FaceKeypointsDetection-ResNet101.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"FaceKeypointsDetection-ResNet101.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"id":"imFPmzLcsYbS","colab_type":"code","outputId":"f9f233bb-7634-4881-bbed-52e33047c36e","executionInfo":{"status":"ok","timestamp":1577260632992,"user_tz":-330,"elapsed":1248,"user":{"displayName":"Singh Akhil","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mA36AceXWq_HrzeD0O1PmRhh5A07fstsh9aa4qxFA=s64","userId":"03591970807351555446"}},"colab":{"base_uri":"https://localhost:8080/","height":36}},"source":["from google.colab import drive\n","drive.mount('/content/drive')\n","\n","from fastai import *\n","from fastai.vision import *\n","from skimage.transform import resize\n","\n","# Setting up paths...1\n","\n","# The directory that has all the data unzipped in it...\n","# Change this to run it on your machine as per appropriate path...\n","ROOT_DIR = \"/content/drive/My Drive/Colab Notebooks/data/facial-keypoints-detection\""],"execution_count":41,"outputs":[{"output_type":"stream","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"1tZav-p0IeSc","colab_type":"code","outputId":"6657a750-9a17-42bf-bc22-69c25b1ba624","executionInfo":{"status":"ok","timestamp":1577258249147,"user_tz":-330,"elapsed":11190,"user":{"displayName":"Singh Akhil","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mA36AceXWq_HrzeD0O1PmRhh5A07fstsh9aa4qxFA=s64","userId":"03591970807351555446"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["trainDf = pd.read_csv(os.path.join(ROOT_DIR,\"training.csv\"))\n","testDf = pd.read_csv(os.path.join(ROOT_DIR,\"test.csv\"))\n","print(trainDf.shape)\n","trainDf.head()"],"execution_count":5,"outputs":[{"output_type":"stream","text":["(7049, 31)\n"],"name":"stdout"},{"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>left_eye_center_x</th>\n","      <th>left_eye_center_y</th>\n","      <th>right_eye_center_x</th>\n","      <th>right_eye_center_y</th>\n","      <th>left_eye_inner_corner_x</th>\n","      <th>left_eye_inner_corner_y</th>\n","      <th>left_eye_outer_corner_x</th>\n","      <th>left_eye_outer_corner_y</th>\n","      <th>right_eye_inner_corner_x</th>\n","      <th>right_eye_inner_corner_y</th>\n","      <th>right_eye_outer_corner_x</th>\n","      <th>right_eye_outer_corner_y</th>\n","      <th>left_eyebrow_inner_end_x</th>\n","      <th>left_eyebrow_inner_end_y</th>\n","      <th>left_eyebrow_outer_end_x</th>\n","      <th>left_eyebrow_outer_end_y</th>\n","      <th>right_eyebrow_inner_end_x</th>\n","      <th>right_eyebrow_inner_end_y</th>\n","      <th>right_eyebrow_outer_end_x</th>\n","      <th>right_eyebrow_outer_end_y</th>\n","      <th>nose_tip_x</th>\n","      <th>nose_tip_y</th>\n","      <th>mouth_left_corner_x</th>\n","      <th>mouth_left_corner_y</th>\n","      <th>mouth_right_corner_x</th>\n","      <th>mouth_right_corner_y</th>\n","      <th>mouth_center_top_lip_x</th>\n","      <th>mouth_center_top_lip_y</th>\n","      <th>mouth_center_bottom_lip_x</th>\n","      <th>mouth_center_bottom_lip_y</th>\n","      <th>Image</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>66.033564</td>\n","      <td>39.002274</td>\n","      <td>30.227008</td>\n","      <td>36.421678</td>\n","      <td>59.582075</td>\n","      <td>39.647423</td>\n","      <td>73.130346</td>\n","      <td>39.969997</td>\n","      <td>36.356571</td>\n","      <td>37.389402</td>\n","      <td>23.452872</td>\n","      <td>37.389402</td>\n","      <td>56.953263</td>\n","      <td>29.033648</td>\n","      <td>80.227128</td>\n","      <td>32.228138</td>\n","      <td>40.227609</td>\n","      <td>29.002322</td>\n","      <td>16.356379</td>\n","      <td>29.647471</td>\n","      <td>44.420571</td>\n","      <td>57.066803</td>\n","      <td>61.195308</td>\n","      <td>79.970165</td>\n","      <td>28.614496</td>\n","      <td>77.388992</td>\n","      <td>43.312602</td>\n","      <td>72.935459</td>\n","      <td>43.130707</td>\n","      <td>84.485774</td>\n","      <td>238 236 237 238 240 240 239 241 241 243 240 23...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>64.332936</td>\n","      <td>34.970077</td>\n","      <td>29.949277</td>\n","      <td>33.448715</td>\n","      <td>58.856170</td>\n","      <td>35.274349</td>\n","      <td>70.722723</td>\n","      <td>36.187166</td>\n","      <td>36.034723</td>\n","      <td>34.361532</td>\n","      <td>24.472511</td>\n","      <td>33.144443</td>\n","      <td>53.987404</td>\n","      <td>28.275949</td>\n","      <td>78.634213</td>\n","      <td>30.405923</td>\n","      <td>42.728851</td>\n","      <td>26.146043</td>\n","      <td>16.865362</td>\n","      <td>27.058860</td>\n","      <td>48.206298</td>\n","      <td>55.660936</td>\n","      <td>56.421447</td>\n","      <td>76.352000</td>\n","      <td>35.122383</td>\n","      <td>76.047660</td>\n","      <td>46.684596</td>\n","      <td>70.266553</td>\n","      <td>45.467915</td>\n","      <td>85.480170</td>\n","      <td>219 215 204 196 204 211 212 200 180 168 178 19...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>65.057053</td>\n","      <td>34.909642</td>\n","      <td>30.903789</td>\n","      <td>34.909642</td>\n","      <td>59.412000</td>\n","      <td>36.320968</td>\n","      <td>70.984421</td>\n","      <td>36.320968</td>\n","      <td>37.678105</td>\n","      <td>36.320968</td>\n","      <td>24.976421</td>\n","      <td>36.603221</td>\n","      <td>55.742526</td>\n","      <td>27.570947</td>\n","      <td>78.887368</td>\n","      <td>32.651621</td>\n","      <td>42.193895</td>\n","      <td>28.135453</td>\n","      <td>16.791158</td>\n","      <td>32.087116</td>\n","      <td>47.557263</td>\n","      <td>53.538947</td>\n","      <td>60.822947</td>\n","      <td>73.014316</td>\n","      <td>33.726316</td>\n","      <td>72.732000</td>\n","      <td>47.274947</td>\n","      <td>70.191789</td>\n","      <td>47.274947</td>\n","      <td>78.659368</td>\n","      <td>144 142 159 180 188 188 184 180 167 132 84 59 ...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>65.225739</td>\n","      <td>37.261774</td>\n","      <td>32.023096</td>\n","      <td>37.261774</td>\n","      <td>60.003339</td>\n","      <td>39.127179</td>\n","      <td>72.314713</td>\n","      <td>38.380967</td>\n","      <td>37.618643</td>\n","      <td>38.754115</td>\n","      <td>25.307270</td>\n","      <td>38.007903</td>\n","      <td>56.433809</td>\n","      <td>30.929864</td>\n","      <td>77.910261</td>\n","      <td>31.665725</td>\n","      <td>41.671513</td>\n","      <td>31.049990</td>\n","      <td>20.458017</td>\n","      <td>29.909343</td>\n","      <td>51.885078</td>\n","      <td>54.166539</td>\n","      <td>65.598887</td>\n","      <td>72.703722</td>\n","      <td>37.245496</td>\n","      <td>74.195478</td>\n","      <td>50.303165</td>\n","      <td>70.091687</td>\n","      <td>51.561183</td>\n","      <td>78.268383</td>\n","      <td>193 192 193 194 194 194 193 192 168 111 50 12 ...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>66.725301</td>\n","      <td>39.621261</td>\n","      <td>32.244810</td>\n","      <td>38.042032</td>\n","      <td>58.565890</td>\n","      <td>39.621261</td>\n","      <td>72.515926</td>\n","      <td>39.884466</td>\n","      <td>36.982380</td>\n","      <td>39.094852</td>\n","      <td>22.506110</td>\n","      <td>38.305237</td>\n","      <td>57.249571</td>\n","      <td>30.672177</td>\n","      <td>77.762945</td>\n","      <td>31.737247</td>\n","      <td>38.035436</td>\n","      <td>30.935382</td>\n","      <td>15.925870</td>\n","      <td>30.672177</td>\n","      <td>43.299534</td>\n","      <td>64.889521</td>\n","      <td>60.671411</td>\n","      <td>77.523239</td>\n","      <td>31.191755</td>\n","      <td>76.997301</td>\n","      <td>44.962748</td>\n","      <td>73.707387</td>\n","      <td>44.227141</td>\n","      <td>86.871166</td>\n","      <td>147 148 160 196 215 214 216 217 219 220 206 18...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["   left_eye_center_x  ...                                              Image\n","0          66.033564  ...  238 236 237 238 240 240 239 241 241 243 240 23...\n","1          64.332936  ...  219 215 204 196 204 211 212 200 180 168 178 19...\n","2          65.057053  ...  144 142 159 180 188 188 184 180 167 132 84 59 ...\n","3          65.225739  ...  193 192 193 194 194 194 193 192 168 111 50 12 ...\n","4          66.725301  ...  147 148 160 196 215 214 216 217 219 220 206 18...\n","\n","[5 rows x 31 columns]"]},"metadata":{"tags":[]},"execution_count":5}]},{"cell_type":"code","metadata":{"id":"SPRLiZBxIeNk","colab_type":"code","colab":{}},"source":["# [TODO] : try median filling\n","trainDf.fillna(method = 'ffill',inplace = True)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"BpFKZpZNIeLF","colab_type":"code","colab":{}},"source":["# trainDf.reset_index(drop = True,inplace = True)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"8r3nZ2FWJCrt","colab_type":"code","colab":{}},"source":["TRAIN_DIR = os.path.join(ROOT_DIR,\"imgs\",\"train\")\n","TEST_DIR = os.path.join(ROOT_DIR,\"imgs\",\"test\")\n","\n","# saveImage(str(trainDf['Image'][0]),96,96,savepath)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"T_Bkt_heF94V","colab_type":"code","outputId":"069c8569-e1f9-4813-cc0c-dcbe9b7eb969","executionInfo":{"status":"ok","timestamp":1577173755588,"user_tz":-330,"elapsed":82668,"user":{"displayName":"Singh Akhil","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mA36AceXWq_HrzeD0O1PmRhh5A07fstsh9aa4qxFA=s64","userId":"03591970807351555446"}},"colab":{"base_uri":"https://localhost:8080/","height":36}},"source":["for i in range(trainDf.shape[0]):\n","    img = trainDf['Image'][i].split(' ')\n","    img = ['0' if x == '' else x for x in img]\n","    img = np.array(img,dtype='uint8').reshape(96,96)\n","    plt.imsave(os.path.join(TRAIN_DIR,f\"{i}.jpg\"),img,cmap='gray')\n","print(\"train imgs are loaded\")"],"execution_count":0,"outputs":[{"output_type":"stream","text":["train imgs are loaded\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"dEmWMIRB4BHn","colab_type":"code","outputId":"c8f9c1a2-88ce-454a-f568-8254d4918562","executionInfo":{"status":"ok","timestamp":1577174578230,"user_tz":-330,"elapsed":22329,"user":{"displayName":"Singh Akhil","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mA36AceXWq_HrzeD0O1PmRhh5A07fstsh9aa4qxFA=s64","userId":"03591970807351555446"}},"colab":{"base_uri":"https://localhost:8080/","height":36}},"source":["for i in range(testDf.shape[0]):\n","    img = testDf['Image'][i].split(' ')\n","    img = ['0' if x == '' else x for x in img]\n","    img = np.array(img,dtype='uint8').reshape(96,96)\n","    plt.imsave(os.path.join(TEST_DIR,f\"{i}.jpg\"),img,cmap='gray')\n","print(\"test imgs are loaded\")"],"execution_count":0,"outputs":[{"output_type":"stream","text":["test imgs are loaded\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"5Lp-hfHY7dxV","colab_type":"code","colab":{}},"source":["# Credits : https://www.kaggle.com/nitron/facial-keypoints-fastai-image-regression\n","\n","def get_locs(flname):\n","    index = int(flname.name[:-4])\n","    targets=[]\n","\n","    unrolled = list(trainDf.loc[index])\n","    \n","    for i in range(len(unrolled)//2):\n","        targets.append([unrolled[i*2+1],unrolled[i*2]])\n","    \n","    return tensor(targets)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"sHkmi3_u8VVT","colab_type":"code","colab":{}},"source":["data = (PointsItemList.from_folder(TRAIN_DIR)\n","        .split_by_rand_pct(0.1,seed=8)\n","        .label_from_func(get_locs)\n","        .add_test_folder(TEST_DIR)\n","        .transform([],size=(96,96))\n","        .databunch(num_workers=0)).normalize(imagenet_stats)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"iMY_NyDA8qQM","colab_type":"code","outputId":"dff3335b-74de-4c0d-f1e8-868209c2cebc","executionInfo":{"status":"ok","timestamp":1577260732391,"user_tz":-330,"elapsed":34628,"user":{"displayName":"Singh Akhil","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mA36AceXWq_HrzeD0O1PmRhh5A07fstsh9aa4qxFA=s64","userId":"03591970807351555446"}},"colab":{"base_uri":"https://localhost:8080/","height":441}},"source":["data.show_batch(3,figsize=(6,6))"],"execution_count":45,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAacAAAGoCAYAAADiuSpNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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c8PmKdND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1m8fwLz7vCov/gv/oHKJVvThEGYmiKlpnNBE9ISy1nFbUCcmbvIHV/arR3hcd9iDzdZ+zc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kwZ26bSLBmJM1CZw6NAhCV9fvnwZ733ve6UjwpVk+zbtwzamLayGKEyTsfl8HoNIsTR02kaOoFQs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\nERQPJ0u4WCyy9V6pVDA5Ock1P/F4HAMDA2wt1+t1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TyQ3lHa7pQEa5LnpGbnvVttnm1j4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\nN6pxZwyX0USCAAAAAElFTkSuQmCC\n","text/plain":["<Figure size 432x432 with 9 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"kOTsyNJW8zHp","colab_type":"code","colab":{}},"source":["# How Tensor.view() works? : https://stackoverflow.com/questions/42479902/how-does-the-view-method-work-in-pytorch\n","# TLDR: Tensor.view is similar to numpy.reshape but it does not copy the tensor into memory (saves memory)\n","\n","def MSELoss(actual,pred):\n","    actual = actual.view(-1,15,2)\n","\n","    tmp = (actual - pred)\n","    loss = (tmp**2).sum(dim=2).sum(dim=1).sum()\n","    return loss\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"uxl1Fx2A-xx3","colab_type":"code","outputId":"c1df7741-b73f-4a01-cb7c-b328e3b5e80c","executionInfo":{"status":"ok","timestamp":1577263446382,"user_tz":-330,"elapsed":2699356,"user":{"displayName":"Singh Akhil","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mA36AceXWq_HrzeD0O1PmRhh5A07fstsh9aa4qxFA=s64","userId":"03591970807351555446"}},"colab":{"base_uri":"https://localhost:8080/","height":298}},"source":["learn = cnn_learner(data,models.resnet101,loss_func=MSELoss)\n","learn.lr_find()\n","learn.recorder.plot()"],"execution_count":47,"outputs":[{"output_type":"display_data","data":{"text/html":[""],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYsAAAEGCAYAAACUzrmNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nexG7Syu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6kxQX4i+LC4Iupc1RWIiIeFIT4/ji2ExeXLWL/WVVQZfTpigsREQauHlKDtW1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\nJ8NEpEm6dFYkQN5hpQrnnDO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size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"gz44nJkZ_CFI","colab_type":"code","outputId":"29457137-37af-4a29-b0f7-68556ad85a75","executionInfo":{"status":"ok","timestamp":1577264711252,"user_tz":-330,"elapsed":1074262,"user":{"displayName":"Singh Akhil","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mA36AceXWq_HrzeD0O1PmRhh5A07fstsh9aa4qxFA=s64","userId":"03591970807351555446"}},"colab":{"base_uri":"https://localhost:8080/","height":363}},"source":["learn.fit_one_cycle(10,max_lr=slice(3e-3,1e-1))"],"execution_count":48,"outputs":[{"output_type":"display_data","data":{"text/html":["<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: left;\">\n","      <th>epoch</th>\n","      <th>train_loss</th>\n","      <th>valid_loss</th>\n","      <th>time</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>0</td>\n","      <td>897.159790</td>\n","      <td>177.373245</td>\n","      <td>12:05</td>\n","    </tr>\n","    <tr>\n","      <td>1</td>\n","      <td>154.176529</td>\n","      <td>10.898947</td>\n","      <td>00:38</td>\n","    </tr>\n","    <tr>\n","      <td>2</td>\n","      <td>27.023493</td>\n","      <td>7.301726</td>\n","      <td>00:39</td>\n","    </tr>\n","    <tr>\n","      <td>3</td>\n","      <td>9.280669</td>\n","      <td>6.824963</td>\n","      <td>00:38</td>\n","    </tr>\n","    <tr>\n","      <td>4</td>\n","      <td>6.587440</td>\n","      <td>6.320252</td>\n","      <td>00:39</td>\n","    </tr>\n","    <tr>\n","      <td>5</td>\n","      <td>6.197590</td>\n","      <td>7.378200</td>\n","      <td>00:38</td>\n","    </tr>\n","    <tr>\n","      <td>6</td>\n","      <td>5.777067</td>\n","      <td>5.990875</td>\n","      <td>00:38</td>\n","    </tr>\n","    <tr>\n","      <td>7</td>\n","      <td>5.321459</td>\n","      <td>6.157947</td>\n","      <td>00:38</td>\n","    </tr>\n","    <tr>\n","      <td>8</td>\n","      <td>4.643307</td>\n","      <td>5.473890</td>\n","      <td>00:38</td>\n","    </tr>\n","    <tr>\n","      <td>9</td>\n","      <td>4.087871</td>\n","      <td>5.040724</td>\n","      <td>00:38</td>\n","    </tr>\n","  </tbody>\n","</table>"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"wqOxFw5j_cby","colab_type":"code","outputId":"a01f4455-9a34-4820-ac5c-23bcd95d389b","executionInfo":{"status":"ok","timestamp":1577264748004,"user_tz":-330,"elapsed":36732,"user":{"displayName":"Singh Akhil","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mA36AceXWq_HrzeD0O1PmRhh5A07fstsh9aa4qxFA=s64","userId":"03591970807351555446"}},"colab":{"base_uri":"https://localhost:8080/","height":298}},"source":["learn.save(os.path.join(ROOT_DIR,\"frozen-resnet101-10epoch-IDKvalMSE\"))\n","learn.unfreeze()\n","learn.lr_find()\n","learn.recorder.plot()"],"execution_count":49,"outputs":[{"output_type":"display_data","data":{"text/html":[""],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYIAAAEGCAYAAABo25JHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSgRPb8r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E97y0k9jwIJYvGmvZtZR7WZoIRCQBuAB4qq3txpj/GmOah4duBjxqJIm/rw/n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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"lTwb7TXvHgl2","colab_type":"code","outputId":"1edb82b8-5228-46a7-e80c-53ac482ccc80","executionInfo":{"status":"ok","timestamp":1577180587132,"user_tz":-330,"elapsed":101756,"user":{"displayName":"Singh Akhil","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mA36AceXWq_HrzeD0O1PmRhh5A07fstsh9aa4qxFA=s64","userId":"03591970807351555446"}},"colab":{"base_uri":"https://localhost:8080/","height":677}},"source":["learn.fit_one_cycle(20,max_lr=9e-7)"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"text/html":["<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: left;\">\n","      <th>epoch</th>\n","      <th>train_loss</th>\n","      <th>valid_loss</th>\n","      <th>time</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>0</td>\n","      <td>3.979948</td>\n","      <td>5.004973</td>\n","      <td>00:34</td>\n","    </tr>\n","    <tr>\n","      <td>1</td>\n","      <td>4.035170</td>\n","      <td>7.642426</td>\n","      <td>00:34</td>\n","    </tr>\n","    <tr>\n","      <td>2</td>\n","      <td>3.935815</td>\n","      <td>5.421720</td>\n","      <td>00:34</td>\n","    </tr>\n","    <tr>\n","      <td>3</td>\n","      <td>3.883795</td>\n","      <td>4.882553</td>\n","      <td>00:34</td>\n","    </tr>\n","    <tr>\n","      <td>4</td>\n","      <td>3.875044</td>\n","      <td>6.226526</td>\n","      <td>00:34</td>\n","    </tr>\n","    <tr>\n","      <td>5</td>\n","      <td>3.624193</td>\n","      <td>5.033089</td>\n","      <td>00:34</td>\n","    </tr>\n","    <tr>\n","      <td>6</td>\n","      <td>3.621421</td>\n","      <td>5.977318</td>\n","      <td>00:35</td>\n","    </tr>\n","    <tr>\n","      <td>7</td>\n","      <td>3.508951</td>\n","      <td>5.707201</td>\n","      <td>00:34</td>\n","    </tr>\n","    <tr>\n","      <td>8</td>\n","      <td>3.553867</td>\n","      <td>5.366906</td>\n","      <td>00:35</td>\n","    </tr>\n","    <tr>\n","      <td>9</td>\n","      <td>3.538146</td>\n","      <td>5.643112</td>\n","      <td>00:34</td>\n","    </tr>\n","    <tr>\n","      <td>10</td>\n","      <td>3.435445</td>\n","      <td>6.428617</td>\n","      <td>00:34</td>\n","    </tr>\n","    <tr>\n","      <td>11</td>\n","      <td>3.412343</td>\n","      <td>5.617450</td>\n","      <td>00:34</td>\n","    </tr>\n","    <tr>\n","      <td>12</td>\n","      <td>3.326002</td>\n","      <td>4.554952</td>\n","      <td>00:34</td>\n","    </tr>\n","    <tr>\n","      <td>13</td>\n","      <td>3.385590</td>\n","      <td>5.762123</td>\n","      <td>00:34</td>\n","    </tr>\n","    <tr>\n","      <td>14</td>\n","      <td>3.296772</td>\n","      <td>5.023932</td>\n","      <td>00:34</td>\n","    </tr>\n","    <tr>\n","      <td>15</td>\n","      <td>3.313578</td>\n","      <td>4.542331</td>\n","      <td>00:34</td>\n","    </tr>\n","    <tr>\n","      <td>16</td>\n","      <td>3.276899</td>\n","      <td>4.475505</td>\n","      <td>00:34</td>\n","    </tr>\n","    <tr>\n","      <td>17</td>\n","      <td>3.284457</td>\n","      <td>5.118698</td>\n","      <td>00:35</td>\n","    </tr>\n","    <tr>\n","      <td>18</td>\n","      <td>3.397022</td>\n","      <td>6.586456</td>\n","      <td>00:34</td>\n","    </tr>\n","    <tr>\n","      <td>19</td>\n","      <td>3.316274</td>\n","      <td>4.507608</td>\n","      <td>00:34</td>\n","    </tr>\n","  </tbody>\n","</table>"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"8tqbTLeSIPMQ","colab_type":"code","outputId":"07e896d2-c2b0-4612-a547-36b35208d107","executionInfo":{"status":"ok","timestamp":1577180620549,"user_tz":-330,"elapsed":30334,"user":{"displayName":"Singh Akhil","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mA36AceXWq_HrzeD0O1PmRhh5A07fstsh9aa4qxFA=s64","userId":"03591970807351555446"}},"colab":{"base_uri":"https://localhost:8080/","height":298}},"source":["learn.lr_find()\n","learn.recorder.plot()"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"text/html":[""],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYgAAAEGCAYAAAB/+QKOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n3a+sU2w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LCSJB5m4oYUyf5t1XP6xHFv/+5ni+99xi7np5FQs37+HXl41KijGXZLNmRxnT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\nRCQmJQgREYlJCUJERGJSghARkZj+P8/MnKeTEnaoAAAAAElFTkSuQmCC\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"lceI6JnfKwDq","colab_type":"code","outputId":"b91ae033-40e2-42a3-bc4b-0a65b98ec382","executionInfo":{"status":"ok","timestamp":1577180992448,"user_tz":-330,"elapsed":345797,"user":{"displayName":"Singh Akhil","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mA36AceXWq_HrzeD0O1PmRhh5A07fstsh9aa4qxFA=s64","userId":"03591970807351555446"}},"colab":{"base_uri":"https://localhost:8080/","height":363}},"source":["learn.fit_one_cycle(10,max_lr=1e-4)"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"text/html":["<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: left;\">\n","      <th>epoch</th>\n","      <th>train_loss</th>\n","      <th>valid_loss</th>\n","      <th>time</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>0</td>\n","      <td>3.335928</td>\n","      <td>4.598642</td>\n","      <td>00:34</td>\n","    </tr>\n","    <tr>\n","      <td>1</td>\n","      <td>3.520589</td>\n","      <td>3.936564</td>\n","      <td>00:34</td>\n","    </tr>\n","    <tr>\n","      <td>2</td>\n","      <td>3.120204</td>\n","      <td>3.435664</td>\n","      <td>00:34</td>\n","    </tr>\n","    <tr>\n","      <td>3</td>\n","      <td>2.739600</td>\n","      <td>3.029156</td>\n","      <td>00:34</td>\n","    </tr>\n","    <tr>\n","      <td>4</td>\n","      <td>2.233401</td>\n","      <td>2.852885</td>\n","      <td>00:34</td>\n","    </tr>\n","    <tr>\n","      <td>5</td>\n","      <td>2.045594</td>\n","      <td>2.650699</td>\n","      <td>00:34</td>\n","    </tr>\n","    <tr>\n","      <td>6</td>\n","      <td>1.736048</td>\n","      <td>2.597580</td>\n","      <td>00:34</td>\n","    </tr>\n","    <tr>\n","      <td>7</td>\n","      <td>1.595823</td>\n","      <td>2.478950</td>\n","      <td>00:34</td>\n","    </tr>\n","    <tr>\n","      <td>8</td>\n","      <td>1.615900</td>\n","      <td>2.520370</td>\n","      <td>00:34</td>\n","    </tr>\n","    <tr>\n","      <td>9</td>\n","      <td>1.448542</td>\n","      <td>2.438833</td>\n","      <td>00:34</td>\n","    </tr>\n","  </tbody>\n","</table>"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"cfuO6jAPLKDL","colab_type":"code","outputId":"9bad88de-5811-4c67-da4c-320075a41e41","executionInfo":{"status":"ok","timestamp":1577181088635,"user_tz":-330,"elapsed":28448,"user":{"displayName":"Singh Akhil","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mA36AceXWq_HrzeD0O1PmRhh5A07fstsh9aa4qxFA=s64","userId":"03591970807351555446"}},"colab":{"base_uri":"https://localhost:8080/","height":36}},"source":["learn.lr_find()"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"text/html":[""],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"QQbhx91HMHZK","colab_type":"code","outputId":"01e5bd3e-ea3f-40bd-d8b8-cdb0b16af814","executionInfo":{"status":"ok","timestamp":1577181089896,"user_tz":-330,"elapsed":28951,"user":{"displayName":"Singh 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size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"w4rtYTlqMJf8","colab_type":"code","outputId":"58fb71ec-9dc7-48f6-f64b-caf29edaec17","executionInfo":{"status":"ok","timestamp":1577181250275,"user_tz":-330,"elapsed":104243,"user":{"displayName":"Singh Akhil","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mA36AceXWq_HrzeD0O1PmRhh5A07fstsh9aa4qxFA=s64","userId":"03591970807351555446"}},"colab":{"base_uri":"https://localhost:8080/","height":143}},"source":["learn.fit_one_cycle(3,max_lr =5e-6)"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"text/html":["<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: left;\">\n","      <th>epoch</th>\n","      <th>train_loss</th>\n","      <th>valid_loss</th>\n","      <th>time</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>0</td>\n","      <td>1.421929</td>\n","      <td>2.447115</td>\n","      <td>00:34</td>\n","    </tr>\n","    <tr>\n","      <td>1</td>\n","      <td>1.460781</td>\n","      <td>2.530812</td>\n","      <td>00:34</td>\n","    </tr>\n","    <tr>\n","      <td>2</td>\n","      <td>1.373475</td>\n","      <td>2.480316</td>\n","      <td>00:34</td>\n","    </tr>\n","  </tbody>\n","</table>"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"tMEi-pGMMUz4","colab_type":"code","outputId":"60ae04e3-445f-447d-c0cf-ffa852b8c5c1","executionInfo":{"status":"ok","timestamp":1577181413349,"user_tz":-330,"elapsed":104431,"user":{"displayName":"Singh Akhil","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mA36AceXWq_HrzeD0O1PmRhh5A07fstsh9aa4qxFA=s64","userId":"03591970807351555446"}},"colab":{"base_uri":"https://localhost:8080/","height":143}},"source":["learn.fit_one_cycle(3,max_lr=1e-5)\n"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"text/html":["<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: left;\">\n","      <th>epoch</th>\n","      <th>train_loss</th>\n","      <th>valid_loss</th>\n","      <th>time</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>0</td>\n","      <td>1.480098</td>\n","      <td>2.501997</td>\n","      <td>00:34</td>\n","    </tr>\n","    <tr>\n","      <td>1</td>\n","      <td>1.429499</td>\n","      <td>2.463293</td>\n","      <td>00:34</td>\n","    </tr>\n","    <tr>\n","      <td>2</td>\n","      <td>1.426233</td>\n","      <td>2.454099</td>\n","      <td>00:34</td>\n","    </tr>\n","  </tbody>\n","</table>"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"FGfvehFBMtj0","colab_type":"code","outputId":"20eac345-3883-4033-8d4a-10da3d157bd4","executionInfo":{"status":"ok","timestamp":1577181753410,"user_tz":-330,"elapsed":29700,"user":{"displayName":"Singh Akhil","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mA36AceXWq_HrzeD0O1PmRhh5A07fstsh9aa4qxFA=s64","userId":"03591970807351555446"}},"colab":{"base_uri":"https://localhost:8080/","height":298}},"source":["learn.save(os.path.join(ROOT_DIR,\"resnet101-36epoch-2.45valMSE\"))\n","learn.lr_find()\n","learn.recorder.plot()"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"text/html":[""],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{"tags":[]}},{"output_type":"stream","text":["LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n"],"name":"stdout"},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYIAAAEGCAYAAABo25JHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n+865vV7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size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"9siIvVOtNG_D","colab_type":"code","outputId":"86802d25-fd3c-4018-a2d1-8dc7f0af2f86","executionInfo":{"status":"ok","timestamp":1577179377571,"user_tz":-330,"elapsed":139925,"user":{"displayName":"Singh Akhil","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mA36AceXWq_HrzeD0O1PmRhh5A07fstsh9aa4qxFA=s64","userId":"03591970807351555446"}},"colab":{"base_uri":"https://localhost:8080/","height":206}},"source":["learn.fit_one_cycle(5,max_lr=3e-3)"],"execution_count":0,"outputs":[{"output_type":"display_data","data":{"text/html":["<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: left;\">\n","      <th>epoch</th>\n","      <th>train_loss</th>\n","      <th>valid_loss</th>\n","      <th>time</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>0</td>\n","      <td>3.371477</td>\n","      <td>7.694940</td>\n","      <td>00:28</td>\n","    </tr>\n","    <tr>\n","      <td>1</td>\n","      <td>3.987474</td>\n","      <td>6.443917</td>\n","      <td>00:27</td>\n","    </tr>\n","    <tr>\n","      <td>2</td>\n","      <td>3.466420</td>\n","      <td>5.299884</td>\n","      <td>00:27</td>\n","    </tr>\n","    <tr>\n","      <td>3</td>\n","      <td>2.722844</td>\n","      <td>2.905257</td>\n","      <td>00:27</td>\n","    </tr>\n","    <tr>\n","      <td>4</td>\n","      <td>2.042373</td>\n","      <td>2.629123</td>\n","      <td>00:27</td>\n","    </tr>\n","  </tbody>\n","</table>"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"TP3bGliBNUwM","colab_type":"code","outputId":"2c0c0a88-fac8-44a2-ec08-d235975b1e9b","executionInfo":{"status":"ok","timestamp":1577183195096,"user_tz":-330,"elapsed":1653,"user":{"displayName":"Singh 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xSnGRMTyvcXpPGdiyfx/o4qXtlSygsbDvDsJ/sYExPCVTNG86Xpo5k0KmzAUMgtrueZj/dSWN7I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\nvtaJx45qAAAAAElFTkSuQmCC\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"0VWhWmG7N6mJ","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":1000},"outputId":"6b4a1153-e06b-4097-f2a6-7c86e74b741c","executionInfo":{"status":"ok","timestamp":1577258477150,"user_tz":-330,"elapsed":15275,"user":{"displayName":"Singh Akhil","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mA36AceXWq_HrzeD0O1PmRhh5A07fstsh9aa4qxFA=s64","userId":"03591970807351555446"}}},"source":["learn.load(os.path.join(ROOT_DIR,\"resnet101-36epoch-2.45valMSE\"))\n"],"execution_count":13,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Learner(data=ImageDataBunch;\n","\n","Train: LabelList (6345 items)\n","x: PointsItemList\n","Image (3, 96, 96),Image (3, 96, 96),Image (3, 96, 96),Image (3, 96, 96),Image (3, 96, 96)\n","y: PointsLabelList\n","ImagePoints (96, 96),ImagePoints (96, 96),ImagePoints (96, 96),ImagePoints (96, 96),ImagePoints (96, 96)\n","Path: /content/drive/My Drive/Colab Notebooks/data/facial-keypoints-detection/imgs/train;\n","\n","Valid: LabelList (704 items)\n","x: PointsItemList\n","Image (3, 96, 96),Image (3, 96, 96),Image (3, 96, 96),Image (3, 96, 96),Image (3, 96, 96)\n","y: PointsLabelList\n","ImagePoints (96, 96),ImagePoints (96, 96),ImagePoints (96, 96),ImagePoints (96, 96),ImagePoints (96, 96)\n","Path: /content/drive/My Drive/Colab Notebooks/data/facial-keypoints-detection/imgs/train;\n","\n","Test: None, model=Sequential(\n","  (0): Sequential(\n","    (0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n","    (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","    (2): ReLU(inplace=True)\n","    (3): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n","    (4): Sequential(\n","      (0): Bottleneck(\n","        (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu): ReLU(inplace=True)\n","        (downsample): Sequential(\n","          (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        )\n","      )\n","      (1): Bottleneck(\n","        (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu): ReLU(inplace=True)\n","      )\n","      (2): Bottleneck(\n","        (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu): ReLU(inplace=True)\n","      )\n","    )\n","    (5): Sequential(\n","      (0): Bottleneck(\n","        (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n","        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu): ReLU(inplace=True)\n","        (downsample): Sequential(\n","          (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n","          (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        )\n","      )\n","      (1): Bottleneck(\n","        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu): ReLU(inplace=True)\n","      )\n","      (2): Bottleneck(\n","        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu): ReLU(inplace=True)\n","      )\n","      (3): Bottleneck(\n","        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu): ReLU(inplace=True)\n","      )\n","    )\n","    (6): Sequential(\n","      (0): Bottleneck(\n","        (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n","        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu): ReLU(inplace=True)\n","        (downsample): Sequential(\n","          (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n","          (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        )\n","      )\n","      (1): Bottleneck(\n","        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu): ReLU(inplace=True)\n","      )\n","      (2): Bottleneck(\n","        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu): ReLU(inplace=True)\n","      )\n","      (3): Bottleneck(\n","        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu): ReLU(inplace=True)\n","      )\n","      (4): Bottleneck(\n","        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu): ReLU(inplace=True)\n","      )\n","      (5): Bottleneck(\n","        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu): ReLU(inplace=True)\n","      )\n","      (6): Bottleneck(\n","        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu): ReLU(inplace=True)\n","      )\n","      (7): Bottleneck(\n","        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu): ReLU(inplace=True)\n","      )\n","      (8): Bottleneck(\n","        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu): ReLU(inplace=True)\n","      )\n","      (9): Bottleneck(\n","        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu): ReLU(inplace=True)\n","      )\n","      (10): Bottleneck(\n","        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu): ReLU(inplace=True)\n","      )\n","      (11): Bottleneck(\n","        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu): ReLU(inplace=True)\n","      )\n","      (12): Bottleneck(\n","        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu): ReLU(inplace=True)\n","      )\n","      (13): Bottleneck(\n","        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu): ReLU(inplace=True)\n","      )\n","      (14): Bottleneck(\n","        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu): ReLU(inplace=True)\n","      )\n","      (15): Bottleneck(\n","        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu): ReLU(inplace=True)\n","      )\n","      (16): Bottleneck(\n","        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu): ReLU(inplace=True)\n","      )\n","      (17): Bottleneck(\n","        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu): ReLU(inplace=True)\n","      )\n","      (18): Bottleneck(\n","        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu): ReLU(inplace=True)\n","      )\n","      (19): Bottleneck(\n","        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu): ReLU(inplace=True)\n","      )\n","      (20): Bottleneck(\n","        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu): ReLU(inplace=True)\n","      )\n","      (21): Bottleneck(\n","        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu): ReLU(inplace=True)\n","      )\n","      (22): Bottleneck(\n","        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu): ReLU(inplace=True)\n","      )\n","    )\n","    (7): Sequential(\n","      (0): Bottleneck(\n","        (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n","        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu): ReLU(inplace=True)\n","        (downsample): Sequential(\n","          (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n","          (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        )\n","      )\n","      (1): Bottleneck(\n","        (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu): ReLU(inplace=True)\n","      )\n","      (2): Bottleneck(\n","        (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","        (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","        (relu): ReLU(inplace=True)\n","      )\n","    )\n","  )\n","  (1): Sequential(\n","    (0): AdaptiveConcatPool2d(\n","      (ap): AdaptiveAvgPool2d(output_size=1)\n","      (mp): AdaptiveMaxPool2d(output_size=1)\n","    )\n","    (1): Flatten()\n","    (2): BatchNorm1d(4096, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","    (3): Dropout(p=0.25, inplace=False)\n","    (4): Linear(in_features=4096, out_features=512, bias=True)\n","    (5): ReLU(inplace=True)\n","    (6): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","    (7): Dropout(p=0.5, inplace=False)\n","    (8): Linear(in_features=512, out_features=30, bias=True)\n","  )\n","), opt_func=functools.partial(<class 'torch.optim.adam.Adam'>, betas=(0.9, 0.99)), loss_func=<function MSELoss at 0x7fb11d525510>, metrics=[], true_wd=True, bn_wd=True, wd=0.01, train_bn=True, path=PosixPath('/content/drive/My Drive/Colab Notebooks/data/facial-keypoints-detection/imgs/train'), model_dir='models', callback_fns=[functools.partial(<class 'fastai.basic_train.Recorder'>, add_time=True, silent=False)], callbacks=[], layer_groups=[Sequential(\n","  (0): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n","  (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (2): ReLU(inplace=True)\n","  (3): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n","  (4): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (5): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (6): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","  (7): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (8): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (9): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (10): ReLU(inplace=True)\n","  (11): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (12): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (13): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (14): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (15): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","  (16): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (17): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (18): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (19): ReLU(inplace=True)\n","  (20): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (21): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (22): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","  (23): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (24): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (25): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (26): ReLU(inplace=True)\n","  (27): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (28): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (29): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n","  (30): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (31): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (32): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (33): ReLU(inplace=True)\n","  (34): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n","  (35): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (36): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (37): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (38): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","  (39): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (40): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (41): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (42): ReLU(inplace=True)\n","  (43): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (44): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (45): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","  (46): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (47): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (48): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (49): ReLU(inplace=True)\n","  (50): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (51): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (52): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","  (53): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (54): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (55): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (56): ReLU(inplace=True)\n","), Sequential(\n","  (0): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n","  (3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (4): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (5): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (6): ReLU(inplace=True)\n","  (7): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n","  (8): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (9): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (10): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (11): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","  (12): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (13): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (14): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (15): ReLU(inplace=True)\n","  (16): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (17): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (18): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","  (19): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (20): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (21): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (22): ReLU(inplace=True)\n","  (23): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (24): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (25): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","  (26): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (27): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (28): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (29): ReLU(inplace=True)\n","  (30): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (31): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (32): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","  (33): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (34): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (35): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (36): ReLU(inplace=True)\n","  (37): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (38): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (39): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","  (40): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (41): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (42): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (43): ReLU(inplace=True)\n","  (44): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (45): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (46): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","  (47): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (48): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (49): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (50): ReLU(inplace=True)\n","  (51): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (52): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (53): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","  (54): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (55): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (56): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (57): ReLU(inplace=True)\n","  (58): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (59): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (60): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","  (61): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (62): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (63): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (64): ReLU(inplace=True)\n","  (65): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (66): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (67): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","  (68): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (69): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (70): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (71): ReLU(inplace=True)\n","  (72): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (73): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (74): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","  (75): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (76): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (77): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (78): ReLU(inplace=True)\n","  (79): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (80): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (81): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","  (82): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (83): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (84): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (85): ReLU(inplace=True)\n","  (86): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (87): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (88): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","  (89): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (90): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (91): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (92): ReLU(inplace=True)\n","  (93): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (94): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (95): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","  (96): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (97): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (98): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (99): ReLU(inplace=True)\n","  (100): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (101): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (102): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","  (103): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (104): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (105): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (106): ReLU(inplace=True)\n","  (107): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (108): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (109): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","  (110): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (111): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (112): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (113): ReLU(inplace=True)\n","  (114): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (115): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (116): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","  (117): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (118): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (119): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (120): ReLU(inplace=True)\n","  (121): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (122): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (123): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","  (124): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (125): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (126): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (127): ReLU(inplace=True)\n","  (128): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (129): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (130): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","  (131): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (132): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (133): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (134): ReLU(inplace=True)\n","  (135): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (136): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (137): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","  (138): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (139): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (140): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (141): ReLU(inplace=True)\n","  (142): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (143): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (144): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","  (145): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (146): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (147): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (148): ReLU(inplace=True)\n","  (149): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (150): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (151): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","  (152): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (153): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (154): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (155): ReLU(inplace=True)\n","  (156): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (157): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (158): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","  (159): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (160): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (161): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (162): ReLU(inplace=True)\n","  (163): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (164): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (165): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n","  (166): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (167): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (168): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (169): ReLU(inplace=True)\n","  (170): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n","  (171): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (172): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (173): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (174): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","  (175): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (176): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (177): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (178): ReLU(inplace=True)\n","  (179): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (180): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (181): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n","  (182): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (183): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n","  (184): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (185): ReLU(inplace=True)\n","), Sequential(\n","  (0): AdaptiveAvgPool2d(output_size=1)\n","  (1): AdaptiveMaxPool2d(output_size=1)\n","  (2): Flatten()\n","  (3): BatchNorm1d(4096, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (4): Dropout(p=0.25, inplace=False)\n","  (5): Linear(in_features=4096, out_features=512, bias=True)\n","  (6): ReLU(inplace=True)\n","  (7): BatchNorm1d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n","  (8): Dropout(p=0.5, inplace=False)\n","  (9): Linear(in_features=512, out_features=30, bias=True)\n",")], add_time=True, silent=False)"]},"metadata":{"tags":[]},"execution_count":13}]},{"cell_type":"code","metadata":{"id":"MJZRxDg97ioa","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":263},"outputId":"b1032c0f-467a-4cb5-9650-230d31a95b35","executionInfo":{"status":"ok","timestamp":1577258755824,"user_tz":-330,"elapsed":1286,"user":{"displayName":"Singh Akhil","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mA36AceXWq_HrzeD0O1PmRhh5A07fstsh9aa4qxFA=s64","userId":"03591970807351555446"}}},"source":["doc(learn.predict)"],"execution_count":16,"outputs":[{"output_type":"display_data","data":{"text/html":["<h4 id=\"Learner.predict\" class=\"doc_header\"><code>predict</code><a href=\"https://github.com/fastai/fastai/blob/master/fastai/basic_train.py#L368\" class=\"source_link\" style=\"float:right\">[source]</a><a class=\"source_link\" data-toggle=\"collapse\" data-target=\"#Learner-predict-pytest\" style=\"float:right; padding-right:10px\">[test]</a></h4><blockquote><p><code>predict</code>(<strong><code>item</code></strong>:<a href=\"https://docs.fast.ai/core.html#ItemBase\"><code>ItemBase</code></a>, <strong><code>return_x</code></strong>:<code>bool</code>=<strong><em><code>False</code></em></strong>, <strong><code>batch_first</code></strong>:<code>bool</code>=<strong><em><code>True</code></em></strong>, <strong><code>with_dropout</code></strong>:<code>bool</code>=<strong><em><code>False</code></em></strong>, <strong>**<code>kwargs</code></strong>)</p>\n","</blockquote>\n","<div class=\"collapse\" id=\"Learner-predict-pytest\"><div class=\"card card-body pytest_card\"><a type=\"button\" data-toggle=\"collapse\" data-target=\"#Learner-predict-pytest\" class=\"close\" aria-label=\"Close\"><span aria-hidden=\"true\">&times;</span></a><p>Tests found for <code>predict</code>:</p><ul><li><code>pytest -sv tests/test_vision_train.py::test_models_meta</code> <a href=\"https://github.com/fastai/fastai/blob/master/tests/test_vision_train.py#L89\" class=\"source_link\" style=\"float:right\">[source]</a></li><li><code>pytest -sv tests/test_vision_train.py::test_preds</code> <a href=\"https://github.com/fastai/fastai/blob/master/tests/test_vision_train.py#L63\" class=\"source_link\" style=\"float:right\">[source]</a></li></ul><p>To run tests please refer to this <a href=\"/dev/test.html#quick-guide\">guide</a>.</p></div></div><p>Return predicted class, label and probabilities for <code>item</code>.</p>\n","<p><a href=\"https://docs.fast.ai/basic_train.html#Learner.predict\" target=\"_blank\" rel=\"noreferrer noopener\">Show in docs</a></p>\n"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"xvodYz4o8mFR","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":371},"outputId":"edb6d129-d2c4-4bfd-e899-1e1880b84af7","executionInfo":{"status":"ok","timestamp":1577260548886,"user_tz":-330,"elapsed":2018,"user":{"displayName":"Singh Akhil","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mA36AceXWq_HrzeD0O1PmRhh5A07fstsh9aa4qxFA=s64","userId":"03591970807351555446"}}},"source":["fn = os.path.join(TEST_DIR,\"15.jpg\")\n","img=open_image(fn=fn)\n","img.show()\n","\n","tar = learn.predict(img)\n","img.show(y=tar[0])"],"execution_count":40,"outputs":[{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAALEAAACxCAYAAACLKVzFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nNCFWOd6ur0dpZET1drudsxGiIE2RVMp2LquXAMyU/KjIS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ZSaddms4k3nn8ef/HSS/j9889jaWnJzajR823wpV6vh7+9rYE928UWiKzX6+HZ7eb1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\nl4GXfBDnMvCSD+JcBl7yQZzLwMv/A3Yz8kmBZYydAAAAAElFTkSuQmCC\n","text/plain":["<Figure size 216x216 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"zoTsA0Sb_E_M","colab_type":"code","colab":{}},"source":[""],"execution_count":0,"outputs":[]}]}