diff --git a/pyproject.toml b/pyproject.toml
index 246bde2..7d52f14 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -13,7 +13,6 @@ process_data = "trap.process_data:main"
blacklist = "trap.tools:blacklist_tracks"
rewrite_tracks = "trap.tools:rewrite_raw_track_files"
-
[tool.poetry.dependencies]
python = "^3.10,<3.12,"
diff --git a/test_custom_rnn.ipynb b/test_custom_rnn.ipynb
index 73d00ff..23bdbff 100644
--- a/test_custom_rnn.ipynb
+++ b/test_custom_rnn.ipynb
@@ -48,8 +48,8 @@
"# SRC_H = \"../DATASETS/hof/webcam20231103-2-homography.txt\"\n",
"SRC_H = None\n",
"CACHE_DIR = \"EXPERIMENTS/cache/hof2/\"\n",
- "SMOOTHING = True # hof-yolo is already smoothed, hof2 isn't\n",
- "SMOOTHING_WINDOW=3 #2"
+ "# SMOOTHING = True # hof-yolo is already smoothed, hof2 isn't\n",
+ "# SMOOTHING_WINDOW=3 #2"
]
},
{
@@ -58,12 +58,14 @@
"metadata": {},
"outputs": [],
"source": [
- "in_fields = ['proj_x', 'proj_y', 'vx', 'vy', 'ax', 'ay']\n",
+ "in_fields = ['x', 'y', 'vx', 'vy', 'ax', 'ay'] #, 'dt'] (WARNING: dt column contains NaN)\n",
"# out_fields = ['v', 'heading']\n",
"# velocity cannot be negative, and heading is circular (modulo), this makes it harder to optimise than a linear space, so try to use components\n",
"# an we can use simple MSE loss (I guess?)\n",
- "out_fields = ['vx', 'vy']\n",
- "window = int(FPS*1.5)"
+ "out_fields = ['dx', 'dy']\n",
+ "SAMPLE_STEP = 5 # 1/5, for 12fps leads to effectively 12/5=2.4fps\n",
+ "GRID_SIZE = 2 # round items on a grid of 2 points per meter (None to disable rounding)\n",
+ "window = 8 #int(FPS*1.5 / SAMPLE_STEP)"
]
},
{
@@ -85,13 +87,13 @@
"print(device)\n",
"\n",
"# Hyperparameters\n",
- "input_size = len(in_fields)\n",
- "hidden_size = 256\n",
- "num_layers = 3\n",
- "output_size = len(out_fields)\n",
+ "input_size = len(in_fields) #in_d\n",
+ "hidden_size = 64 # hidden_d\n",
+ "num_layers = 1 # num_hidden\n",
+ "output_size = len(out_fields) # out_d\n",
"learning_rate = 0.005 #0.01 #0.005\n",
- "batch_size = 256\n",
- "num_epochs = 1000"
+ "batch_size = 512\n",
+ "num_epochs = 1000\n"
]
},
{
@@ -121,727 +123,9 @@
"source": [
"from pathlib import Path\n",
"from trap.tools import load_tracks_from_csv\n",
+ "from trap.tools import filter_short_tracks, normalise_position\n",
"\n",
- "data = load_tracks_from_csv(Path(SRC_CSV), FPS, 2, 5 )"
- ]
- },
- {
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- ],
- "text/plain": [
- " l t w h x \\\n",
- "track_id frame_id \n",
- "1 342 1393.736572 0.000000 67.613647 121.391151 1363.3164 \n",
- " 343 1391.775879 0.852371 78.562622 141.050934 1359.1885 \n",
- " 346 1392.164551 7.758987 85.757324 154.357971 1355.7444 \n",
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- " 32722 1702.384766 750.754517 123.435425 180.945618 1395.4082 \n",
- "\n",
- " y state \n",
- "track_id frame_id \n",
- "1 342 232.92647 2 \n",
- " 343 266.06586 2 \n",
- " 346 297.67404 2 \n",
- " 347 308.20670 2 \n",
- " 348 310.09225 2 \n",
- "... ... ... \n",
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- " 32722 1074.06500 2 \n",
- "\n",
- "[326960 rows x 7 columns]"
- ]
- },
- "execution_count": 6,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "data = pd.read_csv(SRC_CSV, delimiter=\"\\t\", index_col=False, header=None)\n",
- "# data.columns = ['frame_id', 'track_id', 'pos_x', 'pos_y', 'width', 'height']#, '_x', '_y,']\n",
- "data.columns = ['frame_id', 'track_id', 'l', 't', 'w', 'h', 'x', 'y', 'state']#, '_x', '_y,']\n",
- "data['frame_id'] = pd.to_numeric(data['frame_id'], downcast='integer')\n",
- "data['frame_id'] = data['frame_id'] // 10 # compatibility with Trajectron++\n",
- "\n",
- "data.sort_values(by=['track_id', 'frame_id'],inplace=True)\n",
- "\n",
- "data.set_index(['track_id', 'frame_id'])"
+ "data= load_tracks_from_csv(Path(SRC_CSV), FPS, GRID_SIZE, SAMPLE_STEP )"
]
},
{
@@ -850,684 +134,9 @@
"metadata": {},
"outputs": [],
"source": [
- "# cm to meter\n",
- "data['x'] = data['x']/100\n",
- "data['y'] = data['y']/100"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {},
- "outputs": [],
- "source": [
- "data['diff'] = data.groupby(['track_id'])['frame_id'].diff() #.fillna(0)\n",
- "data['diff'] = pd.to_numeric(data['diff'], downcast='integer')"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "326960it [06:37, 821.55it/s] "
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "was: 326960 added: 85138 new length: 412098\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "\n"
- ]
- }
- ],
- "source": [
- "missing=0\n",
- "old_size=len(data)\n",
- "# slow way to append missing steps to the dataset\n",
- "for ind, row in tqdm(data.iterrows()):\n",
- " if row['diff'] > 1:\n",
- " for s in range(1, int(row['diff'])):\n",
- " # add as many entries as missing\n",
- " missing += 1\n",
- " data.loc[len(data)] = [row['frame_id']-s, row['track_id'], np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, 1, 1]\n",
- " # new_frame = [data.loc[ind-1]['frame_id']+s, row['track_id'], np.nan, np.nan, np.nan, np.nan, np.nan]\n",
- " # data.loc[len(data)] = new_frame\n",
- "\n",
- "print('was:', old_size, 'added:', missing, 'new length:', len(data))\n",
- "# now sort, so that the added data is in the right place\n",
- "data.sort_values(by=['track_id', 'frame_id'], inplace=True)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {},
- "outputs": [],
- "source": [
- "# interpolate missing data\n",
- "df=data.copy()\n",
- "df = df.groupby('track_id').apply(lambda group: group.interpolate(method='linear'))\n",
- "df.reset_index(drop=True, inplace=True)\n",
- "data = df\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Running smoother\n"
- ]
- }
- ],
- "source": [
- "from trap.tracker import Smoother\n",
- "\n",
- "if SMOOTHING:\n",
- " df=data.copy()\n",
- " if 'x_raw' not in df:\n",
- " df['x_raw'] = df['x']\n",
- " if 'y_raw' not in df:\n",
- " df['y_raw'] = df['y']\n",
- "\n",
- " print(\"Running smoother\")\n",
- " # print(df)\n",
- " # from tsmoothie.smoother import KalmanSmoother, ConvolutionSmoother\n",
- " smoother = Smoother(convolution=False)\n",
- " def smoothing(data):\n",
- " # smoother = ConvolutionSmoother(window_len=SMOOTHING_WINDOW, window_type='ones', copy=None)\n",
- " return smoother.smooth(data).tolist()\n",
- " # df=df.assign(smooth_data=smoother.smooth_data[0])\n",
- " # return smoother.smooth_data[0].tolist()\n",
- "\n",
- " # operate smoothing per axis\n",
- " df['x'] = df.groupby('track_id')['x_raw'].transform(smoothing)\n",
- " df['y'] = df.groupby('track_id')['y_raw'].transform(smoothing)\n",
- " \n",
- "\n",
- " data = df\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [
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- "
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- "text/plain": [
- " frame_id track_id l t w h \\\n",
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- "1 10.0 1.0 0.000000 565.116699 88.801704 171.334290 \n",
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- "320184 60160.0 3632.0 1834.013672 649.612122 153.686646 160.874023 \n",
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- "320187 60163.0 3632.0 1862.792725 658.719971 141.984253 149.052307 \n",
- "\n",
- " x y state diff x_raw y_raw \n",
- "0 0.855100 7.136193 2.0 NaN 0.881595 7.341152 \n",
- "1 0.873132 7.235233 2.0 1.0 0.870703 7.309168 \n",
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- "\n",
- "[320188 rows x 12 columns]"
- ]
- },
- "execution_count": 12,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# del data['diff']\n",
- "# recalculate diff\n",
- "data['diff'] = data.groupby(['track_id'])['frame_id'].diff()\n",
- "data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
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- " \n",
- " 320184 | \n",
- " 60160.0 | \n",
- " 3632 | \n",
- " 1834.013672 | \n",
- " 649.612122 | \n",
- " 153.686646 | \n",
- " 160.874023 | \n",
- " 15.033432 | \n",
- " 9.870472 | \n",
- " 2.0 | \n",
- " 1.0 | \n",
- " 15.244872 | \n",
- " 10.047117 | \n",
- " 0.083333 | \n",
- "
\n",
- " \n",
- " 320185 | \n",
- " 60161.0 | \n",
- " 3632 | \n",
- " 1845.373047 | \n",
- " 651.249756 | \n",
- " 147.178589 | \n",
- " 153.729248 | \n",
- " 15.211560 | \n",
- " 9.943236 | \n",
- " 2.0 | \n",
- " 1.0 | \n",
- " 15.318496 | \n",
- " 10.015218 | \n",
- " 0.083333 | \n",
- "
\n",
- " \n",
- " 320186 | \n",
- " 60162.0 | \n",
- " 3632 | \n",
- " 1857.388916 | \n",
- " 650.908203 | \n",
- " 136.407349 | \n",
- " 142.354614 | \n",
- " 15.377673 | \n",
- " 10.008965 | \n",
- " 2.0 | \n",
- " 1.0 | \n",
- " 15.400203 | \n",
- " 9.935355 | \n",
- " 0.083333 | \n",
- "
\n",
- " \n",
- " 320187 | \n",
- " 60163.0 | \n",
- " 3632 | \n",
- " 1862.792725 | \n",
- " 658.719971 | \n",
- " 141.984253 | \n",
- " 149.052307 | \n",
- " 15.538255 | \n",
- " 10.075935 | \n",
- " 2.0 | \n",
- " 1.0 | \n",
- " 15.416893 | \n",
- " 10.051785 | \n",
- " 0.083333 | \n",
- "
\n",
- " \n",
- "
\n",
- "
320188 rows × 13 columns
\n",
- "
"
- ],
- "text/plain": [
- " frame_id track_id l t w h \\\n",
- "0 9.0 1 0.000000 565.566162 88.795326 173.917542 \n",
- "1 10.0 1 0.000000 565.116699 88.801704 171.334290 \n",
- "2 11.0 1 0.000000 564.874573 90.596596 177.199951 \n",
- "3 12.0 1 0.000000 564.874268 90.928131 183.125732 \n",
- "4 13.0 1 0.000000 569.931213 86.213280 180.774292 \n",
- "... ... ... ... ... ... ... \n",
- "320183 60159.0 3632 1830.709717 651.257446 150.202515 157.239746 \n",
- "320184 60160.0 3632 1834.013672 649.612122 153.686646 160.874023 \n",
- "320185 60161.0 3632 1845.373047 651.249756 147.178589 153.729248 \n",
- "320186 60162.0 3632 1857.388916 650.908203 136.407349 142.354614 \n",
- "320187 60163.0 3632 1862.792725 658.719971 141.984253 149.052307 \n",
- "\n",
- " x y state diff x_raw y_raw dt \n",
- "0 0.855100 7.136193 2.0 NaN 0.881595 7.341152 NaN \n",
- "1 0.873132 7.235233 2.0 1.0 0.870703 7.309168 0.083333 \n",
- "2 0.890957 7.328989 2.0 1.0 0.901374 7.370044 0.083333 \n",
- "3 0.907784 7.418187 2.0 1.0 0.924360 7.432365 0.083333 \n",
- "4 0.923439 7.505012 2.0 1.0 0.906583 7.456334 0.083333 \n",
- "... ... ... ... ... ... ... ... \n",
- "320183 14.840476 9.786501 2.0 NaN 15.214551 10.027093 NaN \n",
- "320184 15.033432 9.870472 2.0 1.0 15.244872 10.047117 0.083333 \n",
- "320185 15.211560 9.943236 2.0 1.0 15.318496 10.015218 0.083333 \n",
- "320186 15.377673 10.008965 2.0 1.0 15.400203 9.935355 0.083333 \n",
- "320187 15.538255 10.075935 2.0 1.0 15.416893 10.051785 0.083333 \n",
- "\n",
- "[320188 rows x 13 columns]"
- ]
- },
- "execution_count": 13,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "\n",
- "# data['node_type'] = 'PEDESTRIAN' # compatibility with Trajectron++\n",
- "# data['node_id'] = data['track_id'].astype(str)\n",
- "data['track_id'] = pd.to_numeric(data['track_id'], downcast='integer')\n",
- "\n",
- "\n",
- "data['dt'] = data['diff'] * (1/FPS)\n",
- "data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "# position into an average coordinate system. (DO THESE NEED TO BE STORED?)\n",
- "# Don't do this, messes up\n",
- "# data['pos_x'] = data['pos_x'] - data['pos_x'].mean()\n",
- "# data['pos_y'] = data['pos_y'] - data['pos_y'].mean()\n",
- " \n",
- "# data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- ""
- ]
- },
- "execution_count": 15,
- "metadata": {},
- "output_type": "execute_result"
- },
- {
- "data": {
- "image/png": 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- "text/plain": [
- ""
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "data['diff'].hist()"
+ "# create x-norm, y_norm columns\n",
+ "data, mu, std = normalise_position(data)\n",
+ "data = filter_short_tracks(data, window+1)"
]
},
{
@@ -1542,1533 +151,23 @@
"execution_count": null,
"metadata": {},
"outputs": [],
- "source": [
- "if SRC_H is not None:\n",
- " H = np.loadtxt(SRC_H, delimiter=',')\n",
- "else:\n",
- " H = None"
- ]
+ "source": []
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "No H given, probably already projected data?\n"
- ]
- },
- {
- "data": {
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- "text/plain": [
- " frame_id track_id l t w h \\\n",
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- "320187 60163.0 3632 1862.792725 658.719971 141.984253 149.052307 \n",
- "\n",
- " x y state diff x_raw y_raw dt \\\n",
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- "320187 15.538255 10.075935 2.0 1.0 15.416893 10.051785 0.083333 \n",
- "\n",
- " proj_x proj_y \n",
- "0 0.855100 7.136193 \n",
- "1 0.873132 7.235233 \n",
- "2 0.890957 7.328989 \n",
- "3 0.907784 7.418187 \n",
- "4 0.923439 7.505012 \n",
- "... ... ... \n",
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- "\n",
- "[320188 rows x 15 columns]"
- ]
- },
- "execution_count": 17,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "if H is not None:\n",
- " print(\"Projecting data\")\n",
- " data['foot_x'] = data['pos_x'] + 0.5 * data['width']\n",
- " data['foot_y'] = data['pos_y'] + 0.5 * data['height']\n",
- " \n",
- " transformed = cv2.perspectiveTransform(np.array([data[['foot_x','foot_y']].to_numpy()]),H)[0]\n",
- " data['proj_x'], data['proj_y'] = transformed[:,0], transformed[:,1]\n",
- " data['proj_x'] = data['proj_x'].div(100) # cm to m\n",
- " data['proj_y'] = data['proj_y'].div(100) # cm to m\n",
- " # and shift to mean (THES NEED TO BE STORED AND REUSED IN LIVE SETTING)\n",
- " mean_x = data['proj_x'].mean()\n",
- " mean_y = data['proj_y'].mean()\n",
- " data['proj_x'] = data['proj_x'] - data['proj_x'].mean()\n",
- " data['proj_y'] = data['proj_y'] - data['proj_y'].mean()\n",
- "else:\n",
- " print(\"No H given, probably already projected data?\")\n",
- " mean_x = 0\n",
- " mean_y = 0\n",
- " data['proj_x'] = data['x']\n",
- " data['proj_y'] = data['y']\n",
- "data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
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- "name": "stdout",
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- "Deriving displacement, velocity and accelation from x and y\n"
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- "\n",
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- ]
- },
- "execution_count": 18,
- "metadata": {},
- "output_type": "execute_result"
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- ],
- "source": [
- "print(\"Deriving displacement, velocity and accelation from x and y\")\n",
- "data['dx'] = data.groupby(['track_id'])['proj_x'].diff()\n",
- "data['dy'] = data.groupby(['track_id'])['proj_y'].diff()\n",
- "data['vx'] = data['dx'].div(data['dt'], axis=0)\n",
- "data['vy'] = data['dy'].div(data['dt'], axis=0)\n",
- "\n",
- "data['ax'] = data.groupby(['track_id'])['vx'].diff().div(data['dt'], axis=0)\n",
- "data['ay'] = data.groupby(['track_id'])['vy'].diff().div(data['dt'], axis=0)\n",
- "\n",
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- "source": [
- "# then we need the velocity itself\n",
- "data['v'] = np.sqrt(data['vx'].pow(2) + data['vy'].pow(2))\n",
- "# and derive acceleration\n",
- "data['a'] = data.groupby(['track_id'])['v'].diff().div(data['dt'], axis=0)\n",
- "\n",
- "# we can calculate heading based on the velocity components\n",
- "data['heading'] = (np.arctan2(data['vy'], data['vx']) * 180 / np.pi) % 360\n",
- "\n",
- "# and derive it to get the rate of change of the heading\n",
- "data['d_heading'] = data.groupby(['track_id'])['heading'].diff().div(data['dt'], axis=0)\n",
- "\n",
- "data"
- ]
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- {
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- " frame_id track_id l t w h \\\n",
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- "1 10.0 1 0.000000 565.116699 88.801704 171.334290 \n",
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- "[320188 rows x 25 columns]"
- ]
- },
- "execution_count": 20,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "# we can backfill the v and a, so that our model can make estimations\n",
- "# based on these assumed values\n",
- "data['v'] = data.groupby(['track_id'])['v'].bfill()\n",
- "data['a'] = data.groupby(['track_id'])['a'].bfill()\n",
- "\n",
- "data['heading'] = data.groupby(['track_id'])['heading'].bfill()\n",
- "data['d_heading'] = data.groupby(['track_id'])['d_heading'].bfill()\n",
- "data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "312423 items in filtered set, out of 320188 in total set\n"
+ "1606 training tracks, 402 test tracks\n"
]
}
],
"source": [
- "filtered_data = data.groupby(['track_id']).filter(lambda group: len(group) >= window+1) # a lenght of 3 is neccessary to have all relevant derivatives of position\n",
- "filtered_data = filtered_data.set_index(['track_id', 'frame_id']) # use for quick access\n",
- "print(filtered_data.shape[0], \"items in filtered set, out of\", data.shape[0], \"in total set\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "1263 training tracks, 316 test tracks\n"
- ]
- }
- ],
- "source": [
- "track_ids = filtered_data.index.unique('track_id').to_numpy()\n",
+ "track_ids = data.index.unique('track_id').to_numpy()\n",
"np.random.shuffle(track_ids)\n",
"test_offset_idx = int(len(track_ids) * .8)\n",
"training_ids, test_ids = track_ids[:test_offset_idx], track_ids[test_offset_idx:]\n",
@@ -3084,26 +183,19 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "1058\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "0 0\n"
+ "4789\n"
]
},
{
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",
+ "image/png": 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",
"text/plain": [
""
]
@@ -3114,47 +206,47 @@
],
"source": [
"import random\n",
- "if H:\n",
- " img_src = \"../DATASETS/hof/webcam20231103-2.png\"\n",
- " # dst = cv2.warpPerspective(img_src,H,(2500,1920))\n",
- " src_img = cv2.imread(img_src)\n",
- " print(src_img.shape)\n",
- " h1,w1 = src_img.shape[:2]\n",
- " corners = np.float32([[0,0], [w1, 0], [0, h1], [w1, h1]])\n",
+ "# if H:\n",
+ "# img_src = \"../DATASETS/hof/webcam20231103-2.png\"\n",
+ "# # dst = cv2.warpPerspective(img_src,H,(2500,1920))\n",
+ "# src_img = cv2.imread(img_src)\n",
+ "# print(src_img.shape)\n",
+ "# h1,w1 = src_img.shape[:2]\n",
+ "# corners = np.float32([[0,0], [w1, 0], [0, h1], [w1, h1]])\n",
"\n",
- " print(corners)\n",
- " corners_projected = cv2.perspectiveTransform(corners.reshape((-1,4,2)), H)[0]\n",
- " print(corners_projected)\n",
- " [xmin, ymin] = np.int32(corners_projected.min(axis=0).ravel() - 0.5)\n",
- " [xmax, ymax] = np.int32(corners_projected.max(axis=0).ravel() + 0.5)\n",
- " print(xmin, xmax, ymin, ymax)\n",
+ "# print(corners)\n",
+ "# corners_projected = cv2.perspectiveTransform(corners.reshape((-1,4,2)), H)[0]\n",
+ "# print(corners_projected)\n",
+ "# [xmin, ymin] = np.int32(corners_projected.min(axis=0).ravel() - 0.5)\n",
+ "# [xmax, ymax] = np.int32(corners_projected.max(axis=0).ravel() + 0.5)\n",
+ "# print(xmin, xmax, ymin, ymax)\n",
"\n",
- " dst = cv2.warpPerspective(src_img,H, (xmax, ymax))\n",
- " def plot_track(track_id: int):\n",
- " plt.gca().invert_yaxis()\n",
+ "# dst = cv2.warpPerspective(src_img,H, (xmax, ymax))\n",
+ "# def plot_track(track_id: int):\n",
+ "# plt.gca().invert_yaxis()\n",
"\n",
- " plt.imshow(dst, origin='lower', extent=[xmin/100-mean_x, xmax/100-mean_x, ymin/100-mean_y, ymax/100-mean_y])\n",
- " # plot scatter plot with x and y data \n",
+ "# plt.imshow(dst, origin='lower', extent=[xmin/100-mean_x, xmax/100-mean_x, ymin/100-mean_y, ymax/100-mean_y])\n",
+ "# # plot scatter plot with x and y data \n",
" \n",
- " ax = plt.scatter(\n",
- " filtered_data.loc[track_id,:]['proj_x'],\n",
- " filtered_data.loc[track_id,:]['proj_y'],\n",
- " marker=\"*\") \n",
- " ax.axes.invert_yaxis()\n",
- " plt.plot(\n",
- " filtered_data.loc[track_id,:]['proj_x'],\n",
- " filtered_data.loc[track_id,:]['proj_y']\n",
- " )\n",
- "else:\n",
- " def plot_track(track_id: int):\n",
- " ax = plt.scatter(\n",
- " filtered_data.loc[track_id,:]['x'],\n",
- " filtered_data.loc[track_id,:]['y'],\n",
- " marker=\"*\") \n",
- " plt.plot(\n",
- " filtered_data.loc[track_id,:]['proj_x'],\n",
- " filtered_data.loc[track_id,:]['proj_y']\n",
- " )\n",
+ "# ax = plt.scatter(\n",
+ "# filtered_data.loc[track_id,:]['proj_x'],\n",
+ "# filtered_data.loc[track_id,:]['proj_y'],\n",
+ "# marker=\"*\") \n",
+ "# ax.axes.invert_yaxis()\n",
+ "# plt.plot(\n",
+ "# filtered_data.loc[track_id,:]['proj_x'],\n",
+ "# filtered_data.loc[track_id,:]['proj_y']\n",
+ "# )\n",
+ "# else:\n",
+ "def plot_track(track_id: int):\n",
+ " ax = plt.scatter(\n",
+ " data.loc[track_id,:]['x_norm'],\n",
+ " data.loc[track_id,:]['y_norm'],\n",
+ " marker=\"*\") \n",
+ " plt.plot(\n",
+ " data.loc[track_id,:]['x_norm'],\n",
+ " data.loc[track_id,:]['y_norm']\n",
+ " )\n",
"\n",
"# print(filtered_data.loc[track_id,:]['proj_x'])\n",
"# _track_id = 2188\n",
@@ -3165,7 +257,7 @@
"for track_id in random.choices(track_ids, k=100):\n",
" plot_track(track_id)\n",
" \n",
- "print(mean_x, mean_y)"
+ "# print(mean_x, mean_y)"
]
},
{
@@ -3177,62 +269,501 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 10,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
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+ "
80035 rows × 24 columns
\n",
+ "
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+ ],
+ "text/plain": [
+ " l t w h x \\\n",
+ "track_id frame_id \n",
+ "1 342.0 1393.736572 0.000000 67.613647 121.391151 13.244408 \n",
+ " 347.0 1393.844849 12.691238 86.482910 156.264786 13.500384 \n",
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+ " 362.0 1438.374268 115.362549 84.298584 172.143616 13.499658 \n",
+ "... ... ... ... ... ... \n",
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+ " 32707.0 1703.756025 749.703112 131.216670 181.961914 14.000000 \n",
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+ " 32722.0 1702.384766 750.754517 123.435425 180.945618 14.000000 \n",
+ "\n",
+ " y state diff x_raw y_raw ... vx \\\n",
+ "track_id frame_id ... \n",
+ "1 342.0 2.414339 2.0 NaN 13.5 2.5 ... 6.143418e-01 \n",
+ " 347.0 2.993156 2.0 5.0 13.5 3.0 ... 6.143418e-01 \n",
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+ " 357.0 4.282279 2.0 5.0 13.5 4.5 ... -2.209058e-02 \n",
+ " 362.0 4.743787 2.0 5.0 13.5 4.5 ... -1.349331e-03 \n",
+ "... ... ... ... ... ... ... ... \n",
+ "5030 32702.0 10.495261 1.0 5.0 14.0 10.5 ... 1.654143e-12 \n",
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+ " 32722.0 10.499985 2.0 5.0 14.0 10.5 ... 0.000000e+00 \n",
+ "\n",
+ " vy ax ay v a \\\n",
+ "track_id frame_id \n",
+ "1 342.0 1.389160 -1.422342e+00 0.453213 1.518941 0.142097 \n",
+ " 347.0 1.389160 -1.422342e+00 0.453213 1.518941 0.142097 \n",
+ " 352.0 1.577999 -1.422342e+00 0.453213 1.578149 0.142097 \n",
+ " 357.0 1.515896 -1.050958e-01 -0.149049 1.516057 -0.149020 \n",
+ " 362.0 1.107618 4.977900e-02 -0.979866 1.107619 -0.980250 \n",
+ "... ... ... ... ... ... \n",
+ "5030 32702.0 -0.029145 4.967546e-11 0.657171 0.029145 -0.657171 \n",
+ " 32707.0 0.010435 -2.189608e-12 0.094992 0.010435 -0.044905 \n",
+ " 32712.0 0.001334 -1.882654e-12 -0.021841 0.001334 -0.021841 \n",
+ " 32717.0 -0.000350 3.069545e-14 -0.004042 0.000350 -0.002362 \n",
+ " 32722.0 -0.000082 7.162271e-14 0.000644 0.000082 -0.000644 \n",
+ "\n",
+ " heading d_heading x_norm y_norm \n",
+ "track_id frame_id \n",
+ "1 342.0 66.143088 5.536579e+01 0.353449 -1.768217 \n",
+ " 347.0 66.143088 5.536579e+01 0.414443 -1.574517 \n",
+ " 352.0 89.212166 5.536579e+01 0.416598 -1.354485 \n",
+ " 357.0 90.834891 3.894540e+00 0.414404 -1.143113 \n",
+ " 362.0 90.069799 -1.836220e+00 0.414270 -0.988670 \n",
+ "... ... ... ... ... \n",
+ "5030 32702.0 270.000000 1.644812e-08 0.533492 0.936057 \n",
+ " 32707.0 90.000000 -4.320000e+02 0.533492 0.937512 \n",
+ " 32712.0 90.000000 1.416898e-08 0.533492 0.937698 \n",
+ " 32717.0 270.000000 4.320000e+02 0.533492 0.937649 \n",
+ " 32722.0 270.000000 1.172430e-08 0.533492 0.937638 \n",
+ "\n",
+ "[80035 rows x 24 columns]"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"# a=filtered_data.loc[1]\n",
- "# min(a.index.tolist())"
+ "# min(a.index.tolist())\n",
+ "data"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "x False\n",
+ "y False\n",
+ "vx False\n",
+ "vy False\n",
+ "ax False\n",
+ "ay False\n",
+ "dx False\n",
+ "dy False\n"
+ ]
+ }
+ ],
+ "source": [
+ "for field in in_fields + out_fields:\n",
+ " print(field, data[field].isnull().values.any())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
- " 0%| | 0/1263 [00:00, ?it/s]"
+ " 0%| | 0/1606 [00:00, ?it/s]"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- "100%|██████████| 1263/1263 [01:42<00:00, 12.36it/s]\n",
- "100%|██████████| 316/316 [00:26<00:00, 11.75it/s]\n"
+ "100%|██████████| 1606/1606 [00:27<00:00, 58.17it/s]\n",
+ "100%|██████████| 402/402 [00:06<00:00, 60.38it/s]\n"
]
}
],
"source": [
- "\n",
- "\n",
- "\n",
- "def create_dataset(data, track_ids, window):\n",
+ "def create_dataset(data, track_ids, window, only_last=False):\n",
" X, y, = [], []\n",
+ " factor = SAMPLE_STEP if SAMPLE_STEP is not None else 1\n",
" for track_id in tqdm(track_ids):\n",
" df = data.loc[track_id]\n",
+ " # print(df)\n",
" start_frame = min(df.index.tolist())\n",
" for step in range(len(df)-window-1):\n",
- " i = int(start_frame) + step\n",
+ " i = int(start_frame) + (step*factor)\n",
" # print(step, int(start_frame), i)\n",
- " feature = df.loc[i:i+window][in_fields]\n",
+ " feature = df.loc[i:i+(window*factor)][in_fields]\n",
" # target = df.loc[i+1:i+window+1][out_fields]\n",
- " target = df.loc[i+window+1][out_fields]\n",
+ " # print(i, window*factor, factor, i+window*factor+factor, df['idx_in_track'])\n",
+ " # print(i+window*factor+factor)\n",
+ " if only_last:\n",
+ " target = df.loc[i+window*factor+factor][out_fields]\n",
+ " else:\n",
+ " target = df.loc[i+factor:i+window*factor+factor][out_fields]\n",
+ "\n",
" X.append(feature.values)\n",
" y.append(target.values)\n",
" \n",
" return torch.tensor(np.array(X), dtype=torch.float), torch.tensor(np.array(y), dtype=torch.float)\n",
"\n",
- "X_train, y_train = create_dataset(filtered_data, training_ids, window)\n",
- "X_test, y_test = create_dataset(filtered_data, test_ids, window)"
+ "X_train, y_train = create_dataset(data, training_ids, window)\n",
+ "X_test, y_test = create_dataset(data, test_ids, window)"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
@@ -3242,13 +773,15 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 106,
"metadata": {},
"outputs": [],
"source": [
"from torch.utils.data import TensorDataset, DataLoader\n",
"dataset_train = TensorDataset(X_train, y_train)\n",
- "loader_train = DataLoader(dataset_train, shuffle=True, batch_size=8)"
+ "loader_train = DataLoader(dataset_train, shuffle=True, batch_size=batch_size)\n",
+ "dataset_test = TensorDataset(X_test, y_test)\n",
+ "loader_test = DataLoader(dataset_test, shuffle=False, batch_size=batch_size)"
]
},
{
@@ -3258,9 +791,60 @@
"Model give output for all timesteps, this should improve training. But we use only the last timestep for the prediction process"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## RNN"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "class SimpleRnn(nn.Module):\n",
+ " def __init__(self, in_d=2, out_d=2, hidden_d=4, num_hidden=1):\n",
+ " super(SimpleRnn, self).__init__()\n",
+ " self.rnn = nn.RNN(input_size=in_d, hidden_size=hidden_d, num_layers=num_hidden)\n",
+ " self.fc = nn.Linear(hidden_d, out_d)\n",
+ "\n",
+ " def forward(self, x, h0):\n",
+ " r, h = self.rnn(x, h0)\n",
+ " # r = r[:, -1,:]\n",
+ " y = self.fc(r) # no activation on the output\n",
+ " return y, h\n",
+ "rnn = SimpleRnn(input_size, output_size, hidden_size, num_layers).to(device)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## LSTM"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "For optional LSTM-GAN, see https://discuss.pytorch.org/t/how-to-use-lstm-to-construct-gan/12419\n",
+ "\n",
+ "Or VAE (variational Auto encoder):\n",
+ "\n",
+ "> The only constraint on the latent vector representation for traditional autoencoders is that latent vectors should be easily decodable back into the original image. As a result, the latent space $Z$ can become disjoint and non-continuous. Variational autoencoders try to solve this problem. [Alexander van de Kleut](https://avandekleut.github.io/vae/)\n",
+ "\n",
+ "For LSTM based generative VAE: https://github.com/Khamies/LSTM-Variational-AutoEncoder/blob/main/model.py\n",
+ "\n",
+ "http://web.archive.org/web/20210119121802/https://towardsdatascience.com/time-series-generation-with-vae-lstm-5a6426365a1c?gi=29d8b029a386\n",
+ "\n",
+ "https://youtu.be/qJeaCHQ1k2w?si=30aAdqqwvz0DpR-x&t=687 VAE generate mu and sigma of a Normal distribution. Thus, they don't map the input to a single point, but a gausian distribution."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 328,
"metadata": {},
"outputs": [],
"source": [
@@ -3270,31 +854,52 @@
" # num_layers : number of LSTM layers \n",
" def __init__(self, input_size, hidden_size, num_layers): \n",
" super(LSTMModel, self).__init__() #initializes the parent class nn.Module\n",
- " self.lin1 = nn.Linear(input_size, hidden_size//2)\n",
- " self.lstm = nn.LSTM(hidden_size//2, hidden_size, num_layers, batch_first=True)\n",
+ " # We _could_ train the h0: https://discuss.pytorch.org/t/learn-initial-hidden-state-h0-for-rnn/10013 \n",
+ " # self.lin1 = nn.Linear(input_size, hidden_size)\n",
+ " self.num_layers = num_layers\n",
+ " self.hidden_size = hidden_size\n",
+ " self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)\n",
" self.linear = nn.Linear(hidden_size, output_size)\n",
" # self.activation_v = nn.LeakyReLU(.01)\n",
" # self.activation_heading = torch.remainder()\n",
"\n",
- " def forward(self, x): # defines forward pass of the neural network\n",
- " out = self.lin1(x)\n",
- " out, h0 = self.lstm(out)\n",
+ " \n",
+ " def get_hidden_state(self, batch_size, device):\n",
+ " h = torch.zeros(self.num_layers, batch_size, self.hidden_size).to(device)\n",
+ " c = torch.zeros(self.num_layers, batch_size, self.hidden_size).to(device)\n",
+ " return (h, c)\n",
+ "\n",
+ " def forward(self, x, hidden_state): # defines forward pass of the neural network\n",
+ " # out = self.lin1(x)\n",
+ " \n",
+ " out, hidden_state = self.lstm(x, hidden_state)\n",
" # extract only the last time step, see https://machinelearningmastery.com/lstm-for-time-series-prediction-in-pytorch/\n",
" # print(out.shape)\n",
- " out = out[:, -1,:]\n",
+ " # TODO)) Might want to remove this below: as it might improve training\n",
+ " # out = out[:, -1,:]\n",
" # print(out.shape)\n",
" out = self.linear(out)\n",
" \n",
" # torch.remainder(out[1], 360)\n",
" # print('o',out.shape)\n",
- " return out\n",
+ " return out, hidden_state\n",
"\n",
- "model = LSTMModel(input_size, hidden_size, num_layers).to(device)\n"
+ "lstm = LSTMModel(input_size, hidden_size, num_layers).to(device)\n"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 329,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# model = rnn\n",
+ "model = lstm\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 330,
"metadata": {},
"outputs": [],
"source": [
@@ -3304,7 +909,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 331,
"metadata": {},
"outputs": [],
"source": [
@@ -3312,14 +917,25 @@
" # toggle evaluation mode\n",
" model.eval()\n",
" with torch.no_grad():\n",
- " y_pred = model(X_train.to(device=device))\n",
+ " batch_size, seq_len, feature_dim = X_train.shape\n",
+ " y_pred, _ = model(\n",
+ " X_train.to(device=device),\n",
+ " model.get_hidden_state(batch_size, device)\n",
+ " )\n",
" train_rmse = torch.sqrt(loss_fn(y_pred, y_train))\n",
- " y_pred = model(X_test.to(device=device))\n",
+ " # print(y_pred)\n",
+ "\n",
+ " batch_size, seq_len, feature_dim = X_test.shape\n",
+ " y_pred, _ = model(\n",
+ " X_test.to(device=device),\n",
+ " model.get_hidden_state(batch_size, device)\n",
+ " )\n",
+ " # print(loss_fn(y_pred, y_test))\n",
" test_rmse = torch.sqrt(loss_fn(y_pred, y_test))\n",
- " print(\"Epoch %d: train RMSE %.4f, test RMSE %.4f\" % (epoch, train_rmse, test_rmse))\n",
+ " print(\"Epoch ??: train RMSE %.4f, test RMSE %.4f\" % ( train_rmse, test_rmse))\n",
"\n",
"def load_most_recent():\n",
- " paths = list(cache_path.glob(\"checkpoint_*.pt\"))\n",
+ " paths = list(cache_path.glob(f\"checkpoint-{model._get_name()}_*.pt\"))\n",
" if len(paths) < 1:\n",
" print('Nothing found to load')\n",
" return None, None\n",
@@ -3332,7 +948,7 @@
" if path is None:\n",
" if epoch is None:\n",
" raise RuntimeError(\"Either path or epoch must be given\")\n",
- " path = cache_path / f\"checkpoint_{epoch:05d}.pt\"\n",
+ " path = cache_path / f\"checkpoint-{model._get_name()}_{epoch:05d}.pt\"\n",
" else:\n",
" print (path.stem)\n",
" epoch = int(path.stem[-5:])\n",
@@ -3345,7 +961,7 @@
" \n",
"\n",
"def cache(epoch, loss):\n",
- " path = cache_path / f\"checkpoint_{epoch:05d}.pt\"\n",
+ " path = cache_path / f\"checkpoint-{model._get_name()}_{epoch:05d}.pt\"\n",
" print(f\"Cache to {path}\")\n",
" torch.save({\n",
" 'epoch': epoch,\n",
@@ -3355,39 +971,40 @@
" }, path)\n"
]
},
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "TODO)) See [this notebook](https://www.cs.toronto.edu/~lczhang/aps360_20191/lec/w08/rnn.html) For initialization (with random or not) and the use of GRU"
+ ]
+ },
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 332,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Loading EXPERIMENTS/cache/hof2/checkpoint_00005.pt\n",
- "checkpoint_00005\n",
- "starting from epoch 5 (loss: nan)\n"
+ "Loading EXPERIMENTS/cache/hof2/checkpoint-LSTMModel_01000.pt\n",
+ "checkpoint-LSTMModel_01000\n",
+ "starting from epoch 1000 (loss: 0.014368701726198196)\n",
+ "Epoch ??: train RMSE 0.0849, test RMSE 0.0866\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
- " 0%| | 0/95 [00:00, ?it/s]"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- " 4%|▍ | 4/95 [07:37<2:53:27, 114.37s/it]"
+ "0it [00:00, ?it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "Cache to EXPERIMENTS/cache/hof2/checkpoint_00010.pt\n"
+ "Epoch ??: train RMSE 0.0849, test RMSE 0.0866\n"
]
},
{
@@ -3396,22 +1013,6 @@
"text": [
"\n"
]
- },
- {
- "ename": "RuntimeError",
- "evalue": "CUDA out of memory. Tried to allocate 28.40 GiB (GPU 0; 23.59 GiB total capacity; 15.01 GiB already allocated; 7.20 GiB free; 15.03 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mRuntimeError\u001b[0m Traceback (most recent call last)",
- "Cell \u001b[0;32mIn[31], line 31\u001b[0m\n\u001b[1;32m 28\u001b[0m \u001b[38;5;28;01mcontinue\u001b[39;00m\n\u001b[1;32m 30\u001b[0m cache(epoch, loss)\n\u001b[0;32m---> 31\u001b[0m \u001b[43mevaluate\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 33\u001b[0m evaluate()\n",
- "Cell \u001b[0;32mIn[30], line 5\u001b[0m, in \u001b[0;36mevaluate\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m model\u001b[38;5;241m.\u001b[39meval()\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mno_grad():\n\u001b[0;32m----> 5\u001b[0m y_pred \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_train\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mto\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdevice\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdevice\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 6\u001b[0m train_rmse \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39msqrt(loss_fn(y_pred, y_train))\n\u001b[1;32m 7\u001b[0m y_pred \u001b[38;5;241m=\u001b[39m model(X_test\u001b[38;5;241m.\u001b[39mto(device\u001b[38;5;241m=\u001b[39mdevice))\n",
- "File \u001b[0;32m~/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py:1130\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 1126\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1127\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1128\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1129\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1130\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1131\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1132\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
- "Cell \u001b[0;32mIn[28], line 15\u001b[0m, in \u001b[0;36mLSTMModel.forward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, x): \u001b[38;5;66;03m# defines forward pass of the neural network\u001b[39;00m\n\u001b[1;32m 14\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlin1(x)\n\u001b[0;32m---> 15\u001b[0m out, h0 \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlstm\u001b[49m\u001b[43m(\u001b[49m\u001b[43mout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 16\u001b[0m \u001b[38;5;66;03m# extract only the last time step, see https://machinelearningmastery.com/lstm-for-time-series-prediction-in-pytorch/\u001b[39;00m\n\u001b[1;32m 17\u001b[0m \u001b[38;5;66;03m# print(out.shape)\u001b[39;00m\n\u001b[1;32m 18\u001b[0m out \u001b[38;5;241m=\u001b[39m out[:, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m,:]\n",
- "File \u001b[0;32m~/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py:1130\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 1126\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1127\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1128\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1129\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1130\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1131\u001b[0m \u001b[38;5;66;03m# Do not call functions when jit is used\u001b[39;00m\n\u001b[1;32m 1132\u001b[0m full_backward_hooks, non_full_backward_hooks \u001b[38;5;241m=\u001b[39m [], []\n",
- "File \u001b[0;32m~/suspicion/trap/.venv/lib/python3.10/site-packages/torch/nn/modules/rnn.py:769\u001b[0m, in \u001b[0;36mLSTM.forward\u001b[0;34m(self, input, hx)\u001b[0m\n\u001b[1;32m 767\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcheck_forward_args(\u001b[38;5;28minput\u001b[39m, hx, batch_sizes)\n\u001b[1;32m 768\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m batch_sizes \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 769\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[43m_VF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlstm\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_flat_weights\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbias\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnum_layers\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 770\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdropout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtraining\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbidirectional\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbatch_first\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 771\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 772\u001b[0m result \u001b[38;5;241m=\u001b[39m _VF\u001b[38;5;241m.\u001b[39mlstm(\u001b[38;5;28minput\u001b[39m, batch_sizes, hx, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_flat_weights, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbias,\n\u001b[1;32m 773\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnum_layers, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdropout, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtraining, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbidirectional)\n",
- "\u001b[0;31mRuntimeError\u001b[0m: CUDA out of memory. Tried to allocate 28.40 GiB (GPU 0; 23.59 GiB total capacity; 15.01 GiB already allocated; 7.20 GiB free; 15.03 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF"
- ]
}
],
"source": [
@@ -3420,18 +1021,25 @@
" start_epoch = 0\n",
"else:\n",
" print(f\"starting from epoch {start_epoch} (loss: {loss})\")\n",
+ " evaluate()\n",
"\n",
+ "loss_log = []\n",
"# Train Network\n",
"for epoch in tqdm(range(start_epoch+1,num_epochs+1)):\n",
" # toggle train mode\n",
" model.train()\n",
- " for batch_idx, (data, targets) in enumerate(loader_train):\n",
- " # Get data to cuda if possible\n",
- " data = data.to(device=device).squeeze(1)\n",
+ " for batch_idx, (x, targets) in enumerate(loader_train):\n",
+ " # Get x to cuda if possible\n",
+ " x = x.to(device=device).squeeze(1)\n",
" targets = targets.to(device=device)\n",
"\n",
" # forward\n",
- " scores = model(data)\n",
+ " scores, _ = model(\n",
+ " x,\n",
+ " torch.zeros(num_layers, x.shape[2], hidden_size, dtype=torch.float).to(device=device),\n",
+ " torch.zeros(num_layers, x.shape[2], hidden_size, dtype=torch.float).to(device=device)\n",
+ " )\n",
+ " # print(scores)\n",
" loss = loss_fn(scores, targets)\n",
"\n",
" # backward\n",
@@ -3441,6 +1049,8 @@
" # gradient descent update step/adam step\n",
" optimizer.step()\n",
"\n",
+ " loss_log.append(loss.item())\n",
+ "\n",
" if epoch % 5 != 0:\n",
" continue\n",
"\n",
@@ -3452,86 +1062,125 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 333,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# print(loss)\n",
+ "# print(len(loss_log))\n",
+ "# plt.plot(loss_log)\n",
+ "# plt.ylabel('Loss')\n",
+ "# plt.xlabel('iteration')\n",
+ "# plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 335,
"metadata": {},
"outputs": [
{
- "ename": "NameError",
- "evalue": "name 'model' is not defined",
- "output_type": "error",
- "traceback": [
- "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
- "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
- "Cell \u001b[0;32mIn[1], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241m.\u001b[39meval()\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mno_grad():\n\u001b[1;32m 3\u001b[0m y_pred \u001b[38;5;241m=\u001b[39m model(X_train\u001b[38;5;241m.\u001b[39mto(device\u001b[38;5;241m=\u001b[39mdevice))\n",
- "\u001b[0;31mNameError\u001b[0m: name 'model' is not defined"
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "torch.Size([49999, 9, 2]) torch.Size([49999, 9, 2])\n"
]
}
],
"source": [
"model.eval()\n",
+ "\n",
"with torch.no_grad():\n",
- " y_pred = model(X_train.to(device=device))\n",
+ " y_pred, _ = model(X_train.to(device=device),\n",
+ " model.get_hidden_state(X_train.shape[0], device))\n",
+ " \n",
" print(y_pred.shape, y_train.shape)\n",
- "y_train, y_pred"
+ "# y_train, y_pred"
]
},
{
"cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "text/plain": [
- "(0, 0)"
- ]
- },
- "execution_count": 34,
- "metadata": {},
- "output_type": "execute_result"
- }
- ],
- "source": [
- "mean_x, mean_y"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
+ "execution_count": 336,
"metadata": {},
"outputs": [],
"source": [
"import scipy\n",
"\n",
- "\n",
- "def predict_and_plot(feature, steps = 50):\n",
+ "def ceil_away_from_0(a):\n",
+ " return np.sign(a) * np.ceil(np.abs(a))\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 343,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def predict_and_plot(model, feature, steps = 50):\n",
" lenght = feature.shape[0]\n",
+ "\n",
+ " dt = (1/ FPS) * SAMPLE_STEP\n",
+ "\n",
+ " trajectory = feature\n",
+ "\n",
" # feature = filtered_data.loc[_track_id,:].iloc[:5][in_fields].values\n",
" # nxt = filtered_data.loc[_track_id,:].iloc[5][out_fields]\n",
" with torch.no_grad():\n",
+ " # h = torch.zeros(num_layers, window+1, hidden_size, dtype=torch.float).to(device=device)\n",
+ " # c = torch.zeros(num_layers, window+1, hidden_size, dtype=torch.float).to(device=device)\n",
+ " h = torch.zeros(num_layers, 1, hidden_size, dtype=torch.float).to(device=device)\n",
+ " c = torch.zeros(num_layers, 1, hidden_size, dtype=torch.float).to(device=device)\n",
+ " hidden_state = (h, c)\n",
+ " # X = torch.tensor([feature], dtype=torch.float).to(device)\n",
+ " # y, (h, c) = model(X, h, c)\n",
" for i in range(steps):\n",
" # predict_f = scipy.ndimage.uniform_filter(feature)\n",
" # predict_f = scipy.interpolate.splrep(feature[:][0], feature[:][1],)\n",
" # predict_f = scipy.signal.spline_feature(feature, lmbda=.1)\n",
" # bathc size of one, so feature as single item in array\n",
- " X = torch.tensor([feature], dtype=torch.float).to(device)\n",
" # print(X.shape)\n",
- " s = model(X)[0].cpu()\n",
- " \n",
+ " X = torch.tensor([feature], dtype=torch.float).to(device)\n",
+ " # print(type(model))\n",
+ " y, hidden_state, *_ = model(X, hidden_state)\n",
+ " # print(hidden_state.shape)\n",
+ "\n",
+ " s = y[-1][-1].cpu()\n",
+ "\n",
" # proj_x proj_y v heading a d_heading\n",
" # next_step = feature\n",
- " dt = 1/ FPS\n",
+ "\n",
+ " dx, dy = s\n",
+ " \n",
+ " dx = (dx * GRID_SIZE).round() / GRID_SIZE\n",
+ " dy = (dy * GRID_SIZE).round() / GRID_SIZE\n",
+ " vx, vy = dx / dt, dy / dt\n",
+ "\n",
" v = np.sqrt(s[0]**2 + s[1]**2)\n",
" heading = (np.arctan2(s[1], s[0]) * 180 / np.pi) % 360\n",
- " a = (v - feature[-1][2]) / dt\n",
- " d_heading = (heading - feature[-1][5])\n",
+ " # a = (v - feature[-1][2]) / dt\n",
+ " ax = (vx - feature[-1][2]) / dt\n",
+ " ay = (vx - feature[-1][3]) / dt\n",
+ " # d_heading = (heading - feature[-1][5])\n",
" # print(s)\n",
- " feature = np.append(feature, [[feature[-1][0] + s[0]*dt, feature[-1][1] + s[1]*dt, v, heading, a, d_heading ]], axis=0)\n",
+ " # ['x', 'y', 'vx', 'vy', 'ax', 'ay'] \n",
+ " x = feature[-1][0] + dx\n",
+ " y = feature[-1][1] + dy\n",
+ " if GRID_SIZE is not None:\n",
+ " # put points back on grid\n",
+ " x = (x*GRID_SIZE).round() / GRID_SIZE\n",
+ " y = (y*GRID_SIZE).round() / GRID_SIZE\n",
+ "\n",
+ " feature = [[x, y, vx, vy, ax, ay]]\n",
+ " \n",
+ " trajectory = np.append(trajectory, feature, axis=0)\n",
+ " # f = [feature[-1][0] + s[0]*dt, feature[-1][1] + s[1]*dt, v, heading, a, d_heading ]\n",
+ " # feature = np.append(feature, [feature], axis=0)\n",
" \n",
" # print(next_step, nxt)\n",
- " plt.plot(feature[:lenght,0], feature[:lenght,1], c='orange')\n",
- " plt.plot(feature[lenght-1:,0], feature[lenght-1:,1], c='red')\n",
- " plt.scatter(feature[lenght:,0], feature[lenght:,1], c='red')"
+ " # print(trajectory)\n",
+ " plt.plot(trajectory[:lenght,0], trajectory[:lenght,1], c='orange')\n",
+ " plt.plot(trajectory[lenght-1:,0], trajectory[lenght-1:,1], c='red')\n",
+ " plt.scatter(trajectory[lenght:,0], trajectory[lenght:,1], c='red', marker='x')"
]
},
{
@@ -3543,12 +1192,13 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "2515\n"
+ "1301\n",
+ "(10, 6) (10, 6)\n"
]
},
{
"data": {
- "image/png": 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",
+ "image/png": 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",
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