503 lines
19 KiB
Python
503 lines
19 KiB
Python
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import glob
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import random
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import time
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import os
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import os.path as osp
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import cv2
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import matplotlib.pyplot as plt
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import numpy as np
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import torch
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import torch.nn.functional as F
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from utils import torch_utils
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import maskrcnn_benchmark.layers.nms as nms
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from external.lib.nms.cpu_nms import cpu_soft_nms
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# Set printoptions
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torch.set_printoptions(linewidth=1320, precision=5, profile='long')
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np.set_printoptions(linewidth=320, formatter={'float_kind': '{:11.5g}'.format}) # format short g, %precision=5
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def mkdir_if_missing(d):
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if not osp.exists(d):
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os.makedirs(d)
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def float3(x): # format floats to 3 decimals
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return float(format(x, '.3f'))
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def init_seeds(seed=0):
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random.seed(seed)
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np.random.seed(seed)
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torch_utils.init_seeds(seed=seed)
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def load_classes(path):
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"""
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Loads class labels at 'path'
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"""
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fp = open(path, 'r')
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names = fp.read().split('\n')
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return list(filter(None, names)) # filter removes empty strings (such as last line)
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def model_info(model): # Plots a line-by-line description of a PyTorch model
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n_p = sum(x.numel() for x in model.parameters()) # number parameters
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n_g = sum(x.numel() for x in model.parameters() if x.requires_grad) # number gradients
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print('\n%5s %50s %9s %12s %20s %12s %12s' % ('layer', 'name', 'gradient', 'parameters', 'shape', 'mu', 'sigma'))
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for i, (name, p) in enumerate(model.named_parameters()):
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name = name.replace('module_list.', '')
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print('%5g %50s %9s %12g %20s %12.3g %12.3g' % (
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i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std()))
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print('Model Summary: %g layers, %g parameters, %g gradients\n' % (i + 1, n_p, n_g))
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def plot_one_box(x, img, color=None, label=None, line_thickness=None): # Plots one bounding box on image img
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tl = line_thickness or round(0.0004 * max(img.shape[0:2])) + 1 # line thickness
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color = color or [random.randint(0, 255) for _ in range(3)]
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c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))
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cv2.rectangle(img, c1, c2, color, thickness=tl)
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if label:
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tf = max(tl - 1, 1) # font thickness
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t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]
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c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3
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cv2.rectangle(img, c1, c2, color, -1) # filled
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cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA)
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def weights_init_normal(m):
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classname = m.__class__.__name__
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if classname.find('Conv') != -1:
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torch.nn.init.normal_(m.weight.data, 0.0, 0.03)
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elif classname.find('BatchNorm2d') != -1:
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torch.nn.init.normal_(m.weight.data, 1.0, 0.03)
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torch.nn.init.constant_(m.bias.data, 0.0)
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def xyxy2xywh(x):
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# Convert bounding box format from [x1, y1, x2, y2] to [x, y, w, h]
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y = torch.zeros(x.shape) if x.dtype is torch.float32 else np.zeros(x.shape)
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y[:, 0] = (x[:, 0] + x[:, 2]) / 2
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y[:, 1] = (x[:, 1] + x[:, 3]) / 2
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y[:, 2] = x[:, 2] - x[:, 0]
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y[:, 3] = x[:, 3] - x[:, 1]
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return y
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def xywh2xyxy(x):
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# Convert bounding box format from [x, y, w, h] to [x1, y1, x2, y2]
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y = torch.zeros(x.shape) if x.dtype is torch.float32 else np.zeros(x.shape)
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y[:, 0] = (x[:, 0] - x[:, 2] / 2)
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y[:, 1] = (x[:, 1] - x[:, 3] / 2)
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y[:, 2] = (x[:, 0] + x[:, 2] / 2)
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y[:, 3] = (x[:, 1] + x[:, 3] / 2)
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return y
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def scale_coords(img_size, coords, img0_shape):
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# Rescale x1, y1, x2, y2 from 416 to image size
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gain_w = float(img_size[0]) / img0_shape[1] # gain = old / new
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gain_h = float(img_size[1]) / img0_shape[0]
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gain = min(gain_w, gain_h)
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pad_x = (img_size[0] - img0_shape[1] * gain) / 2 # width padding
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pad_y = (img_size[1] - img0_shape[0] * gain) / 2 # height padding
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coords[:, [0, 2]] -= pad_x
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coords[:, [1, 3]] -= pad_y
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coords[:, 0:4] /= gain
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coords[:, :4] = torch.clamp(coords[:, :4], min=0)
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return coords
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def ap_per_class(tp, conf, pred_cls, target_cls):
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""" Compute the average precision, given the recall and precision curves.
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Method originally from https://github.com/rafaelpadilla/Object-Detection-Metrics.
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# Arguments
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tp: True positives (list).
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conf: Objectness value from 0-1 (list).
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pred_cls: Predicted object classes (list).
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target_cls: True object classes (list).
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# Returns
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The average precision as computed in py-faster-rcnn.
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"""
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# lists/pytorch to numpy
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tp, conf, pred_cls, target_cls = np.array(tp), np.array(conf), np.array(pred_cls), np.array(target_cls)
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# Sort by objectness
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i = np.argsort(-conf)
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tp, conf, pred_cls = tp[i], conf[i], pred_cls[i]
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# Find unique classes
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unique_classes = np.unique(np.concatenate((pred_cls, target_cls), 0))
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# Create Precision-Recall curve and compute AP for each class
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ap, p, r = [], [], []
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for c in unique_classes:
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i = pred_cls == c
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n_gt = sum(target_cls == c) # Number of ground truth objects
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n_p = sum(i) # Number of predicted objects
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if (n_p == 0) and (n_gt == 0):
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continue
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elif (n_p == 0) or (n_gt == 0):
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ap.append(0)
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r.append(0)
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p.append(0)
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else:
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# Accumulate FPs and TPs
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fpc = np.cumsum(1 - tp[i])
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tpc = np.cumsum(tp[i])
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# Recall
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recall_curve = tpc / (n_gt + 1e-16)
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r.append(tpc[-1] / (n_gt + 1e-16))
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# Precision
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precision_curve = tpc / (tpc + fpc)
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p.append(tpc[-1] / (tpc[-1] + fpc[-1]))
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# AP from recall-precision curve
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ap.append(compute_ap(recall_curve, precision_curve))
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return np.array(ap), unique_classes.astype('int32'), np.array(r), np.array(p)
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def compute_ap(recall, precision):
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""" Compute the average precision, given the recall and precision curves.
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Code originally from https://github.com/rbgirshick/py-faster-rcnn.
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# Arguments
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recall: The recall curve (list).
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precision: The precision curve (list).
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# Returns
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The average precision as computed in py-faster-rcnn.
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"""
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# correct AP calculation
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# first append sentinel values at the end
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mrec = np.concatenate(([0.], recall, [1.]))
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mpre = np.concatenate(([0.], precision, [0.]))
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# compute the precision envelope
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for i in range(mpre.size - 1, 0, -1):
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mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i])
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# to calculate area under PR curve, look for points
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# where X axis (recall) changes value
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i = np.where(mrec[1:] != mrec[:-1])[0]
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# and sum (\Delta recall) * prec
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ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])
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return ap
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def bbox_iou(box1, box2, x1y1x2y2=False):
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"""
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Returns the IoU of two bounding boxes
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"""
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N, M = len(box1), len(box2)
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if x1y1x2y2:
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# Get the coordinates of bounding boxes
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b1_x1, b1_y1, b1_x2, b1_y2 = box1[:, 0], box1[:, 1], box1[:, 2], box1[:, 3]
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b2_x1, b2_y1, b2_x2, b2_y2 = box2[:, 0], box2[:, 1], box2[:, 2], box2[:, 3]
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else:
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# Transform from center and width to exact coordinates
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b1_x1, b1_x2 = box1[:, 0] - box1[:, 2] / 2, box1[:, 0] + box1[:, 2] / 2
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b1_y1, b1_y2 = box1[:, 1] - box1[:, 3] / 2, box1[:, 1] + box1[:, 3] / 2
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b2_x1, b2_x2 = box2[:, 0] - box2[:, 2] / 2, box2[:, 0] + box2[:, 2] / 2
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b2_y1, b2_y2 = box2[:, 1] - box2[:, 3] / 2, box2[:, 1] + box2[:, 3] / 2
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# get the coordinates of the intersection rectangle
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inter_rect_x1 = torch.max(b1_x1.unsqueeze(1), b2_x1)
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inter_rect_y1 = torch.max(b1_y1.unsqueeze(1), b2_y1)
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inter_rect_x2 = torch.min(b1_x2.unsqueeze(1), b2_x2)
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inter_rect_y2 = torch.min(b1_y2.unsqueeze(1), b2_y2)
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# Intersection area
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inter_area = torch.clamp(inter_rect_x2 - inter_rect_x1, 0) * torch.clamp(inter_rect_y2 - inter_rect_y1, 0)
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# Union Area
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b1_area = ((b1_x2 - b1_x1) * (b1_y2 - b1_y1))
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b1_area = ((b1_x2 - b1_x1) * (b1_y2 - b1_y1)).view(-1,1).expand(N,M)
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b2_area = ((b2_x2 - b2_x1) * (b2_y2 - b2_y1)).view(1,-1).expand(N,M)
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return inter_area / (b1_area + b2_area - inter_area + 1e-16)
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def build_targets_max(target, anchor_wh, nA, nC, nGh, nGw):
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"""
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returns nT, nCorrect, tx, ty, tw, th, tconf, tcls
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"""
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nB = len(target) # number of images in batch
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txy = torch.zeros(nB, nA, nGh, nGw, 2).cuda() # batch size, anchors, grid size
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twh = torch.zeros(nB, nA, nGh, nGw, 2).cuda()
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tconf = torch.LongTensor(nB, nA, nGh, nGw).fill_(0).cuda()
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tcls = torch.ByteTensor(nB, nA, nGh, nGw, nC).fill_(0).cuda() # nC = number of classes
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tid = torch.LongTensor(nB, nA, nGh, nGw, 1).fill_(-1).cuda()
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for b in range(nB):
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t = target[b]
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t_id = t[:, 1].clone().long().cuda()
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t = t[:,[0,2,3,4,5]]
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nTb = len(t) # number of targets
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if nTb == 0:
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continue
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#gxy, gwh = t[:, 1:3] * nG, t[:, 3:5] * nG
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gxy, gwh = t[: , 1:3].clone() , t[:, 3:5].clone()
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gxy[:, 0] = gxy[:, 0] * nGw
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gxy[:, 1] = gxy[:, 1] * nGh
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gwh[:, 0] = gwh[:, 0] * nGw
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gwh[:, 1] = gwh[:, 1] * nGh
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gi = torch.clamp(gxy[:, 0], min=0, max=nGw -1).long()
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gj = torch.clamp(gxy[:, 1], min=0, max=nGh -1).long()
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# Get grid box indices and prevent overflows (i.e. 13.01 on 13 anchors)
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#gi, gj = torch.clamp(gxy.long(), min=0, max=nG - 1).t()
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#gi, gj = gxy.long().t()
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# iou of targets-anchors (using wh only)
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box1 = gwh
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box2 = anchor_wh.unsqueeze(1)
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inter_area = torch.min(box1, box2).prod(2)
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iou = inter_area / (box1.prod(1) + box2.prod(2) - inter_area + 1e-16)
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# Select best iou_pred and anchor
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iou_best, a = iou.max(0) # best anchor [0-2] for each target
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# Select best unique target-anchor combinations
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if nTb > 1:
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_, iou_order = torch.sort(-iou_best) # best to worst
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# Unique anchor selection
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u = torch.stack((gi, gj, a), 0)[:, iou_order]
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# _, first_unique = np.unique(u, axis=1, return_index=True) # first unique indices
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first_unique = return_torch_unique_index(u, torch.unique(u, dim=1)) # torch alternative
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i = iou_order[first_unique]
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# best anchor must share significant commonality (iou) with target
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i = i[iou_best[i] > 0.60] # TODO: examine arbitrary threshold
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if len(i) == 0:
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continue
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a, gj, gi, t = a[i], gj[i], gi[i], t[i]
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t_id = t_id[i]
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if len(t.shape) == 1:
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t = t.view(1, 5)
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else:
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if iou_best < 0.60:
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continue
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tc, gxy, gwh = t[:, 0].long(), t[:, 1:3].clone(), t[:, 3:5].clone()
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gxy[:, 0] = gxy[:, 0] * nGw
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gxy[:, 1] = gxy[:, 1] * nGh
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gwh[:, 0] = gwh[:, 0] * nGw
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gwh[:, 1] = gwh[:, 1] * nGh
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# XY coordinates
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txy[b, a, gj, gi] = gxy - gxy.floor()
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# Width and height
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twh[b, a, gj, gi] = torch.log(gwh / anchor_wh[a]) # yolo method
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# twh[b, a, gj, gi] = torch.sqrt(gwh / anchor_wh[a]) / 2 # power method
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# One-hot encoding of label
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tcls[b, a, gj, gi, tc] = 1
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tconf[b, a, gj, gi] = 1
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tid[b, a, gj, gi] = t_id.unsqueeze(1)
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tbox = torch.cat([txy, twh], -1)
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return tconf, tbox, tid
|
||
|
|
|
||
|
|
|
||
|
|
|
||
|
|
def build_targets_thres(target, anchor_wh, nA, nC, nGh, nGw):
|
||
|
|
ID_THRESH = 0.5
|
||
|
|
FG_THRESH = 0.5
|
||
|
|
BG_THRESH = 0.4
|
||
|
|
nB = len(target) # number of images in batch
|
||
|
|
assert(len(anchor_wh)==nA)
|
||
|
|
|
||
|
|
tbox = torch.zeros(nB, nA, nGh, nGw, 4).cuda() # batch size, anchors, grid size
|
||
|
|
tconf = torch.LongTensor(nB, nA, nGh, nGw).fill_(0).cuda()
|
||
|
|
tid = torch.LongTensor(nB, nA, nGh, nGw, 1).fill_(-1).cuda()
|
||
|
|
for b in range(nB):
|
||
|
|
t = target[b]
|
||
|
|
t_id = t[:, 1].clone().long().cuda()
|
||
|
|
t = t[:,[0,2,3,4,5]]
|
||
|
|
nTb = len(t) # number of targets
|
||
|
|
if nTb == 0:
|
||
|
|
continue
|
||
|
|
|
||
|
|
gxy, gwh = t[: , 1:3].clone() , t[:, 3:5].clone()
|
||
|
|
gxy[:, 0] = gxy[:, 0] * nGw
|
||
|
|
gxy[:, 1] = gxy[:, 1] * nGh
|
||
|
|
gwh[:, 0] = gwh[:, 0] * nGw
|
||
|
|
gwh[:, 1] = gwh[:, 1] * nGh
|
||
|
|
gxy[:, 0] = torch.clamp(gxy[:, 0], min=0, max=nGw -1)
|
||
|
|
gxy[:, 1] = torch.clamp(gxy[:, 1], min=0, max=nGh -1)
|
||
|
|
|
||
|
|
gt_boxes = torch.cat([gxy, gwh], dim=1) # Shape Ngx4 (xc, yc, w, h)
|
||
|
|
|
||
|
|
anchor_mesh = generate_anchor(nGh, nGw, anchor_wh)
|
||
|
|
anchor_list = anchor_mesh.permute(0,2,3,1).contiguous().view(-1, 4) # Shpae (nA x nGh x nGw) x 4
|
||
|
|
#print(anchor_list.shape, gt_boxes.shape)
|
||
|
|
iou_pdist = bbox_iou(anchor_list, gt_boxes) # Shape (nA x nGh x nGw) x Ng
|
||
|
|
iou_max, max_gt_index = torch.max(iou_pdist, dim=1) # Shape (nA x nGh x nGw), both
|
||
|
|
|
||
|
|
iou_map = iou_max.view(nA, nGh, nGw)
|
||
|
|
gt_index_map = max_gt_index.view(nA, nGh, nGw)
|
||
|
|
|
||
|
|
#nms_map = pooling_nms(iou_map, 3)
|
||
|
|
|
||
|
|
id_index = iou_map > ID_THRESH
|
||
|
|
fg_index = iou_map > FG_THRESH
|
||
|
|
bg_index = iou_map < BG_THRESH
|
||
|
|
ign_index = (iou_map < FG_THRESH) * (iou_map > BG_THRESH)
|
||
|
|
tconf[b][fg_index] = 1
|
||
|
|
tconf[b][bg_index] = 0
|
||
|
|
tconf[b][ign_index] = -1
|
||
|
|
|
||
|
|
gt_index = gt_index_map[fg_index]
|
||
|
|
gt_box_list = gt_boxes[gt_index]
|
||
|
|
gt_id_list = t_id[gt_index_map[id_index]]
|
||
|
|
#print(gt_index.shape, gt_index_map[id_index].shape, gt_boxes.shape)
|
||
|
|
if torch.sum(fg_index) > 0:
|
||
|
|
tid[b][id_index] = gt_id_list.unsqueeze(1)
|
||
|
|
fg_anchor_list = anchor_list.view(nA, nGh, nGw, 4)[fg_index]
|
||
|
|
delta_target = encode_delta(gt_box_list, fg_anchor_list)
|
||
|
|
tbox[b][fg_index] = delta_target
|
||
|
|
return tconf, tbox, tid
|
||
|
|
|
||
|
|
def generate_anchor(nGh, nGw, anchor_wh):
|
||
|
|
nA = len(anchor_wh)
|
||
|
|
yy, xx =torch.meshgrid(torch.arange(nGh), torch.arange(nGw))
|
||
|
|
xx, yy = xx.cuda(), yy.cuda()
|
||
|
|
|
||
|
|
mesh = torch.stack([xx, yy], dim=0) # Shape 2, nGh, nGw
|
||
|
|
mesh = mesh.unsqueeze(0).repeat(nA,1,1,1).float() # Shape nA x 2 x nGh x nGw
|
||
|
|
anchor_offset_mesh = anchor_wh.unsqueeze(-1).unsqueeze(-1).repeat(1, 1, nGh,nGw) # Shape nA x 2 x nGh x nGw
|
||
|
|
anchor_mesh = torch.cat([mesh, anchor_offset_mesh], dim=1) # Shape nA x 4 x nGh x nGw
|
||
|
|
return anchor_mesh
|
||
|
|
|
||
|
|
def encode_delta(gt_box_list, fg_anchor_list):
|
||
|
|
px, py, pw, ph = fg_anchor_list[:, 0], fg_anchor_list[:,1], \
|
||
|
|
fg_anchor_list[:, 2], fg_anchor_list[:,3]
|
||
|
|
gx, gy, gw, gh = gt_box_list[:, 0], gt_box_list[:, 1], \
|
||
|
|
gt_box_list[:, 2], gt_box_list[:, 3]
|
||
|
|
dx = (gx - px) / pw
|
||
|
|
dy = (gy - py) / ph
|
||
|
|
dw = torch.log(gw/pw)
|
||
|
|
dh = torch.log(gh/ph)
|
||
|
|
return torch.stack([dx, dy, dw, dh], dim=1)
|
||
|
|
|
||
|
|
def decode_delta(delta, fg_anchor_list):
|
||
|
|
px, py, pw, ph = fg_anchor_list[:, 0], fg_anchor_list[:,1], \
|
||
|
|
fg_anchor_list[:, 2], fg_anchor_list[:,3]
|
||
|
|
dx, dy, dw, dh = delta[:, 0], delta[:, 1], delta[:, 2], delta[:, 3]
|
||
|
|
gx = pw * dx + px
|
||
|
|
gy = ph * dy + py
|
||
|
|
gw = pw * torch.exp(dw)
|
||
|
|
gh = ph * torch.exp(dh)
|
||
|
|
return torch.stack([gx, gy, gw, gh], dim=1)
|
||
|
|
|
||
|
|
def decode_delta_map(delta_map, anchors):
|
||
|
|
'''
|
||
|
|
:param: delta_map, shape (nB, nA, nGh, nGw, 4)
|
||
|
|
:param: anchors, shape (nA,4)
|
||
|
|
'''
|
||
|
|
nB, nA, nGh, nGw, _ = delta_map.shape
|
||
|
|
anchor_mesh = generate_anchor(nGh, nGw, anchors)
|
||
|
|
anchor_mesh = anchor_mesh.permute(0,2,3,1).contiguous() # Shpae (nA x nGh x nGw) x 4
|
||
|
|
anchor_mesh = anchor_mesh.unsqueeze(0).repeat(nB,1,1,1,1)
|
||
|
|
pred_list = decode_delta(delta_map.view(-1,4), anchor_mesh.view(-1,4))
|
||
|
|
pred_map = pred_list.view(nB, nA, nGh, nGw, 4)
|
||
|
|
return pred_map
|
||
|
|
|
||
|
|
|
||
|
|
def pooling_nms(heatmap, kernel=1):
|
||
|
|
pad = (kernel -1 ) // 2
|
||
|
|
hmax = F.max_pool2d(heatmap, (kernel, kernel), stride=1, padding=pad)
|
||
|
|
keep = (hmax == heatmap).float()
|
||
|
|
return keep * heatmap
|
||
|
|
|
||
|
|
def soft_nms(dets, sigma=0.5, Nt=0.3, threshold=0.05, method=1):
|
||
|
|
keep = cpu_soft_nms(np.ascontiguousarray(dets, dtype=np.float32),
|
||
|
|
np.float32(sigma), np.float32(Nt),
|
||
|
|
np.float32(threshold),
|
||
|
|
np.uint8(method))
|
||
|
|
return keep
|
||
|
|
|
||
|
|
def non_max_suppression(prediction, conf_thres=0.5, nms_thres=0.4, method=-1):
|
||
|
|
"""
|
||
|
|
Removes detections with lower object confidence score than 'conf_thres'
|
||
|
|
Non-Maximum Suppression to further filter detections.
|
||
|
|
Returns detections with shape:
|
||
|
|
(x1, y1, x2, y2, object_conf, class_score, class_pred)
|
||
|
|
"""
|
||
|
|
|
||
|
|
output = [None for _ in range(len(prediction))]
|
||
|
|
for image_i, pred in enumerate(prediction):
|
||
|
|
# Filter out confidence scores below threshold
|
||
|
|
# Get score and class with highest confidence
|
||
|
|
|
||
|
|
v = pred[:, 4] > conf_thres
|
||
|
|
v = v.nonzero().squeeze()
|
||
|
|
if len(v.shape) == 0:
|
||
|
|
v = v.unsqueeze(0)
|
||
|
|
|
||
|
|
pred = pred[v]
|
||
|
|
|
||
|
|
# If none are remaining => process next image
|
||
|
|
nP = pred.shape[0]
|
||
|
|
if not nP:
|
||
|
|
continue
|
||
|
|
# From (center x, center y, width, height) to (x1, y1, x2, y2)
|
||
|
|
pred[:, :4] = xywh2xyxy(pred[:, :4])
|
||
|
|
|
||
|
|
|
||
|
|
# Non-maximum suppression
|
||
|
|
if method == -1:
|
||
|
|
nms_indices = nms(pred[:, :4], pred[:, 4], nms_thres)
|
||
|
|
else:
|
||
|
|
dets = pred[:, :5].clone().contiguous().data.cpu().numpy()
|
||
|
|
nms_indices = soft_nms(dets, Nt=nms_thres, method=method)
|
||
|
|
det_max = pred[nms_indices]
|
||
|
|
|
||
|
|
if len(det_max) > 0:
|
||
|
|
# Add max detections to outputs
|
||
|
|
output[image_i] = det_max if output[image_i] is None else torch.cat((output[image_i], det_max))
|
||
|
|
|
||
|
|
return output
|
||
|
|
|
||
|
|
|
||
|
|
def return_torch_unique_index(u, uv):
|
||
|
|
n = uv.shape[1] # number of columns
|
||
|
|
first_unique = torch.zeros(n, device=u.device).long()
|
||
|
|
for j in range(n):
|
||
|
|
first_unique[j] = (uv[:, j:j + 1] == u).all(0).nonzero()[0]
|
||
|
|
|
||
|
|
return first_unique
|
||
|
|
|
||
|
|
|
||
|
|
def strip_optimizer_from_checkpoint(filename='weights/best.pt'):
|
||
|
|
# Strip optimizer from *.pt files for lighter files (reduced by 2/3 size)
|
||
|
|
|
||
|
|
a = torch.load(filename, map_location='cpu')
|
||
|
|
a['optimizer'] = []
|
||
|
|
torch.save(a, filename.replace('.pt', '_lite.pt'))
|
||
|
|
|
||
|
|
|
||
|
|
def plot_results():
|
||
|
|
# Plot YOLO training results file 'results.txt'
|
||
|
|
# import os; os.system('wget https://storage.googleapis.com/ultralytics/yolov3/results_v1.txt')
|
||
|
|
|
||
|
|
plt.figure(figsize=(14, 7))
|
||
|
|
s = ['X + Y', 'Width + Height', 'Confidence', 'Classification', 'Total Loss', 'mAP', 'Recall', 'Precision']
|
||
|
|
files = sorted(glob.glob('results*.txt'))
|
||
|
|
for f in files:
|
||
|
|
results = np.loadtxt(f, usecols=[2, 3, 4, 5, 6, 9, 10, 11]).T # column 11 is mAP
|
||
|
|
x = range(1, results.shape[1])
|
||
|
|
for i in range(8):
|
||
|
|
plt.subplot(2, 4, i + 1)
|
||
|
|
plt.plot(x, results[i, x], marker='.', label=f)
|
||
|
|
plt.title(s[i])
|
||
|
|
if i == 0:
|
||
|
|
plt.legend()
|