From 6c3bbeb682d417a82e17943b3a766acbe1f998cc Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Timoth=C3=A9e=20Barry?= <timothee.barry1@ecl20.ec-lyon.fr>
Date: Fri, 1 Dec 2023 17:16:11 +0100
Subject: [PATCH] exo 4

---
 TD2 Deep Learning.ipynb   | 531 ++++++++++++++++++++++++++++----------
 utils/plot_performance.py |   4 +-
 2 files changed, 400 insertions(+), 135 deletions(-)

diff --git a/TD2 Deep Learning.ipynb b/TD2 Deep Learning.ipynb
index 442c4ad..8ce1f6f 100644
--- a/TD2 Deep Learning.ipynb	
+++ b/TD2 Deep Learning.ipynb	
@@ -31,41 +31,6 @@
     "Install and test PyTorch from  https://pytorch.org/get-started/locally."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "330a42f5",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Requirement already satisfied: torch in c:\\users\\barry\\anaconda3\\lib\\site-packages (2.1.0)\n",
-      "Requirement already satisfied: torchvision in c:\\users\\barry\\anaconda3\\lib\\site-packages (0.16.0)\n",
-      "Requirement already satisfied: filelock in c:\\users\\barry\\anaconda3\\lib\\site-packages (from torch) (3.9.0)\n",
-      "Requirement already satisfied: typing-extensions in c:\\users\\barry\\anaconda3\\lib\\site-packages (from torch) (4.7.1)\n",
-      "Requirement already satisfied: sympy in c:\\users\\barry\\anaconda3\\lib\\site-packages (from torch) (1.11.1)\n",
-      "Requirement already satisfied: networkx in c:\\users\\barry\\anaconda3\\lib\\site-packages (from torch) (3.1)\n",
-      "Requirement already satisfied: jinja2 in c:\\users\\barry\\anaconda3\\lib\\site-packages (from torch) (3.1.2)\n",
-      "Requirement already satisfied: fsspec in c:\\users\\barry\\anaconda3\\lib\\site-packages (from torch) (2023.4.0)\n",
-      "Requirement already satisfied: numpy in c:\\users\\barry\\anaconda3\\lib\\site-packages (from torchvision) (1.24.3)\n",
-      "Requirement already satisfied: requests in c:\\users\\barry\\anaconda3\\lib\\site-packages (from torchvision) (2.31.0)\n",
-      "Requirement already satisfied: pillow!=8.3.*,>=5.3.0 in c:\\users\\barry\\anaconda3\\lib\\site-packages (from torchvision) (9.4.0)\n",
-      "Requirement already satisfied: MarkupSafe>=2.0 in c:\\users\\barry\\anaconda3\\lib\\site-packages (from jinja2->torch) (2.1.1)\n",
-      "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\barry\\anaconda3\\lib\\site-packages (from requests->torchvision) (2.0.4)\n",
-      "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\barry\\anaconda3\\lib\\site-packages (from requests->torchvision) (3.4)\n",
-      "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\users\\barry\\anaconda3\\lib\\site-packages (from requests->torchvision) (1.26.16)\n",
-      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\barry\\anaconda3\\lib\\site-packages (from requests->torchvision) (2023.7.22)\n",
-      "Requirement already satisfied: mpmath>=0.19 in c:\\users\\barry\\anaconda3\\lib\\site-packages (from sympy->torch) (1.3.0)\n",
-      "Note: you may need to restart the kernel to use updated packages.\n"
-     ]
-    }
-   ],
-   "source": [
-    "%pip install torch torchvision"
-   ]
-  },
   {
    "cell_type": "markdown",
    "id": "0882a636",
@@ -77,7 +42,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "b1950f0a",
    "metadata": {},
    "outputs": [],
@@ -120,7 +85,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "6e18f2fd",
    "metadata": {},
    "outputs": [
@@ -154,7 +119,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "462666a2",
    "metadata": {},
    "outputs": [
@@ -236,7 +201,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "317bf070",
    "metadata": {},
    "outputs": [
@@ -299,7 +264,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "4b53f229",
    "metadata": {},
    "outputs": [],
@@ -379,7 +344,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -464,7 +429,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "id": "e93efdfc",
    "metadata": {},
    "outputs": [],
@@ -538,7 +503,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -596,7 +561,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -1056,7 +1021,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -1148,7 +1113,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -1185,7 +1150,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -1266,7 +1231,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 11,
    "id": "b4d13080",
    "metadata": {},
    "outputs": [
@@ -1346,7 +1311,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -1406,7 +1371,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -1430,6 +1395,7 @@
     }
    ],
    "source": [
+    "from utils.print_size_of_model import print_size_of_model\n",
     "resnet = models.resnet50(pretrained=True)\n",
     "resnet.eval()\n",
     "s1 = print_size_of_model(resnet, \"unquantized\")\n",
@@ -1449,15 +1415,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "c:\\Users\\barry\\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\\barry\\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=AlexNet_Weights.IMAGENET1K_V1`. You can also use `weights=AlexNet_Weights.DEFAULT` to get the most up-to-date weights.\n",
       "  warnings.warn(msg)\n"
      ]
@@ -1488,7 +1452,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -1549,13 +1513,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 86,
    "id": "be2d31f5",
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1652,73 +1616,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 87,
    "id": "572d824c",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Users\\barry\\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=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.\n",
-      "  warnings.warn(msg)\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1/10\n",
-      "----------\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\Users\\barry\\anaconda3\\Lib\\site-packages\\torch\\optim\\lr_scheduler.py:136: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n",
-      "  warnings.warn(\"Detected call of `lr_scheduler.step()` before `optimizer.step()`. \"\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "train Loss: 0.6300 Acc: 0.6557\n",
-      "val Loss: 0.2347 Acc: 0.9020\n",
-      "\n",
-      "Epoch 2/10\n",
-      "----------\n",
-      "train Loss: 0.5593 Acc: 0.7336\n",
-      "val Loss: 0.1728 Acc: 0.9608\n",
-      "\n",
-      "Epoch 3/10\n",
-      "----------\n",
-      "train Loss: 0.4961 Acc: 0.7746\n"
-     ]
-    },
-    {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[1;32mc:\\Users\\barry\\OneDrive\\Bureau\\4A\\Deep Learning et IA\\BE\\mod_4_6-td2\\TD2 Deep Learning.ipynb Cell 43\u001b[0m line \u001b[0;36m1\n\u001b[0;32m    <a href='vscode-notebook-cell:/c%3A/Users/barry/OneDrive/Bureau/4A/Deep%20Learning%20et%20IA/BE/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X55sZmlsZQ%3D%3D?line=173'>174</a>\u001b[0m optimizer_conv \u001b[39m=\u001b[39m optim\u001b[39m.\u001b[39mSGD(model\u001b[39m.\u001b[39mfc\u001b[39m.\u001b[39mparameters(), lr\u001b[39m=\u001b[39m\u001b[39m0.001\u001b[39m, momentum\u001b[39m=\u001b[39m\u001b[39m0.9\u001b[39m)\n\u001b[0;32m    <a href='vscode-notebook-cell:/c%3A/Users/barry/OneDrive/Bureau/4A/Deep%20Learning%20et%20IA/BE/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X55sZmlsZQ%3D%3D?line=174'>175</a>\u001b[0m exp_lr_scheduler \u001b[39m=\u001b[39m lr_scheduler\u001b[39m.\u001b[39mStepLR(optimizer_conv, step_size\u001b[39m=\u001b[39m\u001b[39m7\u001b[39m, gamma\u001b[39m=\u001b[39m\u001b[39m0.1\u001b[39m)\n\u001b[1;32m--> <a href='vscode-notebook-cell:/c%3A/Users/barry/OneDrive/Bureau/4A/Deep%20Learning%20et%20IA/BE/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X55sZmlsZQ%3D%3D?line=175'>176</a>\u001b[0m model, epoch_time \u001b[39m=\u001b[39m train_model(\n\u001b[0;32m    <a href='vscode-notebook-cell:/c%3A/Users/barry/OneDrive/Bureau/4A/Deep%20Learning%20et%20IA/BE/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X55sZmlsZQ%3D%3D?line=176'>177</a>\u001b[0m     model, criterion, optimizer_conv, exp_lr_scheduler, num_epochs\u001b[39m=\u001b[39m\u001b[39m10\u001b[39m\n\u001b[0;32m    <a href='vscode-notebook-cell:/c%3A/Users/barry/OneDrive/Bureau/4A/Deep%20Learning%20et%20IA/BE/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X55sZmlsZQ%3D%3D?line=177'>178</a>\u001b[0m )\n",
-      "\u001b[1;32mc:\\Users\\barry\\OneDrive\\Bureau\\4A\\Deep Learning et IA\\BE\\mod_4_6-td2\\TD2 Deep Learning.ipynb Cell 43\u001b[0m line \u001b[0;36m1\n\u001b[0;32m    <a href='vscode-notebook-cell:/c%3A/Users/barry/OneDrive/Bureau/4A/Deep%20Learning%20et%20IA/BE/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X55sZmlsZQ%3D%3D?line=104'>105</a>\u001b[0m running_corrects \u001b[39m=\u001b[39m \u001b[39m0\u001b[39m\n\u001b[0;32m    <a href='vscode-notebook-cell:/c%3A/Users/barry/OneDrive/Bureau/4A/Deep%20Learning%20et%20IA/BE/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X55sZmlsZQ%3D%3D?line=106'>107</a>\u001b[0m \u001b[39m# Iterate over data.\u001b[39;00m\n\u001b[1;32m--> <a href='vscode-notebook-cell:/c%3A/Users/barry/OneDrive/Bureau/4A/Deep%20Learning%20et%20IA/BE/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X55sZmlsZQ%3D%3D?line=107'>108</a>\u001b[0m \u001b[39mfor\u001b[39;00m inputs, labels \u001b[39min\u001b[39;00m dataloaders[phase]:\n\u001b[0;32m    <a href='vscode-notebook-cell:/c%3A/Users/barry/OneDrive/Bureau/4A/Deep%20Learning%20et%20IA/BE/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X55sZmlsZQ%3D%3D?line=108'>109</a>\u001b[0m     inputs \u001b[39m=\u001b[39m inputs\u001b[39m.\u001b[39mto(device)\n\u001b[0;32m    <a href='vscode-notebook-cell:/c%3A/Users/barry/OneDrive/Bureau/4A/Deep%20Learning%20et%20IA/BE/mod_4_6-td2/TD2%20Deep%20Learning.ipynb#X55sZmlsZQ%3D%3D?line=109'>110</a>\u001b[0m     labels \u001b[39m=\u001b[39m labels\u001b[39m.\u001b[39mto(device)\n",
-      "File \u001b[1;32mc:\\Users\\barry\\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\\barry\\anaconda3\\Lib\\site-packages\\torch\\utils\\data\\dataloader.py:1328\u001b[0m, in \u001b[0;36m_MultiProcessingDataLoaderIter._next_data\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1325\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_process_data(data)\n\u001b[0;32m   1327\u001b[0m \u001b[39massert\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_shutdown \u001b[39mand\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_tasks_outstanding \u001b[39m>\u001b[39m \u001b[39m0\u001b[39m\n\u001b[1;32m-> 1328\u001b[0m idx, data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_get_data()\n\u001b[0;32m   1329\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_tasks_outstanding \u001b[39m-\u001b[39m\u001b[39m=\u001b[39m \u001b[39m1\u001b[39m\n\u001b[0;32m   1330\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:\n\u001b[0;32m   1331\u001b[0m     \u001b[39m# Check for _IterableDatasetStopIteration\u001b[39;00m\n",
-      "File \u001b[1;32mc:\\Users\\barry\\anaconda3\\Lib\\site-packages\\torch\\utils\\data\\dataloader.py:1294\u001b[0m, in \u001b[0;36m_MultiProcessingDataLoaderIter._get_data\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1290\u001b[0m     \u001b[39m# In this case, `self._data_queue` is a `queue.Queue`,. But we don't\u001b[39;00m\n\u001b[0;32m   1291\u001b[0m     \u001b[39m# need to call `.task_done()` because we don't use `.join()`.\u001b[39;00m\n\u001b[0;32m   1292\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m   1293\u001b[0m     \u001b[39mwhile\u001b[39;00m \u001b[39mTrue\u001b[39;00m:\n\u001b[1;32m-> 1294\u001b[0m         success, data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_try_get_data()\n\u001b[0;32m   1295\u001b[0m         \u001b[39mif\u001b[39;00m success:\n\u001b[0;32m   1296\u001b[0m             \u001b[39mreturn\u001b[39;00m data\n",
-      "File \u001b[1;32mc:\\Users\\barry\\anaconda3\\Lib\\site-packages\\torch\\utils\\data\\dataloader.py:1132\u001b[0m, in \u001b[0;36m_MultiProcessingDataLoaderIter._try_get_data\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m   1119\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_try_get_data\u001b[39m(\u001b[39mself\u001b[39m, timeout\u001b[39m=\u001b[39m_utils\u001b[39m.\u001b[39mMP_STATUS_CHECK_INTERVAL):\n\u001b[0;32m   1120\u001b[0m     \u001b[39m# Tries to fetch data from `self._data_queue` once for a given timeout.\u001b[39;00m\n\u001b[0;32m   1121\u001b[0m     \u001b[39m# This can also be used as inner loop of fetching without timeout, with\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1129\u001b[0m     \u001b[39m# Returns a 2-tuple:\u001b[39;00m\n\u001b[0;32m   1130\u001b[0m     \u001b[39m#   (bool: whether successfully get data, any: data if successful else None)\u001b[39;00m\n\u001b[0;32m   1131\u001b[0m     \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m-> 1132\u001b[0m         data \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_data_queue\u001b[39m.\u001b[39mget(timeout\u001b[39m=\u001b[39mtimeout)\n\u001b[0;32m   1133\u001b[0m         \u001b[39mreturn\u001b[39;00m (\u001b[39mTrue\u001b[39;00m, data)\n\u001b[0;32m   1134\u001b[0m     \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n\u001b[0;32m   1135\u001b[0m         \u001b[39m# At timeout and error, we manually check whether any worker has\u001b[39;00m\n\u001b[0;32m   1136\u001b[0m         \u001b[39m# failed. Note that this is the only mechanism for Windows to detect\u001b[39;00m\n\u001b[0;32m   1137\u001b[0m         \u001b[39m# worker failures.\u001b[39;00m\n",
-      "File \u001b[1;32mc:\\Users\\barry\\anaconda3\\Lib\\multiprocessing\\queues.py:113\u001b[0m, in \u001b[0;36mQueue.get\u001b[1;34m(self, block, timeout)\u001b[0m\n\u001b[0;32m    111\u001b[0m \u001b[39mif\u001b[39;00m block:\n\u001b[0;32m    112\u001b[0m     timeout \u001b[39m=\u001b[39m deadline \u001b[39m-\u001b[39m time\u001b[39m.\u001b[39mmonotonic()\n\u001b[1;32m--> 113\u001b[0m     \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_poll(timeout):\n\u001b[0;32m    114\u001b[0m         \u001b[39mraise\u001b[39;00m Empty\n\u001b[0;32m    115\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_poll():\n",
-      "File \u001b[1;32mc:\\Users\\barry\\anaconda3\\Lib\\multiprocessing\\connection.py:256\u001b[0m, in \u001b[0;36m_ConnectionBase.poll\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m    254\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_check_closed()\n\u001b[0;32m    255\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_check_readable()\n\u001b[1;32m--> 256\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_poll(timeout)\n",
-      "File \u001b[1;32mc:\\Users\\barry\\anaconda3\\Lib\\multiprocessing\\connection.py:329\u001b[0m, in \u001b[0;36mPipeConnection._poll\u001b[1;34m(self, timeout)\u001b[0m\n\u001b[0;32m    326\u001b[0m \u001b[39mif\u001b[39;00m (\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_got_empty_message \u001b[39mor\u001b[39;00m\n\u001b[0;32m    327\u001b[0m             _winapi\u001b[39m.\u001b[39mPeekNamedPipe(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_handle)[\u001b[39m0\u001b[39m] \u001b[39m!=\u001b[39m \u001b[39m0\u001b[39m):\n\u001b[0;32m    328\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mTrue\u001b[39;00m\n\u001b[1;32m--> 329\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mbool\u001b[39m(wait([\u001b[39mself\u001b[39m], timeout))\n",
-      "File \u001b[1;32mc:\\Users\\barry\\anaconda3\\Lib\\multiprocessing\\connection.py:878\u001b[0m, in \u001b[0;36mwait\u001b[1;34m(object_list, timeout)\u001b[0m\n\u001b[0;32m    875\u001b[0m                 ready_objects\u001b[39m.\u001b[39madd(o)\n\u001b[0;32m    876\u001b[0m                 timeout \u001b[39m=\u001b[39m \u001b[39m0\u001b[39m\n\u001b[1;32m--> 878\u001b[0m     ready_handles \u001b[39m=\u001b[39m _exhaustive_wait(waithandle_to_obj\u001b[39m.\u001b[39mkeys(), timeout)\n\u001b[0;32m    879\u001b[0m \u001b[39mfinally\u001b[39;00m:\n\u001b[0;32m    880\u001b[0m     \u001b[39m# request that overlapped reads stop\u001b[39;00m\n\u001b[0;32m    881\u001b[0m     \u001b[39mfor\u001b[39;00m ov \u001b[39min\u001b[39;00m ov_list:\n",
-      "File \u001b[1;32mc:\\Users\\barry\\anaconda3\\Lib\\multiprocessing\\connection.py:810\u001b[0m, in \u001b[0;36m_exhaustive_wait\u001b[1;34m(handles, timeout)\u001b[0m\n\u001b[0;32m    808\u001b[0m ready \u001b[39m=\u001b[39m []\n\u001b[0;32m    809\u001b[0m \u001b[39mwhile\u001b[39;00m L:\n\u001b[1;32m--> 810\u001b[0m     res \u001b[39m=\u001b[39m _winapi\u001b[39m.\u001b[39mWaitForMultipleObjects(L, \u001b[39mFalse\u001b[39;00m, timeout)\n\u001b[0;32m    811\u001b[0m     \u001b[39mif\u001b[39;00m res \u001b[39m==\u001b[39m WAIT_TIMEOUT:\n\u001b[0;32m    812\u001b[0m         \u001b[39mbreak\u001b[39;00m\n",
-      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import copy\n",
     "import os\n",
@@ -1759,18 +1660,31 @@
     "}\n",
     "\n",
     "data_dir = \"data/hymenoptera_data\"\n",
+    "\n",
+    "\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",
+    "# on split le dataset de validation en deux parties (50% pour le test et 50% pour la validation)\n",
+    "split_ratio = 0.5 \n",
+    "split = int(np.floor(split_ratio * len(image_datasets[\"val\"])))\n",
+    "\n",
+    "image_datasets[\"val\"], image_datasets[\"test\"] = torch.utils.data.random_split(\n",
+    "    image_datasets[\"val\"], [split, len(image_datasets[\"val\"]) - split]\n",
+    ")\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",
+    "    for x in [\"train\", \"val\", \"test\"]\n",
     "}\n",
-    "dataset_sizes = {x: len(image_datasets[x]) for x in [\"train\", \"val\"]}\n",
+    "\n",
+    "\n",
+    "dataset_sizes = {x: len(image_datasets[x]) for x in [\"train\", \"val\", \"test\"]}\n",
+    "\n",
     "class_names = image_datasets[\"train\"].classes\n",
     "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
     "\n",
@@ -1875,28 +1789,117 @@
     "\n",
     "    # Load best model weights\n",
     "    model.load_state_dict(best_model_wts)\n",
-    "    return model, epoch_time\n",
-    "\n",
-    "\n",
+    "    return model, epoch_time"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\barry\\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\\barry\\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=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.\n",
+      "  warnings.warn(msg)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/10\n",
+      "----------\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Users\\barry\\anaconda3\\Lib\\site-packages\\torch\\optim\\lr_scheduler.py:136: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n",
+      "  warnings.warn(\"Detected call of `lr_scheduler.step()` before `optimizer.step()`. \"\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "train Loss: 0.5163 Acc: 0.7254\n",
+      "val Loss: 0.2338 Acc: 0.9342\n",
+      "\n",
+      "Epoch 2/10\n",
+      "----------\n",
+      "train Loss: 0.5390 Acc: 0.7418\n",
+      "val Loss: 0.3705 Acc: 0.8947\n",
+      "\n",
+      "Epoch 3/10\n",
+      "----------\n",
+      "train Loss: 0.5885 Acc: 0.7500\n",
+      "val Loss: 0.3185 Acc: 0.9079\n",
+      "\n",
+      "Epoch 4/10\n",
+      "----------\n",
+      "train Loss: 0.6412 Acc: 0.7500\n",
+      "val Loss: 0.2661 Acc: 0.8947\n",
+      "\n",
+      "Epoch 5/10\n",
+      "----------\n",
+      "train Loss: 0.3248 Acc: 0.8443\n",
+      "val Loss: 0.3609 Acc: 0.8553\n",
+      "\n",
+      "Epoch 6/10\n",
+      "----------\n",
+      "train Loss: 0.3957 Acc: 0.8320\n",
+      "val Loss: 0.3617 Acc: 0.8816\n",
+      "\n",
+      "Epoch 7/10\n",
+      "----------\n",
+      "train Loss: 0.4010 Acc: 0.8156\n",
+      "val Loss: 0.2547 Acc: 0.9342\n",
+      "\n",
+      "Epoch 8/10\n",
+      "----------\n",
+      "train Loss: 0.4060 Acc: 0.8361\n",
+      "val Loss: 0.2530 Acc: 0.9211\n",
+      "\n",
+      "Epoch 9/10\n",
+      "----------\n",
+      "train Loss: 0.3200 Acc: 0.8607\n",
+      "val Loss: 0.2399 Acc: 0.9342\n",
+      "\n",
+      "Epoch 10/10\n",
+      "----------\n",
+      "train Loss: 0.2754 Acc: 0.8893\n",
+      "val Loss: 0.2543 Acc: 0.9079\n",
+      "\n",
+      "Training complete in 1m 45s\n",
+      "Best val Acc: 0.934211\n"
+     ]
+    }
+   ],
+   "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",
+    "resnet18 = torchvision.models.resnet18(pretrained=True)\n",
+    "for param in resnet18.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",
+    "num_ftrs = resnet18.fc.in_features\n",
+    "resnet18.fc = nn.Linear(num_ftrs, 2)\n",
     "# Send the model to the GPU\n",
-    "model = model.to(device)\n",
+    "resnet18 = resnet18.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",
+    "optimizer_conv = optim.SGD(resnet18.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",
+    "resnet18, epoch_time = train_model(\n",
+    "    resnet18, criterion, optimizer_conv, exp_lr_scheduler, num_epochs=10\n",
     ")\n"
    ]
   },
@@ -1916,6 +1919,266 @@
     "Apply ther quantization (post and quantization aware) and evaluate impact on model size and accuracy."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def eval_model(model, test_loader, classes, train_on_gpu = train_on_gpu):\n",
+    "    class_correct = list(0.0 for i in range(2))\n",
+    "    class_total = list(0.0 for i in range(2))\n",
+    "\n",
+    "    model.eval()\n",
+    "    # iterate over test data\n",
+    "    for data, target in test_loader:\n",
+    "        # move tensors to GPU if CUDA is available\n",
+    "        if train_on_gpu:\n",
+    "            data, target = data.cuda(), target.cuda()\n",
+    "        # forward pass: compute predicted outputs by passing inputs to the model\n",
+    "        output = model(data)\n",
+    "        _, pred = torch.max(output, 1)\n",
+    "        # compare predictions to true label\n",
+    "        correct_tensor = pred.eq(target.data.view_as(pred))\n",
+    "        correct = (\n",
+    "            np.squeeze(correct_tensor.numpy())\n",
+    "            if not train_on_gpu\n",
+    "            else np.squeeze(correct_tensor.cpu().numpy())\n",
+    "        )\n",
+    "        # calculate test accuracy for each object class\n",
+    "        try:\n",
+    "            for i in range(len(correct)):  \n",
+    "                label = target.data[i]\n",
+    "                class_correct[label] += list(correct)[i]\n",
+    "                class_total[label] += 1\n",
+    "        except:\n",
+    "            continue\n",
+    "    \n",
+    "    for i in range(2):\n",
+    "        if class_total[i] > 0:\n",
+    "            print(\n",
+    "                \"Test Accuracy of %10s: %2d%% (%2d/%2d)\"\n",
+    "                % (\n",
+    "                    classes[i],\n",
+    "                    100 * class_correct[i] / class_total[i],\n",
+    "                    np.sum(class_correct[i]),\n",
+    "                    np.sum(class_total[i]),\n",
+    "                )\n",
+    "            )\n",
+    "        else:\n",
+    "            print(\"Test Accuracy of %5s: N/A (no training examples)\" % (classes[i]))\n",
+    "\n",
+    "    print(\n",
+    "        \"\\nTest Accuracy (Overall): %2d%% (%2d/%2d)\"\n",
+    "        % (\n",
+    "            100.0 * np.sum(class_correct) / np.sum(class_total),\n",
+    "            np.sum(class_correct),\n",
+    "            np.sum(class_total),\n",
+    "        )\n",
+    "    )\n",
+    "\n",
+    "    return class_correct, class_total\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test Accuracy of       ants: 97% (39/40)\n",
+      "Test Accuracy of       bees: 94% (34/36)\n",
+      "\n",
+      "Test Accuracy (Overall): 96% (73/76)\n"
+     ]
+    }
+   ],
+   "source": [
+    "class_correct, class_total = eval_model(resnet18, dataloaders[\"test\"], class_names)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Modification de la dernière couche du modèle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/10\n",
+      "----------\n",
+      "train Loss: 0.6818 Acc: 0.5656\n",
+      "val Loss: 0.6078 Acc: 0.8947\n",
+      "\n",
+      "Epoch 2/10\n",
+      "----------\n",
+      "train Loss: 0.6477 Acc: 0.6516\n",
+      "val Loss: 0.5307 Acc: 0.8684\n",
+      "\n",
+      "Epoch 3/10\n",
+      "----------\n",
+      "train Loss: 0.6289 Acc: 0.6639\n",
+      "val Loss: 0.4367 Acc: 0.9211\n",
+      "\n",
+      "Epoch 4/10\n",
+      "----------\n",
+      "train Loss: 0.5950 Acc: 0.6762\n",
+      "val Loss: 0.4069 Acc: 0.9342\n",
+      "\n",
+      "Epoch 5/10\n",
+      "----------\n",
+      "train Loss: 0.5238 Acc: 0.7295\n",
+      "val Loss: 0.3025 Acc: 0.9342\n",
+      "\n",
+      "Epoch 6/10\n",
+      "----------\n",
+      "train Loss: 0.5225 Acc: 0.7787\n",
+      "val Loss: 0.2675 Acc: 0.9342\n",
+      "\n",
+      "Epoch 7/10\n",
+      "----------\n",
+      "train Loss: 0.4207 Acc: 0.8156\n",
+      "val Loss: 0.2737 Acc: 0.9474\n",
+      "\n",
+      "Epoch 8/10\n",
+      "----------\n",
+      "train Loss: 0.4205 Acc: 0.8156\n",
+      "val Loss: 0.2533 Acc: 0.9474\n",
+      "\n",
+      "Epoch 9/10\n",
+      "----------\n",
+      "train Loss: 0.4241 Acc: 0.8238\n",
+      "val Loss: 0.2570 Acc: 0.9342\n",
+      "\n",
+      "Epoch 10/10\n",
+      "----------\n",
+      "train Loss: 0.4181 Acc: 0.8115\n",
+      "val Loss: 0.2489 Acc: 0.9342\n",
+      "\n",
+      "Training complete in 2m 23s\n",
+      "Best val Acc: 0.947368\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Download a pre-trained ResNet18 model and freeze its weights\n",
+    "resnet18_2 = torchvision.models.resnet18(pretrained=True)\n",
+    "for param in resnet18.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 = resnet18_2.fc.in_features\n",
+    "\n",
+    "resnet18_2.fc = nn.Sequential(\n",
+    "    nn.Linear(num_ftrs, 256),\n",
+    "    nn.ReLU(),  \n",
+    "    nn.Dropout(0.5),  \n",
+    "    nn.Linear(256, 128),  \n",
+    "    nn.ReLU(),  \n",
+    "    nn.Dropout(0.5),  \n",
+    "    nn.Linear(128, 2),  \n",
+    ")\n",
+    "# Send the model to the GPU\n",
+    "resnet18_2 = resnet18_2.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(resnet18_2.fc.parameters(), lr=0.001, momentum=0.9)\n",
+    "exp_lr_scheduler = lr_scheduler.StepLR(optimizer_conv, step_size=7, gamma=0.1)\n",
+    "resnet18_2, epoch_time = train_model(\n",
+    "    resnet18_2, criterion, optimizer_conv, exp_lr_scheduler, num_epochs=10\n",
+    ")\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Test Accuracy of       ants: 97% (38/39)\n",
+      "Test Accuracy of       bees: 94% (35/37)\n",
+      "\n",
+      "Test Accuracy (Overall): 96% (73/76)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "([38.0, 35.0], [39.0, 37.0])"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "eval_model(resnet18_2, dataloaders[\"test\"], class_names)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Les résultats sont les mêmes que pour le modèle précédent, sur le même jeu de test."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Quantization"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model:  unquantized  \t Size (KB): 44782.148\n",
+      "model:  int8  \t Size (KB): 44779.834\n",
+      "Compression ratio (unquantized to int8):  1.000051675046406\n"
+     ]
+    }
+   ],
+   "source": [
+    "quantized_resnet18 = torch.quantization.quantize_dynamic(resnet18, dtype=torch.qint8)\n",
+    "s1 = print_size_of_model(resnet18, \"unquantized\")\n",
+    "s2 = print_size_of_model(quantized_resnet18, \"int8\")\n",
+    "\n",
+    "print(\"Compression ratio (unquantized to int8): \", float(s1)/float(s2))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Il n'y a quasiment aucune différence. Le modèle doit probablement déjà être en int8, sauf peut-être la dernière couche que l'on vient de modifier."
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "04a263f0",
diff --git a/utils/plot_performance.py b/utils/plot_performance.py
index bfe4caf..76d87a1 100644
--- a/utils/plot_performance.py
+++ b/utils/plot_performance.py
@@ -1,7 +1,7 @@
 import matplotlib.pyplot as plt
 
 
-def plot_performance(train_loss_list, val_loss_list, title = "Performance of Model"):
+def plot_performance(train_loss_list, val_loss_list, title = "Performance of Model", save_path = None):
     # Plot the performance of the model (train loss and validation loss)
     epochs_list = range(len(train_loss_list))
     plt.plot(epochs_list, train_loss_list)
@@ -10,4 +10,6 @@ def plot_performance(train_loss_list, val_loss_list, title = "Performance of Mod
     plt.xlabel("Epoch")
     plt.ylabel("Loss")
     plt.title(title)
+    if save_path is not None:
+        plt.savefig(save_path)
     plt.show()
\ No newline at end of file
-- 
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