diff --git "a/Vaani/LDM/notebooks/_2_Rough-LPIPS.ipynb" "b/Vaani/LDM/notebooks/_2_Rough-LPIPS.ipynb" new file mode 100644--- /dev/null +++ "b/Vaani/LDM/notebooks/_2_Rough-LPIPS.ipynb" @@ -0,0 +1,2554 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import torchvision\n", + "import torch\n", + "import torch.nn as nn\n", + "from torchvision import models, transforms, datasets\n", + "from torch.utils.data import DataLoader\n", + "import matplotlib.pyplot as plt\n", + "from collections import namedtuple\n", + "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Downloading: \"https://download.pytorch.org/models/vgg16-397923af.pth\" to /home/23m1521/.cache/torch/hub/checkpoints/vgg16-397923af.pth\n", + "100%|██████████| 528M/528M [00:04<00:00, 117MB/s] \n" + ] + } + ], + "source": [ + "vgg_pretrained_features = torchvision.models.vgg16(\n", + "weights=torchvision.models.VGG16_Weights.IMAGENET1K_V1\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "========================================================================================================================\n", + "Layer (type (var_name)) Input Shape Output Shape Param # Param %\n", + "========================================================================================================================\n", + "Sequential (Sequential) [1, 3, 224, 224] [1, 512, 7, 7] -- --\n", + "├─Conv2d (0) [1, 3, 224, 224] [1, 64, 224, 224] 1,792 0.01%\n", + "├─ReLU (1) [1, 64, 224, 224] [1, 64, 224, 224] -- --\n", + "├─Conv2d (2) [1, 64, 224, 224] [1, 64, 224, 224] 36,928 0.25%\n", + "├─ReLU (3) [1, 64, 224, 224] [1, 64, 224, 224] -- --\n", + "├─MaxPool2d (4) [1, 64, 224, 224] [1, 64, 112, 112] -- --\n", + "├─Conv2d (5) [1, 64, 112, 112] [1, 128, 112, 112] 73,856 0.50%\n", + "├─ReLU (6) [1, 128, 112, 112] [1, 128, 112, 112] -- --\n", + "├─Conv2d (7) [1, 128, 112, 112] [1, 128, 112, 112] 147,584 1.00%\n", + "├─ReLU (8) [1, 128, 112, 112] [1, 128, 112, 112] -- --\n", + "├─MaxPool2d (9) [1, 128, 112, 112] [1, 128, 56, 56] -- --\n", + "├─Conv2d (10) [1, 128, 56, 56] [1, 256, 56, 56] 295,168 2.01%\n", + "├─ReLU (11) [1, 256, 56, 56] [1, 256, 56, 56] -- --\n", + "├─Conv2d (12) [1, 256, 56, 56] [1, 256, 56, 56] 590,080 4.01%\n", + "├─ReLU (13) [1, 256, 56, 56] [1, 256, 56, 56] -- --\n", + "├─Conv2d (14) [1, 256, 56, 56] [1, 256, 56, 56] 590,080 4.01%\n", + "├─ReLU (15) [1, 256, 56, 56] [1, 256, 56, 56] -- --\n", + "├─MaxPool2d (16) [1, 256, 56, 56] [1, 256, 28, 28] -- --\n", + "├─Conv2d (17) [1, 256, 28, 28] [1, 512, 28, 28] 1,180,160 8.02%\n", + "├─ReLU (18) [1, 512, 28, 28] [1, 512, 28, 28] -- --\n", + "├─Conv2d (19) [1, 512, 28, 28] [1, 512, 28, 28] 2,359,808 16.04%\n", + "├─ReLU (20) [1, 512, 28, 28] [1, 512, 28, 28] -- --\n", + "├─Conv2d (21) [1, 512, 28, 28] [1, 512, 28, 28] 2,359,808 16.04%\n", + "├─ReLU (22) [1, 512, 28, 28] [1, 512, 28, 28] -- --\n", + "├─MaxPool2d (23) [1, 512, 28, 28] [1, 512, 14, 14] -- --\n", + "├─Conv2d (24) [1, 512, 14, 14] [1, 512, 14, 14] 2,359,808 16.04%\n", + "├─ReLU (25) [1, 512, 14, 14] [1, 512, 14, 14] -- --\n", + "├─Conv2d (26) [1, 512, 14, 14] [1, 512, 14, 14] 2,359,808 16.04%\n", + "├─ReLU (27) [1, 512, 14, 14] [1, 512, 14, 14] -- --\n", + "├─Conv2d (28) [1, 512, 14, 14] [1, 512, 14, 14] 2,359,808 16.04%\n", + "├─ReLU (29) [1, 512, 14, 14] [1, 512, 14, 14] -- --\n", + "├─MaxPool2d (30) [1, 512, 14, 14] [1, 512, 7, 7] -- --\n", + "========================================================================================================================\n", + "Total params: 14,714,688\n", + "Trainable params: 14,714,688\n", + "Non-trainable params: 0\n", + "Total mult-adds (Units.GIGABYTES): 15.36\n", + "========================================================================================================================\n", + "Input size (MB): 0.60\n", + "Forward/backward pass size (MB): 108.38\n", + "Params size (MB): 58.86\n", + "Estimated Total Size (MB): 167.84\n", + "========================================================================================================================" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from torchinfo import summary\n", + "\n", + "summary(model=vgg_pretrained_features.features,\n", + " input_size=(1, 3, 224, 224),\n", + " # input_data=x,\n", + " col_names = [\"input_size\", \"output_size\", \"num_params\", \"params_percent\"],\n", + " col_width=20,\n", + " row_settings=[\"var_names\"],\n", + " depth = 5,\n", + " device=device\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Downloading: \"https://download.pytorch.org/models/efficientnet_b0_rwightman-7f5810bc.pth\" to /home/23m1521/.cache/torch/hub/checkpoints/efficientnet_b0_rwightman-7f5810bc.pth\n", + "100%|██████████| 20.5M/20.5M [00:00<00:00, 113MB/s]\n" + ] + } + ], + "source": [ + "efnet_pretrained_features = torchvision.models.efficientnet_b0(\n", + "weights=torchvision.models.EfficientNet_B0_Weights.IMAGENET1K_V1\n", + ").features" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "============================================================================================================================================\n", + "Layer (type (var_name)) Input Shape Output Shape Param # Param %\n", + "============================================================================================================================================\n", + "EfficientNet (EfficientNet) [1, 3, 224, 224] [1, 1000] -- --\n", + "├─Sequential (features) [1, 3, 224, 224] [1, 1280, 7, 7] -- --\n", + "│ └─Conv2dNormActivation (0) [1, 3, 224, 224] [1, 32, 112, 112] -- --\n", + "│ │ └─Conv2d (0) [1, 3, 224, 224] [1, 32, 112, 112] 864 0.02%\n", + "│ │ └─BatchNorm2d (1) [1, 32, 112, 112] [1, 32, 112, 112] 64 0.00%\n", + "│ │ └─SiLU (2) [1, 32, 112, 112] [1, 32, 112, 112] -- --\n", + "│ └─Sequential (1) [1, 32, 112, 112] [1, 16, 112, 112] -- --\n", + "│ │ └─MBConv (0) [1, 32, 112, 112] [1, 16, 112, 112] 1,448 0.03%\n", + "│ └─Sequential (2) [1, 16, 112, 112] [1, 24, 56, 56] -- --\n", + "│ │ └─MBConv (0) [1, 16, 112, 112] [1, 24, 56, 56] 6,004 0.11%\n", + "│ │ └─MBConv (1) [1, 24, 56, 56] [1, 24, 56, 56] 10,710 0.20%\n", + "│ └─Sequential (3) [1, 24, 56, 56] [1, 40, 28, 28] -- --\n", + "│ │ └─MBConv (0) [1, 24, 56, 56] [1, 40, 28, 28] 15,350 0.29%\n", + "│ │ └─MBConv (1) [1, 40, 28, 28] [1, 40, 28, 28] 31,290 0.59%\n", + "│ └─Sequential (4) [1, 40, 28, 28] [1, 80, 14, 14] -- --\n", + "│ │ └─MBConv (0) [1, 40, 28, 28] [1, 80, 14, 14] 37,130 0.70%\n", + "│ │ └─MBConv (1) [1, 80, 14, 14] [1, 80, 14, 14] 102,900 1.95%\n", + "│ │ └─MBConv (2) [1, 80, 14, 14] [1, 80, 14, 14] 102,900 1.95%\n", + "│ └─Sequential (5) [1, 80, 14, 14] [1, 112, 14, 14] -- --\n", + "│ │ └─MBConv (0) [1, 80, 14, 14] [1, 112, 14, 14] 126,004 2.38%\n", + "│ │ └─MBConv (1) [1, 112, 14, 14] [1, 112, 14, 14] 208,572 3.94%\n", + "│ │ └─MBConv (2) [1, 112, 14, 14] [1, 112, 14, 14] 208,572 3.94%\n", + "│ └─Sequential (6) [1, 112, 14, 14] [1, 192, 7, 7] -- --\n", + "│ │ └─MBConv (0) [1, 112, 14, 14] [1, 192, 7, 7] 262,492 4.96%\n", + "│ │ └─MBConv (1) [1, 192, 7, 7] [1, 192, 7, 7] 587,952 11.12%\n", + "│ │ └─MBConv (2) [1, 192, 7, 7] [1, 192, 7, 7] 587,952 11.12%\n", + "│ │ └─MBConv (3) [1, 192, 7, 7] [1, 192, 7, 7] 587,952 11.12%\n", + "│ └─Sequential (7) [1, 192, 7, 7] [1, 320, 7, 7] -- --\n", + "│ │ └─MBConv (0) [1, 192, 7, 7] [1, 320, 7, 7] 717,232 13.56%\n", + "│ └─Conv2dNormActivation (8) [1, 320, 7, 7] [1, 1280, 7, 7] -- --\n", + "│ │ └─Conv2d (0) [1, 320, 7, 7] [1, 1280, 7, 7] 409,600 7.75%\n", + "│ │ └─BatchNorm2d (1) [1, 1280, 7, 7] [1, 1280, 7, 7] 2,560 0.05%\n", + "│ │ └─SiLU (2) [1, 1280, 7, 7] [1, 1280, 7, 7] -- --\n", + "├─AdaptiveAvgPool2d (avgpool) [1, 1280, 7, 7] [1, 1280, 1, 1] -- --\n", + "├─Sequential (classifier) [1, 1280] [1, 1000] -- --\n", + "│ └─Dropout (0) [1, 1280] [1, 1280] -- --\n", + "│ └─Linear (1) [1, 1280] [1, 1000] 1,281,000 24.22%\n", + "============================================================================================================================================\n", + "Total params: 5,288,548\n", + "Trainable params: 5,288,548\n", + "Non-trainable params: 0\n", + "Total mult-adds (Units.MEGABYTES): 385.87\n", + "============================================================================================================================================\n", + "Input size (MB): 0.60\n", + "Forward/backward pass size (MB): 107.89\n", + "Params size (MB): 21.15\n", + "Estimated Total Size (MB): 129.64\n", + "============================================================================================================================================" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from torchinfo import summary\n", + "\n", + "summary(model=efnet_pretrained_features,\n", + " input_size=(1, 3, 224, 224),\n", + " # input_data=x,\n", + " col_names = [\"input_size\", \"output_size\", \"num_params\", \"params_percent\"],\n", + " col_width=20,\n", + " row_settings=[\"var_names\"],\n", + " depth = 3,\n", + " device=device\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "from torchvision import models, transforms, datasets\n", + "from torch.utils.data import DataLoader\n", + "import matplotlib.pyplot as plt\n", + "from collections import namedtuple\n", + "\n", + "# Function to preprocess MNIST images\n", + "def preprocess_mnist(image):\n", + " transform = transforms.Compose([\n", + " transforms.Resize((224, 224)), # Resize to match VGG16 input size\n", + " transforms.Grayscale(num_output_channels=3), # Convert grayscale to 3-channel\n", + " transforms.ToTensor(),\n", + " transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) # Normalize for pretrained models\n", + " ])\n", + " return transform(image)\n", + "\n", + "# Spatial averaging function\n", + "def spatial_average(in_tens, keepdim=True):\n", + " return in_tens.mean([2, 3], keepdim=keepdim)\n", + "\n", + "# VGG16 feature extractor\n", + "class vgg16(nn.Module):\n", + " def __init__(self, requires_grad=False, pretrained=True):\n", + " super(vgg16, self).__init__()\n", + " vgg_pretrained_features = models.vgg16(weights=models.VGG16_Weights.IMAGENET1K_V1).features\n", + " self.slice1 = nn.Sequential()\n", + " self.slice2 = nn.Sequential()\n", + " self.slice3 = nn.Sequential()\n", + " self.slice4 = nn.Sequential()\n", + " self.slice5 = nn.Sequential()\n", + " self.N_slices = 5\n", + " for x in range(4):\n", + " self.slice1.add_module(str(x), vgg_pretrained_features[x])\n", + " for x in range(4, 9):\n", + " self.slice2.add_module(str(x), vgg_pretrained_features[x])\n", + " for x in range(9, 16):\n", + " self.slice3.add_module(str(x), vgg_pretrained_features[x])\n", + " for x in range(16, 23):\n", + " self.slice4.add_module(str(x), vgg_pretrained_features[x])\n", + " for x in range(23, 30):\n", + " self.slice5.add_module(str(x), vgg_pretrained_features[x])\n", + "\n", + " # Freeze vgg model\n", + " if not requires_grad:\n", + " for param in self.parameters():\n", + " param.requires_grad = False\n", + "\n", + " def forward(self, X):\n", + " h = self.slice1(X)\n", + " h_relu1_2 = h\n", + " h = self.slice2(h)\n", + " h_relu2_2 = h\n", + " h = self.slice3(h)\n", + " h_relu3_3 = h\n", + " h = self.slice4(h)\n", + " h_relu4_3 = h\n", + " h = self.slice5(h)\n", + " h_relu5_3 = h\n", + " vgg_outputs = namedtuple(\"VggOutputs\", ['relu1_2', 'relu2_2', 'relu3_3', 'relu4_3', 'relu5_3'])\n", + " out = vgg_outputs(h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3, h_relu5_3)\n", + " return out\n", + "\n", + "# Scaling layer for input normalization\n", + "class ScalingLayer(nn.Module):\n", + " def __init__(self):\n", + " super(ScalingLayer, self).__init__()\n", + " self.register_buffer('shift', torch.Tensor([-.030, -.088, -.188])[None, :, None, None])\n", + " self.register_buffer('scale', torch.Tensor([.458, .448, .450])[None, :, None, None])\n", + "\n", + " def forward(self, inp):\n", + " return (inp - self.shift) / self.scale\n", + "\n", + "# Linear layer for LPIPS\n", + "class NetLinLayer(nn.Module):\n", + " def __init__(self, chn_in, chn_out=1, use_dropout=False):\n", + " super(NetLinLayer, self).__init__()\n", + " layers = [nn.Dropout(), ] if (use_dropout) else []\n", + " layers += [nn.Conv2d(chn_in, chn_out, 1, stride=1, padding=0, bias=False), ]\n", + " self.model = nn.Sequential(*layers)\n", + "\n", + " def forward(self, x):\n", + " return self.model(x)\n", + "\n", + "# LPIPS metric\n", + "class LPIPS(nn.Module):\n", + " def __init__(self, net='vgg', version='0.1', use_dropout=True):\n", + " super(LPIPS, self).__init__()\n", + " self.version = version\n", + " self.scaling_layer = ScalingLayer()\n", + " self.chns = [64, 128, 256, 512, 512]\n", + " self.L = len(self.chns)\n", + " self.net = vgg16(pretrained=True, requires_grad=False)\n", + " self.lin0 = NetLinLayer(self.chns[0], use_dropout=use_dropout)\n", + " self.lin1 = NetLinLayer(self.chns[1], use_dropout=use_dropout)\n", + " self.lin2 = NetLinLayer(self.chns[2], use_dropout=use_dropout)\n", + " self.lin3 = NetLinLayer(self.chns[3], use_dropout=use_dropout)\n", + " self.lin4 = NetLinLayer(self.chns[4], use_dropout=use_dropout)\n", + " self.lins = nn.ModuleList([self.lin0, self.lin1, self.lin2, self.lin3, self.lin4])\n", + " self.eval()\n", + " for param in self.parameters():\n", + " param.requires_grad = False\n", + "\n", + " def forward(self, in0, in1, normalize=False):\n", + " if normalize:\n", + " in0 = 2 * in0 - 1\n", + " in1 = 2 * in1 - 1\n", + " in0_input, in1_input = self.scaling_layer(in0), self.scaling_layer(in1)\n", + " outs0, outs1 = self.net(in0_input), self.net(in1_input)\n", + " diffs = {}\n", + " for kk in range(self.L):\n", + " feats0, feats1 = torch.nn.functional.normalize(outs0[kk], dim=1), torch.nn.functional.normalize(outs1[kk])\n", + " diffs[kk] = (feats0 - feats1) ** 2\n", + " res = [spatial_average(self.lins[kk](diffs[kk]), keepdim=True) for kk in range(self.L)]\n", + " val = sum(res)\n", + " return val\n", + "\n", + "# Load MNIST dataset\n", + "mnist_dataset = datasets.MNIST(root='./data', train=False, download=True, transform=preprocess_mnist)\n", + "mnist_loader = DataLoader(mnist_dataset, batch_size=1, shuffle=True)\n", + "\n", + "# Initialize LPIPS model\n", + "lpips_model = LPIPS(net='vgg').to(torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\"))\n", + "\n", + "# Compare perceptual loss for a few pairs of images\n", + "num_pairs = 5 # Number of image pairs to compare\n", + "for i, (image1, label1) in enumerate(mnist_loader):\n", + " if i >= num_pairs:\n", + " break\n", + " for j, (image2, label2) in enumerate(mnist_loader):\n", + " if j >= num_pairs:\n", + " break\n", + " if i == j:\n", + " continue # Skip comparing the same image\n", + "\n", + " # Move images to device\n", + " image1 = image1.to(device)\n", + " image2 = image2.to(device)\n", + "\n", + " # Compute LPIPS score\n", + " lpips_score = lpips_model(image1, image2, normalize=True).item()\n", + "\n", + " # Print results\n", + " print(f\"Image Pair: {i} (Label: {label1.item()}) vs {j} (Label: {label2.item()})\")\n", + " print(f\"LPIPS Score: {lpips_score:.4f}\")\n", + " print(\"-\" * 50)\n", + "\n", + " # Display images (optional)\n", + " plt.figure(figsize=(4, 2))\n", + " plt.subplot(1, 2, 1)\n", + " plt.imshow(image1.squeeze().cpu().permute(1, 2, 0).numpy()[:, :, 0], cmap='gray')\n", + " plt.title(f\"Image {i} (Label: {label1.item()})\")\n", + " plt.axis('off')\n", + "\n", + " plt.subplot(1, 2, 2)\n", + " plt.imshow(image2.squeeze().cpu().permute(1, 2, 0).numpy()[:, :, 0], cmap='gray')\n", + " plt.title(f\"Image {j} (Label: {label2.item()})\")\n", + " plt.axis('off')\n", + "\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# ============================================================================================================================================\n", + "# Layer (type (var_name)) Input Shape Output Shape Param # Param %\n", + "# ============================================================================================================================================\n", + "# EfficientNet (EfficientNet) [1, 3, 224, 224] [1, 1000] -- --\n", + "# ├─Sequential (features) [1, 3, 224, 224] [1, 1280, 7, 7] -- --\n", + "\n", + "\n", + "# │ └─Conv2dNormActivation (0) [1, 3, 224, 224] [1, 32, 112, 112] -- --\n", + "# │ │ └─Conv2d (0) [1, 3, 224, 224] [1, 32, 112, 112] 864 0.02%\n", + "# │ │ └─BatchNorm2d (1) [1, 32, 112, 112] [1, 32, 112, 112] 64 0.00%\n", + "# │ │ └─SiLU (2) [1, 32, 112, 112] [1, 32, 112, 112] -- --\n", + "\n", + "# │ └─Sequential (1) [1, 32, 112, 112] [1, 16, 112, 112] -- --\n", + "# │ │ └─MBConv (0) [1, 32, 112, 112] [1, 16, 112, 112] 1,448 0.03%\n", + "\n", + "# │ └─Sequential (2) [1, 16, 112, 112] [1, 24, 56, 56] -- --\n", + "# │ │ └─MBConv (0) [1, 16, 112, 112] [1, 24, 56, 56] 6,004 0.11%\n", + "# │ │ └─MBConv (1) [1, 24, 56, 56] [1, 24, 56, 56] 10,710 0.20%\n", + "\n", + "# │ └─Sequential (3) [1, 24, 56, 56] [1, 40, 28, 28] -- --\n", + "# │ │ └─MBConv (0) [1, 24, 56, 56] [1, 40, 28, 28] 15,350 0.29%\n", + "# │ │ └─MBConv (1) [1, 40, 28, 28] [1, 40, 28, 28] 31,290 0.59%\n", + "\n", + "# │ └─Sequential (4) [1, 40, 28, 28] [1, 80, 14, 14] -- --\n", + "# │ │ └─MBConv (0) [1, 40, 28, 28] [1, 80, 14, 14] 37,130 0.70%\n", + "# │ │ └─MBConv (1) [1, 80, 14, 14] [1, 80, 14, 14] 102,900 1.95%\n", + "# │ │ └─MBConv (2) [1, 80, 14, 14] [1, 80, 14, 14] 102,900 1.95%\n", + "\n", + "# │ └─Sequential (5) [1, 80, 14, 14] [1, 112, 14, 14] -- --\n", + "# │ │ └─MBConv (0) [1, 80, 14, 14] [1, 112, 14, 14] 126,004 2.38%\n", + "# │ │ └─MBConv (1) [1, 112, 14, 14] [1, 112, 14, 14] 208,572 3.94%\n", + "# │ │ └─MBConv (2) [1, 112, 14, 14] [1, 112, 14, 14] 208,572 3.94%\n", + "\n", + "# │ └─Sequential (6) [1, 112, 14, 14] [1, 192, 7, 7] -- --\n", + "# │ │ └─MBConv (0) [1, 112, 14, 14] [1, 192, 7, 7] 262,492 4.96%\n", + "# │ │ └─MBConv (1) [1, 192, 7, 7] [1, 192, 7, 7] 587,952 11.12%\n", + "# │ │ └─MBConv (2) [1, 192, 7, 7] [1, 192, 7, 7] 587,952 11.12%\n", + "# │ │ └─MBConv (3) [1, 192, 7, 7] [1, 192, 7, 7] 587,952 11.12%\n", + "\n", + "# │ └─Sequential (7) [1, 192, 7, 7] [1, 320, 7, 7] -- --\n", + "# │ │ └─MBConv (0) [1, 192, 7, 7] [1, 320, 7, 7] 717,232 13.56%\n", + "\n", + "# │ └─Conv2dNormActivation (8) [1, 320, 7, 7] [1, 1280, 7, 7] -- --\n", + "# │ │ └─Conv2d (0) [1, 320, 7, 7] [1, 1280, 7, 7] 409,600 7.75%\n", + "# │ │ └─BatchNorm2d (1) [1, 1280, 7, 7] [1, 1280, 7, 7] 2,560 0.05%\n", + "# │ │ └─SiLU (2) [1, 1280, 7, 7] [1, 1280, 7, 7] -- --" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "efnet_pretrained_features = torchvision.models.efficientnet_b0(\n", + "weights=torchvision.models.EfficientNet_B0_Weights.IMAGENET1K_V1\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [], + "source": [ + "Conv2dNormActivation1 = efnet_pretrained_features.features[0]\n", + "\n", + "MBConv1 = efnet_pretrained_features.features[1][0]\n", + "\n", + "MBConv2 = efnet_pretrained_features.features[2][0]\n", + "MBConv3 = efnet_pretrained_features.features[2][1]\n", + "\n", + "MBConv4 = efnet_pretrained_features.features[3][0]\n", + "MBConv5 = efnet_pretrained_features.features[3][1]\n", + "\n", + "MBConv6 = efnet_pretrained_features.features[4][0]\n", + "MBConv7 = efnet_pretrained_features.features[4][1]\n", + "MBConv8 = efnet_pretrained_features.features[4][2]\n", + "\n", + "MBConv9 = efnet_pretrained_features.features[5][0]\n", + "MBConv10 = efnet_pretrained_features.features[5][1]\n", + "MBConv11 = efnet_pretrained_features.features[5][2]\n", + "\n", + "MBConv12 = efnet_pretrained_features.features[6][0]\n", + "MBConv13 = efnet_pretrained_features.features[6][1]\n", + "MBConv14 = efnet_pretrained_features.features[6][2]\n", + "MBConv15 = efnet_pretrained_features.features[6][3]\n", + "\n", + "MBConv16 = efnet_pretrained_features.features[7][0]\n", + "\n", + "Conv2dNormActivation2 = efnet_pretrained_features.features[8]\n", + "\n", + "\n", + "EfficientNet_Features = nn.Sequential(\n", + " Conv2dNormActivation1,\n", + " MBConv1,\n", + " MBConv2,\n", + " MBConv3,\n", + " MBConv4,\n", + " MBConv5,\n", + " MBConv6,\n", + " MBConv7,\n", + " MBConv8,\n", + " MBConv9,\n", + " \n", + " MBConv10,\n", + " MBConv11,\n", + " \n", + " MBConv12,\n", + " MBConv13,\n", + " \n", + " MBConv14,\n", + " \n", + " MBConv15,\n", + " \n", + " MBConv16,\n", + " \n", + " Conv2dNormActivation2\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "==================================================================================================================================\n", + "Layer (type (var_name)) Input Shape Output Shape Param # Param %\n", + "==================================================================================================================================\n", + "Sequential (Sequential) [32, 3, 224, 224] [32, 1280, 7, 7] -- --\n", + "├─Conv2dNormActivation (0) [32, 3, 224, 224] [32, 32, 112, 112] 928 0.02%\n", + "├─MBConv (1) [32, 32, 112, 112] [32, 16, 112, 112] 1,448 0.04%\n", + "├─MBConv (2) [32, 16, 112, 112] [32, 24, 56, 56] 6,004 0.15%\n", + "├─MBConv (3) [32, 24, 56, 56] [32, 24, 56, 56] 10,710 0.27%\n", + "├─MBConv (4) [32, 24, 56, 56] [32, 40, 28, 28] 15,350 0.38%\n", + "├─MBConv (5) [32, 40, 28, 28] [32, 40, 28, 28] 31,290 0.78%\n", + "├─MBConv (6) [32, 40, 28, 28] [32, 80, 14, 14] 37,130 0.93%\n", + "├─MBConv (7) [32, 80, 14, 14] [32, 80, 14, 14] 102,900 2.57%\n", + "├─MBConv (8) [32, 80, 14, 14] [32, 80, 14, 14] 102,900 2.57%\n", + "├─MBConv (9) [32, 80, 14, 14] [32, 112, 14, 14] 126,004 3.14%\n", + "├─MBConv (10) [32, 112, 14, 14] [32, 112, 14, 14] 208,572 5.20%\n", + "├─MBConv (11) [32, 112, 14, 14] [32, 112, 14, 14] 208,572 5.20%\n", + "├─MBConv (12) [32, 112, 14, 14] [32, 192, 7, 7] 262,492 6.55%\n", + "├─MBConv (13) [32, 192, 7, 7] [32, 192, 7, 7] 587,952 14.67%\n", + "├─MBConv (14) [32, 192, 7, 7] [32, 192, 7, 7] 587,952 14.67%\n", + "├─MBConv (15) [32, 192, 7, 7] [32, 192, 7, 7] 587,952 14.67%\n", + "├─MBConv (16) [32, 192, 7, 7] [32, 320, 7, 7] 717,232 17.90%\n", + "├─Conv2dNormActivation (17) [32, 320, 7, 7] [32, 1280, 7, 7] 412,160 10.28%\n", + "==================================================================================================================================\n", + "Total params: 4,007,548\n", + "Trainable params: 4,007,548\n", + "Non-trainable params: 0\n", + "Total mult-adds (Units.GIGABYTES): 12.31\n", + "==================================================================================================================================\n", + "Input size (MB): 19.27\n", + "Forward/backward pass size (MB): 3452.09\n", + "Params size (MB): 16.03\n", + "Estimated Total Size (MB): 3487.39\n", + "==================================================================================================================================" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from torchinfo import summary\n", + "summary(model=EfficientNet_Features,\n", + " input_size=(32, 3, 224, 224),\n", + " # input_data=x,\n", + " col_names = [\"input_size\", \"output_size\", \"num_params\", \"params_percent\"],\n", + " col_width=20,\n", + " row_settings=[\"var_names\"],\n", + " depth = 1,\n", + " device=device\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total parameters: 4007548\n", + "\n", + "Slice 1:\n", + "Parameters in slice: 434664 (10.85%)\n", + "\n", + "Slice 2:\n", + "Parameters in slice: 417144 (10.41%)\n", + "\n", + "Slice 3:\n", + "Parameters in slice: 850444 (21.22%)\n", + "\n", + "Slice 4:\n", + "Parameters in slice: 587952 (14.67%)\n", + "\n", + "Slice 5:\n", + "Parameters in slice: 587952 (14.67%)\n", + "\n", + "Slice 6:\n", + "Parameters in slice: 717232 (17.90%)\n", + "\n", + "Slice 7:\n", + "Parameters in slice: 412160 (10.28%)\n" + ] + } + ], + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "from torchvision import models\n", + "from collections import OrderedDict\n", + "\n", + "# Load pretrained EfficientNet-B0\n", + "efnet_pretrained_features = models.efficientnet_b0(weights=models.EfficientNet_B0_Weights.IMAGENET1K_V1).features\n", + "\n", + "# List of layers with their parameter counts\n", + "layers = [\n", + " ('Conv2dNormActivation1', efnet_pretrained_features[0]), # 928 parameters\n", + " ('MBConv1', efnet_pretrained_features[1][0]), # 1,448 parameters\n", + " ('MBConv2', efnet_pretrained_features[2][0]), # 6,004 parameters\n", + " ('MBConv3', efnet_pretrained_features[2][1]), # 10,710 parameters\n", + " ('MBConv4', efnet_pretrained_features[3][0]), # 15,350 parameters\n", + " ('MBConv5', efnet_pretrained_features[3][1]), # 31,290 parameters\n", + " ('MBConv6', efnet_pretrained_features[4][0]), # 37,130 parameters\n", + " ('MBConv7', efnet_pretrained_features[4][1]), # 102,900 parameters\n", + " ('MBConv8', efnet_pretrained_features[4][2]), # 102,900 parameters\n", + " ('MBConv9', efnet_pretrained_features[5][0]), # 126,004 parameters\n", + " ('MBConv10', efnet_pretrained_features[5][1]), # 208,572 parameters\n", + " ('MBConv11', efnet_pretrained_features[5][2]), # 208,572 parameters\n", + " ('MBConv12', efnet_pretrained_features[6][0]), # 262,492 parameters\n", + " ('MBConv13', efnet_pretrained_features[6][1]), # 587,952 parameters\n", + " ('MBConv14', efnet_pretrained_features[6][2]), # 587,952 parameters\n", + " ('MBConv15', efnet_pretrained_features[6][3]), # 587,952 parameters\n", + " ('MBConv16', efnet_pretrained_features[7][0]), # 717,232 parameters\n", + " ('Conv2dNormActivation2', efnet_pretrained_features[8]), # 412,160 parameters\n", + "]\n", + "\n", + "# Calculate total parameters\n", + "total_params = sum(sum(p.numel() for p in layer.parameters()) for _, layer in layers)\n", + "print(f\"Total parameters: {total_params}\")\n", + "\n", + "# Calculate cumulative parameters and divide into 5 slices\n", + "slice_params = total_params / 10 # Each slice should have ~20% of the total parameters\n", + "cumulative_params = 0\n", + "slices = []\n", + "current_slice = OrderedDict()\n", + "\n", + "for name, layer in layers:\n", + " layer_params = sum(p.numel() for p in layer.parameters())\n", + " cumulative_params += layer_params\n", + " current_slice[name] = layer\n", + "\n", + " # If cumulative parameters exceed the slice threshold, finalize the slice\n", + " if cumulative_params >= slice_params * (len(slices) + 1):\n", + " slices.append(nn.Sequential(current_slice))\n", + " current_slice = OrderedDict()\n", + "\n", + "# Add the last slice if it has any layers\n", + "if current_slice:\n", + " slices.append(nn.Sequential(current_slice))\n", + "\n", + "# Print the slices\n", + "for i, slice in enumerate(slices):\n", + " print(f\"\\nSlice {i + 1}:\")\n", + " # print(slice)\n", + " slice_params = sum(p.numel() for p in slice.parameters())\n", + " print(f\"Parameters in slice: {slice_params} ({slice_params / total_params * 100:.2f}%)\")" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [], + "source": [ + "blocks = nn.Sequential(OrderedDict([\n", + " ('Conv2dNormActivation1', efnet_pretrained_features[0]),\n", + " ('MBConv1', efnet_pretrained_features[1][0]), \n", + " ('MBConv2', efnet_pretrained_features[2][0]), \n", + " ('MBConv3', efnet_pretrained_features[2][1]), \n", + " ('MBConv4', efnet_pretrained_features[3][0]), \n", + " ('MBConv5', efnet_pretrained_features[3][1]), \n", + " ('MBConv6', efnet_pretrained_features[4][0]), \n", + " ('MBConv7', efnet_pretrained_features[4][1]), \n", + " ('MBConv8', efnet_pretrained_features[4][2]),\n", + " ('MBConv9', efnet_pretrained_features[5][0]),\n", + " ('MBConv10', efnet_pretrained_features[5][1]), \n", + " ('MBConv11', efnet_pretrained_features[5][2]), \n", + " ('MBConv12', efnet_pretrained_features[6][0]),\n", + " ('MBConv13', efnet_pretrained_features[6][1]), \n", + " ('MBConv14', efnet_pretrained_features[6][2]), \n", + " ('MBConv15', efnet_pretrained_features[6][3]),\n", + " ('MBConv16', efnet_pretrained_features[7][0]), \n", + " ('Conv2dNormActivation2', efnet_pretrained_features[8]),\n", + " ]))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(blocks[ 0:9 +1])\n", + "print(\"-\"*100)\n", + "print(blocks[10:11 +1])\n", + "print(\"-\"*100)\n", + "print(blocks[12:13 +1])\n", + "print(\"-\"*100)\n", + "print(blocks[14:14 +1])\n", + "print(\"-\"*100)\n", + "print(blocks[15:15 +1])\n", + "print(\"-\"*100)\n", + "print(blocks[16:16 +1])\n", + "print(\"-\"*100)\n", + "print(blocks[17:17 +1])\n", + "print(\"-\"*100)" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sequential(\n", + " (Conv2dNormActivation1): Conv2dNormActivation(\n", + " (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n", + " (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (2): SiLU(inplace=True)\n", + " )\n", + " (MBConv1): MBConv(\n", + " (block): Sequential(\n", + " (0): Conv2dNormActivation(\n", + " (0): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False)\n", + " (1): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (2): SiLU(inplace=True)\n", + " )\n", + " (1): SqueezeExcitation(\n", + " (avgpool): AdaptiveAvgPool2d(output_size=1)\n", + " (fc1): Conv2d(32, 8, kernel_size=(1, 1), stride=(1, 1))\n", + " (fc2): Conv2d(8, 32, kernel_size=(1, 1), stride=(1, 1))\n", + " (activation): SiLU(inplace=True)\n", + " (scale_activation): Sigmoid()\n", + " )\n", + " (2): Conv2dNormActivation(\n", + " (0): Conv2d(32, 16, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (1): BatchNorm2d(16, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " )\n", + " )\n", + " (stochastic_depth): StochasticDepth(p=0.0, mode=row)\n", + " )\n", + " (MBConv2): MBConv(\n", + " (block): Sequential(\n", + " (0): Conv2dNormActivation(\n", + " (0): Conv2d(16, 96, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (2): SiLU(inplace=True)\n", + " )\n", + " (1): Conv2dNormActivation(\n", + " (0): Conv2d(96, 96, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=96, bias=False)\n", + " (1): BatchNorm2d(96, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (2): SiLU(inplace=True)\n", + " )\n", + " (2): SqueezeExcitation(\n", + " (avgpool): AdaptiveAvgPool2d(output_size=1)\n", + " (fc1): Conv2d(96, 4, kernel_size=(1, 1), stride=(1, 1))\n", + " (fc2): Conv2d(4, 96, kernel_size=(1, 1), stride=(1, 1))\n", + " (activation): SiLU(inplace=True)\n", + " (scale_activation): Sigmoid()\n", + " )\n", + " (3): Conv2dNormActivation(\n", + " (0): Conv2d(96, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (1): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " )\n", + " )\n", + " (stochastic_depth): StochasticDepth(p=0.0125, mode=row)\n", + " )\n", + " (MBConv3): MBConv(\n", + " (block): Sequential(\n", + " (0): Conv2dNormActivation(\n", + " (0): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (2): SiLU(inplace=True)\n", + " )\n", + " (1): Conv2dNormActivation(\n", + " (0): Conv2d(144, 144, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=144, bias=False)\n", + " (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (2): SiLU(inplace=True)\n", + " )\n", + " (2): SqueezeExcitation(\n", + " (avgpool): AdaptiveAvgPool2d(output_size=1)\n", + " (fc1): Conv2d(144, 6, kernel_size=(1, 1), stride=(1, 1))\n", + " (fc2): Conv2d(6, 144, kernel_size=(1, 1), stride=(1, 1))\n", + " (activation): SiLU(inplace=True)\n", + " (scale_activation): Sigmoid()\n", + " )\n", + " (3): Conv2dNormActivation(\n", + " (0): Conv2d(144, 24, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (1): BatchNorm2d(24, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " )\n", + " )\n", + " (stochastic_depth): StochasticDepth(p=0.025, mode=row)\n", + " )\n", + " (MBConv4): MBConv(\n", + " (block): Sequential(\n", + " (0): Conv2dNormActivation(\n", + " (0): Conv2d(24, 144, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (2): SiLU(inplace=True)\n", + " )\n", + " (1): Conv2dNormActivation(\n", + " (0): Conv2d(144, 144, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=144, bias=False)\n", + " (1): BatchNorm2d(144, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (2): SiLU(inplace=True)\n", + " )\n", + " (2): SqueezeExcitation(\n", + " (avgpool): AdaptiveAvgPool2d(output_size=1)\n", + " (fc1): Conv2d(144, 6, kernel_size=(1, 1), stride=(1, 1))\n", + " (fc2): Conv2d(6, 144, kernel_size=(1, 1), stride=(1, 1))\n", + " (activation): SiLU(inplace=True)\n", + " (scale_activation): Sigmoid()\n", + " )\n", + " (3): Conv2dNormActivation(\n", + " (0): Conv2d(144, 40, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (1): BatchNorm2d(40, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " )\n", + " )\n", + " (stochastic_depth): StochasticDepth(p=0.037500000000000006, mode=row)\n", + " )\n", + " (MBConv5): MBConv(\n", + " (block): Sequential(\n", + " (0): Conv2dNormActivation(\n", + " (0): Conv2d(40, 240, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (1): BatchNorm2d(240, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (2): SiLU(inplace=True)\n", + " )\n", + " (1): Conv2dNormActivation(\n", + " (0): Conv2d(240, 240, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=240, bias=False)\n", + " (1): BatchNorm2d(240, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (2): SiLU(inplace=True)\n", + " )\n", + " (2): SqueezeExcitation(\n", + " (avgpool): AdaptiveAvgPool2d(output_size=1)\n", + " (fc1): Conv2d(240, 10, kernel_size=(1, 1), stride=(1, 1))\n", + " (fc2): Conv2d(10, 240, kernel_size=(1, 1), stride=(1, 1))\n", + " (activation): SiLU(inplace=True)\n", + " (scale_activation): Sigmoid()\n", + " )\n", + " (3): Conv2dNormActivation(\n", + " (0): Conv2d(240, 40, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (1): BatchNorm2d(40, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " )\n", + " )\n", + " (stochastic_depth): StochasticDepth(p=0.05, mode=row)\n", + " )\n", + " (MBConv6): MBConv(\n", + " (block): Sequential(\n", + " (0): Conv2dNormActivation(\n", + " (0): Conv2d(40, 240, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (1): BatchNorm2d(240, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (2): SiLU(inplace=True)\n", + " )\n", + " (1): Conv2dNormActivation(\n", + " (0): Conv2d(240, 240, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=240, bias=False)\n", + " (1): BatchNorm2d(240, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (2): SiLU(inplace=True)\n", + " )\n", + " (2): SqueezeExcitation(\n", + " (avgpool): AdaptiveAvgPool2d(output_size=1)\n", + " (fc1): Conv2d(240, 10, kernel_size=(1, 1), stride=(1, 1))\n", + " (fc2): Conv2d(10, 240, kernel_size=(1, 1), stride=(1, 1))\n", + " (activation): SiLU(inplace=True)\n", + " (scale_activation): Sigmoid()\n", + " )\n", + " (3): Conv2dNormActivation(\n", + " (0): Conv2d(240, 80, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (1): BatchNorm2d(80, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " )\n", + " )\n", + " (stochastic_depth): StochasticDepth(p=0.0625, mode=row)\n", + " )\n", + " (MBConv7): MBConv(\n", + " (block): Sequential(\n", + " (0): Conv2dNormActivation(\n", + " (0): Conv2d(80, 480, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (2): SiLU(inplace=True)\n", + " )\n", + " (1): Conv2dNormActivation(\n", + " (0): Conv2d(480, 480, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=480, bias=False)\n", + " (1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (2): SiLU(inplace=True)\n", + " )\n", + " (2): SqueezeExcitation(\n", + " (avgpool): AdaptiveAvgPool2d(output_size=1)\n", + " (fc1): Conv2d(480, 20, kernel_size=(1, 1), stride=(1, 1))\n", + " (fc2): Conv2d(20, 480, kernel_size=(1, 1), stride=(1, 1))\n", + " (activation): SiLU(inplace=True)\n", + " (scale_activation): Sigmoid()\n", + " )\n", + " (3): Conv2dNormActivation(\n", + " (0): Conv2d(480, 80, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (1): BatchNorm2d(80, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " )\n", + " )\n", + " (stochastic_depth): StochasticDepth(p=0.07500000000000001, mode=row)\n", + " )\n", + " (MBConv8): MBConv(\n", + " (block): Sequential(\n", + " (0): Conv2dNormActivation(\n", + " (0): Conv2d(80, 480, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (2): SiLU(inplace=True)\n", + " )\n", + " (1): Conv2dNormActivation(\n", + " (0): Conv2d(480, 480, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=480, bias=False)\n", + " (1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (2): SiLU(inplace=True)\n", + " )\n", + " (2): SqueezeExcitation(\n", + " (avgpool): AdaptiveAvgPool2d(output_size=1)\n", + " (fc1): Conv2d(480, 20, kernel_size=(1, 1), stride=(1, 1))\n", + " (fc2): Conv2d(20, 480, kernel_size=(1, 1), stride=(1, 1))\n", + " (activation): SiLU(inplace=True)\n", + " (scale_activation): Sigmoid()\n", + " )\n", + " (3): Conv2dNormActivation(\n", + " (0): Conv2d(480, 80, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (1): BatchNorm2d(80, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " )\n", + " )\n", + " (stochastic_depth): StochasticDepth(p=0.08750000000000001, mode=row)\n", + " )\n", + " (MBConv9): MBConv(\n", + " (block): Sequential(\n", + " (0): Conv2dNormActivation(\n", + " (0): Conv2d(80, 480, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (2): SiLU(inplace=True)\n", + " )\n", + " (1): Conv2dNormActivation(\n", + " (0): Conv2d(480, 480, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=480, bias=False)\n", + " (1): BatchNorm2d(480, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (2): SiLU(inplace=True)\n", + " )\n", + " (2): SqueezeExcitation(\n", + " (avgpool): AdaptiveAvgPool2d(output_size=1)\n", + " (fc1): Conv2d(480, 20, kernel_size=(1, 1), stride=(1, 1))\n", + " (fc2): Conv2d(20, 480, kernel_size=(1, 1), stride=(1, 1))\n", + " (activation): SiLU(inplace=True)\n", + " (scale_activation): Sigmoid()\n", + " )\n", + " (3): Conv2dNormActivation(\n", + " (0): Conv2d(480, 112, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (1): BatchNorm2d(112, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " )\n", + " )\n", + " (stochastic_depth): StochasticDepth(p=0.1, mode=row)\n", + " )\n", + ")\n", + "----------------------------------------------------------------------------------------------------\n", + "Sequential(\n", + " (MBConv10): MBConv(\n", + " (block): Sequential(\n", + " (0): Conv2dNormActivation(\n", + " (0): Conv2d(112, 672, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (2): SiLU(inplace=True)\n", + " )\n", + " (1): Conv2dNormActivation(\n", + " (0): Conv2d(672, 672, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=672, bias=False)\n", + " (1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (2): SiLU(inplace=True)\n", + " )\n", + " (2): SqueezeExcitation(\n", + " (avgpool): AdaptiveAvgPool2d(output_size=1)\n", + " (fc1): Conv2d(672, 28, kernel_size=(1, 1), stride=(1, 1))\n", + " (fc2): Conv2d(28, 672, kernel_size=(1, 1), stride=(1, 1))\n", + " (activation): SiLU(inplace=True)\n", + " (scale_activation): Sigmoid()\n", + " )\n", + " (3): Conv2dNormActivation(\n", + " (0): Conv2d(672, 112, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (1): BatchNorm2d(112, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " )\n", + " )\n", + " (stochastic_depth): StochasticDepth(p=0.1125, mode=row)\n", + " )\n", + " (MBConv11): MBConv(\n", + " (block): Sequential(\n", + " (0): Conv2dNormActivation(\n", + " (0): Conv2d(112, 672, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (2): SiLU(inplace=True)\n", + " )\n", + " (1): Conv2dNormActivation(\n", + " (0): Conv2d(672, 672, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=672, bias=False)\n", + " (1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (2): SiLU(inplace=True)\n", + " )\n", + " (2): SqueezeExcitation(\n", + " (avgpool): AdaptiveAvgPool2d(output_size=1)\n", + " (fc1): Conv2d(672, 28, kernel_size=(1, 1), stride=(1, 1))\n", + " (fc2): Conv2d(28, 672, kernel_size=(1, 1), stride=(1, 1))\n", + " (activation): SiLU(inplace=True)\n", + " (scale_activation): Sigmoid()\n", + " )\n", + " (3): Conv2dNormActivation(\n", + " (0): Conv2d(672, 112, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (1): BatchNorm2d(112, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " )\n", + " )\n", + " (stochastic_depth): StochasticDepth(p=0.125, mode=row)\n", + " )\n", + ")\n", + "----------------------------------------------------------------------------------------------------\n", + "Sequential(\n", + " (MBConv12): MBConv(\n", + " (block): Sequential(\n", + " (0): Conv2dNormActivation(\n", + " (0): Conv2d(112, 672, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (2): SiLU(inplace=True)\n", + " )\n", + " (1): Conv2dNormActivation(\n", + " (0): Conv2d(672, 672, kernel_size=(5, 5), stride=(2, 2), padding=(2, 2), groups=672, bias=False)\n", + " (1): BatchNorm2d(672, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (2): SiLU(inplace=True)\n", + " )\n", + " (2): SqueezeExcitation(\n", + " (avgpool): AdaptiveAvgPool2d(output_size=1)\n", + " (fc1): Conv2d(672, 28, kernel_size=(1, 1), stride=(1, 1))\n", + " (fc2): Conv2d(28, 672, kernel_size=(1, 1), stride=(1, 1))\n", + " (activation): SiLU(inplace=True)\n", + " (scale_activation): Sigmoid()\n", + " )\n", + " (3): Conv2dNormActivation(\n", + " (0): Conv2d(672, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " )\n", + " )\n", + " (stochastic_depth): StochasticDepth(p=0.1375, mode=row)\n", + " )\n", + " (MBConv13): MBConv(\n", + " (block): Sequential(\n", + " (0): Conv2dNormActivation(\n", + " (0): Conv2d(192, 1152, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (1): BatchNorm2d(1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (2): SiLU(inplace=True)\n", + " )\n", + " (1): Conv2dNormActivation(\n", + " (0): Conv2d(1152, 1152, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=1152, bias=False)\n", + " (1): BatchNorm2d(1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (2): SiLU(inplace=True)\n", + " )\n", + " (2): SqueezeExcitation(\n", + " (avgpool): AdaptiveAvgPool2d(output_size=1)\n", + " (fc1): Conv2d(1152, 48, kernel_size=(1, 1), stride=(1, 1))\n", + " (fc2): Conv2d(48, 1152, kernel_size=(1, 1), stride=(1, 1))\n", + " (activation): SiLU(inplace=True)\n", + " (scale_activation): Sigmoid()\n", + " )\n", + " (3): Conv2dNormActivation(\n", + " (0): Conv2d(1152, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " )\n", + " )\n", + " (stochastic_depth): StochasticDepth(p=0.15000000000000002, mode=row)\n", + " )\n", + ")\n", + "----------------------------------------------------------------------------------------------------\n", + "Sequential(\n", + " (MBConv14): MBConv(\n", + " (block): Sequential(\n", + " (0): Conv2dNormActivation(\n", + " (0): Conv2d(192, 1152, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (1): BatchNorm2d(1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (2): SiLU(inplace=True)\n", + " )\n", + " (1): Conv2dNormActivation(\n", + " (0): Conv2d(1152, 1152, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=1152, bias=False)\n", + " (1): BatchNorm2d(1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (2): SiLU(inplace=True)\n", + " )\n", + " (2): SqueezeExcitation(\n", + " (avgpool): AdaptiveAvgPool2d(output_size=1)\n", + " (fc1): Conv2d(1152, 48, kernel_size=(1, 1), stride=(1, 1))\n", + " (fc2): Conv2d(48, 1152, kernel_size=(1, 1), stride=(1, 1))\n", + " (activation): SiLU(inplace=True)\n", + " (scale_activation): Sigmoid()\n", + " )\n", + " (3): Conv2dNormActivation(\n", + " (0): Conv2d(1152, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " )\n", + " )\n", + " (stochastic_depth): StochasticDepth(p=0.1625, mode=row)\n", + " )\n", + ")\n", + "----------------------------------------------------------------------------------------------------\n", + "Sequential(\n", + " (MBConv15): MBConv(\n", + " (block): Sequential(\n", + " (0): Conv2dNormActivation(\n", + " (0): Conv2d(192, 1152, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (1): BatchNorm2d(1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (2): SiLU(inplace=True)\n", + " )\n", + " (1): Conv2dNormActivation(\n", + " (0): Conv2d(1152, 1152, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), groups=1152, bias=False)\n", + " (1): BatchNorm2d(1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (2): SiLU(inplace=True)\n", + " )\n", + " (2): SqueezeExcitation(\n", + " (avgpool): AdaptiveAvgPool2d(output_size=1)\n", + " (fc1): Conv2d(1152, 48, kernel_size=(1, 1), stride=(1, 1))\n", + " (fc2): Conv2d(48, 1152, kernel_size=(1, 1), stride=(1, 1))\n", + " (activation): SiLU(inplace=True)\n", + " (scale_activation): Sigmoid()\n", + " )\n", + " (3): Conv2dNormActivation(\n", + " (0): Conv2d(1152, 192, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (1): BatchNorm2d(192, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " )\n", + " )\n", + " (stochastic_depth): StochasticDepth(p=0.17500000000000002, mode=row)\n", + " )\n", + ")\n", + "----------------------------------------------------------------------------------------------------\n", + "Sequential(\n", + " (MBConv16): MBConv(\n", + " (block): Sequential(\n", + " (0): Conv2dNormActivation(\n", + " (0): Conv2d(192, 1152, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (1): BatchNorm2d(1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (2): SiLU(inplace=True)\n", + " )\n", + " (1): Conv2dNormActivation(\n", + " (0): Conv2d(1152, 1152, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=1152, bias=False)\n", + " (1): BatchNorm2d(1152, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (2): SiLU(inplace=True)\n", + " )\n", + " (2): SqueezeExcitation(\n", + " (avgpool): AdaptiveAvgPool2d(output_size=1)\n", + " (fc1): Conv2d(1152, 48, kernel_size=(1, 1), stride=(1, 1))\n", + " (fc2): Conv2d(48, 1152, kernel_size=(1, 1), stride=(1, 1))\n", + " (activation): SiLU(inplace=True)\n", + " (scale_activation): Sigmoid()\n", + " )\n", + " (3): Conv2dNormActivation(\n", + " (0): Conv2d(1152, 320, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (1): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " )\n", + " )\n", + " (stochastic_depth): StochasticDepth(p=0.1875, mode=row)\n", + " )\n", + ")\n", + "----------------------------------------------------------------------------------------------------\n", + "Sequential(\n", + " (Conv2dNormActivation2): Conv2dNormActivation(\n", + " (0): Conv2d(320, 1280, kernel_size=(1, 1), stride=(1, 1), bias=False)\n", + " (1): BatchNorm2d(1280, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (2): SiLU(inplace=True)\n", + " )\n", + ")\n", + "----------------------------------------------------------------------------------------------------\n" + ] + } + ], + "source": [ + "blocks[ 0:9 +1]\n", + "blocks[10:11 +1]\n", + "blocks[12:13 +1]\n", + "blocks[14:14 +1]\n", + "blocks[15:15 +1]\n", + "blocks[16:16 +1]\n", + "blocks[17:17 +1]" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "=======================================================================================================================================\n", + "Layer (type (var_name)) Input Shape Output Shape Param # Param %\n", + "=======================================================================================================================================\n", + "Sequential (Sequential) [32, 3, 224, 224] [32, 1280, 7, 7] -- --\n", + "├─Sequential (0) [32, 3, 224, 224] [32, 112, 14, 14] -- --\n", + "│ └─Conv2dNormActivation (Conv2dNormActivation1) [32, 3, 224, 224] [32, 32, 112, 112] 928 0.02%\n", + "│ └─MBConv (MBConv1) [32, 32, 112, 112] [32, 16, 112, 112] 1,448 0.04%\n", + "│ └─MBConv (MBConv2) [32, 16, 112, 112] [32, 24, 56, 56] 6,004 0.15%\n", + "│ └─MBConv (MBConv3) [32, 24, 56, 56] [32, 24, 56, 56] 10,710 0.27%\n", + "│ └─MBConv (MBConv4) [32, 24, 56, 56] [32, 40, 28, 28] 15,350 0.38%\n", + "│ └─MBConv (MBConv5) [32, 40, 28, 28] [32, 40, 28, 28] 31,290 0.78%\n", + "│ └─MBConv (MBConv6) [32, 40, 28, 28] [32, 80, 14, 14] 37,130 0.93%\n", + "│ └─MBConv (MBConv7) [32, 80, 14, 14] [32, 80, 14, 14] 102,900 2.57%\n", + "│ └─MBConv (MBConv8) [32, 80, 14, 14] [32, 80, 14, 14] 102,900 2.57%\n", + "│ └─MBConv (MBConv9) [32, 80, 14, 14] [32, 112, 14, 14] 126,004 3.14%\n", + "├─Sequential (1) [32, 112, 14, 14] [32, 112, 14, 14] -- --\n", + "│ └─MBConv (MBConv10) [32, 112, 14, 14] [32, 112, 14, 14] 208,572 5.20%\n", + "│ └─MBConv (MBConv11) [32, 112, 14, 14] [32, 112, 14, 14] 208,572 5.20%\n", + "├─Sequential (2) [32, 112, 14, 14] [32, 192, 7, 7] -- --\n", + "│ └─MBConv (MBConv12) [32, 112, 14, 14] [32, 192, 7, 7] 262,492 6.55%\n", + "│ └─MBConv (MBConv13) [32, 192, 7, 7] [32, 192, 7, 7] 587,952 14.67%\n", + "├─Sequential (3) [32, 192, 7, 7] [32, 192, 7, 7] -- --\n", + "│ └─MBConv (MBConv14) [32, 192, 7, 7] [32, 192, 7, 7] 587,952 14.67%\n", + "├─Sequential (4) [32, 192, 7, 7] [32, 192, 7, 7] -- --\n", + "│ └─MBConv (MBConv15) [32, 192, 7, 7] [32, 192, 7, 7] 587,952 14.67%\n", + "├─Sequential (5) [32, 192, 7, 7] [32, 320, 7, 7] -- --\n", + "│ └─MBConv (MBConv16) [32, 192, 7, 7] [32, 320, 7, 7] 717,232 17.90%\n", + "├─Sequential (6) [32, 320, 7, 7] [32, 1280, 7, 7] -- --\n", + "│ └─Conv2dNormActivation (Conv2dNormActivation2) [32, 320, 7, 7] [32, 1280, 7, 7] 412,160 10.28%\n", + "=======================================================================================================================================\n", + "Total params: 4,007,548\n", + "Trainable params: 4,007,548\n", + "Non-trainable params: 0\n", + "Total mult-adds (Units.GIGABYTES): 12.31\n", + "=======================================================================================================================================\n", + "Input size (MB): 19.27\n", + "Forward/backward pass size (MB): 3452.09\n", + "Params size (MB): 16.03\n", + "Estimated Total Size (MB): 3487.39\n", + "=======================================================================================================================================" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from torchinfo import summary\n", + "summary(model=nn.Sequential(*slices),\n", + " input_size=(32, 3, 224, 224),\n", + " # input_data=x,\n", + " col_names = [\"input_size\", \"output_size\", \"num_params\", \"params_percent\"],\n", + " col_width=20,\n", + " row_settings=[\"var_names\"],\n", + " depth = 2,\n", + " device=device\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### EfficientNet LPIPS" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "from torchvision import models, transforms, datasets\n", + "from torch.utils.data import DataLoader\n", + "import matplotlib.pyplot as plt\n", + "from collections import namedtuple, OrderedDict\n", + "\n", + "# Function to preprocess MNIST images\n", + "def preprocess_mnist(image):\n", + " transform = transforms.Compose([\n", + " transforms.Resize((224, 224)), # Resize to match EfficientNet input size\n", + " transforms.Grayscale(num_output_channels=3), # Convert grayscale to 3-channel\n", + " transforms.ToTensor(),\n", + " transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) # Normalize for pretrained models\n", + " ])\n", + " return transform(image)\n", + "\n", + "# Spatial averaging function\n", + "def spatial_average(in_tens, keepdim=True):\n", + " return in_tens.mean([2, 3], keepdim=keepdim)\n", + "\n", + "# EfficientNet-B0 feature extractor\n", + "class EfficientNetB0(nn.Module):\n", + " def __init__(self, requires_grad=False, pretrained=True):\n", + " super(EfficientNetB0, self).__init__()\n", + " efnet_pretrained_features = models.efficientnet_b0(\n", + " weights=models.EfficientNet_B0_Weights.IMAGENET1K_V1\n", + " ).features\n", + " blocks = nn.Sequential(OrderedDict([\n", + " ('Conv2dNormActivation1', efnet_pretrained_features[0]),\n", + " ('MBConv1', efnet_pretrained_features[1][0]), \n", + " ('MBConv2', efnet_pretrained_features[2][0]), \n", + " ('MBConv3', efnet_pretrained_features[2][1]), \n", + " ('MBConv4', efnet_pretrained_features[3][0]), \n", + " ('MBConv5', efnet_pretrained_features[3][1]), \n", + " ('MBConv6', efnet_pretrained_features[4][0]), \n", + " ('MBConv7', efnet_pretrained_features[4][1]), \n", + " ('MBConv8', efnet_pretrained_features[4][2]),\n", + " ('MBConv9', efnet_pretrained_features[5][0]),\n", + " ('MBConv10', efnet_pretrained_features[5][1]), \n", + " ('MBConv11', efnet_pretrained_features[5][2]), \n", + " ('MBConv12', efnet_pretrained_features[6][0]),\n", + " ('MBConv13', efnet_pretrained_features[6][1]), \n", + " ('MBConv14', efnet_pretrained_features[6][2]), \n", + " ('MBConv15', efnet_pretrained_features[6][3]),\n", + " ('MBConv16', efnet_pretrained_features[7][0]), \n", + " ('Conv2dNormActivation2', efnet_pretrained_features[8]),\n", + " ]))\n", + " \n", + " self.slice1 = blocks[0:9]\n", + " self.slice2 = blocks[9:11]\n", + " self.slice3 = blocks[11:13]\n", + " self.slice4 = blocks[13:14]\n", + " self.slice5 = blocks[14:15]\n", + " self.slice6 = blocks[15:16]\n", + " self.slice7 = blocks[16:17]\n", + " \n", + " self.N_slices = 7\n", + "\n", + " # Freeze EfficientNet model\n", + " if not requires_grad:\n", + " for param in self.parameters():\n", + " param.requires_grad = False\n", + "\n", + " def forward(self, X):\n", + " h1 = self.slice1(X)\n", + " h2 = self.slice2(h1)\n", + " h3 = self.slice3(h2)\n", + " h4 = self.slice4(h3)\n", + " h5 = self.slice5(h4)\n", + " h6 = self.slice6(h5)\n", + " h7 = self.slice7(h6)\n", + " efnet_outputs = namedtuple(\"EfNetOutputs\", ['h1', 'h2', 'h3', 'h4', 'h5', 'h6', 'h7'])\n", + " out = efnet_outputs(h1, h2, h3, h4, h5, h6, h7)\n", + " return out\n", + "\n", + "# Scaling layer for input normalization\n", + "class ScalingLayer(nn.Module):\n", + " def __init__(self):\n", + " super(ScalingLayer, self).__init__()\n", + " self.register_buffer('shift', torch.Tensor([-.030, -.088, -.188])[None, :, None, None])\n", + " self.register_buffer('scale', torch.Tensor([.458, .448, .450])[None, :, None, None])\n", + "\n", + " def forward(self, inp):\n", + " return (inp - self.shift) / self.scale\n", + "\n", + "# Linear layer for LPIPS\n", + "class NetLinLayer(nn.Module):\n", + " def __init__(self, chn_in, chn_out=1, use_dropout=False):\n", + " super(NetLinLayer, self).__init__()\n", + " layers = [nn.Dropout(), ] if (use_dropout) else []\n", + " layers += [nn.Conv2d(chn_in, chn_out, 1, stride=1, padding=0, bias=False), ]\n", + " self.model = nn.Sequential(*layers)\n", + "\n", + " def forward(self, x):\n", + " return self.model(x)\n", + "\n", + "# LPIPS metric using EfficientNet-B0\n", + "class LPIPS(nn.Module):\n", + " def __init__(self, net='efficientnet', version='0.1', use_dropout=True):\n", + " super(LPIPS, self).__init__()\n", + " self.version = version\n", + " self.scaling_layer = ScalingLayer()\n", + " self.chns = [80, 112, 192, 192, 192, 192, 320] # Output channels for each slice\n", + " self.L = len(self.chns)\n", + " self.net = EfficientNetB0(pretrained=True, requires_grad=False)\n", + " self.lin0 = NetLinLayer(self.chns[0], use_dropout=use_dropout)\n", + " self.lin1 = NetLinLayer(self.chns[1], use_dropout=use_dropout)\n", + " self.lin2 = NetLinLayer(self.chns[2], use_dropout=use_dropout)\n", + " self.lin3 = NetLinLayer(self.chns[3], use_dropout=use_dropout)\n", + " self.lin4 = NetLinLayer(self.chns[4], use_dropout=use_dropout)\n", + " self.lin5 = NetLinLayer(self.chns[5], use_dropout=use_dropout)\n", + " self.lin6 = NetLinLayer(self.chns[6], use_dropout=use_dropout)\n", + " self.lins = nn.ModuleList([self.lin0, self.lin1, self.lin2, self.lin3, self.lin4, self.lin5, self.lin6])\n", + " self.eval()\n", + " for param in self.parameters():\n", + " param.requires_grad = False\n", + "\n", + " def forward(self, in0, in1, normalize=False):\n", + " if normalize:\n", + " in0 = 2 * in0 - 1\n", + " in1 = 2 * in1 - 1\n", + " in0_input, in1_input = self.scaling_layer(in0), self.scaling_layer(in1)\n", + " outs0, outs1 = self.net(in0_input), self.net(in1_input)\n", + " diffs = {}\n", + " for kk in range(self.L):\n", + " feats0 = torch.nn.functional.normalize(outs0[kk], dim=1)\n", + " feats1 = torch.nn.functional.normalize(outs1[kk], dim=1)\n", + " diffs[kk] = (feats0 - feats1) ** 2\n", + " \n", + " # for i in range(self.L):\n", + " # print(f\"Slice {i + 1}: {diffs[i].shape}\")\n", + " \n", + " res = [spatial_average(self.lins[kk](diffs[kk]), keepdim=True) for kk in range(self.L)]\n", + " val = sum(res)\n", + " return val\n", + "# Load MNIST dataset\n", + "mnist_dataset = datasets.MNIST(root='./data', train=False, download=True, transform=preprocess_mnist)\n", + "mnist_loader = DataLoader(mnist_dataset, batch_size=1, shuffle=True)\n", + "\n", + "# Initialize LPIPS model\n", + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "lpips_model = LPIPS(net='efficientnet').to(device)\n", + "\n", + "# Compare perceptual loss for a few pairs of images\n", + "num_pairs = 5 # Number of image pairs to compare\n", + "for i, (image1, label1) in enumerate(mnist_loader):\n", + " if i >= num_pairs:\n", + " break\n", + " for j, (image2, label2) in enumerate(mnist_loader):\n", + " if j >= num_pairs:\n", + " break\n", + " if i == j:\n", + " continue # Skip comparing the same image\n", + "\n", + " # Move images to device\n", + " image1 = image1.to(device)\n", + " image2 = image2.to(device)\n", + "\n", + " # Compute LPIPS score\n", + " lpips_score = lpips_model(image1, image2, normalize=True).item()\n", + "\n", + " # Print results\n", + " print(f\"Image Pair: {i} (Label: {label1.item()}) vs {j} (Label: {label2.item()})\")\n", + " print(f\"LPIPS Score: {lpips_score:.4f}\")\n", + " print(\"-\" * 50)\n", + "\n", + " # Display images (optional)\n", + " plt.figure(figsize=(4, 2))\n", + " plt.subplot(1, 2, 1)\n", + " plt.imshow(image1.squeeze().cpu().permute(1, 2, 0).numpy()[:, :, 0], cmap='gray')\n", + " plt.title(f\"Image {i} (Label: {label1.item()})\")\n", + " plt.axis('off')\n", + "\n", + " plt.subplot(1, 2, 2)\n", + " plt.imshow(image2.squeeze().cpu().permute(1, 2, 0).numpy()[:, :, 0], cmap='gray')\n", + " plt.title(f\"Image {j} (Label: {label2.item()})\")\n", + " plt.axis('off')\n", + "\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Comperesion" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "from torchvision import models, transforms, datasets\n", + "from torch.utils.data import DataLoader\n", + "import matplotlib.pyplot as plt\n", + "from collections import namedtuple, OrderedDict\n", + "from torchmetrics.image.lpip import LearnedPerceptualImagePatchSimilarity\n", + "\n", + "\n", + "# VGG16 feature extractor\n", + "class vgg16(nn.Module):\n", + " def __init__(self):\n", + " super(vgg16, self).__init__()\n", + " vgg_pretrained_features = models.vgg16(weights=models.VGG16_Weights.IMAGENET1K_V1).features\n", + " # vgg_pretrained_features = models.vgg16().features\n", + " self.slice1 = nn.Sequential()\n", + " self.slice2 = nn.Sequential()\n", + " self.slice3 = nn.Sequential()\n", + " self.slice4 = nn.Sequential()\n", + " self.slice5 = nn.Sequential()\n", + " self.N_slices = 5\n", + " for x in range(4):\n", + " self.slice1.add_module(str(x), vgg_pretrained_features[x])\n", + " for x in range(4, 9):\n", + " self.slice2.add_module(str(x), vgg_pretrained_features[x])\n", + " for x in range(9, 16):\n", + " self.slice3.add_module(str(x), vgg_pretrained_features[x])\n", + " for x in range(16, 23):\n", + " self.slice4.add_module(str(x), vgg_pretrained_features[x])\n", + " for x in range(23, 30):\n", + " self.slice5.add_module(str(x), vgg_pretrained_features[x])\n", + "\n", + " # Freeze vgg model\n", + " self.eval()\n", + " for param in self.parameters():\n", + " param.requires_grad = False\n", + " \n", + " def forward(self, X):\n", + " h1 = self.slice1(X)\n", + " h2 = self.slice2(h1)\n", + " h3 = self.slice3(h2)\n", + " h4 = self.slice4(h3)\n", + " h5 = self.slice5(h4)\n", + " vgg_outputs = namedtuple(\"VggOutputs\", ['h1', 'h2', 'h3', 'h4', 'h5'])\n", + " out = vgg_outputs(h1, h2, h3, h4, h5)\n", + " return out\n", + "\n", + "\n", + "# EfficientNet-B0 feature extractor\n", + "class EfficientNetB0(nn.Module):\n", + " def __init__(self, requires_grad=False, pretrained=True):\n", + " super(EfficientNetB0, self).__init__()\n", + " efnet_pretrained_features = models.efficientnet_b0(\n", + " weights=models.EfficientNet_B0_Weights.IMAGENET1K_V1\n", + " ).features\n", + " blocks = nn.Sequential(OrderedDict([\n", + " ('Conv2dNormActivation1', efnet_pretrained_features[0]),\n", + " ('MBConv1', efnet_pretrained_features[1][0]), \n", + " ('MBConv2', efnet_pretrained_features[2][0]), \n", + " ('MBConv3', efnet_pretrained_features[2][1]), \n", + " ('MBConv4', efnet_pretrained_features[3][0]), \n", + " ('MBConv5', efnet_pretrained_features[3][1]), \n", + " ('MBConv6', efnet_pretrained_features[4][0]), \n", + " ('MBConv7', efnet_pretrained_features[4][1]), \n", + " ('MBConv8', efnet_pretrained_features[4][2]),\n", + " ('MBConv9', efnet_pretrained_features[5][0]),\n", + " ('MBConv10', efnet_pretrained_features[5][1]), \n", + " ('MBConv11', efnet_pretrained_features[5][2]), \n", + " ('MBConv12', efnet_pretrained_features[6][0]),\n", + " ('MBConv13', efnet_pretrained_features[6][1]), \n", + " ('MBConv14', efnet_pretrained_features[6][2]), \n", + " ('MBConv15', efnet_pretrained_features[6][3]),\n", + " ('MBConv16', efnet_pretrained_features[7][0]), \n", + " ('Conv2dNormActivation2', efnet_pretrained_features[8]),\n", + " ]))\n", + " \n", + " self.slice1 = blocks[0:9]\n", + " self.slice2 = blocks[9:11]\n", + " self.slice3 = blocks[11:13]\n", + " self.slice4 = blocks[13:14]\n", + " self.slice5 = blocks[14:15]\n", + " self.slice6 = blocks[15:16]\n", + " self.slice7 = blocks[16:17]\n", + " \n", + " self.N_slices = 7\n", + "\n", + " # Freeze EfficientNet model\n", + " self.eval()\n", + " if not requires_grad:\n", + " for param in self.parameters():\n", + " param.requires_grad = False\n", + "\n", + " def forward(self, X):\n", + " h1 = self.slice1(X)\n", + " h2 = self.slice2(h1)\n", + " h3 = self.slice3(h2)\n", + " h4 = self.slice4(h3)\n", + " h5 = self.slice5(h4)\n", + " h6 = self.slice6(h5)\n", + " h7 = self.slice7(h6)\n", + " efnet_outputs = namedtuple(\"EfNetOutputs\", ['h1', 'h2', 'h3', 'h4', 'h5', 'h6', 'h7'])\n", + " out = efnet_outputs(h1, h2, h3, h4, h5, h6, h7)\n", + " return out\n", + "\n", + "\n", + "# Scaling layer for input normalization\n", + "class ScalingLayer(nn.Module):\n", + " def __init__(self):\n", + " super(ScalingLayer, self).__init__()\n", + " self.register_buffer(\"shift\", torch.Tensor([-0.030, -0.088, -0.188])[None, :, None, None], persistent=False)\n", + " self.register_buffer(\"scale\", torch.Tensor([0.458, 0.448, 0.450])[None, :, None, None], persistent=False)\n", + "\n", + " def forward(self, inp):\n", + " return (inp - self.shift) / self.scale\n", + "\n", + "# Linear layer for LPIPS\n", + "class NetLinLayer(nn.Module):\n", + " def __init__(self, chn_in, chn_out=1, use_dropout=False):\n", + " super(NetLinLayer, self).__init__()\n", + " layers = [nn.Dropout(), ] if (use_dropout) else []\n", + " layers += [nn.Conv2d(chn_in, chn_out, 1, stride=1, padding=0, bias=False), ]\n", + " self.model = nn.Sequential(*layers)\n", + "\n", + " def forward(self, x):\n", + " return self.model(x)\n", + "\n", + "\n", + "# Function to preprocess MNIST images\n", + "def preprocess_mnist(image):\n", + " transform = transforms.Compose([\n", + " transforms.Resize((224, 224)), # Resize to match VGG16 input size\n", + " transforms.Grayscale(num_output_channels=3), # Convert grayscale to 3-channel\n", + " transforms.ToTensor(),\n", + " # transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) # Normalize for pretrained models\n", + " ])\n", + " return transform(image)\n", + "\n", + "# Spatial averaging function\n", + "def spatial_average(in_tens, keepdim=True):\n", + " return in_tens.mean([2, 3], keepdim=keepdim)\n", + "\n", + "# vgg LPIPS metric\n", + "import torch.hub\n", + "class LPIPS_VGG(nn.Module):\n", + " def __init__(self, net='vgg', version='0.1', use_dropout=True):\n", + " super(LPIPS_VGG, self).__init__()\n", + " self.version = version\n", + " self.scaling_layer = ScalingLayer()\n", + " self.chns = [64, 128, 256, 512, 512]\n", + " self.L = len(self.chns)\n", + " self.net = vgg16()\n", + " self.lin0 = NetLinLayer(self.chns[0], use_dropout=use_dropout)\n", + " self.lin1 = NetLinLayer(self.chns[1], use_dropout=use_dropout)\n", + " self.lin2 = NetLinLayer(self.chns[2], use_dropout=use_dropout)\n", + " self.lin3 = NetLinLayer(self.chns[3], use_dropout=use_dropout)\n", + " self.lin4 = NetLinLayer(self.chns[4], use_dropout=use_dropout)\n", + " self.lins = nn.ModuleList([self.lin0, self.lin1, self.lin2, self.lin3, self.lin4])\n", + " \n", + " # weights = models.get_weight(\"VGG16_Weights.IMAGENET1K_V1\")\n", + " # self.net.load_state_dict(weights.get_state_dict(), strict=False)\n", + " # import inspect\n", + " # import os\n", + " # model_path = os.path.abspath(os.path.join(inspect.getfile(self.__init__), '..', 'weights/v%s/%s.pth'%(version,net)))\n", + " # print(os.path.isfile(model_path), model_path)\n", + " # model_path = 'vgg16model.pth'\n", + " # self.net.load_state_dict(torch.load(model_path, map_location='cpu'), strict=False) \n", + " \n", + " # --- Orignal url --------------------\n", + " # weights_url = f\"https://github.com/richzhang/PerceptualSimilarity/raw/master/lpips/weights/v{version}/{net}.pth\"\n", + " # --- Orignal Forked url -------------\n", + " weights_url = f\"https://github.com/akuresonite/PerceptualSimilarity-Forked/raw/master/lpips/weights/v{version}/{net}.pth\"\n", + " # --- Orignal torchmetric url --------\n", + " # weights_url = \"https://github.com/Lightning-AI/torchmetrics/raw/master/src/torchmetrics/functional/image/lpips_models/vgg.pth\"\n", + " \n", + " \n", + " # weights_url = r\"https://download.pytorch.org/models/vgg16-397923af.pth\"\n", + " state_dict = torch.hub.load_state_dict_from_url(weights_url, map_location='cpu')\n", + " self.load_state_dict(state_dict, strict=False)\n", + " \n", + " # from torchinfo import summary\n", + " # modelsummary = summary(model=self,\n", + " # input_size=[(1, 3, 224, 224), (1, 3, 224, 224)],\n", + " # # input_size=(1, 3, 224, 224),\n", + " # # input_data=x,\n", + " # col_names = [\"input_size\", \"output_size\", \"num_params\", \"params_percent\"],\n", + " # col_width=20,\n", + " # row_settings=[\"var_names\"],\n", + " # depth = 2,\n", + " # device=device\n", + " # )\n", + " # print(modelsummary)\n", + " \n", + " self.eval()\n", + " for param in self.parameters():\n", + " param.requires_grad = False\n", + " \n", + " def _normalize_tensor(self, in_feat, eps= 1e-8):\n", + " \"\"\"Normalize input tensor.\"\"\"\n", + " norm_factor = torch.sqrt(eps + torch.sum(in_feat**2, dim=1, keepdim=True))\n", + " return in_feat / norm_factor\n", + "\n", + " def forward(self, in0, in1, normalize=False):\n", + " if normalize:\n", + " in0 = 2 * in0 - 1\n", + " in1 = 2 * in1 - 1\n", + " \n", + " in0_input, in1_input = self.scaling_layer(in0), self.scaling_layer(in1)\n", + " # in0_input, in1_input = in0, in1\n", + " \n", + " outs0, outs1 = self.net(in0_input), self.net(in1_input)\n", + " \n", + " diffs = {}\n", + " for kk in range(self.L):\n", + " feats0 = self._normalize_tensor(outs0[kk])\n", + " feats1 = self._normalize_tensor(outs1[kk])\n", + " diffs[kk] = (feats0 - feats1) ** 2\n", + " \n", + " res = [spatial_average(self.lins[kk](diffs[kk]), keepdim=True) for kk in range(self.L)]\n", + " val = sum(res)\n", + " \n", + " return val\n", + "\n", + "# LPIPS metric using EfficientNet-B0\n", + "class LPIPS_EFNET(nn.Module):\n", + " def __init__(self, net='efficientnet', version='0.1', use_dropout=True):\n", + " super(LPIPS_EFNET, self).__init__()\n", + " self.version = version\n", + " self.scaling_layer = ScalingLayer()\n", + " self.chns = [80, 112, 192, 192, 192, 192, 320] # Output channels for each slice\n", + " self.L = len(self.chns)\n", + " self.net = EfficientNetB0(pretrained=True, requires_grad=False)\n", + " self.lin0 = NetLinLayer(self.chns[0], use_dropout=use_dropout)\n", + " self.lin1 = NetLinLayer(self.chns[1], use_dropout=use_dropout)\n", + " self.lin2 = NetLinLayer(self.chns[2], use_dropout=use_dropout)\n", + " self.lin3 = NetLinLayer(self.chns[3], use_dropout=use_dropout)\n", + " self.lin4 = NetLinLayer(self.chns[4], use_dropout=use_dropout)\n", + " self.lin5 = NetLinLayer(self.chns[5], use_dropout=use_dropout)\n", + " self.lin6 = NetLinLayer(self.chns[6], use_dropout=use_dropout)\n", + " self.lins = nn.ModuleList([self.lin0, self.lin1, self.lin2, self.lin3, self.lin4, self.lin5, self.lin6])\n", + " \n", + "\n", + " # import inspect\n", + " # import os\n", + " # model_path = os.path.abspath(os.path.join(inspect.getfile(self.__init__), '..', 'weights/v%s/%s.pth'%(version,net)))\n", + " # print(model_path)\n", + " # self.load_state_dict(torch.load(model_path, map_location='cpu'), strict=False) \n", + " \n", + " self.eval()\n", + " for param in self.parameters():\n", + " param.requires_grad = False\n", + "\n", + " def forward(self, in0, in1, normalize=False):\n", + " if normalize:\n", + " in0 = 2 * in0 - 1\n", + " in1 = 2 * in1 - 1\n", + " in0_input, in1_input = self.scaling_layer(in0), self.scaling_layer(in1)\n", + " in0_input, in1_input = in0, in1\n", + " outs0, outs1 = self.net(in0_input), self.net(in1_input)\n", + " diffs = {}\n", + " for kk in range(self.L):\n", + " feats0 = torch.nn.functional.normalize(outs0[kk], dim=1)\n", + " feats1 = torch.nn.functional.normalize(outs1[kk])\n", + " diffs[kk] = (feats0 - feats1) ** 2\n", + " \n", + " res = [spatial_average(self.lins[kk](diffs[kk]), keepdim=True) for kk in range(self.L)]\n", + " val = sum(res)\n", + " return val\n", + "\n", + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "\n", + "# Load MNIST dataset\n", + "mnist_dataset = datasets.MNIST(root='./data', train=False, download=True, transform=preprocess_mnist)\n", + "mnist_loader = DataLoader(mnist_dataset, batch_size=1, shuffle=True)\n", + "\n", + "# Initialize LPIPS model\n", + "lpips_vgg_model = LPIPS_VGG(net='vgg').to(device)\n", + "lpips_efnet_model = LPIPS_EFNET(net='efficientnet').to(device)\n", + "pl_lpips = LearnedPerceptualImagePatchSimilarity(net_type='vgg', normalize=False).to(device)\n", + "\n", + "# Compare perceptual loss for a few pairs of images\n", + "num_pairs = 5 # Number of image pairs to compare\n", + "for i, (image1, label1) in enumerate(mnist_loader):\n", + " if i >= num_pairs:\n", + " break\n", + " for j, (image2, label2) in enumerate(mnist_loader):\n", + " if j >= num_pairs:\n", + " break\n", + " if i == j:\n", + " continue # Skip comparing the same image\n", + " \n", + " # Move images to device\n", + " with torch.inference_mode():\n", + " image1 = image1.to(device)\n", + " image2 = image2.to(device)\n", + " \n", + "\n", + " # Compute LPIPS score\n", + " lpips_vgg_score = lpips_vgg_model(image1, image2, normalize=False).item()\n", + " lpips_efnet_score = lpips_efnet_model(image1, image2, normalize=False).item()\n", + " pl_lpips_vgg_score = pl_lpips(image1, image2)\n", + "\n", + " # Display images (optional)\n", + " plt.figure(figsize=(4, 2))\n", + " plt.subplot(1, 2, 1)\n", + " plt.imshow(image1.squeeze().cpu().permute(1, 2, 0).numpy()[:, :, 0], cmap='gray')\n", + " plt.title(f\"Image {i} (Label: {label1.item()})\")\n", + " plt.axis('off')\n", + "\n", + " plt.subplot(1, 2, 2)\n", + " plt.imshow(image2.squeeze().cpu().permute(1, 2, 0).numpy()[:, :, 0], cmap='gray')\n", + " plt.title(f\"Image {j} (Label: {label2.item()})\")\n", + " plt.axis('off')\n", + " \n", + " plt.suptitle(f\"LPIPS: {lpips_vgg_score:.4f}, {lpips_efnet_score:.4f}, {pl_lpips_vgg_score:.4f}\", y=1.1)\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "_IncompatibleKeys(missing_keys=['net.slice1.0.weight', 'net.slice1.0.bias', 'net.slice1.2.weight', 'net.slice1.2.bias', 'net.slice2.5.weight', 'net.slice2.5.bias', 'net.slice2.7.weight', 'net.slice2.7.bias', 'net.slice3.10.weight', 'net.slice3.10.bias', 'net.slice3.12.weight', 'net.slice3.12.bias', 'net.slice3.14.weight', 'net.slice3.14.bias', 'net.slice4.17.weight', 'net.slice4.17.bias', 'net.slice4.19.weight', 'net.slice4.19.bias', 'net.slice4.21.weight', 'net.slice4.21.bias', 'net.slice5.24.weight', 'net.slice5.24.bias', 'net.slice5.26.weight', 'net.slice5.26.bias', 'net.slice5.28.weight', 'net.slice5.28.bias', 'lins.0.model.1.weight', 'lins.1.model.1.weight', 'lins.2.model.1.weight', 'lins.3.model.1.weight', 'lins.4.model.1.weight'], unexpected_keys=[])" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "net='vgg'\n", + "version='0.1'\n", + "weights_url = f\"https://github.com/richzhang/PerceptualSimilarity/raw/master/lpips/weights/v{version}/{net}.pth\"\n", + "# weights_url = r\"https://download.pytorch.org/models/vgg16-397923af.pth\"\n", + "state_dict = torch.hub.load_state_dict_from_url(weights_url, map_location='cpu')\n", + "lpips_vgg_model = LPIPS_VGG(net='vgg')\n", + "lpips_vgg_model.load_state_dict(state_dict, strict=False)\n", + "# lpips_vgg_model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Downloading: \"https://github.com/richzhang/PerceptualSimilarity/raw/master/lpips/weights/v0.1/vgg.pth\" to /home/23m1521/.cache/torch/hub/checkpoints/vgg.pth" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(0.) tensor(0.9961) tensor(0.) tensor(1.)\n" + ] + } + ], + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "from torchvision import models, transforms, datasets\n", + "from torch.utils.data import DataLoader\n", + "\n", + "def preprocess_mnist(image):\n", + " transform = transforms.Compose([\n", + " transforms.Resize((224, 224)), # Resize to match VGG16 input size\n", + " transforms.Grayscale(num_output_channels=3), # Convert grayscale to 3-channel\n", + " transforms.ToTensor(),\n", + " # transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) # Normalize for pretrained models\n", + " ])\n", + " return transform(image)\n", + "mnist_dataset = datasets.MNIST(root='./data', train=False, download=True, transform=preprocess_mnist)\n", + "mnist_loader = DataLoader(mnist_dataset, batch_size=1, shuffle=True)\n", + "image1, label1 = next(iter(mnist_loader))\n", + "image2, label2 = next(iter(mnist_loader))\n", + "print(image1.min(), image1.max(), image2.min(), image2.max())\n", + "# image1 = 2 * image1 - 1\n", + "# image2 = 2 * image2 - 1\n", + "# print(image1.min(), image1.max(), image2.min(), image2.max())\n", + "# image1 = scaling_layer(image1)\n", + "# image2 = scaling_layer(image2)\n", + "# print(image1.min(), image1.max(), image2.min(), image2.max())" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "from collections import namedtuple\n", + "import torch\n", + "from torchvision import models as tv\n", + "\n", + "class squeezenet(torch.nn.Module):\n", + " def __init__(self, requires_grad=False, pretrained=True):\n", + " super(squeezenet, self).__init__()\n", + " pretrained_features = tv.squeezenet1_1(pretrained=pretrained).features\n", + " self.slice1 = torch.nn.Sequential()\n", + " self.slice2 = torch.nn.Sequential()\n", + " self.slice3 = torch.nn.Sequential()\n", + " self.slice4 = torch.nn.Sequential()\n", + " self.slice5 = torch.nn.Sequential()\n", + " self.slice6 = torch.nn.Sequential()\n", + " self.slice7 = torch.nn.Sequential()\n", + " self.N_slices = 7\n", + " for x in range(2):\n", + " self.slice1.add_module(str(x), pretrained_features[x])\n", + " for x in range(2,5):\n", + " self.slice2.add_module(str(x), pretrained_features[x])\n", + " for x in range(5, 8):\n", + " self.slice3.add_module(str(x), pretrained_features[x])\n", + " for x in range(8, 10):\n", + " self.slice4.add_module(str(x), pretrained_features[x])\n", + " for x in range(10, 11):\n", + " self.slice5.add_module(str(x), pretrained_features[x])\n", + " for x in range(11, 12):\n", + " self.slice6.add_module(str(x), pretrained_features[x])\n", + " for x in range(12, 13):\n", + " self.slice7.add_module(str(x), pretrained_features[x])\n", + " if not requires_grad:\n", + " for param in self.parameters():\n", + " param.requires_grad = False\n", + "\n", + " def forward(self, X):\n", + " h = self.slice1(X)\n", + " h_relu1 = h\n", + " h = self.slice2(h)\n", + " h_relu2 = h\n", + " h = self.slice3(h)\n", + " h_relu3 = h\n", + " h = self.slice4(h)\n", + " h_relu4 = h\n", + " h = self.slice5(h)\n", + " h_relu5 = h\n", + " h = self.slice6(h)\n", + " h_relu6 = h\n", + " h = self.slice7(h)\n", + " h_relu7 = h\n", + " vgg_outputs = namedtuple(\"SqueezeOutputs\", ['relu1','relu2','relu3','relu4','relu5','relu6','relu7'])\n", + " out = vgg_outputs(h_relu1,h_relu2,h_relu3,h_relu4,h_relu5,h_relu6,h_relu7)\n", + "\n", + " return out\n", + "\n", + "\n", + "class alexnet(torch.nn.Module):\n", + " def __init__(self, requires_grad=False, pretrained=True):\n", + " super(alexnet, self).__init__()\n", + " alexnet_pretrained_features = tv.alexnet(pretrained=pretrained).features\n", + " self.slice1 = torch.nn.Sequential()\n", + " self.slice2 = torch.nn.Sequential()\n", + " self.slice3 = torch.nn.Sequential()\n", + " self.slice4 = torch.nn.Sequential()\n", + " self.slice5 = torch.nn.Sequential()\n", + " self.N_slices = 5\n", + " for x in range(2):\n", + " self.slice1.add_module(str(x), alexnet_pretrained_features[x])\n", + " for x in range(2, 5):\n", + " self.slice2.add_module(str(x), alexnet_pretrained_features[x])\n", + " for x in range(5, 8):\n", + " self.slice3.add_module(str(x), alexnet_pretrained_features[x])\n", + " for x in range(8, 10):\n", + " self.slice4.add_module(str(x), alexnet_pretrained_features[x])\n", + " for x in range(10, 12):\n", + " self.slice5.add_module(str(x), alexnet_pretrained_features[x])\n", + " if not requires_grad:\n", + " for param in self.parameters():\n", + " param.requires_grad = False\n", + "\n", + " def forward(self, X):\n", + " h = self.slice1(X)\n", + " h_relu1 = h\n", + " h = self.slice2(h)\n", + " h_relu2 = h\n", + " h = self.slice3(h)\n", + " h_relu3 = h\n", + " h = self.slice4(h)\n", + " h_relu4 = h\n", + " h = self.slice5(h)\n", + " h_relu5 = h\n", + " alexnet_outputs = namedtuple(\"AlexnetOutputs\", ['relu1', 'relu2', 'relu3', 'relu4', 'relu5'])\n", + " out = alexnet_outputs(h_relu1, h_relu2, h_relu3, h_relu4, h_relu5)\n", + "\n", + " return out\n", + "\n", + "class vgg16(torch.nn.Module):\n", + " def __init__(self, requires_grad=False, pretrained=True):\n", + " super(vgg16, self).__init__()\n", + " # vgg_pretrained_features = tv.vgg16(pretrained=pretrained).features\n", + " vgg_pretrained_features = models.vgg16(weights=models.VGG16_Weights.IMAGENET1K_V1).features\n", + " self.slice1 = torch.nn.Sequential()\n", + " self.slice2 = torch.nn.Sequential()\n", + " self.slice3 = torch.nn.Sequential()\n", + " self.slice4 = torch.nn.Sequential()\n", + " self.slice5 = torch.nn.Sequential()\n", + " self.N_slices = 5\n", + " for x in range(4):\n", + " self.slice1.add_module(str(x), vgg_pretrained_features[x])\n", + " for x in range(4, 9):\n", + " self.slice2.add_module(str(x), vgg_pretrained_features[x])\n", + " for x in range(9, 16):\n", + " self.slice3.add_module(str(x), vgg_pretrained_features[x])\n", + " for x in range(16, 23):\n", + " self.slice4.add_module(str(x), vgg_pretrained_features[x])\n", + " for x in range(23, 30):\n", + " self.slice5.add_module(str(x), vgg_pretrained_features[x])\n", + " if not requires_grad:\n", + " for param in self.parameters():\n", + " param.requires_grad = False\n", + "\n", + " def forward(self, X):\n", + " h = self.slice1(X)\n", + " h_relu1_2 = h\n", + " h = self.slice2(h)\n", + " h_relu2_2 = h\n", + " h = self.slice3(h)\n", + " h_relu3_3 = h\n", + " h = self.slice4(h)\n", + " h_relu4_3 = h\n", + " h = self.slice5(h)\n", + " h_relu5_3 = h\n", + " vgg_outputs = namedtuple(\"VggOutputs\", ['relu1_2', 'relu2_2', 'relu3_3', 'relu4_3', 'relu5_3'])\n", + " out = vgg_outputs(h_relu1_2, h_relu2_2, h_relu3_3, h_relu4_3, h_relu5_3)\n", + "\n", + " return out\n", + "\n", + "\n", + "\n", + "class resnet(torch.nn.Module):\n", + " def __init__(self, requires_grad=False, pretrained=True, num=18):\n", + " super(resnet, self).__init__()\n", + " if(num==18):\n", + " self.net = tv.resnet18(pretrained=pretrained)\n", + " elif(num==34):\n", + " self.net = tv.resnet34(pretrained=pretrained)\n", + " elif(num==50):\n", + " self.net = tv.resnet50(pretrained=pretrained)\n", + " elif(num==101):\n", + " self.net = tv.resnet101(pretrained=pretrained)\n", + " elif(num==152):\n", + " self.net = tv.resnet152(pretrained=pretrained)\n", + " self.N_slices = 5\n", + "\n", + " self.conv1 = self.net.conv1\n", + " self.bn1 = self.net.bn1\n", + " self.relu = self.net.relu\n", + " self.maxpool = self.net.maxpool\n", + " self.layer1 = self.net.layer1\n", + " self.layer2 = self.net.layer2\n", + " self.layer3 = self.net.layer3\n", + " self.layer4 = self.net.layer4\n", + "\n", + " def forward(self, X):\n", + " h = self.conv1(X)\n", + " h = self.bn1(h)\n", + " h = self.relu(h)\n", + " h_relu1 = h\n", + " h = self.maxpool(h)\n", + " h = self.layer1(h)\n", + " h_conv2 = h\n", + " h = self.layer2(h)\n", + " h_conv3 = h\n", + " h = self.layer3(h)\n", + " h_conv4 = h\n", + " h = self.layer4(h)\n", + " h_conv5 = h\n", + "\n", + " outputs = namedtuple(\"Outputs\", ['relu1','conv2','conv3','conv4','conv5'])\n", + " out = outputs(h_relu1, h_conv2, h_conv3, h_conv4, h_conv5)\n", + "\n", + " return out" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "import torch.nn.init as init\n", + "from torch.autograd import Variable\n", + "import numpy as np\n", + "import torch.nn\n", + "\n", + "def spatial_average(in_tens, keepdim=True):\n", + " return in_tens.mean([2,3],keepdim=keepdim)\n", + "\n", + "def normalize_tensor(in_feat,eps=1e-10):\n", + " norm_factor = torch.sqrt(torch.sum(in_feat**2,dim=1,keepdim=True))\n", + " return in_feat/(norm_factor+eps)\n", + "\n", + "def upsample(in_tens, out_HW=(64,64)): # assumes scale factor is same for H and W\n", + " in_H, in_W = in_tens.shape[2], in_tens.shape[3]\n", + " return nn.Upsample(size=out_HW, mode='bilinear', align_corners=False)(in_tens)\n", + "\n", + "# Learned perceptual metric\n", + "class LPIPS(nn.Module):\n", + " def __init__(self, pretrained=True, net='alex', version='0.1', lpips=True, spatial=False, \n", + " pnet_rand=False, pnet_tune=False, use_dropout=True, model_path=None, eval_mode=True, verbose=True):\n", + "\n", + " super(LPIPS, self).__init__()\n", + " if(verbose):\n", + " print('Setting up [%s] perceptual loss: trunk [%s], v[%s], spatial [%s]'%\n", + " ('LPIPS' if lpips else 'baseline', net, version, 'on' if spatial else 'off'))\n", + "\n", + " self.pnet_type = net\n", + " self.pnet_tune = pnet_tune\n", + " self.pnet_rand = pnet_rand\n", + " self.spatial = spatial\n", + " self.lpips = lpips # false means baseline of just averaging all layers\n", + " self.version = version\n", + " self.scaling_layer = ScalingLayer()\n", + "\n", + " if(self.pnet_type in ['vgg','vgg16']):\n", + " net_type = vgg16\n", + " self.chns = [64,128,256,512,512]\n", + " elif(self.pnet_type=='alex'):\n", + " net_type = alexnet\n", + " self.chns = [64,192,384,256,256]\n", + " elif(self.pnet_type=='squeeze'):\n", + " net_type = squeezenet\n", + " self.chns = [64,128,256,384,384,512,512]\n", + " self.L = len(self.chns)\n", + "\n", + " self.net = net_type(pretrained=not self.pnet_rand, requires_grad=self.pnet_tune)\n", + "\n", + " if(lpips):\n", + " self.lin0 = NetLinLayer(self.chns[0], use_dropout=use_dropout)\n", + " self.lin1 = NetLinLayer(self.chns[1], use_dropout=use_dropout)\n", + " self.lin2 = NetLinLayer(self.chns[2], use_dropout=use_dropout)\n", + " self.lin3 = NetLinLayer(self.chns[3], use_dropout=use_dropout)\n", + " self.lin4 = NetLinLayer(self.chns[4], use_dropout=use_dropout)\n", + " self.lins = [self.lin0,self.lin1,self.lin2,self.lin3,self.lin4]\n", + " if(self.pnet_type=='squeeze'): # 7 layers for squeezenet\n", + " self.lin5 = NetLinLayer(self.chns[5], use_dropout=use_dropout)\n", + " self.lin6 = NetLinLayer(self.chns[6], use_dropout=use_dropout)\n", + " self.lins+=[self.lin5,self.lin6]\n", + " self.lins = nn.ModuleList(self.lins)\n", + "\n", + " if(pretrained):\n", + " if(model_path is None):\n", + " import inspect\n", + " import os\n", + " model_path = os.path.abspath(os.path.join(inspect.getfile(self.__init__), '..', 'weights/v%s/%s.pth'%(version,net)))\n", + "\n", + " if(verbose):\n", + " print('Loading model from: %s'%model_path)\n", + " # self.load_state_dict(torch.load(model_path, map_location='cpu'), strict=False) \n", + "\n", + " if(eval_mode):\n", + " self.eval()\n", + " for param in self.parameters():\n", + " param.requires_grad = False\n", + "\n", + " def forward(self, in0, in1, retPerLayer=False, normalize=False):\n", + " if normalize: # turn on this flag if input is [0,1] so it can be adjusted to [-1, +1]\n", + " in0 = 2 * in0 - 1\n", + " in1 = 2 * in1 - 1\n", + "\n", + " # v0.0 - original release had a bug, where input was not scaled\n", + " in0_input, in1_input = (self.scaling_layer(in0), self.scaling_layer(in1)) if self.version=='0.1' else (in0, in1)\n", + " outs0, outs1 = self.net.forward(in0_input), self.net.forward(in1_input)\n", + " feats0, feats1, diffs = {}, {}, {}\n", + "\n", + " for kk in range(self.L):\n", + " feats0[kk], feats1[kk] = normalize_tensor(outs0[kk]), normalize_tensor(outs1[kk])\n", + " diffs[kk] = (feats0[kk]-feats1[kk])**2\n", + "\n", + " if(self.lpips):\n", + " if(self.spatial):\n", + " res = [upsample(self.lins[kk](diffs[kk]), out_HW=in0.shape[2:]) for kk in range(self.L)]\n", + " else:\n", + " res = [spatial_average(self.lins[kk](diffs[kk]), keepdim=True) for kk in range(self.L)]\n", + " else:\n", + " if(self.spatial):\n", + " res = [upsample(diffs[kk].sum(dim=1,keepdim=True), out_HW=in0.shape[2:]) for kk in range(self.L)]\n", + " else:\n", + " res = [spatial_average(diffs[kk].sum(dim=1,keepdim=True), keepdim=True) for kk in range(self.L)]\n", + "\n", + " val = 0\n", + " for l in range(self.L):\n", + " val += res[l]\n", + " \n", + " if(retPerLayer):\n", + " return (val, res)\n", + " else:\n", + " return val\n", + "\n", + "\n", + "class ScalingLayer(nn.Module):\n", + " def __init__(self):\n", + " super(ScalingLayer, self).__init__()\n", + " self.register_buffer('shift', torch.Tensor([-.030,-.088,-.188])[None,:,None,None])\n", + " self.register_buffer('scale', torch.Tensor([.458,.448,.450])[None,:,None,None])\n", + "\n", + " def forward(self, inp):\n", + " return (inp - self.shift) / self.scale\n", + "\n", + "\n", + "class NetLinLayer(nn.Module):\n", + " ''' A single linear layer which does a 1x1 conv '''\n", + " def __init__(self, chn_in, chn_out=1, use_dropout=False):\n", + " super(NetLinLayer, self).__init__()\n", + "\n", + " layers = [nn.Dropout(),] if(use_dropout) else []\n", + " layers += [nn.Conv2d(chn_in, chn_out, 1, stride=1, padding=0, bias=False),]\n", + " self.model = nn.Sequential(*layers)\n", + "\n", + " def forward(self, x):\n", + " return self.model(x)\n", + "\n", + "class Dist2LogitLayer(nn.Module):\n", + " ''' takes 2 distances, puts through fc layers, spits out value between [0,1] (if use_sigmoid is True) '''\n", + " def __init__(self, chn_mid=32, use_sigmoid=True):\n", + " super(Dist2LogitLayer, self).__init__()\n", + "\n", + " layers = [nn.Conv2d(5, chn_mid, 1, stride=1, padding=0, bias=True),]\n", + " layers += [nn.LeakyReLU(0.2,True),]\n", + " layers += [nn.Conv2d(chn_mid, chn_mid, 1, stride=1, padding=0, bias=True),]\n", + " layers += [nn.LeakyReLU(0.2,True),]\n", + " layers += [nn.Conv2d(chn_mid, 1, 1, stride=1, padding=0, bias=True),]\n", + " if(use_sigmoid):\n", + " layers += [nn.Sigmoid(),]\n", + " self.model = nn.Sequential(*layers)\n", + "\n", + " def forward(self,d0,d1,eps=0.1):\n", + " return self.model.forward(torch.cat((d0,d1,d0-d1,d0/(d1+eps),d1/(d0+eps)),dim=1))" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Setting up [LPIPS] perceptual loss: trunk [vgg], v[0.1], spatial [off]\n", + "Loading model from: /tmp/ipykernel_2359133/weights/v0.1/vgg.pth\n" + ] + } + ], + "source": [ + "loss_fn_vgg = LPIPS(net='vgg')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[[[0.3723]]]], grad_fn=)" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "img0 = torch.zeros(1,3,64,64) # image should be RGB, IMPORTANT: normalized to [-1,1]\n", + "img1 = torch.zeros(1,3,64,64)\n", + "d = loss_fn_vgg(image1, image2, normalize=True)\n", + "d" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(0.) tensor(1.) tensor(0.) tensor(1.)\n" + ] + } + ], + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "from torchvision import models, transforms, datasets\n", + "from torch.utils.data import DataLoader\n", + "\n", + "def preprocess_mnist(image):\n", + " transform = transforms.Compose([\n", + " transforms.Resize((224, 224)), # Resize to match VGG16 input size\n", + " transforms.Grayscale(num_output_channels=3), # Convert grayscale to 3-channel\n", + " transforms.ToTensor(),\n", + " # transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) # Normalize for pretrained models\n", + " ])\n", + " return transform(image)\n", + "mnist_dataset = datasets.MNIST(root='./data', train=False, download=True, transform=preprocess_mnist)\n", + "mnist_loader = DataLoader(mnist_dataset, batch_size=1, shuffle=True)\n", + "image1, label1 = next(iter(mnist_loader))\n", + "image2, label2 = next(iter(mnist_loader))\n", + "print(image1.min(), image1.max(), image2.min(), image2.max())" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Setting up [LPIPS] perceptual loss: trunk [alex], v[0.1], spatial [off]\n", + "AShish\n", + "Setting up [LPIPS] perceptual loss: trunk [vgg], v[0.1], spatial [off]\n", + "AShish\n" + ] + }, + { + "data": { + "text/plain": [ + "0.0" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import lpips\n", + "loss_fn_alex = lpips.LPIPS(net='alex') # best forward scores\n", + "loss_fn_vgg = lpips.LPIPS(net='vgg') # closer to \"traditional\" perceptual loss, when used for optimization\n", + "\n", + "import torch\n", + "img0 = torch.zeros(1,3,64,64) # image should be RGB, IMPORTANT: normalized to [-1,1]\n", + "img1 = torch.zeros(1,3,64,64)\n", + "d = loss_fn_alex(img0, img1)\n", + "d.item()" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.3265250027179718" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "loss_fn_vgg(image1, image2, normalize=False).item()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "cuda_env2", + "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": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}