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Add CNN Pytoch implementation model
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Orif Milod committed Mar 14, 2024
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env/
build/
.eggs/
**/data/**
239 changes: 239 additions & 0 deletions example/image_models/cnn_pytorch.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"import torch.nn as nn\n",
"import torch.nn.functional as F\n",
"import torchvision\n",
"from torchvision.transforms import v2\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/omilod/Desktop/projects/GigaTorch/env/lib/python3.11/site-packages/torchvision/transforms/v2/_deprecated.py:41: UserWarning: The transform `ToTensor()` is deprecated and will be removed in a future release. Instead, please use `v2.Compose([v2.ToImage(), v2.ToDtype(torch.float32, scale=True)])`.\n",
" warnings.warn(\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Files already downloaded and verified\n",
"Files already downloaded and verified\n"
]
}
],
"source": [
"# Device configuration\n",
"device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
"\n",
"num_epochs = 10\n",
"batch_size = 4\n",
"learning_rate = 0.001\n",
"\n",
"transform = v2.Compose(\n",
" [v2.ToTensor(),\n",
" v2.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])\n",
"\n",
"train_dataset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)\n",
"test_dataset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)\n",
"\n",
"tarin_loder = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)\n",
"test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=False)\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoche [1/10], Step [2000/12500], Loss: 2.2467\n",
"Epoche [1/10], Step [4000/12500], Loss: 2.2539\n",
"Epoche [1/10], Step [6000/12500], Loss: 2.2477\n",
"Epoche [1/10], Step [8000/12500], Loss: 2.4960\n",
"Epoche [1/10], Step [10000/12500], Loss: 2.4319\n",
"Epoche [1/10], Step [12000/12500], Loss: 2.3993\n",
"Epoche [2/10], Step [2000/12500], Loss: 1.7446\n",
"Epoche [2/10], Step [4000/12500], Loss: 1.7958\n",
"Epoche [2/10], Step [6000/12500], Loss: 1.9598\n",
"Epoche [2/10], Step [8000/12500], Loss: 2.0397\n",
"Epoche [2/10], Step [10000/12500], Loss: 1.8597\n",
"Epoche [2/10], Step [12000/12500], Loss: 1.1347\n",
"Epoche [3/10], Step [2000/12500], Loss: 1.2750\n",
"Epoche [3/10], Step [4000/12500], Loss: 1.4700\n",
"Epoche [3/10], Step [6000/12500], Loss: 0.8299\n",
"Epoche [3/10], Step [8000/12500], Loss: 2.4020\n",
"Epoche [3/10], Step [10000/12500], Loss: 0.8445\n",
"Epoche [3/10], Step [12000/12500], Loss: 1.2414\n",
"Epoche [4/10], Step [2000/12500], Loss: 1.1043\n",
"Epoche [4/10], Step [4000/12500], Loss: 1.6044\n",
"Epoche [4/10], Step [6000/12500], Loss: 1.8810\n",
"Epoche [4/10], Step [8000/12500], Loss: 1.4032\n",
"Epoche [4/10], Step [10000/12500], Loss: 1.9440\n",
"Epoche [4/10], Step [12000/12500], Loss: 1.4680\n",
"Epoche [5/10], Step [2000/12500], Loss: 0.9280\n",
"Epoche [5/10], Step [4000/12500], Loss: 1.0829\n",
"Epoche [5/10], Step [6000/12500], Loss: 0.8902\n",
"Epoche [5/10], Step [8000/12500], Loss: 1.4200\n",
"Epoche [5/10], Step [10000/12500], Loss: 2.7336\n",
"Epoche [5/10], Step [12000/12500], Loss: 1.7483\n",
"Epoche [6/10], Step [2000/12500], Loss: 1.4049\n",
"Epoche [6/10], Step [4000/12500], Loss: 0.8819\n",
"Epoche [6/10], Step [6000/12500], Loss: 2.0509\n",
"Epoche [6/10], Step [8000/12500], Loss: 1.5775\n",
"Epoche [6/10], Step [10000/12500], Loss: 1.4660\n",
"Epoche [6/10], Step [12000/12500], Loss: 1.2865\n",
"Epoche [7/10], Step [2000/12500], Loss: 1.3137\n",
"Epoche [7/10], Step [4000/12500], Loss: 1.3415\n",
"Epoche [7/10], Step [6000/12500], Loss: 0.9533\n",
"Epoche [7/10], Step [8000/12500], Loss: 1.1399\n",
"Epoche [7/10], Step [10000/12500], Loss: 1.3637\n",
"Epoche [7/10], Step [12000/12500], Loss: 0.6954\n",
"Epoche [8/10], Step [2000/12500], Loss: 0.9204\n",
"Epoche [8/10], Step [4000/12500], Loss: 1.1020\n",
"Epoche [8/10], Step [6000/12500], Loss: 1.0856\n",
"Epoche [8/10], Step [8000/12500], Loss: 1.2919\n",
"Epoche [8/10], Step [10000/12500], Loss: 0.5048\n",
"Epoche [8/10], Step [12000/12500], Loss: 1.6188\n",
"Epoche [9/10], Step [2000/12500], Loss: 0.7271\n",
"Epoche [9/10], Step [4000/12500], Loss: 0.8624\n",
"Epoche [9/10], Step [6000/12500], Loss: 1.6987\n",
"Epoche [9/10], Step [8000/12500], Loss: 0.8891\n",
"Epoche [9/10], Step [10000/12500], Loss: 1.0421\n",
"Epoche [9/10], Step [12000/12500], Loss: 0.9009\n",
"Epoche [10/10], Step [2000/12500], Loss: 1.8559\n",
"Epoche [10/10], Step [4000/12500], Loss: 1.9273\n",
"Epoche [10/10], Step [6000/12500], Loss: 1.1227\n",
"Epoche [10/10], Step [8000/12500], Loss: 1.3718\n",
"Epoche [10/10], Step [10000/12500], Loss: 1.6195\n",
"Epoche [10/10], Step [12000/12500], Loss: 0.9517\n",
"Finished training\n",
"Accuracy of the network: 58.32 %\n",
"Accuracy: 73.4 %\n",
"Accuracy: 60.8 %\n",
"Accuracy: 39.0 %\n",
"Accuracy: 38.7 %\n",
"Accuracy: 50.4 %\n",
"Accuracy: 38.7 %\n",
"Accuracy: 73.6 %\n",
"Accuracy: 66.8 %\n",
"Accuracy: 75.0 %\n",
"Accuracy: 66.8 %\n"
]
}
],
"source": [
"class ConvNet(nn.Module):\n",
" def __init__(self) -> None:\n",
" super().__init__()\n",
" self.conv1 = nn.Conv2d(3, 6, 5)\n",
" self.pool = nn.MaxPool2d(2, 2)\n",
" self.conv2 = nn.Conv2d(6, 16, 5)\n",
" self.fc1 = nn.Linear(16 * 5 * 5, 120)\n",
" self.fc2 = nn.Linear(120, 84)\n",
" self.fc3 = nn.Linear(84, 10)\n",
"\n",
" def forward(self, x):\n",
" # -> n, 3, 32, 32\n",
" x = self.pool(F.relu(self.conv1(x))) # -> n, 6, 14, 14\n",
" x = self.pool(F.relu(self.conv2(x))) # -> n, 16, 5, 5\n",
" x = x.view(-1, 16 * 5 * 5) # -> n, 400\n",
" x = F.relu(self.fc1(x)) # -> n, 120\n",
" x = F.relu(self.fc2(x)) # -> n, 84\n",
" x = self.fc3(x) # -> n, 10\n",
" return x\n",
"\n",
"model = ConvNet()\n",
"model.to(device)\n",
"critertion = nn.CrossEntropyLoss()\n",
"optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)\n",
"\n",
"n_total_steps = len(tarin_loder)\n",
"for epoch in range(num_epochs):\n",
" for i, (images, labels) in enumerate(tarin_loder):\n",
" images = images.to(device)\n",
" labels = labels.to(device)\n",
"\n",
" # Forward pass\n",
" outputs = model(images)\n",
" loss = critertion(outputs, labels)\n",
"\n",
" # Backward and optimize\n",
" optimizer.zero_grad()\n",
" loss.backward()\n",
" optimizer.step()\n",
"\n",
" if (i+1) % 2000 == 0:\n",
" print(f'Epoche [{epoch+1}/{num_epochs}], Step [{i+1}/{n_total_steps}], Loss: {loss.item():.4f}')\n",
"print(\"Finished training\")\n",
"\n",
"\n",
"with torch.no_grad():\n",
" n_correct = 0\n",
" n_samples = 0\n",
" n_class_correct = [0 for i in range(10)]\n",
" n_class_samples = [0 for i in range(10)]\n",
" for images, labels in test_loader:\n",
" images = images.to(device)\n",
" labels = labels.to(device)\n",
" outputs = model(images)\n",
" # max returns (value ,index)\n",
" _, predicted = torch.max(outputs, 1)\n",
" n_samples += labels.size(0)\n",
" n_correct += (predicted == labels).sum().item()\n",
" \n",
" for i in range(batch_size):\n",
" label = labels[i]\n",
" pred = predicted[i]\n",
" if (label == pred):\n",
" n_class_correct[label] += 1\n",
" n_class_samples[label] += 1\n",
"\n",
" acc = 100.0 * n_correct / n_samples\n",
" print(f'Accuracy of the network: {acc} %')\n",
"\n",
" for i in range(10):\n",
" acc = 100.0 * n_class_correct[i] / n_class_samples[i]\n",
" print(f'Accuracy: {acc} %')\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "env",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.7"
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},
"nbformat": 4,
"nbformat_minor": 2
}

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