notebooks/resnet_detect.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "aa82f556-83ca-44af-b459-60497f343ea6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from torchvision import datasets\n",
    "import xml.etree.ElementTree as ET"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e4ec1bc7-2fb0-4cd9-a4ea-eeb4521a171b",
   "metadata": {},
   "outputs": [],
   "source": [
    "resnet = torchvision.models.detection.fasterrcnn_resnet50_fpn_v2(weights=torchvision.models.detection.FasterRCNN_ResNet50_FPN_V2_Weights.COCO_V1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "17aa19bd-eeba-43c3-98b7-eb7805d74ad5",
   "metadata": {},
   "outputs": [],
   "source": [
    "resnet.roi_heads.box_predictor.cls_score = torch.nn.Linear(1024,37)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7c865dc8-b958-4dc6-bd02-94fd24b3c4bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FasterRCNN(\n",
       "  (transform): GeneralizedRCNNTransform(\n",
       "      Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
       "      Resize(min_size=(800,), max_size=1333, mode='bilinear')\n",
       "  )\n",
       "  (backbone): BackboneWithFPN(\n",
       "    (body): IntermediateLayerGetter(\n",
       "      (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "      (layer1): Sequential(\n",
       "        (0): Bottleneck(\n",
       "          (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace=True)\n",
       "          (downsample): Sequential(\n",
       "            (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (1): Bottleneck(\n",
       "          (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace=True)\n",
       "        )\n",
       "        (2): Bottleneck(\n",
       "          (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace=True)\n",
       "        )\n",
       "      )\n",
       "      (layer2): Sequential(\n",
       "        (0): Bottleneck(\n",
       "          (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace=True)\n",
       "          (downsample): Sequential(\n",
       "            (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "            (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (1): Bottleneck(\n",
       "          (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace=True)\n",
       "        )\n",
       "        (2): Bottleneck(\n",
       "          (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace=True)\n",
       "        )\n",
       "        (3): Bottleneck(\n",
       "          (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace=True)\n",
       "        )\n",
       "      )\n",
       "      (layer3): Sequential(\n",
       "        (0): Bottleneck(\n",
       "          (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace=True)\n",
       "          (downsample): Sequential(\n",
       "            (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "            (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (1): Bottleneck(\n",
       "          (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace=True)\n",
       "        )\n",
       "        (2): Bottleneck(\n",
       "          (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace=True)\n",
       "        )\n",
       "        (3): Bottleneck(\n",
       "          (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace=True)\n",
       "        )\n",
       "        (4): Bottleneck(\n",
       "          (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace=True)\n",
       "        )\n",
       "        (5): Bottleneck(\n",
       "          (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace=True)\n",
       "        )\n",
       "      )\n",
       "      (layer4): Sequential(\n",
       "        (0): Bottleneck(\n",
       "          (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace=True)\n",
       "          (downsample): Sequential(\n",
       "            (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "            (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (1): Bottleneck(\n",
       "          (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace=True)\n",
       "        )\n",
       "        (2): Bottleneck(\n",
       "          (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace=True)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (fpn): FeaturePyramidNetwork(\n",
       "      (inner_blocks): ModuleList(\n",
       "        (0): Conv2dNormActivation(\n",
       "          (0): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (1): Conv2dNormActivation(\n",
       "          (0): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (2): Conv2dNormActivation(\n",
       "          (0): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (3): Conv2dNormActivation(\n",
       "          (0): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (layer_blocks): ModuleList(\n",
       "        (0-3): 4 x Conv2dNormActivation(\n",
       "          (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (extra_blocks): LastLevelMaxPool()\n",
       "    )\n",
       "  )\n",
       "  (rpn): RegionProposalNetwork(\n",
       "    (anchor_generator): AnchorGenerator()\n",
       "    (head): RPNHead(\n",
       "      (conv): Sequential(\n",
       "        (0): Conv2dNormActivation(\n",
       "          (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "          (1): ReLU(inplace=True)\n",
       "        )\n",
       "        (1): Conv2dNormActivation(\n",
       "          (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "          (1): ReLU(inplace=True)\n",
       "        )\n",
       "      )\n",
       "      (cls_logits): Conv2d(256, 3, kernel_size=(1, 1), stride=(1, 1))\n",
       "      (bbox_pred): Conv2d(256, 12, kernel_size=(1, 1), stride=(1, 1))\n",
       "    )\n",
       "  )\n",
       "  (roi_heads): RoIHeads(\n",
       "    (box_roi_pool): MultiScaleRoIAlign(featmap_names=['0', '1', '2', '3'], output_size=(7, 7), sampling_ratio=2)\n",
       "    (box_head): FastRCNNConvFCHead(\n",
       "      (0): Conv2dNormActivation(\n",
       "        (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (2): ReLU(inplace=True)\n",
       "      )\n",
       "      (1): Conv2dNormActivation(\n",
       "        (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (2): ReLU(inplace=True)\n",
       "      )\n",
       "      (2): Conv2dNormActivation(\n",
       "        (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (2): ReLU(inplace=True)\n",
       "      )\n",
       "      (3): Conv2dNormActivation(\n",
       "        (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (2): ReLU(inplace=True)\n",
       "      )\n",
       "      (4): Flatten(start_dim=1, end_dim=-1)\n",
       "      (5): Linear(in_features=12544, out_features=1024, bias=True)\n",
       "      (6): ReLU(inplace=True)\n",
       "    )\n",
       "    (box_predictor): FastRCNNPredictor(\n",
       "      (cls_score): Linear(in_features=1024, out_features=37, bias=True)\n",
       "      (bbox_pred): Linear(in_features=1024, out_features=364, bias=True)\n",
       "    )\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resnet.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1eda5c3a-9e31-431a-97ae-642c6f391741",
   "metadata": {},
   "outputs": [],
   "source": [
    "my_gpu = torch.device(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "79965d84-cbe4-4748-ad4b-73a8cc5e25a9",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FasterRCNN(\n",
       "  (transform): GeneralizedRCNNTransform(\n",
       "      Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
       "      Resize(min_size=(800,), max_size=1333, mode='bilinear')\n",
       "  )\n",
       "  (backbone): BackboneWithFPN(\n",
       "    (body): IntermediateLayerGetter(\n",
       "      (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
       "      (layer1): Sequential(\n",
       "        (0): Bottleneck(\n",
       "          (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace=True)\n",
       "          (downsample): Sequential(\n",
       "            (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (1): Bottleneck(\n",
       "          (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace=True)\n",
       "        )\n",
       "        (2): Bottleneck(\n",
       "          (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace=True)\n",
       "        )\n",
       "      )\n",
       "      (layer2): Sequential(\n",
       "        (0): Bottleneck(\n",
       "          (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace=True)\n",
       "          (downsample): Sequential(\n",
       "            (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "            (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (1): Bottleneck(\n",
       "          (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace=True)\n",
       "        )\n",
       "        (2): Bottleneck(\n",
       "          (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace=True)\n",
       "        )\n",
       "        (3): Bottleneck(\n",
       "          (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace=True)\n",
       "        )\n",
       "      )\n",
       "      (layer3): Sequential(\n",
       "        (0): Bottleneck(\n",
       "          (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace=True)\n",
       "          (downsample): Sequential(\n",
       "            (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "            (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (1): Bottleneck(\n",
       "          (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace=True)\n",
       "        )\n",
       "        (2): Bottleneck(\n",
       "          (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace=True)\n",
       "        )\n",
       "        (3): Bottleneck(\n",
       "          (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace=True)\n",
       "        )\n",
       "        (4): Bottleneck(\n",
       "          (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace=True)\n",
       "        )\n",
       "        (5): Bottleneck(\n",
       "          (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace=True)\n",
       "        )\n",
       "      )\n",
       "      (layer4): Sequential(\n",
       "        (0): Bottleneck(\n",
       "          (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace=True)\n",
       "          (downsample): Sequential(\n",
       "            (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "            (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (1): Bottleneck(\n",
       "          (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace=True)\n",
       "        )\n",
       "        (2): Bottleneck(\n",
       "          (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (relu): ReLU(inplace=True)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (fpn): FeaturePyramidNetwork(\n",
       "      (inner_blocks): ModuleList(\n",
       "        (0): Conv2dNormActivation(\n",
       "          (0): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (1): Conv2dNormActivation(\n",
       "          (0): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (2): Conv2dNormActivation(\n",
       "          (0): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (3): Conv2dNormActivation(\n",
       "          (0): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (layer_blocks): ModuleList(\n",
       "        (0-3): 4 x Conv2dNormActivation(\n",
       "          (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (extra_blocks): LastLevelMaxPool()\n",
       "    )\n",
       "  )\n",
       "  (rpn): RegionProposalNetwork(\n",
       "    (anchor_generator): AnchorGenerator()\n",
       "    (head): RPNHead(\n",
       "      (conv): Sequential(\n",
       "        (0): Conv2dNormActivation(\n",
       "          (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "          (1): ReLU(inplace=True)\n",
       "        )\n",
       "        (1): Conv2dNormActivation(\n",
       "          (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "          (1): ReLU(inplace=True)\n",
       "        )\n",
       "      )\n",
       "      (cls_logits): Conv2d(256, 3, kernel_size=(1, 1), stride=(1, 1))\n",
       "      (bbox_pred): Conv2d(256, 12, kernel_size=(1, 1), stride=(1, 1))\n",
       "    )\n",
       "  )\n",
       "  (roi_heads): RoIHeads(\n",
       "    (box_roi_pool): MultiScaleRoIAlign(featmap_names=['0', '1', '2', '3'], output_size=(7, 7), sampling_ratio=2)\n",
       "    (box_head): FastRCNNConvFCHead(\n",
       "      (0): Conv2dNormActivation(\n",
       "        (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (2): ReLU(inplace=True)\n",
       "      )\n",
       "      (1): Conv2dNormActivation(\n",
       "        (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (2): ReLU(inplace=True)\n",
       "      )\n",
       "      (2): Conv2dNormActivation(\n",
       "        (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (2): ReLU(inplace=True)\n",
       "      )\n",
       "      (3): Conv2dNormActivation(\n",
       "        (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        (2): ReLU(inplace=True)\n",
       "      )\n",
       "      (4): Flatten(start_dim=1, end_dim=-1)\n",
       "      (5): Linear(in_features=12544, out_features=1024, bias=True)\n",
       "      (6): ReLU(inplace=True)\n",
       "    )\n",
       "    (box_predictor): FastRCNNPredictor(\n",
       "      (cls_score): Linear(in_features=1024, out_features=37, bias=True)\n",
       "      (bbox_pred): Linear(in_features=1024, out_features=364, bias=True)\n",
       "    )\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resnet.to(my_gpu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5a6f8377-3f9e-4283-aca9-8e9a583b4192",
   "metadata": {},
   "outputs": [],
   "source": [
    "def collate_fn(batch):\n",
    "    # Create stacked image tensor\n",
    "    images_tensor = torch.Tensor(len(batch),batch[0][0].shape[0],batch[0][0].shape[1],batch[0][0].shape[2])\n",
    "    # Create list of dicts for target\n",
    "    target = []\n",
    "    # Populate stacked image tensor\n",
    "    for img_idx in range(len(batch)):\n",
    "        images_tensor[img_idx,:,:,:] = batch[img_idx][0]\n",
    "        target.append(batch[img_idx][1])\n",
    "    return images_tensor, target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0e395279-b44d-4e8a-8686-e3fcd4b0609d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset does not contain detection annotations for every sample. Filtering to includeonly those that do.\n"
     ]
    }
   ],
   "source": [
    "img_transform = torchvision.transforms.Compose([torchvision.transforms.ToTensor(),torchvision.transforms.Normalize(mean=[.5,.5,.5],std=[.5,.5,.5])])\n",
    "trainset = datasets.oxford_iiit_pet.OxfordIIITPet('G:/datasets/',transform=img_transform,target_types=\"detection\")#,target_transform=target_transform)\n",
    "#testset = datasets.oxford_iiit_pet.OxfordIIITPet('G:/datasets/',split='test',transform=img_transform)#,target_transform=target_transform)\n",
    "trainloader = DataLoader(trainset,batch_size=1,shuffle=True,collate_fn=collate_fn)\n",
    "#testloader = DataLoader(testset,batch_size=1)\n",
    "optim = torch.optim.SGD(resnet.parameters(),lr=.01)\n",
    "clsloss = torch.nn.CrossEntropyLoss()\n",
    "boxloss = torch.nn.SmoothL1Loss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9bf4d159-2084-4d1a-b866-f34d907a956e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.37045468896627426\n",
      "0.25539525438100097\n",
      "0.25222555024549365\n",
      "0.24660854984074831\n",
      "0.23838667191565036\n",
      "0.2345642502605915\n",
      "0.23136780703440307\n",
      "0.24342724978923796\n",
      "0.22387953208759426\n",
      "0.233151125498116\n",
      "0.22778567245230077\n",
      "0.2197857626527548\n",
      "0.22477713625878096\n",
      "0.2259934852272272\n",
      "0.21465887296944858\n",
      "0.20871706575155258\n",
      "0.21436699392274022\n",
      "0.2137922832556069\n",
      "0.19960345277562738\n",
      "0.21025376420468092\n",
      "0.19117709428071974\n",
      "0.19930586202070116\n",
      "0.20021679051220417\n",
      "0.19958070129156114\n",
      "0.20036218106746673\n",
      "0.2080717207863927\n",
      "0.1986175873503089\n",
      "0.19510630063712597\n",
      "0.2055497158318758\n",
      "0.18715420201420785\n",
      "0.1931317364796996\n",
      "0.19091499967500566\n",
      "0.1859787502512336\n",
      "0.19152750175446273\n",
      "0.1780490466579795\n",
      "0.18326394928619266\n"
     ]
    }
   ],
   "source": [
    "loss_total = 0\n",
    "step_total = 0\n",
    "for epoch in range(1):\n",
    "    for img,target in trainloader:\n",
    "        img = img.to(my_gpu)\n",
    "        for tar in target:\n",
    "            tar['boxes'] = tar['boxes'].to(my_gpu)\n",
    "            tar['labels'] = tar['labels'].to(my_gpu)\n",
    "        optim.zero_grad()\n",
    "        output = resnet(img,target)\n",
    "        losses = sum(loss for loss in output.values())\n",
    "        losses.backward()\n",
    "        optim.step()\n",
    "        losses.detach()\n",
    "        torch.cuda.empty_cache()\n",
    "        ld = losses.cpu().item()\n",
    "        loss_total += ld\n",
    "        step_total += 1\n",
    "        if step_total % 100 == 0:\n",
    "            print(loss_total/step_total)\n",
    "            loss_total = 0\n",
    "            step_total = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "40e85e2f-6a07-4312-90f5-f8a04bd3ef7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = img.detach().cpu().numpy().squeeze(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "2b763e87-ce42-4370-8044-6718dd4b16c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "x = np.moveaxis(x,0,-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "96557e9d-90c2-40a5-bf02-f063a70f6e53",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1dad1f9a-4598-4752-ad38-4be27ff1569e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'boxes': tensor([[ 73.9994,  25.2703, 171.0898, 130.2941]], device='cuda:0',\n",
       "         grad_fn=<StackBackward0>),\n",
       "  'labels': tensor([1], device='cuda:0'),\n",
       "  'scores': tensor([0.9994], device='cuda:0', grad_fn=<IndexBackward0>)}]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outdict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "de91a9f6-fdf2-4d48-85af-9b266cd01bf6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,12))\n",
    "#plt.imshow(x)\n",
    "xmin = outdict[0]['boxes'][0,0].detach().cpu().item()\n",
    "ymin = outdict[0]['boxes'][0,1].detach().cpu().item()\n",
    "xmax = outdict[0]['boxes'][0,2].detach().cpu().item()\n",
    "ymax = outdict[0]['boxes'][0,3].detach().cpu().item()\n",
    "x = cv2.rectangle(cv2.UMat((x+1)/2),(int(round(xmin)),int(round(ymin))),(int(round(xmax)),int(round(ymax))),(0.,1.,0.),3)\n",
    "plt.imshow(x.get())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "2cfd361d-62b5-4c63-b199-a965cae53193",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.0"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.min(x.get())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d355bf2-5323-4f11-a36c-ce32e76da721",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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