From 778f3ecd0b34da1b64d5d22e5371e423b9f1a7ff Mon Sep 17 00:00:00 2001
From: zineb15022001 <zineb.kabbaj@etu.ec-lyon.fr>
Date: Fri, 1 Dec 2023 17:15:11 +0100
Subject: [PATCH] Update TD2 Deep Learning.ipynb
---
TD2 Deep Learning.ipynb | 79 +++++++++++++++++++++++++++++++++++++++++
1 file changed, 79 insertions(+)
diff --git a/TD2 Deep Learning.ipynb b/TD2 Deep Learning.ipynb
index bb92c82..baa86cc 100644
--- a/TD2 Deep Learning.ipynb
+++ b/TD2 Deep Learning.ipynb
@@ -2910,6 +2910,85 @@
"eval_model(model, test_dataloader, criterion)\n"
]
},
+ {
+ "cell_type": "code",
+ "execution_count": 147,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Original Model:\n",
+ "Test Loss: 0.1839 Test Acc: 0.9477\n",
+ "\n",
+ "Quantized Model:\n",
+ "Test Loss: 0.1839 Test Acc: 0.9477\n",
+ "\n",
+ "Original model size: 43.21 MB\n",
+ "Quantized model size: 42.83 MB\n"
+ ]
+ }
+ ],
+ "source": [
+ "import torch.quantization\n",
+ "\n",
+ "# Function to evaluate the quantized model on a test set\n",
+ "def eval_model_quantized(model, dataloader, criterion, device):\n",
+ " model.eval()\n",
+ " running_loss = 0.0\n",
+ " running_corrects = 0\n",
+ "\n",
+ " for inputs, labels in dataloader:\n",
+ " inputs = inputs.to(device)\n",
+ " labels = labels.to(device)\n",
+ "\n",
+ " with torch.no_grad():\n",
+ " outputs = model(inputs)\n",
+ " _, preds = torch.max(outputs, 1)\n",
+ " loss = criterion(outputs, labels)\n",
+ "\n",
+ " running_loss += loss.item() * inputs.size(0)\n",
+ " running_corrects += torch.sum(preds == labels.data)\n",
+ "\n",
+ " loss = running_loss / len(dataloader.dataset)\n",
+ " acc = running_corrects.double() / len(dataloader.dataset)\n",
+ "\n",
+ " print(\"Test Loss: {:.4f} Test Acc: {:.4f}\".format(loss, acc))\n",
+ " return acc\n",
+ "\n",
+ "# Post-training quantization\n",
+ "quantized_model = torch.quantization.quantize_dynamic(\n",
+ " model, # Original model\n",
+ " {torch.nn.Linear}, # Specify the type of layers to be quantized\n",
+ " dtype=torch.qint8 # Specify the quantization data type\n",
+ ")\n",
+ "\n",
+ "# Evaluate the original and quantized models on the test set\n",
+ "print(\"Original Model:\")\n",
+ "original_model_acc = eval_model(model, test_dataloader, criterion)\n",
+ "\n",
+ "print(\"\\nQuantized Model:\")\n",
+ "quantized_model_acc = eval_model_quantized(quantized_model, test_dataloader, criterion, device)\n",
+ "\n",
+ "# Compare the sizes of the original and quantized models\n",
+ "def get_size(model):\n",
+ " torch.save(model.state_dict(), \"temp.pth\")\n",
+ " size = os.path.getsize(\"temp.pth\") / (1024.0 ** 2) # Size in megabytes\n",
+ " os.remove(\"temp.pth\")\n",
+ " return size\n",
+ "\n",
+ "original_model_size = get_size(model)\n",
+ "quantized_model_size = get_size(quantized_model)\n",
+ "original_model_size = get_size(model)\n",
+ "quantized_model_size = get_size(quantized_model)\n",
+ "\n",
+ "print(f\"\\nOriginal model size: {original_model_size:.2f} MB\")\n",
+ "print(f\"Quantized model size: {quantized_model_size:.2f} MB\")\n",
+ "\n",
+ "\n"
+ ]
+ },
{
"cell_type": "markdown",
"id": "04a263f0",
--
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