playground.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "from PIL import Image\n",
    "from io import BytesIO\n",
    "from glasses.interpretability import GradCam, SaliencyMap, ScoreCam\n",
    "from torchvision.transforms import Normalize\n",
    "from glasses.models import AutoModel, AutoTransform\n",
    "\n",
    "model = AutoModel.from_pretrained('resnet18')\n",
    "tr = AutoTransform.from_name('resnet18')\n",
    "r = requests.get('https://i.insider.com/5df126b679d7570ad2044f3e?width=700&format=jpeg&auto=webp')\n",
    "im = Image.open(BytesIO(r.content))\n",
    "# un normalize when done\n",
    "postprocessing = Normalize(-tr.transforms[-1].mean / tr.transforms[-1].std, (1.0 /tr.transforms[-1].std))\n",
    "# # apply preprocessing\n",
    "x =  tr(im).unsqueeze(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = model.interpret(x, using=GradCam(), postprocessing=postprocessing).show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from glasses.interpretability.GradCam import GradCamResult"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch import nn\n",
    "from typing import Callable\n",
    "\n",
    "class TorchCamAdapter(GradCam):\n",
    "    \n",
    "    def __init__(self, cam_def: Callable):\n",
    "        self.cam_def = cam_def\n",
    "        \n",
    "    def __call__(\n",
    "        self,\n",
    "        x: torch.Tensor,\n",
    "        module: nn.Module,\n",
    "        layer: nn.Module = None,\n",
    "        target: int = None,\n",
    "        ctx: torch.Tensor = None,\n",
    "        postprocessing: Callable[[torch.Tensor], torch.Tensor] = None,\n",
    "    ) -> GradCamResult:\n",
    "        cam_extractor = self.cam_def(module)\n",
    "        out = module(x)\n",
    "        activation_map = cam_extractor(out.squeeze(0).argmax().item(), out)\n",
    "        return GradCamResult(x, activation_map, postprocessing)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'': ResNet(\n",
       "   (encoder): ResNetEncoder(\n",
       "     (stem): ResNetStem(\n",
       "       (0): ConvBnAct(\n",
       "         (conv): Conv2dPad(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
       "         (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "         (act): ReLU(inplace=True)\n",
       "       )\n",
       "       (1): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "     )\n",
       "     (layers): ModuleList(\n",
       "       (0): ResNetLayer(\n",
       "         (0): ResNetBasicBlock(\n",
       "           (block): Sequential(\n",
       "             (conv1): Conv2dPad(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "             (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "             (act1): ReLU(inplace=True)\n",
       "             (conv2): Conv2dPad(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "             (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           )\n",
       "           (shortcut): Identity()\n",
       "           (act): ReLU(inplace=True)\n",
       "         )\n",
       "         (1): ResNetBasicBlock(\n",
       "           (block): Sequential(\n",
       "             (conv1): Conv2dPad(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "             (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "             (act1): ReLU(inplace=True)\n",
       "             (conv2): Conv2dPad(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "             (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           )\n",
       "           (shortcut): Identity()\n",
       "           (act): ReLU(inplace=True)\n",
       "         )\n",
       "       )\n",
       "       (1): ResNetLayer(\n",
       "         (0): ResNetBasicBlock(\n",
       "           (block): Sequential(\n",
       "             (conv1): Conv2dPad(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "             (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "             (act1): ReLU(inplace=True)\n",
       "             (conv2): Conv2dPad(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "             (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           )\n",
       "           (shortcut): ResNetShorcut(\n",
       "             (conv): Conv2dPad(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "             (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           )\n",
       "           (act): ReLU(inplace=True)\n",
       "         )\n",
       "         (1): ResNetBasicBlock(\n",
       "           (block): Sequential(\n",
       "             (conv1): Conv2dPad(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "             (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "             (act1): ReLU(inplace=True)\n",
       "             (conv2): Conv2dPad(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "             (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           )\n",
       "           (shortcut): Identity()\n",
       "           (act): ReLU(inplace=True)\n",
       "         )\n",
       "       )\n",
       "       (2): ResNetLayer(\n",
       "         (0): ResNetBasicBlock(\n",
       "           (block): Sequential(\n",
       "             (conv1): Conv2dPad(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "             (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "             (act1): ReLU(inplace=True)\n",
       "             (conv2): Conv2dPad(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "             (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           )\n",
       "           (shortcut): ResNetShorcut(\n",
       "             (conv): Conv2dPad(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "             (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           )\n",
       "           (act): ReLU(inplace=True)\n",
       "         )\n",
       "         (1): ResNetBasicBlock(\n",
       "           (block): Sequential(\n",
       "             (conv1): Conv2dPad(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "             (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "             (act1): ReLU(inplace=True)\n",
       "             (conv2): Conv2dPad(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "             (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           )\n",
       "           (shortcut): Identity()\n",
       "           (act): ReLU(inplace=True)\n",
       "         )\n",
       "       )\n",
       "       (3): ResNetLayer(\n",
       "         (0): ResNetBasicBlock(\n",
       "           (block): Sequential(\n",
       "             (conv1): Conv2dPad(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "             (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "             (act1): ReLU(inplace=True)\n",
       "             (conv2): Conv2dPad(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "             (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           )\n",
       "           (shortcut): ResNetShorcut(\n",
       "             (conv): Conv2dPad(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "             (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           )\n",
       "           (act): ReLU(inplace=True)\n",
       "         )\n",
       "         (1): ResNetBasicBlock(\n",
       "           (block): Sequential(\n",
       "             (conv1): Conv2dPad(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "             (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "             (act1): ReLU(inplace=True)\n",
       "             (conv2): Conv2dPad(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "             (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           )\n",
       "           (shortcut): Identity()\n",
       "           (act): ReLU(inplace=True)\n",
       "         )\n",
       "       )\n",
       "     )\n",
       "   )\n",
       "   (head): ResNetHead(\n",
       "     (pool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
       "     (flat): Flatten(start_dim=1, end_dim=-1)\n",
       "     (fc): Linear(in_features=512, out_features=1000, bias=True)\n",
       "   )\n",
       " ),\n",
       " 'encoder': ResNetEncoder(\n",
       "   (stem): ResNetStem(\n",
       "     (0): ConvBnAct(\n",
       "       (conv): Conv2dPad(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
       "       (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "       (act): ReLU(inplace=True)\n",
       "     )\n",
       "     (1): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "   )\n",
       "   (layers): ModuleList(\n",
       "     (0): ResNetLayer(\n",
       "       (0): ResNetBasicBlock(\n",
       "         (block): Sequential(\n",
       "           (conv1): Conv2dPad(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "           (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (act1): ReLU(inplace=True)\n",
       "           (conv2): Conv2dPad(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "           (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "         )\n",
       "         (shortcut): Identity()\n",
       "         (act): ReLU(inplace=True)\n",
       "       )\n",
       "       (1): ResNetBasicBlock(\n",
       "         (block): Sequential(\n",
       "           (conv1): Conv2dPad(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "           (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (act1): ReLU(inplace=True)\n",
       "           (conv2): Conv2dPad(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "           (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "         )\n",
       "         (shortcut): Identity()\n",
       "         (act): ReLU(inplace=True)\n",
       "       )\n",
       "     )\n",
       "     (1): ResNetLayer(\n",
       "       (0): ResNetBasicBlock(\n",
       "         (block): Sequential(\n",
       "           (conv1): Conv2dPad(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "           (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (act1): ReLU(inplace=True)\n",
       "           (conv2): Conv2dPad(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "           (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "         )\n",
       "         (shortcut): ResNetShorcut(\n",
       "           (conv): Conv2dPad(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "           (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "         )\n",
       "         (act): ReLU(inplace=True)\n",
       "       )\n",
       "       (1): ResNetBasicBlock(\n",
       "         (block): Sequential(\n",
       "           (conv1): Conv2dPad(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "           (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (act1): ReLU(inplace=True)\n",
       "           (conv2): Conv2dPad(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "           (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "         )\n",
       "         (shortcut): Identity()\n",
       "         (act): ReLU(inplace=True)\n",
       "       )\n",
       "     )\n",
       "     (2): ResNetLayer(\n",
       "       (0): ResNetBasicBlock(\n",
       "         (block): Sequential(\n",
       "           (conv1): Conv2dPad(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "           (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (act1): ReLU(inplace=True)\n",
       "           (conv2): Conv2dPad(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "           (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "         )\n",
       "         (shortcut): ResNetShorcut(\n",
       "           (conv): Conv2dPad(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "           (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "         )\n",
       "         (act): ReLU(inplace=True)\n",
       "       )\n",
       "       (1): ResNetBasicBlock(\n",
       "         (block): Sequential(\n",
       "           (conv1): Conv2dPad(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "           (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (act1): ReLU(inplace=True)\n",
       "           (conv2): Conv2dPad(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "           (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "         )\n",
       "         (shortcut): Identity()\n",
       "         (act): ReLU(inplace=True)\n",
       "       )\n",
       "     )\n",
       "     (3): ResNetLayer(\n",
       "       (0): ResNetBasicBlock(\n",
       "         (block): Sequential(\n",
       "           (conv1): Conv2dPad(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "           (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (act1): ReLU(inplace=True)\n",
       "           (conv2): Conv2dPad(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "           (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "         )\n",
       "         (shortcut): ResNetShorcut(\n",
       "           (conv): Conv2dPad(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "           (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "         )\n",
       "         (act): ReLU(inplace=True)\n",
       "       )\n",
       "       (1): ResNetBasicBlock(\n",
       "         (block): Sequential(\n",
       "           (conv1): Conv2dPad(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "           (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "           (act1): ReLU(inplace=True)\n",
       "           (conv2): Conv2dPad(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "           (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "         )\n",
       "         (shortcut): Identity()\n",
       "         (act): ReLU(inplace=True)\n",
       "       )\n",
       "     )\n",
       "   )\n",
       " ),\n",
       " 'encoder.stem': ResNetStem(\n",
       "   (0): ConvBnAct(\n",
       "     (conv): Conv2dPad(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
       "     (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "     (act): ReLU(inplace=True)\n",
       "   )\n",
       "   (1): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       " ),\n",
       " 'encoder.stem.0': ConvBnAct(\n",
       "   (conv): Conv2dPad(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
       "   (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "   (act): ReLU(inplace=True)\n",
       " ),\n",
       " 'encoder.stem.0.conv': Conv2dPad(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False),\n",
       " 'encoder.stem.0.bn': BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True),\n",
       " 'encoder.stem.0.act': ReLU(inplace=True),\n",
       " 'encoder.stem.1': MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False),\n",
       " 'encoder.layers': ModuleList(\n",
       "   (0): ResNetLayer(\n",
       "     (0): ResNetBasicBlock(\n",
       "       (block): Sequential(\n",
       "         (conv1): Conv2dPad(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "         (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "         (act1): ReLU(inplace=True)\n",
       "         (conv2): Conv2dPad(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "         (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "       )\n",
       "       (shortcut): Identity()\n",
       "       (act): ReLU(inplace=True)\n",
       "     )\n",
       "     (1): ResNetBasicBlock(\n",
       "       (block): Sequential(\n",
       "         (conv1): Conv2dPad(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "         (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "         (act1): ReLU(inplace=True)\n",
       "         (conv2): Conv2dPad(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "         (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "       )\n",
       "       (shortcut): Identity()\n",
       "       (act): ReLU(inplace=True)\n",
       "     )\n",
       "   )\n",
       "   (1): ResNetLayer(\n",
       "     (0): ResNetBasicBlock(\n",
       "       (block): Sequential(\n",
       "         (conv1): Conv2dPad(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "         (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "         (act1): ReLU(inplace=True)\n",
       "         (conv2): Conv2dPad(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "         (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "       )\n",
       "       (shortcut): ResNetShorcut(\n",
       "         (conv): Conv2dPad(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "         (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "       )\n",
       "       (act): ReLU(inplace=True)\n",
       "     )\n",
       "     (1): ResNetBasicBlock(\n",
       "       (block): Sequential(\n",
       "         (conv1): Conv2dPad(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "         (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "         (act1): ReLU(inplace=True)\n",
       "         (conv2): Conv2dPad(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "         (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "       )\n",
       "       (shortcut): Identity()\n",
       "       (act): ReLU(inplace=True)\n",
       "     )\n",
       "   )\n",
       "   (2): ResNetLayer(\n",
       "     (0): ResNetBasicBlock(\n",
       "       (block): Sequential(\n",
       "         (conv1): Conv2dPad(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "         (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "         (act1): ReLU(inplace=True)\n",
       "         (conv2): Conv2dPad(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "         (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "       )\n",
       "       (shortcut): ResNetShorcut(\n",
       "         (conv): Conv2dPad(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "         (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "       )\n",
       "       (act): ReLU(inplace=True)\n",
       "     )\n",
       "     (1): ResNetBasicBlock(\n",
       "       (block): Sequential(\n",
       "         (conv1): Conv2dPad(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "         (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "         (act1): ReLU(inplace=True)\n",
       "         (conv2): Conv2dPad(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "         (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "       )\n",
       "       (shortcut): Identity()\n",
       "       (act): ReLU(inplace=True)\n",
       "     )\n",
       "   )\n",
       "   (3): ResNetLayer(\n",
       "     (0): ResNetBasicBlock(\n",
       "       (block): Sequential(\n",
       "         (conv1): Conv2dPad(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "         (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "         (act1): ReLU(inplace=True)\n",
       "         (conv2): Conv2dPad(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "         (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "       )\n",
       "       (shortcut): ResNetShorcut(\n",
       "         (conv): Conv2dPad(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "         (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "       )\n",
       "       (act): ReLU(inplace=True)\n",
       "     )\n",
       "     (1): ResNetBasicBlock(\n",
       "       (block): Sequential(\n",
       "         (conv1): Conv2dPad(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "         (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "         (act1): ReLU(inplace=True)\n",
       "         (conv2): Conv2dPad(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "         (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "       )\n",
       "       (shortcut): Identity()\n",
       "       (act): ReLU(inplace=True)\n",
       "     )\n",
       "   )\n",
       " ),\n",
       " 'encoder.layers.0': ResNetLayer(\n",
       "   (0): ResNetBasicBlock(\n",
       "     (block): Sequential(\n",
       "       (conv1): Conv2dPad(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "       (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "       (act1): ReLU(inplace=True)\n",
       "       (conv2): Conv2dPad(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "       (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "     )\n",
       "     (shortcut): Identity()\n",
       "     (act): ReLU(inplace=True)\n",
       "   )\n",
       "   (1): ResNetBasicBlock(\n",
       "     (block): Sequential(\n",
       "       (conv1): Conv2dPad(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "       (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "       (act1): ReLU(inplace=True)\n",
       "       (conv2): Conv2dPad(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "       (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "     )\n",
       "     (shortcut): Identity()\n",
       "     (act): ReLU(inplace=True)\n",
       "   )\n",
       " ),\n",
       " 'encoder.layers.0.0': ResNetBasicBlock(\n",
       "   (block): Sequential(\n",
       "     (conv1): Conv2dPad(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "     (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "     (act1): ReLU(inplace=True)\n",
       "     (conv2): Conv2dPad(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "     (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "   )\n",
       "   (shortcut): Identity()\n",
       "   (act): ReLU(inplace=True)\n",
       " ),\n",
       " 'encoder.layers.0.0.block': Sequential(\n",
       "   (conv1): Conv2dPad(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "   (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "   (act1): ReLU(inplace=True)\n",
       "   (conv2): Conv2dPad(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "   (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       " ),\n",
       " 'encoder.layers.0.0.block.conv1': Conv2dPad(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False),\n",
       " 'encoder.layers.0.0.block.bn1': BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True),\n",
       " 'encoder.layers.0.0.block.act1': ReLU(inplace=True),\n",
       " 'encoder.layers.0.0.block.conv2': Conv2dPad(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False),\n",
       " 'encoder.layers.0.0.block.bn2': BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True),\n",
       " 'encoder.layers.0.0.shortcut': Identity(),\n",
       " 'encoder.layers.0.0.act': ReLU(inplace=True),\n",
       " 'encoder.layers.0.1': ResNetBasicBlock(\n",
       "   (block): Sequential(\n",
       "     (conv1): Conv2dPad(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "     (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "     (act1): ReLU(inplace=True)\n",
       "     (conv2): Conv2dPad(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "     (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "   )\n",
       "   (shortcut): Identity()\n",
       "   (act): ReLU(inplace=True)\n",
       " ),\n",
       " 'encoder.layers.0.1.block': Sequential(\n",
       "   (conv1): Conv2dPad(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "   (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "   (act1): ReLU(inplace=True)\n",
       "   (conv2): Conv2dPad(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "   (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       " ),\n",
       " 'encoder.layers.0.1.block.conv1': Conv2dPad(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False),\n",
       " 'encoder.layers.0.1.block.bn1': BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True),\n",
       " 'encoder.layers.0.1.block.act1': ReLU(inplace=True),\n",
       " 'encoder.layers.0.1.block.conv2': Conv2dPad(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False),\n",
       " 'encoder.layers.0.1.block.bn2': BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True),\n",
       " 'encoder.layers.0.1.shortcut': Identity(),\n",
       " 'encoder.layers.0.1.act': ReLU(inplace=True),\n",
       " 'encoder.layers.1': ResNetLayer(\n",
       "   (0): ResNetBasicBlock(\n",
       "     (block): Sequential(\n",
       "       (conv1): Conv2dPad(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "       (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "       (act1): ReLU(inplace=True)\n",
       "       (conv2): Conv2dPad(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "       (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "     )\n",
       "     (shortcut): ResNetShorcut(\n",
       "       (conv): Conv2dPad(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "       (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "     )\n",
       "     (act): ReLU(inplace=True)\n",
       "   )\n",
       "   (1): ResNetBasicBlock(\n",
       "     (block): Sequential(\n",
       "       (conv1): Conv2dPad(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "       (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "       (act1): ReLU(inplace=True)\n",
       "       (conv2): Conv2dPad(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "       (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "     )\n",
       "     (shortcut): Identity()\n",
       "     (act): ReLU(inplace=True)\n",
       "   )\n",
       " ),\n",
       " 'encoder.layers.1.0': ResNetBasicBlock(\n",
       "   (block): Sequential(\n",
       "     (conv1): Conv2dPad(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "     (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "     (act1): ReLU(inplace=True)\n",
       "     (conv2): Conv2dPad(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "     (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "   )\n",
       "   (shortcut): ResNetShorcut(\n",
       "     (conv): Conv2dPad(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "     (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "   )\n",
       "   (act): ReLU(inplace=True)\n",
       " ),\n",
       " 'encoder.layers.1.0.block': Sequential(\n",
       "   (conv1): Conv2dPad(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "   (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "   (act1): ReLU(inplace=True)\n",
       "   (conv2): Conv2dPad(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "   (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       " ),\n",
       " 'encoder.layers.1.0.block.conv1': Conv2dPad(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False),\n",
       " 'encoder.layers.1.0.block.bn1': BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True),\n",
       " 'encoder.layers.1.0.block.act1': ReLU(inplace=True),\n",
       " 'encoder.layers.1.0.block.conv2': Conv2dPad(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False),\n",
       " 'encoder.layers.1.0.block.bn2': BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True),\n",
       " 'encoder.layers.1.0.shortcut': ResNetShorcut(\n",
       "   (conv): Conv2dPad(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "   (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       " ),\n",
       " 'encoder.layers.1.0.shortcut.conv': Conv2dPad(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False),\n",
       " 'encoder.layers.1.0.shortcut.bn': BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True),\n",
       " 'encoder.layers.1.0.act': ReLU(inplace=True),\n",
       " 'encoder.layers.1.1': ResNetBasicBlock(\n",
       "   (block): Sequential(\n",
       "     (conv1): Conv2dPad(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "     (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "     (act1): ReLU(inplace=True)\n",
       "     (conv2): Conv2dPad(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "     (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "   )\n",
       "   (shortcut): Identity()\n",
       "   (act): ReLU(inplace=True)\n",
       " ),\n",
       " 'encoder.layers.1.1.block': Sequential(\n",
       "   (conv1): Conv2dPad(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "   (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "   (act1): ReLU(inplace=True)\n",
       "   (conv2): Conv2dPad(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "   (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       " ),\n",
       " 'encoder.layers.1.1.block.conv1': Conv2dPad(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False),\n",
       " 'encoder.layers.1.1.block.bn1': BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True),\n",
       " 'encoder.layers.1.1.block.act1': ReLU(inplace=True),\n",
       " 'encoder.layers.1.1.block.conv2': Conv2dPad(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False),\n",
       " 'encoder.layers.1.1.block.bn2': BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True),\n",
       " 'encoder.layers.1.1.shortcut': Identity(),\n",
       " 'encoder.layers.1.1.act': ReLU(inplace=True),\n",
       " 'encoder.layers.2': ResNetLayer(\n",
       "   (0): ResNetBasicBlock(\n",
       "     (block): Sequential(\n",
       "       (conv1): Conv2dPad(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "       (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "       (act1): ReLU(inplace=True)\n",
       "       (conv2): Conv2dPad(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "       (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "     )\n",
       "     (shortcut): ResNetShorcut(\n",
       "       (conv): Conv2dPad(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "       (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "     )\n",
       "     (act): ReLU(inplace=True)\n",
       "   )\n",
       "   (1): ResNetBasicBlock(\n",
       "     (block): Sequential(\n",
       "       (conv1): Conv2dPad(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "       (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "       (act1): ReLU(inplace=True)\n",
       "       (conv2): Conv2dPad(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "       (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "     )\n",
       "     (shortcut): Identity()\n",
       "     (act): ReLU(inplace=True)\n",
       "   )\n",
       " ),\n",
       " 'encoder.layers.2.0': ResNetBasicBlock(\n",
       "   (block): Sequential(\n",
       "     (conv1): Conv2dPad(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "     (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "     (act1): ReLU(inplace=True)\n",
       "     (conv2): Conv2dPad(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "     (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "   )\n",
       "   (shortcut): ResNetShorcut(\n",
       "     (conv): Conv2dPad(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "     (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "   )\n",
       "   (act): ReLU(inplace=True)\n",
       " ),\n",
       " 'encoder.layers.2.0.block': Sequential(\n",
       "   (conv1): Conv2dPad(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "   (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "   (act1): ReLU(inplace=True)\n",
       "   (conv2): Conv2dPad(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "   (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       " ),\n",
       " 'encoder.layers.2.0.block.conv1': Conv2dPad(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False),\n",
       " 'encoder.layers.2.0.block.bn1': BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True),\n",
       " 'encoder.layers.2.0.block.act1': ReLU(inplace=True),\n",
       " 'encoder.layers.2.0.block.conv2': Conv2dPad(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False),\n",
       " 'encoder.layers.2.0.block.bn2': BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True),\n",
       " 'encoder.layers.2.0.shortcut': ResNetShorcut(\n",
       "   (conv): Conv2dPad(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "   (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       " ),\n",
       " 'encoder.layers.2.0.shortcut.conv': Conv2dPad(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False),\n",
       " 'encoder.layers.2.0.shortcut.bn': BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True),\n",
       " 'encoder.layers.2.0.act': ReLU(inplace=True),\n",
       " 'encoder.layers.2.1': ResNetBasicBlock(\n",
       "   (block): Sequential(\n",
       "     (conv1): Conv2dPad(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "     (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "     (act1): ReLU(inplace=True)\n",
       "     (conv2): Conv2dPad(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "     (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "   )\n",
       "   (shortcut): Identity()\n",
       "   (act): ReLU(inplace=True)\n",
       " ),\n",
       " 'encoder.layers.2.1.block': Sequential(\n",
       "   (conv1): Conv2dPad(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "   (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "   (act1): ReLU(inplace=True)\n",
       "   (conv2): Conv2dPad(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "   (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       " ),\n",
       " 'encoder.layers.2.1.block.conv1': Conv2dPad(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False),\n",
       " 'encoder.layers.2.1.block.bn1': BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True),\n",
       " 'encoder.layers.2.1.block.act1': ReLU(inplace=True),\n",
       " 'encoder.layers.2.1.block.conv2': Conv2dPad(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False),\n",
       " 'encoder.layers.2.1.block.bn2': BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True),\n",
       " 'encoder.layers.2.1.shortcut': Identity(),\n",
       " 'encoder.layers.2.1.act': ReLU(inplace=True),\n",
       " 'encoder.layers.3': ResNetLayer(\n",
       "   (0): ResNetBasicBlock(\n",
       "     (block): Sequential(\n",
       "       (conv1): Conv2dPad(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "       (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "       (act1): ReLU(inplace=True)\n",
       "       (conv2): Conv2dPad(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "       (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "     )\n",
       "     (shortcut): ResNetShorcut(\n",
       "       (conv): Conv2dPad(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "       (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "     )\n",
       "     (act): ReLU(inplace=True)\n",
       "   )\n",
       "   (1): ResNetBasicBlock(\n",
       "     (block): Sequential(\n",
       "       (conv1): Conv2dPad(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "       (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "       (act1): ReLU(inplace=True)\n",
       "       (conv2): Conv2dPad(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "       (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "     )\n",
       "     (shortcut): Identity()\n",
       "     (act): ReLU(inplace=True)\n",
       "   )\n",
       " ),\n",
       " 'encoder.layers.3.0': ResNetBasicBlock(\n",
       "   (block): Sequential(\n",
       "     (conv1): Conv2dPad(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "     (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "     (act1): ReLU(inplace=True)\n",
       "     (conv2): Conv2dPad(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "     (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "   )\n",
       "   (shortcut): ResNetShorcut(\n",
       "     (conv): Conv2dPad(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "     (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "   )\n",
       "   (act): ReLU(inplace=True)\n",
       " ),\n",
       " 'encoder.layers.3.0.block': Sequential(\n",
       "   (conv1): Conv2dPad(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "   (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "   (act1): ReLU(inplace=True)\n",
       "   (conv2): Conv2dPad(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "   (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       " ),\n",
       " 'encoder.layers.3.0.block.conv1': Conv2dPad(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False),\n",
       " 'encoder.layers.3.0.block.bn1': BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True),\n",
       " 'encoder.layers.3.0.block.act1': ReLU(inplace=True),\n",
       " 'encoder.layers.3.0.block.conv2': Conv2dPad(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False),\n",
       " 'encoder.layers.3.0.block.bn2': BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True),\n",
       " 'encoder.layers.3.0.shortcut': ResNetShorcut(\n",
       "   (conv): Conv2dPad(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "   (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       " ),\n",
       " 'encoder.layers.3.0.shortcut.conv': Conv2dPad(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False),\n",
       " 'encoder.layers.3.0.shortcut.bn': BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True),\n",
       " 'encoder.layers.3.0.act': ReLU(inplace=True),\n",
       " 'encoder.layers.3.1': ResNetBasicBlock(\n",
       "   (block): Sequential(\n",
       "     (conv1): Conv2dPad(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "     (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "     (act1): ReLU(inplace=True)\n",
       "     (conv2): Conv2dPad(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "     (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "   )\n",
       "   (shortcut): Identity()\n",
       "   (act): ReLU(inplace=True)\n",
       " ),\n",
       " 'encoder.layers.3.1.block': Sequential(\n",
       "   (conv1): Conv2dPad(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "   (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "   (act1): ReLU(inplace=True)\n",
       "   (conv2): Conv2dPad(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "   (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       " ),\n",
       " 'encoder.layers.3.1.block.conv1': Conv2dPad(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False),\n",
       " 'encoder.layers.3.1.block.bn1': BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True),\n",
       " 'encoder.layers.3.1.block.act1': ReLU(inplace=True),\n",
       " 'encoder.layers.3.1.block.conv2': Conv2dPad(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False),\n",
       " 'encoder.layers.3.1.block.bn2': BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True),\n",
       " 'encoder.layers.3.1.shortcut': Identity(),\n",
       " 'encoder.layers.3.1.act': ReLU(inplace=True),\n",
       " 'head': ResNetHead(\n",
       "   (pool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
       "   (flat): Flatten(start_dim=1, end_dim=-1)\n",
       "   (fc): Linear(in_features=512, out_features=1000, bias=True)\n",
       " ),\n",
       " 'head.pool': AdaptiveAvgPool2d(output_size=(1, 1)),\n",
       " 'head.flat': Flatten(start_dim=1, end_dim=-1),\n",
       " 'head.fc': Linear(in_features=512, out_features=1000, bias=True)}"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict(model.named_modules())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:no value was provided for `target_layer`, thus set to 'encoder'.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from torchcam.cams import ScoreCAM\n",
    "\n",
    "\n",
    "_ = model.interpret(x, using=TorchCamAdapter(ScoreCAM), postprocessing=postprocessing).show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:no value was provided for `target_layer`, thus set to 'encoder'.\n",
      "WARNING:root:no value was provided for `fc_layer`, thus set to 'head.fc'.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "torch.Size([7, 7])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from torchcam.cams import CAM\n",
    "\n",
    "cam_extractor = CAM(model)\n",
    "# Preprocess yodata:image/png;base64,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ur data and feed it to the model\n",
    "out = model(x)\n",
    "# Retrieve the CAM by passing the class index and the model output\n",
    "activation_map = cam_extractor(out.squeeze(0).argmax().item(), out)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "GradCamResult(x, activation_map, postprocessing).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "  Using cached sotabencheval-0.0.38-py3-none-any.whl (55 kB)\n",
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      "  Using cached sotabenchapi-0.0.16-py3-none-any.whl (20 kB)\n",
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      "\u001b[?25hCollecting typing\n",
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      "Installing collected packages: urllib3, idna, chardet, typing, tabulate, soupsieve, requests, portalocker, Cython, click, sotabenchapi, sacrebleu, pycocotools, beautifulsoup4, sotabencheval\n",
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      "  Attempting uninstall: requests\n",
      "    Found existing installation: requests 2.26.0\n",
      "    Uninstalling requests-2.26.0:\n",
      "      Successfully uninstalled requests-2.26.0\n",
      "  Attempting uninstall: click\n",
      "    Found existing installation: click 8.0.3\n",
      "    Uninstalling click-8.0.3:\n",
      "      Successfully uninstalled click-8.0.3\n",
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "black 21.11b1 requires click>=7.1.2, but you have click 7.0 which is incompatible.\u001b[0m\n",
      "Successfully installed Cython-0.29.24 beautifulsoup4-4.10.0 chardet-3.0.4 click-7.0 idna-2.8 portalocker-2.3.2 pycocotools-2.0.3 requests-2.22.0 sacrebleu-1.4.1 sotabenchapi-0.0.16 sotabencheval-0.0.38 soupsieve-2.3.1 tabulate-0.8.3 typing-3.7.4.3 urllib3-1.25.11\n"
     ]
    }
   ],
   "source": [
    "!pip install sotabencheval"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import timm\n",
    "import torch\n",
    "from glasses.models import AutoModel, AutoTransform\n",
    "from transfer_weights import clone_model\n",
    "from torchvision.models import mobilenet_v2\n",
    "from glasses.models.classification.resnet import ResNet, ResNetBottleneckBlock, ResNetShorcutD, ResNetStemT\n",
    "from glasses.nn.att import ECA, WithAtt\n",
    "\n",
    "from functools import partial\n",
    "\n",
    "key = 'regnety_064'\n",
    "\n",
    "src = timm.create_model(key, pretrained=True)\n",
    "\n",
    "dst = AutoModel.from_name(key)\n",
    "tr = AutoTransform.from_name('resnet26')\n",
    "\n",
    "x = torch.randn((1, 3, 224,224))\n",
    "\n",
    "dst = clone_model(src, dst, x)\n",
    "\n",
    "# dst.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "===============================================================================================\n",
       "Layer (type:depth-idx)                        Output Shape              Param #\n",
       "===============================================================================================\n",
       "RegNet                                        --                        --\n",
       "├─ConvBnAct: 1-1                              [1, 32, 112, 112]         --\n",
       "│    └─Conv2d: 2-1                            [1, 32, 112, 112]         864\n",
       "│    └─BatchNormAct2d: 2-2                    [1, 32, 112, 112]         --\n",
       "│    │    └─ReLU: 3-1                         [1, 32, 112, 112]         --\n",
       "├─RegStage: 1-2                               [1, 144, 56, 56]          --\n",
       "│    └─Bottleneck: 2-3                        [1, 144, 56, 56]          --\n",
       "│    │    └─ConvBnAct: 3-2                    [1, 144, 112, 112]        4,896\n",
       "│    │    └─ConvBnAct: 3-3                    [1, 144, 56, 56]          93,600\n",
       "│    │    └─SEModule: 3-4                     [1, 144, 56, 56]          2,456\n",
       "│    │    └─ConvBnAct: 3-5                    [1, 144, 56, 56]          21,024\n",
       "│    │    └─ConvBnAct: 3-6                    [1, 144, 56, 56]          4,896\n",
       "│    │    └─ReLU: 3-7                         [1, 144, 56, 56]          --\n",
       "│    └─Bottleneck: 2-4                        [1, 144, 56, 56]          --\n",
       "│    │    └─ConvBnAct: 3-8                    [1, 144, 56, 56]          21,024\n",
       "│    │    └─ConvBnAct: 3-9                    [1, 144, 56, 56]          93,600\n",
       "│    │    └─SEModule: 3-10                    [1, 144, 56, 56]          10,548\n",
       "│    │    └─ConvBnAct: 3-11                   [1, 144, 56, 56]          21,024\n",
       "│    │    └─ReLU: 3-12                        [1, 144, 56, 56]          --\n",
       "├─RegStage: 1-3                               [1, 288, 28, 28]          --\n",
       "│    └─Bottleneck: 2-5                        [1, 288, 28, 28]          --\n",
       "│    │    └─ConvBnAct: 3-13                   [1, 288, 56, 56]          42,048\n",
       "│    │    └─ConvBnAct: 3-14                   [1, 288, 28, 28]          187,200\n",
       "│    │    └─SEModule: 3-15                    [1, 288, 28, 28]          21,060\n",
       "│    │    └─ConvBnAct: 3-16                   [1, 288, 28, 28]          83,520\n",
       "│    │    └─ConvBnAct: 3-17                   [1, 288, 28, 28]          42,048\n",
       "│    │    └─ReLU: 3-18                        [1, 288, 28, 28]          --\n",
       "│    └─Bottleneck: 2-6                        [1, 288, 28, 28]          --\n",
       "│    │    └─ConvBnAct: 3-19                   [1, 288, 28, 28]          83,520\n",
       "│    │    └─ConvBnAct: 3-20                   [1, 288, 28, 28]          187,200\n",
       "│    │    └─SEModule: 3-21                    [1, 288, 28, 28]          41,832\n",
       "│    │    └─ConvBnAct: 3-22                   [1, 288, 28, 28]          83,520\n",
       "│    │    └─ReLU: 3-23                        [1, 288, 28, 28]          --\n",
       "│    └─Bottleneck: 2-7                        [1, 288, 28, 28]          --\n",
       "│    │    └─ConvBnAct: 3-24                   [1, 288, 28, 28]          83,520\n",
       "│    │    └─ConvBnAct: 3-25                   [1, 288, 28, 28]          187,200\n",
       "│    │    └─SEModule: 3-26                    [1, 288, 28, 28]          41,832\n",
       "│    │    └─ConvBnAct: 3-27                   [1, 288, 28, 28]          83,520\n",
       "│    │    └─ReLU: 3-28                        [1, 288, 28, 28]          --\n",
       "│    └─Bottleneck: 2-8                        [1, 288, 28, 28]          --\n",
       "│    │    └─ConvBnAct: 3-29                   [1, 288, 28, 28]          83,520\n",
       "│    │    └─ConvBnAct: 3-30                   [1, 288, 28, 28]          187,200\n",
       "│    │    └─SEModule: 3-31                    [1, 288, 28, 28]          41,832\n",
       "│    │    └─ConvBnAct: 3-32                   [1, 288, 28, 28]          83,520\n",
       "│    │    └─ReLU: 3-33                        [1, 288, 28, 28]          --\n",
       "│    └─Bottleneck: 2-9                        [1, 288, 28, 28]          --\n",
       "│    │    └─ConvBnAct: 3-34                   [1, 288, 28, 28]          83,520\n",
       "│    │    └─ConvBnAct: 3-35                   [1, 288, 28, 28]          187,200\n",
       "│    │    └─SEModule: 3-36                    [1, 288, 28, 28]          41,832\n",
       "│    │    └─ConvBnAct: 3-37                   [1, 288, 28, 28]          83,520\n",
       "│    │    └─ReLU: 3-38                        [1, 288, 28, 28]          --\n",
       "│    └─Bottleneck: 2-10                       [1, 288, 28, 28]          --\n",
       "│    │    └─ConvBnAct: 3-39                   [1, 288, 28, 28]          83,520\n",
       "│    │    └─ConvBnAct: 3-40                   [1, 288, 28, 28]          187,200\n",
       "│    │    └─SEModule: 3-41                    [1, 288, 28, 28]          41,832\n",
       "│    │    └─ConvBnAct: 3-42                   [1, 288, 28, 28]          83,520\n",
       "│    │    └─ReLU: 3-43                        [1, 288, 28, 28]          --\n",
       "│    └─Bottleneck: 2-11                       [1, 288, 28, 28]          --\n",
       "│    │    └─ConvBnAct: 3-44                   [1, 288, 28, 28]          83,520\n",
       "│    │    └─ConvBnAct: 3-45                   [1, 288, 28, 28]          187,200\n",
       "│    │    └─SEModule: 3-46                    [1, 288, 28, 28]          41,832\n",
       "│    │    └─ConvBnAct: 3-47                   [1, 288, 28, 28]          83,520\n",
       "│    │    └─ReLU: 3-48                        [1, 288, 28, 28]          --\n",
       "├─RegStage: 1-4                               [1, 576, 14, 14]          --\n",
       "│    └─Bottleneck: 2-12                       [1, 576, 14, 14]          --\n",
       "│    │    └─ConvBnAct: 3-49                   [1, 576, 28, 28]          167,040\n",
       "│    │    └─ConvBnAct: 3-50                   [1, 576, 14, 14]          374,400\n",
       "│    │    └─SEModule: 3-51                    [1, 576, 14, 14]          83,592\n",
       "│    │    └─ConvBnAct: 3-52                   [1, 576, 14, 14]          332,928\n",
       "│    │    └─ConvBnAct: 3-53                   [1, 576, 14, 14]          167,040\n",
       "│    │    └─ReLU: 3-54                        [1, 576, 14, 14]          --\n",
       "│    └─Bottleneck: 2-13                       [1, 576, 14, 14]          --\n",
       "│    │    └─ConvBnAct: 3-55                   [1, 576, 14, 14]          332,928\n",
       "│    │    └─ConvBnAct: 3-56                   [1, 576, 14, 14]          374,400\n",
       "│    │    └─SEModule: 3-57                    [1, 576, 14, 14]          166,608\n",
       "│    │    └─ConvBnAct: 3-58                   [1, 576, 14, 14]          332,928\n",
       "│    │    └─ReLU: 3-59                        [1, 576, 14, 14]          --\n",
       "│    └─Bottleneck: 2-14                       [1, 576, 14, 14]          --\n",
       "│    │    └─ConvBnAct: 3-60                   [1, 576, 14, 14]          332,928\n",
       "│    │    └─ConvBnAct: 3-61                   [1, 576, 14, 14]          374,400\n",
       "│    │    └─SEModule: 3-62                    [1, 576, 14, 14]          166,608\n",
       "│    │    └─ConvBnAct: 3-63                   [1, 576, 14, 14]          332,928\n",
       "│    │    └─ReLU: 3-64                        [1, 576, 14, 14]          --\n",
       "│    └─Bottleneck: 2-15                       [1, 576, 14, 14]          --\n",
       "│    │    └─ConvBnAct: 3-65                   [1, 576, 14, 14]          332,928\n",
       "│    │    └─ConvBnAct: 3-66                   [1, 576, 14, 14]          374,400\n",
       "│    │    └─SEModule: 3-67                    [1, 576, 14, 14]          166,608\n",
       "│    │    └─ConvBnAct: 3-68                   [1, 576, 14, 14]          332,928\n",
       "│    │    └─ReLU: 3-69                        [1, 576, 14, 14]          --\n",
       "│    └─Bottleneck: 2-16                       [1, 576, 14, 14]          --\n",
       "│    │    └─ConvBnAct: 3-70                   [1, 576, 14, 14]          332,928\n",
       "│    │    └─ConvBnAct: 3-71                   [1, 576, 14, 14]          374,400\n",
       "│    │    └─SEModule: 3-72                    [1, 576, 14, 14]          166,608\n",
       "│    │    └─ConvBnAct: 3-73                   [1, 576, 14, 14]          332,928\n",
       "│    │    └─ReLU: 3-74                        [1, 576, 14, 14]          --\n",
       "│    └─Bottleneck: 2-17                       [1, 576, 14, 14]          --\n",
       "│    │    └─ConvBnAct: 3-75                   [1, 576, 14, 14]          332,928\n",
       "│    │    └─ConvBnAct: 3-76                   [1, 576, 14, 14]          374,400\n",
       "│    │    └─SEModule: 3-77                    [1, 576, 14, 14]          166,608\n",
       "│    │    └─ConvBnAct: 3-78                   [1, 576, 14, 14]          332,928\n",
       "│    │    └─ReLU: 3-79                        [1, 576, 14, 14]          --\n",
       "│    └─Bottleneck: 2-18                       [1, 576, 14, 14]          --\n",
       "│    │    └─ConvBnAct: 3-80                   [1, 576, 14, 14]          332,928\n",
       "│    │    └─ConvBnAct: 3-81                   [1, 576, 14, 14]          374,400\n",
       "│    │    └─SEModule: 3-82                    [1, 576, 14, 14]          166,608\n",
       "│    │    └─ConvBnAct: 3-83                   [1, 576, 14, 14]          332,928\n",
       "│    │    └─ReLU: 3-84                        [1, 576, 14, 14]          --\n",
       "│    └─Bottleneck: 2-19                       [1, 576, 14, 14]          --\n",
       "│    │    └─ConvBnAct: 3-85                   [1, 576, 14, 14]          332,928\n",
       "│    │    └─ConvBnAct: 3-86                   [1, 576, 14, 14]          374,400\n",
       "│    │    └─SEModule: 3-87                    [1, 576, 14, 14]          166,608\n",
       "│    │    └─ConvBnAct: 3-88                   [1, 576, 14, 14]          332,928\n",
       "│    │    └─ReLU: 3-89                        [1, 576, 14, 14]          --\n",
       "│    └─Bottleneck: 2-20                       [1, 576, 14, 14]          --\n",
       "│    │    └─ConvBnAct: 3-90                   [1, 576, 14, 14]          332,928\n",
       "│    │    └─ConvBnAct: 3-91                   [1, 576, 14, 14]          374,400\n",
       "│    │    └─SEModule: 3-92                    [1, 576, 14, 14]          166,608\n",
       "│    │    └─ConvBnAct: 3-93                   [1, 576, 14, 14]          332,928\n",
       "│    │    └─ReLU: 3-94                        [1, 576, 14, 14]          --\n",
       "│    └─Bottleneck: 2-21                       [1, 576, 14, 14]          --\n",
       "│    │    └─ConvBnAct: 3-95                   [1, 576, 14, 14]          332,928\n",
       "│    │    └─ConvBnAct: 3-96                   [1, 576, 14, 14]          374,400\n",
       "│    │    └─SEModule: 3-97                    [1, 576, 14, 14]          166,608\n",
       "│    │    └─ConvBnAct: 3-98                   [1, 576, 14, 14]          332,928\n",
       "│    │    └─ReLU: 3-99                        [1, 576, 14, 14]          --\n",
       "│    └─Bottleneck: 2-22                       [1, 576, 14, 14]          --\n",
       "│    │    └─ConvBnAct: 3-100                  [1, 576, 14, 14]          332,928\n",
       "│    │    └─ConvBnAct: 3-101                  [1, 576, 14, 14]          374,400\n",
       "│    │    └─SEModule: 3-102                   [1, 576, 14, 14]          166,608\n",
       "│    │    └─ConvBnAct: 3-103                  [1, 576, 14, 14]          332,928\n",
       "│    │    └─ReLU: 3-104                       [1, 576, 14, 14]          --\n",
       "│    └─Bottleneck: 2-23                       [1, 576, 14, 14]          --\n",
       "│    │    └─ConvBnAct: 3-105                  [1, 576, 14, 14]          332,928\n",
       "│    │    └─ConvBnAct: 3-106                  [1, 576, 14, 14]          374,400\n",
       "│    │    └─SEModule: 3-107                   [1, 576, 14, 14]          166,608\n",
       "│    │    └─ConvBnAct: 3-108                  [1, 576, 14, 14]          332,928\n",
       "│    │    └─ReLU: 3-109                       [1, 576, 14, 14]          --\n",
       "│    └─Bottleneck: 2-24                       [1, 576, 14, 14]          --\n",
       "│    │    └─ConvBnAct: 3-110                  [1, 576, 14, 14]          332,928\n",
       "│    │    └─ConvBnAct: 3-111                  [1, 576, 14, 14]          374,400\n",
       "│    │    └─SEModule: 3-112                   [1, 576, 14, 14]          166,608\n",
       "│    │    └─ConvBnAct: 3-113                  [1, 576, 14, 14]          332,928\n",
       "│    │    └─ReLU: 3-114                       [1, 576, 14, 14]          --\n",
       "│    └─Bottleneck: 2-25                       [1, 576, 14, 14]          --\n",
       "│    │    └─ConvBnAct: 3-115                  [1, 576, 14, 14]          332,928\n",
       "│    │    └─ConvBnAct: 3-116                  [1, 576, 14, 14]          374,400\n",
       "│    │    └─SEModule: 3-117                   [1, 576, 14, 14]          166,608\n",
       "│    │    └─ConvBnAct: 3-118                  [1, 576, 14, 14]          332,928\n",
       "│    │    └─ReLU: 3-119                       [1, 576, 14, 14]          --\n",
       "├─RegStage: 1-5                               [1, 1296, 7, 7]           --\n",
       "│    └─Bottleneck: 2-26                       [1, 1296, 7, 7]           --\n",
       "│    │    └─ConvBnAct: 3-120                  [1, 1296, 14, 14]         749,088\n",
       "│    │    └─ConvBnAct: 3-121                  [1, 1296, 7, 7]           842,400\n",
       "│    │    └─SEModule: 3-122                   [1, 1296, 7, 7]           374,688\n",
       "│    │    └─ConvBnAct: 3-123                  [1, 1296, 7, 7]           1,682,208\n",
       "│    │    └─ConvBnAct: 3-124                  [1, 1296, 7, 7]           749,088\n",
       "│    │    └─ReLU: 3-125                       [1, 1296, 7, 7]           --\n",
       "│    └─Bottleneck: 2-27                       [1, 1296, 7, 7]           --\n",
       "│    │    └─ConvBnAct: 3-126                  [1, 1296, 7, 7]           1,682,208\n",
       "│    │    └─ConvBnAct: 3-127                  [1, 1296, 7, 7]           842,400\n",
       "│    │    └─SEModule: 3-128                   [1, 1296, 7, 7]           841,428\n",
       "│    │    └─ConvBnAct: 3-129                  [1, 1296, 7, 7]           1,682,208\n",
       "│    │    └─ReLU: 3-130                       [1, 1296, 7, 7]           --\n",
       "├─ClassifierHead: 1-6                         [1, 1000]                 --\n",
       "│    └─SelectAdaptivePool2d: 2-28             [1, 1296]                 --\n",
       "│    │    └─AdaptiveAvgPool2d: 3-131          [1, 1296, 1, 1]           --\n",
       "│    │    └─Flatten: 3-132                    [1, 1296]                 --\n",
       "│    └─Linear: 2-29                           [1, 1000]                 1,297,000\n",
       "│    └─Identity: 2-30                         [1, 1000]                 --\n",
       "===============================================================================================\n",
       "Total params: 30,500,820\n",
       "Trainable params: 30,500,820\n",
       "Non-trainable params: 0\n",
       "Total mult-adds (G): 6.36\n",
       "===============================================================================================\n",
       "Input size (MB): 0.60\n",
       "Forward/backward pass size (MB): 131.26\n",
       "Params size (MB): 122.00\n",
       "Estimated Total Size (MB): 253.86\n",
       "==============================================================================================="
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from torchinfo import summary\n",
    "\n",
    "summary(src, (1, 3, 224,224))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "===================================================================================================================\n",
       "Layer (type:depth-idx)                                            Output Shape              Param #\n",
       "===================================================================================================================\n",
       "RegNet                                                            --                        --\n",
       "├─ResNetEncoder: 1                                                --                        --\n",
       "│    └─ModuleList: 2-1                                            --                        --\n",
       "├─ResNetEncoder: 1-1                                              [1, 1296, 7, 7]           --\n",
       "│    └─ConvNormAct: 2-2                                           [1, 32, 112, 112]         --\n",
       "│    │    └─Conv2dPad: 3-1                                        [1, 32, 112, 112]         864\n",
       "│    │    └─BatchNorm2d: 3-2                                      [1, 32, 112, 112]         64\n",
       "│    │    └─ReLU: 3-3                                             [1, 32, 112, 112]         --\n",
       "│    └─ModuleList: 2-1                                            --                        --\n",
       "│    │    └─ResNetLayer: 3-4                                      [1, 144, 56, 56]          273,068\n",
       "│    │    └─ResNetLayer: 3-5                                      [1, 288, 28, 28]          2,752,308\n",
       "│    │    └─ResNetLayer: 3-6                                      [1, 576, 14, 14]          16,814,232\n",
       "│    │    └─ResNetLayer: 3-7                                      [1, 1296, 7, 7]           9,445,716\n",
       "├─ResNetHead: 1-2                                                 [1, 1000]                 --\n",
       "│    └─AdaptiveAvgPool2d: 2-3                                     [1, 1296, 1, 1]           --\n",
       "│    └─Flatten: 2-4                                               [1, 1296]                 --\n",
       "│    └─Linear: 2-5                                                [1, 1000]                 1,297,000\n",
       "===================================================================================================================\n",
       "Total params: 30,583,252\n",
       "Trainable params: 30,583,252\n",
       "Non-trainable params: 0\n",
       "Total mult-adds (G): 6.36\n",
       "===================================================================================================================\n",
       "Input size (MB): 0.60\n",
       "Forward/backward pass size (MB): 262.38\n",
       "Params size (MB): 122.33\n",
       "Estimated Total Size (MB): 385.32\n",
       "==================================================================================================================="
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary(dst, (1, 3, 224,224), depth=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using downloaded and verified file: ./imagenet_val_targets.pkl\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2510c95bc69140a7ac00c85264b33396",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/782 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.9375, None, None)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "benchmark(src, tr, fast=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using downloaded and verified file: ./imagenet_val_targets.pkl\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0b8fa05a3b4544d7991d4d13e3bba5f6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/782 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.9375, None, None)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "benchmark(dst, tr, fast=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Intepretability\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'glasses.utils.weights.storage.hubs.HFModelHub' has no attribute 'from_pretrained'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-22-c5d08159099a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mglasses\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodels\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mAutoModel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mAutoTransform\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mAutoModel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_pretrained\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'resnet18'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcuda\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m \u001b[0mcfg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mAutoTransform\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'resnet18'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrequests\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'https://i.insider.com/5df126b679d7570ad2044f3e?width=700&format=jpeg&auto=webp'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Documents/torcheyes/glasses/models/AutoModel.py\u001b[0m in \u001b[0;36mfrom_pretrained\u001b[0;34m(name, *args, **kwargs)\u001b[0m\n\u001b[1;32m    231\u001b[0m             \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mModule\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mA\u001b[0m \u001b[0mfully\u001b[0m \u001b[0minstantiated\u001b[0m \u001b[0mpretrained\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    232\u001b[0m         \"\"\"\n\u001b[0;32m--> 233\u001b[0;31m         \u001b[0mweights\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mAutoModel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprovider\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    234\u001b[0m         \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mAutoModel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    235\u001b[0m         \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_state_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mweights\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Documents/torcheyes/glasses/utils/weights/PretrainedWeightsProvider.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    187\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    188\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mStateDict\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 189\u001b[0;31m         \u001b[0mweights\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstorage\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    190\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mweights\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Documents/torcheyes/glasses/utils/weights/storage/HuggingFaceStorage.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mStateDict\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m         \u001b[0mstate_dict\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mHFModelHub\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_pretrained\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{self.ORGANIZATION}/{key}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     27\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mstate_dict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     28\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: module 'glasses.utils.weights.storage.hubs.HFModelHub' has no attribute 'from_pretrained'"
     ]
    }
   ],
   "source": [
    "import requests\n",
    "from PIL import Image\n",
    "from io import BytesIO\n",
    "from glasses.interpretability import GradCam, SaliencyMap, ScoreCam\n",
    "from torchvision.transforms import Normalize\n",
    "from glasses.models import AutoModel, AutoTransform\n",
    "\n",
    "model = AutoModel.from_pretrained('resnet18').cuda()\n",
    "cfg = AutoTransform.from_name('resnet18')\n",
    "r = requests.get('https://i.insider.com/5df126b679d7570ad2044f3e?width=700&format=jpeg&auto=webp')\n",
    "im = Image.open(BytesIO(r.content))\n",
    "# un normalize when done\n",
    "postprocessing = Normalize(-cfg.mean / cfg.std, (1.0 / cfg.std))\n",
    "# # apply preprocessing\n",
    "x =  cfg.transform(im).unsqueeze(0).cuda()\n",
    "# _ = model.interpret(x, using=GradCam(), postprocessing=postprocessing).show()\n",
    "\n",
    "\n",
    "ScoreCam()(x, model, postprocessing=postprocessing).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "GradCam()(x, model, postprocessing=postprocessing).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[[[0.8032, 0.8032, 0.8032,  ..., 0.0316, 0.0316, 0.0316],\n",
       "          [0.8032, 0.8032, 0.8032,  ..., 0.0316, 0.0316, 0.0316],\n",
       "          [0.8032, 0.8032, 0.8032,  ..., 0.0316, 0.0316, 0.0316],\n",
       "          ...,\n",
       "          [0.0354, 0.0354, 0.0354,  ..., 0.9403, 0.9403, 0.9403],\n",
       "          [0.0354, 0.0354, 0.0354,  ..., 0.9403, 0.9403, 0.9403],\n",
       "          [0.0354, 0.0354, 0.0354,  ..., 0.9403, 0.9403, 0.9403]],\n",
       "\n",
       "         [[0.9359, 0.9359, 0.9359,  ..., 0.7071, 0.7071, 0.7071],\n",
       "          [0.9359, 0.9359, 0.9359,  ..., 0.7071, 0.7071, 0.7071],\n",
       "          [0.9359, 0.9359, 0.9359,  ..., 0.7071, 0.7071, 0.7071],\n",
       "          ...,\n",
       "          [1.0000, 1.0000, 1.0000,  ..., 0.2538, 0.2538, 0.2538],\n",
       "          [1.0000, 1.0000, 1.0000,  ..., 0.2538, 0.2538, 0.2538],\n",
       "          [1.0000, 1.0000, 1.0000,  ..., 0.2538, 0.2538, 0.2538]],\n",
       "\n",
       "         [[0.2173, 0.2173, 0.2173,  ..., 0.7678, 0.7678, 0.7678],\n",
       "          [0.2173, 0.2173, 0.2173,  ..., 0.7678, 0.7678, 0.7678],\n",
       "          [0.2173, 0.2173, 0.2173,  ..., 0.7678, 0.7678, 0.7678],\n",
       "          ...,\n",
       "          [0.0104, 0.0104, 0.0104,  ..., 1.0000, 1.0000, 1.0000],\n",
       "          [0.0104, 0.0104, 0.0104,  ..., 1.0000, 1.0000, 1.0000],\n",
       "          [0.0104, 0.0104, 0.0104,  ..., 1.0000, 1.0000, 1.0000]],\n",
       "\n",
       "         ...,\n",
       "\n",
       "         [[1.0000, 1.0000, 1.0000,  ..., 1.0000, 1.0000, 1.0000],\n",
       "          [1.0000, 1.0000, 1.0000,  ..., 1.0000, 1.0000, 1.0000],\n",
       "          [1.0000, 1.0000, 1.0000,  ..., 1.0000, 1.0000, 1.0000],\n",
       "          ...,\n",
       "          [0.8188, 0.8188, 0.8188,  ..., 0.0000, 0.0000, 0.0000],\n",
       "          [0.8188, 0.8188, 0.8188,  ..., 0.0000, 0.0000, 0.0000],\n",
       "          [0.8188, 0.8188, 0.8188,  ..., 0.0000, 0.0000, 0.0000]],\n",
       "\n",
       "         [[0.3917, 0.3917, 0.3917,  ..., 0.7994, 0.7994, 0.7994],\n",
       "          [0.3917, 0.3917, 0.3917,  ..., 0.7994, 0.7994, 0.7994],\n",
       "          [0.3917, 0.3917, 0.3917,  ..., 0.7994, 0.7994, 0.7994],\n",
       "          ...,\n",
       "          [0.0000, 0.0000, 0.0000,  ..., 0.6858, 0.6858, 0.6858],\n",
       "          [0.0000, 0.0000, 0.0000,  ..., 0.6858, 0.6858, 0.6858],\n",
       "          [0.0000, 0.0000, 0.0000,  ..., 0.6858, 0.6858, 0.6858]],\n",
       "\n",
       "         [[0.2471, 0.2471, 0.2471,  ..., 0.0716, 0.0716, 0.0716],\n",
       "          [0.2471, 0.2471, 0.2471,  ..., 0.0716, 0.0716, 0.0716],\n",
       "          [0.2471, 0.2471, 0.2471,  ..., 0.0716, 0.0716, 0.0716],\n",
       "          ...,\n",
       "          [0.1593, 0.1593, 0.1593,  ..., 0.4666, 0.4666, 0.4666],\n",
       "          [0.1593, 0.1593, 0.1593,  ..., 0.4666, 0.4666, 0.4666],\n",
       "          [0.1593, 0.1593, 0.1593,  ..., 0.4666, 0.4666, 0.4666]]],\n",
       "\n",
       "\n",
       "        [[[0.6398, 0.6398, 0.6398,  ..., 0.0416, 0.0416, 0.0416],\n",
       "          [0.6398, 0.6398, 0.6398,  ..., 0.0416, 0.0416, 0.0416],\n",
       "          [0.6398, 0.6398, 0.6398,  ..., 0.0416, 0.0416, 0.0416],\n",
       "          ...,\n",
       "          [0.8411, 0.8411, 0.8411,  ..., 0.1784, 0.1784, 0.1784],\n",
       "          [0.8411, 0.8411, 0.8411,  ..., 0.1784, 0.1784, 0.1784],\n",
       "          [0.8411, 0.8411, 0.8411,  ..., 0.1784, 0.1784, 0.1784]],\n",
       "\n",
       "         [[0.3818, 0.3818, 0.3818,  ..., 0.0566, 0.0566, 0.0566],\n",
       "          [0.3818, 0.3818, 0.3818,  ..., 0.0566, 0.0566, 0.0566],\n",
       "          [0.3818, 0.3818, 0.3818,  ..., 0.0566, 0.0566, 0.0566],\n",
       "          ...,\n",
       "          [0.2268, 0.2268, 0.2268,  ..., 0.0000, 0.0000, 0.0000],\n",
       "          [0.2268, 0.2268, 0.2268,  ..., 0.0000, 0.0000, 0.0000],\n",
       "          [0.2268, 0.2268, 0.2268,  ..., 0.0000, 0.0000, 0.0000]],\n",
       "\n",
       "         [[0.0000, 0.0000, 0.0000,  ..., 0.7033, 0.7033, 0.7033],\n",
       "          [0.0000, 0.0000, 0.0000,  ..., 0.7033, 0.7033, 0.7033],\n",
       "          [0.0000, 0.0000, 0.0000,  ..., 0.7033, 0.7033, 0.7033],\n",
       "          ...,\n",
       "          [0.0000, 0.0000, 0.0000,  ..., 0.5288, 0.5288, 0.5288],\n",
       "          [0.0000, 0.0000, 0.0000,  ..., 0.5288, 0.5288, 0.5288],\n",
       "          [0.0000, 0.0000, 0.0000,  ..., 0.5288, 0.5288, 0.5288]],\n",
       "\n",
       "         ...,\n",
       "\n",
       "         [[0.9279, 0.9279, 0.9279,  ..., 0.0000, 0.0000, 0.0000],\n",
       "          [0.9279, 0.9279, 0.9279,  ..., 0.0000, 0.0000, 0.0000],\n",
       "          [0.9279, 0.9279, 0.9279,  ..., 0.0000, 0.0000, 0.0000],\n",
       "          ...,\n",
       "          [0.3421, 0.3421, 0.3421,  ..., 0.4403, 0.4403, 0.4403],\n",
       "          [0.3421, 0.3421, 0.3421,  ..., 0.4403, 0.4403, 0.4403],\n",
       "          [0.3421, 0.3421, 0.3421,  ..., 0.4403, 0.4403, 0.4403]],\n",
       "\n",
       "         [[1.0000, 1.0000, 1.0000,  ..., 0.2003, 0.2003, 0.2003],\n",
       "          [1.0000, 1.0000, 1.0000,  ..., 0.2003, 0.2003, 0.2003],\n",
       "          [1.0000, 1.0000, 1.0000,  ..., 0.2003, 0.2003, 0.2003],\n",
       "          ...,\n",
       "          [0.6529, 0.6529, 0.6529,  ..., 0.1199, 0.1199, 0.1199],\n",
       "          [0.6529, 0.6529, 0.6529,  ..., 0.1199, 0.1199, 0.1199],\n",
       "          [0.6529, 0.6529, 0.6529,  ..., 0.1199, 0.1199, 0.1199]],\n",
       "\n",
       "         [[0.2930, 0.2930, 0.2930,  ..., 0.1648, 0.1648, 0.1648],\n",
       "          [0.2930, 0.2930, 0.2930,  ..., 0.1648, 0.1648, 0.1648],\n",
       "          [0.2930, 0.2930, 0.2930,  ..., 0.1648, 0.1648, 0.1648],\n",
       "          ...,\n",
       "          [0.5801, 0.5801, 0.5801,  ..., 0.6907, 0.6907, 0.6907],\n",
       "          [0.5801, 0.5801, 0.5801,  ..., 0.6907, 0.6907, 0.6907],\n",
       "          [0.5801, 0.5801, 0.5801,  ..., 0.6907, 0.6907, 0.6907]]]])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from einops import rearrange\n",
    "import torch\n",
    "import torch.nn.functional as F\n",
    "from\n",
    "\n",
    "a = torch.rand((2, 10, 5, 5))\n",
    "\n",
    "# rear = rearrange(a, 'b c h w -> b (c h w)',)\n",
    "\n",
    "a = F.interpolate(a, size=(224, 224), mode='bilinear', align_corners=False)\n",
    "# scaling\n",
    "# rear.shape\n",
    "mins = a.min(keepdim=True, dim=1).values\n",
    "maxs = a.max(keepdim=True, dim=1).values\n",
    "scaling = (a - mins) / (maxs - mins)\n",
    "scaling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Benchmark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from glasses.models import AutoModel, AutoTransform\n",
    "import torch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ViT(\n",
       "  (embedding): PatchEmbedding(\n",
       "    (projection): Sequential(\n",
       "      (0): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16))\n",
       "      (1): Rearrange('b e (h) (w) -> b (h w) e')\n",
       "    )\n",
       "    (tokens): ViTTokens()\n",
       "  )\n",
       "  (encoder): TransformerEncoder(\n",
       "    (layers): ModuleList(\n",
       "      (0): TransformerEncoderBlock(\n",
       "        (0): ResidualAdd(\n",
       "          (fn): Sequential(\n",
       "            (0): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "            (1): MultiHeadAttention(\n",
       "              (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
       "              (att_drop): Dropout(p=0.0, inplace=False)\n",
       "              (projection): Sequential(\n",
       "                (0): Linear(in_features=768, out_features=768, bias=True)\n",
       "                (1): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (1): ResidualAdd(\n",
       "          (fn): Sequential(\n",
       "            (0): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "            (1): FeedForwardBlock(\n",
       "              (0): Linear(in_features=768, out_features=3072, bias=True)\n",
       "              (1): GELU()\n",
       "              (2): Dropout(p=0.0, inplace=False)\n",
       "              (3): Linear(in_features=3072, out_features=768, bias=True)\n",
       "              (4): Dropout(p=0.0, inplace=False)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "      (1): TransformerEncoderBlock(\n",
       "        (0): ResidualAdd(\n",
       "          (fn): Sequential(\n",
       "            (0): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "            (1): MultiHeadAttention(\n",
       "              (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
       "              (att_drop): Dropout(p=0.0, inplace=False)\n",
       "              (projection): Sequential(\n",
       "                (0): Linear(in_features=768, out_features=768, bias=True)\n",
       "                (1): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (1): ResidualAdd(\n",
       "          (fn): Sequential(\n",
       "            (0): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "            (1): FeedForwardBlock(\n",
       "              (0): Linear(in_features=768, out_features=3072, bias=True)\n",
       "              (1): GELU()\n",
       "              (2): Dropout(p=0.0, inplace=False)\n",
       "              (3): Linear(in_features=3072, out_features=768, bias=True)\n",
       "              (4): Dropout(p=0.0, inplace=False)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "      (2): TransformerEncoderBlock(\n",
       "        (0): ResidualAdd(\n",
       "          (fn): Sequential(\n",
       "            (0): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "            (1): MultiHeadAttention(\n",
       "              (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
       "              (att_drop): Dropout(p=0.0, inplace=False)\n",
       "              (projection): Sequential(\n",
       "                (0): Linear(in_features=768, out_features=768, bias=True)\n",
       "                (1): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (1): ResidualAdd(\n",
       "          (fn): Sequential(\n",
       "            (0): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "            (1): FeedForwardBlock(\n",
       "              (0): Linear(in_features=768, out_features=3072, bias=True)\n",
       "              (1): GELU()\n",
       "              (2): Dropout(p=0.0, inplace=False)\n",
       "              (3): Linear(in_features=3072, out_features=768, bias=True)\n",
       "              (4): Dropout(p=0.0, inplace=False)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "      (3): TransformerEncoderBlock(\n",
       "        (0): ResidualAdd(\n",
       "          (fn): Sequential(\n",
       "            (0): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "            (1): MultiHeadAttention(\n",
       "              (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
       "              (att_drop): Dropout(p=0.0, inplace=False)\n",
       "              (projection): Sequential(\n",
       "                (0): Linear(in_features=768, out_features=768, bias=True)\n",
       "                (1): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (1): ResidualAdd(\n",
       "          (fn): Sequential(\n",
       "            (0): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "            (1): FeedForwardBlock(\n",
       "              (0): Linear(in_features=768, out_features=3072, bias=True)\n",
       "              (1): GELU()\n",
       "              (2): Dropout(p=0.0, inplace=False)\n",
       "              (3): Linear(in_features=3072, out_features=768, bias=True)\n",
       "              (4): Dropout(p=0.0, inplace=False)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "      (4): TransformerEncoderBlock(\n",
       "        (0): ResidualAdd(\n",
       "          (fn): Sequential(\n",
       "            (0): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "            (1): MultiHeadAttention(\n",
       "              (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
       "              (att_drop): Dropout(p=0.0, inplace=False)\n",
       "              (projection): Sequential(\n",
       "                (0): Linear(in_features=768, out_features=768, bias=True)\n",
       "                (1): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (1): ResidualAdd(\n",
       "          (fn): Sequential(\n",
       "            (0): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "            (1): FeedForwardBlock(\n",
       "              (0): Linear(in_features=768, out_features=3072, bias=True)\n",
       "              (1): GELU()\n",
       "              (2): Dropout(p=0.0, inplace=False)\n",
       "              (3): Linear(in_features=3072, out_features=768, bias=True)\n",
       "              (4): Dropout(p=0.0, inplace=False)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "      (5): TransformerEncoderBlock(\n",
       "        (0): ResidualAdd(\n",
       "          (fn): Sequential(\n",
       "            (0): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "            (1): MultiHeadAttention(\n",
       "              (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
       "              (att_drop): Dropout(p=0.0, inplace=False)\n",
       "              (projection): Sequential(\n",
       "                (0): Linear(in_features=768, out_features=768, bias=True)\n",
       "                (1): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (1): ResidualAdd(\n",
       "          (fn): Sequential(\n",
       "            (0): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "            (1): FeedForwardBlock(\n",
       "              (0): Linear(in_features=768, out_features=3072, bias=True)\n",
       "              (1): GELU()\n",
       "              (2): Dropout(p=0.0, inplace=False)\n",
       "              (3): Linear(in_features=3072, out_features=768, bias=True)\n",
       "              (4): Dropout(p=0.0, inplace=False)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "      (6): TransformerEncoderBlock(\n",
       "        (0): ResidualAdd(\n",
       "          (fn): Sequential(\n",
       "            (0): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "            (1): MultiHeadAttention(\n",
       "              (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
       "              (att_drop): Dropout(p=0.0, inplace=False)\n",
       "              (projection): Sequential(\n",
       "                (0): Linear(in_features=768, out_features=768, bias=True)\n",
       "                (1): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (1): ResidualAdd(\n",
       "          (fn): Sequential(\n",
       "            (0): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "            (1): FeedForwardBlock(\n",
       "              (0): Linear(in_features=768, out_features=3072, bias=True)\n",
       "              (1): GELU()\n",
       "              (2): Dropout(p=0.0, inplace=False)\n",
       "              (3): Linear(in_features=3072, out_features=768, bias=True)\n",
       "              (4): Dropout(p=0.0, inplace=False)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "      (7): TransformerEncoderBlock(\n",
       "        (0): ResidualAdd(\n",
       "          (fn): Sequential(\n",
       "            (0): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "            (1): MultiHeadAttention(\n",
       "              (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
       "              (att_drop): Dropout(p=0.0, inplace=False)\n",
       "              (projection): Sequential(\n",
       "                (0): Linear(in_features=768, out_features=768, bias=True)\n",
       "                (1): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (1): ResidualAdd(\n",
       "          (fn): Sequential(\n",
       "            (0): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "            (1): FeedForwardBlock(\n",
       "              (0): Linear(in_features=768, out_features=3072, bias=True)\n",
       "              (1): GELU()\n",
       "              (2): Dropout(p=0.0, inplace=False)\n",
       "              (3): Linear(in_features=3072, out_features=768, bias=True)\n",
       "              (4): Dropout(p=0.0, inplace=False)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "      (8): TransformerEncoderBlock(\n",
       "        (0): ResidualAdd(\n",
       "          (fn): Sequential(\n",
       "            (0): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "            (1): MultiHeadAttention(\n",
       "              (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
       "              (att_drop): Dropout(p=0.0, inplace=False)\n",
       "              (projection): Sequential(\n",
       "                (0): Linear(in_features=768, out_features=768, bias=True)\n",
       "                (1): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (1): ResidualAdd(\n",
       "          (fn): Sequential(\n",
       "            (0): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "            (1): FeedForwardBlock(\n",
       "              (0): Linear(in_features=768, out_features=3072, bias=True)\n",
       "              (1): GELU()\n",
       "              (2): Dropout(p=0.0, inplace=False)\n",
       "              (3): Linear(in_features=3072, out_features=768, bias=True)\n",
       "              (4): Dropout(p=0.0, inplace=False)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "      (9): TransformerEncoderBlock(\n",
       "        (0): ResidualAdd(\n",
       "          (fn): Sequential(\n",
       "            (0): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "            (1): MultiHeadAttention(\n",
       "              (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
       "              (att_drop): Dropout(p=0.0, inplace=False)\n",
       "              (projection): Sequential(\n",
       "                (0): Linear(in_features=768, out_features=768, bias=True)\n",
       "                (1): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (1): ResidualAdd(\n",
       "          (fn): Sequential(\n",
       "            (0): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "            (1): FeedForwardBlock(\n",
       "              (0): Linear(in_features=768, out_features=3072, bias=True)\n",
       "              (1): GELU()\n",
       "              (2): Dropout(p=0.0, inplace=False)\n",
       "              (3): Linear(in_features=3072, out_features=768, bias=True)\n",
       "              (4): Dropout(p=0.0, inplace=False)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "      (10): TransformerEncoderBlock(\n",
       "        (0): ResidualAdd(\n",
       "          (fn): Sequential(\n",
       "            (0): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "            (1): MultiHeadAttention(\n",
       "              (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
       "              (att_drop): Dropout(p=0.0, inplace=False)\n",
       "              (projection): Sequential(\n",
       "                (0): Linear(in_features=768, out_features=768, bias=True)\n",
       "                (1): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (1): ResidualAdd(\n",
       "          (fn): Sequential(\n",
       "            (0): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "            (1): FeedForwardBlock(\n",
       "              (0): Linear(in_features=768, out_features=3072, bias=True)\n",
       "              (1): GELU()\n",
       "              (2): Dropout(p=0.0, inplace=False)\n",
       "              (3): Linear(in_features=3072, out_features=768, bias=True)\n",
       "              (4): Dropout(p=0.0, inplace=False)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "      (11): TransformerEncoderBlock(\n",
       "        (0): ResidualAdd(\n",
       "          (fn): Sequential(\n",
       "            (0): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "            (1): MultiHeadAttention(\n",
       "              (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
       "              (att_drop): Dropout(p=0.0, inplace=False)\n",
       "              (projection): Sequential(\n",
       "                (0): Linear(in_features=768, out_features=768, bias=True)\n",
       "                (1): Dropout(p=0.0, inplace=False)\n",
       "              )\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "        (1): ResidualAdd(\n",
       "          (fn): Sequential(\n",
       "            (0): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "            (1): FeedForwardBlock(\n",
       "              (0): Linear(in_features=768, out_features=3072, bias=True)\n",
       "              (1): GELU()\n",
       "              (2): Dropout(p=0.0, inplace=False)\n",
       "              (3): Linear(in_features=3072, out_features=768, bias=True)\n",
       "              (4): Dropout(p=0.0, inplace=False)\n",
       "            )\n",
       "          )\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (norm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
       "  )\n",
       "  (head): ViTClassificationHead(\n",
       "    (pool): Lambda()\n",
       "    (fc): Linear(in_features=768, out_features=1000, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = AutoModel.from_name('vit_base_patch16_224')\n",
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Parameter containing:\n",
       "tensor([[[-1.0900e+00,  5.9232e-01, -1.3475e+00, -8.8945e-03,  2.8032e-01,\n",
       "          -2.5296e+00, -1.2493e+00,  1.9137e-01, -8.4429e-01, -3.9187e-01,\n",
       "          -1.3274e-01,  1.9846e-01,  2.0594e-01,  3.8294e-01,  1.4616e+00,\n",
       "           7.3132e-02, -2.6978e-02,  1.2118e-01, -7.7703e-01,  7.8420e-01,\n",
       "          -1.9390e+00,  2.5998e+00,  1.5004e+00, -1.4453e+00, -1.0452e+00,\n",
       "          -3.9081e-01,  6.5189e-02, -6.0795e-01, -6.4043e-01, -1.3401e+00,\n",
       "           5.5202e-01, -6.9359e-01,  1.5587e+00,  6.1924e-01, -4.4311e-02,\n",
       "           1.2512e+00,  8.5140e-01,  2.0568e+00,  2.7636e-01,  4.0293e-01,\n",
       "           1.1603e+00,  1.2421e+00,  2.1889e-01, -2.3521e-01, -6.4439e-01,\n",
       "           9.5910e-01,  1.3049e+00,  2.0507e+00, -1.1315e-01,  1.2872e+00,\n",
       "           4.0509e-02, -1.9550e-01,  3.4365e-01, -3.1178e-01, -1.2283e+00,\n",
       "          -3.5690e-02,  5.9273e-02,  8.8650e-01,  8.9549e-01, -5.8535e-01,\n",
       "           6.3476e-01, -7.6959e-01,  4.5018e-01, -6.3732e-01, -5.4927e-01,\n",
       "           4.6874e-01, -1.5319e+00, -1.8565e+00,  5.1462e-01, -1.0542e+00,\n",
       "          -9.3327e-01, -1.0785e-01, -8.1992e-01, -1.8084e+00, -7.4689e-01,\n",
       "          -4.3132e-01,  2.6189e-01,  7.5025e-01, -3.1078e-02, -1.2794e-01,\n",
       "           1.2726e-01,  6.6611e-03,  1.6012e+00,  3.2580e-01,  8.5679e-02,\n",
       "          -1.7322e+00,  3.5419e-01, -9.8321e-01, -2.2448e+00, -4.1546e-01,\n",
       "           2.1053e-01, -1.3528e-01,  1.5467e+00, -6.7335e-01, -4.7675e-01,\n",
       "          -9.3845e-01, -5.5074e-01, -3.2094e-01, -5.1937e-01, -8.1332e-01,\n",
       "           6.1044e-01,  2.9012e+00,  4.9418e-01,  2.4208e+00,  5.6871e-01,\n",
       "          -2.5625e-02,  1.0912e+00, -2.9923e+00,  7.9205e-02,  1.4133e+00,\n",
       "          -9.6248e-01, -1.4989e+00,  5.8437e-01,  1.0214e+00,  8.5368e-02,\n",
       "          -7.3181e-01,  6.2549e-01, -3.3619e-02,  5.0234e-01,  4.9483e-01,\n",
       "           1.2996e-01, -7.6516e-01, -4.5927e-01,  5.5060e-01,  1.6467e+00,\n",
       "          -4.5678e-01,  1.0321e+00,  3.5360e-01, -2.2235e+00, -4.9513e-01,\n",
       "           1.3871e+00, -8.5038e-02,  6.9938e-02,  2.5721e-01,  3.2807e-01,\n",
       "          -8.7622e-01,  4.5472e-02,  2.2461e-01,  4.4584e-01,  7.8802e-01,\n",
       "           4.3910e-03,  9.2062e-01, -1.3662e+00, -5.1785e-02, -9.7134e-02,\n",
       "          -5.5681e-01,  6.4208e-01, -2.1335e+00, -9.4939e-01, -1.0725e-01,\n",
       "           7.9762e-01, -6.6328e-01, -3.7971e-01,  4.1706e-01, -4.2290e-01,\n",
       "          -5.3523e-01, -9.5426e-01, -1.0227e-01, -1.7574e-01,  1.2518e-01,\n",
       "          -4.2900e-02,  1.0310e+00, -1.0658e+00, -2.9768e+00,  9.4370e-02,\n",
       "           8.7603e-02, -2.9738e-01,  4.9579e-01,  9.3580e-01, -1.2140e+00,\n",
       "           6.7486e-01, -9.5397e-01, -1.8202e+00,  2.5582e-01, -1.3199e+00,\n",
       "           7.5853e-01,  5.7308e-01, -9.9339e-01, -8.8300e-01, -3.8961e-01,\n",
       "          -1.7053e-01,  1.3032e+00, -1.2577e+00, -1.6207e+00, -1.8583e+00,\n",
       "          -1.3336e-01,  4.3480e-01,  1.0299e+00,  1.4491e+00,  6.5932e-01,\n",
       "          -3.7181e-02, -2.1047e+00, -9.0154e-01,  6.0392e-02,  1.4666e+00,\n",
       "           1.9248e+00,  1.1382e+00,  1.5762e+00,  6.4158e-01,  1.5410e+00,\n",
       "           1.5976e+00, -2.0962e+00, -1.3901e+00, -1.0869e+00, -1.1602e+00,\n",
       "           3.5641e-01,  1.9545e+00, -5.6843e-01,  1.9785e-01, -1.2140e+00,\n",
       "           6.9817e-01, -8.3747e-01, -3.0752e-01, -6.9357e-02, -3.1526e-01,\n",
       "           4.8144e-03, -2.5217e-01,  1.5838e+00,  1.8952e+00,  9.2576e-01,\n",
       "           7.2924e-01,  7.4291e-01,  2.9076e-01, -1.0978e+00,  8.6329e-01,\n",
       "          -1.0081e+00, -1.0405e+00, -9.2291e-01, -1.3979e-01,  9.8224e-02,\n",
       "          -4.4015e-01, -6.2625e-01,  2.7156e-01,  1.3953e+00,  1.6370e-01,\n",
       "           1.1385e+00,  9.6359e-01,  5.0721e-01,  1.9046e-01, -2.0339e+00,\n",
       "          -1.0061e-01, -7.7351e-01, -7.8947e-01,  1.4525e+00,  1.8582e+00,\n",
       "          -9.0120e-01, -5.6805e-01, -7.2366e-01, -1.1086e+00, -1.6612e-01,\n",
       "           2.5675e-02, -4.1961e-01, -7.9940e-01,  1.5448e+00,  1.5532e+00,\n",
       "          -1.0710e+00, -8.1679e-01,  9.4079e-01,  9.5346e-02, -1.2771e-01,\n",
       "          -1.2069e+00,  3.0788e+00, -5.8542e-02,  1.4630e+00,  1.0304e-01,\n",
       "           2.1589e+00, -1.4353e+00,  1.3017e+00, -6.6899e-01,  8.1822e-02,\n",
       "          -9.0460e-01,  1.3085e+00, -1.3874e+00, -1.5780e+00,  9.1415e-01,\n",
       "          -1.5004e+00,  1.1185e-01, -5.3694e-01,  5.0390e-01, -1.0917e+00,\n",
       "          -3.2231e-01,  6.9508e-01, -9.7843e-01,  6.4026e-01,  6.8062e-01,\n",
       "           6.5405e-01, -4.5502e-01,  7.4053e-01, -1.3150e+00, -2.1695e+00,\n",
       "           2.4951e-01,  1.1186e-01, -7.9547e-01,  1.5309e-01,  1.6735e+00,\n",
       "           4.3535e-01,  6.7528e-01, -1.1907e+00,  1.3842e+00,  1.4155e+00,\n",
       "          -1.2830e+00,  4.8537e-01,  6.9995e-01,  4.0068e-01,  3.1991e-01,\n",
       "           2.2908e+00,  3.9141e-02,  1.1580e+00,  1.1082e+00, -1.0205e+00,\n",
       "          -6.4855e-01, -4.7265e-01,  3.8882e-01,  1.1385e+00,  6.4845e-01,\n",
       "           3.3756e-01, -7.0649e-01, -1.9557e+00, -8.5767e-01,  2.8132e-01,\n",
       "           8.0932e-01,  1.3471e+00,  1.8383e-01, -1.6618e-01, -3.8722e-01,\n",
       "          -2.4470e-01,  6.5527e-01,  1.5484e-01, -4.9395e-01,  1.0782e+00,\n",
       "          -9.6313e-01,  1.5246e-01,  4.6766e-01,  1.0884e-01, -3.9877e-01,\n",
       "           6.3453e-01,  2.3032e+00,  1.9180e+00,  9.0551e-01,  8.2178e-02,\n",
       "           4.2137e-01,  1.1123e+00, -5.2491e-01,  7.4049e-01, -1.1056e+00,\n",
       "          -2.3380e+00, -8.3061e-01, -5.3465e-01, -6.4004e-01,  6.5386e-01,\n",
       "           3.9099e-01, -7.9797e-01,  2.8115e-01, -7.3093e-02,  1.0892e+00,\n",
       "          -1.1192e+00, -5.6473e-01,  8.4140e-01,  1.0740e+00,  1.7930e-01,\n",
       "          -2.2187e-01,  1.2363e+00, -8.6513e-01, -1.0110e+00, -1.1728e+00,\n",
       "          -2.2888e+00,  1.0034e+00, -4.8461e-01, -1.0824e+00, -7.4733e-01,\n",
       "          -1.9479e+00,  1.7330e-01, -4.0416e-01, -2.3888e-01, -1.2456e-01,\n",
       "          -2.5553e-01, -4.0166e-02, -3.6764e-01,  1.5359e+00,  9.4066e-01,\n",
       "          -1.3969e-01, -4.6935e-01, -5.8249e-01,  5.7440e-01,  1.4132e+00,\n",
       "          -1.8693e+00, -1.3813e+00,  1.3932e+00, -2.8791e-01,  4.1152e-01,\n",
       "           5.5816e-01, -1.0274e+00, -1.0443e+00,  1.3560e+00, -4.5734e-01,\n",
       "           4.8372e-01, -1.1856e+00,  1.1947e+00, -5.2107e-01,  7.2829e-01,\n",
       "           7.5493e-01,  6.6788e-01, -2.0896e-01,  4.8523e-01,  8.2262e-01,\n",
       "           9.8797e-01,  1.3810e+00,  1.3595e+00,  7.7131e-01,  1.7739e+00,\n",
       "          -1.0825e+00,  3.4881e-01, -4.8890e-01,  1.2136e+00,  3.7662e-01,\n",
       "          -9.1033e-01,  6.5249e-01,  2.3791e-01, -8.6310e-01,  1.2974e+00,\n",
       "           5.6838e-01,  7.3629e-01, -9.7425e-01, -2.9722e-01, -1.5587e+00,\n",
       "           3.6263e-01, -1.9623e-01,  1.2352e+00, -3.5189e-01, -1.7154e+00,\n",
       "           1.6313e-01,  1.1602e+00,  8.0493e-01, -5.8007e-01, -6.6657e-01,\n",
       "           2.7709e-01,  6.9484e-01,  8.1018e-01,  8.1950e-01,  1.6425e+00,\n",
       "           1.5437e-01,  2.0072e+00, -4.4718e-01, -8.9735e-01,  1.1052e+00,\n",
       "           8.8772e-01,  1.6360e+00, -8.2787e-01, -9.9990e-01, -9.7302e-01,\n",
       "          -2.6320e-01,  1.0759e+00,  5.2235e-01, -1.3533e-01, -3.9964e-01,\n",
       "           2.9398e-01, -1.5023e-01,  3.9871e-01, -1.0475e+00, -3.7889e-01,\n",
       "          -1.9862e-01, -1.3813e+00, -8.6888e-01, -2.4549e+00, -2.2968e-01,\n",
       "           5.4831e-01,  4.9464e-01, -2.2210e-01,  1.6621e+00, -8.5390e-01,\n",
       "          -4.7426e-01, -1.2215e+00,  2.6082e-01,  7.8131e-01, -1.2321e+00,\n",
       "           7.4482e-01, -5.0444e-01, -1.9976e+00,  6.0403e-01, -8.4497e-01,\n",
       "           4.1903e-01,  1.1805e+00,  8.1306e-02, -8.5309e-01,  2.2782e-01,\n",
       "          -3.7042e-01,  4.3260e-01, -1.1336e+00, -9.7417e-01, -2.2201e+00,\n",
       "           4.4058e-01, -6.2459e-01,  7.5020e-03, -2.7010e-01, -6.7692e-01,\n",
       "           2.8807e-01, -2.4484e+00,  8.3555e-01, -1.0111e+00, -9.2949e-01,\n",
       "           4.8208e-01, -3.1039e-01, -2.4060e+00, -1.3419e-01,  1.3995e+00,\n",
       "          -9.2050e-01,  1.1411e+00,  1.5227e+00, -1.4104e-01,  1.7461e+00,\n",
       "           3.5730e-02, -4.5474e-01,  6.5016e-01, -2.6094e-01,  3.0947e-01,\n",
       "          -6.4080e-01, -1.1928e+00,  2.8778e-01,  1.1537e+00, -1.2832e-01,\n",
       "          -2.7913e+00,  3.1604e-02,  1.4573e+00, -2.2058e-01,  1.1436e+00,\n",
       "           3.8431e-01, -1.7020e+00,  1.9599e-01,  1.9123e+00, -2.1642e+00,\n",
       "          -1.5608e+00,  5.4421e-01, -1.7773e-01,  3.9388e-01, -1.5898e+00,\n",
       "          -1.1156e-01,  1.7062e-01, -5.9853e-01,  6.0199e-01,  1.6157e+00,\n",
       "           1.5776e+00,  4.4625e-01, -1.4673e+00, -5.0395e-01, -5.8981e-01,\n",
       "           1.2758e+00,  8.2441e-01,  9.2240e-01, -1.0312e+00,  9.8475e-02,\n",
       "           9.4924e-01, -1.6235e+00, -3.5080e-01, -1.9698e-01,  1.2541e-01,\n",
       "           2.5835e-01,  5.8370e-01,  6.8603e-02,  2.3831e-01, -1.4062e+00,\n",
       "          -6.6909e-01, -1.2951e+00, -1.4112e-01, -7.6895e-01,  5.3437e-01,\n",
       "          -1.1799e-01, -9.0051e-01,  1.8413e+00,  2.9967e-01,  1.7692e+00,\n",
       "          -9.1848e-01,  4.7715e-02,  3.8569e-01, -8.6352e-01, -2.6434e-01,\n",
       "           1.0756e+00,  7.7464e-02,  2.4063e-02,  7.2420e-01, -5.8407e-01,\n",
       "          -3.6077e-02, -7.2690e-01, -2.6187e-01, -7.5544e-01, -3.7882e-01,\n",
       "          -6.7151e-01,  5.1613e-02, -9.0002e-02,  1.2097e+00, -6.6205e-01,\n",
       "           1.2923e+00, -8.0329e-02,  1.0073e+00, -4.6025e-01, -1.7284e+00,\n",
       "           9.0676e-01,  2.5060e+00,  5.1357e-01,  2.2029e-01, -3.9936e-02,\n",
       "          -1.6847e+00, -6.1498e-01, -3.7015e-02, -1.6992e+00,  1.3033e+00,\n",
       "          -1.0206e+00,  7.5245e-02, -2.2145e+00, -1.5437e+00, -1.1924e+00,\n",
       "           1.1770e+00, -6.0050e-01, -3.9392e-01, -4.0685e-01,  3.0883e-01,\n",
       "           2.3546e+00, -3.5284e-01, -7.0271e-02,  8.2141e-01, -1.0619e+00,\n",
       "           1.5304e-01,  1.3038e+00,  8.1289e-02, -5.0256e-01, -4.7806e-01,\n",
       "           1.0880e+00,  5.9374e-01,  1.6556e+00,  8.8321e-01,  5.2498e-01,\n",
       "           8.6540e-01, -9.6427e-01, -5.4538e-01, -2.0624e+00, -9.5101e-01,\n",
       "           1.5097e+00,  1.3741e+00,  6.7275e-01, -1.5131e+00, -5.2218e-01,\n",
       "           7.9845e-01, -8.3951e-02, -5.1966e-01, -3.0574e-01,  3.2924e-01,\n",
       "          -1.3496e-01,  1.0154e+00, -1.7979e+00, -1.4562e+00,  5.6701e-01,\n",
       "          -3.5247e-01, -8.0082e-01,  1.4418e+00, -1.5711e+00,  3.9429e-01,\n",
       "          -1.3331e+00, -5.8025e-01,  3.7940e-01, -2.2804e+00,  2.2093e-01,\n",
       "          -7.0911e-01, -2.8341e-01, -1.7835e+00, -3.8241e-01,  1.3491e-03,\n",
       "           1.1205e+00, -8.0571e-01, -1.0484e+00, -3.8249e-01, -7.3989e-02,\n",
       "           8.7036e-01,  2.5033e-01, -9.3811e-01, -1.0347e+00, -1.5896e+00,\n",
       "           6.0197e-01,  1.1407e-02, -1.3800e+00,  6.0260e-01, -3.0351e-01,\n",
       "           1.1836e+00, -1.6408e+00,  6.1079e-01, -1.7683e+00,  1.6654e+00,\n",
       "          -2.4656e+00, -1.7454e+00,  8.5995e-01, -5.0630e-01, -3.7902e-01,\n",
       "          -4.3593e-01,  1.3127e-01, -6.2834e-01,  9.2038e-01,  1.0179e-02,\n",
       "           4.1303e-01,  1.1736e+00,  7.2534e-01,  2.2219e-01,  1.1086e+00,\n",
       "           6.3542e-01,  1.8113e-01,  9.1850e-01,  7.8797e-01, -1.9280e-01,\n",
       "           1.9326e+00, -2.0830e-01, -5.4141e-01,  7.5186e-01,  3.0698e-01,\n",
       "           8.6154e-01,  2.8947e-01, -4.3507e-01,  3.3811e-02,  1.2198e+00,\n",
       "          -3.8588e-01,  7.4613e-01, -2.3643e-03,  7.4513e-02, -1.2532e-01,\n",
       "           2.0901e+00,  1.3635e-01, -2.8220e-01,  1.4528e-01, -1.4518e+00,\n",
       "           6.9878e-01,  8.5450e-02, -1.6456e+00,  8.7270e-01,  2.6731e-01,\n",
       "          -3.2155e-02,  2.0820e-01,  1.6496e+00,  3.7624e-01,  7.5232e-01,\n",
       "          -3.2907e-01,  7.9029e-02, -9.9868e-01,  9.6144e-01,  1.5720e+00,\n",
       "          -1.6811e+00, -2.6061e-02,  2.8053e-01, -7.7090e-01, -2.1711e+00,\n",
       "           2.6635e+00, -1.2178e+00, -1.2020e+00, -1.8276e+00,  3.5189e-01,\n",
       "          -1.1718e+00,  1.3765e+00,  3.2913e-01, -3.0725e-01, -3.7107e-01,\n",
       "          -5.2215e-01, -6.3230e-03,  7.5891e-01, -2.4976e+00, -9.5139e-01,\n",
       "           4.2494e-01, -8.3677e-01,  1.8784e+00,  1.1923e+00,  2.8611e-01,\n",
       "           7.2239e-01,  1.6482e+00, -4.3692e-01]]], requires_grad=True)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from glasses.models.classification.deit import DeiT\n",
    "DeiT().embedding.tokens.dist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading: \"https://github.com/facebookresearch/deit/archive/main.zip\" to /home/zuppif/.cache/torch/hub/main.zip\n",
      "Downloading: \"https://dl.fbaipublicfiles.com/deit/deit_base_distilled_patch16_224-df68dfff.pth\" to /home/zuppif/.cache/torch/hub/checkpoints/deit_base_distilled_patch16_224-df68dfff.pth\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2062dd83b5ee4974be5247286febf6b6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0.00/333M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torch\n",
    "model = torch.hub.load('facebookresearch/deit:main', 'deit_base_distilled_patch16_224', pretrained=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DistilledVisionTransformer(\n",
       "  (patch_embed): PatchEmbed(\n",
       "    (proj): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16))\n",
       "  )\n",
       "  (pos_drop): Dropout(p=0.0, inplace=False)\n",
       "  (blocks): ModuleList(\n",
       "    (0): Block(\n",
       "      (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
       "      (attn): Attention(\n",
       "        (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
       "        (attn_drop): Dropout(p=0.0, inplace=False)\n",
       "        (proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "        (proj_drop): Dropout(p=0.0, inplace=False)\n",
       "      )\n",
       "      (drop_path): Identity()\n",
       "      (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
       "      (mlp): Mlp(\n",
       "        (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
       "        (act): GELU()\n",
       "        (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
       "        (drop): Dropout(p=0.0, inplace=False)\n",
       "      )\n",
       "    )\n",
       "    (1): Block(\n",
       "      (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
       "      (attn): Attention(\n",
       "        (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
       "        (attn_drop): Dropout(p=0.0, inplace=False)\n",
       "        (proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "        (proj_drop): Dropout(p=0.0, inplace=False)\n",
       "      )\n",
       "      (drop_path): Identity()\n",
       "      (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
       "      (mlp): Mlp(\n",
       "        (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
       "        (act): GELU()\n",
       "        (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
       "        (drop): Dropout(p=0.0, inplace=False)\n",
       "      )\n",
       "    )\n",
       "    (2): Block(\n",
       "      (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
       "      (attn): Attention(\n",
       "        (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
       "        (attn_drop): Dropout(p=0.0, inplace=False)\n",
       "        (proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "        (proj_drop): Dropout(p=0.0, inplace=False)\n",
       "      )\n",
       "      (drop_path): Identity()\n",
       "      (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
       "      (mlp): Mlp(\n",
       "        (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
       "        (act): GELU()\n",
       "        (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
       "        (drop): Dropout(p=0.0, inplace=False)\n",
       "      )\n",
       "    )\n",
       "    (3): Block(\n",
       "      (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
       "      (attn): Attention(\n",
       "        (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
       "        (attn_drop): Dropout(p=0.0, inplace=False)\n",
       "        (proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "        (proj_drop): Dropout(p=0.0, inplace=False)\n",
       "      )\n",
       "      (drop_path): Identity()\n",
       "      (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
       "      (mlp): Mlp(\n",
       "        (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
       "        (act): GELU()\n",
       "        (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
       "        (drop): Dropout(p=0.0, inplace=False)\n",
       "      )\n",
       "    )\n",
       "    (4): Block(\n",
       "      (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
       "      (attn): Attention(\n",
       "        (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
       "        (attn_drop): Dropout(p=0.0, inplace=False)\n",
       "        (proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "        (proj_drop): Dropout(p=0.0, inplace=False)\n",
       "      )\n",
       "      (drop_path): Identity()\n",
       "      (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
       "      (mlp): Mlp(\n",
       "        (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
       "        (act): GELU()\n",
       "        (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
       "        (drop): Dropout(p=0.0, inplace=False)\n",
       "      )\n",
       "    )\n",
       "    (5): Block(\n",
       "      (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
       "      (attn): Attention(\n",
       "        (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
       "        (attn_drop): Dropout(p=0.0, inplace=False)\n",
       "        (proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "        (proj_drop): Dropout(p=0.0, inplace=False)\n",
       "      )\n",
       "      (drop_path): Identity()\n",
       "      (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
       "      (mlp): Mlp(\n",
       "        (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
       "        (act): GELU()\n",
       "        (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
       "        (drop): Dropout(p=0.0, inplace=False)\n",
       "      )\n",
       "    )\n",
       "    (6): Block(\n",
       "      (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
       "      (attn): Attention(\n",
       "        (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
       "        (attn_drop): Dropout(p=0.0, inplace=False)\n",
       "        (proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "        (proj_drop): Dropout(p=0.0, inplace=False)\n",
       "      )\n",
       "      (drop_path): Identity()\n",
       "      (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
       "      (mlp): Mlp(\n",
       "        (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
       "        (act): GELU()\n",
       "        (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
       "        (drop): Dropout(p=0.0, inplace=False)\n",
       "      )\n",
       "    )\n",
       "    (7): Block(\n",
       "      (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
       "      (attn): Attention(\n",
       "        (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
       "        (attn_drop): Dropout(p=0.0, inplace=False)\n",
       "        (proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "        (proj_drop): Dropout(p=0.0, inplace=False)\n",
       "      )\n",
       "      (drop_path): Identity()\n",
       "      (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
       "      (mlp): Mlp(\n",
       "        (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
       "        (act): GELU()\n",
       "        (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
       "        (drop): Dropout(p=0.0, inplace=False)\n",
       "      )\n",
       "    )\n",
       "    (8): Block(\n",
       "      (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
       "      (attn): Attention(\n",
       "        (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
       "        (attn_drop): Dropout(p=0.0, inplace=False)\n",
       "        (proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "        (proj_drop): Dropout(p=0.0, inplace=False)\n",
       "      )\n",
       "      (drop_path): Identity()\n",
       "      (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
       "      (mlp): Mlp(\n",
       "        (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
       "        (act): GELU()\n",
       "        (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
       "        (drop): Dropout(p=0.0, inplace=False)\n",
       "      )\n",
       "    )\n",
       "    (9): Block(\n",
       "      (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
       "      (attn): Attention(\n",
       "        (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
       "        (attn_drop): Dropout(p=0.0, inplace=False)\n",
       "        (proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "        (proj_drop): Dropout(p=0.0, inplace=False)\n",
       "      )\n",
       "      (drop_path): Identity()\n",
       "      (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
       "      (mlp): Mlp(\n",
       "        (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
       "        (act): GELU()\n",
       "        (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
       "        (drop): Dropout(p=0.0, inplace=False)\n",
       "      )\n",
       "    )\n",
       "    (10): Block(\n",
       "      (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
       "      (attn): Attention(\n",
       "        (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
       "        (attn_drop): Dropout(p=0.0, inplace=False)\n",
       "        (proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "        (proj_drop): Dropout(p=0.0, inplace=False)\n",
       "      )\n",
       "      (drop_path): Identity()\n",
       "      (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
       "      (mlp): Mlp(\n",
       "        (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
       "        (act): GELU()\n",
       "        (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
       "        (drop): Dropout(p=0.0, inplace=False)\n",
       "      )\n",
       "    )\n",
       "    (11): Block(\n",
       "      (norm1): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
       "      (attn): Attention(\n",
       "        (qkv): Linear(in_features=768, out_features=2304, bias=True)\n",
       "        (attn_drop): Dropout(p=0.0, inplace=False)\n",
       "        (proj): Linear(in_features=768, out_features=768, bias=True)\n",
       "        (proj_drop): Dropout(p=0.0, inplace=False)\n",
       "      )\n",
       "      (drop_path): Identity()\n",
       "      (norm2): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
       "      (mlp): Mlp(\n",
       "        (fc1): Linear(in_features=768, out_features=3072, bias=True)\n",
       "        (act): GELU()\n",
       "        (fc2): Linear(in_features=3072, out_features=768, bias=True)\n",
       "        (drop): Dropout(p=0.0, inplace=False)\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (norm): LayerNorm((768,), eps=1e-06, elementwise_affine=True)\n",
       "  (head): Linear(in_features=768, out_features=1000, bias=True)\n",
       "  (head_dist): Linear(in_features=768, out_features=1000, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using cache found in /home/zuppif/.cache/torch/hub/facebookresearch_deit_main\n",
      "Using cache found in /home/zuppif/.cache/torch/hub/facebookresearch_deit_main\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from glasses.models import AutoModel, AutoTransform, EfficientNetLite\n",
    "import timm\n",
    "from transfer_weights import clone_model, deit_clone\n",
    "from benchmark import benchmark\n",
    "from glasses.models.classification.vit import ViTTokens\n",
    "\n",
    "src = torch.hub.load('facebookresearch/deit:main', 'deit_small_distilled_patch16_224', pretrained=True).eval()\n",
    "dst = deit_clone('deit_small_patch16_224').eval()\n",
    "# src = timm.create_model('vit_base_patch16_224', pretrained='True')\n",
    "# dst = AutoModel.from_name('vit_base_patch16_224')\n",
    "transform = AutoTransform.from_name('vit_base_patch16_224').transform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using downloaded and verified file: ./imagenet_val_targets.pkl\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
       "version_major": 2,
       "version_minor": 0
      },
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     "data": {
      "text/plain": [
       "(0.80258, 0.9512, 113.77364897727966)"
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     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "benchmark(dst.cuda(), transform, 256)"
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  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using downloaded and verified file: ./imagenet_val_targets.pkl\n"
     ]
    },
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      "/home/zuppif/anaconda3/envs/dl/lib/python3.8/site-packages/torch/cuda/memory.py:231: FutureWarning: torch.cuda.reset_max_memory_allocated now calls torch.cuda.reset_peak_memory_stats, which resets /all/ peak memory stats.\n",
      "  warnings.warn(\n"
     ]
    },
    {
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       "(0.80258, 0.9512, 117.3070387840271)"
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   "cell_type": "code",
   "execution_count": 12,
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   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "===============================================================================================\n",
      "Layer (type:depth-idx)                        Output Shape              Param #\n",
      "===============================================================================================\n",
      "├─PatchEmbedding: 1-1                         [1, 198, 384]             --\n",
      "|    └─Sequential: 2-1                        [1, 196, 384]             --\n",
      "|    |    └─Conv2d: 3-1                       [1, 384, 14, 14]          295,296\n",
      "|    |    └─Rearrange: 3-2                    [1, 196, 384]             --\n",
      "|    └─DeiTTokens: 2-2                        [1, 1, 384]               768\n",
      "├─TransformerEncoder: 1-2                     [1, 198, 384]             --\n",
      "|    └─ModuleList: 2                          []                        --\n",
      "|    |    └─TransformerEncoderBlock: 3-3      [1, 198, 384]             1,774,464\n",
      "|    |    └─TransformerEncoderBlock: 3-4      [1, 198, 384]             1,774,464\n",
      "|    |    └─TransformerEncoderBlock: 3-5      [1, 198, 384]             1,774,464\n",
      "|    |    └─TransformerEncoderBlock: 3-6      [1, 198, 384]             1,774,464\n",
      "|    |    └─TransformerEncoderBlock: 3-7      [1, 198, 384]             1,774,464\n",
      "|    |    └─TransformerEncoderBlock: 3-8      [1, 198, 384]             1,774,464\n",
      "|    |    └─TransformerEncoderBlock: 3-9      [1, 198, 384]             1,774,464\n",
      "|    |    └─TransformerEncoderBlock: 3-10     [1, 198, 384]             1,774,464\n",
      "|    |    └─TransformerEncoderBlock: 3-11     [1, 198, 384]             1,774,464\n",
      "|    |    └─TransformerEncoderBlock: 3-12     [1, 198, 384]             1,774,464\n",
      "|    |    └─TransformerEncoderBlock: 3-13     [1, 198, 384]             1,774,464\n",
      "|    |    └─TransformerEncoderBlock: 3-14     [1, 198, 384]             1,774,464\n",
      "|    └─LayerNorm: 2-3                         [1, 198, 384]             768\n",
      "├─DeiTClassificationHead: 1-3                 [1, 1000]                 --\n",
      "|    └─Linear: 2-4                            [1, 1000]                 385,000\n",
      "|    └─Linear: 2-5                            [1, 1000]                 385,000\n",
      "===============================================================================================\n",
      "Total params: 22,360,400\n",
      "Trainable params: 22,360,400\n",
      "Non-trainable params: 0\n",
      "Total mult-adds (M): 123.66\n",
      "===============================================================================================\n",
      "Input size (MB): 0.60\n",
      "Forward/backward pass size (MB): 1.23\n",
      "Params size (MB): 89.44\n",
      "Estimated Total Size (MB): 91.27\n",
      "===============================================================================================\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "===============================================================================================\n",
       "Layer (type:depth-idx)                        Output Shape              Param #\n",
       "===============================================================================================\n",
       "├─PatchEmbedding: 1-1                         [1, 198, 384]             --\n",
       "|    └─Sequential: 2-1                        [1, 196, 384]             --\n",
       "|    |    └─Conv2d: 3-1                       [1, 384, 14, 14]          295,296\n",
       "|    |    └─Rearrange: 3-2                    [1, 196, 384]             --\n",
       "|    └─DeiTTokens: 2-2                        [1, 1, 384]               768\n",
       "├─TransformerEncoder: 1-2                     [1, 198, 384]             --\n",
       "|    └─ModuleList: 2                          []                        --\n",
       "|    |    └─TransformerEncoderBlock: 3-3      [1, 198, 384]             1,774,464\n",
       "|    |    └─TransformerEncoderBlock: 3-4      [1, 198, 384]             1,774,464\n",
       "|    |    └─TransformerEncoderBlock: 3-5      [1, 198, 384]             1,774,464\n",
       "|    |    └─TransformerEncoderBlock: 3-6      [1, 198, 384]             1,774,464\n",
       "|    |    └─TransformerEncoderBlock: 3-7      [1, 198, 384]             1,774,464\n",
       "|    |    └─TransformerEncoderBlock: 3-8      [1, 198, 384]             1,774,464\n",
       "|    |    └─TransformerEncoderBlock: 3-9      [1, 198, 384]             1,774,464\n",
       "|    |    └─TransformerEncoderBlock: 3-10     [1, 198, 384]             1,774,464\n",
       "|    |    └─TransformerEncoderBlock: 3-11     [1, 198, 384]             1,774,464\n",
       "|    |    └─TransformerEncoderBlock: 3-12     [1, 198, 384]             1,774,464\n",
       "|    |    └─TransformerEncoderBlock: 3-13     [1, 198, 384]             1,774,464\n",
       "|    |    └─TransformerEncoderBlock: 3-14     [1, 198, 384]             1,774,464\n",
       "|    └─LayerNorm: 2-3                         [1, 198, 384]             768\n",
       "├─DeiTClassificationHead: 1-3                 [1, 1000]                 --\n",
       "|    └─Linear: 2-4                            [1, 1000]                 385,000\n",
       "|    └─Linear: 2-5                            [1, 1000]                 385,000\n",
       "===============================================================================================\n",
       "Total params: 22,360,400\n",
       "Trainable params: 22,360,400\n",
       "Non-trainable params: 0\n",
       "Total mult-adds (M): 123.66\n",
       "===============================================================================================\n",
       "Input size (MB): 0.60\n",
       "Forward/backward pass size (MB): 1.23\n",
       "Params size (MB): 89.44\n",
       "Estimated Total Size (MB): 91.27\n",
       "==============================================================================================="
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
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