5_w2, dim=0) + self.epsilon) + # Connections for P5_0, P5_1 and P4_2 to P5_2 respectively + p5_out = self.conv5_down( + self.swish(weight[0] * p5_in + weight[1] * p5_up + weight[2] * self.p5_downsample(p4_out))) + + # Weights for P6_0, P6_1 and P5_2 to P6_2 + p6_w2 = self.p6_w2_relu(self.p6_w2) + weight = p6_w2 / (torch.sum(p6_w2, dim=0) + self.epsilon) + # Connections for P6_0, P6_1 and P5_2 to P6_2 respectively + p6_out = self.conv6_down( + self.swish(weight[0] * p6_in + weight[1] * p6_up + weight[2] * self.p6_downsample(p5_out))) + + # Weights for P7_0 and P6_2 to P7_2 + p7_w2 = self.p7_w2_relu(self.p7_w2) + weight = p7_w2 / (torch.sum(p7_w2, dim=0) + self.epsilon) + # Connections for P7_0 and P6_2 to P7_2 + p7_out = self.conv7_down(self.swish(weight[0] * p7_in + weight[1] * self.p7_downsample(p6_out))) + + return p3_out, p4_out, p5_out, p6_out, p7_out + + def _forward(self, inputs): + if self.first_time: + p3, p4, p5 = inputs + + p6_in = self.p5_to_p6(p5) + p7_in = self.p6_to_p7(p6_in) + if self.use_p8: + p8_in = self.p7_to_p8(p7_in) + + p3_in = self.p3_down_channel(p3) + p4_in = self.p4_down_channel(p4) + p5_in = self.p5_down_channel(p5) + + else: + if self.use_p8: + # P3_0, P4_0, P5_0, P6_0, P7_0 and P8_0 + p3_in, p4_in, p5_in, p6_in, p7_in, p8_in = inputs + else: + # P3_0, P4_0, P5_0, P6_0 and P7_0 + p3_in, p4_in, p5_in, p6_in, p7_in = inputs + + if self.use_p8: + # P8_0 to P8_2 + + # Connections for P7_0 and P8_0 to P7_1 respectively + p7_up = self.conv7_up(self.swish(p7_in + self.p7_upsample(p8_in))) + + # Connections for P6_0 and P7_0 to P6_1 respectively + p6_up = self.conv6_up(self.swish(p6_in + self.p6_upsample(p7_up))) + else: + # P7_0 to P7_2 + + # Connections for P6_0 and P7_0 to P6_1 respectively + p6_up = self.conv6_up(self.swish(p6_in + self.p6_upsample(p7_in))) + + # Connections for P5_0 and P6_1 to P5_1 respectively + p5_up = self.conv5_up(self.swish(p5_in + self.p5_upsample(p6_up))) + + # Connections for P4_0 and P5_1 to P4_1 respectively + p4_up = self.conv4_up(self.swish(p4_in + self.p4_upsample(p5_up))) + + # Connections for P3_0 and P4_1 to P3_2 respectively + p3_out = self.conv3_up(self.swish(p3_in + self.p3_upsample(p4_up))) + + if self.first_time: + p4_in = self.p4_down_channel_2(p4) + p5_in = self.p5_down_channel_2(p5) + + # Connections for P4_0, P4_1 and P3_2 to P4_2 respectively + p4_out = self.conv4_down( + self.swish(p4_in + p4_up + self.p4_downsample(p3_out))) + + # Connections for P5_0, P5_1 and P4_2 to P5_2 respectively + p5_out = self.conv5_down( + self.swish(p5_in + p5_up + self.p5_downsample(p4_out))) + + # Connections for P6_0, P6_1 and P5_2 to P6_2 respectively + p6_out = self.conv6_down( + self.swish(p6_in + p6_up + self.p6_downsample(p5_out))) + + if self.use_p8: + # Connections for P7_0, P7_1 and P6_2 to P7_2 respectively + p7_out = self.conv7_down( + self.swish(p7_in + p7_up + self.p7_downsample(p6_out))) + + # Connections for P8_0 and P7_2 to P8_2 + p8_out = self.conv8_down(self.swish(p8_in + self.p8_downsample(p7_out))) + + return p3_out, p4_out, p5_out, p6_out, p7_out, p8_out + else: + # Connections for P7_0 and P6_2 to P7_2 + p7_out = self.conv7_down(self.swish(p7_in + self.p7_downsample(p6_out))) + + return p3_out, p4_out, p5_out, p6_out, p7_out + + +class Regressor(nn.Module): + """ + modified by Zylo117 + """ + + def __init__(self, in_channels, num_anchors, num_layers, pyramid_levels=5, onnx_export=False): + super(Regressor, self).__init__() + self.num_layers = num_layers + + self.conv_list = nn.ModuleList( + [SeparableConvBlock(in_channels, in_channels, norm=False, activation=False) for i in range(num_layers)]) + self.bn_list = nn.ModuleList( + [nn.ModuleList([nn.BatchNorm2d(in_channels, momentum=0.01, eps=1e-3) for i in range(num_layers)]) for j in + range(pyramid_levels)]) + self.header = SeparableConvBlock(in_channels, num_anchors * 4, norm=False, activation=False) + self.swish = MemoryEfficientSwish() if not onnx_export else Swish() + + def forward(self, inputs): + feats = [] + for feat, bn_list in zip(inputs, self.bn_list): + for i, bn, conv in zip(range(self.num_layers), bn_list, self.conv_list): + feat = conv(feat) + feat = bn(feat) + feat = self.swish(feat) + feat = self.header(feat) + + feat = feat.permute(0, 2, 3, 1) + feat = feat.contiguous().view(feat.shape[0], -1, 4) + + feats.append(feat) + + feats = torch.cat(feats, dim=1) + + return feats + + +class Classifier(nn.Module): + """ + modified by Zylo117 + """ + + def __init__(self, in_channels, num_anchors, num_classes, num_layers, pyramid_levels=5, onnx_export=False): + super(Classifier, self).__init__() + self.num_anchors = num_anchors + self.num_classes = num_classes + self.num_layers = num_layers + self.conv_list = nn.ModuleList( + [SeparableConvBlock(in_channels, in_channels, norm=False, activation=False) for i in range(num_layers)]) + self.bn_list = nn.ModuleList( + [nn.ModuleList([nn.BatchNorm2d(in_channels, momentum=0.01, eps=1e-3) for i in range(num_layers)]) for j in + range(pyramid_levels)]) + self.header = SeparableConvBlock(in_channels, num_anchors * num_classes, norm=False, activation=False) + self.swish = MemoryEfficientSwish() if not onnx_export else Swish() + + def forward(self, inputs): + feats = [] + for feat, bn_list in zip(inputs, self.bn_list): + for i, bn, conv in zip(range(self.num_layers), bn_list, self.conv_list): + feat = conv(feat) + feat = bn(feat) + feat = self.swish(feat) + feat = self.header(feat) + + feat = feat.permute(0, 2, 3, 1) + feat = feat.contiguous().view(feat.shape[0], feat.shape[1], feat.shape[2], self.num_anchors, + self.num_classes) + feat = feat.contiguous().view(feat.shape[0], -1, self.num_classes) + + feats.append(feat) + + feats = torch.cat(feats, dim=1) + feats = feats.sigmoid() + + return feats + + +class EfficientNet(nn.Module): + """ + modified by Zylo117 + """ + + def __init__(self, compound_coef, load_weights=False): + super(EfficientNet, self).__init__() + model = EffNet.from_pretrained(f'efficientnet-b{compound_coef}', load_weights) + del model._conv_head + del model._bn1 + del model._avg_pooling + del model._dropout + del model._fc + self.model = model + + def forward(self, x): + x = self.model._conv_stem(x) + x = self.model._bn0(x) + x = self.model._swish(x) + feature_maps = [] + + # TODO: temporarily storing extra tensor last_x and del it later might not be a good idea, + # try recording stride changing when creating efficientnet, + # and then apply it here. + last_x = None + for idx, block in enumerate(self.model._blocks): + drop_connect_rate = self.model._global_params.drop_connect_rate + if drop_connect_rate: + drop_connect_rate *= float(idx) / len(self.model._blocks) + x = block(x, drop_connect_rate=drop_connect_rate) + + if block._depthwise_conv.stride == [2, 2]: + feature_maps.append(last_x) + elif idx == len(self.model._blocks) - 1: + feature_maps.append(x) + last_x = x + del last_x + return feature_maps[1:] + + +if __name__ == '__main__': + from tensorboardX import SummaryWriter + + + def count_parameters(model): + return sum(p.numel() for p in model.parameters() if p.requires_grad) diff --git a/detection/models/detection/efficientdet/process.py b/detection/models/detection/efficientdet/process.py new file mode 100644 index 0000000..be5869d --- /dev/null +++ b/detection/models/detection/efficientdet/process.py @@ -0,0 +1,314 @@ +# Author: Zylo117 + +import math +import os +import uuid +from glob import glob +from typing import Union + +import cv2 +import numpy as np +import torch +#import webcolors +from torch import nn +from torch.nn.init import _calculate_fan_in_and_fan_out, _no_grad_normal_ +from torchvision.ops.boxes import batched_nms + +from .sync_batchnorm import SynchronizedBatchNorm2d + + +def invert_affine(metas: Union[float, list, tuple], preds): + for i in range(len(preds)): + if len(preds[i]['rois']) == 0: + continue + else: + if metas is float: + preds[i]['rois'][:, [0, 2]] = preds[i]['rois'][:, [0, 2]] / metas + preds[i]['rois'][:, [1, 3]] = preds[i]['rois'][:, [1, 3]] / metas + else: + new_w, new_h, old_w, old_h, padding_w, padding_h = metas[i] + preds[i]['rois'][:, [0, 2]] = preds[i]['rois'][:, [0, 2]] / (new_w / old_w) + preds[i]['rois'][:, [1, 3]] = preds[i]['rois'][:, [1, 3]] / (new_h / old_h) + return preds + + +def aspectaware_resize_padding(image, width, height, interpolation=None, means=None): + old_h, old_w, c = image.shape + if old_w > old_h: + new_w = width + new_h = int(width / old_w * old_h) + else: + new_w = int(height / old_h * old_w) + new_h = height + + canvas = np.zeros((height, height, c), np.float32) + if means is not None: + canvas[...] = means + + if new_w != old_w or new_h != old_h: + if interpolation is None: + image = cv2.resize(image, (new_w, new_h)) + else: + image = cv2.resize(image, (new_w, new_h), interpolation=interpolation) + + padding_h = height - new_h + padding_w = width - new_w + + if c > 1: + canvas[:new_h, :new_w] = image + else: + if len(image.shape) == 2: + canvas[:new_h, :new_w, 0] = image + else: + canvas[:new_h, :new_w] = image + + return canvas, new_w, new_h, old_w, old_h, padding_w, padding_h, + + +def preprocess(*image_path, max_size=512, mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)): + ori_imgs = [cv2.imread(img_path) for img_path in image_path] + normalized_imgs = [(img[..., ::-1] / 255 - mean) / std for img in ori_imgs] + imgs_meta = [aspectaware_resize_padding(img, max_size, max_size, + means=None) for img in normalized_imgs] + framed_imgs = [img_meta[0] for img_meta in imgs_meta] + framed_metas = [img_meta[1:] for img_meta in imgs_meta] + + return ori_imgs, framed_imgs, framed_metas + + +def preprocess_video(*frame_from_video, max_size=512, mean=(0.406, 0.456, 0.485), std=(0.225, 0.224, 0.229)): + ori_imgs = frame_from_video + normalized_imgs = [(img[..., ::-1] / 255 - mean) / std for img in ori_imgs] + imgs_meta = [aspectaware_resize_padding(img, max_size, max_size, + means=None) for img in normalized_imgs] + framed_imgs = [img_meta[0] for img_meta in imgs_meta] + framed_metas = [img_meta[1:] for img_meta in imgs_meta] + + return ori_imgs, framed_imgs, framed_metas + + +def postprocess(x, anchors, regression, classification, regressBoxes, clipBoxes, threshold=0.2, iou_threshold=0.2): + transformed_anchors = regressBoxes(anchors, regression) + transformed_anchors = clipBoxes(transformed_anchors, x) + scores = torch.max(classification, dim=2, keepdim=True)[0] + scores_over_thresh = (scores > threshold)[:, :, 0] + out = [] + for i in range(x.shape[0]): + if scores_over_thresh[i].sum() == 0: + out.append({ + 'rois': np.array(()), + 'class_ids': np.array(()), + 'scores': np.array(()), + }) + continue + + classification_per = classification[i, scores_over_thresh[i, :], ...].permute(1, 0) + transformed_anchors_per = transformed_anchors[i, scores_over_thresh[i, :], ...] + scores_per = scores[i, scores_over_thresh[i, :], ...] + scores_, classes_ = classification_per.max(dim=0) + anchors_nms_idx = batched_nms(transformed_anchors_per, scores_per[:, 0], classes_, iou_threshold=iou_threshold) + + if anchors_nms_idx.shape[0] != 0: + classes_ = classes_[anchors_nms_idx] + scores_ = scores_[anchors_nms_idx] + boxes_ = transformed_anchors_per[anchors_nms_idx, :] + + out.append({ + 'rois': boxes_.cpu().numpy(), + 'class_ids': classes_.cpu().numpy(), + 'scores': scores_.cpu().numpy(), + }) + else: + out.append({ + 'rois': np.array(()), + 'class_ids': np.array(()), + 'scores': np.array(()), + }) + + return out + + +def display(preds, imgs, obj_list, imshow=True, imwrite=False): + for i in range(len(imgs)): + if len(preds[i]['rois']) == 0: + continue + + imgs[i] = imgs[i].copy() + + for j in range(len(preds[i]['rois'])): + (x1, y1, x2, y2) = preds[i]['rois'][j].astype(np.int) + obj = obj_list[preds[i]['class_ids'][j]] + score = float(preds[i]['scores'][j]) + + plot_one_box(imgs[i], [x1, y1, x2, y2], label=obj, score=score, + color=color_list[get_index_label(obj, obj_list)]) + if imshow: + cv2.imshow('img', imgs[i]) + cv2.waitKey(0) + + if imwrite: + os.makedirs('test/', exist_ok=True) + cv2.imwrite(f'test/{uuid.uuid4().hex}.jpg', imgs[i]) + + +def replace_w_sync_bn(m): + for var_name in dir(m): + target_attr = getattr(m, var_name) + if type(target_attr) == torch.nn.BatchNorm2d: + num_features = target_attr.num_features + eps = target_attr.eps + momentum = target_attr.momentum + affine = target_attr.affine + + # get parameters + running_mean = target_attr.running_mean + running_var = target_attr.running_var + if affine: + weight = target_attr.weight + bias = target_attr.bias + + setattr(m, var_name, + SynchronizedBatchNorm2d(num_features, eps, momentum, affine)) + + target_attr = getattr(m, var_name) + # set parameters + target_attr.running_mean = running_mean + target_attr.running_var = running_var + if affine: + target_attr.weight = weight + target_attr.bias = bias + + for var_name, children in m.named_children(): + replace_w_sync_bn(children) + + +class CustomDataParallel(nn.DataParallel): + """ + force splitting data to all gpus instead of sending all data to cuda:0 and then moving around. + """ + + def __init__(self, module, num_gpus): + super().__init__(module) + self.num_gpus = num_gpus + + def scatter(self, inputs, kwargs, device_ids): + # More like scatter and data prep at the same time. The point is we prep the data in such a way + # that no scatter is necessary, and there's no need to shuffle stuff around different GPUs. + devices = ['cuda:' + str(x) for x in range(self.num_gpus)] + splits = inputs[0].shape[0] // self.num_gpus + + if splits == 0: + raise Exception('Batchsize must be greater than num_gpus.') + + return [(inputs[0][splits * device_idx: splits * (device_idx + 1)].to(f'cuda:{device_idx}', non_blocking=True), + inputs[1][splits * device_idx: splits * (device_idx + 1)].to(f'cuda:{device_idx}', non_blocking=True)) + for device_idx in range(len(devices))], \ + [kwargs] * len(devices) + + +def get_last_weights(weights_path): + weights_path = glob(weights_path + f'/*.pth') + weights_path = sorted(weights_path, + key=lambda x: int(x.rsplit('_')[-1].rsplit('.')[0]), + reverse=True)[0] + print(f'using weights {weights_path}') + return weights_path + + +def init_weights(model): + for name, module in model.named_modules(): + is_conv_layer = isinstance(module, nn.Conv2d) + + if is_conv_layer: + if "conv_list" or "header" in name: + variance_scaling_(module.weight.data) + else: + nn.init.kaiming_uniform_(module.weight.data) + + if module.bias is not None: + if "classifier.header" in name: + bias_value = -np.log((1 - 0.01) / 0.01) + torch.nn.init.constant_(module.bias, bias_value) + else: + module.bias.data.zero_() + + +def variance_scaling_(tensor, gain=1.): + # type: (Tensor, float) -> Tensor + r""" + initializer for SeparableConv in Regressor/Classifier + reference: https://keras.io/zh/initializers/ VarianceScaling + """ + fan_in, fan_out = _calculate_fan_in_and_fan_out(tensor) + std = math.sqrt(gain / float(fan_in)) + + return _no_grad_normal_(tensor, 0., std) + + +STANDARD_COLORS = [ + 'LawnGreen', 'Chartreuse', 'Aqua', 'Beige', 'Azure', 'BlanchedAlmond', 'Bisque', + 'Aquamarine', 'BlueViolet', 'BurlyWood', 'CadetBlue', 'AntiqueWhite', + 'Chocolate', 'Coral', 'CornflowerBlue', 'Cornsilk', 'Crimson', 'Cyan', + 'DarkCyan', 'DarkGoldenRod', 'DarkGrey', 'DarkKhaki', 'DarkOrange', + 'DarkOrchid', 'DarkSalmon', 'DarkSeaGreen', 'DarkTurquoise', 'DarkViolet', + 'DeepPink', 'DeepSkyBlue', 'DodgerBlue', 'FireBrick', 'FloralWhite', + 'ForestGreen', 'Fuchsia', 'Gainsboro', 'GhostWhite', 'Gold', 'GoldenRod', + 'Salmon', 'Tan', 'HoneyDew', 'HotPink', 'IndianRed', 'Ivory', 'Khaki', + 'Lavender', 'LavenderBlush', 'AliceBlue', 'LemonChiffon', 'LightBlue', + 'LightCoral', 'LightCyan', 'LightGoldenRodYellow', 'LightGray', 'LightGrey', + 'LightGreen', 'LightPink', 'LightSalmon', 'LightSeaGreen', 'LightSkyBlue', + 'LightSlateGray', 'LightSlateGrey', 'LightSteelBlue', 'LightYellow', 'Lime', + 'LimeGreen', 'Linen', 'Magenta', 'MediumAquaMarine', 'MediumOrchid', + 'MediumPurple', 'MediumSeaGreen', 'MediumSlateBlue', 'MediumSpringGreen', + 'MediumTurquoise', 'MediumVioletRed', 'MintCream', 'MistyRose', 'Moccasin', + 'NavajoWhite', 'OldLace', 'Olive', 'OliveDrab', 'Orange', 'OrangeRed', + 'Orchid', 'PaleGoldenRod', 'PaleGreen', 'PaleTurquoise', 'PaleVioletRed', + 'PapayaWhip', 'PeachPuff', 'Peru', 'Pink', 'Plum', 'PowderBlue', 'Purple', + 'Red', 'RosyBrown', 'RoyalBlue', 'SaddleBrown', 'Green', 'SandyBrown', + 'SeaGreen', 'SeaShell', 'Sienna', 'Silver', 'SkyBlue', 'SlateBlue', + 'SlateGray', 'SlateGrey', 'Snow', 'SpringGreen', 'SteelBlue', 'GreenYellow', + 'Teal', 'Thistle', 'Tomato', 'Turquoise', 'Violet', 'Wheat', 'White', + 'WhiteSmoke', 'Yellow', 'YellowGreen' +] + + +def from_colorname_to_bgr(color): + rgb_color = webcolors.name_to_rgb(color) + result = (rgb_color.blue, rgb_color.green, rgb_color.red) + return result + + +def standard_to_bgr(list_color_name): + standard = [] + for i in range(len(list_color_name) - 36): # -36 used to match the len(obj_list) + standard.append(from_colorname_to_bgr(list_color_name[i])) + return standard + + +def get_index_label(label, obj_list): + index = int(obj_list.index(label)) + return index + + +def plot_one_box(img, coord, label=None, score=None, color=None, line_thickness=None): + tl = line_thickness or int(round(0.001 * max(img.shape[0:2]))) # line thickness + color = color + c1, c2 = (int(coord[0]), int(coord[1])), (int(coord[2]), int(coord[3])) + cv2.rectangle(img, c1, c2, color, thickness=tl) + if label: + tf = max(tl - 2, 1) # font thickness + s_size = cv2.getTextSize(str('{:.0%}'.format(score)), 0, fontScale=float(tl) / 3, thickness=tf)[0] + t_size = cv2.getTextSize(label, 0, fontScale=float(tl) / 3, thickness=tf)[0] + c2 = c1[0] + t_size[0] + s_size[0] + 15, c1[1] - t_size[1] - 3 + cv2.rectangle(img, c1, c2, color, -1) # filled + cv2.putText(img, '{}: {:.0%}'.format(label, score), (c1[0], c1[1] - 2), 0, float(tl) / 3, [0, 0, 0], + thickness=tf, lineType=cv2.FONT_HERSHEY_SIMPLEX) + + +#color_list = standard_to_bgr(STANDARD_COLORS) + + +def boolean_string(s): + if s not in {'False', 'True'}: + raise ValueError('Not a valid boolean string') + return s == 'True' diff --git a/detection/models/detection/efficientdet/sync_batchnorm/__init__.py b/detection/models/detection/efficientdet/sync_batchnorm/__init__.py new file mode 100644 index 0000000..a10989f --- /dev/null +++ b/detection/models/detection/efficientdet/sync_batchnorm/__init__.py @@ -0,0 +1,13 @@ +# -*- coding: utf-8 -*- +# File : __init__.py +# Author : Jiayuan Mao +# Email : maojiayuan@gmail.com +# Date : 27/01/2018 +# +# This file is part of Synchronized-BatchNorm-PyTorch. +# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch +# Distributed under MIT License. + +from .batchnorm import SynchronizedBatchNorm1d, SynchronizedBatchNorm2d, SynchronizedBatchNorm3d +from .batchnorm import patch_sync_batchnorm, convert_model +from .replicate import DataParallelWithCallback, patch_replication_callback diff --git a/detection/models/detection/efficientdet/sync_batchnorm/batchnorm.py b/detection/models/detection/efficientdet/sync_batchnorm/batchnorm.py new file mode 100644 index 0000000..e1bf74f --- /dev/null +++ b/detection/models/detection/efficientdet/sync_batchnorm/batchnorm.py @@ -0,0 +1,394 @@ +# -*- coding: utf-8 -*- +# File : batchnorm.py +# Author : Jiayuan Mao +# Email : maojiayuan@gmail.com +# Date : 27/01/2018 +# +# This file is part of Synchronized-BatchNorm-PyTorch. +# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch +# Distributed under MIT License. + +import collections +import contextlib + +import torch +import torch.nn.functional as F + +from torch.nn.modules.batchnorm import _BatchNorm + +try: + from torch.nn.parallel._functions import ReduceAddCoalesced, Broadcast +except ImportError: + ReduceAddCoalesced = Broadcast = None + +try: + from jactorch.parallel.comm import SyncMaster + from jactorch.parallel.data_parallel import JacDataParallel as DataParallelWithCallback +except ImportError: + from .comm import SyncMaster + from .replicate import DataParallelWithCallback + +__all__ = [ + 'SynchronizedBatchNorm1d', 'SynchronizedBatchNorm2d', 'SynchronizedBatchNorm3d', + 'patch_sync_batchnorm', 'convert_model' +] + + +def _sum_ft(tensor): + """sum over the first and last dimention""" + return tensor.sum(dim=0).sum(dim=-1) + + +def _unsqueeze_ft(tensor): + """add new dimensions at the front and the tail""" + return tensor.unsqueeze(0).unsqueeze(-1) + + +_ChildMessage = collections.namedtuple('_ChildMessage', ['sum', 'ssum', 'sum_size']) +_MasterMessage = collections.namedtuple('_MasterMessage', ['sum', 'inv_std']) + + +class _SynchronizedBatchNorm(_BatchNorm): + def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True): + assert ReduceAddCoalesced is not None, 'Can not use Synchronized Batch Normalization without CUDA support.' + + super(_SynchronizedBatchNorm, self).__init__(num_features, eps=eps, momentum=momentum, affine=affine) + + self._sync_master = SyncMaster(self._data_parallel_master) + + self._is_parallel = False + self._parallel_id = None + self._slave_pipe = None + + def forward(self, input): + # If it is not parallel computation or is in evaluation mode, use PyTorch's implementation. + if not (self._is_parallel and self.training): + return F.batch_norm( + input, self.running_mean, self.running_var, self.weight, self.bias, + self.training, self.momentum, self.eps) + + # Resize the input to (B, C, -1). + input_shape = input.size() + input = input.view(input.size(0), self.num_features, -1) + + # Compute the sum and square-sum. + sum_size = input.size(0) * input.size(2) + input_sum = _sum_ft(input) + input_ssum = _sum_ft(input ** 2) + + # Reduce-and-broadcast the statistics. + if self._parallel_id == 0: + mean, inv_std = self._sync_master.run_master(_ChildMessage(input_sum, input_ssum, sum_size)) + else: + mean, inv_std = self._slave_pipe.run_slave(_ChildMessage(input_sum, input_ssum, sum_size)) + + # Compute the output. + if self.affine: + # MJY:: Fuse the multiplication for speed. + output = (input - _unsqueeze_ft(mean)) * _unsqueeze_ft(inv_std * self.weight) + _unsqueeze_ft(self.bias) + else: + output = (input - _unsqueeze_ft(mean)) * _unsqueeze_ft(inv_std) + + # Reshape it. + return output.view(input_shape) + + def __data_parallel_replicate__(self, ctx, copy_id): + self._is_parallel = True + self._parallel_id = copy_id + + # parallel_id == 0 means master device. + if self._parallel_id == 0: + ctx.sync_master = self._sync_master + else: + self._slave_pipe = ctx.sync_master.register_slave(copy_id) + + def _data_parallel_master(self, intermediates): + """Reduce the sum and square-sum, compute the statistics, and broadcast it.""" + + # Always using same "device order" makes the ReduceAdd operation faster. + # Thanks to:: Tete Xiao (http://tetexiao.com/) + intermediates = sorted(intermediates, key=lambda i: i[1].sum.get_device()) + + to_reduce = [i[1][:2] for i in intermediates] + to_reduce = [j for i in to_reduce for j in i] # flatten + target_gpus = [i[1].sum.get_device() for i in intermediates] + + sum_size = sum([i[1].sum_size for i in intermediates]) + sum_, ssum = ReduceAddCoalesced.apply(target_gpus[0], 2, *to_reduce) + mean, inv_std = self._compute_mean_std(sum_, ssum, sum_size) + + broadcasted = Broadcast.apply(target_gpus, mean, inv_std) + + outputs = [] + for i, rec in enumerate(intermediates): + outputs.append((rec[0], _MasterMessage(*broadcasted[i*2:i*2+2]))) + + return outputs + + def _compute_mean_std(self, sum_, ssum, size): + """Compute the mean and standard-deviation with sum and square-sum. This method + also maintains the moving average on the master device.""" + assert size > 1, 'BatchNorm computes unbiased standard-deviation, which requires size > 1.' + mean = sum_ / size + sumvar = ssum - sum_ * mean + unbias_var = sumvar / (size - 1) + bias_var = sumvar / size + + if hasattr(torch, 'no_grad'): + with torch.no_grad(): + self.running_mean = (1 - self.momentum) * self.running_mean + self.momentum * mean.data + self.running_var = (1 - self.momentum) * self.running_var + self.momentum * unbias_var.data + else: + self.running_mean = (1 - self.momentum) * self.running_mean + self.momentum * mean.data + self.running_var = (1 - self.momentum) * self.running_var + self.momentum * unbias_var.data + + return mean, bias_var.clamp(self.eps) ** -0.5 + + +class SynchronizedBatchNorm1d(_SynchronizedBatchNorm): + r"""Applies Synchronized Batch Normalization over a 2d or 3d input that is seen as a + mini-batch. + + .. math:: + + y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta + + This module differs from the built-in PyTorch BatchNorm1d as the mean and + standard-deviation are reduced across all devices during training. + + For example, when one uses `nn.DataParallel` to wrap the network during + training, PyTorch's implementation normalize the tensor on each device using + the statistics only on that device, which accelerated the computation and + is also easy to implement, but the statistics might be inaccurate. + Instead, in this synchronized version, the statistics will be computed + over all training samples distributed on multiple devices. + + Note that, for one-GPU or CPU-only case, this module behaves exactly same + as the built-in PyTorch implementation. + + The mean and standard-deviation are calculated per-dimension over + the mini-batches and gamma and beta are learnable parameter vectors + of size C (where C is the input size). + + During training, this layer keeps a running estimate of its computed mean + and variance. The running sum is kept with a default momentum of 0.1. + + During evaluation, this running mean/variance is used for normalization. + + Because the BatchNorm is done over the `C` dimension, computing statistics + on `(N, L)` slices, it's common terminology to call this Temporal BatchNorm + + Args: + num_features: num_features from an expected input of size + `batch_size x num_features [x width]` + eps: a value added to the denominator for numerical stability. + Default: 1e-5 + momentum: the value used for the running_mean and running_var + computation. Default: 0.1 + affine: a boolean value that when set to ``True``, gives the layer learnable + affine parameters. Default: ``True`` + + Shape:: + - Input: :math:`(N, C)` or :math:`(N, C, L)` + - Output: :math:`(N, C)` or :math:`(N, C, L)` (same shape as input) + + Examples: + >>> # With Learnable Parameters + >>> m = SynchronizedBatchNorm1d(100) + >>> # Without Learnable Parameters + >>> m = SynchronizedBatchNorm1d(100, affine=False) + >>> input = torch.autograd.Variable(torch.randn(20, 100)) + >>> output = m(input) + """ + + def _check_input_dim(self, input): + if input.dim() != 2 and input.dim() != 3: + raise ValueError('expected 2D or 3D input (got {}D input)' + .format(input.dim())) + super(SynchronizedBatchNorm1d, self)._check_input_dim(input) + + +class SynchronizedBatchNorm2d(_SynchronizedBatchNorm): + r"""Applies Batch Normalization over a 4d input that is seen as a mini-batch + of 3d inputs + + .. math:: + + y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta + + This module differs from the built-in PyTorch BatchNorm2d as the mean and + standard-deviation are reduced across all devices during training. + + For example, when one uses `nn.DataParallel` to wrap the network during + training, PyTorch's implementation normalize the tensor on each device using + the statistics only on that device, which accelerated the computation and + is also easy to implement, but the statistics might be inaccurate. + Instead, in this synchronized version, the statistics will be computed + over all training samples distributed on multiple devices. + + Note that, for one-GPU or CPU-only case, this module behaves exactly same + as the built-in PyTorch implementation. + + The mean and standard-deviation are calculated per-dimension over + the mini-batches and gamma and beta are learnable parameter vectors + of size C (where C is the input size). + + During training, this layer keeps a running estimate of its computed mean + and variance. The running sum is kept with a default momentum of 0.1. + + During evaluation, this running mean/variance is used for normalization. + + Because the BatchNorm is done over the `C` dimension, computing statistics + on `(N, H, W)` slices, it's common terminology to call this Spatial BatchNorm + + Args: + num_features: num_features from an expected input of + size batch_size x num_features x height x width + eps: a value added to the denominator for numerical stability. + Default: 1e-5 + momentum: the value used for the running_mean and running_var + computation. Default: 0.1 + affine: a boolean value that when set to ``True``, gives the layer learnable + affine parameters. Default: ``True`` + + Shape:: + - Input: :math:`(N, C, H, W)` + - Output: :math:`(N, C, H, W)` (same shape as input) + + Examples: + >>> # With Learnable Parameters + >>> m = SynchronizedBatchNorm2d(100) + >>> # Without Learnable Parameters + >>> m = SynchronizedBatchNorm2d(100, affine=False) + >>> input = torch.autograd.Variable(torch.randn(20, 100, 35, 45)) + >>> output = m(input) + """ + + def _check_input_dim(self, input): + if input.dim() != 4: + raise ValueError('expected 4D input (got {}D input)' + .format(input.dim())) + super(SynchronizedBatchNorm2d, self)._check_input_dim(input) + + +class SynchronizedBatchNorm3d(_SynchronizedBatchNorm): + r"""Applies Batch Normalization over a 5d input that is seen as a mini-batch + of 4d inputs + + .. math:: + + y = \frac{x - mean[x]}{ \sqrt{Var[x] + \epsilon}} * gamma + beta + + This module differs from the built-in PyTorch BatchNorm3d as the mean and + standard-deviation are reduced across all devices during training. + + For example, when one uses `nn.DataParallel` to wrap the network during + training, PyTorch's implementation normalize the tensor on each device using + the statistics only on that device, which accelerated the computation and + is also easy to implement, but the statistics might be inaccurate. + Instead, in this synchronized version, the statistics will be computed + over all training samples distributed on multiple devices. + + Note that, for one-GPU or CPU-only case, this module behaves exactly same + as the built-in PyTorch implementation. + + The mean and standard-deviation are calculated per-dimension over + the mini-batches and gamma and beta are learnable parameter vectors + of size C (where C is the input size). + + During training, this layer keeps a running estimate of its computed mean + and variance. The running sum is kept with a default momentum of 0.1. + + During evaluation, this running mean/variance is used for normalization. + + Because the BatchNorm is done over the `C` dimension, computing statistics + on `(N, D, H, W)` slices, it's common terminology to call this Volumetric BatchNorm + or Spatio-temporal BatchNorm + + Args: + num_features: num_features from an expected input of + size batch_size x num_features x depth x height x width + eps: a value added to the denominator for numerical stability. + Default: 1e-5 + momentum: the value used for the running_mean and running_var + computation. Default: 0.1 + affine: a boolean value that when set to ``True``, gives the layer learnable + affine parameters. Default: ``True`` + + Shape:: + - Input: :math:`(N, C, D, H, W)` + - Output: :math:`(N, C, D, H, W)` (same shape as input) + + Examples: + >>> # With Learnable Parameters + >>> m = SynchronizedBatchNorm3d(100) + >>> # Without Learnable Parameters + >>> m = SynchronizedBatchNorm3d(100, affine=False) + >>> input = torch.autograd.Variable(torch.randn(20, 100, 35, 45, 10)) + >>> output = m(input) + """ + + def _check_input_dim(self, input): + if input.dim() != 5: + raise ValueError('expected 5D input (got {}D input)' + .format(input.dim())) + super(SynchronizedBatchNorm3d, self)._check_input_dim(input) + + +@contextlib.contextmanager +def patch_sync_batchnorm(): + import torch.nn as nn + + backup = nn.BatchNorm1d, nn.BatchNorm2d, nn.BatchNorm3d + + nn.BatchNorm1d = SynchronizedBatchNorm1d + nn.BatchNorm2d = SynchronizedBatchNorm2d + nn.BatchNorm3d = SynchronizedBatchNorm3d + + yield + + nn.BatchNorm1d, nn.BatchNorm2d, nn.BatchNorm3d = backup + + +def convert_model(module): + """Traverse the input module and its child recursively + and replace all instance of torch.nn.modules.batchnorm.BatchNorm*N*d + to SynchronizedBatchNorm*N*d + + Args: + module: the input module needs to be convert to SyncBN model + + Examples: + >>> import torch.nn as nn + >>> import torchvision + >>> # m is a standard pytorch model + >>> m = torchvision.models.resnet18(True) + >>> m = nn.DataParallel(m) + >>> # after convert, m is using SyncBN + >>> m = convert_model(m) + """ + if isinstance(module, torch.nn.DataParallel): + mod = module.module + mod = convert_model(mod) + mod = DataParallelWithCallback(mod, device_ids=module.device_ids) + return mod + + mod = module + for pth_module, sync_module in zip([torch.nn.modules.batchnorm.BatchNorm1d, + torch.nn.modules.batchnorm.BatchNorm2d, + torch.nn.modules.batchnorm.BatchNorm3d], + [SynchronizedBatchNorm1d, + SynchronizedBatchNorm2d, + SynchronizedBatchNorm3d]): + if isinstance(module, pth_module): + mod = sync_module(module.num_features, module.eps, module.momentum, module.affine) + mod.running_mean = module.running_mean + mod.running_var = module.running_var + if module.affine: + mod.weight.data = module.weight.data.clone().detach() + mod.bias.data = module.bias.data.clone().detach() + + for name, child in module.named_children(): + mod.add_module(name, convert_model(child)) + + return mod diff --git a/detection/models/detection/efficientdet/sync_batchnorm/batchnorm_reimpl.py b/detection/models/detection/efficientdet/sync_batchnorm/batchnorm_reimpl.py new file mode 100644 index 0000000..18145c3 --- /dev/null +++ b/detection/models/detection/efficientdet/sync_batchnorm/batchnorm_reimpl.py @@ -0,0 +1,74 @@ +#! /usr/bin/env python3 +# -*- coding: utf-8 -*- +# File : batchnorm_reimpl.py +# Author : acgtyrant +# Date : 11/01/2018 +# +# This file is part of Synchronized-BatchNorm-PyTorch. +# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch +# Distributed under MIT License. + +import torch +import torch.nn as nn +import torch.nn.init as init + +__all__ = ['BatchNorm2dReimpl'] + + +class BatchNorm2dReimpl(nn.Module): + """ + A re-implementation of batch normalization, used for testing the numerical + stability. + + Author: acgtyrant + See also: + https://github.com/vacancy/Synchronized-BatchNorm-PyTorch/issues/14 + """ + def __init__(self, num_features, eps=1e-5, momentum=0.1): + super().__init__() + + self.num_features = num_features + self.eps = eps + self.momentum = momentum + self.weight = nn.Parameter(torch.empty(num_features)) + self.bias = nn.Parameter(torch.empty(num_features)) + self.register_buffer('running_mean', torch.zeros(num_features)) + self.register_buffer('running_var', torch.ones(num_features)) + self.reset_parameters() + + def reset_running_stats(self): + self.running_mean.zero_() + self.running_var.fill_(1) + + def reset_parameters(self): + self.reset_running_stats() + init.uniform_(self.weight) + init.zeros_(self.bias) + + def forward(self, input_): + batchsize, channels, height, width = input_.size() + numel = batchsize * height * width + input_ = input_.permute(1, 0, 2, 3).contiguous().view(channels, numel) + sum_ = input_.sum(1) + sum_of_square = input_.pow(2).sum(1) + mean = sum_ / numel + sumvar = sum_of_square - sum_ * mean + + self.running_mean = ( + (1 - self.momentum) * self.running_mean + + self.momentum * mean.detach() + ) + unbias_var = sumvar / (numel - 1) + self.running_var = ( + (1 - self.momentum) * self.running_var + + self.momentum * unbias_var.detach() + ) + + bias_var = sumvar / numel + inv_std = 1 / (bias_var + self.eps).pow(0.5) + output = ( + (input_ - mean.unsqueeze(1)) * inv_std.unsqueeze(1) * + self.weight.unsqueeze(1) + self.bias.unsqueeze(1)) + + return output.view(channels, batchsize, height, width).permute(1, 0, 2, 3).contiguous() + diff --git a/detection/models/detection/efficientdet/sync_batchnorm/comm.py b/detection/models/detection/efficientdet/sync_batchnorm/comm.py new file mode 100644 index 0000000..922f8c4 --- /dev/null +++ b/detection/models/detection/efficientdet/sync_batchnorm/comm.py @@ -0,0 +1,137 @@ +# -*- coding: utf-8 -*- +# File : comm.py +# Author : Jiayuan Mao +# Email : maojiayuan@gmail.com +# Date : 27/01/2018 +# +# This file is part of Synchronized-BatchNorm-PyTorch. +# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch +# Distributed under MIT License. + +import queue +import collections +import threading + +__all__ = ['FutureResult', 'SlavePipe', 'SyncMaster'] + + +class FutureResult(object): + """A thread-safe future implementation. Used only as one-to-one pipe.""" + + def __init__(self): + self._result = None + self._lock = threading.Lock() + self._cond = threading.Condition(self._lock) + + def put(self, result): + with self._lock: + assert self._result is None, 'Previous result has\'t been fetched.' + self._result = result + self._cond.notify() + + def get(self): + with self._lock: + if self._result is None: + self._cond.wait() + + res = self._result + self._result = None + return res + + +_MasterRegistry = collections.namedtuple('MasterRegistry', ['result']) +_SlavePipeBase = collections.namedtuple('_SlavePipeBase', ['identifier', 'queue', 'result']) + + +class SlavePipe(_SlavePipeBase): + """Pipe for master-slave communication.""" + + def run_slave(self, msg): + self.queue.put((self.identifier, msg)) + ret = self.result.get() + self.queue.put(True) + return ret + + +class SyncMaster(object): + """An abstract `SyncMaster` object. + + - During the replication, as the data parallel will trigger an callback of each module, all slave devices should + call `register(id)` and obtain an `SlavePipe` to communicate with the master. + - During the forward pass, master device invokes `run_master`, all messages from slave devices will be collected, + and passed to a registered callback. + - After receiving the messages, the master device should gather the information and determine to message passed + back to each slave devices. + """ + + def __init__(self, master_callback): + """ + + Args: + master_callback: a callback to be invoked after having collected messages from slave devices. + """ + self._master_callback = master_callback + self._queue = queue.Queue() + self._registry = collections.OrderedDict() + self._activated = False + + def __getstate__(self): + return {'master_callback': self._master_callback} + + def __setstate__(self, state): + self.__init__(state['master_callback']) + + def register_slave(self, identifier): + """ + Register an slave device. + + Args: + identifier: an identifier, usually is the device id. + + Returns: a `SlavePipe` object which can be used to communicate with the master device. + + """ + if self._activated: + assert self._queue.empty(), 'Queue is not clean before next initialization.' + self._activated = False + self._registry.clear() + future = FutureResult() + self._registry[identifier] = _MasterRegistry(future) + return SlavePipe(identifier, self._queue, future) + + def run_master(self, master_msg): + """ + Main entry for the master device in each forward pass. + The messages were first collected from each devices (including the master device), and then + an callback will be invoked to compute the message to be sent back to each devices + (including the master device). + + Args: + master_msg: the message that the master want to send to itself. This will be placed as the first + message when calling `master_callback`. For detailed usage, see `_SynchronizedBatchNorm` for an example. + + Returns: the message to be sent back to the master device. + + """ + self._activated = True + + intermediates = [(0, master_msg)] + for i in range(self.nr_slaves): + intermediates.append(self._queue.get()) + + results = self._master_callback(intermediates) + assert results[0][0] == 0, 'The first result should belongs to the master.' + + for i, res in results: + if i == 0: + continue + self._registry[i].result.put(res) + + for i in range(self.nr_slaves): + assert self._queue.get() is True + + return results[0][1] + + @property + def nr_slaves(self): + return len(self._registry) diff --git a/detection/models/detection/efficientdet/sync_batchnorm/replicate.py b/detection/models/detection/efficientdet/sync_batchnorm/replicate.py new file mode 100644 index 0000000..b71c7b8 --- /dev/null +++ b/detection/models/detection/efficientdet/sync_batchnorm/replicate.py @@ -0,0 +1,94 @@ +# -*- coding: utf-8 -*- +# File : replicate.py +# Author : Jiayuan Mao +# Email : maojiayuan@gmail.com +# Date : 27/01/2018 +# +# This file is part of Synchronized-BatchNorm-PyTorch. +# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch +# Distributed under MIT License. + +import functools + +from torch.nn.parallel.data_parallel import DataParallel + +__all__ = [ + 'CallbackContext', + 'execute_replication_callbacks', + 'DataParallelWithCallback', + 'patch_replication_callback' +] + + +class CallbackContext(object): + pass + + +def execute_replication_callbacks(modules): + """ + Execute an replication callback `__data_parallel_replicate__` on each module created by original replication. + + The callback will be invoked with arguments `__data_parallel_replicate__(ctx, copy_id)` + + Note that, as all modules are isomorphism, we assign each sub-module with a context + (shared among multiple copies of this module on different devices). + Through this context, different copies can share some information. + + We guarantee that the callback on the master copy (the first copy) will be called ahead of calling the callback + of any slave copies. + """ + master_copy = modules[0] + nr_modules = len(list(master_copy.modules())) + ctxs = [CallbackContext() for _ in range(nr_modules)] + + for i, module in enumerate(modules): + for j, m in enumerate(module.modules()): + if hasattr(m, '__data_parallel_replicate__'): + m.__data_parallel_replicate__(ctxs[j], i) + + +class DataParallelWithCallback(DataParallel): + """ + Data Parallel with a replication callback. + + An replication callback `__data_parallel_replicate__` of each module will be invoked after being created by + original `replicate` function. + The callback will be invoked with arguments `__data_parallel_replicate__(ctx, copy_id)` + + Examples: + > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False) + > sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1]) + # sync_bn.__data_parallel_replicate__ will be invoked. + """ + + def replicate(self, module, device_ids): + modules = super(DataParallelWithCallback, self).replicate(module, device_ids) + execute_replication_callbacks(modules) + return modules + + +def patch_replication_callback(data_parallel): + """ + Monkey-patch an existing `DataParallel` object. Add the replication callback. + Useful when you have customized `DataParallel` implementation. + + Examples: + > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False) + > sync_bn = DataParallel(sync_bn, device_ids=[0, 1]) + > patch_replication_callback(sync_bn) + # this is equivalent to + > sync_bn = SynchronizedBatchNorm1d(10, eps=1e-5, affine=False) + > sync_bn = DataParallelWithCallback(sync_bn, device_ids=[0, 1]) + """ + + assert isinstance(data_parallel, DataParallel) + + old_replicate = data_parallel.replicate + + @functools.wraps(old_replicate) + def new_replicate(module, device_ids): + modules = old_replicate(module, device_ids) + execute_replication_callbacks(modules) + return modules + + data_parallel.replicate = new_replicate diff --git a/detection/models/detection/efficientdet/sync_batchnorm/unittest.py b/detection/models/detection/efficientdet/sync_batchnorm/unittest.py new file mode 100644 index 0000000..bed56f1 --- /dev/null +++ b/detection/models/detection/efficientdet/sync_batchnorm/unittest.py @@ -0,0 +1,29 @@ +# -*- coding: utf-8 -*- +# File : unittest.py +# Author : Jiayuan Mao +# Email : maojiayuan@gmail.com +# Date : 27/01/2018 +# +# This file is part of Synchronized-BatchNorm-PyTorch. +# https://github.com/vacancy/Synchronized-BatchNorm-PyTorch +# Distributed under MIT License. + +import unittest +import torch + + +class TorchTestCase(unittest.TestCase): + def assertTensorClose(self, x, y): + adiff = float((x - y).abs().max()) + if (y == 0).all(): + rdiff = 'NaN' + else: + rdiff = float((adiff / y).abs().max()) + + message = ( + 'Tensor close check failed\n' + 'adiff={}\n' + 'rdiff={}\n' + ).format(adiff, rdiff) + self.assertTrue(torch.allclose(x, y), message) + diff --git a/detection/models/detection/efficientdet/utils.py b/detection/models/detection/efficientdet/utils.py new file mode 100644 index 0000000..50559b2 --- /dev/null +++ b/detection/models/detection/efficientdet/utils.py @@ -0,0 +1,139 @@ +import itertools +import torch +import torch.nn as nn +import numpy as np + + +class BBoxTransform(nn.Module): + def forward(self, anchors, regression): + """ + decode_box_outputs adapted from https://github.com/google/automl/blob/master/efficientdet/anchors.py + + Args: + anchors: [batchsize, boxes, (y1, x1, y2, x2)] + regression: [batchsize, boxes, (dy, dx, dh, dw)] + + Returns: + + """ + y_centers_a = (anchors[..., 0] + anchors[..., 2]) / 2 + x_centers_a = (anchors[..., 1] + anchors[..., 3]) / 2 + ha = anchors[..., 2] - anchors[..., 0] + wa = anchors[..., 3] - anchors[..., 1] + + w = regression[..., 3].exp() * wa + h = regression[..., 2].exp() * ha + + y_centers = regression[..., 0] * ha + y_centers_a + x_centers = regression[..., 1] * wa + x_centers_a + + ymin = y_centers - h / 2. + xmin = x_centers - w / 2. + ymax = y_centers + h / 2. + xmax = x_centers + w / 2. + + return torch.stack([xmin, ymin, xmax, ymax], dim=2) + + +class ClipBoxes(nn.Module): + + def __init__(self): + super(ClipBoxes, self).__init__() + + def forward(self, boxes, img): + batch_size, num_channels, height, width = img.shape + + boxes[:, :, 0] = torch.clamp(boxes[:, :, 0], min=0) + boxes[:, :, 1] = torch.clamp(boxes[:, :, 1], min=0) + + boxes[:, :, 2] = torch.clamp(boxes[:, :, 2], max=width - 1) + boxes[:, :, 3] = torch.clamp(boxes[:, :, 3], max=height - 1) + + return boxes + + +class Anchors(nn.Module): + """ + adapted and modified from https://github.com/google/automl/blob/master/efficientdet/anchors.py by Zylo117 + """ + + def __init__(self, anchor_scale=4., pyramid_levels=None, **kwargs): + super().__init__() + self.anchor_scale = anchor_scale + + if pyramid_levels is None: + self.pyramid_levels = [3, 4, 5, 6, 7] + else: + self.pyramid_levels = pyramid_levels + + self.strides = kwargs.get('strides', [2 ** x for x in self.pyramid_levels]) + self.scales = np.array(kwargs.get('scales', [2 ** 0, 2 ** (1.0 / 3.0), 2 ** (2.0 / 3.0)])) + self.ratios = kwargs.get('ratios', [(1.0, 1.0), (1.4, 0.7), (0.7, 1.4)]) + + self.last_anchors = {} + self.last_shape = None + + def forward(self, image, dtype=torch.float32): + """Generates multiscale anchor boxes. + + Args: + image_size: integer number of input image size. The input image has the + same dimension for width and height. The image_size should be divided by + the largest feature stride 2^max_level. + anchor_scale: float number representing the scale of size of the base + anchor to the feature stride 2^level. + anchor_configs: a dictionary with keys as the levels of anchors and + values as a list of anchor configuration. + + Returns: + anchor_boxes: a numpy array with shape [N, 4], which stacks anchors on all + feature levels. + Raises: + ValueError: input size must be the multiple of largest feature stride. + """ + image_shape = image.shape[2:] + + if image_shape == self.last_shape and image.device in self.last_anchors: + return self.last_anchors[image.device] + + if self.last_shape is None or self.last_shape != image_shape: + self.last_shape = image_shape + + if dtype == torch.float16: + dtype = np.float16 + else: + dtype = np.float32 + + boxes_all = [] + for stride in self.strides: + boxes_level = [] + for scale, ratio in itertools.product(self.scales, self.ratios): + if image_shape[1] % stride != 0: + raise ValueError('input size must be divided by the stride.') + base_anchor_size = self.anchor_scale * stride * scale + anchor_size_x_2 = base_anchor_size * ratio[0] / 2.0 + anchor_size_y_2 = base_anchor_size * ratio[1] / 2.0 + + x = np.arange(stride / 2, image_shape[1], stride) + y = np.arange(stride / 2, image_shape[0], stride) + xv, yv = np.meshgrid(x, y) + xv = xv.reshape(-1) + yv = yv.reshape(-1) + + # y1,x1,y2,x2 + boxes = np.vstack((yv - anchor_size_y_2, xv - anchor_size_x_2, + yv + anchor_size_y_2, xv + anchor_size_x_2)) + boxes = np.swapaxes(boxes, 0, 1) + boxes_level.append(np.expand_dims(boxes, axis=1)) + # concat anchors on the same level to the reshape NxAx4 + boxes_level = np.concatenate(boxes_level, axis=1) + boxes_all.append(boxes_level.reshape([-1, 4])) + + anchor_boxes = np.vstack(boxes_all) + + anchor_boxes = torch.from_numpy(anchor_boxes.astype(dtype)).to(image.device) + anchor_boxes = anchor_boxes.unsqueeze(0) + + # save it for later use to reduce overhead + self.last_anchors[image.device] = anchor_boxes + return anchor_boxes diff --git a/detection/models/detection/yolo/__init__.py b/detection/models/detection/yolo/__init__.py new file mode 100644 index 0000000..e28dae1 --- /dev/null +++ b/detection/models/detection/yolo/__init__.py @@ -0,0 +1,96 @@ +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from detectron2.structures import Instances, Boxes + +from .backbone import Darknet +from .utils import (xy_to_cxcy, + non_max_suppression) +from . import constants as C + + +class YOLOv3Model(nn.Module): + """YOLO V3 model: + https://github.com/eriklindernoren/PyTorch-YOLOv3.git + """ + + def __init__(self, cfg_name, model_args=None): + super().__init__() + num_classes = model_args.get("num_classes", None) + self.conf_threshold = model_args.get("conf_threshold", 0.8) + self.nms_threshold = model_args.get("nms_threshold", 0.4) + pretrained = model_args.get("pretrained", False) + ignore_width = model_args.get("ignore_width", 0) + cfg_path = C.CONFIGS[cfg_name] + self.model = Darknet(cfg_path, + num_classes=num_classes, + ignore_width=ignore_width) + + @staticmethod + def to_numpy(v): + if isinstance(v, np.ndarray): + return v + else: + return v.detach().cpu().numpy() + + def forward(self, x): + """ + To N x (img_id, class_id, cx, cy, w, h) format + """ + N = len(x) + imgs = torch.stack([sample['image'].float() for sample in x]) + width = imgs.shape[2] + height = imgs.shape[3] + if height != 416 or width != 416: + raise ValueError( + f"Input images must of size 416 x 416 but is {width} x {height}") + + annotations = [] + for i, sample in enumerate(x): + instances = sample['instances'] + boxes = self.to_numpy(instances.gt_boxes.tensor) + class_ids = self.to_numpy(instances.gt_classes) + for class_id, box in zip(class_ids, boxes): + cx, cy, w, h = xy_to_cxcy(box, width, height) + annotations.append([i, class_id, cx, cy, w, h]) + annotations = np.stack(annotations, 0) + annotations = torch.from_numpy(annotations).float() + return self.model(imgs, annotations)[0] + + def infer(self, x): + """ + From N x (xmin, ymin, xmax, ymax, conf, cls_conf_1, cls_conf_2, ..., cls_conf_k) format + """ + imgs = torch.stack([sample['image'].float() for sample in x]) + width = imgs.shape[2] + height = imgs.shape[3] + if height != 416 or width != 416: + raise ValueError( + f"Input images must of size 416 x 416 but is {width} x {height}") + rois = self.model.infer(imgs) + rois = non_max_suppression(rois, + self.conf_threshold, + self.nms_threshold) + outs = [] + for sample_input, sample_output in zip(x, rois): + instances = Instances( + (sample_input['height'], sample_input['width'])) + print(sample_output) + if sample_output is not None and len(sample_output): + instances.pred_boxes = Boxes(sample_output[:, :4]) + instances.scores = torch.tensor(sample_output[:, 4]) + class_conf, class_id = sample_output[:, 5:].max(1) + instances.pred_classes = torch.tensor(class_id) + outs.append({"instances": instances}) + return outs + + +class YOLOv3(YOLOv3Model): + def __init__(self, model_args=None): + super().__init__("yolov3", model_args) + + +class YOLOv3Tiny(YOLOv3Model): + def __init__(self, model_args=None): + super().__init__("yolov3-tiny", model_args) diff --git a/detection/models/detection/yolo/backbone.py b/detection/models/detection/yolo/backbone.py new file mode 100644 index 0000000..1c40cd9 --- /dev/null +++ b/detection/models/detection/yolo/backbone.py @@ -0,0 +1,408 @@ +''' +ABOUT THIS SCRIPT: +This is a yolov3 implementation that constructs the appropriate +yolov3 model layers and performs forward runs as per these modules + +This script is a slightly modified version of the follwoing repo: +https://github.com/eriklindernoren/PyTorch-YOLOv3.git + +''' + +import numpy as np +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.autograd import Variable + +from .utils import (slice_boundary, + parse_model_config, + to_cpu, + build_targets) + +from . import constants as C + + +def create_modules(module_defs, ignore_width): + """ + Constructs module list of layer blocks from module configuration in module_defs + """ + hyperparams = module_defs.pop(0) + output_filters = [int(hyperparams["channels"])] + module_list = nn.ModuleList() + for module_i, module_def in enumerate(module_defs): + modules = nn.Sequential() + + if module_def["type"] == "convolutional": + bn = int(module_def["batch_normalize"]) + filters = int(module_def["filters"]) + kernel_size = int(module_def["size"]) + pad = (kernel_size - 1) // 2 + modules.add_module( + f"conv_{module_i}", + nn.Conv2d( + in_channels=output_filters[-1], + out_channels=filters, + kernel_size=kernel_size, + stride=int(module_def["stride"]), + padding=pad, + bias=not bn, + ), + ) + if bn: + modules.add_module(f"batch_norm_{module_i}", nn.BatchNorm2d( + filters, momentum=0.9, eps=1e-5)) + if module_def["activation"] == "leaky": + modules.add_module(f"leaky_{module_i}", nn.LeakyReLU(0.1)) + + elif module_def["type"] == "maxpool": + kernel_size = int(module_def["size"]) + stride = int(module_def["stride"]) + if kernel_size == 2 and stride == 1: + modules.add_module( + f"_debug_padding_{module_i}", nn.ZeroPad2d((0, 1, 0, 1))) + maxpool = nn.MaxPool2d( + kernel_size=kernel_size, + stride=stride, + padding=int( + (kernel_size - 1) // 2)) + modules.add_module(f"maxpool_{module_i}", maxpool) + + elif module_def["type"] == "upsample": + upsample = Upsample(scale_factor=int( + module_def["stride"]), mode="nearest") + modules.add_module(f"upsample_{module_i}", upsample) + + elif module_def["type"] == "route": + layers = [int(x) for x in module_def["layers"].split(",")] + filters = sum([output_filters[1:][i] for i in layers]) + modules.add_module(f"route_{module_i}", EmptyLayer()) + + elif module_def["type"] == "shortcut": + filters = output_filters[1:][int(module_def["from"])] + modules.add_module(f"shortcut_{module_i}", EmptyLayer()) + + elif module_def["type"] == "yolo": + anchor_idxs = [int(x) for x in module_def["mask"].split(",")] + # Extract anchors + anchors = [int(x) for x in module_def["anchors"].split(",")] + anchors = [(anchors[i], anchors[i + 1]) + for i in range(0, len(anchors), 2)] + anchors = [anchors[i] for i in anchor_idxs] + num_classes = int(module_def["classes"]) + img_size = int(hyperparams["height"]) + # Define detection layer + yolo_layer = YOLOLayer(anchors, num_classes, ignore_width, img_size) + modules.add_module(f"yolo_{module_i}", yolo_layer) + # Register module list and number of output filters + module_list.append(modules) + output_filters.append(filters) + + return hyperparams, module_list + + +class Upsample(nn.Module): + """ nn.Upsample is deprecated """ + + def __init__(self, scale_factor, mode="nearest"): + super(Upsample, self).__init__() + self.scale_factor = scale_factor + self.mode = mode + + def forward(self, x): + x = F.interpolate(x, scale_factor=self.scale_factor, mode=self.mode) + return x + + +class EmptyLayer(nn.Module): + """Placeholder for 'route' and 'shortcut' layers""" + + def __init__(self): + super(EmptyLayer, self).__init__() + + +class YOLOLayer(nn.Module): + """Detection layer""" + + def __init__(self, anchors, num_classes, ignore_width=32, img_dim=416): + super(YOLOLayer, self).__init__() + self.anchors = anchors + self.num_anchors = len(anchors) + self.num_classes = num_classes + self.ignore_width = ignore_width + self.ignore_thres = 0.5 + self.mse_loss = nn.MSELoss() + self.bce_loss = nn.BCELoss() + self.obj_scale = 1 + self.noobj_scale = 100 + self.metrics = {} + self.img_dim = img_dim + self.grid_size = 0 # grid size + + def compute_grid_offsets(self, grid_size, cuda=True): + self.grid_size = grid_size + g = self.grid_size + FloatTensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor + self.stride = self.img_dim / self.grid_size + # Calculate offsets for each grid + self.grid_x = torch.arange(g).repeat( + g, 1).view([1, 1, g, g]).type(FloatTensor) + self.grid_y = torch.arange(g).repeat( + g, 1).t().view([1, 1, g, g]).type(FloatTensor) + self.scaled_anchors = FloatTensor( + [(a_w / self.stride, a_h / self.stride) for a_w, a_h in self.anchors]) + self.anchor_w = self.scaled_anchors[:, 0:1].view( + (1, self.num_anchors, 1, 1)) + self.anchor_h = self.scaled_anchors[:, 1:2].view( + (1, self.num_anchors, 1, 1)) + + def forward(self, x, targets=None, image_size=416, return_metrics=False): + # Tensors for cuda support + FloatTensor = torch.cuda.FloatTensor if x.is_cuda else torch.FloatTensor + self.img_dim = image_size + num_samples = x.size(0) + grid_size = x.size(2) + + prediction = ( + x.view(num_samples, self.num_anchors, + self.num_classes + 5, grid_size, grid_size) + .permute(0, 1, 3, 4, 2) + .contiguous() + ) + + # Get outputs + x = torch.sigmoid(prediction[..., 0]) # Center x + y = torch.sigmoid(prediction[..., 1]) # Center y + w = prediction[..., 2] # Width + h = prediction[..., 3] # Height + pred_conf = torch.sigmoid(prediction[..., 4]) # Conf + pred_cls = torch.sigmoid(prediction[..., 5:]) # Cls pred. + + if grid_size != self.grid_size: + self.compute_grid_offsets(grid_size, cuda=x.is_cuda) + + # Add offset and scale with anchors + pred_boxes = FloatTensor(prediction[..., :4].shape) + pred_boxes[..., 0] = x.data + self.grid_x + pred_boxes[..., 1] = y.data + self.grid_y + pred_boxes[..., 2] = torch.exp(w.data) * self.anchor_w + pred_boxes[..., 3] = torch.exp(h.data) * self.anchor_h + + # Only keep predictions inside the boundary + # Note: Due to FPN, predictions across different scales are combined + # Need to adjust slice boundary accordingly + assert (grid_size * self.ignore_width) % C.SIZE == 0 + boundary = grid_size * self.ignore_width // C.SIZE + output = torch.cat( + (slice_boundary( + pred_boxes, boundary).view( + num_samples, -1, 4) * self.stride, slice_boundary( + pred_conf, boundary).view( + num_samples, -1, 1), pred_cls.view( + num_samples, -1, self.num_classes), ), -1, ) + if targets is None: + return output, 0 + + iou_scores, obj_mask, noobj_mask, tx, ty, tw, th, tconf =\ + build_targets( + pred_boxes=pred_boxes, + target=targets, + anchors=self.scaled_anchors, + ignore_thres=self.ignore_thres, + ) + + # Remove the boundary from predictions, ground truth, and masks + # when computing the loss. + tensors = [pred_boxes, pred_conf, tconf, x, tx, y, ty, + w, tw, h, th, iou_scores, obj_mask, noobj_mask] + (pred_boxes, pred_conf, tconf, x, tx, y, ty, + w, tw, h, th, iou_scores, obj_mask, noobj_mask) = [ + slice_boundary(tensor, boundary) + for tensor in tensors + ] + # Loss : Mask outputs to ignore non-existing objects (except with conf. + # loss) + loss_x = self.mse_loss(x[obj_mask.bool()], tx[obj_mask.bool()]) + loss_y = self.mse_loss(y[obj_mask.bool()], ty[obj_mask.bool()]) + loss_w = self.mse_loss(w[obj_mask.bool()], tw[obj_mask.bool()]) + loss_h = self.mse_loss(h[obj_mask.bool()], th[obj_mask.bool()]) + loss_conf_obj = self.bce_loss( + pred_conf[obj_mask.bool()], tconf[obj_mask.bool()]) + loss_conf_noobj = self.bce_loss( + pred_conf[noobj_mask.bool()], tconf[noobj_mask.bool()]) + loss_conf = self.obj_scale * loss_conf_obj + self.noobj_scale * loss_conf_noobj + + if obj_mask.bool().sum().item() == 0: + total_loss = self.noobj_scale * loss_conf_noobj + else: + # Ignore useless classification loss + total_loss = loss_x + loss_y + loss_w + loss_h + loss_conf + + if torch.isnan(total_loss).item(): + import pdb + pdb.set_trace() + + if not return_metrics: + return output, total_loss + else: + # Metrics + conf_obj = pred_conf[obj_mask.bool()].mean() + conf_noobj = pred_conf[noobj_mask.bool()].mean() + conf50 = (pred_conf > 0.5).float() + iou50 = (iou_scores > 0.5).float() + iou75 = (iou_scores > 0.75).float() + detected_mask = conf50 * tconf + precision = torch.sum(iou50 * detected_mask) / \ + (conf50.sum() + 1e-16) + recall50 = torch.sum(iou50 * detected_mask) / \ + (obj_mask.sum() + 1e-16) + recall75 = torch.sum(iou75 * detected_mask) / \ + (obj_mask.sum() + 1e-16) + + self.metrics = { + "loss": to_cpu(total_loss).item(), + "x": to_cpu(loss_x).item(), + "y": to_cpu(loss_y).item(), + "w": to_cpu(loss_w).item(), + "h": to_cpu(loss_h).item(), + "conf": to_cpu(loss_conf).item(), + "recall50": to_cpu(recall50).item(), + "recall75": to_cpu(recall75).item(), + "precision": to_cpu(precision).item(), + "conf_obj": to_cpu(conf_obj).item(), + "conf_noobj": to_cpu(conf_noobj).item(), + "grid_size": grid_size, + } + return output, total_loss, self.metrics + + +class Darknet(nn.Module): + """YOLOv3 object detection model""" + + def __init__(self, config_path, ignore_width, num_classes=80, img_size=416): + super(Darknet, self).__init__() + self.module_defs = parse_model_config(config_path, num_classes) + self.hyperparams, self.module_list = create_modules( + self.module_defs, ignore_width) + self.yolo_layers = [ + layer[0] for layer in self.module_list if hasattr( + layer[0], "metrics")] + self.img_size = img_size + self.seen = 0 + self.header_info = np.array([0, 0, 0, self.seen, 0], dtype=np.int32) + + def forward(self, x, targets): + img_dim = x.shape[2] + loss = 0 + layer_outputs, yolo_outputs = [], [] + for i, (module_def, module) in enumerate( + zip(self.module_defs, self.module_list)): + if module_def["type"] in ["convolutional", "upsample", "maxpool"]: + x = module(x) + elif module_def["type"] == "route": + x = torch.cat([layer_outputs[int(layer_i)] + for layer_i in module_def["layers"].split(",")], 1) + elif module_def["type"] == "shortcut": + layer_i = int(module_def["from"]) + x = layer_outputs[-1] + layer_outputs[layer_i] + elif module_def["type"] == "yolo": + outputs = module[0](x, targets, img_dim) + x, layer_loss = module[0](x, targets, img_dim) + loss += layer_loss + yolo_outputs.append(x) + layer_outputs.append(x) + yolo_outputs = to_cpu(torch.cat(yolo_outputs, 1)) + return loss, yolo_outputs + + def infer(self, x): + loss, yolo_outputs = self.forward(x, None) + return yolo_outputs + + def load_darknet_weights(self, weights_path): + """Parses and loads the weights stored in 'weights_path'""" + + # Open the weights file + with open(weights_path, "rb") as f: + # First five are header values + header = np.fromfile(f, dtype=np.int32, count=5) + self.header_info = header # Needed to write header when saving weights + self.seen = header[3] # number of images seen during training + weights = np.fromfile(f, dtype=np.float32) # The rest are weights + + # Establish cutoff for loading backbone weights + cutoff = None + if "darknet53.conv.74" in weights_path: + cutoff = 75 + + ptr = 0 + for i, (module_def, module) in enumerate( + zip(self.module_defs, self.module_list)): + if i == cutoff: + break + if module_def["type"] == "convolutional": + conv_layer = module[0] + if module_def["batch_normalize"]: + # Load BN bias, weights, running mean and running variance + bn_layer = module[1] + num_b = bn_layer.bias.numel() # Number of biases + # Bias + bn_b = torch.from_numpy( + weights[ptr: ptr + num_b]).view_as(bn_layer.bias) + bn_layer.bias.data.copy_(bn_b) + ptr += num_b + # Weight + bn_w = torch.from_numpy( + weights[ptr: ptr + num_b]).view_as(bn_layer.weight) + bn_layer.weight.data.copy_(bn_w) + ptr += num_b + # Running Mean + bn_rm = torch.from_numpy( + weights[ptr: ptr + num_b]).view_as(bn_layer.running_mean) + bn_layer.running_mean.data.copy_(bn_rm) + ptr += num_b + # Running Var + bn_rv = torch.from_numpy( + weights[ptr: ptr + num_b]).view_as(bn_layer.running_var) + bn_layer.running_var.data.copy_(bn_rv) + ptr += num_b + else: + # Load conv. bias + num_b = conv_layer.bias.numel() + conv_b = torch.from_numpy( + weights[ptr: ptr + num_b]).view_as(conv_layer.bias) + conv_layer.bias.data.copy_(conv_b) + ptr += num_b + # Load conv. weights + num_w = conv_layer.weight.numel() + conv_w = torch.from_numpy( + weights[ptr: ptr + num_w]).view_as(conv_layer.weight) + conv_layer.weight.data.copy_(conv_w) + ptr += num_w + + def save_darknet_weights(self, path, cutoff=-1): + """ + @:param path - path of the new weights file + @:param cutoff - save layers between 0 and cutoff (cutoff = -1 -> all are saved) + """ + fp = open(path, "wb") + self.header_info[3] = self.seen + self.header_info.tofile(fp) + + # Iterate through layers + for i, (module_def, module) in enumerate( + zip(self.module_defs[:cutoff], self.module_list[:cutoff])): + if module_def["type"] == "convolutional": + conv_layer = module[0] + # If batch norm, load bn first + if module_def["batch_normalize"]: + bn_layer = module[1] + bn_layer.bias.data.cpu().numpy().tofile(fp) + bn_layer.weight.data.cpu().numpy().tofile(fp) + bn_layer.running_mean.data.cpu().numpy().tofile(fp) + bn_layer.running_var.data.cpu().numpy().tofile(fp) + # Load conv bias + else: + conv_layer.bias.data.cpu().numpy().tofile(fp) + # Load conv weights + conv_layer.weight.data.cpu().numpy().tofile(fp) + + fp.close() diff --git a/detection/models/detection/yolo/constants.py b/detection/models/detection/yolo/constants.py new file mode 100644 index 0000000..f055806 --- /dev/null +++ b/detection/models/detection/yolo/constants.py @@ -0,0 +1,7 @@ +import os + + +SIZE = 416 +CONFIG_DIR = os.path.dirname(__file__) +CONFIGS = {"yolov3": os.path.join(CONFIG_DIR, "yolov3.cfg"), + "yolov3-tiny": os.path.join(CONFIG_DIR, "yolov3-tiny.cfg")} diff --git a/detection/models/detection/yolo/utils.py b/detection/models/detection/yolo/utils.py new file mode 100644 index 0000000..db71c12 --- /dev/null +++ b/detection/models/detection/yolo/utils.py @@ -0,0 +1,292 @@ +"""Define Logger class for logging information to stdout and disk.""" +import collections +import os +import json +import torch +import numpy as np +import time +import torchvision +from os.path import join + + +def xywh2xyxy(x): + y = x.new(x.shape) + y[..., 0] = x[..., 0] - x[..., 2] / 2 + y[..., 1] = x[..., 1] - x[..., 3] / 2 + y[..., 2] = x[..., 0] + x[..., 2] / 2 + y[..., 3] = x[..., 1] + x[..., 3] / 2 + return y + + +def bbox_iou(box1, box2, x1y1x2y2=True): + """ + Returns the IoU of two bounding boxes + """ + if not x1y1x2y2: + # Transform from center and width to exact coordinates + b1_x1, b1_x2 = box1[:, 0] - box1[:, 2] / 2, box1[:, 0] + box1[:, 2] / 2 + b1_y1, b1_y2 = box1[:, 1] - box1[:, 3] / 2, box1[:, 1] + box1[:, 3] / 2 + b2_x1, b2_x2 = box2[:, 0] - box2[:, 2] / 2, box2[:, 0] + box2[:, 2] / 2 + b2_y1, b2_y2 = box2[:, 1] - box2[:, 3] / 2, box2[:, 1] + box2[:, 3] / 2 + else: + # Get the coordinates of bounding boxes + b1_x1, b1_y1, b1_x2, b1_y2 = (box1[:, 0], box1[:, 1], + box1[:, 2], box1[:, 3]) + b2_x1, b2_y1, b2_x2, b2_y2 = (box2[:, 0], box2[:, 1], + box2[:, 2], box2[:, 3]) + + # get the corrdinates of the intersection rectangle + inter_rect_x1 = torch.max(b1_x1, b2_x1) + inter_rect_y1 = torch.max(b1_y1, b2_y1) + inter_rect_x2 = torch.min(b1_x2, b2_x2) + inter_rect_y2 = torch.min(b1_y2, b2_y2) + # Intersection area + inter_area = (torch.clamp(inter_rect_x2 - inter_rect_x1 + 1, min=0) * + torch.clamp(inter_rect_y2 - inter_rect_y1 + 1, min=0)) + # Union Area + b1_area = (b1_x2 - b1_x1 + 1) * (b1_y2 - b1_y1 + 1) + b2_area = (b2_x2 - b2_x1 + 1) * (b2_y2 - b2_y1 + 1) + + iou = inter_area / (b1_area + b2_area - inter_area + 1e-16) + + return iou + + +def bbox_wh_iou(wh1, wh2): + wh2 = wh2.t() + w1, h1 = wh1[0], wh1[1] + w2, h2 = wh2[0], wh2[1] + inter_area = torch.min(w1, w2) * torch.min(h1, h2) + union_area = (w1 * h1 + 1e-16) + w2 * h2 - inter_area + return inter_area / union_area + + +def build_targets(pred_boxes, target, anchors, ignore_thres): + + ByteTensor = torch.cuda.ByteTensor if pred_boxes.is_cuda\ + else torch.ByteTensor + FloatTensor = torch.cuda.FloatTensor if pred_boxes.is_cuda\ + else torch.FloatTensor + + nB = pred_boxes.size(0) + nA = pred_boxes.size(1) + nG = pred_boxes.size(2) + + # Output tensors + obj_mask = ByteTensor(nB, nA, nG, nG).fill_(0) + noobj_mask = ByteTensor(nB, nA, nG, nG).fill_(1) + iou_scores = FloatTensor(nB, nA, nG, nG).fill_(0) + tx = FloatTensor(nB, nA, nG, nG).fill_(0) + ty = FloatTensor(nB, nA, nG, nG).fill_(0) + tw = FloatTensor(nB, nA, nG, nG).fill_(0) + th = FloatTensor(nB, nA, nG, nG).fill_(0) + + # Convert to position relative to box + target_boxes = target[:, 2:6] * nG + gxy = target_boxes[:, :2] + gwh = target_boxes[:, 2:] + # Get anchors with best iou + ious = torch.stack([bbox_wh_iou(anchor, gwh) for anchor in anchors]) + best_ious, best_n = ious.max(0) + # Separate target values + b, target_labels = target[:, :2].long().t() + gx, gy = gxy.t() + gw, gh = gwh.t() + gi, gj = gxy.long().t() + # Set masks + obj_mask[b, best_n, gj, gi] = 1 + noobj_mask[b, best_n, gj, gi] = 0 + + # Set noobj mask to zero where iou exceeds ignore threshold + for i, anchor_ious in enumerate(ious.t()): + noobj_mask[b[i], anchor_ious > ignore_thres, gj[i], gi[i]] = 0 + + # Coordinates + tx[b, best_n, gj, gi] = gx - gx.floor() + ty[b, best_n, gj, gi] = gy - gy.floor() + # Width and height + tw[b, best_n, gj, gi] = torch.log(gw / anchors[best_n][:, 0] + 1e-16) + th[b, best_n, gj, gi] = torch.log(gh / anchors[best_n][:, 1] + 1e-16) + + iou_scores[b, best_n, gj, gi] = bbox_iou( + pred_boxes[b, best_n, gj, gi], target_boxes, x1y1x2y2=False) + + tconf = obj_mask.float() + return (iou_scores, obj_mask, noobj_mask, + tx, ty, tw, th, tconf) + + +def slice_boundary(t, width): + """Assumes shape (B, C, W, H, ...).""" + if not isinstance(width, int): + raise ValueError(f"ignore_width must be an integer. Got {width}.") + if width < 0: + raise ValueError(f"ignore_width must be positive. Got {width}.") + if width > t.shape[2] // 2: + raise ValueError("ignore_width * 2 must be less than image dim. " + + f"Got {width}.") + + if width != 0: + return t[:, :, width:-width, width:-width].contiguous() + else: + return t + + +def parse_model_config(path, num_classes=80): + """Parses the yolo-v3 layer configuration file and returns module definitions""" + file = open(path, 'r') + lines = file.read().split('\n') + lines = [x for x in lines if x and not x.startswith('#')] + lines = [x.rstrip().lstrip() + for x in lines] # get rid of fringe whitespaces + module_defs = [] + for line in lines: + if line.startswith('['): # This marks the start of a new block + module_defs.append({}) + module_defs[-1]['type'] = line[1:-1].rstrip() + if module_defs[-1]['type'] == 'convolutional': + module_defs[-1]['batch_normalize'] = 0 + else: + key, value = line.split("=") + value = value.strip() + module_defs[-1][key.rstrip()] = value.strip() + + # Overwrite number of classes + yolo_layers = [] + for i, module_def in enumerate(module_defs): + if module_def['type'] == 'yolo': + yolo_layers.append(i) + module_defs[i]['classes'] = str(num_classes) + + for i in yolo_layers: + module_defs[i - 1]['filters'] = str((num_classes + 5) * 3) + + return module_defs + + +def parse_data_config(path): + """Parses the data configuration file""" + options = dict() + options['gpus'] = '0,1,2,3' + options['num_workers'] = '10' + with open(path, 'r') as fp: + lines = fp.readlines() + for line in lines: + line = line.strip() + if line == '' or line.startswith('#'): + continue + key, value = line.split('=') + options[key.strip()] = value.strip() + return options + + +def to_cpu(tensor): + return tensor.detach().cpu() + + +def xy_to_cxcy(xy, height, width): + return [(xy[0] + xy[2]) / 2 / width, + (xy[1] + xy[3]) / 2 / height, + (xy[2] - xy[0]) / width, + (xy[3] - xy[1]) / height] + + +def non_max_suppression( + prediction, + conf_thres=0.25, + iou_thres=0.45, + classes=None, + agnostic=False, + labels=()): + """Performs Non-Maximum Suppression (NMS) on inference results + Returns: + detections with shape: nx6 (x1, y1, x2, y2, conf, cls) + """ + + nc = prediction.shape[2] - 5 # number of classes + xc = prediction[..., 4] > conf_thres # candidates + + # Settings + # (pixels) minimum and maximum box width and height + min_wh, max_wh = 2, 4096 + max_det = 300 # maximum number of detections per image + max_nms = 30000 # maximum number of boxes into torchvision.ops.nms() + time_limit = 20.0 # seconds to quit after + redundant = True # require redundant detections + multi_label = nc > 1 # multiple labels per box (adds 0.5ms/img) + merge = False # use merge-NMS + + t = time.time() + output = [torch.zeros((0, 6), device=prediction.device) + ] * prediction.shape[0] + for xi, x in enumerate(prediction): # image index, image inference + # Apply constraints + # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0 # + # width-height + x = x[xc[xi]] # confidence + + # Cat apriori labels if autolabelling + if labels and len(labels[xi]): + l = labels[xi] + v = torch.zeros((len(l), nc + 5), device=x.device) + v[:, :4] = l[:, 1:5] # box + v[:, 4] = 1.0 # conf + v[range(len(l)), l[:, 0].long() + 5] = 1.0 # cls + x = torch.cat((x, v), 0) + + # If none remain process next image + if not x.shape[0]: + continue + + # Compute conf + x[:, 5:] *= x[:, 4:5] # conf = obj_conf * cls_conf + + # Box (center x, center y, width, height) to (x1, y1, x2, y2) + box = xywh2xyxy(x[:, :4]) + + # Detections matrix nx6 (xyxy, conf, cls) + if multi_label: + i, j = (x[:, 5:] > conf_thres).nonzero(as_tuple=False).T + x = torch.cat((box[i], x[i, j + 5, None], j[:, None].float()), 1) + else: # best class only + conf, j = x[:, 5:].max(1, keepdim=True) + x = torch.cat((box, conf, j.float()), 1)[conf.view(-1) > conf_thres] + + # Filter by class + if classes is not None: + x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)] + + # Apply finite constraint + # if not torch.isfinite(x).all(): + # x = x[torch.isfinite(x).all(1)] + + # Check shape + n = x.shape[0] # number of boxes + if not n: # no boxes + continue + elif n > max_nms: # excess boxes + # sort by confidence + x = x[x[:, 4].argsort(descending=True)[:max_nms]] + + # Batched NMS + c = x[:, 5:6] * (0 if agnostic else max_wh) # classes + boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores + i = torchvision.ops.nms(boxes, scores, iou_thres) # NMS + if i.shape[0] > max_det: # limit detections + i = i[:max_det] + if merge and ( + 1 < n < 3E3): # Merge NMS (boxes merged using weighted mean) + # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4) + iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix + weights = iou * scores[None] # box weights + x[i, :4] = torch.mm(weights, x[:, :4]).float( + ) / weights.sum(1, keepdim=True) # merged boxes + if redundant: + i = i[iou.sum(1) > 1] # require redundancy + + output[xi] = x[i] + if (time.time() - t) > time_limit: + print(f'WARNING: NMS time limit {time_limit}s exceeded') + break # time limit exceeded + + return output diff --git a/detection/models/detection/yolo/yolov3-tiny.cfg b/detection/models/detection/yolo/yolov3-tiny.cfg new file mode 100644 index 0000000..ae068aa --- /dev/null +++ b/detection/models/detection/yolo/yolov3-tiny.cfg @@ -0,0 +1,206 @@ +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=64 +subdivisions=2 +width=416 +height=416 +channels=3 +momentum=0.9 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.001 +burn_in=1000 +max_batches = 500200 +policy=steps +steps=400000,450000 +scales=.1,.1 + +# 0 +[convolutional] +batch_normalize=1 +filters=16 +size=3 +stride=1 +pad=1 +activation=leaky + +# 1 +[maxpool] +size=2 +stride=2 + +# 2 +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +# 3 +[maxpool] +size=2 +stride=2 + +# 4 +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +# 5 +[maxpool] +size=2 +stride=2 + +# 6 +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +# 7 +[maxpool] +size=2 +stride=2 + +# 8 +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +# 9 +[maxpool] +size=2 +stride=2 + +# 10 +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +# 11 +[maxpool] +size=2 +stride=1 + +# 12 +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +########### + +# 13 +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +# 14 +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +# 15 +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + + +# 16 +[yolo] +mask = 3,4,5 +anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319 +classes=80 +num=6 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 + +# 17 +[route] +layers = -4 + +# 18 +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +# 19 +[upsample] +stride=2 + +# 20 +[route] +layers = -1, 8 + +# 21 +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +# 22 +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + +# 23 +[yolo] +mask = 1,2,3 +anchors = 10,14, 23,27, 37,58, 81,82, 135,169, 344,319 +classes=80 +num=6 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 diff --git a/detection/models/detection/yolo/yolov3.cfg b/detection/models/detection/yolo/yolov3.cfg new file mode 100644 index 0000000..946e015 --- /dev/null +++ b/detection/models/detection/yolo/yolov3.cfg @@ -0,0 +1,788 @@ +[net] +# Testing +#batch=1 +#subdivisions=1 +# Training +batch=16 +subdivisions=1 +width=416 +height=416 +channels=3 +momentum=0.9 +decay=0.0005 +angle=0 +saturation = 1.5 +exposure = 1.5 +hue=.1 + +learning_rate=0.001 +burn_in=1000 +max_batches = 500200 +policy=steps +steps=400000,450000 +scales=.1,.1 + +[convolutional] +batch_normalize=1 +filters=32 +size=3 +stride=1 +pad=1 +activation=leaky + +# Downsample + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=32 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=64 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +# Downsample + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=2 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=1024 +size=3 +stride=1 +pad=1 +activation=leaky + +[shortcut] +from=-3 +activation=linear + +###################### + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=512 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=1024 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 6,7,8 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=80 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 + + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 61 + + + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=256 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=512 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 3,4,5 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=80 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 + + + +[route] +layers = -4 + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[upsample] +stride=2 + +[route] +layers = -1, 36 + + + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +batch_normalize=1 +filters=128 +size=1 +stride=1 +pad=1 +activation=leaky + +[convolutional] +batch_normalize=1 +size=3 +stride=1 +pad=1 +filters=256 +activation=leaky + +[convolutional] +size=1 +stride=1 +pad=1 +filters=255 +activation=linear + + +[yolo] +mask = 0,1,2 +anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326 +classes=80 +num=9 +jitter=.3 +ignore_thresh = .7 +truth_thresh = 1 +random=1 diff --git a/detection/plots/.keepme b/detection/plots/.keepme new file mode 100644 index 0000000..e69de29 diff --git a/detection/sandbox/.keepme b/detection/sandbox/.keepme new file mode 100644 index 0000000..e69de29 diff --git a/detection/util/.keepme b/detection/util/.keepme new file mode 100644 index 0000000..e69de29 diff --git a/detection/util/__init__.py b/detection/util/__init__.py new file mode 100644 index 0000000..8243204 --- /dev/null +++ b/detection/util/__init__.py @@ -0,0 +1,2 @@ +from .constants import * +from .util import Args, init_exp_folder, get_concat_h_cut diff --git a/detection/util/constants.py b/detection/util/constants.py new file mode 100644 index 0000000..7e590a7 --- /dev/null +++ b/detection/util/constants.py @@ -0,0 +1,25 @@ +"""Define constants to be used throughout the repository.""" +import os +from detectron2.data.catalog import Metadata +# Main paths + +# Dataset constants +IMAGENET_MEAN = [0.485, 0.456, 0.406] +IMAGENET_STD = [0.229, 0.224, 0.225] + +# US latitude/longitude boundaries +US_N = 49.4 +US_S = 24.5 +US_E = -66.93 +US_W = -124.784 + +# Test image +TEST_IMG_PATH = [".circleci/images/test_image.png"] * 2 + + +SANDBOX_PATH = './sandbox' +TB_PATH = os.path.join(SANDBOX_PATH, 'tb') + +META = Metadata() +META.thing_classes = ["Camera", "Camera"] +META.thing_colors = [[20, 200, 60], [11, 119, 32]] diff --git a/detection/util/nni.py b/detection/util/nni.py new file mode 100644 index 0000000..ed70c95 --- /dev/null +++ b/detection/util/nni.py @@ -0,0 +1,45 @@ +import os +import nni +import time +import logging +import json +import traceback +from glob import glob + + +def _cast_value(v): + if v == "True": + v = True + elif v == "False": + v = False + elif v == "None": + v = None + return v + + +def run_nni(train_func, test_func): + try: + params = nni.get_next_parameter() + params = {k: _cast_value(v) for k, v in params.items()} + params['exp_name'] = "nni" + str(time.time()) + logging.info("Final Params:") + logging.info(params) + + save_dir, exp_name = train_func(**params) + ckpt_reg = os.path.join(save_dir, exp_name, "*.ckpt") + print(ckpt_reg) + ckpt_path = list(glob(ckpt_reg))[-1] + + test_func(ckpt_path=ckpt_path) + + except RuntimeError as re: + if 'out of memory' in str(re): + time.sleep(600) + params['batch_size'] = int(0.5 * params['batch_size']) + train(**params) + else: + traceback.print_exc() + nni.report_final_result(-1) + except Exception as e: + traceback.print_exc() + nni.report_final_result(-2) diff --git a/detection/util/sbatch_template.sh b/detection/util/sbatch_template.sh new file mode 100755 index 0000000..5f9f4f2 --- /dev/null +++ b/detection/util/sbatch_template.sh @@ -0,0 +1,31 @@ +#!/bin/bash +#SBATCH --partition=deep --qos=normal +#SBATCH --nodes=1 +#SBATCH --cpus-per-task=4 +#SBATCH --mem=16G + +# only use the following on partition with GPUs +#SBATCH --gres=gpu:1 + +#SBATCH --job-name="NAME" +#SBATCH --output=/deep/group/aicc-bootcamp/wind/job_logs/NAME-%j.out + +# only use the following if you want email notification +####SBATCH --mail-user=youremailaddress +####SBATCH --mail-type=ALL + +# list out some useful information (optional) +echo "SLURM_JOBID="$SLURM_JOBID +echo "SLURM_JOB_NODELIST"=$SLURM_JOB_NODELIST +echo "SLURM_NNODES"=$SLURM_NNODES +echo "SLURMTMPDIR="$SLURMTMPDIR +echo "working directory = "$SLURM_SUBMIT_DIR + +# sample process (list hostnames of the nodes you've requested) +NPROCS=`srun --nodes=${SLURM_NNODES} bash -c 'hostname' |wc -l` +echo NPROCS=$NPROCS + +COMMAND + +# done +echo "Done" diff --git a/detection/util/util.py b/detection/util/util.py new file mode 100644 index 0000000..ca55ff0 --- /dev/null +++ b/detection/util/util.py @@ -0,0 +1,70 @@ +import json +import os +from os.path import join +from PIL import Image + + +LIGHTNING_CKPT_PATH = 'lightning_logs/version_0/checkpoints/' +LIGHTNING_TB_PATH = 'lightning_logs/version_0/' +LIGHTNING_METRICS_PATH = 'lightning_logs/version_0/metrics.csv' + + +class Args(dict): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + self.__dict__.update(args[0]) + + def __getattr__(self, name): + if name in self: + return self[name] + raise AttributeError("No such attribute: " + name) + + def __setattr__(self, name, value): + self[name] = value + + def __delattr__(self, name): + if name in self: + del self[name] + else: + AttributeError("No such attribute: " + name) + + +def init_exp_folder(args): + save_dir = os.path.abspath(args.get("save_dir")) + exp_name = args.get("exp_name") + exp_path = join(save_dir, exp_name) + exp_metrics_path = join(exp_path, "metrics.csv") + exp_tb_path = join(exp_path, "tb") + global_tb_path = args.get("tb_path") + global_tb_exp_path = join(global_tb_path, exp_name) + if os.environ.get('LOCAL_RANK') is not None: + return + + # init exp path + if os.path.exists(exp_path): + raise FileExistsError(f"Experiment path [{exp_path}] already exists!") + os.makedirs(exp_path, exist_ok=True) + + os.makedirs(global_tb_path, exist_ok=True) + if os.path.exists(global_tb_exp_path): + raise FileExistsError(f"Experiment exists in the global " + f"Tensorboard path [{global_tb_path}]!") + os.makedirs(global_tb_path, exist_ok=True) + + # dump hyper-parameters/arguments + with open(join(save_dir, exp_name, "args.json"), "w") as f: + json.dump(args, f) + + # ln -s for metrics + os.symlink(join(exp_path, LIGHTNING_METRICS_PATH), + exp_metrics_path) + + # ln -s for tb + os.symlink(join(exp_path, LIGHTNING_TB_PATH), exp_tb_path) + os.symlink(exp_tb_path, global_tb_exp_path) + +def get_concat_h_cut(im1, im2): + dst = Image.new('RGB', (im1.width + im2.width, min(im1.height, im2.height))) + dst.paste(im1, (0, 0)) + dst.paste(im2, (im1.width, 0)) + return dst diff --git a/main.py b/main.py new file mode 100644 index 0000000..9225d91 --- /dev/null +++ b/main.py @@ -0,0 +1,11 @@ +import fire + +from plot import plot_all +from streetview import (download_streetview_image + calculate_coverage, + calculate_zone, + calculate_road_length) + + +if __name__ == "__main__": + fire.Fire() diff --git a/plot/__init__.py b/plot/__init__.py new file mode 100644 index 0000000..87f16c9 --- /dev/null +++ b/plot/__init__.py @@ -0,0 +1,11 @@ +from .spatial_distribution import (plot_spatial_distribution, + plot_prepost, + plot_post, + plot_samples) +from .coverage import plot_coverage +from .precision_recall_curve import plot_precision_recall + +def plot_all(): + plot_spatial_distribution() + plot_coverage() + plot_precision_recall() diff --git a/plot/coverage.py b/plot/coverage.py new file mode 100644 index 0000000..c47038c --- /dev/null +++ b/plot/coverage.py @@ -0,0 +1,42 @@ +import pandas as pd +from matplotlib import pyplot as plt +from util import constants as C +from scipy.stats.mstats import winsorize +import numpy as np +import matplotlib.collections as collections +import seaborn as sb +import matplotlib + +LABEL = [('SF', 'San Francisco, California, USA'), ('Chicago', + 'Chicago, Illinois, USA'), ('NYC', 'New York City, New York, USA')] + + +def plot_coverage(): + plt.figure(figsize=(8, 4)) + font = {'family': 'normal', + 'weight': 'normal', + 'size': 15} + + matplotlib.rc('font', **font) + T = 60 + COLOR = ['C0', 'C1', 'C2', 'C3', 'C4', 'C5', 'C6', 'C7'] + for i, (name, place) in enumerate(LABEL): + data = pd.read_csv( + f"/home/haosheng/dataset/camera/sample/meta_0228/{name}_coverage.csv") + sb.kdeplot(data.coverage, label=place.split(",")[0], linewidth=2) + threshold = np.clip(data.coverage, 0, T).mean() + plt.axvline(x=threshold, linestyle='-.', color=COLOR[i]) + print(f"Average coverage for city {place}: {threshold}") + plt.xlim([0, 120]) + + plt.legend(loc='upper right') + plt.xlabel("Estimated Road Segment Coverage (meter)") + plt.ylabel("Probability Density") + + t = np.arange(T, 130, 0.01) + collection = collections.BrokenBarHCollection.span_where( + t, ymin=0, ymax=1, where=t > 0, facecolor='gray', alpha=0.15) + ax = plt.gca() + ax.add_collection(collection) + plt.subplots_adjust(bottom=0.2) + plt.savefig("figures/coverage.png") diff --git a/plot/precision_recall_curve.py b/plot/precision_recall_curve.py new file mode 100644 index 0000000..284de41 --- /dev/null +++ b/plot/precision_recall_curve.py @@ -0,0 +1,29 @@ +from sklearn.metrics import precision_recall_curve, precision_score, recall_score +from matplotlib import pyplot as plt +import pandas as pd +import numpy as np +from pathlib import Path +from tqdm import tqdm +import seaborn as sb +import cv2 as cv + +def plot_precision_recall(): + data = pd.read_csv("/home/haosheng/dataset/camera/test/test_result.csv") + plt.figure(figsize=(8,6)) + sb.set_style("white") + for f in [50, 200, 500, 1000]: + data_plot = data.query(f"f == {f}") + sb.lineplot(x="p", y="recall", + data=data_plot, + label=f"Pixel threshold: {f}", + linewidth=2.5, + ci=None) + plt.xlim([0.145,1.05]) + plt.ylim([0,1.05]) + plt.axvline(x=0.583333, ymin=0, ymax=0.6, linestyle='-.', color='gray') + plt.axhline(y=0.624400, xmin=0, xmax=0.48, linestyle='-.', color='gray') + plt.plot(0.583333, 0.624400,'ro') + plt.xlabel("Precision") + plt.ylabel("Recall") + plt.legend() + plt.savefig("figures/precision_recall.png") diff --git a/plot/spatial_distribution.py b/plot/spatial_distribution.py new file mode 100644 index 0000000..6bcbb3e --- /dev/null +++ b/plot/spatial_distribution.py @@ -0,0 +1,125 @@ +from util import constants as C +import osmnx as ox +import pandas as pd +import pickle as pkl +from tqdm import tqdm +from matplotlib import pyplot as plt +import seaborn as sb +sb.set() + + +def plot_samples( + meta_file_path="/home/haosheng/dataset/camera/deployment/verified_0425.csv"): + data = pd.read_csv(meta_file_path) + for city, place in list(C.CITIES.items()): + with open(f"/home/haosheng/dataset/camera/shape/graph/{city}.pkl", "rb") as f: + G = pkl.load(f) + ox.plot.plot_graph(G, + figsize=(12, 12), + bgcolor='white', + node_color='#696969', + edge_color="#A9A9A9", + edge_linewidth=0.8, + node_size=0, + edge_alpha=0.5, + save=False, + show=False) + sample = data.query(f'city == "{city}"') + + plt.scatter( + sample.lon_anchor, + sample.lat_anchor, + s=0.2, + c='blue', + alpha=1) + plt.tight_layout() + plt.savefig(f"figures/samples_{city}.png") + print(f"Save figure to [figures/samples_{city}.png]") + + +def plot_prepost( + meta_file_path="/home/haosheng/dataset/camera/deployment/verified_prepost_0425.csv"): + data = pd.read_csv(meta_file_path) + + for city, place in list(C.CITIES.items())[:10]: + with open(f"/home/haosheng/dataset/camera/shape/graph/{city}.pkl", "rb") as f: + G = pkl.load(f) + ox.plot.plot_graph(G, + figsize=(12, 12), + bgcolor='white', + node_color='#696969', + edge_color="#A9A9A9", + edge_linewidth=0.8, + node_size=0, + edge_alpha=0.5, + save=False, + show=False) + + print("Generating the plot .. ") + + pre = data.query( + f'camera_count > 0 and split == "pre" and city == "{city}"') + post = data.query( + f'camera_count > 0 and split == "post" and city == "{city}"') + + plt.scatter( + pre.lon_anchor, + pre.lat_anchor, + s=150, + facecolors='none', + edgecolors='red', + linewidth=2.0, + marker='o') + plt.scatter( + post.lon_anchor, + post.lat_anchor, + s=120, + c='black', + marker='x') + plt.tight_layout() + plt.savefig(f"figures/prepost_spatial_distribution_{city}.png") + print( + f"Save figure to [figures/prepost_spatial_distribution_{city}.png]") + + +def plot_post( + meta_file_path="/home/haosheng/dataset/camera/deployment/verified_0425.csv"): + data = pd.read_csv(meta_file_path) + for city, place in C.CITIES.items(): + with open(f"/home/haosheng/dataset/camera/shape/graph/{city}.pkl", "rb") as f: + G = pkl.load(f) + + ox.plot.plot_graph(G, + figsize=(12, 12), + bgcolor='white', + node_color='#696969', + edge_color="#A9A9A9", + edge_linewidth=0.8, + node_size=0, + edge_alpha=0.5, + save=False, + show=False) + + print("Generating the plot .. ") + + pre = data.query(f'camera_count > 0 and city == "{city}"') + post = data.query(f'camera_count > 0 and city == "{city}"') + + plt.scatter( + pre.lon_anchor, + pre.lat_anchor, + color='red', + #color='#BE0000', + s=30, + linewidth=2.0, + marker='o', + alpha=1) + plt.tight_layout() + plt.savefig(f"figures/post_spatial_distribution_{city}.png") + print(f"Save figure to [figures/post_spatial_distribution_{city}.png]") + + +def plot_spatial_distribution(): + plot_samples() + plot_prepost() + plot_post() diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..6fb90df --- /dev/null +++ b/requirements.txt @@ -0,0 +1,17 @@ +pytorch-lightning==1.1.4 +test-tube==0.7.1 +numpy==1.17.2 +tqdm>=4.36.1 +pretrainedmodels==0.7.4 +Pillow==6.2.0 +fire==0.2.1 +tensorboardX==1.9 +streamlit==0.53.0 +albumentations==0.4.6 +imgaug==0.4.0 +pytorch-ignite +scikit-learn==0.23.2 +seaborn==0.10.1 +segmentation-models-pytorch +torch==1.8.1+cu102 +torchvision==0.9.1+cu102 diff --git a/streetview/__init__.py b/streetview/__init__.py new file mode 100644 index 0000000..13c1fa3 --- /dev/null +++ b/streetview/__init__.py @@ -0,0 +1,7 @@ +from .download import download_streetview_image +from .sample import random_points, random_stratified_points +from .coverage import calculate_coverage +from .zoning import calculate_zone +from .road import calculate_road_length + + diff --git a/streetview/coverage.py b/streetview/coverage.py new file mode 100644 index 0000000..d84a5cd --- /dev/null +++ b/streetview/coverage.py @@ -0,0 +1,75 @@ +import os +import osmnx as ox +from shapely.geometry import Point +from shapely.ops import nearest_points +from geopy import distance +from tqdm import tqdm +import pickle as pkl +import pandas as pd +import geopandas as gpd +import multiprocessing +import numpy as np + +from util import constants as C + + +def get_buildings(city, city_tag): + tags = tags = {'building': True} + building_path = f"/share/data/camera/shape/building/{city}.pkl" + if False:#os.path.exists(building_path): + with open(building_path, "rb") as f: + gdf = pkl.load(f) + else: + gdf = ox.geometries_from_place(city_tag, tags) + with open(building_path, "wb") as f: + pkl.dump(gdf, f) + rows = [] + for rid, row in tqdm(gdf.iterrows(), total=len(gdf)): + if isinstance(row['geometry'], Point): + continue + row['centroid_lat'] = row['geometry'].centroid.y + row['centroid_lon'] = row['geometry'].centroid.x + rows.append(row) + buildings = gpd.GeoDataFrame(rows) + return buildings + +def get_coverage(lat, lon, buildings, t=0.005, default=50): + dist = default + try: + near_buildings = buildings.query(f"{lat-t} < centroid_lat < {lat+t} and \ + {lon-t} < centroid_lon < {lon+t}") + for rid, row in near_buildings.iterrows(): + building = row['geometry'] + + p = nearest_points(building, Point(lon, lat))[0] + _lat, _lon = p.y, p.x + _dist = distance.distance((lat, lon), (_lat, _lon)).m + dist = min(dist, _dist) + + except Exception as e: + print(str(e)) + pass + return 2 * dist + +def get_coverage_df(rtuple): + global buildings + rid, row = rtuple + lat, lon = row['lat'], row['lon'] + row['coverage'] = get_coverage(lat, lon, buildings) + return row + +def calculate_coverage(meta_path="/share/data/camera/deployment/verified_0425.csv"): + df = pd.read_csv(meta_path) + dfs = [] + for city, place in list(C.CITIES.items())[:10]: + print(f"Load building footprint [{place}]..") + buildings = get_buildings(city, place) + pano = df.query(f"city == '{city}'") + print(f"Start coverage calculation ..") + with multiprocessing.Pool(50) as p: + rows = list(tqdm(p.imap(get_coverage_df, pano.iterrows()), + total=len(pano), + smoothing=0.1)) + pano = pd.DataFrame(rows) + dfs.append(pano) + pd.concat(dfs).to_csv("/share/data/camera/deployment/verified_0425_coverage.csv", index=False) diff --git a/streetview/download.py b/streetview/download.py new file mode 100644 index 0000000..a01a5f4 --- /dev/null +++ b/streetview/download.py @@ -0,0 +1,130 @@ +import time +import traceback +import sys +import random +import hashlib +import hmac +import base64 +import fire +import os +import logging +import pandas as pd +import numpy as np +import multiprocessing as mp +from tqdm import tqdm +import requests as r +import urllib.parse as urlparse + +from util import constants as C + + +def _init_downloader(*args): + global downloader + downloader = SVImageDownloader(*args) + + +def _download(key): + global downloader + return downloader.download(key) + + +class SVImageDownloader: + def __init__(self, + key_to_sec, + save_dir, + sleep_time=0.0): + self.key_to_sec = key_to_sec + self.sleep_time = sleep_time + self.save_dir = save_dir + + def get_url(self, panoid, head, keysec): + key, secret = keysec + url = (f"https://maps.googleapis.com/maps/api/streetview?" + f"size={C.SV_SIZE}&pano={panoid}&fov={C.SV_FOV}&" + f"heading={head}&pitch={C.SV_PITCH}&key={key}") + url = urlparse.urlparse(url) + + # We only need to sign the path+query part of the string + url_to_sign = url.path + "?" + url.query + # Decode the private key into its binary format + # We need to decode the URL-encoded private key + decoded_key = base64.urlsafe_b64decode(secret) + + # Create a signature using the private key and the URL-encoded + # string using HMAC SHA1. This signature will be binary. + signature = hmac.new(decoded_key, + str.encode(url_to_sign), + hashlib.sha1) + + # Encode the binary signature into base64 for use within a URL + encoded_signature = base64.urlsafe_b64encode(signature.digest()) + original_url = f'{url.scheme}://{url.netloc}{url.path}?{url.query}' + + return original_url + "&signature=" + encoded_signature.decode() + + def download_image(self, + panoid, + head, + keysec, + save_path, + ): + os.makedirs(save_path, exist_ok=True) + url = self.get_url(panoid, head, keysec) + resp = r.get(url) + img_binary = resp._content + write_path = os.path.join(save_path, f'{panoid}_{head}.jpg') + with open(write_path, "wb+") as f: + f.write(img_binary) + + def download(self, rtuple): + rid, row = rtuple + time.sleep(np.random.rand() * self.sleep_time) + head = row['heading'] + try: + key_idx = rid % len(self.key_to_sec) + keysec = list(self.key_to_sec)[key_idx] + self.download_image(panoid=row['panoid'], + head=head, + keysec=keysec, + save_path=self.save_dir) + except BaseException as e: + traceback.print_exception(*sys.exc_info()) + return {"panoid": row['panoid'], + "heading": head, + "exception": str(e)} + return {"panoid": None} + +class ParallelSVImageDownloader: + def __init__(self, + key_to_sec, + save_dir, + sleep_time=0.0, + nthread=10, + ): + self.key_to_sec = key_to_sec + self.save_dir = save_dir + self.sleep_time = sleep_time + self.nthread = nthread + os.makedirs(self.save_dir, exist_ok=True) + + def download(self, df, sample_frac=1.0): + df = df.sample(frac=sample_frac) + + print("Start downloading ...") + with mp.Pool(self.nthread, + initializer=_init_downloader, + initargs=(self.key_to_sec, self.save_dir, self.sleep_time)) as p: + df = list(tqdm(p.imap(_download, df.iterrows()), + total=len(df), + smoothing=0.1)) + + image_errors = pd.DataFrame(df) + image_errors.dropna(subset=['panoid'], inplace=True) + return image_errors + + +def download_streetview_image(key, sec): + df = pd.read_csv("data/meta.csv") + downloader = ParallelSVImageDownloader(key_to_sec=[(key, sec)], + save_dir="./data/image") + downloader.download(df) diff --git a/streetview/evaluate.py b/streetview/evaluate.py new file mode 100644 index 0000000..d61cc33 --- /dev/null +++ b/streetview/evaluate.py @@ -0,0 +1,71 @@ +import seaborn as sb +import numpy as np +import osmnx as ox +from geopy.distance import distance +from matplotlib import pyplot as plt + + +def evaluate_coverage_distance(df): + sb.set_style("dark") + f, axes = plt.subplots(2, 2, figsize=(12,8)) + axes[0][0].title.set_text(f"Coverage of [2011-2015]: {len(df)} / 5000 = {len(df)/5000*100:.2f}%") + axes[0][1].title.set_text(f"Coverage of [2016-2020]: {len(df)} / 5000 = {len(df)/5000*100:.2f}%") + sb.countplot(x="year_pre", data=df, ax=axes[0][0], palette=['#432371']) + sb.countplot(x="year_post", data=df, ax=axes[0][1], palette=["#FAAE7B"]) + axes[0][0].set_xlabel('') + axes[0][1].set_xlabel('') + + d1, i1 = zip(*get_closest_distances(df, 'pre')) + d2, i2 = zip(*get_closest_distances(df, 'post')) + sb.lineplot(x=range(len(d1)), y=d1, ax=axes[1][0]) + sb.lineplot(x=range(len(d2)), y=d2, ax=axes[1][1]) + axes[1][0].title.set_text(f"Top 50 closest distance of [2011-2015] panoramas") + axes[1][1].title.set_text(f"Top 50 closest distance of [2016-2020] panoramas") + return f + +def get_closest_distances(df, suffix='pre', n=50): + lat = df[f'lat_{suffix}'].values + lon = df[f'lon_{suffix}'].values + D = np.sqrt(np.square(lat[:,np.newaxis] - lat) + np.square(lon[:,np.newaxis] - lon)) + D = np.tril(D) + np.triu(np.ones_like(D)) + d = [] + for i in range(n): + x, y = np.unravel_index(D.argmin(), D.shape) + _d = distance((lat[x], lon[x]), (lat[y], lon[y])).m + d.append((_d, x)) + D[x,:] = D[:,x] = 1 + return sorted(d) + +def evaluate_spatial_distribution(df, city): + sb.set_style("white") + G = ox.graph_from_place(city, network_type='drive') + try: + G = ox.simplify_graph(G) + except: + G = G + + f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24,12)) + ox.plot.plot_graph(G, + ax=ax1, + bgcolor='white', + node_color='#696969', + edge_color="#A9A9A9", + edge_linewidth=0.8, + node_size=0, + save=False, + show=False) + ax1.scatter(df.lon_anchor, df.lat_anchor, s=3, c='red', alpha=0.5) + ax1.scatter(df.lon_pre, df.lat_pre, s=3, c='blue', alpha=0.5) + + ox.plot.plot_graph(G, + ax=ax2, + bgcolor='white', + node_color='#696969', + edge_color="#A9A9A9", + edge_linewidth=0.8, + node_size=0, + save=False, + show=False) + ax2.scatter(df.lon_anchor, df.lat_anchor, s=3, c='red', alpha=0.5) + ax2.scatter(df.lon_post, df.lat_post, s=3, c='blue', alpha=0.5) + plt.show() \ No newline at end of file diff --git a/streetview/plot.py b/streetview/plot.py new file mode 100644 index 0000000..dce35bf --- /dev/null +++ b/streetview/plot.py @@ -0,0 +1,39 @@ +from matplotlib import pyplot as plt +import seaborn as sb +import osmnx as ox + + +def evaluate_spatial_distribution(df): + sb.set_style("white") + G = ox.graph_from_place('San Francisco, California, USA', + network_type='drive') + try: + G = ox.simplify_graph(G) + except: + G = G + + f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24,12)) + ox.plot.plot_graph(G, + ax=ax1, + bgcolor='white', + node_color='#696969', + edge_color="#A9A9A9", + edge_linewidth=0.8, + node_size=0, + save=False, + show=False) + ax1.scatter(df.lon_anchor, df.lat_anchor, s=3, c='red', alpha=0.5) + ax1.scatter(df.lon_pre, df.lat_pre, s=3, c='blue', alpha=0.5) + + ox.plot.plot_graph(G, + ax=ax2, + bgcolor='white', + node_color='#696969', + edge_color="#A9A9A9", + edge_linewidth=0.8, + node_size=0, + save=False, + show=False) + ax2.scatter(df.lon_anchor, df.lat_anchor, s=3, c='red', alpha=0.5) + ax2.scatter(df.lon_post, df.lat_post, s=3, c='blue', alpha=0.5) + plt.show() \ No newline at end of file diff --git a/streetview/road.py b/streetview/road.py new file mode 100644 index 0000000..0ec908d --- /dev/null +++ b/streetview/road.py @@ -0,0 +1,28 @@ +import os +import osmnx as ox +import pandas as pd +from tqdm import tqdm +import geopandas as gpd + +from util import constants as C + + +def calculate_road_length(): + metas = [] + for name, place in tqdm(C.CITIES.items(), total=len(C.CITIES)): + meta = ox.geocode_to_gdf(place) + meta = meta.to_crs('EPSG:3395') + meta['area'] = meta.geometry.apply(lambda x: x.area / 1e6) + + G = ox.graph_from_place(place, network_type='drive') + try: + G = ox.simplify_graph(G) + except: + G = G + gdf = ox.utils_graph.graph_to_gdfs(G, nodes=False, edges=True) + meta['length'] = gdf['length'].sum() / 1e3 + metas.append(meta) + stats = gpd.GeoDataFrame(pd.concat(metas))[['display_name', 'area', 'length']] \ + .rename(columns={"area": "area(km^2)", "length": "length(km)"}) + print(stats) + \ No newline at end of file diff --git a/streetview/sample.py b/streetview/sample.py new file mode 100644 index 0000000..d93fff6 --- /dev/null +++ b/streetview/sample.py @@ -0,0 +1,173 @@ +import numpy as np +import pandas as pd +from tqdm import tqdm +import random +import os +from geopy.distance import distance +from shapely.geometry import MultiPoint + +from .util import get_heading + +def random_points(edges, + n=100, + d=None, + verbose=False): + m = len(edges) + lengths = edges['length'].tolist() + total_length = edges.sum()['length'] + lengths_normalized = [l/total_length for l in lengths] + + rows = [] + points = [] + indices = np.random.choice(range(m), + size=2*n, + p=lengths_normalized) + pbar = tqdm(total=n) + i = j = 0 + while i < n: + index = indices[j] + row = edges.iloc[index] + u, v, key = edges.index[index] + line = row['geometry'] + offset = np.random.rand() * line.length + point = line.interpolate(offset) + lat = point.y + lon = point.x + flag = 1 + if d is not None: + for _lat, _lon in points: + _d = np.sqrt(np.square(lat-_lat) + np.square(lon-_lon)) + if _d < 1e-4 and distance((lat, lon), (_lat, _lon)).m < d: + flag = 0 + break + if flag: + i += 1 + pbar.update(1) + start = line.interpolate(offset*0.9) + end = line.interpolate(min(line.length, offset*1.1)) + heading = get_heading(start.y, start.x, end.y, end.x) + rows.append({"lat": lat, + "lon": lon, + "id": i, + "u": u, + "v": v, + "heading": heading, + "offset": offset, + "key": key}) + points.append((lat, lon)) + j += 1 + pbar.close() + return pd.DataFrame(rows) + +def random_stratified_points(edges, n=10): + m = len(edges) + rows = [] + for index in range(len(edges)): + row = edges.iloc[index] + u, v, key = edges.index[index] + line = row['geometry'] + + for _ in range(n): + offset = np.random.rand() * line.length + point = line.interpolate(offset) + lat = point.y + lon = point.x + rows.append({"lat": lat, + "lon": lon, + "u": u, + "v": v, + "key": key}) + return pd.DataFrame(rows) + +def select_panoid(meta, + n=5000, + distance=10, + selection="closest", + seed=123): + YEARS = ["2010 0.5)) % 360 + merged['heading_pre'] = merged['heading_pre'].apply(int) + merged['heading_post'] = merged['heading_post'].apply(int) + return merged + +def select_panoid_recent(meta, + year, + n=5000, + distance=10, + seed=123): + + # Set random seed + np.random.seed(seed) + random.seed(seed) + + # Filter by distance + meta = meta.query(f"distance < {distance}") + meta = meta.query(f"year >= {year}") + + # Sample anchor points + meta_sample = meta.drop_duplicates(['id']).sample(n, replace=False) + lat_anchor_chosen = meta_sample.lat_anchor.unique() + lon_anchor_chosen = meta_sample.lon_anchor.unique() + + # Sample for pre and post + meta_sub = meta[meta.lat_anchor.isin(lat_anchor_chosen)] + meta_sub = meta_sub[meta_sub.lon_anchor.isin(lon_anchor_chosen)] + + # Select panoid + + meta = meta_sub.sort_values(['lat_anchor','lon_anchor', 'distance']) \ + .groupby(['lat_anchor','lon_anchor']) \ + .first().reset_index() + + # Random select the orthogonal heading + meta['road_heading'] = meta.heading + meta['heading'] = (meta.heading + 360 + 90 - 180 * (np.random.rand(n) > 0.5)) % 360 + meta['heading'] = meta['heading'].apply(int) + meta['year'] = meta['year'].apply(int) + meta['month'] = meta['month'].apply(int) + meta['save_path'] = meta.apply(get_path, 1) + return meta + +def get_path(row): + panoid = row['panoid'] + heading = row['heading'] + return os.path.join("/scratch/haosheng/camera/", panoid[:2], panoid[2:4], panoid[4:6], panoid[6:], f"{heading}.png") diff --git a/streetview/util.py b/streetview/util.py new file mode 100644 index 0000000..ab68e6b --- /dev/null +++ b/streetview/util.py @@ -0,0 +1,8 @@ +from geographiclib.geodesic import Geodesic + + +def get_heading(lat1, lon1, lat2, lon2): + return Geodesic.WGS84.Inverse(lat1, lon1, lat2, lon2)['azi1'] + +#def is_close(lat1, lon1, lat2, lon2, d=None): + \ No newline at end of file diff --git a/streetview/zoning.py b/streetview/zoning.py new file mode 100644 index 0000000..aa43470 --- /dev/null +++ b/streetview/zoning.py @@ -0,0 +1,78 @@ +import geopandas as gpd +from geopy import distance +import pandas as pd +from shapely.geometry import Point +import numpy as np +from shapely.ops import nearest_points +from sklearn.neighbors import KDTree +from tqdm import tqdm +from matplotlib import pyplot as plt +import sys + +from util import constants as C + +CITIES = [('NYC', 'New York'), ('SF', 'San Francisco'), ('Seattle', 'Seattle'), ('Boston', 'Boston'), ('Chicago', 'Chicago'), ('Philadelphia', 'Philadelphia'), ('DC', 'Washington'), + ('LA', 'Los Angeles'), ('Baltimore', 'Baltimore'), ('Milwaukee', 'Milwaukee')] + +class Zoning: + def __init__(self, path): + self.path = path + self.gdf = gpd.read_file(self.path) + self.zone_type = self.gdf.zone_type.tolist() + self._get_centroids() + + def _get_centroids(self): + centroids = self.gdf.centroid + coords = [] + for i, c in enumerate(centroids): + if c is None or self.zone_type[i] == 'roads': + coords.append([10000, 10000]) + else: + coords.append([c.y, c.x]) + self.coords = KDTree(np.array(coords), leaf_size=30) + + def get_zone(self, lat, lon, n=-1, return_polygon=False): + if n == -1: + ind = range(len(self.gdf)) + else: + ind = self.coords.query(np.array([lat, lon])[np.newaxis,:], k=n, return_distance=False).flatten() + dist = 10000 + zone_type = None + zone = None + for i in list(ind): + _zone = self.gdf.geometry.iloc[i] + #for p in nearest_points(_zone, Point(lon, lat)): + p = nearest_points(_zone, Point(lon, lat))[0] + _lat, _lon = p.y, p.x + _dist = distance.distance((lat, lon), (_lat, _lon)).m + if _dist < dist: + zone_type = self.zone_type[i] + dist = _dist + zone = _zone + if return_polygon: + return zone_type, dist, zone + else: + return zone_type, dist + +def calculate_zone(meta_path="/share/data/camera/deployment/verified_0425.csv"): + + df = pd.read_csv(meta_path) + dfs = [] + for city, city_tag in CITIES: + print(f"Loading zoning shapefile for [{city_tag}]..") + try: + zone = Zoning(f"/share/data/camera/zoning/{city_tag}_zoning_clean.shp") + except Exception as e: + print(str(e)) + continue + + final = df.query(f"city == '{city}'") + rows = [] + for rid, row in tqdm(final.iterrows(), total=len(final)): + z, d = zone.get_zone(row['lat'], row['lon'], n=5) + row['zone_type'] = z + row['zone_distance'] = d + rows.append(row) + zone_final = pd.DataFrame(rows) + dfs.append(zone_final) + pd.concat(dfs).to_csv("/share/data/camera/deployment/verified_0425_zone.csv", index=False) diff --git a/util/constants.py b/util/constants.py new file mode 100644 index 0000000..8bc32ed --- /dev/null +++ b/util/constants.py @@ -0,0 +1,22 @@ +# SV configuration +SV_FOV = '90' +SV_SIZE = '640x640' +SV_PITCH = '0' + +# Network configuration +CITIES = {'NYC': 'New York City, New York, USA', + 'SF': 'San Francisco, California, USA', + 'Seattle': 'Seattle, Washington, USA', + 'Boston': 'Boston, Massachusetts, USA', + 'Chicago': 'Chicago, Illinois, USA', + 'Philadelphia': 'Philadelphia, Pennsylvania, USA', + 'DC': 'Washington, D.C, USA', + 'LA': 'Los Angeles, California, USA', + 'Baltimore': 'Baltimore, Maryland, USA', + 'Milwaukee': 'Milwaukee, Wisconsin, USA', + 'London': 'London, UK', + 'Paris': 'Paris, France', + 'Tokyo': 'Tokyo, Japan', + 'Bangkok': 'Bangkok, Thailand', + 'Singapore': 'Singapore, Singapore', + 'Seoul': 'Seoul, South Korea'}

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