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\u001b[01;34mpred_Street\u001b[0m/\n"," Pre-trained_Social_Distancing_Tool.ipynb\n"," \u001b[01;34m__pycache__\u001b[0m/\n","'Social_Distancing_Tool final12.ipynb'\n"," Social_Distancing_Tool_Final_Backbone.ipynb\n","'Social Distancing Tool.ipynb'\n"," \u001b[01;34mStreet__\u001b[0m/\n"," \u001b[01;34mStreet___\u001b[0m/\n"," street.mp4\n"," \u001b[01;34mstreets\u001b[0m/\n"," streets_rate.avi\n"," Street_video.avi\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"otsZRIbJbpiO","colab_type":"code","colab":{}},"source":["import cv2\n","import os\n","import pandas as pd\n","import matplotlib.pyplot as plt\n","import numpy as np\n","import tensorflow as tf"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"SGS_Gb_CbrjI","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":72},"executionInfo":{"status":"ok","timestamp":1597110214092,"user_tz":240,"elapsed":8065,"user":{"displayName":"Srinidhi 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is deprecated. Use the functions in the public API at pandas.testing instead.\n","  import pandas.util.testing as tm\n"],"name":"stderr"}]},{"cell_type":"code","metadata":{"id":"FGBeX7p5btmi","colab_type":"code","colab":{}},"source":["device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\") "],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"phLQyqidbv1C","colab_type":"code","colab":{}},"source":["#Function to extract frames\n","def extractFrames(pathIn, pathOut):\n","   #directory path, where my video images will be stored\n","   os.mkdir(pathOut)\n","   #Capture video from video file\n","   cap = cv2.VideoCapture(pathIn)\n","#Counter Variable\n","   count = 0\n","   while (cap.isOpened()):\n","     # Capture frame-by-frame\n","     ret, frame = cap.read()\n","     if ret == True:\n","       # save frame as JPEG file\n","       cv2.imwrite(os.path.join(pathOut, \"frame{:d}.jpg\".format(count)), frame)\n","       count += 1\n","     else:\n","       break\n","    # When everything done, release the capture\n","   cap.release()\n","   cv2.destroyAllWindows()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"lMb5HVjdbxuI","colab_type":"code","colab":{}},"source":["extractFrames('/content/drive/My Drive/Colab Notebooks/Social Distancing Tool/Bridge.mp4',\n","              '/content/drive/My Drive/Colab Notebooks/Social Distancing Tool/Bridge__')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"rPwMfNh_b0hK","colab_type":"code","colab":{}},"source":["extractFrames('/content/drive/My Drive/Colab Notebooks/Social Distancing Tool/street.mp4',\n","              '/content/drive/My Drive/Colab Notebooks/Social Distancing Tool/Street__')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"1_ndNcMkb2sB","colab_type":"code","colab":{}},"source":["Labels_BB= pd.read_csv('/content/drive/My Drive/Colab Notebooks/Social Distancing Tool/BB_Label Data.csv')"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"LYzQ0hUJb4lz","colab_type":"code","colab":{}},"source":["##frames = [Labels_BB_1,Labels_BB_2]\n","\n","##Labels_BB = pd.concat(frames)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"r4nC_lZDb6e5","colab_type":"code","colab":{}},"source":["Labels_BB_imp = Labels_BB[['#filename','region_id','region_shape_attributes']]"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"KH0p0MBQb-tL","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"executionInfo":{"status":"ok","timestamp":1597110247158,"user_tz":240,"elapsed":497,"user":{"displayName":"Srinidhi 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Dannamaneni","photoUrl":"","userId":"14652330968581622724"}},"outputId":"11f54492-a682-44a7-e5c7-16184172d659"},"source":["len(Bridge_frames)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["480"]},"metadata":{"tags":[]},"execution_count":14}]},{"cell_type":"code","metadata":{"id":"kYVsLcLwcHQD","colab_type":"code","colab":{}},"source":["re_size = [cv2.resize(np.float32(img),(600,600), interpolation=cv2.INTER_LINEAR) for img in Bridge_frames] "],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"rFTWENZIcHqJ","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"executionInfo":{"status":"ok","timestamp":1597110432412,"user_tz":240,"elapsed":160917,"user":{"displayName":"Srinidhi Dannamaneni","photoUrl":"","userId":"14652330968581622724"}},"outputId":"437e8ae3-2c9c-49b3-f40d-85b6cb47fe5f"},"source":["len(re_size)\n"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["480"]},"metadata":{"tags":[]},"execution_count":16}]},{"cell_type":"code","metadata":{"id":"cQ1t9KqScKFT","colab_type":"code","colab":{}},"source":["import pandas as pd\n","import ast"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"JRk8UU_ucNxt","colab_type":"code","colab":{}},"source":["eval_dict = []\n","for i in Labels_BB_imp['region_shape_attributes']:\n","  eval_dict.append(ast.literal_eval(i))"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"-y24RDWacP6b","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"executionInfo":{"status":"ok","timestamp":1597110432418,"user_tz":240,"elapsed":152586,"user":{"displayName":"Srinidhi Dannamaneni","photoUrl":"","userId":"14652330968581622724"}},"outputId":"554600b8-0699-4439-cf2c-c5277f6ce013"},"source":["eval_dict[1]"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'height': 172, 'name': 'rect', 'width': 52, 'x': 311, 'y': 145}"]},"metadata":{"tags":[]},"execution_count":19}]},{"cell_type":"code","metadata":{"id":"Flc8DvTZcRv6","colab_type":"code","colab":{}},"source":["new_table = pd.DataFrame(columns = ['filename','class_id'])"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"yT01vTpYcTuU","colab_type":"code","colab":{}},"source":["new_table['filename'] = Labels_BB_imp['#filename']\n"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"3aOdrcSicVce","colab_type":"code","colab":{}},"source":["new_table['class_id'] = Labels_BB_imp['region_id']"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"37vM6PPXcXQZ","colab_type":"code","colab":{}},"source":["for i in range(0,len(eval_dict)):\n","  split_table = pd.DataFrame(eval_dict)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"o9iiz9kMcZLs","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":203},"executionInfo":{"status":"ok","timestamp":1597110438188,"user_tz":240,"elapsed":152368,"user":{"displayName":"Srinidhi Dannamaneni","photoUrl":"","userId":"14652330968581622724"}},"outputId":"8f94f465-81b4-487e-b989-93958944d1c9"},"source":["split_table.head(5)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>name</th>\n","      <th>x</th>\n","      <th>y</th>\n","      <th>width</th>\n","      <th>height</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>rect</td>\n","      <td>72</td>\n","      <td>393</td>\n","      <td>52</td>\n","      <td>180</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>rect</td>\n","      <td>311</td>\n","      <td>145</td>\n","      <td>52</td>\n","      <td>172</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>rect</td>\n","      <td>273</td>\n","      <td>28</td>\n","      <td>33</td>\n","      <td>129</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>rect</td>\n","      <td>343</td>\n","      <td>20</td>\n","      <td>31</td>\n","      <td>123</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>rect</td>\n","      <td>75</td>\n","      <td>389</td>\n","      <td>46</td>\n","      <td>177</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   name    x    y  width  height\n","0  rect   72  393     52     180\n","1  rect  311  145     52     172\n","2  rect  273   28     33     129\n","3  rect  343   20     31     123\n","4  rect   75  389     46     177"]},"metadata":{"tags":[]},"execution_count":24}]},{"cell_type":"code","metadata":{"id":"mFQHHZ-GcbCA","colab_type":"code","colab":{}},"source":["new_table['x'] = split_table['x']\n","new_table['y'] = split_table['y']\n","new_table['width'] = split_table['width']\n","new_table['height'] = split_table['height']"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"K1Snf2jncctr","colab_type":"code","colab":{}},"source":["new_table['xmin'] = new_table.apply(lambda row: row.x - \n","                                  (row.width/2), axis = 1)\n","new_table['ymin'] = new_table.apply(lambda row: row.y - \n","                                  (row.height/2), axis = 1)\n","new_table['xmax'] = new_table.apply(lambda row: row.x + \n","                                  (row.width/2), axis = 1)\n","new_table['ymax'] = new_table.apply(lambda row: row.y + \n","                                  (row.height/2), axis = 1)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"9geze9Yrcevx","colab_type":"code","colab":{}},"source":["same_calss = new_table"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"mJhDfwHIchSl","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"executionInfo":{"status":"ok","timestamp":1597110438324,"user_tz":240,"elapsed":149038,"user":{"displayName":"Srinidhi Dannamaneni","photoUrl":"","userId":"14652330968581622724"}},"outputId":"c95086cd-60fc-42a6-d87b-c8e924d4c3a2"},"source":["same_calss['class_id'].unique()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([0, 1, 2, 3, 4, 5])"]},"metadata":{"tags":[]},"execution_count":28}]},{"cell_type":"code","metadata":{"id":"h1qAfCrMcj6Y","colab_type":"code","colab":{}},"source":["Row_list =[] \n","  \n","# Iterate over each row \n","for index, rows in same_calss.iterrows(): \n","    # Create list for the current row \n","    my_list =[rows.xmin, rows.ymin, rows.xmax, rows.ymax] \n","      \n","    # append the list to the final list \n","    Row_list.append(my_list) "],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"8QmrDAAfclw1","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"executionInfo":{"status":"ok","timestamp":1597110438968,"user_tz":240,"elapsed":147647,"user":{"displayName":"Srinidhi Dannamaneni","photoUrl":"","userId":"14652330968581622724"}},"outputId":"adbfaf7f-7370-468f-b366-f260b910f399"},"source":["Row_list[1]"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[285.0, 59.0, 337.0, 231.0]"]},"metadata":{"tags":[]},"execution_count":30}]},{"cell_type":"code","metadata":{"id":"olmLCl0xcnnQ","colab_type":"code","colab":{}},"source":["same_calss['x1_y1_x2_y2'] = Row_list"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"0E3WdB6zcpRh","colab_type":"code","colab":{}},"source":["bb_combined = same_calss"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"8Jd9Nik1crln","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":203},"executionInfo":{"status":"ok","timestamp":1597110438976,"user_tz":240,"elapsed":144665,"user":{"displayName":"Srinidhi Dannamaneni","photoUrl":"","userId":"14652330968581622724"}},"outputId":"2c656b3e-f27b-4775-a72e-efc03f92faa4"},"source":["bb_combined.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>filename</th>\n","      <th>class_id</th>\n","      <th>x</th>\n","      <th>y</th>\n","      <th>width</th>\n","      <th>height</th>\n","      <th>xmin</th>\n","      <th>ymin</th>\n","      <th>xmax</th>\n","      <th>ymax</th>\n","      <th>x1_y1_x2_y2</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>resize0.jpg</td>\n","      <td>0</td>\n","      <td>72</td>\n","      <td>393</td>\n","      <td>52</td>\n","      <td>180</td>\n","      <td>46.0</td>\n","      <td>303.0</td>\n","      <td>98.0</td>\n","      <td>483.0</td>\n","      <td>[46.0, 303.0, 98.0, 483.0]</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>resize0.jpg</td>\n","      <td>1</td>\n","      <td>311</td>\n","      <td>145</td>\n","      <td>52</td>\n","      <td>172</td>\n","      <td>285.0</td>\n","      <td>59.0</td>\n","      <td>337.0</td>\n","      <td>231.0</td>\n","      <td>[285.0, 59.0, 337.0, 231.0]</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>resize0.jpg</td>\n","      <td>2</td>\n","      <td>273</td>\n","      <td>28</td>\n","      <td>33</td>\n","      <td>129</td>\n","      <td>256.5</td>\n","      <td>-36.5</td>\n","      <td>289.5</td>\n","      <td>92.5</td>\n","      <td>[256.5, -36.5, 289.5, 92.5]</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>resize0.jpg</td>\n","      <td>3</td>\n","      <td>343</td>\n","      <td>20</td>\n","      <td>31</td>\n","      <td>123</td>\n","      <td>327.5</td>\n","      <td>-41.5</td>\n","      <td>358.5</td>\n","      <td>81.5</td>\n","      <td>[327.5, -41.5, 358.5, 81.5]</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>resize1.jpg</td>\n","      <td>0</td>\n","      <td>75</td>\n","      <td>389</td>\n","      <td>46</td>\n","      <td>177</td>\n","      <td>52.0</td>\n","      <td>300.5</td>\n","      <td>98.0</td>\n","      <td>477.5</td>\n","      <td>[52.0, 300.5, 98.0, 477.5]</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["      filename  class_id    x  ...   xmax   ymax                  x1_y1_x2_y2\n","0  resize0.jpg         0   72  ...   98.0  483.0   [46.0, 303.0, 98.0, 483.0]\n","1  resize0.jpg         1  311  ...  337.0  231.0  [285.0, 59.0, 337.0, 231.0]\n","2  resize0.jpg         2  273  ...  289.5   92.5  [256.5, -36.5, 289.5, 92.5]\n","3  resize0.jpg         3  343  ...  358.5   81.5  [327.5, -41.5, 358.5, 81.5]\n","4  resize1.jpg         0   75  ...   98.0  477.5   [52.0, 300.5, 98.0, 477.5]\n","\n","[5 rows x 11 columns]"]},"metadata":{"tags":[]},"execution_count":33}]},{"cell_type":"code","metadata":{"id":"Zjsipy5DcziK","colab_type":"code","colab":{}},"source":["columns = ['x','y','width','height','xmin','ymin','xmax','ymax']\n","bb_combined = bb_combined.drop(columns,axis=1)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"bbhOi017c2Q-","colab_type":"code","colab":{}},"source":["new_same_calss = bb_combined.groupby('filename',as_index=False).agg(lambda x: list(x))"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"JnZlauNuc477","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":87},"executionInfo":{"status":"ok","timestamp":1597110439321,"user_tz":240,"elapsed":138616,"user":{"displayName":"Srinidhi 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dtype='object')"]},"metadata":{"tags":[]},"execution_count":37}]},{"cell_type":"code","metadata":{"id":"Nrs3JhZzc83y","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":203},"executionInfo":{"status":"ok","timestamp":1597110439339,"user_tz":240,"elapsed":136991,"user":{"displayName":"Srinidhi Dannamaneni","photoUrl":"","userId":"14652330968581622724"}},"outputId":"e2d4d47f-6411-4f47-f696-cdc288a83dc9"},"source":["new_same_calss.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>filename</th>\n","      <th>class_id</th>\n","      <th>x1_y1_x2_y2</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>resize0.jpg</td>\n","      <td>[0, 1, 2, 3]</td>\n","      <td>[[46.0, 303.0, 98.0, 483.0], [285.0, 59.0, 337...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>resize1.jpg</td>\n","      <td>[0, 1, 2, 3]</td>\n","      <td>[[52.0, 300.5, 98.0, 477.5], [286.0, 71.5, 336...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>resize10.jpg</td>\n","      <td>[0, 1, 2, 3]</td>\n","      <td>[[65.5, 263.5, 124.5, 450.5], [283.5, 89.0, 32...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>resize100.jpg</td>\n","      <td>[0, 1, 2, 3, 4]</td>\n","      <td>[[267.5, 87.5, 300.5, 232.5], [99.0, 133.5, 14...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>resize101.jpg</td>\n","      <td>[0, 1, 2, 3, 4]</td>\n","      <td>[[274.5, 89.0, 305.5, 231.0], [96.5, 138.5, 14...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["        filename  ...                                        x1_y1_x2_y2\n","0    resize0.jpg  ...  [[46.0, 303.0, 98.0, 483.0], [285.0, 59.0, 337...\n","1    resize1.jpg  ...  [[52.0, 300.5, 98.0, 477.5], [286.0, 71.5, 336...\n","2   resize10.jpg  ...  [[65.5, 263.5, 124.5, 450.5], [283.5, 89.0, 32...\n","3  resize100.jpg  ...  [[267.5, 87.5, 300.5, 232.5], [99.0, 133.5, 14...\n","4  resize101.jpg  ...  [[274.5, 89.0, 305.5, 231.0], [96.5, 138.5, 14...\n","\n","[5 rows x 3 columns]"]},"metadata":{"tags":[]},"execution_count":38}]},{"cell_type":"code","metadata":{"id":"XsYKzInzc_JG","colab_type":"code","colab":{}},"source":["tensor_class_id = []\n","for i in new_same_calss.class_id:\n","  x = torch.LongTensor(i)\n","  tensor_class_id.append(x)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"Akr5UOZ5dBM-","colab_type":"code","colab":{}},"source":["tensor_x1_y1_x2_y2 = []\n","for i in new_same_calss.x1_y1_x2_y2:\n","  x = torch.Tensor(i)\n","  tensor_x1_y1_x2_y2.append(x)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"ode9UixJdDEW","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":87},"executionInfo":{"status":"ok","timestamp":1597110439349,"user_tz":240,"elapsed":134568,"user":{"displayName":"Srinidhi Dannamaneni","photoUrl":"","userId":"14652330968581622724"}},"outputId":"d7faf1c3-2740-4c19-c90d-15fc2cdabcf6"},"source":["tensor_x1_y1_x2_y2[1]"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["tensor([[ 52.0000, 300.5000,  98.0000, 477.5000],\n","        [286.0000,  71.5000, 336.0000, 236.5000],\n","        [257.0000, -41.5000, 289.0000,  93.5000],\n","        [331.0000, -41.0000, 357.0000,  81.0000]])"]},"metadata":{"tags":[]},"execution_count":41}]},{"cell_type":"code","metadata":{"id":"xzrqoBL_dFBW","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"executionInfo":{"status":"ok","timestamp":1597110531045,"user_tz":240,"elapsed":555,"user":{"displayName":"Srinidhi Dannamaneni","photoUrl":"","userId":"14652330968581622724"}},"outputId":"b1cc0329-c598-48e6-ec6c-2166de36f1b1"},"source":["tensor_class_id[1]"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["tensor([0, 1, 2, 3])"]},"metadata":{"tags":[]},"execution_count":42}]},{"cell_type":"code","metadata":{"id":"lyB8ejxkdGyx","colab_type":"code","colab":{}},"source":["dict_boxes_labels = []\n","for i in range(0,len(new_same_calss)):\n","    dict_boxes_labels.append({'boxes':tensor_x1_y1_x2_y2[i],'labels':tensor_class_id[i]})"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"eMKRYXSodIpj","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"executionInfo":{"status":"ok","timestamp":1597110533003,"user_tz":240,"elapsed":536,"user":{"displayName":"Srinidhi Dannamaneni","photoUrl":"","userId":"14652330968581622724"}},"outputId":"0ec2912c-9555-4146-939f-37ce0033355a"},"source":["len(dict_boxes_labels)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["480"]},"metadata":{"tags":[]},"execution_count":44}]},{"cell_type":"code","metadata":{"id":"RKaz3oHzdKbI","colab_type":"code","colab":{}},"source":["bb_lables_dict_table = new_same_calss"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"0b4yEq7SdMDo","colab_type":"code","colab":{}},"source":["bb_lables_dict_table['dict_boxes_labels'] = dict_boxes_labels"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"JshGAbEWdO7O","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"executionInfo":{"status":"ok","timestamp":1597110535103,"user_tz":240,"elapsed":397,"user":{"displayName":"Srinidhi Dannamaneni","photoUrl":"","userId":"14652330968581622724"}},"outputId":"9316b82b-f052-4712-d3b7-9ca1d5e5a6ed"},"source":["bb_lables_dict_table.columns"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Index(['filename', 'class_id', 'x1_y1_x2_y2', 'dict_boxes_labels'], dtype='object')"]},"metadata":{"tags":[]},"execution_count":47}]},{"cell_type":"code","metadata":{"id":"TgkYAWtadQpE","colab_type":"code","colab":{}},"source":["import re"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"b4f8QFVQdTAe","colab_type":"code","colab":{}},"source":["final_data = bb_lables_dict_table"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"YxhB0mbUdU1q","colab_type":"code","colab":{}},"source":["image_array = []\n","for i in range(0,len(re_size)):\n","   number = re.findall('\\d+',bb_lables_dict_table.filename[i])\n","   image_array.append(re_size[int(number[0])])  "],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"lW9W0fspdWyF","colab_type":"code","colab":{}},"source":["final_data['image_array'] = image_array"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"-Ntn3qZXdYnh","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"executionInfo":{"status":"ok","timestamp":1597110538866,"user_tz":240,"elapsed":957,"user":{"displayName":"Srinidhi Dannamaneni","photoUrl":"","userId":"14652330968581622724"}},"outputId":"1d0e06c8-265d-4607-ef0c-5dfd7d26b57f"},"source":["len(final_data)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["480"]},"metadata":{"tags":[]},"execution_count":52}]},{"cell_type":"code","metadata":{"id":"AdpNBv2qdaQW","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":288},"executionInfo":{"status":"ok","timestamp":1597110550727,"user_tz":240,"elapsed":11908,"user":{"displayName":"Srinidhi Dannamaneni","photoUrl":"","userId":"14652330968581622724"}},"outputId":"8a87219b-b0c6-462b-f15a-5b8bc598a851"},"source":["final_data.head()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>filename</th>\n","      <th>class_id</th>\n","      <th>x1_y1_x2_y2</th>\n","      <th>dict_boxes_labels</th>\n","      <th>image_array</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>resize0.jpg</td>\n","      <td>[0, 1, 2, 3]</td>\n","      <td>[[46.0, 303.0, 98.0, 483.0], [285.0, 59.0, 337...</td>\n","      <td>{'boxes': [[tensor(46.), tensor(303.), tensor(...</td>\n","      <td>[[[201.99998, 206.99998, 203.0], [201.99998, 2...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>resize1.jpg</td>\n","      <td>[0, 1, 2, 3]</td>\n","      <td>[[52.0, 300.5, 98.0, 477.5], [286.0, 71.5, 336...</td>\n","      <td>{'boxes': [[tensor(52.), tensor(300.5000), ten...</td>\n","      <td>[[[201.99998, 206.99998, 203.0], [201.99998, 2...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>resize10.jpg</td>\n","      <td>[0, 1, 2, 3]</td>\n","      <td>[[65.5, 263.5, 124.5, 450.5], [283.5, 89.0, 32...</td>\n","      <td>{'boxes': [[tensor(65.5000), tensor(263.5000),...</td>\n","      <td>[[[201.99998, 206.99998, 203.0], [201.99998, 2...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>resize100.jpg</td>\n","      <td>[0, 1, 2, 3, 4]</td>\n","      <td>[[267.5, 87.5, 300.5, 232.5], [99.0, 133.5, 14...</td>\n","      <td>{'boxes': [[tensor(267.5000), tensor(87.5000),...</td>\n","      <td>[[[203.0, 208.99998, 206.99998], [203.0, 208.9...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>resize101.jpg</td>\n","      <td>[0, 1, 2, 3, 4]</td>\n","      <td>[[274.5, 89.0, 305.5, 231.0], [96.5, 138.5, 14...</td>\n","      <td>{'boxes': [[tensor(274.5000), tensor(89.), ten...</td>\n","      <td>[[[203.0, 208.99998, 206.99998], [203.0, 208.9...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["        filename  ...                                        image_array\n","0    resize0.jpg  ...  [[[201.99998, 206.99998, 203.0], [201.99998, 2...\n","1    resize1.jpg  ...  [[[201.99998, 206.99998, 203.0], [201.99998, 2...\n","2   resize10.jpg  ...  [[[201.99998, 206.99998, 203.0], [201.99998, 2...\n","3  resize100.jpg  ...  [[[203.0, 208.99998, 206.99998], [203.0, 208.9...\n","4  resize101.jpg  ...  [[[203.0, 208.99998, 206.99998], [203.0, 208.9...\n","\n","[5 rows x 5 columns]"]},"metadata":{"tags":[]},"execution_count":53}]},{"cell_type":"code","metadata":{"id":"YWdNcn7RdcIC","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":104},"executionInfo":{"status":"ok","timestamp":1597110550729,"user_tz":240,"elapsed":11094,"user":{"displayName":"Srinidhi Dannamaneni","photoUrl":"","userId":"14652330968581622724"}},"outputId":"85da7f89-920a-4893-ac8a-8f3a2f4d6ef5"},"source":["final_data['dict_boxes_labels'][150]"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'boxes': tensor([[365.5000, -32.0000, 384.5000,  50.0000],\n","         [348.0000,  -7.0000, 376.0000, 107.0000],\n","         [150.0000,  37.0000, 190.0000, 193.0000],\n","         [417.0000, -20.0000, 441.0000,  22.0000]]),\n"," 'labels': tensor([0, 1, 2, 3])}"]},"metadata":{"tags":[]},"execution_count":54}]},{"cell_type":"code","metadata":{"id":"vHDTlKpgdefw","colab_type":"code","colab":{}},"source":["tensor_image_array = []\n","for i in final_data.image_array:\n","  x = torch.FloatTensor(i)\n","  y = torch.reshape(x,(3,600,600))\n","  tensor_image_array.append(y)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"-8Fj5zOWdgqO","colab_type":"code","colab":{}},"source":["imgs = tensor_image_array\n","targets = final_data['dict_boxes_labels']"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"MIxYjGApdiWC","colab_type":"code","colab":{}},"source":["Input_Data=[]\n","for i in range(0,len(imgs)):\n","  Input_Data.append((imgs[i],targets[i]))"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"rOviPvL5dj_7","colab_type":"code","colab":{}},"source":["from sklearn.model_selection import train_test_split\n","train_data, test_data= train_test_split(Input_Data, test_size=0.05)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"yxpLETsSdlyK","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"executionInfo":{"status":"ok","timestamp":1597110550744,"user_tz":240,"elapsed":4477,"user":{"displayName":"Srinidhi Dannamaneni","photoUrl":"","userId":"14652330968581622724"}},"outputId":"81c92f87-8ffd-46ca-8681-48f106dc949b"},"source":["len(train_data)"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["456"]},"metadata":{"tags":[]},"execution_count":59}]},{"cell_type":"code","metadata":{"id":"rv-Y5zHXdnXQ","colab_type":"code","colab":{}},"source":["x,y = train_data[0]"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"ilTBv6D2dpLM","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":87},"executionInfo":{"status":"ok","timestamp":1597110550748,"user_tz":240,"elapsed":2260,"user":{"displayName":"Srinidhi Dannamaneni","photoUrl":"","userId":"14652330968581622724"}},"outputId":"11fcc582-322e-4bdf-90de-e85f58a4021c"},"source":["y"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'boxes': tensor([[384.0000, -50.0000, 410.0000,  56.0000],\n","         [403.5000, -25.0000, 424.5000,  27.0000],\n","         [-25.0000, 309.0000,  25.0000, 461.0000]]),\n"," 'labels': tensor([0, 1, 2])}"]},"metadata":{"tags":[]},"execution_count":61}]},{"cell_type":"code","metadata":{"id":"JbCSVGpudq-3","colab_type":"code","colab":{}},"source":["sizes = []\n","for i in tensor_image_array:\n","  x = i.shape\n","  sizes.append(x)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"E3_3zM5SdtAX","colab_type":"code","colab":{}},"source":["import sys\n","sys.path.append('/content/drive/My Drive/Colab Notebooks/Social Distancing Tool')\n","\n","from mpdl import set_seed\n","from mpdl import train_network, Flatten, View, weight_reset"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"IAHpDKiTd1o_","colab_type":"code","colab":{}},"source":["def collate_fn(batch):\n","    \"\"\"\n","    batch is going to contain a python list of objects. In our case, our data loader returns (Tensor, Dict) pairs\n","    The FasterRCNN algorithm wants a List[Tensors] and a List[Dict]. So we will use this function to convert the \n","    batch of data into the form we want, and then give it to the Dataloader to use\n","    \"\"\"\n","    imgs = []\n","    labels = []\n","    for img, label in batch:\n","        imgs.append(img)\n","        labels.append(label)\n","    return imgs, labels\n","\n","train_loader = DataLoader(train_data, batch_size=8, shuffle=True, collate_fn=collate_fn)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"_bheUthZd3MW","colab_type":"code","colab":{}},"source":["#How many channels are in the input?\n","C = 3\n","#How many classes are there?\n","classes = 6\n","\n","n_filters = 32"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"YggcrAtSd5-T","colab_type":"code","colab":{}},"source":["backbone = nn.Sequential(\n","    nn.Conv2d(C, n_filters, (3,3), padding=1),    \n","    nn.BatchNorm2d(n_filters),\n","    nn.LeakyReLU(), \n","    nn.Conv2d(n_filters, n_filters, (3,3), padding=1), \n","    nn.BatchNorm2d(n_filters),\n","    nn.LeakyReLU(), \n","    nn.MaxPool2d((2,2),stride=(3,4)), \n","    nn.Conv2d(n_filters, 2*n_filters, (3,3), padding=1), \n","    nn.BatchNorm2d(2*n_filters),\n","    nn.LeakyReLU(),                     \n","    nn.Conv2d(2*n_filters, 2*n_filters, (3,3), padding=1), \n","    nn.BatchNorm2d(2*n_filters), \n","    nn.LeakyReLU(),\n","    nn.MaxPool2d((2,2),stride=(2,2)), \n","    nn.Conv2d(2*n_filters, 4*n_filters, (3,3), padding=1), \n","    nn.BatchNorm2d(4*n_filters),\n","    nn.LeakyReLU(),\n","    nn.Conv2d(4*n_filters, 4*n_filters, (3,3), padding=1), \n","    nn.BatchNorm2d(4*n_filters),\n","    nn.LeakyReLU(),\n","    nn.MaxPool2d((2,2),stride=(2,2)), \n","    nn.Conv2d(4*n_filters, 8*n_filters, (3,3), padding=1), \n","    nn.BatchNorm2d(8*n_filters),\n","    nn.LeakyReLU(),\n","    nn.Conv2d(8*n_filters, 8*n_filters, (3,3), padding=1), \n","    nn.BatchNorm2d(8*n_filters),\n","    nn.LeakyReLU(),\n","    nn.MaxPool2d((2,2),stride=(2,2)),\n","    nn.Conv2d(8*n_filters, 16*n_filters, (3,3), padding=1), \n","    nn.BatchNorm2d(16*n_filters),\n","    nn.LeakyReLU(),\n","    nn.Conv2d(16*n_filters, 16*n_filters, (3,3), padding=1), \n","    nn.BatchNorm2d(16*n_filters),\n","    nn.LeakyReLU(), \n",")\n","\n","backbone.out_channels = n_filters*16"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"v3Blt4Wud7tl","colab_type":"code","colab":{}},"source":["model2 = FasterRCNN(backbone, num_classes=6, \n","                #image_mean = [0.5], image_std = [0.229],\n","                #if they were color images\n","                 image_mean = (0.5, 0.5, 0.5), image_std = (0.229, 0.229, 0.229)\n","                  )"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"y3OBG7IPd9ji","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":154,"referenced_widgets":["d0af107511dc457e87d33a39d635db13","8b78e9d2282941178c690af6fab3d37c","3b8f157c81e14061a2888a869c423f1c","f2dcb8e4c976497cb43e0ccfa525657d","6cac715448e649a6b170857edf1e8ee7","e3a49b17e2f34c3380c982f664083ae5","3eb53b4ff9544b8992fb1f89fe95cdd0","6f095a9e72c54c6c8297ebea6dbe5584","a917842145964a51b74afda710cff882","491443b7b2c84d699769fd69c232f27a","f49e56d741974e86a52c90a6eee4b070","3ff8790793c245c19ac09b7f572635ca","46b793508f6f4a9091a9c9aab7807a55","8b46083336904aad96e922f99f10f599","2ce21c8a9eba4957833c78f2da5eeb3a","353500bd0a3a4ce6a8d1c22f21b0a0c0","ab224b5c079342dbab63842d6c9d61e6","3c6c4cc1f58348d2b42f49068a52ef5b","998d04a3060942d99a3a84824c204c17","815af29175004805817bc03cf24fd571","4e9ca75b7997427286371b070d1be66b","e9bcc84794414c70bafcb5f78c056551","1a487e0d4b834b20abd19c23bd662404","0c96555b10374ddabb4d62fbb634f1f1","2c1e1e5ce695407995d5c790be3223d8","e147a7f1005f45e6b6076b5a03d3231a","28bedb1e3b00453ea53fa436665e3144","fc4bfe7e27d84cb49e3a4b98df75c40a","3593b670c0194a1791e0a0a101b1825b","036d78113e9243288886b7b3ed1bd9b8","4d7e5903cb5d447f9a7d18353509da9b","5f7f045d5621408abcef3c9da5754761","338678f7f3ff4104aa9a37b9b7150071","a0864d1488694211918336c2a44a195c","8d32ac6ce21c40188e256cef6a1bfff4","4297f80c253b4ecdad37d7301c1c1693","a98e7f6fe3bc4223a403c44771fc5316","583348057cb74b97820478e7ba850af1","26b9589a66c2494494894fee1057de7f","c8bc2079ba194c2ebb8c35700afb1b1d","d68988c8455149dea6ca3784167d23fb","a9524e2cebd74b55aed6055f718dd5ca","84c093476ac346aca06dea0375f38d5b","7bf09e1ddb314471bbdaac86f9a62747","a815e68bce17454fad04cc769909c43c","163e7832052d41439f973139b3c91ca2","bff098eddc334e6893494914fc6409b6","065e24ee7b154c4f926b8b300d0d2061","c3e081072fc24e01a11d039f2bd30c51","42fb0fbf499b48319be9ba6734780e1a","98db86893449488abd4248b5330691f6","663229adad3b40feb58d39be39383dc9","0fd5a539d8f7445fbafbf3d40a292609","3f1f25b3028f49479ba0ceadb612942f","a674eca367cc4a5482eea51d1c66a896","0cea78f8205941ed9b9e9da7bafdf071","b83b859caff34e6ab02c5cef6124edfb","a4d1e7011c1d4a289146b0fc43db3374","d493dc500d3948b4b22b7094685fb772","cb67ddc8756949528c01a22f9a2baa81","0474b5ab36384e43861cd15c9297a983","e64fd939b001467e8d36137ab20b4d3d","15a2e77194b64f9a97bcfc472d0f9d8a","a1278cf9145b46cab16f1be1b3eedabd","21a48402e4e543ee905aa5b9b9a83ada","2d75feb0fc1e4443a79081a38fb76161","06b265762a5d46478dad982a3323510c","0bf9fad0b60d409c8997131d4e2b6eb9","62147921fe174a478888cb7f7ab45a33","52c5ff5688af4df3aeb5d3ac0557b5b3","10c46b9202044e8f9535498c97fd7824","488722cea45e45a28663039bf8a6bcfd","5f5cdc89c17c4cc095397deb1f6465d1","4fa08c0b057f44efb10637c46aab95b7","93c5892f49cb4e0093189193c720ee51","d271d65ac7404c2295a1f63ec9e56180","520cfbf957d048a68a5d1ad3c4f0e1d3","078fa1aabbd24fd59710f4686706ed63","8ff376734e1d477aa9345074b72a63ff","6341eb4910084797ad7ec7d7edbf5c0b","c4802553a6fb4a119ed0bad031ed8863","da5e086c84f04096897ae5402fd826b2","db794660397141778e8d0d02243d3c47","20bdb96f03fd42f8b8cc504905777b07","c419f8542b8f48888abcb7831eae41ab","639ef909b3444e8f8aa4c78d5a4f42cb","626d3c9e60bb46208bccd3d939e07c09","a13f4dd63a6841f58b0007ff0d71c97c"]},"executionInfo":{"status":"ok","timestamp":1597143071276,"user_tz":240,"elapsed":32512586,"user":{"displayName":"Srinidhi Dannamaneni","photoUrl":"","userId":"14652330968581622724"}},"outputId":"4567890c-8991-43b8-e1af-8f7571a96923"},"source":["model2 = model2.train()\n","model2.to(device)\n","optimizer = torch.optim.AdamW(model2.parameters())\n","\n","loss_hist = []\n","\n","for epoch in tqdm(range(10), desc=\"Epoch\", disable=False):\n","    running_loss = 0.0\n","    for inputs, labels in tqdm(train_loader, desc=\"Train Batch\", leave=False, disable=False):\n","        #Move the batch to the device we are using. \n","        inputs = [i.to(device) for i in inputs]\n","#         labels = labels.to(device)\n","        labels = [dict([(k,v.to(device)) for k,v in d.items()]) for d in labels]\n","\n","        optimizer.zero_grad()\n","\n","        losses = model2(inputs, labels)\n","        # Compute loss.\n","        loss = 0\n","        for partial_loss in losses.values():\n","            loss += partial_loss\n","\n","        loss.backward()\n","        \n","        optimizer.step()\n","\n","        running_loss += loss.item()\n","        \n","        loss_hist.append(loss.item())"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"d0af107511dc457e87d33a39d635db13","version_minor":0,"version_major":2},"text/plain":["HBox(children=(FloatProgress(value=0.0, description='Epoch', max=10.0, style=ProgressStyle(description_width='…"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"a917842145964a51b74afda710cff882","version_minor":0,"version_major":2},"text/plain":["HBox(children=(FloatProgress(value=0.0, description='Train Batch', max=57.0, style=ProgressStyle(description_w…"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/torchvision/ops/boxes.py:101: UserWarning: This overload of nonzero is deprecated:\n","\tnonzero()\n","Consider using one of the following signatures instead:\n","\tnonzero(*, bool as_tuple) (Triggered internally at  /pytorch/torch/csrc/utils/python_arg_parser.cpp:766.)\n","  keep = keep.nonzero().squeeze(1)\n"],"name":"stderr"},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"ab224b5c079342dbab63842d6c9d61e6","version_minor":0,"version_major":2},"text/plain":["HBox(children=(FloatProgress(value=0.0, description='Train Batch', max=57.0, style=ProgressStyle(description_w…"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"2c1e1e5ce695407995d5c790be3223d8","version_minor":0,"version_major":2},"text/plain":["HBox(children=(FloatProgress(value=0.0, description='Train Batch', max=57.0, style=ProgressStyle(description_w…"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"338678f7f3ff4104aa9a37b9b7150071","version_minor":0,"version_major":2},"text/plain":["HBox(children=(FloatProgress(value=0.0, description='Train Batch', max=57.0, style=ProgressStyle(description_w…"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"d68988c8455149dea6ca3784167d23fb","version_minor":0,"version_major":2},"text/plain":["HBox(children=(FloatProgress(value=0.0, description='Train Batch', max=57.0, style=ProgressStyle(description_w…"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"c3e081072fc24e01a11d039f2bd30c51","version_minor":0,"version_major":2},"text/plain":["HBox(children=(FloatProgress(value=0.0, description='Train Batch', max=57.0, style=ProgressStyle(description_w…"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"b83b859caff34e6ab02c5cef6124edfb","version_minor":0,"version_major":2},"text/plain":["HBox(children=(FloatProgress(value=0.0, description='Train Batch', max=57.0, style=ProgressStyle(description_w…"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"21a48402e4e543ee905aa5b9b9a83ada","version_minor":0,"version_major":2},"text/plain":["HBox(children=(FloatProgress(value=0.0, description='Train Batch', max=57.0, style=ProgressStyle(description_w…"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"5f5cdc89c17c4cc095397deb1f6465d1","version_minor":0,"version_major":2},"text/plain":["HBox(children=(FloatProgress(value=0.0, description='Train Batch', max=57.0, style=ProgressStyle(description_w…"]},"metadata":{"tags":[]}},{"output_type":"display_data","data":{"application/vnd.jupyter.widget-view+json":{"model_id":"c4802553a6fb4a119ed0bad031ed8863","version_minor":0,"version_major":2},"text/plain":["HBox(children=(FloatProgress(value=0.0, description='Train Batch', max=57.0, style=ProgressStyle(description_w…"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"obXoCMRrd_J5","colab_type":"code","colab":{}},"source":["PATH = '/content/drive/My Drive/Colab Notebooks/Social Distancing Tool/model_SelfTrain.pt'\n","torch.save(model2.state_dict(), PATH)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"iFA_H8FUeCih","colab_type":"code","colab":{}},"source":["model2 = model2.eval()\n","model2= model2.to(device)"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"k6Jl54BfMpxJ","colab_type":"code","colab":{}},"source":["import matplotlib.patches as patches\n","def plotDetection(ax, abs_pos, label=None):\n","    x1, y1, x2, y2 = abs_pos\n","    \n","    rect = patches.Rectangle((x1,y1),x2-x1,y2-y1,linewidth=1,edgecolor='r',facecolor='none')\n","    ax.add_patch(rect)\n","    if label is not None:\n","        plt.text(x1+0.5, y1, label, color='black', bbox=dict(facecolor='white', edgecolor='white', pad=1.0))\n","    \n","    return "],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"6eYpWjeUaBFt","colab_type":"code","colab":{}},"source":["def showPreds(img, pred, iou_max_overlap=0.5, min_score=0.05, label_names=None):\n","    \"\"\"\n","    img: the original image object detection was performed on\n","    pred: the output dictionary from FasterRCNN for evaluation on img\n","    iou_max_overlap: the iou threshold at which non-maximum suppression will be performed\n","    min_score: the minimum RPN network score to consider an object\n","    \"\"\"\n","    fig,ax = plt.subplots(1)\n","    img = img.cpu().numpy()\n","    if img.shape[0] == 1:\n","        ax.imshow(img[0,:])\n","    else:\n","        ax.imshow(np.moveaxis(img, 0, 2))\n","    boxes = pred['boxes'].cpu()\n","    labels = pred['labels'].cpu()\n","    scores = pred['scores'].cpu()\n","    \n","    selected = nms(boxes, scores, iou_max_overlap).cpu().numpy()\n","    \n","    for i in selected:\n","        if scores[i].item() > min_score:\n","            if label_names is None:\n","                label = str(labels[i].item())\n","            else:\n","                label = label_names[labels[i].item()]\n","            plotDetection(ax, boxes[i].cpu().numpy(), label=label)\n","    \n","    plt.show()"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"728G4N3-ltJe","colab_type":"code","colab":{}},"source":["x,y = test_data[0]"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"tT3vCuwnado5","colab_type":"code","colab":{}},"source":["\n","    ## as pytorch models only accept tensors and shape inspecific way converting to tensors and chnaging shape t0 (3,720,1280)\n","with torch.no_grad():                                            \n","  pred = model2([x])"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"jBvoAExKizwp","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":628},"executionInfo":{"status":"ok","timestamp":1597143075069,"user_tz":240,"elapsed":32505250,"user":{"displayName":"Srinidhi Dannamaneni","photoUrl":"","userId":"14652330968581622724"}},"outputId":"69234bc9-026a-4364-df72-2f8207e1fd2d"},"source":["pred[0]                         #coordinates, labels, scores of the prediction of an image\n","                                #there are multiple predictions\n","                                #score is also very less"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'boxes': tensor([[287.6707,  66.6648, 334.2833, 225.8144],\n","         [ 39.0332, 294.9340, 101.8755, 526.0132],\n","         [291.0624,  51.9614, 311.6388, 261.2302],\n","         [299.5331,  53.5878, 319.3089, 235.4733],\n","         [ 60.0336, 266.5402, 119.5310, 503.6440],\n","         [284.4784,  72.6872, 305.5305, 269.9460],\n","         [308.8372,   3.0053, 330.1629, 227.5575],\n","         [281.9531, 111.2188, 322.9257, 254.1202],\n","         [303.0660,  60.1467, 343.6287, 203.0968],\n","         [ 18.6436, 354.0460,  78.7637, 552.7528],\n","         [313.7089,   4.3723, 359.1384, 153.2505],\n","         [  0.0000, 379.8773,  35.5270, 580.6762],\n","         [273.1059, 102.2881, 308.3529, 244.7855],\n","         [314.5067,  56.6704, 335.0117, 201.2442],\n","         [ 70.6061, 209.1265, 122.5446, 410.2245],\n","         [  0.0000, 302.0448,  66.4618, 515.7721],\n","         [ 37.5198, 295.1083, 103.5556, 524.8768],\n","         [102.3974, 160.0752, 123.2486, 444.9744],\n","         [  0.0000, 395.2138,  16.4007, 558.7050],\n","         [331.7133,   0.0000, 352.7798, 156.5439],\n","         [262.6680,  88.8579, 315.9664, 276.0148],\n","         [323.2906,   0.0000, 344.6266, 169.3167],\n","         [337.6469,   0.0000, 378.8022, 103.9384],\n","         [ 96.0876, 150.3773, 116.2293, 430.0416],\n","         [259.4047, 134.2444, 298.1856, 275.6153],\n","         [  0.0000, 484.6184,   9.6872, 588.2330],\n","         [  0.0000, 491.1990,  22.8185, 588.7556],\n","         [273.2640,  79.6805, 295.2251, 304.8544],\n","         [ 83.5755, 201.6993, 143.1767, 447.7289]]),\n"," 'labels': tensor([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 3, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1,\n","         3, 1, 1, 1, 1]),\n"," 'scores': tensor([0.3712, 0.3024, 0.2641, 0.2517, 0.2295, 0.2206, 0.2178, 0.1920, 0.1866,\n","         0.1456, 0.1433, 0.1381, 0.1362, 0.1264, 0.1161, 0.0955, 0.0905, 0.0861,\n","         0.0806, 0.0792, 0.0789, 0.0778, 0.0744, 0.0735, 0.0651, 0.0636, 0.0623,\n","         0.0531, 0.0524])}"]},"metadata":{"tags":[]},"execution_count":75}]},{"cell_type":"code","metadata":{"id":"ZQpu--QEiumB","colab_type":"code","colab":{}},"source":["from torchvision.ops import nms"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"g6Afyqc2zrf8","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":286},"executionInfo":{"status":"ok","timestamp":1597159171565,"user_tz":240,"elapsed":669,"user":{"displayName":"Srinidhi Dannamaneni","photoUrl":"","userId":"14652330968581622724"}},"outputId":"a4c03da9-be5e-4d1c-83d9-302b87fc9a5e"},"source":["showPreds(x, pred[0], iou_max_overlap=0.5, min_score=0.9)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"],"name":"stderr"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"5VF1t2Y8xK-m","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":286},"executionInfo":{"status":"ok","timestamp":1597153760911,"user_tz":240,"elapsed":598,"user":{"displayName":"Srinidhi Dannamaneni","photoUrl":"","userId":"14652330968581622724"}},"outputId":"9a96f995-40f0-4ec3-b8fc-f5e50ceb6fbc"},"source":["showPreds(x, pred[0], iou_max_overlap=0.5, min_score=0.3)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"],"name":"stderr"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAQEAAAD8CAYAAAB3lxGOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAARGElEQVR4nO3db4xc1X3G8e/D2sYEKGZhbVleYxPFUoRQA2hFsEBWCkoENIp5QRBuVEywZKmlEhGVUtNKbSP1RdIXIUGtSK06qqkSA80f2ULUxDVEVV+EsA7mfxw2yMheYbwQbEiAgM2vL+7ZeFj2z+zunTl39zwfaTTnnntn7m93x4/PPXNnriICMyvXabkLMLO8HAJmhXMImBXOIWBWOIeAWeEcAmaF60gISLpW0gFJQ5K2dGIfZlYP1X2egKQe4FfAZ4HDwBPAhoh4vtYdmVktOjESuBwYioiXIuI94H5gfQf2Y2Y1WNCB51wBHGpZPgx8erIHnH/++bF69eoOlGJmo/bt2/daRPSN7e9ECLRF0mZgM8AFF1zA4OBgrlLMiiDp5fH6O3E4MAysbFnuT30fEhFbI2IgIgb6+j4STmbWJZ0IgSeANZIulLQIuBnY1YH9mFkNaj8ciIgTkv4KeAToAb4bEc/VvR8zq0dH5gQi4mHg4U48t5nVy2cMmhXOIWBWOIeAWeEcAmaFcwiYFc4hYFY4h4BZ4RwCZoVzCJgVziFgVjiHgFnhHALWtttuu42lS5dy8cUX5y7FauQQsLbdeuut7N69O3cZVjOHgLVt3bp19Pb25i7DauYQMCucQ8CscA4Bs8I5BMwK5xCwtm3YsIG1a9dy4MAB+vv72bZtW+6SrAbZrjtgc8+OHTsmXilN/uBVq+DgwVrrsXo4BKweU13TcqqQsGx8OGBWOIeAWeEcAtaescP5d4F6r2pvmXhOwGZmce4CrC4eCVgzBPBe7iLK5BCwZvgAeCd3EWWaMgQkfVfSUUnPtvT1Stoj6cV0f27ql6R7JA1JelrSZZ0s3uaR04CzcxdRpnZGAv8BXDumbwuwNyLWAHvTMsB1wJp02wzcW0+ZNu+dBF7LXUSZpgyBiPhf4DdjutcD21N7O3BDS/99UfkZsETS8rqKtXlsAbA0dxFlmumcwLKIeCW1jwDLUnsFcKhlu8Opz8waatYTgxERzOAdY0mbJQ1KGhwZGZltGWY2QzMNgVdHh/np/mjqHwZWtmzXn/o+IiK2RsRARAz09fXNsAwzm62ZhsAuYGNqbwR2tvTfkt4luAI43nLYYGYNNOUZg5J2AJ8Bzpd0GPgH4OvAg5I2AS8DN6XNHwauB4aAt4Evd6BmM6vRlCEQERsmWHXNONsGcPtsizKz7vEZg2aFcwiYFc4hYFY4h4BZ4RwCZoVzCJgVziFgVjiHgFnhHAJmhXMImBXOIWBWOIeAWeEcAmaFcwiYFc4hYFY4h4BZ4RwCZoVzCDTKjL642WxWHAKNcgJ4P3cRVhhfmrxRenIXYAXySKBRTmNO/knep7qqsM1JHgnY7PlVNKfNwf925rOTVPMCc4zSzeYkh0CjvEN1zRaz7vFArlHOyl2AFcgjAbPCOQTMCucQMCvclCEgaaWkxyQ9L+k5SXek/l5JeyS9mO7PTf2SdI+kIUlPS7qs0z+Emc1cOyOBE8BfR8RFwBXA7ZIuArYAeyNiDbA3LQNcB6xJt83AvbVXbWa1mTIEIuKViPhFar8FvACsANYD29Nm24EbUns9cF9UfgYskbS89srNrBbTmhOQtBq4FHgcWBYRr6RVR4Blqb0CONTysMOpb+xzbZY0KGlwZGRkmmWbWV3aDgFJZwE/BL4SEW+2rouIaX8GNiK2RsRARAz09fVN56FmVqO2QkDSQqoA+F5E/Ch1vzo6zE/3R1P/MLCy5eH9qc/MGqiddwcEbANeiIhvtqzaBWxM7Y3Azpb+W9K7BFcAx1sOG8ysYdo5bfhK4M+BZyTtT31/C3wdeFDSJuBl4Ka07mHgemCI6kT4L9dasZnVasoQiIj/Y+LPiF0zzvYB3D7LusysS3zGoFnhHAJmhXMImBXO3ydg7Vm1CjSLrw9ataq+WqxWDgFrz8GDuSuwDvHhgFnhHALWMAG8lbuIojgErIH8Zavd5BCwBlqYu4CiOASsgc7IXUBRHALWQK/lLqAoDgFrIF+evZscAtZAvrppNzkErIE8Eugmh4BZ4RwC1kB+WXaTf9vWQCun3sRq4w8QWcOIib/IyjrBIwGzwjkEzArnEDAr3LwIgdtuu42lS5dy8cUX5y7FbM6ZFyFw6623snv37txlmM1J8yIE1q1bR29vb+4yzOakeRECZjZzDgGzwjkEzArXzlWJF0v6uaSnJD0n6Wup/0JJj0sakvSApEWp//S0PJTWr+7sj2Bms9HOSOD3wNUR8SngEuDadMnxbwB3R8QngDeATWn7TcAbqf/utF1HbdiwgbVr13LgwAH6+/vZtm3bqZXSzG6rV3e6bLNGaOeqxAH8Ni0uTLcArgb+LPVvB/4RuBdYn9oAPwD+RZLS83TEjh07Jl45093O5mo7ZnNIW3MCknok7QeOAnuAXwPHIuJE2uQwsCK1VwCHANL648B54zznZkmDkgZHRkZm91OY2Yy1FQIRcTIiLgH6gcuBT852xxGxNSIGImKgr69vZk9y5MhsyzAr3rTeHYiIY8BjwFpgiaTRw4l+YDi1h0kfCE/rzwFer6XasZYv78jTmpWknXcH+iQtSe0zgM8CL1CFwY1ps43AztTelZZJ6x/t5HyAmc1OO18qshzYLqmHKjQejIiHJD0P3C/pn4AngdEp+W3Af0oaAn4D3NyBus2sJu28O/A0cOk4/S9RzQ+M7X8X+GIt1ZlZx/mMQbPCOQTMCucQMCucQ8CscA4Bs8I5BMwK5xAwK5xDwKxwDgGzwjkEzArnEDArnEPArHAOAbPCOQTMCucQMCucQ8CscA4Bs8I5BMwK5xCweeN3wDu5i5iDHAJdE8AHuYuY194F3stdxBzUzrcNm80JvbkLmKMcAl2jdOuE0cs6lH39xLE//fupzy/yyfn3M28EpYdAqw84FbvvU80VBNXlsOzDHALzQidHGXOTqCa8Rn8rp2espekcAjYvtcZiT7rZ+PzugFnhHAJmhWs7BCT1SHpS0kNp+UJJj0sakvSApEWp//S0PJTWr+5M6WYT+4BT75nY5KYzEriD6pLko74B3B0RnwDeADal/k3AG6n/7rSdWVeN4LMH29VWCEjqB/4U+Pe0LOBq4Adpk+3ADam9Pi2T1l+TtjfrmjPxrHe72h0JfAv4KqfOez0POBYRJ9LyYWBFaq8ADgGk9cfT9oU6SXUyawC/z1xLOXrwhFe7pvw9Sfo8cDQi9tW5Y0mbJQ1KGhwZGanzqRtGnHqDym9Udcti/NtuVztheSXwBUkHgfupDgO+DSyRNDri6geGU3sYWAmQ1p8DvD72SSNia0QMRMRAX1/frH6IZjuN6uU40QmsvwVOUI0UPJVVF58+1b4pQyAi7oqI/ohYDdwMPBoRXwIeA25Mm20Edqb2rrRMWv9oRPjVPaHfUk1hncxdiBVqNodNfwPcKWmI6ph/W+rfBpyX+u8EtsyuxPmuB3gLfwjWcpnWBGpE/BT4aWq/BFw+zjbvAl+sobZCLKQaBXjwann4XZTszkw3T2NZHg6B7BbmLsAK57dSzQrnEDArnEPArHAOAbPCOQTMCucQMCucQ8CscA4Bs8I5BMwK5xAwK5xDwKxwDgGzwjkEzArnEDArnEPArHAOAbPCOQTMCucQMCucQ8CscA4Bs8I5BMwK5xDI6gN8+THLzSGQ1VtUlyBzCFg+DoGsguq6A8JBYLk4BLI7RnUdQoeA5dFWCEg6KOkZSfslDaa+Xkl7JL2Y7s9N/ZJ0j6QhSU9LuqyTP8Dct4BTcwNm3TedkcCfRMQlETGQlrcAeyNiDbCXU1cfvg5Yk26bgXvrKnb+EfA2VQiY5TGbw4H1wPbU3g7c0NJ/X1R+BiyRtHwW+5nHeqj+BD34qsSWS7shEMBPJO2TtDn1LYuIV1L7CLAstVcAh1oeezj12Ud8DPgjTk0OmnVfu1clvioihiUtBfZI+mXryogISdM6qE1hshngggsumM5D55HTqCYFF+AgsFzaGglExHC6Pwr8GLgceHV0mJ/uj6bNh4GVLQ/vT31jn3NrRAxExEBfX9/Mf4I5L/CcgOU0ZQhIOlPS2aNt4HPAs8AuYGPabCOwM7V3AbekdwmuAI63HDbYR5yGRwCWUzuHA8uAH0sa3f77EbFb0hPAg5I2AS8DN6XtHwauB4aopr6/XHvV84onBS2vKUMgIl4CPjVO/+vANeP0B3B7LdUV4WzgJA4Cy6XdiUHrqJ7cBVjBfNqwWeEcAmaFcwiYFW5uzwmsWgXq0ITaqlWded6OGT3fYIL5hTeARcCZ3avI5oa5HQIHD+auoEECeBM4d/zV5+A3IGxcczsErIWoPocwAR/42QT80ui6AI534HmF32q0mXAIdNXol4r+Dn+3oDWFQ6DrfgcsxgFgTeE5ga57E3+9gjWJRwJdtzh3AWYf4hDout7cBZh9iEOg6/wFItYsDoGu84SgNYtDoKuE52KtaRwCZoVzCJgVziFgVjiHgFnhHAJmhXMImBXOIWBWOIeATWL0o882nzkEbBLvpVsrXztxvnEI2CTeTrexjnS7EOsgh4DNgA8R5hOHgE2T8JeizC8OAbPC+SNtNglfqKAEDgGbxMncBVgXKCL/JI+kt4ADuetocT7wWu4ixmhaTa5nck2rB2BVRPSN7WzKSOBARAzkLmKUpMEm1QPNq8n1TK5p9UzGE4NmhXMImBWuKSGwNXcBYzStHmheTa5nck2rZ0KNmBg0s3yaMhIws0yyh4CkayUdkDQkaUuX9vldSUclPdvS1ytpj6QX0/25qV+S7kn1PS3psg7Us1LSY5Kel/ScpDty1iRpsaSfS3oq1fO11H+hpMfTfh+QtCj1n56Wh9L61XXW01JXj6QnJT3UkHoOSnpG0n5Jg6kv2+toxiIi2w3oAX4NfBxYBDwFXNSF/a4DLgOeben7Z2BLam8BvpHa1wP/TXX63BXA4x2oZzlwWWqfDfwKuChXTel5z0rthcDjaT8PAjen/u8Af5Hafwl8J7VvBh7o0N/tTuD7wENpOXc9B4Hzx/Rlex3N+OfIunNYCzzSsnwXcFeX9r16TAgcAJan9nKqcxcA/g3YMN52HaxtJ/DZJtQEfAz4BfBpqpNfFoz92wGPAGtTe0HaTjXX0Q/sBa4GHkr/mLLVk557vBDI/jeb7i334cAK4FDL8mHyfURtWUS8ktpHgGWp3dUa09D1Uqr/fbPVlIbe+4GjwB6qEduxiDgxzj7/UE9afxw4r856gG8BX+XUN5qcl7keqD5T/RNJ+yRtTn2NeB1NR1POGGyUiAhJXX/bRNJZwA+Br0TEm9KpD/B0u6aIOAlcImkJ8GPgk93a91iSPg8cjYh9kj6Tq45xXBURw5KWAnsk/bJ1Za7X0XTlHgkMAytblvtTXw6vSloOkO6Ppv6u1ChpIVUAfC8iftSEmgAi4hjwGNVwe4mk0f84Wvf5h3rS+nOA12ss40rgC5IOAvdTHRJ8O2M9AETEcLo/ShWUl9OAv9l05Q6BJ4A1aZZ3EdUkzq5MtewCNqb2Rqrj8tH+W9Ls7hXA8ZbhXi1U/Ze/DXghIr6ZuyZJfWkEgKQzqOYnXqAKgxsnqGe0zhuBRyMd+NYhIu6KiP6IWE31Gnk0Ir6Uqx4ASWdKOnu0DXwOeJaMr6MZyz0pQTVr+iuqY86/69I+dwCvAO9THZttojpm3Au8CPwP0Ju2FfCvqb5ngIEO1HMV1fHl08D+dLs+V03AHwNPpnqeBf4+9X8c+DkwBPwXcHrqX5yWh9L6j3fwb/cZTr07kK2etO+n0u250dduztfRTG8+Y9CscLkPB8wsM4eAWeEcAmaFcwiYFc4hYFY4h4BZ4RwCZoVzCJgV7v8BoDEPNyfBInUAAAAASUVORK5CYII=\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"OPXuLrstxfhZ","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":289},"executionInfo":{"status":"ok","timestamp":1597153776974,"user_tz":240,"elapsed":576,"user":{"displayName":"Srinidhi Dannamaneni","photoUrl":"","userId":"14652330968581622724"}},"outputId":"3993f1ff-8ea7-40a7-f0e1-cfe0cbf2ff50"},"source":["showPreds(x, pred[0], iou_max_overlap=0.5, min_score=0.2)"],"execution_count":null,"outputs":[{"output_type":"stream","text":["Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"],"name":"stderr"},{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[],"needs_background":"light"}}]},{"cell_type":"code","metadata":{"id":"Dtybh4LC7bym","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1597143075473,"user_tz":240,"elapsed":32503228,"user":{"displayName":"Srinidhi Dannamaneni","photoUrl":"","userId":"14652330968581622724"}},"outputId":"dd48ef03-dd8c-43cd-d559-4f523119c5be"},"source":["model2.eval()"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["FasterRCNN(\n","  (transform): GeneralizedRCNNTransform(\n","      Normalize(mean=(0.5, 0.5, 0.5), std=(0.229, 0.229, 0.229))\n","      Resize(min_size=(800,), max_size=1333, mode='bilinear')\n","  )\n","  (backbone): Sequential(\n","    (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","    (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","    (2): LeakyReLU(negative_slope=0.01)\n","    (3): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","    (4): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","    (5): LeakyReLU(negative_slope=0.01)\n","    (6): MaxPool2d(kernel_size=(2, 2), stride=(3, 4), padding=0, dilation=1, ceil_mode=False)\n","    (7): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","    (8): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","    (9): LeakyReLU(negative_slope=0.01)\n","    (10): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","    (11): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","    (12): LeakyReLU(negative_slope=0.01)\n","    (13): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False)\n","    (14): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","    (15): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","    (16): LeakyReLU(negative_slope=0.01)\n","    (17): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","    (18): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","    (19): LeakyReLU(negative_slope=0.01)\n","    (20): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False)\n","    (21): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","    (22): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","    (23): LeakyReLU(negative_slope=0.01)\n","    (24): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","    (25): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","    (26): LeakyReLU(negative_slope=0.01)\n","    (27): MaxPool2d(kernel_size=(2, 2), stride=(2, 2), padding=0, dilation=1, ceil_mode=False)\n","    (28): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","    (29): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","    (30): LeakyReLU(negative_slope=0.01)\n","    (31): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","    (32): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","    (33): LeakyReLU(negative_slope=0.01)\n","  )\n","  (rpn): RegionProposalNetwork(\n","    (anchor_generator): AnchorGenerator()\n","    (head): RPNHead(\n","      (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n","      (cls_logits): Conv2d(512, 3, kernel_size=(1, 1), stride=(1, 1))\n","      (bbox_pred): Conv2d(512, 12, kernel_size=(1, 1), stride=(1, 1))\n","    )\n","  )\n","  (roi_heads): RoIHeads(\n","    (box_roi_pool): MultiScaleRoIAlign()\n","    (box_head): TwoMLPHead(\n","      (fc6): Linear(in_features=25088, out_features=1024, bias=True)\n","      (fc7): Linear(in_features=1024, out_features=1024, bias=True)\n","    )\n","    (box_predictor): FastRCNNPredictor(\n","      (cls_score): Linear(in_features=1024, out_features=6, bias=True)\n","      (bbox_pred): Linear(in_features=1024, out_features=24, bias=True)\n","    )\n","  )\n",")"]},"metadata":{"tags":[]},"execution_count":78}]},{"cell_type":"code","metadata":{"id":"WIypxPEGfDCK","colab_type":"code","colab":{}},"source":[""],"execution_count":null,"outputs":[]}]}