diff --git a/TD2 Deep Learning.ipynb b/TD2 Deep Learning.ipynb index 975918d059923d79b3df00ac9ea9f4d947640ea2..e5f12d3cb23bf5802d15d07fc791f60e5a48eefe 100644 --- a/TD2 Deep Learning.ipynb +++ b/TD2 Deep Learning.ipynb @@ -33,7 +33,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "id": "330a42f5", "metadata": {}, "outputs": [ @@ -77,7 +77,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "id": "b1950f0a", "metadata": {}, "outputs": [ @@ -85,34 +85,34 @@ "name": "stdout", "output_type": "stream", "text": [ - "tensor([[ 0.3653, 0.6776, 1.4290, 1.3045, -0.1440, -1.9016, 0.1427, 0.6754,\n", - " 0.0791, 0.6423],\n", - " [-1.3009, 0.1227, 0.4001, 0.6688, 0.1672, -0.5949, 0.3957, -0.6071,\n", - " -0.7747, 0.6197],\n", - " [-0.7347, -1.5540, 2.3525, 0.1084, 0.1178, 0.5596, 0.6267, 2.1786,\n", - " -0.5310, -0.6559],\n", - " [ 0.6326, -1.0263, 0.3332, -0.1291, 0.1675, -0.1014, 1.3175, 0.3264,\n", - " -0.1400, 0.7431],\n", - " [ 0.4699, 0.9845, -1.4050, 1.1468, 0.7983, 1.0263, -1.6672, 0.1562,\n", - " -0.0875, -1.9664],\n", - " [-0.3761, -0.8523, 1.5731, -2.0885, -1.5779, 0.6759, 0.4770, 1.5133,\n", - " -1.4350, -0.5716],\n", - " [ 0.0985, -0.1337, -0.3850, 0.3503, -0.4130, -0.7820, -1.1305, 1.0061,\n", - " 0.0298, -1.4626],\n", - " [-0.0387, -1.7999, -2.1245, 0.2555, 0.1214, 0.5655, 0.5005, 1.0409,\n", - " 0.8113, -0.2322],\n", - " [ 2.1456, 0.3775, 0.8248, 0.8468, 0.8631, -0.0429, -1.5679, -0.6221,\n", - " -1.1605, 0.5963],\n", - " [ 0.1601, 0.2023, -0.9813, 0.1316, 0.1114, -1.8421, 0.6188, -0.3290,\n", - " 0.6238, 0.3155],\n", - " [-0.3864, -0.5559, 0.4249, -1.0155, -0.9137, 0.1228, -0.3569, 1.1107,\n", - " -0.5542, 1.2470],\n", - " [-0.6112, -0.5138, 1.1420, -0.0729, 1.1220, -0.1792, 1.0880, 0.8450,\n", - " 0.6158, -0.9575],\n", - " [ 0.9272, 0.1329, 0.4858, -0.5643, -0.1636, -0.2209, 0.9413, 0.1729,\n", - " 0.4400, 0.2477],\n", - " [-0.2307, 2.0693, 0.0898, 1.8634, 0.1166, 0.2212, 0.9382, -0.6915,\n", - " -1.9567, 0.2097]])\n", + "tensor([[-0.2657, 1.4264, -0.7658, 0.2888, -0.0724, -1.4032, 1.4979, -0.5388,\n", + " -1.8618, 1.5451],\n", + " [ 0.2315, -0.7664, 0.2843, -0.7651, -0.3544, 0.7568, 1.5988, 1.5724,\n", + " 0.5084, -1.5090],\n", + " [ 0.8253, 1.3846, -0.5591, 1.6799, 0.3691, -0.2530, -0.8940, -0.1759,\n", + " -1.2923, -0.2397],\n", + " [-0.1594, -0.9202, -0.0974, -1.9886, 0.7394, 0.0280, 0.7205, 0.7733,\n", + " 1.5885, -0.0933],\n", + " [ 0.3625, 0.2128, -1.8266, 0.0509, -1.0923, 1.5304, 1.6000, 0.2908,\n", + " 1.4650, -0.5551],\n", + " [ 1.9715, 1.6207, -0.5833, -0.2766, -0.8677, -0.3057, 0.6769, -0.2114,\n", + " -1.0948, 0.1133],\n", + " [-0.3803, -1.9537, -1.4493, 1.8527, -0.1894, 0.1226, 0.9199, 0.3932,\n", + " -0.2974, -0.5101],\n", + " [ 1.6058, -0.4255, -1.2330, 2.5125, 0.1017, 0.2285, -1.8927, 0.8053,\n", + " -0.3359, -0.0566],\n", + " [-0.1663, -0.1439, 2.7656, -0.5761, 1.2288, 0.3111, 0.7889, -2.0122,\n", + " -0.5605, 1.6705],\n", + " [ 1.4450, 0.0175, 1.2517, -2.3707, 2.2402, -0.4936, -0.0806, 1.0950,\n", + " -0.7469, -0.5404],\n", + " [ 0.3399, 0.4550, 0.6310, -0.5896, 0.1144, 0.3110, 2.4287, 1.3147,\n", + " -1.1029, -2.1430],\n", + " [-0.4746, 2.1541, 0.2555, 0.5946, 0.6905, 0.3468, 0.2744, 2.1437,\n", + " -0.4742, -1.3799],\n", + " [-0.2635, 1.3382, -1.1008, 1.2666, -0.3079, 1.2363, 0.0359, 0.2133,\n", + " 0.7574, -0.9682],\n", + " [-0.1775, 0.5077, 1.4268, 0.2454, 0.3230, 1.2289, 0.7614, 0.0073,\n", + " 1.8001, -1.5034]])\n", "AlexNet(\n", " (features): Sequential(\n", " (0): Conv2d(3, 64, kernel_size=(11, 11), stride=(4, 4), padding=(2, 2))\n", @@ -182,7 +182,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "id": "6e18f2fd", "metadata": {}, "outputs": [ @@ -216,7 +216,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "id": "462666a2", "metadata": {}, "outputs": [ @@ -297,7 +297,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "id": "317bf070", "metadata": {}, "outputs": [ @@ -361,7 +361,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "id": "4b53f229", "metadata": {}, "outputs": [ @@ -369,30 +369,33 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch: 0 \tTraining Loss: 43.453638 \tValidation Loss: 38.117901\n", - "Validation loss decreased (inf --> 38.117901). Saving model ...\n", - "Epoch: 1 \tTraining Loss: 33.786905 \tValidation Loss: 30.608687\n", - "Validation loss decreased (38.117901 --> 30.608687). Saving model ...\n", - "Epoch: 2 \tTraining Loss: 29.978750 \tValidation Loss: 28.626190\n", - "Validation loss decreased (30.608687 --> 28.626190). Saving model ...\n", - "Epoch: 3 \tTraining Loss: 27.777584 \tValidation Loss: 27.198099\n", - "Validation loss decreased (28.626190 --> 27.198099). Saving model ...\n", - "Epoch: 4 \tTraining Loss: 26.117933 \tValidation Loss: 26.415911\n", - "Validation loss decreased (27.198099 --> 26.415911). Saving model ...\n", - "Epoch: 5 \tTraining Loss: 24.786261 \tValidation Loss: 24.554481\n", - "Validation loss decreased (26.415911 --> 24.554481). Saving model ...\n", - "Epoch: 6 \tTraining Loss: 23.703873 \tValidation Loss: 24.357461\n", - "Validation loss decreased (24.554481 --> 24.357461). Saving model ...\n", - "Epoch: 7 \tTraining Loss: 22.748076 \tValidation Loss: 24.332178\n", - "Validation loss decreased (24.357461 --> 24.332178). Saving model ...\n", - "Epoch: 8 \tTraining Loss: 21.790853 \tValidation Loss: 23.261406\n", - "Validation loss decreased (24.332178 --> 23.261406). Saving model ...\n", - "Epoch: 9 \tTraining Loss: 20.925274 \tValidation Loss: 23.353505\n", - "Epoch: 10 \tTraining Loss: 20.174014 \tValidation Loss: 22.972180\n", - "Validation loss decreased (23.261406 --> 22.972180). Saving model ...\n", - "Epoch: 11 \tTraining Loss: 19.419566 \tValidation Loss: 22.647662\n", - "Validation loss decreased (22.972180 --> 22.647662). Saving model ...\n", - "Epoch: 12 \tTraining Loss: 18.719525 \tValidation Loss: 22.919457\n" + "Epoch: 0 \tTraining Loss: 45.727068 \tValidation Loss: 43.318010\n", + "Validation loss decreased (inf --> 43.318010). Saving model ...\n", + "Epoch: 1 \tTraining Loss: 38.408429 \tValidation Loss: 33.652277\n", + "Validation loss decreased (43.318010 --> 33.652277). Saving model ...\n", + "Epoch: 2 \tTraining Loss: 32.319058 \tValidation Loss: 30.753993\n", + "Validation loss decreased (33.652277 --> 30.753993). Saving model ...\n", + "Epoch: 3 \tTraining Loss: 29.549846 \tValidation Loss: 28.169669\n", + "Validation loss decreased (30.753993 --> 28.169669). Saving model ...\n", + "Epoch: 4 \tTraining Loss: 27.655829 \tValidation Loss: 27.654481\n", + "Validation loss decreased (28.169669 --> 27.654481). Saving model ...\n", + "Epoch: 5 \tTraining Loss: 26.222008 \tValidation Loss: 25.873263\n", + "Validation loss decreased (27.654481 --> 25.873263). Saving model ...\n", + "Epoch: 6 \tTraining Loss: 25.089649 \tValidation Loss: 25.455413\n", + "Validation loss decreased (25.873263 --> 25.455413). Saving model ...\n", + "Epoch: 7 \tTraining Loss: 24.132566 \tValidation Loss: 24.524804\n", + "Validation loss decreased (25.455413 --> 24.524804). Saving model ...\n", + "Epoch: 8 \tTraining Loss: 23.214103 \tValidation Loss: 23.883503\n", + "Validation loss decreased (24.524804 --> 23.883503). Saving model ...\n", + "Epoch: 9 \tTraining Loss: 22.452441 \tValidation Loss: 23.670456\n", + "Validation loss decreased (23.883503 --> 23.670456). Saving model ...\n", + "Epoch: 10 \tTraining Loss: 21.695399 \tValidation Loss: 23.130459\n", + "Validation loss decreased (23.670456 --> 23.130459). Saving model ...\n", + "Epoch: 11 \tTraining Loss: 21.011460 \tValidation Loss: 23.008460\n", + "Validation loss decreased (23.130459 --> 23.008460). Saving model ...\n", + "Epoch: 12 \tTraining Loss: 20.418336 \tValidation Loss: 23.139267\n", + "Epoch: 13 \tTraining Loss: 19.765256 \tValidation Loss: 22.010464\n", + "Validation loss decreased (23.008460 --> 22.010464). Saving model ...\n" ] }, { @@ -402,15 +405,15 @@ "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "\u001b[1;32md:\\Users\\lucil\\Documents\\S9\\Apprentissage profond\\mod_4_6-td2\\TD2 Deep Learning.ipynb Cell 15\u001b[0m line \u001b[0;36m3\n\u001b[0;32m <a href='vscode-notebook-cell:/d%3A/Users/lucil/Documents/S9/Apprentissage%20profond/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X20sZmlsZQ%3D%3D?line=33'>34</a>\u001b[0m \u001b[39m# Validate the model\u001b[39;00m\n\u001b[0;32m <a href='vscode-notebook-cell:/d%3A/Users/lucil/Documents/S9/Apprentissage%20profond/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X20sZmlsZQ%3D%3D?line=34'>35</a>\u001b[0m model\u001b[39m.\u001b[39meval()\n\u001b[1;32m---> <a href='vscode-notebook-cell:/d%3A/Users/lucil/Documents/S9/Apprentissage%20profond/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X20sZmlsZQ%3D%3D?line=35'>36</a>\u001b[0m \u001b[39mfor\u001b[39;00m data, target \u001b[39min\u001b[39;00m valid_loader:\n\u001b[0;32m <a href='vscode-notebook-cell:/d%3A/Users/lucil/Documents/S9/Apprentissage%20profond/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X20sZmlsZQ%3D%3D?line=36'>37</a>\u001b[0m \u001b[39m# Move tensors to GPU if CUDA is available\u001b[39;00m\n\u001b[0;32m <a href='vscode-notebook-cell:/d%3A/Users/lucil/Documents/S9/Apprentissage%20profond/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X20sZmlsZQ%3D%3D?line=37'>38</a>\u001b[0m \u001b[39mif\u001b[39;00m train_on_gpu:\n\u001b[0;32m <a href='vscode-notebook-cell:/d%3A/Users/lucil/Documents/S9/Apprentissage%20profond/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X20sZmlsZQ%3D%3D?line=38'>39</a>\u001b[0m data, target \u001b[39m=\u001b[39m data\u001b[39m.\u001b[39mcuda(), target\u001b[39m.\u001b[39mcuda()\n", - "File \u001b[1;32mc:\\Users\\lucil\\anaconda3\\Lib\\site-packages\\torch\\utils\\data\\dataloader.py:630\u001b[0m, in \u001b[0;36m_BaseDataLoaderIter.__next__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 627\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_sampler_iter \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m 628\u001b[0m \u001b[39m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[39;00m\n\u001b[0;32m 629\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_reset() \u001b[39m# type: ignore[call-arg]\u001b[39;00m\n\u001b[1;32m--> 630\u001b[0m data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_next_data()\n\u001b[0;32m 631\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_num_yielded \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m1\u001b[39m\n\u001b[0;32m 632\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_dataset_kind \u001b[39m==\u001b[39m _DatasetKind\u001b[39m.\u001b[39mIterable \u001b[39mand\u001b[39;00m \\\n\u001b[0;32m 633\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_IterableDataset_len_called \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m \\\n\u001b[0;32m 634\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_num_yielded \u001b[39m>\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_IterableDataset_len_called:\n", - "File \u001b[1;32mc:\\Users\\lucil\\anaconda3\\Lib\\site-packages\\torch\\utils\\data\\dataloader.py:674\u001b[0m, in \u001b[0;36m_SingleProcessDataLoaderIter._next_data\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 672\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_next_data\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[0;32m 673\u001b[0m index \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_next_index() \u001b[39m# may raise StopIteration\u001b[39;00m\n\u001b[1;32m--> 674\u001b[0m data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_dataset_fetcher\u001b[39m.\u001b[39mfetch(index) \u001b[39m# may raise StopIteration\u001b[39;00m\n\u001b[0;32m 675\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_pin_memory:\n\u001b[0;32m 676\u001b[0m data \u001b[39m=\u001b[39m _utils\u001b[39m.\u001b[39mpin_memory\u001b[39m.\u001b[39mpin_memory(data, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_pin_memory_device)\n", - "File \u001b[1;32mc:\\Users\\lucil\\anaconda3\\Lib\\site-packages\\torch\\utils\\data\\_utils\\fetch.py:51\u001b[0m, in \u001b[0;36m_MapDatasetFetcher.fetch\u001b[1;34m(self, possibly_batched_index)\u001b[0m\n\u001b[0;32m 49\u001b[0m data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdataset\u001b[39m.\u001b[39m__getitems__(possibly_batched_index)\n\u001b[0;32m 50\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m---> 51\u001b[0m data \u001b[39m=\u001b[39m [\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdataset[idx] \u001b[39mfor\u001b[39;00m idx \u001b[39min\u001b[39;00m possibly_batched_index]\n\u001b[0;32m 52\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m 53\u001b[0m data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdataset[possibly_batched_index]\n", - "File \u001b[1;32mc:\\Users\\lucil\\anaconda3\\Lib\\site-packages\\torch\\utils\\data\\_utils\\fetch.py:51\u001b[0m, in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m 49\u001b[0m data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdataset\u001b[39m.\u001b[39m__getitems__(possibly_batched_index)\n\u001b[0;32m 50\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m---> 51\u001b[0m data \u001b[39m=\u001b[39m [\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdataset[idx] \u001b[39mfor\u001b[39;00m idx \u001b[39min\u001b[39;00m possibly_batched_index]\n\u001b[0;32m 52\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m 53\u001b[0m data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdataset[possibly_batched_index]\n", - "File \u001b[1;32mc:\\Users\\lucil\\anaconda3\\Lib\\site-packages\\torchvision\\datasets\\cifar.py:118\u001b[0m, in \u001b[0;36mCIFAR10.__getitem__\u001b[1;34m(self, index)\u001b[0m\n\u001b[0;32m 115\u001b[0m img \u001b[39m=\u001b[39m Image\u001b[39m.\u001b[39mfromarray(img)\n\u001b[0;32m 117\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtransform \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m--> 118\u001b[0m img \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtransform(img)\n\u001b[0;32m 120\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtarget_transform \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m 121\u001b[0m target \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtarget_transform(target)\n", - "File \u001b[1;32mc:\\Users\\lucil\\anaconda3\\Lib\\site-packages\\torchvision\\transforms\\transforms.py:95\u001b[0m, in \u001b[0;36mCompose.__call__\u001b[1;34m(self, img)\u001b[0m\n\u001b[0;32m 93\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__call__\u001b[39m(\u001b[39mself\u001b[39m, img):\n\u001b[0;32m 94\u001b[0m \u001b[39mfor\u001b[39;00m t \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtransforms:\n\u001b[1;32m---> 95\u001b[0m img \u001b[39m=\u001b[39m t(img)\n\u001b[0;32m 96\u001b[0m \u001b[39mreturn\u001b[39;00m img\n", - "File \u001b[1;32mc:\\Users\\lucil\\anaconda3\\Lib\\site-packages\\torchvision\\transforms\\transforms.py:137\u001b[0m, in \u001b[0;36mToTensor.__call__\u001b[1;34m(self, pic)\u001b[0m\n\u001b[0;32m 129\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__call__\u001b[39m(\u001b[39mself\u001b[39m, pic):\n\u001b[0;32m 130\u001b[0m \u001b[39m \u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m 131\u001b[0m \u001b[39m Args:\u001b[39;00m\n\u001b[0;32m 132\u001b[0m \u001b[39m pic (PIL Image or numpy.ndarray): Image to be converted to tensor.\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 135\u001b[0m \u001b[39m Tensor: Converted image.\u001b[39;00m\n\u001b[0;32m 136\u001b[0m \u001b[39m \"\"\"\u001b[39;00m\n\u001b[1;32m--> 137\u001b[0m \u001b[39mreturn\u001b[39;00m F\u001b[39m.\u001b[39mto_tensor(pic)\n", - "File \u001b[1;32mc:\\Users\\lucil\\anaconda3\\Lib\\site-packages\\torchvision\\transforms\\functional.py:174\u001b[0m, in \u001b[0;36mto_tensor\u001b[1;34m(pic)\u001b[0m\n\u001b[0;32m 172\u001b[0m img \u001b[39m=\u001b[39m img\u001b[39m.\u001b[39mpermute((\u001b[39m2\u001b[39m, \u001b[39m0\u001b[39m, \u001b[39m1\u001b[39m))\u001b[39m.\u001b[39mcontiguous()\n\u001b[0;32m 173\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(img, torch\u001b[39m.\u001b[39mByteTensor):\n\u001b[1;32m--> 174\u001b[0m \u001b[39mreturn\u001b[39;00m img\u001b[39m.\u001b[39mto(dtype\u001b[39m=\u001b[39mdefault_float_dtype)\u001b[39m.\u001b[39mdiv(\u001b[39m255\u001b[39m)\n\u001b[0;32m 175\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m 176\u001b[0m \u001b[39mreturn\u001b[39;00m img\n", + "\u001b[1;32md:\\Users\\lucil\\Documents\\S9\\Apprentissage profond\\mod_4_6-td2\\TD2 Deep Learning.ipynb Cell 15\u001b[0m line \u001b[0;36m2\n\u001b[0;32m <a href='vscode-notebook-cell:/d%3A/Users/lucil/Documents/S9/Apprentissage%20profond/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X20sZmlsZQ%3D%3D?line=21'>22</a>\u001b[0m optimizer\u001b[39m.\u001b[39mzero_grad()\n\u001b[0;32m <a href='vscode-notebook-cell:/d%3A/Users/lucil/Documents/S9/Apprentissage%20profond/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X20sZmlsZQ%3D%3D?line=22'>23</a>\u001b[0m \u001b[39m# Forward pass: compute predicted outputs by passing inputs to the model\u001b[39;00m\n\u001b[1;32m---> <a href='vscode-notebook-cell:/d%3A/Users/lucil/Documents/S9/Apprentissage%20profond/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X20sZmlsZQ%3D%3D?line=23'>24</a>\u001b[0m output \u001b[39m=\u001b[39m model(data)\n\u001b[0;32m <a href='vscode-notebook-cell:/d%3A/Users/lucil/Documents/S9/Apprentissage%20profond/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X20sZmlsZQ%3D%3D?line=24'>25</a>\u001b[0m \u001b[39m# Calculate the batch loss\u001b[39;00m\n\u001b[0;32m <a href='vscode-notebook-cell:/d%3A/Users/lucil/Documents/S9/Apprentissage%20profond/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X20sZmlsZQ%3D%3D?line=25'>26</a>\u001b[0m loss \u001b[39m=\u001b[39m criterion(output, target)\n", + "File \u001b[1;32mc:\\Users\\lucil\\anaconda3\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1516\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_compiled_call_impl(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs) \u001b[39m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m 1517\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m-> 1518\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_call_impl(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n", + "File \u001b[1;32mc:\\Users\\lucil\\anaconda3\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1522\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m 1523\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m 1524\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_pre_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m 1525\u001b[0m \u001b[39mor\u001b[39;00m _global_backward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m 1526\u001b[0m \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1527\u001b[0m \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m 1529\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m 1530\u001b[0m result \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n", + "\u001b[1;32md:\\Users\\lucil\\Documents\\S9\\Apprentissage profond\\mod_4_6-td2\\TD2 Deep Learning.ipynb Cell 15\u001b[0m line \u001b[0;36m1\n\u001b[0;32m <a href='vscode-notebook-cell:/d%3A/Users/lucil/Documents/S9/Apprentissage%20profond/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X20sZmlsZQ%3D%3D?line=16'>17</a>\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mforward\u001b[39m(\u001b[39mself\u001b[39m, x):\n\u001b[0;32m <a href='vscode-notebook-cell:/d%3A/Users/lucil/Documents/S9/Apprentissage%20profond/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X20sZmlsZQ%3D%3D?line=17'>18</a>\u001b[0m x \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpool(F\u001b[39m.\u001b[39mrelu(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mconv1(x)))\n\u001b[1;32m---> <a href='vscode-notebook-cell:/d%3A/Users/lucil/Documents/S9/Apprentissage%20profond/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X20sZmlsZQ%3D%3D?line=18'>19</a>\u001b[0m x \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpool(F\u001b[39m.\u001b[39mrelu(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mconv2(x)))\n\u001b[0;32m <a href='vscode-notebook-cell:/d%3A/Users/lucil/Documents/S9/Apprentissage%20profond/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X20sZmlsZQ%3D%3D?line=19'>20</a>\u001b[0m x \u001b[39m=\u001b[39m x\u001b[39m.\u001b[39mview(\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m, \u001b[39m16\u001b[39m \u001b[39m*\u001b[39m \u001b[39m5\u001b[39m \u001b[39m*\u001b[39m \u001b[39m5\u001b[39m)\n\u001b[0;32m <a href='vscode-notebook-cell:/d%3A/Users/lucil/Documents/S9/Apprentissage%20profond/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X20sZmlsZQ%3D%3D?line=20'>21</a>\u001b[0m x \u001b[39m=\u001b[39m F\u001b[39m.\u001b[39mrelu(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfc1(x))\n", + "File \u001b[1;32mc:\\Users\\lucil\\anaconda3\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1518\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1516\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_compiled_call_impl(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs) \u001b[39m# type: ignore[misc]\u001b[39;00m\n\u001b[0;32m 1517\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m-> 1518\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_call_impl(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n", + "File \u001b[1;32mc:\\Users\\lucil\\anaconda3\\Lib\\site-packages\\torch\\nn\\modules\\module.py:1527\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1522\u001b[0m \u001b[39m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[0;32m 1523\u001b[0m \u001b[39m# this function, and just call forward.\u001b[39;00m\n\u001b[0;32m 1524\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_backward_pre_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_hooks \u001b[39mor\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_forward_pre_hooks\n\u001b[0;32m 1525\u001b[0m \u001b[39mor\u001b[39;00m _global_backward_pre_hooks \u001b[39mor\u001b[39;00m _global_backward_hooks\n\u001b[0;32m 1526\u001b[0m \u001b[39mor\u001b[39;00m _global_forward_hooks \u001b[39mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[1;32m-> 1527\u001b[0m \u001b[39mreturn\u001b[39;00m forward_call(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m 1529\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m 1530\u001b[0m result \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n", + "File \u001b[1;32mc:\\Users\\lucil\\anaconda3\\Lib\\site-packages\\torch\\nn\\modules\\pooling.py:166\u001b[0m, in \u001b[0;36mMaxPool2d.forward\u001b[1;34m(self, input)\u001b[0m\n\u001b[0;32m 165\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mforward\u001b[39m(\u001b[39mself\u001b[39m, \u001b[39minput\u001b[39m: Tensor):\n\u001b[1;32m--> 166\u001b[0m \u001b[39mreturn\u001b[39;00m F\u001b[39m.\u001b[39mmax_pool2d(\u001b[39minput\u001b[39m, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mkernel_size, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstride,\n\u001b[0;32m 167\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpadding, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdilation, ceil_mode\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mceil_mode,\n\u001b[0;32m 168\u001b[0m return_indices\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mreturn_indices)\n", + "File \u001b[1;32mc:\\Users\\lucil\\anaconda3\\Lib\\site-packages\\torch\\_jit_internal.py:488\u001b[0m, in \u001b[0;36mboolean_dispatch.<locals>.fn\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 486\u001b[0m \u001b[39mreturn\u001b[39;00m if_true(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m 487\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m--> 488\u001b[0m \u001b[39mreturn\u001b[39;00m if_false(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n", + "File \u001b[1;32mc:\\Users\\lucil\\anaconda3\\Lib\\site-packages\\torch\\nn\\functional.py:791\u001b[0m, in \u001b[0;36m_max_pool2d\u001b[1;34m(input, kernel_size, stride, padding, dilation, ceil_mode, return_indices)\u001b[0m\n\u001b[0;32m 789\u001b[0m \u001b[39mif\u001b[39;00m stride \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m 790\u001b[0m stride \u001b[39m=\u001b[39m torch\u001b[39m.\u001b[39mjit\u001b[39m.\u001b[39mannotate(List[\u001b[39mint\u001b[39m], [])\n\u001b[1;32m--> 791\u001b[0m \u001b[39mreturn\u001b[39;00m torch\u001b[39m.\u001b[39mmax_pool2d(\u001b[39minput\u001b[39m, kernel_size, stride, padding, dilation, ceil_mode)\n", "\u001b[1;31mKeyboardInterrupt\u001b[0m: " ] } @@ -490,7 +493,7 @@ "id": "13e1df74", "metadata": {}, "source": [ - "Does overfit occur? If so, do an early stopping." + "Overfit occurs so we do an early stopping at epoch 13 (should have been done at epoch 12)." ] }, { @@ -501,7 +504,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] @@ -514,7 +517,7 @@ "import matplotlib.pyplot as plt\n", "\n", "n_epochs_overfit = 13 #Otherwise len(train_lost_list) < n_epochs\n", - "plt.plot(range(n_epochs_overfit), train_loss_list)\n", + "plt.plot(range(n_epochs_overfit), train_loss_list[:-1])\n", "plt.xlabel(\"Epoch\")\n", "plt.ylabel(\"Loss\")\n", "plt.title(\"Performance of Model 1\")\n", @@ -539,20 +542,20 @@ "name": "stdout", "output_type": "stream", "text": [ - "Test Loss: 22.235297\n", + "Test Loss: 22.185146\n", "\n", - "Test Accuracy of airplane: 52% (523/1000)\n", - "Test Accuracy of automobile: 84% (849/1000)\n", - "Test Accuracy of bird: 34% (341/1000)\n", - "Test Accuracy of cat: 43% (432/1000)\n", - "Test Accuracy of deer: 66% (662/1000)\n", - "Test Accuracy of dog: 44% (448/1000)\n", - "Test Accuracy of frog: 74% (746/1000)\n", - "Test Accuracy of horse: 64% (647/1000)\n", - "Test Accuracy of ship: 83% (836/1000)\n", - "Test Accuracy of truck: 64% (649/1000)\n", + "Test Accuracy of airplane: 62% (628/1000)\n", + "Test Accuracy of automobile: 74% (749/1000)\n", + "Test Accuracy of bird: 42% (429/1000)\n", + "Test Accuracy of cat: 38% (385/1000)\n", + "Test Accuracy of deer: 48% (482/1000)\n", + "Test Accuracy of dog: 48% (480/1000)\n", + "Test Accuracy of frog: 73% (734/1000)\n", + "Test Accuracy of horse: 71% (712/1000)\n", + "Test Accuracy of ship: 78% (781/1000)\n", + "Test Accuracy of truck: 71% (718/1000)\n", "\n", - "Test Accuracy (Overall): 61% (6133/10000)\n" + "Test Accuracy (Overall): 60% (6098/10000)\n" ] } ], @@ -722,7 +725,7 @@ "print(model_1)\n", "# move tensors to GPU if CUDA is available\n", "if train_on_gpu:\n", - " model.cuda()" + " model_1.cuda()" ] }, { @@ -741,31 +744,37 @@ "name": "stdout", "output_type": "stream", "text": [ - "Epoch: 0 \tTraining Loss: 45.348058 \tValidation Loss: 41.718214\n", - "Validation loss decreased (inf --> 41.718214). Saving model_1 ...\n", - "Epoch: 1 \tTraining Loss: 39.649087 \tValidation Loss: 35.754235\n", - "Validation loss decreased (41.718214 --> 35.754235). Saving model_1 ...\n", - "Epoch: 2 \tTraining Loss: 35.008029 \tValidation Loss: 31.420939\n", - "Validation loss decreased (35.754235 --> 31.420939). Saving model_1 ...\n", - "Epoch: 3 \tTraining Loss: 32.138094 \tValidation Loss: 28.863286\n", - "Validation loss decreased (31.420939 --> 28.863286). Saving model_1 ...\n", - "Epoch: 4 \tTraining Loss: 30.218731 \tValidation Loss: 28.003921\n", - "Validation loss decreased (28.863286 --> 28.003921). Saving model_1 ...\n", - "Epoch: 5 \tTraining Loss: 28.807953 \tValidation Loss: 26.228902\n", - "Validation loss decreased (28.003921 --> 26.228902). Saving model_1 ...\n", - "Epoch: 6 \tTraining Loss: 27.365782 \tValidation Loss: 25.497843\n", - "Validation loss decreased (26.228902 --> 25.497843). Saving model_1 ...\n", - "Epoch: 7 \tTraining Loss: 26.038266 \tValidation Loss: 23.508494\n", - "Validation loss decreased (25.497843 --> 23.508494). Saving model_1 ...\n", - "Epoch: 8 \tTraining Loss: 24.863525 \tValidation Loss: 23.421283\n", - "Validation loss decreased (23.508494 --> 23.421283). Saving model_1 ...\n", - "Epoch: 9 \tTraining Loss: 23.610995 \tValidation Loss: 21.928674\n", - "Validation loss decreased (23.421283 --> 21.928674). Saving model_1 ...\n", - "Epoch: 10 \tTraining Loss: 22.689530 \tValidation Loss: 21.890606\n", - "Validation loss decreased (21.928674 --> 21.890606). Saving model_1 ...\n", - "Epoch: 11 \tTraining Loss: 21.605674 \tValidation Loss: 20.122198\n", - "Validation loss decreased (21.890606 --> 20.122198). Saving model_1 ...\n", - "Epoch: 12 \tTraining Loss: 20.795100 \tValidation Loss: 20.151628\n" + "Epoch: 0 \tTraining Loss: 45.999779 \tValidation Loss: 45.708036\n", + "Validation loss decreased (inf --> 45.708036). Saving model_1 ...\n", + "Epoch: 1 \tTraining Loss: 42.228754 \tValidation Loss: 38.043084\n", + "Validation loss decreased (45.708036 --> 38.043084). Saving model_1 ...\n", + "Epoch: 2 \tTraining Loss: 36.859957 \tValidation Loss: 32.964439\n", + "Validation loss decreased (38.043084 --> 32.964439). Saving model_1 ...\n", + "Epoch: 3 \tTraining Loss: 33.854479 \tValidation Loss: 31.206484\n", + "Validation loss decreased (32.964439 --> 31.206484). Saving model_1 ...\n", + "Epoch: 4 \tTraining Loss: 31.841331 \tValidation Loss: 29.466390\n", + "Validation loss decreased (31.206484 --> 29.466390). Saving model_1 ...\n", + "Epoch: 5 \tTraining Loss: 30.123153 \tValidation Loss: 27.320879\n", + "Validation loss decreased (29.466390 --> 27.320879). Saving model_1 ...\n", + "Epoch: 6 \tTraining Loss: 28.501253 \tValidation Loss: 25.977184\n", + "Validation loss decreased (27.320879 --> 25.977184). Saving model_1 ...\n", + "Epoch: 7 \tTraining Loss: 26.864250 \tValidation Loss: 23.841341\n", + "Validation loss decreased (25.977184 --> 23.841341). Saving model_1 ...\n", + "Epoch: 8 \tTraining Loss: 25.504650 \tValidation Loss: 22.691753\n", + "Validation loss decreased (23.841341 --> 22.691753). Saving model_1 ...\n", + "Epoch: 9 \tTraining Loss: 24.086911 \tValidation Loss: 21.352982\n", + "Validation loss decreased (22.691753 --> 21.352982). Saving model_1 ...\n", + "Epoch: 10 \tTraining Loss: 22.880436 \tValidation Loss: 20.317365\n", + "Validation loss decreased (21.352982 --> 20.317365). Saving model_1 ...\n", + "Epoch: 11 \tTraining Loss: 21.789587 \tValidation Loss: 19.749537\n", + "Validation loss decreased (20.317365 --> 19.749537). Saving model_1 ...\n", + "Epoch: 12 \tTraining Loss: 20.766126 \tValidation Loss: 19.134493\n", + "Validation loss decreased (19.749537 --> 19.134493). Saving model_1 ...\n", + "Epoch: 13 \tTraining Loss: 19.737510 \tValidation Loss: 18.760638\n", + "Validation loss decreased (19.134493 --> 18.760638). Saving model_1 ...\n", + "Epoch: 14 \tTraining Loss: 19.023474 \tValidation Loss: 17.555831\n", + "Validation loss decreased (18.760638 --> 17.555831). Saving model_1 ...\n", + "Epoch: 15 \tTraining Loss: 18.147850 \tValidation Loss: 17.598172\n" ] }, { @@ -775,9 +784,15 @@ "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "\u001b[1;32md:\\Users\\lucil\\Documents\\S9\\Apprentissage profond\\mod_4_6-td2\\TD2 Deep Learning.ipynb Cell 24\u001b[0m line \u001b[0;36m2\n\u001b[0;32m <a href='vscode-notebook-cell:/d%3A/Users/lucil/Documents/S9/Apprentissage%20profond/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X56sZmlsZQ%3D%3D?line=25'>26</a>\u001b[0m loss \u001b[39m=\u001b[39m criterion(output, target)\n\u001b[0;32m <a href='vscode-notebook-cell:/d%3A/Users/lucil/Documents/S9/Apprentissage%20profond/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X56sZmlsZQ%3D%3D?line=26'>27</a>\u001b[0m \u001b[39m# Backward pass: compute gradient of the loss with respect to model parameters\u001b[39;00m\n\u001b[1;32m---> <a href='vscode-notebook-cell:/d%3A/Users/lucil/Documents/S9/Apprentissage%20profond/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X56sZmlsZQ%3D%3D?line=27'>28</a>\u001b[0m loss\u001b[39m.\u001b[39mbackward()\n\u001b[0;32m <a href='vscode-notebook-cell:/d%3A/Users/lucil/Documents/S9/Apprentissage%20profond/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X56sZmlsZQ%3D%3D?line=28'>29</a>\u001b[0m \u001b[39m# Perform a single optimization step (parameter update)\u001b[39;00m\n\u001b[0;32m <a href='vscode-notebook-cell:/d%3A/Users/lucil/Documents/S9/Apprentissage%20profond/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X56sZmlsZQ%3D%3D?line=29'>30</a>\u001b[0m optimizer\u001b[39m.\u001b[39mstep()\n", - "File \u001b[1;32mc:\\Users\\lucil\\anaconda3\\Lib\\site-packages\\torch\\_tensor.py:492\u001b[0m, in \u001b[0;36mTensor.backward\u001b[1;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[0;32m 482\u001b[0m \u001b[39mif\u001b[39;00m has_torch_function_unary(\u001b[39mself\u001b[39m):\n\u001b[0;32m 483\u001b[0m \u001b[39mreturn\u001b[39;00m handle_torch_function(\n\u001b[0;32m 484\u001b[0m Tensor\u001b[39m.\u001b[39mbackward,\n\u001b[0;32m 485\u001b[0m (\u001b[39mself\u001b[39m,),\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 490\u001b[0m inputs\u001b[39m=\u001b[39minputs,\n\u001b[0;32m 491\u001b[0m )\n\u001b[1;32m--> 492\u001b[0m torch\u001b[39m.\u001b[39mautograd\u001b[39m.\u001b[39mbackward(\n\u001b[0;32m 493\u001b[0m \u001b[39mself\u001b[39m, gradient, retain_graph, create_graph, inputs\u001b[39m=\u001b[39minputs\n\u001b[0;32m 494\u001b[0m )\n", - "File \u001b[1;32mc:\\Users\\lucil\\anaconda3\\Lib\\site-packages\\torch\\autograd\\__init__.py:251\u001b[0m, in \u001b[0;36mbackward\u001b[1;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[0;32m 246\u001b[0m retain_graph \u001b[39m=\u001b[39m create_graph\n\u001b[0;32m 248\u001b[0m \u001b[39m# The reason we repeat the same comment below is that\u001b[39;00m\n\u001b[0;32m 249\u001b[0m \u001b[39m# some Python versions print out the first line of a multi-line function\u001b[39;00m\n\u001b[0;32m 250\u001b[0m \u001b[39m# calls in the traceback and some print out the last line\u001b[39;00m\n\u001b[1;32m--> 251\u001b[0m Variable\u001b[39m.\u001b[39m_execution_engine\u001b[39m.\u001b[39mrun_backward( \u001b[39m# Calls into the C++ engine to run the backward pass\u001b[39;00m\n\u001b[0;32m 252\u001b[0m tensors,\n\u001b[0;32m 253\u001b[0m grad_tensors_,\n\u001b[0;32m 254\u001b[0m retain_graph,\n\u001b[0;32m 255\u001b[0m create_graph,\n\u001b[0;32m 256\u001b[0m inputs,\n\u001b[0;32m 257\u001b[0m allow_unreachable\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m,\n\u001b[0;32m 258\u001b[0m accumulate_grad\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m,\n\u001b[0;32m 259\u001b[0m )\n", + "\u001b[1;32md:\\Users\\lucil\\Documents\\S9\\Apprentissage profond\\mod_4_6-td2\\TD2 Deep Learning.ipynb Cell 24\u001b[0m line \u001b[0;36m1\n\u001b[0;32m <a href='vscode-notebook-cell:/d%3A/Users/lucil/Documents/S9/Apprentissage%20profond/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X32sZmlsZQ%3D%3D?line=14'>15</a>\u001b[0m \u001b[39m# Train the model\u001b[39;00m\n\u001b[0;32m <a href='vscode-notebook-cell:/d%3A/Users/lucil/Documents/S9/Apprentissage%20profond/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X32sZmlsZQ%3D%3D?line=15'>16</a>\u001b[0m model_1\u001b[39m.\u001b[39mtrain()\n\u001b[1;32m---> <a href='vscode-notebook-cell:/d%3A/Users/lucil/Documents/S9/Apprentissage%20profond/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X32sZmlsZQ%3D%3D?line=16'>17</a>\u001b[0m \u001b[39mfor\u001b[39;00m data, target \u001b[39min\u001b[39;00m train_loader:\n\u001b[0;32m <a href='vscode-notebook-cell:/d%3A/Users/lucil/Documents/S9/Apprentissage%20profond/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X32sZmlsZQ%3D%3D?line=17'>18</a>\u001b[0m \u001b[39m# Move tensors to GPU if CUDA is available\u001b[39;00m\n\u001b[0;32m <a href='vscode-notebook-cell:/d%3A/Users/lucil/Documents/S9/Apprentissage%20profond/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X32sZmlsZQ%3D%3D?line=18'>19</a>\u001b[0m \u001b[39mif\u001b[39;00m train_on_gpu:\n\u001b[0;32m <a href='vscode-notebook-cell:/d%3A/Users/lucil/Documents/S9/Apprentissage%20profond/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X32sZmlsZQ%3D%3D?line=19'>20</a>\u001b[0m data, target \u001b[39m=\u001b[39m data\u001b[39m.\u001b[39mcuda(), target\u001b[39m.\u001b[39mcuda()\n", + "File \u001b[1;32mc:\\Users\\lucil\\anaconda3\\Lib\\site-packages\\torch\\utils\\data\\dataloader.py:630\u001b[0m, in \u001b[0;36m_BaseDataLoaderIter.__next__\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 627\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_sampler_iter \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m 628\u001b[0m \u001b[39m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[39;00m\n\u001b[0;32m 629\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_reset() \u001b[39m# type: ignore[call-arg]\u001b[39;00m\n\u001b[1;32m--> 630\u001b[0m data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_next_data()\n\u001b[0;32m 631\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_num_yielded \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m1\u001b[39m\n\u001b[0;32m 632\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_dataset_kind \u001b[39m==\u001b[39m _DatasetKind\u001b[39m.\u001b[39mIterable \u001b[39mand\u001b[39;00m \\\n\u001b[0;32m 633\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_IterableDataset_len_called \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39mand\u001b[39;00m \\\n\u001b[0;32m 634\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_num_yielded \u001b[39m>\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_IterableDataset_len_called:\n", + "File \u001b[1;32mc:\\Users\\lucil\\anaconda3\\Lib\\site-packages\\torch\\utils\\data\\dataloader.py:674\u001b[0m, in \u001b[0;36m_SingleProcessDataLoaderIter._next_data\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 672\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_next_data\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[0;32m 673\u001b[0m index \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_next_index() \u001b[39m# may raise StopIteration\u001b[39;00m\n\u001b[1;32m--> 674\u001b[0m data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_dataset_fetcher\u001b[39m.\u001b[39mfetch(index) \u001b[39m# may raise StopIteration\u001b[39;00m\n\u001b[0;32m 675\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_pin_memory:\n\u001b[0;32m 676\u001b[0m data \u001b[39m=\u001b[39m _utils\u001b[39m.\u001b[39mpin_memory\u001b[39m.\u001b[39mpin_memory(data, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_pin_memory_device)\n", + "File \u001b[1;32mc:\\Users\\lucil\\anaconda3\\Lib\\site-packages\\torch\\utils\\data\\_utils\\fetch.py:51\u001b[0m, in \u001b[0;36m_MapDatasetFetcher.fetch\u001b[1;34m(self, possibly_batched_index)\u001b[0m\n\u001b[0;32m 49\u001b[0m data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdataset\u001b[39m.\u001b[39m__getitems__(possibly_batched_index)\n\u001b[0;32m 50\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m---> 51\u001b[0m data \u001b[39m=\u001b[39m [\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdataset[idx] \u001b[39mfor\u001b[39;00m idx \u001b[39min\u001b[39;00m possibly_batched_index]\n\u001b[0;32m 52\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m 53\u001b[0m data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdataset[possibly_batched_index]\n", + "File \u001b[1;32mc:\\Users\\lucil\\anaconda3\\Lib\\site-packages\\torch\\utils\\data\\_utils\\fetch.py:51\u001b[0m, in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m 49\u001b[0m data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdataset\u001b[39m.\u001b[39m__getitems__(possibly_batched_index)\n\u001b[0;32m 50\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m---> 51\u001b[0m data \u001b[39m=\u001b[39m [\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdataset[idx] \u001b[39mfor\u001b[39;00m idx \u001b[39min\u001b[39;00m possibly_batched_index]\n\u001b[0;32m 52\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m 53\u001b[0m data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mdataset[possibly_batched_index]\n", + "File \u001b[1;32mc:\\Users\\lucil\\anaconda3\\Lib\\site-packages\\torchvision\\datasets\\cifar.py:118\u001b[0m, in \u001b[0;36mCIFAR10.__getitem__\u001b[1;34m(self, index)\u001b[0m\n\u001b[0;32m 115\u001b[0m img \u001b[39m=\u001b[39m Image\u001b[39m.\u001b[39mfromarray(img)\n\u001b[0;32m 117\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtransform \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m--> 118\u001b[0m img \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtransform(img)\n\u001b[0;32m 120\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtarget_transform \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m 121\u001b[0m target \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtarget_transform(target)\n", + "File \u001b[1;32mc:\\Users\\lucil\\anaconda3\\Lib\\site-packages\\torchvision\\transforms\\transforms.py:95\u001b[0m, in \u001b[0;36mCompose.__call__\u001b[1;34m(self, img)\u001b[0m\n\u001b[0;32m 93\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__call__\u001b[39m(\u001b[39mself\u001b[39m, img):\n\u001b[0;32m 94\u001b[0m \u001b[39mfor\u001b[39;00m t \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtransforms:\n\u001b[1;32m---> 95\u001b[0m img \u001b[39m=\u001b[39m t(img)\n\u001b[0;32m 96\u001b[0m \u001b[39mreturn\u001b[39;00m img\n", + "File \u001b[1;32mc:\\Users\\lucil\\anaconda3\\Lib\\site-packages\\torchvision\\transforms\\transforms.py:137\u001b[0m, in \u001b[0;36mToTensor.__call__\u001b[1;34m(self, pic)\u001b[0m\n\u001b[0;32m 129\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__call__\u001b[39m(\u001b[39mself\u001b[39m, pic):\n\u001b[0;32m 130\u001b[0m \u001b[39m \u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m 131\u001b[0m \u001b[39m Args:\u001b[39;00m\n\u001b[0;32m 132\u001b[0m \u001b[39m pic (PIL Image or numpy.ndarray): Image to be converted to tensor.\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 135\u001b[0m \u001b[39m Tensor: Converted image.\u001b[39;00m\n\u001b[0;32m 136\u001b[0m \u001b[39m \"\"\"\u001b[39;00m\n\u001b[1;32m--> 137\u001b[0m \u001b[39mreturn\u001b[39;00m F\u001b[39m.\u001b[39mto_tensor(pic)\n", + "File \u001b[1;32mc:\\Users\\lucil\\anaconda3\\Lib\\site-packages\\torchvision\\transforms\\functional.py:172\u001b[0m, in \u001b[0;36mto_tensor\u001b[1;34m(pic)\u001b[0m\n\u001b[0;32m 170\u001b[0m img \u001b[39m=\u001b[39m img\u001b[39m.\u001b[39mview(pic\u001b[39m.\u001b[39msize[\u001b[39m1\u001b[39m], pic\u001b[39m.\u001b[39msize[\u001b[39m0\u001b[39m], F_pil\u001b[39m.\u001b[39mget_image_num_channels(pic))\n\u001b[0;32m 171\u001b[0m \u001b[39m# put it from HWC to CHW format\u001b[39;00m\n\u001b[1;32m--> 172\u001b[0m img \u001b[39m=\u001b[39m img\u001b[39m.\u001b[39mpermute((\u001b[39m2\u001b[39m, \u001b[39m0\u001b[39m, \u001b[39m1\u001b[39m))\u001b[39m.\u001b[39mcontiguous()\n\u001b[0;32m 173\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(img, torch\u001b[39m.\u001b[39mByteTensor):\n\u001b[0;32m 174\u001b[0m \u001b[39mreturn\u001b[39;00m img\u001b[39m.\u001b[39mto(dtype\u001b[39m=\u001b[39mdefault_float_dtype)\u001b[39m.\u001b[39mdiv(\u001b[39m255\u001b[39m)\n", "\u001b[1;31mKeyboardInterrupt\u001b[0m: " ] } @@ -861,12 +876,12 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "<Figure size 640x480 with 1 Axes>" ] @@ -878,12 +893,12 @@ "source": [ "import matplotlib.pyplot as plt\n", "\n", - "n_epochs_overfit_1 = 13 #Otherwise len(train_lost_list) < n_epochs\n", - "plt.plot(range(n_epochs_overfit), train_loss_list, label = \"Model 0\")\n", - "plt.plot(range(n_epochs_overfit), train_loss_list_1, color = \"green\", label = \"Model 1\")\n", + "n_epochs_overfit = 13\n", + "plt.plot(range(n_epochs_overfit), train_loss_list[:-1], label = \"Model 0\")\n", + "plt.plot(range(n_epochs_overfit), train_loss_list_1[:-3], color = \"green\", label = \"Model 1\")\n", "plt.xlabel(\"Epoch\")\n", "plt.ylabel(\"Loss\")\n", - "plt.title(\"Comparison of Performande for of Model 0 and Model 1\")\n", + "plt.title(\"Comparison of Performance for of Model 0 and Model 1\")\n", "plt.show()" ] }, @@ -923,7 +938,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 25, "id": "ef623c26", "metadata": {}, "outputs": [ @@ -931,16 +946,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "model: fp32 \t Size (KB): 251.278\n" + "model: fp32 \t Size (KB): 102523.238\n" ] }, { "data": { "text/plain": [ - "251278" + "102523238" ] }, - "execution_count": 15, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } @@ -1230,10 +1245,38 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "b4d13080", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\lucil\\anaconda3\\Lib\\site-packages\\torchvision\\models\\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n", + " warnings.warn(\n", + "c:\\Users\\lucil\\anaconda3\\Lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet50_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet50_Weights.DEFAULT` to get the most up-to-date weights.\n", + " warnings.warn(msg)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Predicted class is: Golden Retriever\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import json\n", "from PIL import Image\n", @@ -1296,131 +1339,303 @@ ] }, { - "cell_type": "markdown", - "id": "5d57da4b", + "cell_type": "code", + "execution_count": null, "metadata": {}, + "outputs": [], "source": [ - "## Exercise 4: Transfer Learning\n", - " \n", - " \n", - "For this work, we will use a pre-trained model (ResNet18) as a descriptor extractor and will refine the classification by training only the last fully connected layer of the network. Thus, the output layer of the pre-trained network will be replaced by a layer adapted to the new classes to be recognized which will be in our case ants and bees.\n", - "Download and unzip in your working directory the dataset available at the address :\n", - " \n", - "https://download.pytorch.org/tutorial/hymenoptera_data.zip\n", - " \n", - "Execute the following code in order to display some images of the dataset." + "Trying the code with other images downloaded from the internet :" ] }, { "cell_type": "code", - "execution_count": null, - "id": "be2d31f5", + "execution_count": 30, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\lucil\\anaconda3\\Lib\\site-packages\\torchvision\\models\\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n", + " warnings.warn(\n", + "c:\\Users\\lucil\\anaconda3\\Lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet50_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet50_Weights.DEFAULT` to get the most up-to-date weights.\n", + " warnings.warn(msg)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Predicted class is: Golden Retriever\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ + "import json\n", + "from PIL import Image\n", "import os\n", "\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import torch\n", - "import torchvision\n", - "from torchvision import datasets, transforms\n", + "# Choose a list of images to pass through the model\n", + "list_test_images = [\"dog.png\"]\n", "\n", - "# Data augmentation and normalization for training\n", - "# Just normalization for validation\n", - "data_transforms = {\n", - " \"train\": transforms.Compose(\n", - " [\n", - " transforms.RandomResizedCrop(\n", - " 224\n", - " ), # ImageNet models were trained on 224x224 images\n", - " transforms.RandomHorizontalFlip(), # flip horizontally 50% of the time - increases train set variability\n", - " transforms.ToTensor(), # convert it to a PyTorch tensor\n", - " transforms.Normalize(\n", - " [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]\n", - " ), # ImageNet models expect this norm\n", - " ]\n", - " ),\n", - " \"val\": transforms.Compose(\n", - " [\n", - " transforms.Resize(256),\n", - " transforms.CenterCrop(224),\n", - " transforms.ToTensor(),\n", - " transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n", - " ]\n", - " ),\n", - "}\n", + "# Configure matplotlib for pretty inline plots\n", + "#%matplotlib inline\n", + "#%config InlineBackend.figure_format = 'retina'\n", "\n", - "data_dir = \"hymenoptera_data\"\n", - "# Create train and validation datasets and loaders\n", - "image_datasets = {\n", - " x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x])\n", - " for x in [\"train\", \"val\"]\n", - "}\n", - "dataloaders = {\n", - " x: torch.utils.data.DataLoader(\n", - " image_datasets[x], batch_size=4, shuffle=True, num_workers=0\n", - " )\n", - " for x in [\"train\", \"val\"]\n", - "}\n", - "dataset_sizes = {x: len(image_datasets[x]) for x in [\"train\", \"val\"]}\n", - "class_names = image_datasets[\"train\"].classes\n", - "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n", + "# Prepare the labels\n", + "with open(\"imagenet-simple-labels.json\") as f:\n", + " labels = json.load(f)\n", "\n", - "# Helper function for displaying images\n", - "def imshow(inp, title=None):\n", - " \"\"\"Imshow for Tensor.\"\"\"\n", - " inp = inp.numpy().transpose((1, 2, 0))\n", - " mean = np.array([0.485, 0.456, 0.406])\n", - " std = np.array([0.229, 0.224, 0.225])\n", + "# First prepare the transformations: resize the image to what the model was trained on and convert it to a tensor\n", + "data_transform = transforms.Compose(\n", + " [\n", + " transforms.Resize((224, 224)),\n", + " transforms.ToTensor(),\n", + " transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n", + " ]\n", + ")\n", "\n", - " # Un-normalize the images\n", - " inp = std * inp + mean\n", - " # Clip just in case\n", - " inp = np.clip(inp, 0, 1)\n", - " plt.imshow(inp)\n", - " if title is not None:\n", - " plt.title(title)\n", - " plt.pause(0.001) # pause a bit so that plots are updated\n", - " plt.show()\n", + "# Download the model if it's not there already. It will take a bit on the first run, after that it's fast\n", + "model = models.resnet50(pretrained=True)\n", + "# Send the model to the GPU\n", + "# model.cuda()\n", + "# Set layers such as dropout and batchnorm in evaluation mode\n", + "model.eval()\n", "\n", + "for image in list_test_images:\n", + " # Load the image\n", "\n", - "# Get a batch of training data\n", - "inputs, classes = next(iter(dataloaders[\"train\"]))\n", + " image = Image.open(test_image)\n", + " plt.imshow(image), plt.xticks([]), plt.yticks([])\n", "\n", - "# Make a grid from batch\n", - "out = torchvision.utils.make_grid(inputs)\n", + " # Now apply the transformation, expand the batch dimension, and send the image to the GPU\n", + " # image = data_transform(image).unsqueeze(0).cuda()\n", + " image = data_transform(image).unsqueeze(0)\n", "\n", - "imshow(out, title=[class_names[x] for x in classes])\n", - "\n" + " \n", + "\n", + " # Get the 1000-dimensional model output\n", + " out = model(image)\n", + "\n", + " # Find the predicted class\n", + " print(\"Predicted class is: {}\".format(labels[out.argmax()]))" ] }, { "cell_type": "markdown", - "id": "bbd48800", "metadata": {}, "source": [ - "Now, execute the following code which uses a pre-trained model ResNet18 having replaced the output layer for the ants/bees classification and performs the model training by only changing the weights of this output layer." + "Now we want to try with the quantized model. \n", + "First we calculate the size of the initial model using the fonction defined in the second exercise." ] }, { "cell_type": "code", - "execution_count": null, - "id": "572d824c", + "execution_count": 26, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "model: fp32 \t Size (KB): 102523.238\n" + ] + }, + { + "data": { + "text/plain": [ + "102523238" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "import copy\n", - "import os\n", - "import time\n", + "# Model size\n", + "print_size_of_model(model, \"fp32\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we quantize the model and calculate its size." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "c:\\Users\\lucil\\anaconda3\\Lib\\site-packages\\torchvision\\models\\_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n", + " warnings.warn(\n", + "c:\\Users\\lucil\\anaconda3\\Lib\\site-packages\\torchvision\\models\\_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet50_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet50_Weights.DEFAULT` to get the most up-to-date weights.\n", + " warnings.warn(msg)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "model: int8 \t Size (KB): 96379.996\n" + ] + }, + { + "data": { + "text/plain": [ + "96379996" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import torch.quantization\n", "\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import torch\n", - "import torch.nn as nn\n", - "import torch.optim as optim\n", + "# Download the model if it's not there already.\n", + "model = models.resnet50(pretrained=True)\n", + "\n", + "quantized_model = torch.quantization.quantize_dynamic(model, dtype=torch.qint8)\n", + "print_size_of_model(quantized_model, \"int8\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And we check if the quantized model is still able to classify the previous images :" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Predicted class is: Golden Retriever\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import json\n", + "from PIL import Image\n", + "import os\n", + "\n", + "# Choose a list of images to pass through the model\n", + "list_test_images = [\"dog.png\"]\n", + "\n", + "# Configure matplotlib for pretty inline plots\n", + "#%matplotlib inline\n", + "#%config InlineBackend.figure_format = 'retina'\n", + "\n", + "# Prepare the labels\n", + "with open(\"imagenet-simple-labels.json\") as f:\n", + " labels = json.load(f)\n", + "\n", + "# First prepare the transformations: resize the image to what the model was trained on and convert it to a tensor\n", + "data_transform = transforms.Compose(\n", + " [\n", + " transforms.Resize((224, 224)),\n", + " transforms.ToTensor(),\n", + " transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n", + " ]\n", + ")\n", + "\n", + "# Send the model to the GPU\n", + "# model.cuda()\n", + "# Set layers such as dropout and batchnorm in evaluation mode\n", + "quantized_model.eval()\n", + "\n", + "for image in list_test_images:\n", + " # Load the image\n", + "\n", + " image = Image.open(test_image)\n", + " plt.imshow(image), plt.xticks([]), plt.yticks([])\n", + "\n", + " # Now apply the transformation, expand the batch dimension, and send the image to the GPU\n", + " # image = data_transform(image).unsqueeze(0).cuda()\n", + " image = data_transform(image).unsqueeze(0)\n", + "\n", + " \n", + "\n", + " # Get the 1000-dimensional model output\n", + " out = quantized_model(image)\n", + "\n", + " # Find the predicted class\n", + " print(\"Predicted class is: {}\".format(labels[out.argmax()]))" + ] + }, + { + "cell_type": "markdown", + "id": "5d57da4b", + "metadata": {}, + "source": [ + "## Exercise 4: Transfer Learning\n", + " \n", + " \n", + "For this work, we will use a pre-trained model (ResNet18) as a descriptor extractor and will refine the classification by training only the last fully connected layer of the network. Thus, the output layer of the pre-trained network will be replaced by a layer adapted to the new classes to be recognized which will be in our case ants and bees.\n", + "Download and unzip in your working directory the dataset available at the address :\n", + " \n", + "https://download.pytorch.org/tutorial/hymenoptera_data.zip\n", + " \n", + "Execute the following code in order to display some images of the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "be2d31f5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import os\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import torch\n", "import torchvision\n", - "from torch.optim import lr_scheduler\n", "from torchvision import datasets, transforms\n", "\n", "# Data augmentation and normalization for training\n", @@ -1456,6 +1671,162 @@ "}\n", "dataloaders = {\n", " x: torch.utils.data.DataLoader(\n", + " image_datasets[x], batch_size=4, shuffle=True, num_workers=0\n", + " )\n", + " for x in [\"train\", \"val\"]\n", + "}\n", + "dataset_sizes = {x: len(image_datasets[x]) for x in [\"train\", \"val\"]}\n", + "class_names = image_datasets[\"train\"].classes\n", + "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n", + "\n", + "# Helper function for displaying images\n", + "def imshow(inp, title=None):\n", + " \"\"\"Imshow for Tensor.\"\"\"\n", + " inp = inp.numpy().transpose((1, 2, 0))\n", + " mean = np.array([0.485, 0.456, 0.406])\n", + " std = np.array([0.229, 0.224, 0.225])\n", + "\n", + " # Un-normalize the images\n", + " inp = std * inp + mean\n", + " # Clip just in case\n", + " inp = np.clip(inp, 0, 1)\n", + " plt.imshow(inp)\n", + " if title is not None:\n", + " plt.title(title)\n", + " plt.pause(0.001) # pause a bit so that plots are updated\n", + " plt.show()\n", + "\n", + "\n", + "# Get a batch of training data\n", + "inputs, classes = next(iter(dataloaders[\"train\"]))\n", + "\n", + "# Make a grid from batch\n", + "out = torchvision.utils.make_grid(inputs)\n", + "\n", + "imshow(out, title=[class_names[x] for x in classes])\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "bbd48800", + "metadata": {}, + "source": [ + "Now, execute the following code which uses a pre-trained model ResNet18 having replaced the output layer for the ants/bees classification and performs the model training by only changing the weights of this output layer." + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "572d824c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch 1/10\n", + "----------\n", + "train Loss: 0.5453 Acc: 0.7008\n", + "val Loss: 0.2199 Acc: 0.9281\n", + "\n", + "Epoch 2/10\n", + "----------\n", + "train Loss: 0.6431 Acc: 0.6885\n", + "val Loss: 0.1848 Acc: 0.9477\n", + "\n", + "Epoch 3/10\n", + "----------\n", + "train Loss: 0.5283 Acc: 0.7377\n", + "val Loss: 0.1921 Acc: 0.9477\n", + "\n", + "Epoch 4/10\n", + "----------\n", + "train Loss: 0.4287 Acc: 0.8197\n", + "val Loss: 0.2242 Acc: 0.9281\n", + "\n", + "Epoch 5/10\n", + "----------\n", + "train Loss: 0.6329 Acc: 0.7254\n", + "val Loss: 0.1941 Acc: 0.9412\n", + "\n", + "Epoch 6/10\n", + "----------\n", + "train Loss: 0.4771 Acc: 0.7869\n", + "val Loss: 0.3280 Acc: 0.8693\n", + "\n", + "Epoch 7/10\n", + "----------\n", + "train Loss: 0.3549 Acc: 0.8402\n", + "val Loss: 0.1751 Acc: 0.9542\n", + "\n", + "Epoch 8/10\n", + "----------\n", + "train Loss: 0.3525 Acc: 0.8279\n", + "val Loss: 0.1632 Acc: 0.9542\n", + "\n", + "Epoch 9/10\n", + "----------\n", + "train Loss: 0.3126 Acc: 0.8648\n", + "val Loss: 0.1709 Acc: 0.9542\n", + "\n", + "Epoch 10/10\n", + "----------\n", + "train Loss: 0.3212 Acc: 0.8689\n", + "val Loss: 0.1691 Acc: 0.9477\n", + "\n", + "Training complete in 8m 5s\n", + "Best val Acc: 0.954248\n" + ] + } + ], + "source": [ + "import copy\n", + "import os\n", + "import time\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import torch\n", + "import torch.nn as nn\n", + "import torch.optim as optim\n", + "import torchvision\n", + "from torch.optim import lr_scheduler\n", + "from torchvision import datasets, transforms\n", + "\n", + "# Data augmentation and normalization for training\n", + "# Just normalization for validation\n", + "data_transforms = {\n", + " \"train\": transforms.Compose(\n", + " [\n", + " transforms.RandomResizedCrop(\n", + " 224\n", + " ), # ImageNet models were trained on 224x224 images\n", + " transforms.RandomHorizontalFlip(), # flip horizontally 50% of the time - increases train set variability\n", + " transforms.ToTensor(), # convert it to a PyTorch tensor\n", + " transforms.Normalize(\n", + " [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]\n", + " ), # ImageNet models expect this norm\n", + " ]\n", + " ),\n", + " \"val\": transforms.Compose(\n", + " [\n", + " transforms.Resize(256),\n", + " transforms.CenterCrop(224),\n", + " transforms.ToTensor(),\n", + " transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n", + " ]\n", + " ),\n", + "}\n", + "\n", + "data_dir = \"hymenoptera_data\"\n", + "# Create train validation datasets and loaders\n", + "image_datasets = {\n", + " x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x])\n", + " for x in [\"train\", \"val\"]\n", + "}\n", + "dataloaders = {\n", + " x: torch.utils.data.DataLoader(\n", " image_datasets[x], batch_size=4, shuffle=True, num_workers=4\n", " )\n", " for x in [\"train\", \"val\"]\n", @@ -1606,6 +1977,305 @@ "Apply ther quantization (post and quantization aware) and evaluate impact on model size and accuracy." ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "ename": "FileNotFoundError", + "evalue": "[WinError 3] Le chemin d’accès spécifié est introuvable: 'hymenoptera_data\\\\test'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32md:\\Users\\lucil\\Documents\\S9\\Apprentissage profond\\mod_4_6-td2\\TD2 Deep Learning.ipynb Cell 56\u001b[0m line \u001b[0;36m4\n\u001b[0;32m <a href='vscode-notebook-cell:/d%3A/Users/lucil/Documents/S9/Apprentissage%20profond/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y111sZmlsZQ%3D%3D?line=46'>47</a>\u001b[0m data_dir \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mhymenoptera_data\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m <a href='vscode-notebook-cell:/d%3A/Users/lucil/Documents/S9/Apprentissage%20profond/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y111sZmlsZQ%3D%3D?line=47'>48</a>\u001b[0m \u001b[39m# Create train, validation and test datasets and loaders\u001b[39;00m\n\u001b[1;32m---> <a href='vscode-notebook-cell:/d%3A/Users/lucil/Documents/S9/Apprentissage%20profond/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y111sZmlsZQ%3D%3D?line=48'>49</a>\u001b[0m image_datasets \u001b[39m=\u001b[39m {\n\u001b[0;32m <a href='vscode-notebook-cell:/d%3A/Users/lucil/Documents/S9/Apprentissage%20profond/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y111sZmlsZQ%3D%3D?line=49'>50</a>\u001b[0m x: datasets\u001b[39m.\u001b[39mImageFolder(os\u001b[39m.\u001b[39mpath\u001b[39m.\u001b[39mjoin(data_dir, x), data_transforms[x])\n\u001b[0;32m <a href='vscode-notebook-cell:/d%3A/Users/lucil/Documents/S9/Apprentissage%20profond/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y111sZmlsZQ%3D%3D?line=50'>51</a>\u001b[0m \u001b[39mfor\u001b[39;00m x \u001b[39min\u001b[39;00m [\u001b[39m\"\u001b[39m\u001b[39mtrain\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mval\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mtest\u001b[39m\u001b[39m\"\u001b[39m]\n\u001b[0;32m <a href='vscode-notebook-cell:/d%3A/Users/lucil/Documents/S9/Apprentissage%20profond/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y111sZmlsZQ%3D%3D?line=51'>52</a>\u001b[0m }\n\u001b[0;32m <a href='vscode-notebook-cell:/d%3A/Users/lucil/Documents/S9/Apprentissage%20profond/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y111sZmlsZQ%3D%3D?line=52'>53</a>\u001b[0m dataloaders \u001b[39m=\u001b[39m {\n\u001b[0;32m <a href='vscode-notebook-cell:/d%3A/Users/lucil/Documents/S9/Apprentissage%20profond/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y111sZmlsZQ%3D%3D?line=53'>54</a>\u001b[0m x: torch\u001b[39m.\u001b[39mutils\u001b[39m.\u001b[39mdata\u001b[39m.\u001b[39mDataLoader(\n\u001b[0;32m <a href='vscode-notebook-cell:/d%3A/Users/lucil/Documents/S9/Apprentissage%20profond/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y111sZmlsZQ%3D%3D?line=54'>55</a>\u001b[0m image_datasets[x], batch_size\u001b[39m=\u001b[39m\u001b[39m4\u001b[39m, shuffle\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m, num_workers\u001b[39m=\u001b[39m\u001b[39m4\u001b[39m\n\u001b[0;32m <a href='vscode-notebook-cell:/d%3A/Users/lucil/Documents/S9/Apprentissage%20profond/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y111sZmlsZQ%3D%3D?line=55'>56</a>\u001b[0m )\n\u001b[0;32m <a href='vscode-notebook-cell:/d%3A/Users/lucil/Documents/S9/Apprentissage%20profond/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y111sZmlsZQ%3D%3D?line=56'>57</a>\u001b[0m \u001b[39mfor\u001b[39;00m x \u001b[39min\u001b[39;00m [\u001b[39m\"\u001b[39m\u001b[39mtrain\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mval\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mtest\u001b[39m\u001b[39m\"\u001b[39m]\n\u001b[0;32m <a href='vscode-notebook-cell:/d%3A/Users/lucil/Documents/S9/Apprentissage%20profond/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y111sZmlsZQ%3D%3D?line=57'>58</a>\u001b[0m }\n\u001b[0;32m <a href='vscode-notebook-cell:/d%3A/Users/lucil/Documents/S9/Apprentissage%20profond/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y111sZmlsZQ%3D%3D?line=60'>61</a>\u001b[0m dataset_sizes \u001b[39m=\u001b[39m {x: \u001b[39mlen\u001b[39m(image_datasets[x]) \u001b[39mfor\u001b[39;00m x \u001b[39min\u001b[39;00m [\u001b[39m\"\u001b[39m\u001b[39mtrain\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mval\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mtest\u001b[39m\u001b[39m\"\u001b[39m]}\n", + "\u001b[1;32md:\\Users\\lucil\\Documents\\S9\\Apprentissage profond\\mod_4_6-td2\\TD2 Deep Learning.ipynb Cell 56\u001b[0m line \u001b[0;36m5\n\u001b[0;32m <a href='vscode-notebook-cell:/d%3A/Users/lucil/Documents/S9/Apprentissage%20profond/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y111sZmlsZQ%3D%3D?line=46'>47</a>\u001b[0m data_dir \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mhymenoptera_data\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m <a href='vscode-notebook-cell:/d%3A/Users/lucil/Documents/S9/Apprentissage%20profond/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y111sZmlsZQ%3D%3D?line=47'>48</a>\u001b[0m \u001b[39m# Create train, validation and test datasets and loaders\u001b[39;00m\n\u001b[0;32m <a href='vscode-notebook-cell:/d%3A/Users/lucil/Documents/S9/Apprentissage%20profond/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y111sZmlsZQ%3D%3D?line=48'>49</a>\u001b[0m image_datasets \u001b[39m=\u001b[39m {\n\u001b[1;32m---> <a href='vscode-notebook-cell:/d%3A/Users/lucil/Documents/S9/Apprentissage%20profond/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y111sZmlsZQ%3D%3D?line=49'>50</a>\u001b[0m x: datasets\u001b[39m.\u001b[39mImageFolder(os\u001b[39m.\u001b[39mpath\u001b[39m.\u001b[39mjoin(data_dir, x), data_transforms[x])\n\u001b[0;32m <a href='vscode-notebook-cell:/d%3A/Users/lucil/Documents/S9/Apprentissage%20profond/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y111sZmlsZQ%3D%3D?line=50'>51</a>\u001b[0m \u001b[39mfor\u001b[39;00m x \u001b[39min\u001b[39;00m [\u001b[39m\"\u001b[39m\u001b[39mtrain\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mval\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mtest\u001b[39m\u001b[39m\"\u001b[39m]\n\u001b[0;32m <a href='vscode-notebook-cell:/d%3A/Users/lucil/Documents/S9/Apprentissage%20profond/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y111sZmlsZQ%3D%3D?line=51'>52</a>\u001b[0m }\n\u001b[0;32m <a href='vscode-notebook-cell:/d%3A/Users/lucil/Documents/S9/Apprentissage%20profond/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y111sZmlsZQ%3D%3D?line=52'>53</a>\u001b[0m dataloaders \u001b[39m=\u001b[39m {\n\u001b[0;32m <a href='vscode-notebook-cell:/d%3A/Users/lucil/Documents/S9/Apprentissage%20profond/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y111sZmlsZQ%3D%3D?line=53'>54</a>\u001b[0m x: torch\u001b[39m.\u001b[39mutils\u001b[39m.\u001b[39mdata\u001b[39m.\u001b[39mDataLoader(\n\u001b[0;32m <a href='vscode-notebook-cell:/d%3A/Users/lucil/Documents/S9/Apprentissage%20profond/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y111sZmlsZQ%3D%3D?line=54'>55</a>\u001b[0m image_datasets[x], batch_size\u001b[39m=\u001b[39m\u001b[39m4\u001b[39m, shuffle\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m, num_workers\u001b[39m=\u001b[39m\u001b[39m4\u001b[39m\n\u001b[0;32m <a href='vscode-notebook-cell:/d%3A/Users/lucil/Documents/S9/Apprentissage%20profond/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y111sZmlsZQ%3D%3D?line=55'>56</a>\u001b[0m )\n\u001b[0;32m <a href='vscode-notebook-cell:/d%3A/Users/lucil/Documents/S9/Apprentissage%20profond/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y111sZmlsZQ%3D%3D?line=56'>57</a>\u001b[0m \u001b[39mfor\u001b[39;00m x \u001b[39min\u001b[39;00m [\u001b[39m\"\u001b[39m\u001b[39mtrain\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mval\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mtest\u001b[39m\u001b[39m\"\u001b[39m]\n\u001b[0;32m <a href='vscode-notebook-cell:/d%3A/Users/lucil/Documents/S9/Apprentissage%20profond/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y111sZmlsZQ%3D%3D?line=57'>58</a>\u001b[0m }\n\u001b[0;32m <a href='vscode-notebook-cell:/d%3A/Users/lucil/Documents/S9/Apprentissage%20profond/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#Y111sZmlsZQ%3D%3D?line=60'>61</a>\u001b[0m dataset_sizes \u001b[39m=\u001b[39m {x: \u001b[39mlen\u001b[39m(image_datasets[x]) \u001b[39mfor\u001b[39;00m x \u001b[39min\u001b[39;00m [\u001b[39m\"\u001b[39m\u001b[39mtrain\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mval\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39mtest\u001b[39m\u001b[39m\"\u001b[39m]}\n", + "File \u001b[1;32mc:\\Users\\lucil\\anaconda3\\Lib\\site-packages\\torchvision\\datasets\\folder.py:309\u001b[0m, in \u001b[0;36mImageFolder.__init__\u001b[1;34m(self, root, transform, target_transform, loader, is_valid_file)\u001b[0m\n\u001b[0;32m 301\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__init__\u001b[39m(\n\u001b[0;32m 302\u001b[0m \u001b[39mself\u001b[39m,\n\u001b[0;32m 303\u001b[0m root: \u001b[39mstr\u001b[39m,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 307\u001b[0m is_valid_file: Optional[Callable[[\u001b[39mstr\u001b[39m], \u001b[39mbool\u001b[39m]] \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m,\n\u001b[0;32m 308\u001b[0m ):\n\u001b[1;32m--> 309\u001b[0m \u001b[39msuper\u001b[39m()\u001b[39m.\u001b[39m\u001b[39m__init__\u001b[39m(\n\u001b[0;32m 310\u001b[0m root,\n\u001b[0;32m 311\u001b[0m loader,\n\u001b[0;32m 312\u001b[0m IMG_EXTENSIONS \u001b[39mif\u001b[39;00m is_valid_file \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m \u001b[39melse\u001b[39;00m \u001b[39mNone\u001b[39;00m,\n\u001b[0;32m 313\u001b[0m transform\u001b[39m=\u001b[39mtransform,\n\u001b[0;32m 314\u001b[0m target_transform\u001b[39m=\u001b[39mtarget_transform,\n\u001b[0;32m 315\u001b[0m is_valid_file\u001b[39m=\u001b[39mis_valid_file,\n\u001b[0;32m 316\u001b[0m )\n\u001b[0;32m 317\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mimgs \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39msamples\n", + "File \u001b[1;32mc:\\Users\\lucil\\anaconda3\\Lib\\site-packages\\torchvision\\datasets\\folder.py:144\u001b[0m, in \u001b[0;36mDatasetFolder.__init__\u001b[1;34m(self, root, loader, extensions, transform, target_transform, is_valid_file)\u001b[0m\n\u001b[0;32m 134\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m__init__\u001b[39m(\n\u001b[0;32m 135\u001b[0m \u001b[39mself\u001b[39m,\n\u001b[0;32m 136\u001b[0m root: \u001b[39mstr\u001b[39m,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 141\u001b[0m is_valid_file: Optional[Callable[[\u001b[39mstr\u001b[39m], \u001b[39mbool\u001b[39m]] \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m,\n\u001b[0;32m 142\u001b[0m ) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m 143\u001b[0m \u001b[39msuper\u001b[39m()\u001b[39m.\u001b[39m\u001b[39m__init__\u001b[39m(root, transform\u001b[39m=\u001b[39mtransform, target_transform\u001b[39m=\u001b[39mtarget_transform)\n\u001b[1;32m--> 144\u001b[0m classes, class_to_idx \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mfind_classes(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mroot)\n\u001b[0;32m 145\u001b[0m samples \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmake_dataset(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mroot, class_to_idx, extensions, is_valid_file)\n\u001b[0;32m 147\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mloader \u001b[39m=\u001b[39m loader\n", + "File \u001b[1;32mc:\\Users\\lucil\\anaconda3\\Lib\\site-packages\\torchvision\\datasets\\folder.py:218\u001b[0m, in \u001b[0;36mDatasetFolder.find_classes\u001b[1;34m(self, directory)\u001b[0m\n\u001b[0;32m 191\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mfind_classes\u001b[39m(\u001b[39mself\u001b[39m, directory: \u001b[39mstr\u001b[39m) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m Tuple[List[\u001b[39mstr\u001b[39m], Dict[\u001b[39mstr\u001b[39m, \u001b[39mint\u001b[39m]]:\n\u001b[0;32m 192\u001b[0m \u001b[39m \u001b[39m\u001b[39m\"\"\"Find the class folders in a dataset structured as follows::\u001b[39;00m\n\u001b[0;32m 193\u001b[0m \n\u001b[0;32m 194\u001b[0m \u001b[39m directory/\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 216\u001b[0m \u001b[39m (Tuple[List[str], Dict[str, int]]): List of all classes and dictionary mapping each class to an index.\u001b[39;00m\n\u001b[0;32m 217\u001b[0m \u001b[39m \"\"\"\u001b[39;00m\n\u001b[1;32m--> 218\u001b[0m \u001b[39mreturn\u001b[39;00m find_classes(directory)\n", + "File \u001b[1;32mc:\\Users\\lucil\\anaconda3\\Lib\\site-packages\\torchvision\\datasets\\folder.py:40\u001b[0m, in \u001b[0;36mfind_classes\u001b[1;34m(directory)\u001b[0m\n\u001b[0;32m 35\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mfind_classes\u001b[39m(directory: \u001b[39mstr\u001b[39m) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m Tuple[List[\u001b[39mstr\u001b[39m], Dict[\u001b[39mstr\u001b[39m, \u001b[39mint\u001b[39m]]:\n\u001b[0;32m 36\u001b[0m \u001b[39m \u001b[39m\u001b[39m\"\"\"Finds the class folders in a dataset.\u001b[39;00m\n\u001b[0;32m 37\u001b[0m \n\u001b[0;32m 38\u001b[0m \u001b[39m See :class:`DatasetFolder` for details.\u001b[39;00m\n\u001b[0;32m 39\u001b[0m \u001b[39m \"\"\"\u001b[39;00m\n\u001b[1;32m---> 40\u001b[0m classes \u001b[39m=\u001b[39m \u001b[39msorted\u001b[39m(entry\u001b[39m.\u001b[39mname \u001b[39mfor\u001b[39;00m entry \u001b[39min\u001b[39;00m os\u001b[39m.\u001b[39mscandir(directory) \u001b[39mif\u001b[39;00m entry\u001b[39m.\u001b[39mis_dir())\n\u001b[0;32m 41\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m classes:\n\u001b[0;32m 42\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mFileNotFoundError\u001b[39;00m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mCouldn\u001b[39m\u001b[39m'\u001b[39m\u001b[39mt find any class folder in \u001b[39m\u001b[39m{\u001b[39;00mdirectory\u001b[39m}\u001b[39;00m\u001b[39m.\u001b[39m\u001b[39m\"\u001b[39m)\n", + "\u001b[1;31mFileNotFoundError\u001b[0m: [WinError 3] Le chemin d’accès spécifié est introuvable: 'hymenoptera_data\\\\test'" + ] + } + ], + "source": [ + "\n", + "import copy\n", + "import os\n", + "import time\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import torch\n", + "import torch.nn as nn\n", + "import torch.optim as optim\n", + "import torchvision\n", + "from torch.optim import lr_scheduler\n", + "from torchvision import datasets, transforms\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "# Data augmentation and normalization for training\n", + "# Just normalization for validation\n", + "data_transforms = {\n", + " \"train\": transforms.Compose(\n", + " [\n", + " transforms.RandomResizedCrop(\n", + " 224\n", + " ), # ImageNet models were trained on 224x224 images\n", + " transforms.RandomHorizontalFlip(), # flip horizontally 50% of the time - increases train set variability\n", + " transforms.ToTensor(), # convert it to a PyTorch tensor\n", + " transforms.Normalize(\n", + " [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]\n", + " ), # ImageNet models expect this norm\n", + " ]\n", + " ),\n", + " \"val\": transforms.Compose(\n", + " [\n", + " transforms.Resize(256),\n", + " transforms.CenterCrop(224),\n", + " transforms.ToTensor(),\n", + " transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\n", + " ]\n", + " ),\n", + "}\n", + "\n", + "\n", + "data_dir = \"hymenoptera_data\"\n", + "\n", + "\n", + "\n", + "# Create train, validation and test datasets and loaders\n", + "\n", + "train = datasets.ImageFolder(os.path.join(data_dir, \"train\"), data_transforms[\"train\"])\n", + "val = datasets.ImageFolder(os.path.join(data_dir, \"val\"), data_transforms[\"val\"])\n", + "\n", + "test = train_test_split(val, test_size = len(val)/2, random_state = 42)\n", + "\n", + "image_datasets = {\n", + " \"train\" : train\n", + " \"val\" : val\n", + " \"test\" : test\n", + "}\n", + "\n", + "dataloaders = {\n", + " \"train\" : torch.utils.data.DataLoader(train, batch_size=4, shuffle=True, num_workers=4)\n", + " \"val\" : torch.utils.data.DataLoader(val, batch_size=4, shuffle=True, num_workers=4)\n", + " \"test\" : torch.utils.data.DataLoader(test, batch_size=4, shuffle=True, num_workers=4)\n", + "}\n", + "\n", + "\n", + "dataset_sizes = {x: len(image_datasets[x]) for x in [\"train\", \"val\", \"test\"]}\n", + "class_names = image_datasets[\"train\"].classes\n", + "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n", + "\n", + "# Helper function for displaying images\n", + "def imshow(inp, title=None):\n", + " \"\"\"Imshow for Tensor.\"\"\"\n", + " inp = inp.numpy().transpose((1, 2, 0))\n", + " mean = np.array([0.485, 0.456, 0.406])\n", + " std = np.array([0.229, 0.224, 0.225])\n", + "\n", + " # Un-normalize the images\n", + " inp = std * inp + mean\n", + " # Clip just in case\n", + " inp = np.clip(inp, 0, 1)\n", + " plt.imshow(inp)\n", + " if title is not None:\n", + " plt.title(title)\n", + " plt.pause(0.001) # pause a bit so that plots are updated\n", + " plt.show()\n", + "\n", + "\n", + "# Get a batch of training data\n", + "# inputs, classes = next(iter(dataloaders['train']))\n", + "\n", + "# Make a grid from batch\n", + "# out = torchvision.utils.make_grid(inputs)\n", + "\n", + "# imshow(out, title=[class_names[x] for x in classes])\n", + "# training\n", + "\n", + "\n", + "def train_model(model, criterion, optimizer, scheduler, num_epochs=25):\n", + " since = time.time()\n", + "\n", + " best_model_wts = copy.deepcopy(model.state_dict())\n", + " best_acc = 0.0\n", + "\n", + " epoch_time = [] # we'll keep track of the time needed for each epoch\n", + "\n", + " for epoch in range(num_epochs):\n", + " epoch_start = time.time()\n", + " print(\"Epoch {}/{}\".format(epoch + 1, num_epochs))\n", + " print(\"-\" * 10)\n", + "\n", + " # Each epoch has a training and validation phase\n", + " for phase in [\"train\", \"val\"]:\n", + " if phase == \"train\":\n", + " scheduler.step()\n", + " model.train() # Set model to training mode\n", + " else:\n", + " model.eval() # Set model to evaluate mode\n", + "\n", + " running_loss = 0.0\n", + " running_corrects = 0\n", + "\n", + " # Iterate over data.\n", + " for inputs, labels in dataloaders[phase]:\n", + " inputs = inputs.to(device)\n", + " labels = labels.to(device)\n", + "\n", + " # zero the parameter gradients\n", + " optimizer.zero_grad()\n", + "\n", + " # Forward\n", + " # Track history if only in training phase\n", + " with torch.set_grad_enabled(phase == \"train\"):\n", + " outputs = model(inputs)\n", + " _, preds = torch.max(outputs, 1)\n", + " loss = criterion(outputs, labels)\n", + "\n", + " # backward + optimize only if in training phase\n", + " if phase == \"train\":\n", + " loss.backward()\n", + " optimizer.step()\n", + "\n", + " # Statistics\n", + " running_loss += loss.item() * inputs.size(0)\n", + " running_corrects += torch.sum(preds == labels.data)\n", + "\n", + " epoch_loss = running_loss / dataset_sizes[phase]\n", + " epoch_acc = running_corrects.double() / dataset_sizes[phase]\n", + "\n", + " print(\"{} Loss: {:.4f} Acc: {:.4f}\".format(phase, epoch_loss, epoch_acc))\n", + "\n", + " # Deep copy the model\n", + " if phase == \"val\" and epoch_acc > best_acc:\n", + " best_acc = epoch_acc\n", + " best_model_wts = copy.deepcopy(model.state_dict())\n", + "\n", + " # Add the epoch time\n", + " t_epoch = time.time() - epoch_start\n", + " epoch_time.append(t_epoch)\n", + " print()\n", + "\n", + " time_elapsed = time.time() - since\n", + " print(\n", + " \"Training complete in {:.0f}m {:.0f}s\".format(\n", + " time_elapsed // 60, time_elapsed % 60\n", + " )\n", + " )\n", + " print(\"Best val Acc: {:4f}\".format(best_acc))\n", + "\n", + " # Load best model weights\n", + " model.load_state_dict(best_model_wts)\n", + " return model, epoch_time\n", + "\n", + "\n", + "# Download a pre-trained ResNet18 model and freeze its weights\n", + "model = torchvision.models.resnet18(pretrained=True)\n", + "for param in model.parameters():\n", + " param.requires_grad = False\n", + "\n", + "# Replace the final fully connected layer\n", + "# Parameters of newly constructed modules have requires_grad=True by default\n", + "num_ftrs = model.fc.in_features\n", + "model.fc = nn.Linear(num_ftrs, 2)\n", + "# Send the model to the GPU\n", + "model = model.to(device)\n", + "# Set the loss function\n", + "criterion = nn.CrossEntropyLoss()\n", + "\n", + "# Observe that only the parameters of the final layer are being optimized\n", + "optimizer_conv = optim.SGD(model.fc.parameters(), lr=0.001, momentum=0.9)\n", + "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=7, gamma=0.1)\n", + "model, epoch_time = train_model(\n", + " model, criterion, optimizer_conv, exp_lr_scheduler, num_epochs=10\n", + ")\n", + "\n", + "# The function eval_model evaluates the model on a different test set\n", + "def eval_model(model, criterion):\n", + "\n", + " phase = \"test\"\n", + "\n", + " model.eval()\n", + "\n", + " running_loss = 0.0\n", + " running_corrects = 0\n", + "\n", + " # Iterate over data.\n", + " for inputs, labels in dataloaders[phase]:\n", + " inputs = inputs.to(device)\n", + " labels = labels.to(device)\n", + "\n", + " # Forward\n", + " with torch.set_grad_enabled(False):\n", + " outputs = model(inputs)\n", + " _, preds = torch.max(outputs, 1)\n", + " loss = criterion(outputs, labels)\n", + " \n", + " # Statistics\n", + " running_loss += loss.item() * inputs.size(0)\n", + " running_corrects += torch.sum(preds == labels.data)\n", + " \n", + " eval_loss = running_loss / dataset_sizes[phase]\n", + " eval_acc = running_corrects.double() / dataset_sizes[phase]\n", + "\n", + " print(\"{} Loss: {:.4f} Acc: {:.4f}\".format(phase, eval_loss, eval_acc))\n", + "\n", + " return eval_loss, eval_acc\n", + "\n", + "\n", + "# Now we can evaluate the model\n", + "print('Evaluation of the model :')\n", + "eval_model(model, criterion)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Download a pre-trained ResNet18 model and freeze its weights\n", + "model = torchvision.models.resnet18(pretrained=True)\n", + "for param in model.parameters():\n", + " param.requires_grad = False\n", + "\n", + "# Replace the final fully connected layer with a set of two layers\n", + "# Parameters of newly constructed modules have requires_grad=True by default\n", + "num_ftrs = model.fc.in_features\n", + "model.fc = nn.Sequential(\n", + " nn.Linear(num_ftrs, 256), # Adding a new fully connected layer\n", + " nn.ReLU(), # Applying ReLU activation function\n", + " nn.Dropout(0.5), # Dropout layer with p=0.5\n", + " nn.Linear(256, 2) # Final fully connected layer for binary classification\n", + ")\n", + "# Send the model to the GPU\n", + "model = model.to(device)\n", + "# Set the loss function\n", + "criterion = nn.CrossEntropyLoss()\n", + "\n", + "# Observe that only the parameters of the final layer are being optimized\n", + "optimizer_conv = optim.SGD(model.fc.parameters(), lr=0.001, momentum=0.9)\n", + "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=7, gamma=0.1)\n", + "model, epoch_time = train_model(\n", + " model, criterion, optimizer_conv, exp_lr_scheduler, num_epochs=10\n", + ")\n", + "\n" + ] + }, { "cell_type": "markdown", "id": "04a263f0",