demo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import time\n",
    "import PIL\n",
    "from PIL import Image\n",
    "import torch\n",
    "import torchvision\n",
    "from torchvision.models.detection.faster_rcnn import FastRCNNPredictor\n",
    "from torchvision.models.detection.keypoint_rcnn import KeypointRCNNPredictor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KeypointRCNN(\n",
       "  (transform): GeneralizedRCNNTransform()\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): FrozenBatchNorm2d()\n",
       "      (relu): ReLU(inplace)\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): FrozenBatchNorm2d()\n",
       "          (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): FrozenBatchNorm2d()\n",
       "          (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn3): FrozenBatchNorm2d()\n",
       "          (relu): ReLU(inplace)\n",
       "          (downsample): Sequential(\n",
       "            (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (1): FrozenBatchNorm2d()\n",
       "          )\n",
       "        )\n",
       "        (1): Bottleneck(\n",
       "          (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn1): FrozenBatchNorm2d()\n",
       "          (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): FrozenBatchNorm2d()\n",
       "          (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn3): FrozenBatchNorm2d()\n",
       "          (relu): ReLU(inplace)\n",
       "        )\n",
       "        (2): Bottleneck(\n",
       "          (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn1): FrozenBatchNorm2d()\n",
       "          (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): FrozenBatchNorm2d()\n",
       "          (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn3): FrozenBatchNorm2d()\n",
       "          (relu): ReLU(inplace)\n",
       "        )\n",
       "      )\n",
       "      (layer2): Sequential(\n",
       "        (0): Bottleneck(\n",
       "          (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn1): FrozenBatchNorm2d()\n",
       "          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "          (bn2): FrozenBatchNorm2d()\n",
       "          (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn3): FrozenBatchNorm2d()\n",
       "          (relu): ReLU(inplace)\n",
       "          (downsample): Sequential(\n",
       "            (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "            (1): FrozenBatchNorm2d()\n",
       "          )\n",
       "        )\n",
       "        (1): Bottleneck(\n",
       "          (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn1): FrozenBatchNorm2d()\n",
       "          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): FrozenBatchNorm2d()\n",
       "          (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn3): FrozenBatchNorm2d()\n",
       "          (relu): ReLU(inplace)\n",
       "        )\n",
       "        (2): Bottleneck(\n",
       "          (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn1): FrozenBatchNorm2d()\n",
       "          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): FrozenBatchNorm2d()\n",
       "          (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn3): FrozenBatchNorm2d()\n",
       "          (relu): ReLU(inplace)\n",
       "        )\n",
       "        (3): Bottleneck(\n",
       "          (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn1): FrozenBatchNorm2d()\n",
       "          (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): FrozenBatchNorm2d()\n",
       "          (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn3): FrozenBatchNorm2d()\n",
       "          (relu): ReLU(inplace)\n",
       "        )\n",
       "      )\n",
       "      (layer3): Sequential(\n",
       "        (0): Bottleneck(\n",
       "          (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn1): FrozenBatchNorm2d()\n",
       "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "          (bn2): FrozenBatchNorm2d()\n",
       "          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn3): FrozenBatchNorm2d()\n",
       "          (relu): ReLU(inplace)\n",
       "          (downsample): Sequential(\n",
       "            (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "            (1): FrozenBatchNorm2d()\n",
       "          )\n",
       "        )\n",
       "        (1): Bottleneck(\n",
       "          (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn1): FrozenBatchNorm2d()\n",
       "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): FrozenBatchNorm2d()\n",
       "          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn3): FrozenBatchNorm2d()\n",
       "          (relu): ReLU(inplace)\n",
       "        )\n",
       "        (2): Bottleneck(\n",
       "          (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn1): FrozenBatchNorm2d()\n",
       "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): FrozenBatchNorm2d()\n",
       "          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn3): FrozenBatchNorm2d()\n",
       "          (relu): ReLU(inplace)\n",
       "        )\n",
       "        (3): Bottleneck(\n",
       "          (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn1): FrozenBatchNorm2d()\n",
       "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): FrozenBatchNorm2d()\n",
       "          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn3): FrozenBatchNorm2d()\n",
       "          (relu): ReLU(inplace)\n",
       "        )\n",
       "        (4): Bottleneck(\n",
       "          (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn1): FrozenBatchNorm2d()\n",
       "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): FrozenBatchNorm2d()\n",
       "          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn3): FrozenBatchNorm2d()\n",
       "          (relu): ReLU(inplace)\n",
       "        )\n",
       "        (5): Bottleneck(\n",
       "          (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn1): FrozenBatchNorm2d()\n",
       "          (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): FrozenBatchNorm2d()\n",
       "          (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn3): FrozenBatchNorm2d()\n",
       "          (relu): ReLU(inplace)\n",
       "        )\n",
       "      )\n",
       "      (layer4): Sequential(\n",
       "        (0): Bottleneck(\n",
       "          (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn1): FrozenBatchNorm2d()\n",
       "          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "          (bn2): FrozenBatchNorm2d()\n",
       "          (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn3): FrozenBatchNorm2d()\n",
       "          (relu): ReLU(inplace)\n",
       "          (downsample): Sequential(\n",
       "            (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "            (1): FrozenBatchNorm2d()\n",
       "          )\n",
       "        )\n",
       "        (1): Bottleneck(\n",
       "          (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn1): FrozenBatchNorm2d()\n",
       "          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): FrozenBatchNorm2d()\n",
       "          (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn3): FrozenBatchNorm2d()\n",
       "          (relu): ReLU(inplace)\n",
       "        )\n",
       "        (2): Bottleneck(\n",
       "          (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn1): FrozenBatchNorm2d()\n",
       "          (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn2): FrozenBatchNorm2d()\n",
       "          (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn3): FrozenBatchNorm2d()\n",
       "          (relu): ReLU(inplace)\n",
       "        )\n",
       "      )\n",
       "    )\n",
       "    (fpn): FeaturePyramidNetwork(\n",
       "      (inner_blocks): ModuleList(\n",
       "        (0): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))\n",
       "        (1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))\n",
       "        (2): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1))\n",
       "        (3): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1))\n",
       "      )\n",
       "      (layer_blocks): ModuleList(\n",
       "        (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "        (3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      )\n",
       "      (extra_blocks): LastLevelMaxPool()\n",
       "    )\n",
       "  )\n",
       "  (rpn): RegionProposalNetwork(\n",
       "    (anchor_generator): AnchorGenerator()\n",
       "    (head): RPNHead(\n",
       "      (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\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()\n",
       "    (box_head): TwoMLPHead(\n",
       "      (fc6): Linear(in_features=12544, out_features=1024, bias=True)\n",
       "      (fc7): Linear(in_features=1024, out_features=1024, bias=True)\n",
       "    )\n",
       "    (box_predictor): FastRCNNPredictor(\n",
       "      (cls_score): Linear(in_features=1024, out_features=2, bias=True)\n",
       "      (bbox_pred): Linear(in_features=1024, out_features=8, bias=True)\n",
       "    )\n",
       "    (keypoint_roi_pool): MultiScaleRoIAlign()\n",
       "    (keypoint_head): KeypointRCNNHeads(\n",
       "      (0): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      (1): ReLU(inplace)\n",
       "      (2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      (3): ReLU(inplace)\n",
       "      (4): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      (5): ReLU(inplace)\n",
       "      (6): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      (7): ReLU(inplace)\n",
       "      (8): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      (9): ReLU(inplace)\n",
       "      (10): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      (11): ReLU(inplace)\n",
       "      (12): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      (13): ReLU(inplace)\n",
       "      (14): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
       "      (15): ReLU(inplace)\n",
       "    )\n",
       "    (keypoint_predictor): KeypointRCNNPredictor(\n",
       "      (kps_score_lowres): ConvTranspose2d(512, 8, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1))\n",
       "    )\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = torchvision.models.detection.keypointrcnn_resnet50_fpn(pretrained=True)\n",
    "\n",
    "model.roi_heads.box_predictor = FastRCNNPredictor(1024, 2)\n",
    "\n",
    "model.roi_heads.keypoint_predictor = KeypointRCNNPredictor(512, 8)\n",
    "\n",
    "model.load_state_dict(torch.load('model_h'))\n",
    "\n",
    "model.to(torch.device('cuda'))\n",
    "\n",
    "model.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'boxes': tensor([[164.2905, 152.0016, 411.1442, 409.1567]], device='cuda:0',\n",
      "       grad_fn=<StackBackward>), 'labels': tensor([1], device='cuda:0'), 'scores': tensor([1.0000], device='cuda:0', grad_fn=<IndexBackward>), 'keypoints': tensor([[[270.1462, 275.8646,   1.0000],\n",
      "         [270.1462, 275.8646,   1.0000],\n",
      "         [270.1462, 275.8646,   1.0000],\n",
      "         [379.0018, 358.1543,   1.0000],\n",
      "         [270.1462, 275.8646,   1.0000],\n",
      "         [270.1462, 275.8646,   1.0000],\n",
      "         [270.1462, 275.8646,   1.0000],\n",
      "         [379.0018, 358.1543,   1.0000]]], device='cuda:0',\n",
      "       grad_fn=<CopySlices>), 'keypoints_scores': tensor([[5.3614, 5.4884, 6.4521, 6.3925, 6.6776, 6.3214, 5.5231, 5.2892]],\n",
      "       device='cuda:0', grad_fn=<CopySlices>)}]\n",
      "Inference time: 1.7870311737060547\n"
     ]
    }
   ],
   "source": [
    "image = PIL.Image.open('C:\\\\Users\\\\TCM\\\\keypoints\\\\data\\\\keypoints_data\\\\images\\\\1011.png')\n",
    "\n",
    "image_tensor = torchvision.transforms.functional.to_tensor(image).to(torch.device('cuda'))\n",
    "\n",
    "start = time.time()\n",
    "output = model([image_tensor])\n",
    "end = time.time()\n",
    "\n",
    "print(output)\n",
    "print('Inference time: {}'.format(end - start))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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