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"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Loading necessary Libraries"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import os \n",
"import torch\n",
"import torchvision\n",
"from torch import nn \n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'cuda'"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
"device"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Train,Test path"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"('data\\\\retinal_oct\\\\test', 'data\\\\retinal_oct\\\\train')"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_dir = \"data\\\\retinal_oct\\\\train\"\n",
"test_dir = \"data\\\\retinal_oct\\\\test\"\n",
"test_dir, train_dir"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Printing classnames"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['CNV', 'DME', 'DRUSEN', 'NORMAL']\n",
"['CNV', 'DME', 'DRUSEN', 'NORMAL']\n"
]
}
],
"source": [
"print(os.listdir(train_dir))\n",
"print(os.listdir(test_dir))"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"from torch.utils.data import DataLoader\n",
"from torchvision import datasets,transforms"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Creating data transforms"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"data_transform = transforms.Compose([\n",
" transforms.Resize(size=(224,224)),\n",
" transforms.RandomHorizontalFlip(p=0.5),\n",
" transforms.ToTensor()\n",
"])"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Compose(\n",
" Resize(size=(224, 224), interpolation=bilinear, max_size=None, antialias=True)\n",
" RandomHorizontalFlip(p=0.5)\n",
" ToTensor()\n",
")"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_transform"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Turning images into pytorch tensors"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train data: \n",
"Dataset ImageFolder\n",
" Number of datapoints: 13720\n",
" Root location: data\\retinal_oct\\train\n",
" StandardTransform\n",
"Transform: Compose(\n",
" Resize(size=(224, 224), interpolation=bilinear, max_size=None, antialias=True)\n",
" RandomHorizontalFlip(p=0.5)\n",
" ToTensor()\n",
" )\n",
" Test data: \n",
" Dataset ImageFolder\n",
" Number of datapoints: 973\n",
" Root location: data\\retinal_oct\\test\n",
" StandardTransform\n",
"Transform: Compose(\n",
" Resize(size=(224, 224), interpolation=bilinear, max_size=None, antialias=True)\n",
" RandomHorizontalFlip(p=0.5)\n",
" ToTensor()\n",
" )\n"
]
}
],
"source": [
"from torchvision import datasets\n",
"train_data = datasets.ImageFolder(root=train_dir,\n",
" transform= data_transform,\n",
" target_transform=None)\n",
"\n",
"test_data = datasets.ImageFolder(root=test_dir,\n",
" transform=data_transform,\n",
" target_transform=None)\n",
"\n",
"print(f\"Train data: \\n{train_data}\\n Test data: \\n {test_data}\")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['CNV', 'DME', 'DRUSEN', 'NORMAL']"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"class_names = train_data.classes\n",
"class_names"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'CNV': 0, 'DME': 1, 'DRUSEN': 2, 'NORMAL': 3}"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"class_dict = train_data.class_to_idx\n",
"class_dict"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Image tensor:\n",
"tensor([[[0.0000, 0.0510, 0.0314, ..., 0.0510, 0.0431, 0.0078],\n",
" [0.0471, 0.0745, 0.0745, ..., 0.0627, 0.0588, 0.0275],\n",
" [0.0980, 0.0667, 0.0863, ..., 0.0745, 0.0745, 0.0392],\n",
" ...,\n",
" [0.0196, 0.0235, 0.0235, ..., 0.0353, 0.0235, 0.0000],\n",
" [0.0235, 0.0235, 0.0275, ..., 0.0314, 0.0196, 0.0000],\n",
" [0.0275, 0.0275, 0.0275, ..., 0.0275, 0.0157, 0.0000]],\n",
"\n",
" [[0.0000, 0.0510, 0.0314, ..., 0.0510, 0.0431, 0.0078],\n",
" [0.0471, 0.0745, 0.0745, ..., 0.0627, 0.0588, 0.0275],\n",
" [0.0980, 0.0667, 0.0863, ..., 0.0745, 0.0745, 0.0392],\n",
" ...,\n",
" [0.0196, 0.0235, 0.0235, ..., 0.0353, 0.0235, 0.0000],\n",
" [0.0235, 0.0235, 0.0275, ..., 0.0314, 0.0196, 0.0000],\n",
" [0.0275, 0.0275, 0.0275, ..., 0.0275, 0.0157, 0.0000]],\n",
"\n",
" [[0.0000, 0.0510, 0.0314, ..., 0.0510, 0.0431, 0.0078],\n",
" [0.0471, 0.0745, 0.0745, ..., 0.0627, 0.0588, 0.0275],\n",
" [0.0980, 0.0667, 0.0863, ..., 0.0745, 0.0745, 0.0392],\n",
" ...,\n",
" [0.0196, 0.0235, 0.0235, ..., 0.0353, 0.0235, 0.0000],\n",
" [0.0235, 0.0235, 0.0275, ..., 0.0314, 0.0196, 0.0000],\n",
" [0.0275, 0.0275, 0.0275, ..., 0.0275, 0.0157, 0.0000]]])\n",
"Image shape: torch.Size([3, 224, 224])\n",
"Image datatype: torch.float32\n",
"Image label: 0\n",
"Label datatype: <class 'int'>\n"
]
}
],
"source": [
"img, label = train_data[0][0], train_data[0][1]\n",
"print(f\"Image tensor:\\n{img}\")\n",
"print(f\"Image shape: {img.shape}\")\n",
"print(f\"Image datatype: {img.dtype}\")\n",
"print(f\"Image label: {label}\")\n",
"print(f\"Label datatype: {type(label)}\")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Original shape: torch.Size([3, 224, 224])\n",
"Permuted shape: torch.Size([224, 224, 3])\n"
]
}
],
"source": [
"image_permute = img.permute(1,2,0)\n",
"print(f\"Original shape: {img.shape}\")\n",
"print(f\"Permuted shape: {image_permute.shape}\")\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'CNV')"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 400x500 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt \n",
"plt.figure(figsize=(4,5))\n",
"plt.imshow(img.permute(1,2,0))\n",
"plt.axis(\"off\")\n",
"plt.title(class_names[label])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Turning dataset into dataloaders\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"batch_size = 8\n",
"num_workers = os.cpu_count()\n",
"\n",
"from torch.utils.data import DataLoader\n",
"train_dataloader = DataLoader(dataset=train_data,\n",
" batch_size=8,\n",
" num_workers=num_workers,\n",
" shuffle=True)\n",
"\n",
"test_dataloader = DataLoader(dataset=test_data,\n",
" batch_size=8,\n",
" num_workers=num_workers,\n",
" shuffle=False)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(<torch.utils.data.dataloader.DataLoader at 0x2320bbef7f0>,\n",
" <torch.utils.data.dataloader.DataLoader at 0x2320bbee650>)"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_dataloader,test_dataloader"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(<torch.utils.data.dataloader.DataLoader at 0x2321422bd30>,\n",
" <torch.utils.data.dataloader.DataLoader at 0x2321422a2c0>,\n",
" ['CNV', 'DME', 'DRUSEN', 'NORMAL'])"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Create training and testing DataLoaders as well as get a list of class names\n",
"import src.data_setup as data_setup\n",
"train_dataloader, test_dataloader, class_names = data_setup.create_dataloaders(train_dir=train_dir,\n",
" test_dir=test_dir,\n",
" transform=data_transform, # resize, convert images to between 0 & 1 and normalize them\n",
" batch_size=32) # set mini-batch size to 32\n",
"\n",
"train_dataloader, test_dataloader, class_names"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Image shape: torch.Size([32, 3, 224, 224])\n",
"Label shape: torch.Size([32])\n"
]
}
],
"source": [
"img, label = next(iter(train_dataloader))\n",
"\n",
"\n",
"print(f\"Image shape: {img.shape}\")\n",
"print(f\"Label shape: {label.shape}\")"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(['CNV', 'DME', 'DRUSEN', 'NORMAL'],\n",
" {'CNV': 0, 'DME': 1, 'DRUSEN': 2, 'NORMAL': 3})"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_data.classes , train_data.class_to_idx"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Data augmentation"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"from torchvision.transforms import Compose, Resize, RandomHorizontalFlip, ToTensor, Normalize\n",
"from PIL import Image\n",
"\n",
"train_data_augmented = transforms.Compose([\n",
" transforms.Resize((224,224), interpolation=Image.BILINEAR),\n",
" transforms.RandomHorizontalFlip(p=0.5),\n",
" transforms.TrivialAugmentWide(num_magnitude_bins=31),\n",
" transforms.ToTensor(),\n",
" Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
"])\n",
"\n",
"test_data_augmented = transforms.Compose([\n",
" transforms.Resize((224,224), interpolation=Image.BILINEAR),\n",
" transforms.ToTensor(),\n",
" Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
"])"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(Compose(\n",
" Resize(size=(224, 224), interpolation=bilinear, max_size=None, antialias=True)\n",
" RandomHorizontalFlip(p=0.5)\n",
" TrivialAugmentWide(num_magnitude_bins=31, interpolation=InterpolationMode.NEAREST, fill=None)\n",
" ToTensor()\n",
" Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
" ),\n",
" Compose(\n",
" Resize(size=(224, 224), interpolation=bilinear, max_size=None, antialias=True)\n",
" ToTensor()\n",
" Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
" ))"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_data_augmented, test_data_augmented"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"from torchvision.datasets import ImageFolder\n",
"train_data_augmented = ImageFolder(root='data\\\\retinal_oct\\\\train', transform=train_data_augmented)\n",
"test_data_augmented = ImageFolder(root='data\\\\retinal_oct\\\\train', transform=test_data_augmented)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"import os \n",
"batch_size = 8\n",
"num_workers = os.cpu_count()\n",
"torch.manual_seed(42)\n",
"train_dataloader_augmented = DataLoader(train_data_augmented,\n",
" batch_size=batch_size,\n",
" shuffle=True,\n",
" num_workers=num_workers)\n",
"\n",
"test_dataloader_augmented = DataLoader(test_data_augmented,\n",
" batch_size=batch_size,\n",
" shuffle=False,\n",
" num_workers=num_workers)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 800x800 with 8 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def show_images(images, labels, class_names):\n",
" fig, axes = plt.subplots(figsize=(8, 8), nrows=2, ncols=4)\n",
" for i in range(8):\n",
" ax = axes[i // 4, i % 4]\n",
" img = images[i].permute(1, 2, 0)\n",
" ax.imshow(img)\n",
" ax.axis('off')\n",
" ax.set_title(class_names[labels[i]])\n",
" plt.show()\n",
"\n",
"# Get a batch of training data\n",
"images, labels = next(iter(train_dataloader))\n",
"\n",
"# Show images\n",
"show_images(images, labels, class_names)\n"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(torch.Size([3, 224, 224]), tensor(1))"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"image_batch, label_batch = next(iter(train_dataloader))\n",
"\n",
"# Get a single image from the batch\n",
"image, label = image_batch[0], label_batch[0]\n",
"\n",
"# View the batch shapes\n",
"image.shape, label"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Optic Model architecture"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using cuda for inference\n"
]
}
],
"source": [
"import torch\n",
"from PIL import Image\n",
"import torchvision.transforms as transforms\n",
"import numpy as np\n",
"import json\n",
"import requests\n",
"import matplotlib.pyplot as plt\n",
"import warnings\n",
"warnings.filterwarnings('ignore')\n",
"%matplotlib inline\n",
"\n",
"device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
"print(f'Using {device} for inference')"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"ResNet(\n",
" (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
" (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
" (layer1): Sequential(\n",
" (0): Bottleneck(\n",
" (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" (downsample): Sequential(\n",
" (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" (1): Bottleneck(\n",
" (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" (2): Bottleneck(\n",
" (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" )\n",
" (layer2): Sequential(\n",
" (0): Bottleneck(\n",
" (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" (downsample): Sequential(\n",
" (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
" (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" (1): Bottleneck(\n",
" (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" (2): Bottleneck(\n",
" (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" (3): Bottleneck(\n",
" (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" )\n",
" (layer3): Sequential(\n",
" (0): Bottleneck(\n",
" (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" (downsample): Sequential(\n",
" (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
" (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" (1): Bottleneck(\n",
" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" (2): Bottleneck(\n",
" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" (3): Bottleneck(\n",
" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" (4): Bottleneck(\n",
" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" (5): Bottleneck(\n",
" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" )\n",
" (layer4): Sequential(\n",
" (0): Bottleneck(\n",
" (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" (downsample): Sequential(\n",
" (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
" (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" (1): Bottleneck(\n",
" (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" (2): Bottleneck(\n",
" (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" )\n",
" (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
" (fc): Linear(in_features=2048, out_features=1000, bias=True)\n",
")"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"weights = torchvision.models.ResNet50_Weights.DEFAULT\n",
"model = torchvision.models.resnet50(weights=weights).to(device)\n",
"model"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"========================================================================================================================\n",
"Layer (type (var_name)) Input Shape Output Shape Param # Trainable\n",
"========================================================================================================================\n",
"ResNet (ResNet) [8, 3, 224, 224] [8, 1000] -- True\n",
"├─Conv2d (conv1) [8, 3, 224, 224] [8, 64, 112, 112] 9,408 True\n",
"├─BatchNorm2d (bn1) [8, 64, 112, 112] [8, 64, 112, 112] 128 True\n",
"├─ReLU (relu) [8, 64, 112, 112] [8, 64, 112, 112] -- --\n",
"├─MaxPool2d (maxpool) [8, 64, 112, 112] [8, 64, 56, 56] -- --\n",
"├─Sequential (layer1) [8, 64, 56, 56] [8, 256, 56, 56] -- True\n",
"│ └─Bottleneck (0) [8, 64, 56, 56] [8, 256, 56, 56] -- True\n",
"│ │ └─Conv2d (conv1) [8, 64, 56, 56] [8, 64, 56, 56] 4,096 True\n",
"│ │ └─BatchNorm2d (bn1) [8, 64, 56, 56] [8, 64, 56, 56] 128 True\n",
"│ │ └─ReLU (relu) [8, 64, 56, 56] [8, 64, 56, 56] -- --\n",
"│ │ └─Conv2d (conv2) [8, 64, 56, 56] [8, 64, 56, 56] 36,864 True\n",
"│ │ └─BatchNorm2d (bn2) [8, 64, 56, 56] [8, 64, 56, 56] 128 True\n",
"│ │ └─ReLU (relu) [8, 64, 56, 56] [8, 64, 56, 56] -- --\n",
"│ │ └─Conv2d (conv3) [8, 64, 56, 56] [8, 256, 56, 56] 16,384 True\n",
"│ │ └─BatchNorm2d (bn3) [8, 256, 56, 56] [8, 256, 56, 56] 512 True\n",
"│ │ └─Sequential (downsample) [8, 64, 56, 56] [8, 256, 56, 56] 16,896 True\n",
"│ │ └─ReLU (relu) [8, 256, 56, 56] [8, 256, 56, 56] -- --\n",
"│ └─Bottleneck (1) [8, 256, 56, 56] [8, 256, 56, 56] -- True\n",
"│ │ └─Conv2d (conv1) [8, 256, 56, 56] [8, 64, 56, 56] 16,384 True\n",
"│ │ └─BatchNorm2d (bn1) [8, 64, 56, 56] [8, 64, 56, 56] 128 True\n",
"│ │ └─ReLU (relu) [8, 64, 56, 56] [8, 64, 56, 56] -- --\n",
"│ │ └─Conv2d (conv2) [8, 64, 56, 56] [8, 64, 56, 56] 36,864 True\n",
"│ │ └─BatchNorm2d (bn2) [8, 64, 56, 56] [8, 64, 56, 56] 128 True\n",
"│ │ └─ReLU (relu) [8, 64, 56, 56] [8, 64, 56, 56] -- --\n",
"│ │ └─Conv2d (conv3) [8, 64, 56, 56] [8, 256, 56, 56] 16,384 True\n",
"│ │ └─BatchNorm2d (bn3) [8, 256, 56, 56] [8, 256, 56, 56] 512 True\n",
"│ │ └─ReLU (relu) [8, 256, 56, 56] [8, 256, 56, 56] -- --\n",
"│ └─Bottleneck (2) [8, 256, 56, 56] [8, 256, 56, 56] -- True\n",
"│ │ └─Conv2d (conv1) [8, 256, 56, 56] [8, 64, 56, 56] 16,384 True\n",
"│ │ └─BatchNorm2d (bn1) [8, 64, 56, 56] [8, 64, 56, 56] 128 True\n",
"│ │ └─ReLU (relu) [8, 64, 56, 56] [8, 64, 56, 56] -- --\n",
"│ │ └─Conv2d (conv2) [8, 64, 56, 56] [8, 64, 56, 56] 36,864 True\n",
"│ │ └─BatchNorm2d (bn2) [8, 64, 56, 56] [8, 64, 56, 56] 128 True\n",
"│ │ └─ReLU (relu) [8, 64, 56, 56] [8, 64, 56, 56] -- --\n",
"│ │ └─Conv2d (conv3) [8, 64, 56, 56] [8, 256, 56, 56] 16,384 True\n",
"│ │ └─BatchNorm2d (bn3) [8, 256, 56, 56] [8, 256, 56, 56] 512 True\n",
"│ │ └─ReLU (relu) [8, 256, 56, 56] [8, 256, 56, 56] -- --\n",
"├─Sequential (layer2) [8, 256, 56, 56] [8, 512, 28, 28] -- True\n",
"│ └─Bottleneck (0) [8, 256, 56, 56] [8, 512, 28, 28] -- True\n",
"│ │ └─Conv2d (conv1) [8, 256, 56, 56] [8, 128, 56, 56] 32,768 True\n",
"│ │ └─BatchNorm2d (bn1) [8, 128, 56, 56] [8, 128, 56, 56] 256 True\n",
"│ │ └─ReLU (relu) [8, 128, 56, 56] [8, 128, 56, 56] -- --\n",
"│ │ └─Conv2d (conv2) [8, 128, 56, 56] [8, 128, 28, 28] 147,456 True\n",
"│ │ └─BatchNorm2d (bn2) [8, 128, 28, 28] [8, 128, 28, 28] 256 True\n",
"│ │ └─ReLU (relu) [8, 128, 28, 28] [8, 128, 28, 28] -- --\n",
"│ │ └─Conv2d (conv3) [8, 128, 28, 28] [8, 512, 28, 28] 65,536 True\n",
"│ │ └─BatchNorm2d (bn3) [8, 512, 28, 28] [8, 512, 28, 28] 1,024 True\n",
"│ │ └─Sequential (downsample) [8, 256, 56, 56] [8, 512, 28, 28] 132,096 True\n",
"│ │ └─ReLU (relu) [8, 512, 28, 28] [8, 512, 28, 28] -- --\n",
"│ └─Bottleneck (1) [8, 512, 28, 28] [8, 512, 28, 28] -- True\n",
"│ │ └─Conv2d (conv1) [8, 512, 28, 28] [8, 128, 28, 28] 65,536 True\n",
"│ │ └─BatchNorm2d (bn1) [8, 128, 28, 28] [8, 128, 28, 28] 256 True\n",
"│ │ └─ReLU (relu) [8, 128, 28, 28] [8, 128, 28, 28] -- --\n",
"│ │ └─Conv2d (conv2) [8, 128, 28, 28] [8, 128, 28, 28] 147,456 True\n",
"│ │ └─BatchNorm2d (bn2) [8, 128, 28, 28] [8, 128, 28, 28] 256 True\n",
"│ │ └─ReLU (relu) [8, 128, 28, 28] [8, 128, 28, 28] -- --\n",
"│ │ └─Conv2d (conv3) [8, 128, 28, 28] [8, 512, 28, 28] 65,536 True\n",
"│ │ └─BatchNorm2d (bn3) [8, 512, 28, 28] [8, 512, 28, 28] 1,024 True\n",
"│ │ └─ReLU (relu) [8, 512, 28, 28] [8, 512, 28, 28] -- --\n",
"│ └─Bottleneck (2) [8, 512, 28, 28] [8, 512, 28, 28] -- True\n",
"│ │ └─Conv2d (conv1) [8, 512, 28, 28] [8, 128, 28, 28] 65,536 True\n",
"│ │ └─BatchNorm2d (bn1) [8, 128, 28, 28] [8, 128, 28, 28] 256 True\n",
"│ │ └─ReLU (relu) [8, 128, 28, 28] [8, 128, 28, 28] -- --\n",
"│ │ └─Conv2d (conv2) [8, 128, 28, 28] [8, 128, 28, 28] 147,456 True\n",
"│ │ └─BatchNorm2d (bn2) [8, 128, 28, 28] [8, 128, 28, 28] 256 True\n",
"│ │ └─ReLU (relu) [8, 128, 28, 28] [8, 128, 28, 28] -- --\n",
"│ │ └─Conv2d (conv3) [8, 128, 28, 28] [8, 512, 28, 28] 65,536 True\n",
"│ │ └─BatchNorm2d (bn3) [8, 512, 28, 28] [8, 512, 28, 28] 1,024 True\n",
"│ │ └─ReLU (relu) [8, 512, 28, 28] [8, 512, 28, 28] -- --\n",
"│ └─Bottleneck (3) [8, 512, 28, 28] [8, 512, 28, 28] -- True\n",
"│ │ └─Conv2d (conv1) [8, 512, 28, 28] [8, 128, 28, 28] 65,536 True\n",
"│ │ └─BatchNorm2d (bn1) [8, 128, 28, 28] [8, 128, 28, 28] 256 True\n",
"│ │ └─ReLU (relu) [8, 128, 28, 28] [8, 128, 28, 28] -- --\n",
"│ │ └─Conv2d (conv2) [8, 128, 28, 28] [8, 128, 28, 28] 147,456 True\n",
"│ │ └─BatchNorm2d (bn2) [8, 128, 28, 28] [8, 128, 28, 28] 256 True\n",
"│ │ └─ReLU (relu) [8, 128, 28, 28] [8, 128, 28, 28] -- --\n",
"│ │ └─Conv2d (conv3) [8, 128, 28, 28] [8, 512, 28, 28] 65,536 True\n",
"│ │ └─BatchNorm2d (bn3) [8, 512, 28, 28] [8, 512, 28, 28] 1,024 True\n",
"│ │ └─ReLU (relu) [8, 512, 28, 28] [8, 512, 28, 28] -- --\n",
"├─Sequential (layer3) [8, 512, 28, 28] [8, 1024, 14, 14] -- True\n",
"│ └─Bottleneck (0) [8, 512, 28, 28] [8, 1024, 14, 14] -- True\n",
"│ │ └─Conv2d (conv1) [8, 512, 28, 28] [8, 256, 28, 28] 131,072 True\n",
"│ │ └─BatchNorm2d (bn1) [8, 256, 28, 28] [8, 256, 28, 28] 512 True\n",
"│ │ └─ReLU (relu) [8, 256, 28, 28] [8, 256, 28, 28] -- --\n",
"│ │ └─Conv2d (conv2) [8, 256, 28, 28] [8, 256, 14, 14] 589,824 True\n",
"│ │ └─BatchNorm2d (bn2) [8, 256, 14, 14] [8, 256, 14, 14] 512 True\n",
"│ │ └─ReLU (relu) [8, 256, 14, 14] [8, 256, 14, 14] -- --\n",
"│ │ └─Conv2d (conv3) [8, 256, 14, 14] [8, 1024, 14, 14] 262,144 True\n",
"│ │ └─BatchNorm2d (bn3) [8, 1024, 14, 14] [8, 1024, 14, 14] 2,048 True\n",
"│ │ └─Sequential (downsample) [8, 512, 28, 28] [8, 1024, 14, 14] 526,336 True\n",
"│ │ └─ReLU (relu) [8, 1024, 14, 14] [8, 1024, 14, 14] -- --\n",
"│ └─Bottleneck (1) [8, 1024, 14, 14] [8, 1024, 14, 14] -- True\n",
"│ │ └─Conv2d (conv1) [8, 1024, 14, 14] [8, 256, 14, 14] 262,144 True\n",
"│ │ └─BatchNorm2d (bn1) [8, 256, 14, 14] [8, 256, 14, 14] 512 True\n",
"│ │ └─ReLU (relu) [8, 256, 14, 14] [8, 256, 14, 14] -- --\n",
"│ │ └─Conv2d (conv2) [8, 256, 14, 14] [8, 256, 14, 14] 589,824 True\n",
"│ │ └─BatchNorm2d (bn2) [8, 256, 14, 14] [8, 256, 14, 14] 512 True\n",
"│ │ └─ReLU (relu) [8, 256, 14, 14] [8, 256, 14, 14] -- --\n",
"│ │ └─Conv2d (conv3) [8, 256, 14, 14] [8, 1024, 14, 14] 262,144 True\n",
"│ │ └─BatchNorm2d (bn3) [8, 1024, 14, 14] [8, 1024, 14, 14] 2,048 True\n",
"│ │ └─ReLU (relu) [8, 1024, 14, 14] [8, 1024, 14, 14] -- --\n",
"│ └─Bottleneck (2) [8, 1024, 14, 14] [8, 1024, 14, 14] -- True\n",
"│ │ └─Conv2d (conv1) [8, 1024, 14, 14] [8, 256, 14, 14] 262,144 True\n",
"│ │ └─BatchNorm2d (bn1) [8, 256, 14, 14] [8, 256, 14, 14] 512 True\n",
"│ │ └─ReLU (relu) [8, 256, 14, 14] [8, 256, 14, 14] -- --\n",
"│ │ └─Conv2d (conv2) [8, 256, 14, 14] [8, 256, 14, 14] 589,824 True\n",
"│ │ └─BatchNorm2d (bn2) [8, 256, 14, 14] [8, 256, 14, 14] 512 True\n",
"│ │ └─ReLU (relu) [8, 256, 14, 14] [8, 256, 14, 14] -- --\n",
"│ │ └─Conv2d (conv3) [8, 256, 14, 14] [8, 1024, 14, 14] 262,144 True\n",
"│ │ └─BatchNorm2d (bn3) [8, 1024, 14, 14] [8, 1024, 14, 14] 2,048 True\n",
"│ │ └─ReLU (relu) [8, 1024, 14, 14] [8, 1024, 14, 14] -- --\n",
"│ └─Bottleneck (3) [8, 1024, 14, 14] [8, 1024, 14, 14] -- True\n",
"│ │ └─Conv2d (conv1) [8, 1024, 14, 14] [8, 256, 14, 14] 262,144 True\n",
"│ │ └─BatchNorm2d (bn1) [8, 256, 14, 14] [8, 256, 14, 14] 512 True\n",
"│ │ └─ReLU (relu) [8, 256, 14, 14] [8, 256, 14, 14] -- --\n",
"│ │ └─Conv2d (conv2) [8, 256, 14, 14] [8, 256, 14, 14] 589,824 True\n",
"│ │ └─BatchNorm2d (bn2) [8, 256, 14, 14] [8, 256, 14, 14] 512 True\n",
"│ │ └─ReLU (relu) [8, 256, 14, 14] [8, 256, 14, 14] -- --\n",
"│ │ └─Conv2d (conv3) [8, 256, 14, 14] [8, 1024, 14, 14] 262,144 True\n",
"│ │ └─BatchNorm2d (bn3) [8, 1024, 14, 14] [8, 1024, 14, 14] 2,048 True\n",
"│ │ └─ReLU (relu) [8, 1024, 14, 14] [8, 1024, 14, 14] -- --\n",
"│ └─Bottleneck (4) [8, 1024, 14, 14] [8, 1024, 14, 14] -- True\n",
"│ │ └─Conv2d (conv1) [8, 1024, 14, 14] [8, 256, 14, 14] 262,144 True\n",
"│ │ └─BatchNorm2d (bn1) [8, 256, 14, 14] [8, 256, 14, 14] 512 True\n",
"│ │ └─ReLU (relu) [8, 256, 14, 14] [8, 256, 14, 14] -- --\n",
"│ │ └─Conv2d (conv2) [8, 256, 14, 14] [8, 256, 14, 14] 589,824 True\n",
"│ │ └─BatchNorm2d (bn2) [8, 256, 14, 14] [8, 256, 14, 14] 512 True\n",
"│ │ └─ReLU (relu) [8, 256, 14, 14] [8, 256, 14, 14] -- --\n",
"│ │ └─Conv2d (conv3) [8, 256, 14, 14] [8, 1024, 14, 14] 262,144 True\n",
"│ │ └─BatchNorm2d (bn3) [8, 1024, 14, 14] [8, 1024, 14, 14] 2,048 True\n",
"│ │ └─ReLU (relu) [8, 1024, 14, 14] [8, 1024, 14, 14] -- --\n",
"│ └─Bottleneck (5) [8, 1024, 14, 14] [8, 1024, 14, 14] -- True\n",
"│ │ └─Conv2d (conv1) [8, 1024, 14, 14] [8, 256, 14, 14] 262,144 True\n",
"│ │ └─BatchNorm2d (bn1) [8, 256, 14, 14] [8, 256, 14, 14] 512 True\n",
"│ │ └─ReLU (relu) [8, 256, 14, 14] [8, 256, 14, 14] -- --\n",
"│ │ └─Conv2d (conv2) [8, 256, 14, 14] [8, 256, 14, 14] 589,824 True\n",
"│ │ └─BatchNorm2d (bn2) [8, 256, 14, 14] [8, 256, 14, 14] 512 True\n",
"│ │ └─ReLU (relu) [8, 256, 14, 14] [8, 256, 14, 14] -- --\n",
"│ │ └─Conv2d (conv3) [8, 256, 14, 14] [8, 1024, 14, 14] 262,144 True\n",
"│ │ └─BatchNorm2d (bn3) [8, 1024, 14, 14] [8, 1024, 14, 14] 2,048 True\n",
"│ │ └─ReLU (relu) [8, 1024, 14, 14] [8, 1024, 14, 14] -- --\n",
"├─Sequential (layer4) [8, 1024, 14, 14] [8, 2048, 7, 7] -- True\n",
"│ └─Bottleneck (0) [8, 1024, 14, 14] [8, 2048, 7, 7] -- True\n",
"│ │ └─Conv2d (conv1) [8, 1024, 14, 14] [8, 512, 14, 14] 524,288 True\n",
"│ │ └─BatchNorm2d (bn1) [8, 512, 14, 14] [8, 512, 14, 14] 1,024 True\n",
"│ │ └─ReLU (relu) [8, 512, 14, 14] [8, 512, 14, 14] -- --\n",
"│ │ └─Conv2d (conv2) [8, 512, 14, 14] [8, 512, 7, 7] 2,359,296 True\n",
"│ │ └─BatchNorm2d (bn2) [8, 512, 7, 7] [8, 512, 7, 7] 1,024 True\n",
"│ │ └─ReLU (relu) [8, 512, 7, 7] [8, 512, 7, 7] -- --\n",
"│ │ └─Conv2d (conv3) [8, 512, 7, 7] [8, 2048, 7, 7] 1,048,576 True\n",
"│ │ └─BatchNorm2d (bn3) [8, 2048, 7, 7] [8, 2048, 7, 7] 4,096 True\n",
"│ │ └─Sequential (downsample) [8, 1024, 14, 14] [8, 2048, 7, 7] 2,101,248 True\n",
"│ │ └─ReLU (relu) [8, 2048, 7, 7] [8, 2048, 7, 7] -- --\n",
"│ └─Bottleneck (1) [8, 2048, 7, 7] [8, 2048, 7, 7] -- True\n",
"│ │ └─Conv2d (conv1) [8, 2048, 7, 7] [8, 512, 7, 7] 1,048,576 True\n",
"│ │ └─BatchNorm2d (bn1) [8, 512, 7, 7] [8, 512, 7, 7] 1,024 True\n",
"│ │ └─ReLU (relu) [8, 512, 7, 7] [8, 512, 7, 7] -- --\n",
"│ │ └─Conv2d (conv2) [8, 512, 7, 7] [8, 512, 7, 7] 2,359,296 True\n",
"│ │ └─BatchNorm2d (bn2) [8, 512, 7, 7] [8, 512, 7, 7] 1,024 True\n",
"│ │ └─ReLU (relu) [8, 512, 7, 7] [8, 512, 7, 7] -- --\n",
"│ │ └─Conv2d (conv3) [8, 512, 7, 7] [8, 2048, 7, 7] 1,048,576 True\n",
"│ │ └─BatchNorm2d (bn3) [8, 2048, 7, 7] [8, 2048, 7, 7] 4,096 True\n",
"│ │ └─ReLU (relu) [8, 2048, 7, 7] [8, 2048, 7, 7] -- --\n",
"│ └─Bottleneck (2) [8, 2048, 7, 7] [8, 2048, 7, 7] -- True\n",
"│ │ └─Conv2d (conv1) [8, 2048, 7, 7] [8, 512, 7, 7] 1,048,576 True\n",
"│ │ └─BatchNorm2d (bn1) [8, 512, 7, 7] [8, 512, 7, 7] 1,024 True\n",
"│ │ └─ReLU (relu) [8, 512, 7, 7] [8, 512, 7, 7] -- --\n",
"│ │ └─Conv2d (conv2) [8, 512, 7, 7] [8, 512, 7, 7] 2,359,296 True\n",
"│ │ └─BatchNorm2d (bn2) [8, 512, 7, 7] [8, 512, 7, 7] 1,024 True\n",
"│ │ └─ReLU (relu) [8, 512, 7, 7] [8, 512, 7, 7] -- --\n",
"│ │ └─Conv2d (conv3) [8, 512, 7, 7] [8, 2048, 7, 7] 1,048,576 True\n",
"│ │ └─BatchNorm2d (bn3) [8, 2048, 7, 7] [8, 2048, 7, 7] 4,096 True\n",
"│ │ └─ReLU (relu) [8, 2048, 7, 7] [8, 2048, 7, 7] -- --\n",
"├─AdaptiveAvgPool2d (avgpool) [8, 2048, 7, 7] [8, 2048, 1, 1] -- --\n",
"├─Linear (fc) [8, 2048] [8, 1000] 2,049,000 True\n",
"========================================================================================================================\n",
"Total params: 25,557,032\n",
"Trainable params: 25,557,032\n",
"Non-trainable params: 0\n",
"Total mult-adds (G): 32.71\n",
"========================================================================================================================\n",
"Input size (MB): 4.82\n",
"Forward/backward pass size (MB): 1422.65\n",
"Params size (MB): 102.23\n",
"Estimated Total Size (MB): 1529.70\n",
"========================================================================================================================"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from torchinfo import summary\n",
"\n",
"summary(model=model, \n",
" input_size=(8, 3, 224, 224),\n",
" col_names=[\"input_size\", \"output_size\", \"num_params\", \"trainable\"],\n",
" col_width=20,\n",
" row_settings=[\"var_names\"]\n",
") "
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"#Freeze all layers\n",
"for param in model.parameters():\n",
" param.requires_grad = False"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
"# Unfreeze last layer\n",
"for param in model.fc.parameters():\n",
" param.requires_grad = True"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"========================================================================================================================\n",
"Layer (type (var_name)) Input Shape Output Shape Param # Trainable\n",
"========================================================================================================================\n",
"ResNet (ResNet) [8, 3, 224, 224] [8, 1000] -- Partial\n",
"├─Conv2d (conv1) [8, 3, 224, 224] [8, 64, 112, 112] (9,408) False\n",
"├─BatchNorm2d (bn1) [8, 64, 112, 112] [8, 64, 112, 112] (128) False\n",
"├─ReLU (relu) [8, 64, 112, 112] [8, 64, 112, 112] -- --\n",
"├─MaxPool2d (maxpool) [8, 64, 112, 112] [8, 64, 56, 56] -- --\n",
"├─Sequential (layer1) [8, 64, 56, 56] [8, 256, 56, 56] -- False\n",
"│ └─Bottleneck (0) [8, 64, 56, 56] [8, 256, 56, 56] -- False\n",
"│ │ └─Conv2d (conv1) [8, 64, 56, 56] [8, 64, 56, 56] (4,096) False\n",
"│ │ └─BatchNorm2d (bn1) [8, 64, 56, 56] [8, 64, 56, 56] (128) False\n",
"│ │ └─ReLU (relu) [8, 64, 56, 56] [8, 64, 56, 56] -- --\n",
"│ │ └─Conv2d (conv2) [8, 64, 56, 56] [8, 64, 56, 56] (36,864) False\n",
"│ │ └─BatchNorm2d (bn2) [8, 64, 56, 56] [8, 64, 56, 56] (128) False\n",
"│ │ └─ReLU (relu) [8, 64, 56, 56] [8, 64, 56, 56] -- --\n",
"│ │ └─Conv2d (conv3) [8, 64, 56, 56] [8, 256, 56, 56] (16,384) False\n",
"│ │ └─BatchNorm2d (bn3) [8, 256, 56, 56] [8, 256, 56, 56] (512) False\n",
"│ │ └─Sequential (downsample) [8, 64, 56, 56] [8, 256, 56, 56] (16,896) False\n",
"│ │ └─ReLU (relu) [8, 256, 56, 56] [8, 256, 56, 56] -- --\n",
"│ └─Bottleneck (1) [8, 256, 56, 56] [8, 256, 56, 56] -- False\n",
"│ │ └─Conv2d (conv1) [8, 256, 56, 56] [8, 64, 56, 56] (16,384) False\n",
"│ │ └─BatchNorm2d (bn1) [8, 64, 56, 56] [8, 64, 56, 56] (128) False\n",
"│ │ └─ReLU (relu) [8, 64, 56, 56] [8, 64, 56, 56] -- --\n",
"│ │ └─Conv2d (conv2) [8, 64, 56, 56] [8, 64, 56, 56] (36,864) False\n",
"│ │ └─BatchNorm2d (bn2) [8, 64, 56, 56] [8, 64, 56, 56] (128) False\n",
"│ │ └─ReLU (relu) [8, 64, 56, 56] [8, 64, 56, 56] -- --\n",
"│ │ └─Conv2d (conv3) [8, 64, 56, 56] [8, 256, 56, 56] (16,384) False\n",
"│ │ └─BatchNorm2d (bn3) [8, 256, 56, 56] [8, 256, 56, 56] (512) False\n",
"│ │ └─ReLU (relu) [8, 256, 56, 56] [8, 256, 56, 56] -- --\n",
"│ └─Bottleneck (2) [8, 256, 56, 56] [8, 256, 56, 56] -- False\n",
"│ │ └─Conv2d (conv1) [8, 256, 56, 56] [8, 64, 56, 56] (16,384) False\n",
"│ │ └─BatchNorm2d (bn1) [8, 64, 56, 56] [8, 64, 56, 56] (128) False\n",
"│ │ └─ReLU (relu) [8, 64, 56, 56] [8, 64, 56, 56] -- --\n",
"│ │ └─Conv2d (conv2) [8, 64, 56, 56] [8, 64, 56, 56] (36,864) False\n",
"│ │ └─BatchNorm2d (bn2) [8, 64, 56, 56] [8, 64, 56, 56] (128) False\n",
"│ │ └─ReLU (relu) [8, 64, 56, 56] [8, 64, 56, 56] -- --\n",
"│ │ └─Conv2d (conv3) [8, 64, 56, 56] [8, 256, 56, 56] (16,384) False\n",
"│ │ └─BatchNorm2d (bn3) [8, 256, 56, 56] [8, 256, 56, 56] (512) False\n",
"│ │ └─ReLU (relu) [8, 256, 56, 56] [8, 256, 56, 56] -- --\n",
"├─Sequential (layer2) [8, 256, 56, 56] [8, 512, 28, 28] -- False\n",
"│ └─Bottleneck (0) [8, 256, 56, 56] [8, 512, 28, 28] -- False\n",
"│ │ └─Conv2d (conv1) [8, 256, 56, 56] [8, 128, 56, 56] (32,768) False\n",
"│ │ └─BatchNorm2d (bn1) [8, 128, 56, 56] [8, 128, 56, 56] (256) False\n",
"│ │ └─ReLU (relu) [8, 128, 56, 56] [8, 128, 56, 56] -- --\n",
"│ │ └─Conv2d (conv2) [8, 128, 56, 56] [8, 128, 28, 28] (147,456) False\n",
"│ │ └─BatchNorm2d (bn2) [8, 128, 28, 28] [8, 128, 28, 28] (256) False\n",
"│ │ └─ReLU (relu) [8, 128, 28, 28] [8, 128, 28, 28] -- --\n",
"│ │ └─Conv2d (conv3) [8, 128, 28, 28] [8, 512, 28, 28] (65,536) False\n",
"│ │ └─BatchNorm2d (bn3) [8, 512, 28, 28] [8, 512, 28, 28] (1,024) False\n",
"│ │ └─Sequential (downsample) [8, 256, 56, 56] [8, 512, 28, 28] (132,096) False\n",
"│ │ └─ReLU (relu) [8, 512, 28, 28] [8, 512, 28, 28] -- --\n",
"│ └─Bottleneck (1) [8, 512, 28, 28] [8, 512, 28, 28] -- False\n",
"│ │ └─Conv2d (conv1) [8, 512, 28, 28] [8, 128, 28, 28] (65,536) False\n",
"│ │ └─BatchNorm2d (bn1) [8, 128, 28, 28] [8, 128, 28, 28] (256) False\n",
"│ │ └─ReLU (relu) [8, 128, 28, 28] [8, 128, 28, 28] -- --\n",
"│ │ └─Conv2d (conv2) [8, 128, 28, 28] [8, 128, 28, 28] (147,456) False\n",
"│ │ └─BatchNorm2d (bn2) [8, 128, 28, 28] [8, 128, 28, 28] (256) False\n",
"│ │ └─ReLU (relu) [8, 128, 28, 28] [8, 128, 28, 28] -- --\n",
"│ │ └─Conv2d (conv3) [8, 128, 28, 28] [8, 512, 28, 28] (65,536) False\n",
"│ │ └─BatchNorm2d (bn3) [8, 512, 28, 28] [8, 512, 28, 28] (1,024) False\n",
"│ │ └─ReLU (relu) [8, 512, 28, 28] [8, 512, 28, 28] -- --\n",
"│ └─Bottleneck (2) [8, 512, 28, 28] [8, 512, 28, 28] -- False\n",
"│ │ └─Conv2d (conv1) [8, 512, 28, 28] [8, 128, 28, 28] (65,536) False\n",
"│ │ └─BatchNorm2d (bn1) [8, 128, 28, 28] [8, 128, 28, 28] (256) False\n",
"│ │ └─ReLU (relu) [8, 128, 28, 28] [8, 128, 28, 28] -- --\n",
"│ │ └─Conv2d (conv2) [8, 128, 28, 28] [8, 128, 28, 28] (147,456) False\n",
"│ │ └─BatchNorm2d (bn2) [8, 128, 28, 28] [8, 128, 28, 28] (256) False\n",
"│ │ └─ReLU (relu) [8, 128, 28, 28] [8, 128, 28, 28] -- --\n",
"│ │ └─Conv2d (conv3) [8, 128, 28, 28] [8, 512, 28, 28] (65,536) False\n",
"│ │ └─BatchNorm2d (bn3) [8, 512, 28, 28] [8, 512, 28, 28] (1,024) False\n",
"│ │ └─ReLU (relu) [8, 512, 28, 28] [8, 512, 28, 28] -- --\n",
"│ └─Bottleneck (3) [8, 512, 28, 28] [8, 512, 28, 28] -- False\n",
"│ │ └─Conv2d (conv1) [8, 512, 28, 28] [8, 128, 28, 28] (65,536) False\n",
"│ │ └─BatchNorm2d (bn1) [8, 128, 28, 28] [8, 128, 28, 28] (256) False\n",
"│ │ └─ReLU (relu) [8, 128, 28, 28] [8, 128, 28, 28] -- --\n",
"│ │ └─Conv2d (conv2) [8, 128, 28, 28] [8, 128, 28, 28] (147,456) False\n",
"│ │ └─BatchNorm2d (bn2) [8, 128, 28, 28] [8, 128, 28, 28] (256) False\n",
"│ │ └─ReLU (relu) [8, 128, 28, 28] [8, 128, 28, 28] -- --\n",
"│ │ └─Conv2d (conv3) [8, 128, 28, 28] [8, 512, 28, 28] (65,536) False\n",
"│ │ └─BatchNorm2d (bn3) [8, 512, 28, 28] [8, 512, 28, 28] (1,024) False\n",
"│ │ └─ReLU (relu) [8, 512, 28, 28] [8, 512, 28, 28] -- --\n",
"├─Sequential (layer3) [8, 512, 28, 28] [8, 1024, 14, 14] -- False\n",
"│ └─Bottleneck (0) [8, 512, 28, 28] [8, 1024, 14, 14] -- False\n",
"│ │ └─Conv2d (conv1) [8, 512, 28, 28] [8, 256, 28, 28] (131,072) False\n",
"│ │ └─BatchNorm2d (bn1) [8, 256, 28, 28] [8, 256, 28, 28] (512) False\n",
"│ │ └─ReLU (relu) [8, 256, 28, 28] [8, 256, 28, 28] -- --\n",
"│ │ └─Conv2d (conv2) [8, 256, 28, 28] [8, 256, 14, 14] (589,824) False\n",
"│ │ └─BatchNorm2d (bn2) [8, 256, 14, 14] [8, 256, 14, 14] (512) False\n",
"│ │ └─ReLU (relu) [8, 256, 14, 14] [8, 256, 14, 14] -- --\n",
"│ │ └─Conv2d (conv3) [8, 256, 14, 14] [8, 1024, 14, 14] (262,144) False\n",
"│ │ └─BatchNorm2d (bn3) [8, 1024, 14, 14] [8, 1024, 14, 14] (2,048) False\n",
"│ │ └─Sequential (downsample) [8, 512, 28, 28] [8, 1024, 14, 14] (526,336) False\n",
"│ │ └─ReLU (relu) [8, 1024, 14, 14] [8, 1024, 14, 14] -- --\n",
"│ └─Bottleneck (1) [8, 1024, 14, 14] [8, 1024, 14, 14] -- False\n",
"│ │ └─Conv2d (conv1) [8, 1024, 14, 14] [8, 256, 14, 14] (262,144) False\n",
"│ │ └─BatchNorm2d (bn1) [8, 256, 14, 14] [8, 256, 14, 14] (512) False\n",
"│ │ └─ReLU (relu) [8, 256, 14, 14] [8, 256, 14, 14] -- --\n",
"│ │ └─Conv2d (conv2) [8, 256, 14, 14] [8, 256, 14, 14] (589,824) False\n",
"│ │ └─BatchNorm2d (bn2) [8, 256, 14, 14] [8, 256, 14, 14] (512) False\n",
"│ │ └─ReLU (relu) [8, 256, 14, 14] [8, 256, 14, 14] -- --\n",
"│ │ └─Conv2d (conv3) [8, 256, 14, 14] [8, 1024, 14, 14] (262,144) False\n",
"│ │ └─BatchNorm2d (bn3) [8, 1024, 14, 14] [8, 1024, 14, 14] (2,048) False\n",
"│ │ └─ReLU (relu) [8, 1024, 14, 14] [8, 1024, 14, 14] -- --\n",
"│ └─Bottleneck (2) [8, 1024, 14, 14] [8, 1024, 14, 14] -- False\n",
"│ │ └─Conv2d (conv1) [8, 1024, 14, 14] [8, 256, 14, 14] (262,144) False\n",
"│ │ └─BatchNorm2d (bn1) [8, 256, 14, 14] [8, 256, 14, 14] (512) False\n",
"│ │ └─ReLU (relu) [8, 256, 14, 14] [8, 256, 14, 14] -- --\n",
"│ │ └─Conv2d (conv2) [8, 256, 14, 14] [8, 256, 14, 14] (589,824) False\n",
"│ │ └─BatchNorm2d (bn2) [8, 256, 14, 14] [8, 256, 14, 14] (512) False\n",
"│ │ └─ReLU (relu) [8, 256, 14, 14] [8, 256, 14, 14] -- --\n",
"│ │ └─Conv2d (conv3) [8, 256, 14, 14] [8, 1024, 14, 14] (262,144) False\n",
"│ │ └─BatchNorm2d (bn3) [8, 1024, 14, 14] [8, 1024, 14, 14] (2,048) False\n",
"│ │ └─ReLU (relu) [8, 1024, 14, 14] [8, 1024, 14, 14] -- --\n",
"│ └─Bottleneck (3) [8, 1024, 14, 14] [8, 1024, 14, 14] -- False\n",
"│ │ └─Conv2d (conv1) [8, 1024, 14, 14] [8, 256, 14, 14] (262,144) False\n",
"│ │ └─BatchNorm2d (bn1) [8, 256, 14, 14] [8, 256, 14, 14] (512) False\n",
"│ │ └─ReLU (relu) [8, 256, 14, 14] [8, 256, 14, 14] -- --\n",
"│ │ └─Conv2d (conv2) [8, 256, 14, 14] [8, 256, 14, 14] (589,824) False\n",
"│ │ └─BatchNorm2d (bn2) [8, 256, 14, 14] [8, 256, 14, 14] (512) False\n",
"│ │ └─ReLU (relu) [8, 256, 14, 14] [8, 256, 14, 14] -- --\n",
"│ │ └─Conv2d (conv3) [8, 256, 14, 14] [8, 1024, 14, 14] (262,144) False\n",
"│ │ └─BatchNorm2d (bn3) [8, 1024, 14, 14] [8, 1024, 14, 14] (2,048) False\n",
"│ │ └─ReLU (relu) [8, 1024, 14, 14] [8, 1024, 14, 14] -- --\n",
"│ └─Bottleneck (4) [8, 1024, 14, 14] [8, 1024, 14, 14] -- False\n",
"│ │ └─Conv2d (conv1) [8, 1024, 14, 14] [8, 256, 14, 14] (262,144) False\n",
"│ │ └─BatchNorm2d (bn1) [8, 256, 14, 14] [8, 256, 14, 14] (512) False\n",
"│ │ └─ReLU (relu) [8, 256, 14, 14] [8, 256, 14, 14] -- --\n",
"│ │ └─Conv2d (conv2) [8, 256, 14, 14] [8, 256, 14, 14] (589,824) False\n",
"│ │ └─BatchNorm2d (bn2) [8, 256, 14, 14] [8, 256, 14, 14] (512) False\n",
"│ │ └─ReLU (relu) [8, 256, 14, 14] [8, 256, 14, 14] -- --\n",
"│ │ └─Conv2d (conv3) [8, 256, 14, 14] [8, 1024, 14, 14] (262,144) False\n",
"│ │ └─BatchNorm2d (bn3) [8, 1024, 14, 14] [8, 1024, 14, 14] (2,048) False\n",
"│ │ └─ReLU (relu) [8, 1024, 14, 14] [8, 1024, 14, 14] -- --\n",
"│ └─Bottleneck (5) [8, 1024, 14, 14] [8, 1024, 14, 14] -- False\n",
"│ │ └─Conv2d (conv1) [8, 1024, 14, 14] [8, 256, 14, 14] (262,144) False\n",
"│ │ └─BatchNorm2d (bn1) [8, 256, 14, 14] [8, 256, 14, 14] (512) False\n",
"│ │ └─ReLU (relu) [8, 256, 14, 14] [8, 256, 14, 14] -- --\n",
"│ │ └─Conv2d (conv2) [8, 256, 14, 14] [8, 256, 14, 14] (589,824) False\n",
"│ │ └─BatchNorm2d (bn2) [8, 256, 14, 14] [8, 256, 14, 14] (512) False\n",
"│ │ └─ReLU (relu) [8, 256, 14, 14] [8, 256, 14, 14] -- --\n",
"│ │ └─Conv2d (conv3) [8, 256, 14, 14] [8, 1024, 14, 14] (262,144) False\n",
"│ │ └─BatchNorm2d (bn3) [8, 1024, 14, 14] [8, 1024, 14, 14] (2,048) False\n",
"│ │ └─ReLU (relu) [8, 1024, 14, 14] [8, 1024, 14, 14] -- --\n",
"├─Sequential (layer4) [8, 1024, 14, 14] [8, 2048, 7, 7] -- False\n",
"│ └─Bottleneck (0) [8, 1024, 14, 14] [8, 2048, 7, 7] -- False\n",
"│ │ └─Conv2d (conv1) [8, 1024, 14, 14] [8, 512, 14, 14] (524,288) False\n",
"│ │ └─BatchNorm2d (bn1) [8, 512, 14, 14] [8, 512, 14, 14] (1,024) False\n",
"│ │ └─ReLU (relu) [8, 512, 14, 14] [8, 512, 14, 14] -- --\n",
"│ │ └─Conv2d (conv2) [8, 512, 14, 14] [8, 512, 7, 7] (2,359,296) False\n",
"│ │ └─BatchNorm2d (bn2) [8, 512, 7, 7] [8, 512, 7, 7] (1,024) False\n",
"│ │ └─ReLU (relu) [8, 512, 7, 7] [8, 512, 7, 7] -- --\n",
"│ │ └─Conv2d (conv3) [8, 512, 7, 7] [8, 2048, 7, 7] (1,048,576) False\n",
"│ │ └─BatchNorm2d (bn3) [8, 2048, 7, 7] [8, 2048, 7, 7] (4,096) False\n",
"│ │ └─Sequential (downsample) [8, 1024, 14, 14] [8, 2048, 7, 7] (2,101,248) False\n",
"│ │ └─ReLU (relu) [8, 2048, 7, 7] [8, 2048, 7, 7] -- --\n",
"│ └─Bottleneck (1) [8, 2048, 7, 7] [8, 2048, 7, 7] -- False\n",
"│ │ └─Conv2d (conv1) [8, 2048, 7, 7] [8, 512, 7, 7] (1,048,576) False\n",
"│ │ └─BatchNorm2d (bn1) [8, 512, 7, 7] [8, 512, 7, 7] (1,024) False\n",
"│ │ └─ReLU (relu) [8, 512, 7, 7] [8, 512, 7, 7] -- --\n",
"│ │ └─Conv2d (conv2) [8, 512, 7, 7] [8, 512, 7, 7] (2,359,296) False\n",
"│ │ └─BatchNorm2d (bn2) [8, 512, 7, 7] [8, 512, 7, 7] (1,024) False\n",
"│ │ └─ReLU (relu) [8, 512, 7, 7] [8, 512, 7, 7] -- --\n",
"│ │ └─Conv2d (conv3) [8, 512, 7, 7] [8, 2048, 7, 7] (1,048,576) False\n",
"│ │ └─BatchNorm2d (bn3) [8, 2048, 7, 7] [8, 2048, 7, 7] (4,096) False\n",
"│ │ └─ReLU (relu) [8, 2048, 7, 7] [8, 2048, 7, 7] -- --\n",
"│ └─Bottleneck (2) [8, 2048, 7, 7] [8, 2048, 7, 7] -- False\n",
"│ │ └─Conv2d (conv1) [8, 2048, 7, 7] [8, 512, 7, 7] (1,048,576) False\n",
"│ │ └─BatchNorm2d (bn1) [8, 512, 7, 7] [8, 512, 7, 7] (1,024) False\n",
"│ │ └─ReLU (relu) [8, 512, 7, 7] [8, 512, 7, 7] -- --\n",
"│ │ └─Conv2d (conv2) [8, 512, 7, 7] [8, 512, 7, 7] (2,359,296) False\n",
"│ │ └─BatchNorm2d (bn2) [8, 512, 7, 7] [8, 512, 7, 7] (1,024) False\n",
"│ │ └─ReLU (relu) [8, 512, 7, 7] [8, 512, 7, 7] -- --\n",
"│ │ └─Conv2d (conv3) [8, 512, 7, 7] [8, 2048, 7, 7] (1,048,576) False\n",
"│ │ └─BatchNorm2d (bn3) [8, 2048, 7, 7] [8, 2048, 7, 7] (4,096) False\n",
"│ │ └─ReLU (relu) [8, 2048, 7, 7] [8, 2048, 7, 7] -- --\n",
"├─AdaptiveAvgPool2d (avgpool) [8, 2048, 7, 7] [8, 2048, 1, 1] -- --\n",
"├─Linear (fc) [8, 2048] [8, 1000] 2,049,000 True\n",
"========================================================================================================================\n",
"Total params: 25,557,032\n",
"Trainable params: 2,049,000\n",
"Non-trainable params: 23,508,032\n",
"Total mult-adds (G): 32.71\n",
"========================================================================================================================\n",
"Input size (MB): 4.82\n",
"Forward/backward pass size (MB): 1422.65\n",
"Params size (MB): 102.23\n",
"Estimated Total Size (MB): 1529.70\n",
"========================================================================================================================"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"summary(model=model, \n",
" input_size=(8, 3, 224, 224),\n",
" col_names=[\"input_size\", \"output_size\", \"num_params\", \"trainable\"],\n",
" col_width=20,\n",
" row_settings=[\"var_names\"]\n",
") "
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ResNet(\n",
" (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
" (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
" (layer1): Sequential(\n",
" (0): Bottleneck(\n",
" (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" (downsample): Sequential(\n",
" (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" (1): Bottleneck(\n",
" (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" (2): Bottleneck(\n",
" (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" )\n",
" (layer2): Sequential(\n",
" (0): Bottleneck(\n",
" (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" (downsample): Sequential(\n",
" (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
" (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" (1): Bottleneck(\n",
" (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" (2): Bottleneck(\n",
" (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" (3): Bottleneck(\n",
" (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" )\n",
" (layer3): Sequential(\n",
" (0): Bottleneck(\n",
" (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" (downsample): Sequential(\n",
" (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
" (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" (1): Bottleneck(\n",
" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" (2): Bottleneck(\n",
" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" (3): Bottleneck(\n",
" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" (4): Bottleneck(\n",
" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" (5): Bottleneck(\n",
" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" )\n",
" (layer4): Sequential(\n",
" (0): Bottleneck(\n",
" (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" (downsample): Sequential(\n",
" (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
" (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" )\n",
" )\n",
" (1): Bottleneck(\n",
" (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" (2): Bottleneck(\n",
" (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
" (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
" (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
" (relu): ReLU(inplace=True)\n",
" )\n",
" )\n",
" (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
" (fc): Linear(in_features=2048, out_features=4, bias=True)\n",
")\n"
]
}
],
"source": [
"import torch\n",
"import torch.nn as nn\n",
"import torchvision.models as models\n",
"\n",
"def resnet_output(num_classes):\n",
" model = models.resnet50(pretrained=True)\n",
"\n",
" input_size = model.fc.in_features\n",
"\n",
" # Defining the new output layer\n",
" new_fc = nn.Linear(input_size, num_classes)\n",
"\n",
" # Replacing the existing output layer with the new one\n",
" model.fc = new_fc\n",
"\n",
" return model\n",
"\n",
"# Example usage:\n",
"num_classes = 4 # Example number of classes\n",
"model = resnet_output(num_classes)\n",
"print(model)\n"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"========================================================================================================================\n",
"Layer (type (var_name)) Input Shape Output Shape Param # Trainable\n",
"========================================================================================================================\n",
"ResNet (ResNet) [8, 3, 224, 224] [8, 4] -- True\n",
"├─Conv2d (conv1) [8, 3, 224, 224] [8, 64, 112, 112] 9,408 True\n",
"├─BatchNorm2d (bn1) [8, 64, 112, 112] [8, 64, 112, 112] 128 True\n",
"├─ReLU (relu) [8, 64, 112, 112] [8, 64, 112, 112] -- --\n",
"├─MaxPool2d (maxpool) [8, 64, 112, 112] [8, 64, 56, 56] -- --\n",
"├─Sequential (layer1) [8, 64, 56, 56] [8, 256, 56, 56] -- True\n",
"│ └─Bottleneck (0) [8, 64, 56, 56] [8, 256, 56, 56] -- True\n",
"│ │ └─Conv2d (conv1) [8, 64, 56, 56] [8, 64, 56, 56] 4,096 True\n",
"│ │ └─BatchNorm2d (bn1) [8, 64, 56, 56] [8, 64, 56, 56] 128 True\n",
"│ │ └─ReLU (relu) [8, 64, 56, 56] [8, 64, 56, 56] -- --\n",
"│ │ └─Conv2d (conv2) [8, 64, 56, 56] [8, 64, 56, 56] 36,864 True\n",
"│ │ └─BatchNorm2d (bn2) [8, 64, 56, 56] [8, 64, 56, 56] 128 True\n",
"│ │ └─ReLU (relu) [8, 64, 56, 56] [8, 64, 56, 56] -- --\n",
"│ │ └─Conv2d (conv3) [8, 64, 56, 56] [8, 256, 56, 56] 16,384 True\n",
"│ │ └─BatchNorm2d (bn3) [8, 256, 56, 56] [8, 256, 56, 56] 512 True\n",
"│ │ └─Sequential (downsample) [8, 64, 56, 56] [8, 256, 56, 56] 16,896 True\n",
"│ │ └─ReLU (relu) [8, 256, 56, 56] [8, 256, 56, 56] -- --\n",
"│ └─Bottleneck (1) [8, 256, 56, 56] [8, 256, 56, 56] -- True\n",
"│ │ └─Conv2d (conv1) [8, 256, 56, 56] [8, 64, 56, 56] 16,384 True\n",
"│ │ └─BatchNorm2d (bn1) [8, 64, 56, 56] [8, 64, 56, 56] 128 True\n",
"│ │ └─ReLU (relu) [8, 64, 56, 56] [8, 64, 56, 56] -- --\n",
"│ │ └─Conv2d (conv2) [8, 64, 56, 56] [8, 64, 56, 56] 36,864 True\n",
"│ │ └─BatchNorm2d (bn2) [8, 64, 56, 56] [8, 64, 56, 56] 128 True\n",
"│ │ └─ReLU (relu) [8, 64, 56, 56] [8, 64, 56, 56] -- --\n",
"│ │ └─Conv2d (conv3) [8, 64, 56, 56] [8, 256, 56, 56] 16,384 True\n",
"│ │ └─BatchNorm2d (bn3) [8, 256, 56, 56] [8, 256, 56, 56] 512 True\n",
"│ │ └─ReLU (relu) [8, 256, 56, 56] [8, 256, 56, 56] -- --\n",
"│ └─Bottleneck (2) [8, 256, 56, 56] [8, 256, 56, 56] -- True\n",
"│ │ └─Conv2d (conv1) [8, 256, 56, 56] [8, 64, 56, 56] 16,384 True\n",
"│ │ └─BatchNorm2d (bn1) [8, 64, 56, 56] [8, 64, 56, 56] 128 True\n",
"│ │ └─ReLU (relu) [8, 64, 56, 56] [8, 64, 56, 56] -- --\n",
"│ │ └─Conv2d (conv2) [8, 64, 56, 56] [8, 64, 56, 56] 36,864 True\n",
"│ │ └─BatchNorm2d (bn2) [8, 64, 56, 56] [8, 64, 56, 56] 128 True\n",
"│ │ └─ReLU (relu) [8, 64, 56, 56] [8, 64, 56, 56] -- --\n",
"│ │ └─Conv2d (conv3) [8, 64, 56, 56] [8, 256, 56, 56] 16,384 True\n",
"│ │ └─BatchNorm2d (bn3) [8, 256, 56, 56] [8, 256, 56, 56] 512 True\n",
"│ │ └─ReLU (relu) [8, 256, 56, 56] [8, 256, 56, 56] -- --\n",
"├─Sequential (layer2) [8, 256, 56, 56] [8, 512, 28, 28] -- True\n",
"│ └─Bottleneck (0) [8, 256, 56, 56] [8, 512, 28, 28] -- True\n",
"│ │ └─Conv2d (conv1) [8, 256, 56, 56] [8, 128, 56, 56] 32,768 True\n",
"│ │ └─BatchNorm2d (bn1) [8, 128, 56, 56] [8, 128, 56, 56] 256 True\n",
"│ │ └─ReLU (relu) [8, 128, 56, 56] [8, 128, 56, 56] -- --\n",
"│ │ └─Conv2d (conv2) [8, 128, 56, 56] [8, 128, 28, 28] 147,456 True\n",
"│ │ └─BatchNorm2d (bn2) [8, 128, 28, 28] [8, 128, 28, 28] 256 True\n",
"│ │ └─ReLU (relu) [8, 128, 28, 28] [8, 128, 28, 28] -- --\n",
"│ │ └─Conv2d (conv3) [8, 128, 28, 28] [8, 512, 28, 28] 65,536 True\n",
"│ │ └─BatchNorm2d (bn3) [8, 512, 28, 28] [8, 512, 28, 28] 1,024 True\n",
"│ │ └─Sequential (downsample) [8, 256, 56, 56] [8, 512, 28, 28] 132,096 True\n",
"│ │ └─ReLU (relu) [8, 512, 28, 28] [8, 512, 28, 28] -- --\n",
"│ └─Bottleneck (1) [8, 512, 28, 28] [8, 512, 28, 28] -- True\n",
"│ │ └─Conv2d (conv1) [8, 512, 28, 28] [8, 128, 28, 28] 65,536 True\n",
"│ │ └─BatchNorm2d (bn1) [8, 128, 28, 28] [8, 128, 28, 28] 256 True\n",
"│ │ └─ReLU (relu) [8, 128, 28, 28] [8, 128, 28, 28] -- --\n",
"│ │ └─Conv2d (conv2) [8, 128, 28, 28] [8, 128, 28, 28] 147,456 True\n",
"│ │ └─BatchNorm2d (bn2) [8, 128, 28, 28] [8, 128, 28, 28] 256 True\n",
"│ │ └─ReLU (relu) [8, 128, 28, 28] [8, 128, 28, 28] -- --\n",
"│ │ └─Conv2d (conv3) [8, 128, 28, 28] [8, 512, 28, 28] 65,536 True\n",
"│ │ └─BatchNorm2d (bn3) [8, 512, 28, 28] [8, 512, 28, 28] 1,024 True\n",
"│ │ └─ReLU (relu) [8, 512, 28, 28] [8, 512, 28, 28] -- --\n",
"│ └─Bottleneck (2) [8, 512, 28, 28] [8, 512, 28, 28] -- True\n",
"│ │ └─Conv2d (conv1) [8, 512, 28, 28] [8, 128, 28, 28] 65,536 True\n",
"│ │ └─BatchNorm2d (bn1) [8, 128, 28, 28] [8, 128, 28, 28] 256 True\n",
"│ │ └─ReLU (relu) [8, 128, 28, 28] [8, 128, 28, 28] -- --\n",
"│ │ └─Conv2d (conv2) [8, 128, 28, 28] [8, 128, 28, 28] 147,456 True\n",
"│ │ └─BatchNorm2d (bn2) [8, 128, 28, 28] [8, 128, 28, 28] 256 True\n",
"│ │ └─ReLU (relu) [8, 128, 28, 28] [8, 128, 28, 28] -- --\n",
"│ │ └─Conv2d (conv3) [8, 128, 28, 28] [8, 512, 28, 28] 65,536 True\n",
"│ │ └─BatchNorm2d (bn3) [8, 512, 28, 28] [8, 512, 28, 28] 1,024 True\n",
"│ │ └─ReLU (relu) [8, 512, 28, 28] [8, 512, 28, 28] -- --\n",
"│ └─Bottleneck (3) [8, 512, 28, 28] [8, 512, 28, 28] -- True\n",
"│ │ └─Conv2d (conv1) [8, 512, 28, 28] [8, 128, 28, 28] 65,536 True\n",
"│ │ └─BatchNorm2d (bn1) [8, 128, 28, 28] [8, 128, 28, 28] 256 True\n",
"│ │ └─ReLU (relu) [8, 128, 28, 28] [8, 128, 28, 28] -- --\n",
"│ │ └─Conv2d (conv2) [8, 128, 28, 28] [8, 128, 28, 28] 147,456 True\n",
"│ │ └─BatchNorm2d (bn2) [8, 128, 28, 28] [8, 128, 28, 28] 256 True\n",
"│ │ └─ReLU (relu) [8, 128, 28, 28] [8, 128, 28, 28] -- --\n",
"│ │ └─Conv2d (conv3) [8, 128, 28, 28] [8, 512, 28, 28] 65,536 True\n",
"│ │ └─BatchNorm2d (bn3) [8, 512, 28, 28] [8, 512, 28, 28] 1,024 True\n",
"│ │ └─ReLU (relu) [8, 512, 28, 28] [8, 512, 28, 28] -- --\n",
"├─Sequential (layer3) [8, 512, 28, 28] [8, 1024, 14, 14] -- True\n",
"│ └─Bottleneck (0) [8, 512, 28, 28] [8, 1024, 14, 14] -- True\n",
"│ │ └─Conv2d (conv1) [8, 512, 28, 28] [8, 256, 28, 28] 131,072 True\n",
"│ │ └─BatchNorm2d (bn1) [8, 256, 28, 28] [8, 256, 28, 28] 512 True\n",
"│ │ └─ReLU (relu) [8, 256, 28, 28] [8, 256, 28, 28] -- --\n",
"│ │ └─Conv2d (conv2) [8, 256, 28, 28] [8, 256, 14, 14] 589,824 True\n",
"│ │ └─BatchNorm2d (bn2) [8, 256, 14, 14] [8, 256, 14, 14] 512 True\n",
"│ │ └─ReLU (relu) [8, 256, 14, 14] [8, 256, 14, 14] -- --\n",
"│ │ └─Conv2d (conv3) [8, 256, 14, 14] [8, 1024, 14, 14] 262,144 True\n",
"│ │ └─BatchNorm2d (bn3) [8, 1024, 14, 14] [8, 1024, 14, 14] 2,048 True\n",
"│ │ └─Sequential (downsample) [8, 512, 28, 28] [8, 1024, 14, 14] 526,336 True\n",
"│ │ └─ReLU (relu) [8, 1024, 14, 14] [8, 1024, 14, 14] -- --\n",
"│ └─Bottleneck (1) [8, 1024, 14, 14] [8, 1024, 14, 14] -- True\n",
"│ │ └─Conv2d (conv1) [8, 1024, 14, 14] [8, 256, 14, 14] 262,144 True\n",
"│ │ └─BatchNorm2d (bn1) [8, 256, 14, 14] [8, 256, 14, 14] 512 True\n",
"│ │ └─ReLU (relu) [8, 256, 14, 14] [8, 256, 14, 14] -- --\n",
"│ │ └─Conv2d (conv2) [8, 256, 14, 14] [8, 256, 14, 14] 589,824 True\n",
"│ │ └─BatchNorm2d (bn2) [8, 256, 14, 14] [8, 256, 14, 14] 512 True\n",
"│ │ └─ReLU (relu) [8, 256, 14, 14] [8, 256, 14, 14] -- --\n",
"│ │ └─Conv2d (conv3) [8, 256, 14, 14] [8, 1024, 14, 14] 262,144 True\n",
"│ │ └─BatchNorm2d (bn3) [8, 1024, 14, 14] [8, 1024, 14, 14] 2,048 True\n",
"│ │ └─ReLU (relu) [8, 1024, 14, 14] [8, 1024, 14, 14] -- --\n",
"│ └─Bottleneck (2) [8, 1024, 14, 14] [8, 1024, 14, 14] -- True\n",
"│ │ └─Conv2d (conv1) [8, 1024, 14, 14] [8, 256, 14, 14] 262,144 True\n",
"│ │ └─BatchNorm2d (bn1) [8, 256, 14, 14] [8, 256, 14, 14] 512 True\n",
"│ │ └─ReLU (relu) [8, 256, 14, 14] [8, 256, 14, 14] -- --\n",
"│ │ └─Conv2d (conv2) [8, 256, 14, 14] [8, 256, 14, 14] 589,824 True\n",
"│ │ └─BatchNorm2d (bn2) [8, 256, 14, 14] [8, 256, 14, 14] 512 True\n",
"│ │ └─ReLU (relu) [8, 256, 14, 14] [8, 256, 14, 14] -- --\n",
"│ │ └─Conv2d (conv3) [8, 256, 14, 14] [8, 1024, 14, 14] 262,144 True\n",
"│ │ └─BatchNorm2d (bn3) [8, 1024, 14, 14] [8, 1024, 14, 14] 2,048 True\n",
"│ │ └─ReLU (relu) [8, 1024, 14, 14] [8, 1024, 14, 14] -- --\n",
"│ └─Bottleneck (3) [8, 1024, 14, 14] [8, 1024, 14, 14] -- True\n",
"│ │ └─Conv2d (conv1) [8, 1024, 14, 14] [8, 256, 14, 14] 262,144 True\n",
"│ │ └─BatchNorm2d (bn1) [8, 256, 14, 14] [8, 256, 14, 14] 512 True\n",
"│ │ └─ReLU (relu) [8, 256, 14, 14] [8, 256, 14, 14] -- --\n",
"│ │ └─Conv2d (conv2) [8, 256, 14, 14] [8, 256, 14, 14] 589,824 True\n",
"│ │ └─BatchNorm2d (bn2) [8, 256, 14, 14] [8, 256, 14, 14] 512 True\n",
"│ │ └─ReLU (relu) [8, 256, 14, 14] [8, 256, 14, 14] -- --\n",
"│ │ └─Conv2d (conv3) [8, 256, 14, 14] [8, 1024, 14, 14] 262,144 True\n",
"│ │ └─BatchNorm2d (bn3) [8, 1024, 14, 14] [8, 1024, 14, 14] 2,048 True\n",
"│ │ └─ReLU (relu) [8, 1024, 14, 14] [8, 1024, 14, 14] -- --\n",
"│ └─Bottleneck (4) [8, 1024, 14, 14] [8, 1024, 14, 14] -- True\n",
"│ │ └─Conv2d (conv1) [8, 1024, 14, 14] [8, 256, 14, 14] 262,144 True\n",
"│ │ └─BatchNorm2d (bn1) [8, 256, 14, 14] [8, 256, 14, 14] 512 True\n",
"│ │ └─ReLU (relu) [8, 256, 14, 14] [8, 256, 14, 14] -- --\n",
"│ │ └─Conv2d (conv2) [8, 256, 14, 14] [8, 256, 14, 14] 589,824 True\n",
"│ │ └─BatchNorm2d (bn2) [8, 256, 14, 14] [8, 256, 14, 14] 512 True\n",
"│ │ └─ReLU (relu) [8, 256, 14, 14] [8, 256, 14, 14] -- --\n",
"│ │ └─Conv2d (conv3) [8, 256, 14, 14] [8, 1024, 14, 14] 262,144 True\n",
"│ │ └─BatchNorm2d (bn3) [8, 1024, 14, 14] [8, 1024, 14, 14] 2,048 True\n",
"│ │ └─ReLU (relu) [8, 1024, 14, 14] [8, 1024, 14, 14] -- --\n",
"│ └─Bottleneck (5) [8, 1024, 14, 14] [8, 1024, 14, 14] -- True\n",
"│ │ └─Conv2d (conv1) [8, 1024, 14, 14] [8, 256, 14, 14] 262,144 True\n",
"│ │ └─BatchNorm2d (bn1) [8, 256, 14, 14] [8, 256, 14, 14] 512 True\n",
"│ │ └─ReLU (relu) [8, 256, 14, 14] [8, 256, 14, 14] -- --\n",
"│ │ └─Conv2d (conv2) [8, 256, 14, 14] [8, 256, 14, 14] 589,824 True\n",
"│ │ └─BatchNorm2d (bn2) [8, 256, 14, 14] [8, 256, 14, 14] 512 True\n",
"│ │ └─ReLU (relu) [8, 256, 14, 14] [8, 256, 14, 14] -- --\n",
"│ │ └─Conv2d (conv3) [8, 256, 14, 14] [8, 1024, 14, 14] 262,144 True\n",
"│ │ └─BatchNorm2d (bn3) [8, 1024, 14, 14] [8, 1024, 14, 14] 2,048 True\n",
"│ │ └─ReLU (relu) [8, 1024, 14, 14] [8, 1024, 14, 14] -- --\n",
"├─Sequential (layer4) [8, 1024, 14, 14] [8, 2048, 7, 7] -- True\n",
"│ └─Bottleneck (0) [8, 1024, 14, 14] [8, 2048, 7, 7] -- True\n",
"│ │ └─Conv2d (conv1) [8, 1024, 14, 14] [8, 512, 14, 14] 524,288 True\n",
"│ │ └─BatchNorm2d (bn1) [8, 512, 14, 14] [8, 512, 14, 14] 1,024 True\n",
"│ │ └─ReLU (relu) [8, 512, 14, 14] [8, 512, 14, 14] -- --\n",
"│ │ └─Conv2d (conv2) [8, 512, 14, 14] [8, 512, 7, 7] 2,359,296 True\n",
"│ │ └─BatchNorm2d (bn2) [8, 512, 7, 7] [8, 512, 7, 7] 1,024 True\n",
"│ │ └─ReLU (relu) [8, 512, 7, 7] [8, 512, 7, 7] -- --\n",
"│ │ └─Conv2d (conv3) [8, 512, 7, 7] [8, 2048, 7, 7] 1,048,576 True\n",
"│ │ └─BatchNorm2d (bn3) [8, 2048, 7, 7] [8, 2048, 7, 7] 4,096 True\n",
"│ │ └─Sequential (downsample) [8, 1024, 14, 14] [8, 2048, 7, 7] 2,101,248 True\n",
"│ │ └─ReLU (relu) [8, 2048, 7, 7] [8, 2048, 7, 7] -- --\n",
"│ └─Bottleneck (1) [8, 2048, 7, 7] [8, 2048, 7, 7] -- True\n",
"│ │ └─Conv2d (conv1) [8, 2048, 7, 7] [8, 512, 7, 7] 1,048,576 True\n",
"│ │ └─BatchNorm2d (bn1) [8, 512, 7, 7] [8, 512, 7, 7] 1,024 True\n",
"│ │ └─ReLU (relu) [8, 512, 7, 7] [8, 512, 7, 7] -- --\n",
"│ │ └─Conv2d (conv2) [8, 512, 7, 7] [8, 512, 7, 7] 2,359,296 True\n",
"│ │ └─BatchNorm2d (bn2) [8, 512, 7, 7] [8, 512, 7, 7] 1,024 True\n",
"│ │ └─ReLU (relu) [8, 512, 7, 7] [8, 512, 7, 7] -- --\n",
"│ │ └─Conv2d (conv3) [8, 512, 7, 7] [8, 2048, 7, 7] 1,048,576 True\n",
"│ │ └─BatchNorm2d (bn3) [8, 2048, 7, 7] [8, 2048, 7, 7] 4,096 True\n",
"│ │ └─ReLU (relu) [8, 2048, 7, 7] [8, 2048, 7, 7] -- --\n",
"│ └─Bottleneck (2) [8, 2048, 7, 7] [8, 2048, 7, 7] -- True\n",
"│ │ └─Conv2d (conv1) [8, 2048, 7, 7] [8, 512, 7, 7] 1,048,576 True\n",
"│ │ └─BatchNorm2d (bn1) [8, 512, 7, 7] [8, 512, 7, 7] 1,024 True\n",
"│ │ └─ReLU (relu) [8, 512, 7, 7] [8, 512, 7, 7] -- --\n",
"│ │ └─Conv2d (conv2) [8, 512, 7, 7] [8, 512, 7, 7] 2,359,296 True\n",
"│ │ └─BatchNorm2d (bn2) [8, 512, 7, 7] [8, 512, 7, 7] 1,024 True\n",
"│ │ └─ReLU (relu) [8, 512, 7, 7] [8, 512, 7, 7] -- --\n",
"│ │ └─Conv2d (conv3) [8, 512, 7, 7] [8, 2048, 7, 7] 1,048,576 True\n",
"│ │ └─BatchNorm2d (bn3) [8, 2048, 7, 7] [8, 2048, 7, 7] 4,096 True\n",
"│ │ └─ReLU (relu) [8, 2048, 7, 7] [8, 2048, 7, 7] -- --\n",
"├─AdaptiveAvgPool2d (avgpool) [8, 2048, 7, 7] [8, 2048, 1, 1] -- --\n",
"├─Linear (fc) [8, 2048] [8, 4] 8,196 True\n",
"========================================================================================================================\n",
"Total params: 23,516,228\n",
"Trainable params: 23,516,228\n",
"Non-trainable params: 0\n",
"Total mult-adds (G): 32.70\n",
"========================================================================================================================\n",
"Input size (MB): 4.82\n",
"Forward/backward pass size (MB): 1422.59\n",
"Params size (MB): 94.06\n",
"Estimated Total Size (MB): 1521.47\n",
"========================================================================================================================"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"summary(model, \n",
" input_size=(8, 3, 224, 224), # make sure this is \"input_size\", not \"input_shape\" (batch_size, color_channels, height, width)\n",
" verbose=0,\n",
" col_names=[\"input_size\", \"output_size\", \"num_params\", \"trainable\"],\n",
" col_width=20,\n",
" row_settings=[\"var_names\"]\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"loss_fn = nn.CrossEntropyLoss()\n",
"optimizer = torch.optim.Adam(model.parameters(), lr=0.001, betas=(0.9, 0.99))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Training the model"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" 0%| | 0/20 [00:00<?, ?it/s]"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch: 1 | train_loss: 1.267 | train_acc: 0.408 | test_loss: 1.268 | test_acc: 0.456\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
" 5%|▌ | 1/20 [06:51<2:10:12, 411.21s/it]"
]
},
{
"name": "stdout",
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"Epoch: 20 | train_loss: 0.285 | train_acc: 0.894 | test_loss: 0.213 | test_acc: 0.930\n"
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],
"source": [
"torch.manual_seed(42)\n",
"torch.cuda.manual_seed(42)\n",
"import src.engine as engine\n",
"from timeit import default_timer as timer \n",
"\n",
"start_time = timer()\n",
"results = engine.train(model=model,\n",
" train_dataloader=train_dataloader_augmented,\n",
" test_dataloader=test_dataloader_augmented,\n",
" optimizer=optimizer,\n",
" loss_fn=loss_fn,\n",
" epochs=20,\n",
" device=device)"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"from src.helper_function import plot_loss_curves \n",
"plot_loss_curves(results)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.10.14"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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