diff --git "a/ViTPose/easy_ViTPose/easy_ViTPose/.ipynb_checkpoints/ViTPose_Inference-checkpoint.ipynb" "b/ViTPose/easy_ViTPose/easy_ViTPose/.ipynb_checkpoints/ViTPose_Inference-checkpoint.ipynb" new file mode 100644--- /dev/null +++ "b/ViTPose/easy_ViTPose/easy_ViTPose/.ipynb_checkpoints/ViTPose_Inference-checkpoint.ipynb" @@ -0,0 +1,2591 @@ +{ + "cells": [ + { + "cell_type": "raw", + "id": "4b8faee1-d07c-481e-9470-b4756e2936ba", + "metadata": { + "jupyter": { + "source_hidden": true + } + }, + "source": [ + "import cv2\n", + "from easy_ViTPose import VitInference\n", + "import matplotlib.pyplot as plt\n", + "\n", + "# Image to run inference RGB format\n", + "img = cv2.imread('testVITPOSE.jpg')\n", + "img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n", + "\n", + "# set is_video=True to enable tracking in video inference\n", + "# be sure to use VitInference.reset() function to reset the tracker after each video\n", + "# There are a few flags that allows to customize VitInference, be sure to check the class definition\n", + "model_path = r'C:\\Users\\user\\ViTPose/ckpts/vitpose-s-coco_25.pth'\n", + "yolo_path = r'C:\\Users\\user\\ViTPose/yolov8s.pt'\n", + "\n", + "# If you want to use MPS (on new macbooks) use the torch checkpoints for both ViTPose and Yolo\n", + "# If device is None will try to use cuda -> mps -> cpu (otherwise specify 'cpu', 'mps' or 'cuda')\n", + "# dataset and det_class parameters can be inferred from the ckpt name, but you can specify them.\n", + "model = VitInference(model_path, yolo_path, model_name='s', yolo_size=320, is_video=False, device=\"cuda\")\n", + "\n", + "# Infer keypoints, output is a dict where keys are person ids and values are keypoints (np.ndarray (25, 3): (y, x, score))\n", + "# If is_video=True the IDs will be consistent among the ordered video frames.\n", + "keypoints = model.inference(img)\n", + "\n", + "# call model.reset() after each video\n", + "\n", + "img = model.draw(show_yolo=True) # Returns RGB image with drawings\n", + "plt.imshow(img)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "772c119d-0e34-488a-bcec-40e0007155aa", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "9e2a99d2-ece2-4f00-b9e1-e130099026bd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import torch\n", + "torch.cuda.is_available()" + ] + }, + { + "cell_type": "markdown", + "id": "ea96beea-c174-45c4-9119-e3db40f18793", + "metadata": {}, + "source": [ + "# Training the ViT_Pose" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ba4a27fc-20e0-433f-9de5-65320f963af9", + "metadata": {}, + "outputs": [], + "source": [ + "# Copyright (c) OpenMMLab. All rights reserved.\n", + "import argparse\n", + "import copy\n", + "import os\n", + "import os.path as osp\n", + "import time\n", + "import warnings\n", + "import click\n", + "import yaml\n", + "\n", + "from glob import glob\n", + "\n", + "import torch\n", + "import torch.distributed as dist\n", + "\n", + "from vit_utils.util import init_random_seed, set_random_seed\n", + "from vit_utils.dist_util import get_dist_info, init_dist\n", + "from vit_utils.logging import get_root_logger\n", + "\n", + "import configs.ViTPose_small_coco_256x192 as s_cfg\n", + "# import configs.ViTPose_base_coco_256x192 as b_cfg\n", + "# import configs.ViTPose_large_coco_256x192 as l_cfg\n", + "# import configs.ViTPose_huge_coco_256x192 as h_cfg\n", + "\n", + "from vit_models.model import ViTPose\n", + "from datasets.COCO import COCODataset\n", + "from vit_utils.train_valid_fn import train_model" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "4ef1a26d-9303-4859-9112-bedea1dd46e8", + "metadata": {}, + "outputs": [], + "source": [ + "__file__ = r\"C:\\Users\\user\\ViTPose\\easy_ViTPose\\easy_ViTPose\"\n", + "CUR_PATH = osp.dirname(__file__)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "21be3367-277e-4d8c-8fca-d7524235c21b", + "metadata": {}, + "outputs": [], + "source": [ + "model_name = 's'\n", + "config_path = 'config.yaml'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "16e707e1-e55b-4c44-abc3-fd217a60b381", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "2d5c73bb-6486-4513-af1c-3c37c08e80f8", + "metadata": {}, + "source": [ + "### Loading the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "41ea408e-c350-48d5-bf88-d9a2a68322c2", + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "\n", + "# Load the JSON file\n", + "with open(r\"D:\\ViTPose\\Evaluating\\annotations\\person_keypoints_val2017.json\", 'r') as f:\n", + " coco_data = json.load(f)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7bee2d84-71c7-4f53-8154-3b7340bd8708", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "590ad908-cc80-4c4e-a2af-88450a2aa77e", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "c1fa1c4b-75ff-4ed9-a46d-90c639acae41", + "metadata": {}, + "outputs": [], + "source": [ + "# Create a mapping of image_id to file_name\n", + "image_id_to_filename = {img['id']: img['file_name'] for img in coco_data['images']}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8d8b2585-0ef4-4dc2-86ef-ab6d5d799075", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0b83f079-efd1-416b-ab6c-29e80e668cff", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1dde6eac-a62d-49b1-ad27-a39c114e0f1b", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "37899bdc-69a4-4271-8fd7-aa03148883a2", + "metadata": {}, + "outputs": [], + "source": [ + "# # Example: Process keypoints for one annotation\n", + "# for ann in annotations:\n", + "# keypoints = ann['keypoints']\n", + "# keypoints_array = [keypoints[i:i + 3] for i in range(0, len(keypoints), 3)]\n", + "# print(\"Keypoints:\", keypoints_array)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c9942e11-9394-4f67-a5ed-2ee700d33625", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b030648f-8e1f-4abe-8351-a7ae34cb3bb2", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "9b30c5c1-6657-4102-8cd6-3f68b100bf61", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from PIL import Image\n", + "import numpy as np\n", + "\n", + "data_dir = r'D:\\ViTPose\\Evaluating\\val2017\\\\'\n", + "dataset = []\n", + "\n", + "for ann in coco_data['annotations']:\n", + " image_id = ann['image_id']\n", + " #print(\"image_id: \", image_id)\n", + " file_name = image_id_to_filename[image_id]\n", + " #print(\"file_name: \", file_name)\n", + " image_path = os.path.join(data_dir, file_name)\n", + " #print(\"image_path: \", image_path)\n", + " # Load the image\n", + " if not os.path.exists(image_path):\n", + " continue\n", + " image = Image.open(image_path).convert('RGB')\n", + " \n", + " # Process keypoints\n", + " keypoints = ann['keypoints']\n", + " keypoints_array = np.array([keypoints[i:i + 3] for i in range(0, len(keypoints), 3)])\n", + " \n", + " # Collect data\n", + " dataset.append((image, keypoints_array))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3a9c3c26-7d13-4103-97c1-f5d51cef5584", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "47734b7b-3c0f-4e58-abe6-bc6a70dc29c9", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "397133\n", + "000000397133.jpg\n" + ] + } + ], + "source": [ + "print(coco_data['images'][0]['id'])\n", + "print(coco_data['images'][0]['file_name'])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "9463a1ea-09be-4138-b6d7-501a4f25c2f1", + "metadata": {}, + "outputs": [], + "source": [ + "# Apply scaling transformation for each keypoint\n", + "def resize_keypoints(keypoints, scale_w, scale_h):\n", + " resized_keypoints = keypoints.clone()\n", + " \n", + " for j in range(keypoints.shape[0]):\n", + " x, y, visibility = keypoints[j]\n", + " # Only resize if visibility > 0 (to ignore invisible keypoints)\n", + " if visibility > 0:\n", + " resized_keypoints[j, 0] = int(x * scale_w)\n", + " resized_keypoints[j, 1] = int(y * scale_h)\n", + " \n", + " return resized_keypoints\n", + "\n", + "\n", + "\n", + "def transformKeypoint(img, target_shape, keypoints):\n", + " orig_width, orig_height = img.width, img.height\n", + " (target_width, target_height) = target_shape\n", + " \n", + " # Scaling factors for width and height\n", + " scale_w = target_width / orig_width\n", + " scale_h = target_height / orig_height\n", + " # Resized keypoints\n", + " resized_keypoints = resize_keypoints(keypoints, scale_w, scale_h)\n", + " \n", + " # Print the resized keypoints\n", + " #print(\"Resized Keypoints:\\n\", resized_keypoints)\n", + " return resized_keypoints\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "012be853-653e-4651-9861-fc2e11a00a00", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "be1f8f9d-2c11-4ddc-a646-e193b79d3829", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "3faf2805-5a62-4f5a-967b-4e7ec1c32c87", + "metadata": {}, + "outputs": [], + "source": [ + "target_shape = (208, 208)\n", + "import torch\n", + "from torch.utils.data import Dataset\n", + "\n", + "class COCOKeypointsDataset(Dataset):\n", + " def __init__(self, json_path, images_dir, transform=None, transformKP = None):\n", + " with open(json_path, 'r') as f:\n", + " coco_data = json.load(f)\n", + " \n", + " self.image_id_to_filename = {img['id']: img['file_name'] for img in coco_data['images']}\n", + " self.annotations = coco_data['annotations']\n", + " self.images_dir = images_dir\n", + " self.transform = transform\n", + "\n", + " def __len__(self):\n", + " return len(self.annotations)\n", + "\n", + " def __getitem__(self, idx):\n", + " # Get annotation\n", + " ann = self.annotations[idx]\n", + " image_id = ann['image_id']\n", + " file_name = self.image_id_to_filename[image_id]\n", + " image_path = os.path.join(self.images_dir, file_name)\n", + " \n", + " # Load image\n", + " image = Image.open(image_path).convert('RGB')\n", + " \n", + " # Process keypoints\n", + " keypoints = ann['keypoints']\n", + " keypoints = torch.tensor([keypoints[i:i + 3] for i in range(0, len(keypoints), 3)], dtype=torch.float32) \n", + " keypoints = transformKeypoint(image, target_shape, keypoints)\n", + " #print(\"keypoints: \", keypoints)\n", + " \n", + " # Apply transformations\n", + " if self.transform:\n", + " image = self.transform(image)\n", + " \n", + " return image, keypoints\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "960b56d0-391d-4cd5-886c-c951d5e5bf63", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ccddf3cf-5b28-4fad-a467-917a53d19d63", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "47ebf732-8f35-4caa-85ec-5dabb6e95e93", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "05380b1e-f0d4-4dcc-84de-147726da3ac4", + "metadata": {}, + "outputs": [], + "source": [ + "from torchvision import transforms\n", + "\n", + "transform = transforms.Compose([\n", + " transforms.Resize((208, 208)),\n", + " transforms.ToTensor(),\n", + " transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n", + "])\n", + "\n", + "dataset = COCOKeypointsDataset(\n", + " json_path=r\"D:\\ViTPose\\Evaluating\\annotations\\person_keypoints_val2017.json\",\n", + " images_dir=r'D:\\ViTPose\\Evaluating\\val2017\\\\',\n", + " transform=transform\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "62efbb61-7d6e-4b8d-9fe5-537ca0f7ee04", + "metadata": {}, + "outputs": [], + "source": [ + "from torch.utils.data import DataLoader\n", + "dataloader = DataLoader(dataset, batch_size=4, shuffle=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8c2a8ab8-5496-462d-a58b-f87f06e427bf", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "d9d940c5-13f6-4b24-a887-28f08f370d04", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Images shape: torch.Size([4, 3, 208, 208])\n", + "Keypoints shape: torch.Size([4, 17, 3])\n", + "keypoints: tensor([[[150., 88., 2.],\n", + " [152., 88., 2.],\n", + " [149., 87., 2.],\n", + " [153., 88., 2.],\n", + " [147., 88., 2.],\n", + " [157., 92., 2.],\n", + " [145., 93., 2.],\n", + " [162., 98., 2.],\n", + " [141., 98., 1.],\n", + " [164., 100., 1.],\n", + " [ 0., 0., 0.],\n", + " [156., 103., 1.],\n", + " [148., 103., 1.],\n", + " [ 0., 0., 0.],\n", + " [ 0., 0., 0.],\n", + " [ 0., 0., 0.],\n", + " [ 0., 0., 0.]],\n", + "\n", + " [[ 61., 59., 2.],\n", + " [ 62., 57., 2.],\n", + " [ 60., 57., 2.],\n", + " [ 66., 58., 2.],\n", + " [ 0., 0., 0.],\n", + " [ 72., 69., 2.],\n", + " [ 59., 69., 2.],\n", + " [ 78., 85., 2.],\n", + " [ 59., 80., 2.],\n", + " [ 67., 90., 2.],\n", + " [ 59., 91., 2.],\n", + " [ 73., 102., 2.],\n", + " [ 64., 102., 2.],\n", + " [ 70., 126., 2.],\n", + " [ 58., 123., 2.],\n", + " [ 77., 150., 2.],\n", + " [ 62., 150., 2.]],\n", + "\n", + " [[ 0., 0., 0.],\n", + " [ 0., 0., 0.],\n", + " [ 0., 0., 0.],\n", + " [ 0., 0., 0.],\n", + " [ 0., 0., 0.],\n", + " [ 76., 206., 2.],\n", + " [ 92., 186., 2.],\n", + " [ 0., 0., 0.],\n", + " [ 94., 159., 2.],\n", + " [ 0., 0., 0.],\n", + " [ 89., 137., 2.],\n", + " [ 0., 0., 0.],\n", + " [ 0., 0., 0.],\n", + " [ 0., 0., 0.],\n", + " [ 0., 0., 0.],\n", + " [ 0., 0., 0.],\n", + " [ 0., 0., 0.]],\n", + "\n", + " [[ 65., 66., 2.],\n", + " [ 68., 63., 2.],\n", + " [ 57., 64., 2.],\n", + " [ 71., 67., 2.],\n", + " [ 48., 68., 2.],\n", + " [ 61., 82., 2.],\n", + " [ 50., 80., 2.],\n", + " [ 58., 100., 2.],\n", + " [ 54., 83., 2.],\n", + " [ 74., 110., 2.],\n", + " [ 53., 82., 2.],\n", + " [ 54., 128., 2.],\n", + " [ 45., 131., 2.],\n", + " [ 58., 148., 2.],\n", + " [ 44., 156., 2.],\n", + " [ 58., 177., 2.],\n", + " [ 39., 186., 2.]]])\n" + ] + } + ], + "source": [ + "for images, keypoints in dataloader:\n", + " print(\"Images shape:\", images.shape)\n", + " print(\"Keypoints shape:\", keypoints.shape)\n", + " print(\"keypoints: \", keypoints)\n", + " break" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b26cfb19-15d6-47a6-8114-6ef1385d6fbc", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1e5e00bc-e79f-408f-bd3c-7663225cde47", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e24981bc-7a91-45ba-bcc5-d79a7604b8b3", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "670e81c3-27a2-43ef-a149-f4d88d7214c4", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "97e0ee06-e303-4a6c-8cda-eb6087693980", + "metadata": {}, + "source": [ + "### Ending loading the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "8334085f-85d4-4889-9f7f-71e8b7b6adc5", + "metadata": {}, + "outputs": [], + "source": [ + "cfg = {'s':s_cfg}.get(model_name.lower())" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "b7b53af5-0822-47a4-be61-6f8b2de9f9c6", + "metadata": {}, + "outputs": [], + "source": [ + "# Load config.yaml\n", + "with open(config_path, 'r') as f:\n", + " cfg_yaml = yaml.load(f, Loader=yaml.SafeLoader)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "7ef13d0b-2fd5-4313-9822-8b1626da933f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'log_level': 'logging.INFO',\n", + " 'seed': 0,\n", + " 'gpu_ids': [0],\n", + " 'deterministic': True,\n", + " 'cudnn_benchmark': True,\n", + " 'resume_from': 'C:/Users/user/ViTPose/ckpts/vitpose-s-coco_25.pth',\n", + " 'launcher': 'none',\n", + " 'use_amp': False,\n", + " 'validate': True,\n", + " 'autoscale_lr': False,\n", + " 'dist_params': '...'}" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cfg_yaml" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "64402e77-c6b8-49bb-90db-6693de309268", + "metadata": {}, + "outputs": [], + "source": [ + "for k, v in cfg_yaml.items():\n", + " if hasattr(cfg, k):\n", + " raise ValueError(f\"Already exists {k} in config\")\n", + " else:\n", + " cfg.__setattr__(k, v)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "98b406f9-d79a-40ed-af8a-e73d4808776a", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "057c9e72-4693-4387-a141-a844159b6410", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "88841210-3245-47d4-8fea-22a3fdff04ab", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "765d06d7-dcc9-46a0-a9bc-9569ff314eec", + "metadata": {}, + "outputs": [], + "source": [ + "# set cudnn_benchmark\n", + "if cfg.cudnn_benchmark:\n", + " torch.backends.cudnn.benchmark = True" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "14bcd3c1-01e4-4308-a693-7cba5b28ec97", + "metadata": {}, + "outputs": [], + "source": [ + "# Set work directory (session-level)\n", + "if not hasattr(cfg, 'work_dir'):\n", + " cfg.__setattr__('work_dir', f\"{CUR_PATH}/runs/train\")" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "fe3506fa-36d0-461e-8f18-856a35ae3268", + "metadata": {}, + "outputs": [], + "source": [ + "if not osp.exists(cfg.work_dir):\n", + " os.makedirs(cfg.work_dir)\n", + "session_list = sorted(glob(f\"{cfg.work_dir}/*\"))\n", + "if len(session_list) == 0:\n", + " session = 1\n", + "else:\n", + " session = int(os.path.basename(session_list[-1])) + 1\n", + "session_dir = osp.join(cfg.work_dir, str(session).zfill(3))\n", + "os.makedirs(session_dir)\n", + "cfg.__setattr__('work_dir', session_dir)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "aaffeee8-3d10-4cb8-ab1b-98f79bdb9910", + "metadata": {}, + "outputs": [], + "source": [ + "if cfg.autoscale_lr:\n", + " # apply the linear scaling rule (https://arxiv.org/abs/1706.02677)\n", + " cfg.optimizer['lr'] = cfg.optimizer['lr'] * len(cfg.gpu_ids) / 8" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7c022f17-9135-4317-a8ca-e7d4b51b4d1a", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "bc6c864a-6cf0-4599-aa2a-0de7f6fd19ec", + "metadata": {}, + "outputs": [], + "source": [ + "# init distributed env first, since logger depends on the dist info.\n", + "if cfg.launcher == 'none':\n", + " distributed = False\n", + " if len(cfg.gpu_ids) > 1:\n", + " warnings.warn(\n", + " f\"We treat {cfg['gpu_ids']} as gpu-ids, and reset to \"\n", + " f\"{cfg['gpu_ids'][0:1]} as gpu-ids to avoid potential error in \"\n", + " \"non-distribute training time.\")\n", + " cfg.gpu_ids = cfg.gpu_ids[0:1]\n", + "else:\n", + " distributed = True\n", + " init_dist(cfg.launcher, **cfg.dist_params)\n", + " # re-set gpu_ids with distributed training mode\n", + " _, world_size = get_dist_info()\n", + " cfg.gpu_ids = range(world_size)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "935e56e5-d5d1-4f19-bba0-6d659665c570", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "3881144e-53b7-4a2b-b878-0d8c43a17d82", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2024-12-24 17:08:26,262 - vit_utils - INFO - Distributed training: False\n", + "2024-12-24 17:08:26,263 - vit_utils - INFO - Set random seed to 0, deterministic: True\n" + ] + } + ], + "source": [ + "# init the logger before other steps\n", + "timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())\n", + "log_file = osp.join(session_dir, f'{timestamp}.log')\n", + "logger = get_root_logger(log_file=log_file)\n", + "\n", + "# init the meta dict to record some important information such as\n", + "# environment info and seed, which will be logged\n", + "meta = dict()\n", + "\n", + "# log some basic info\n", + "logger.info(f'Distributed training: {distributed}')\n", + "\n", + "# set random seeds\n", + "seed = init_random_seed(cfg.seed)\n", + "logger.info(f\"Set random seed to {seed}, \"\n", + " f\"deterministic: {cfg.deterministic}\")\n", + "set_random_seed(seed, deterministic=cfg.deterministic)\n", + "meta['seed'] = seed" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e0588f6f-6fe0-4ac6-9fcf-a2c744e68091", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "5878400f-1165-4301-aad8-aef907113a4a", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\user\\AppData\\Local\\Temp\\ipykernel_5392\\1963230343.py:5: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n", + " ckpt_state = torch.load(cfg.resume_from) #['state_dict']\n" + ] + } + ], + "source": [ + "# Set model\n", + "model = ViTPose(cfg.model)\n", + "if cfg.resume_from:\n", + " # Load ckpt partially\n", + " ckpt_state = torch.load(cfg.resume_from) #['state_dict']\n", + " ckpt_state.pop('keypoint_head.final_layer.bias')\n", + " ckpt_state.pop('keypoint_head.final_layer.weight')\n", + " model.load_state_dict(ckpt_state, strict=False)\n", + "\n", + " # freeze the backbone, leave the head to be finetuned\n", + " model.backbone.frozen_stages = model.backbone.depth - 1\n", + " model.backbone.freeze_ffn = True\n", + " model.backbone.freeze_attn = True\n", + " model.backbone._freeze_stages()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "52cef111-bc9c-417d-9770-7595408863be", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + 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Block(\n", + " (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n", + " (attn): Attention(\n", + " (qkv): Linear(in_features=384, out_features=1152, bias=True)\n", + " (attn_drop): Dropout(p=0.0, inplace=False)\n", + " (proj): Linear(in_features=384, out_features=384, bias=True)\n", + " (proj_drop): Dropout(p=0.0, inplace=False)\n", + " )\n", + " (drop_path): DropPath(p=0.09090909361839294)\n", + " (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n", + " (mlp): Mlp(\n", + " (fc1): Linear(in_features=384, out_features=1536, bias=True)\n", + " (act): GELU(approximate='none')\n", + " (fc2): Linear(in_features=1536, out_features=384, bias=True)\n", + " (drop): Dropout(p=0.0, inplace=False)\n", + " )\n", + " )\n", + " (11): Block(\n", + " (norm1): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n", + " (attn): Attention(\n", + " (qkv): Linear(in_features=384, out_features=1152, bias=True)\n", + " (attn_drop): Dropout(p=0.0, inplace=False)\n", + " (proj): Linear(in_features=384, out_features=384, bias=True)\n", + " (proj_drop): Dropout(p=0.0, inplace=False)\n", + " )\n", + " (drop_path): DropPath(p=0.10000000149011612)\n", + " (norm2): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n", + " (mlp): Mlp(\n", + " (fc1): Linear(in_features=384, out_features=1536, bias=True)\n", + " (act): GELU(approximate='none')\n", + " (fc2): Linear(in_features=1536, out_features=384, bias=True)\n", + " (drop): Dropout(p=0.0, inplace=False)\n", + " )\n", + " )\n", + " )\n", + " (last_norm): LayerNorm((384,), eps=1e-06, elementwise_affine=True)\n", + " )\n", + " (keypoint_head): TopdownHeatmapSimpleHead(\n", + " (deconv_layers): Sequential(\n", + " (0): ConvTranspose2d(384, 256, kernel_size=(4, 4), stride=(2, 2), 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", + " (3): ConvTranspose2d(256, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n", + " (4): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (5): ReLU(inplace=True)\n", + " )\n", + " (final_layer): Conv2d(256, 17, kernel_size=(1, 1), stride=(1, 1))\n", + " )\n", + ")" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "851da2fd-707c-4dc9-bca0-99ac5530374a", + "metadata": {}, + "outputs": [], + "source": [ + "# Set dataset\n", + "datasets_train = dataloader\n", + "datasets_valid = dataloader" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4d352a68-dccd-4d94-86c7-deb716545df4", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "5af32dc2-8a9c-4abf-bc31-1454d64622e2", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Batch 1:\n", + " - Images: torch.Size([4, 3, 208, 208])\n", + " - Labels: tensor([[[ 83., 46., 2.],\n", + " [ 0., 0., 0.],\n", + " [ 83., 44., 2.],\n", + " [ 0., 0., 0.],\n", + " [ 79., 44., 2.],\n", + " [ 83., 53., 2.],\n", + " [ 75., 54., 2.],\n", + " [ 86., 64., 2.],\n", + " [ 78., 70., 2.],\n", + " [ 90., 78., 2.],\n", + " [ 87., 79., 2.],\n", + " [ 83., 80., 2.],\n", + " [ 78., 81., 2.],\n", + " [ 0., 0., 0.],\n", + " [ 80., 99., 2.],\n", + " [ 0., 0., 0.],\n", + " [ 74., 121., 2.]],\n", + "\n", + " [[ 0., 0., 0.],\n", + " [ 0., 0., 0.],\n", + " [ 0., 0., 0.],\n", + " [200., 83., 2.],\n", + " [ 0., 0., 0.],\n", + " [192., 150., 2.],\n", + " [ 0., 0., 0.],\n", + " [ 0., 0., 0.],\n", + " [ 0., 0., 0.],\n", + " [ 0., 0., 0.],\n", + " [ 0., 0., 0.],\n", + " [ 0., 0., 0.],\n", + " [ 0., 0., 0.],\n", + " [ 0., 0., 0.],\n", + " [ 0., 0., 0.],\n", + " [ 0., 0., 0.],\n", + " [ 0., 0., 0.]],\n", + "\n", + " [[ 0., 0., 0.],\n", + " [ 0., 0., 0.],\n", + " [ 0., 0., 0.],\n", + " [ 0., 0., 0.],\n", + " [ 74., 48., 2.],\n", + " [ 63., 59., 2.],\n", + " [ 64., 60., 2.],\n", + " [ 70., 82., 2.],\n", + " [ 72., 85., 2.],\n", + " [ 83., 83., 2.],\n", + " [ 76., 62., 2.],\n", + " [ 58., 107., 2.],\n", + " [ 58., 109., 2.],\n", + " [ 83., 87., 2.],\n", + " [ 84., 98., 2.],\n", + " [ 75., 115., 1.],\n", + " [ 77., 141., 2.]],\n", + "\n", + " [[ 0., 0., 0.],\n", + " [ 0., 0., 0.],\n", + " [ 0., 0., 0.],\n", + " [ 0., 0., 0.],\n", + " [ 0., 0., 0.],\n", + " [ 0., 0., 0.],\n", + " [ 0., 0., 0.],\n", + " [ 0., 0., 0.],\n", + " [ 0., 0., 0.],\n", + " [ 0., 0., 0.],\n", + " [ 0., 0., 0.],\n", + " [ 0., 0., 0.],\n", + " [ 0., 0., 0.],\n", + " [ 0., 0., 0.],\n", + " [ 0., 0., 0.],\n", + " [ 0., 0., 0.],\n", + " [ 0., 0., 0.]]])\n" + ] + } + ], + "source": [ + "# Iterate Through the DataLoader\n", + "for batch_idx, (images, labels) in enumerate(dataloader):\n", + " print(f\"Batch {batch_idx + 1}:\")\n", + " print(f\" - Images: {images.shape}\") # Shape: (batch_size, 3, H, W)\n", + " print(f\" - Labels: {labels}\") # Tensor of labels\n", + " # Perform operations on images and labels (e.g., training)\n", + " break" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "c7000fcb-4487-4671-a88c-635da8e17d93", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([17, 3])" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "labels[0].shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "68165efc-54b3-4774-81d7-5e87b409219f", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "raw", + "id": "48811727-c7b5-48ec-8d2e-234e14cd1312", + "metadata": {}, + "source": [ + "train_model(\n", + " model=model,\n", + " datasets_train=datasets_train,\n", + " datasets_valid=datasets_valid,\n", + " cfg=cfg,\n", + " distributed=distributed,\n", + " validate=cfg.validate,\n", + " timestamp=timestamp,\n", + " meta=meta\n", + " )" + ] + }, + { + "cell_type": "raw", + "id": "62b37ca0-46a6-49e9-8d7b-5f0c69b93f20", + "metadata": { + "jupyter": { + "source_hidden": true + } + }, + "source": [ + "import os.path as osp\n", + "\n", + "import torch\n", + "import torch.nn as nn\n", + "\n", + "from vit_models.losses import JointsMSELoss\n", + "from vit_models.optimizer import LayerDecayOptimizer\n", + "\n", + "from torch.nn.parallel import DataParallel, DistributedDataParallel\n", + "from torch.nn.utils import clip_grad_norm_\n", + "from torch.optim import AdamW\n", + "from torch.optim.lr_scheduler import LambdaLR, MultiStepLR\n", + "from torch.utils.data import DataLoader, Dataset\n", + "from torch.utils.data.distributed import DistributedSampler\n", + "from torch.cuda.amp import autocast, GradScaler\n", + "from tqdm import tqdm\n", + "from time import time\n", + "\n", + "\n", + "logger = get_root_logger()\n", + "\n", + " \n", + "dataloaders_train = datasets_train\n", + "dataloaders_valid = datasets_valid\n", + "# put model on gpus\n", + "if distributed:\n", + " find_unused_parameters = cfg.get('find_unused_parameters', False)\n", + " # Sets the `find_unused_parameters` parameter in\n", + " # torch.nn.parallel.DistributedDataParallel\n", + "\n", + " model = DistributedDataParallel(\n", + " module=model, \n", + " device_ids=[torch.cuda.current_device()], \n", + " broadcast_buffers=False, \n", + " find_unused_parameters=find_unused_parameters)\n", + "else:\n", + " model = DataParallel(model, device_ids=cfg.gpu_ids)\n", + "\n", + "# Loss function\n", + "criterion = JointsMSELoss(use_target_weight=cfg.model['keypoint_head']['loss_keypoint']['use_target_weight'])\n", + "\n", + "# Optimizer\n", + "optimizer = AdamW(model.parameters(), lr=cfg.optimizer['lr'], betas=cfg.optimizer['betas'], weight_decay=cfg.optimizer['weight_decay'])\n", + "\n", + "# Layer-wise learning rate decay\n", + "lr_mult = [cfg.optimizer['paramwise_cfg']['layer_decay_rate']] * cfg.optimizer['paramwise_cfg']['num_layers']\n", + "layerwise_optimizer = LayerDecayOptimizer(optimizer, lr_mult)\n", + "\n", + "\n", + "# Learning rate scheduler (MultiStepLR)\n", + "milestones = cfg.lr_config['step']\n", + "gamma = 0.1\n", + "scheduler = MultiStepLR(optimizer, milestones, gamma)\n", + "\n", + "# Warm-up scheduler\n", + "num_warmup_steps = cfg.lr_config['warmup_iters'] # Number of warm-up steps\n", + "warmup_factor = cfg.lr_config['warmup_ratio'] # Initial learning rate = warmup_factor * learning_rate\n", + "warmup_scheduler = LambdaLR(\n", + " optimizer,\n", + " lr_lambda=lambda step: warmup_factor + (1.0 - warmup_factor) * step / num_warmup_steps\n", + ")\n", + "\n", + "# AMP setting\n", + "if cfg.use_amp:\n", + " logger.info(\"Using Automatic Mixed Precision (AMP) training...\")\n", + " # Create a GradScaler object for FP16 training\n", + " scaler = GradScaler()\n", + "\n", + "# Logging config\n", + "total_params = sum(p.numel() for p in model.parameters() if p.requires_grad)\n", + "logger.info(f'''\\n\n", + "#========= [Train Configs] =========#\n", + "# - Num GPUs: {len(cfg.gpu_ids)}\n", + "# - Batch size (per gpu): {cfg.data['samples_per_gpu']}\n", + "# - LR: {cfg.optimizer['lr']: .6f}\n", + "# - Num params: {total_params:,d}\n", + "# - AMP: {cfg.use_amp}\n", + "#===================================# \n", + "''')\n", + "\n", + "global_step = 0\n", + "for dataloader in dataloaders_train:\n", + " print(\"start training\")\n", + " for epoch in range(cfg.total_epochs):\n", + " model.train()\n", + " train_pbar = tqdm(dataloader)\n", + " total_loss = 0\n", + " tic = time()\n", + " for batch_idx, batch in enumerate(train_pbar):\n", + " layerwise_optimizer.zero_grad()\n", + " \n", + " images, targets, target_weights, __ = batch\n", + " images = images.to('cuda').unsqueeze(0)\n", + " targets = targets.to('cuda').unsqueeze(0)\n", + " target_weights = target_weights.to('cuda')\n", + " \n", + " if cfg.use_amp:\n", + " with autocast():\n", + " outputs = model(images)\n", + " loss = criterion(outputs, targets, target_weights)\n", + " scaler.scale(loss).backward()\n", + " clip_grad_norm_(model.parameters(), **cfg.optimizer_config['grad_clip'])\n", + " scaler.step(layerwise_optimizer)\n", + " scaler.update()\n", + " else:\n", + " print(images.shape)\n", + " outputs = model(images)\n", + " print(\"outputs: \", outputs.shape)\n", + " print(\"targets: \", targets.shape)\n", + " loss = criterion(outputs, targets, target_weights)\n", + " loss.backward()\n", + " clip_grad_norm_(model.parameters(), **cfg.optimizer_config['grad_clip'])\n", + " layerwise_optimizer.step()\n", + " \n", + " if global_step < num_warmup_steps:\n", + " warmup_scheduler.step()\n", + " global_step += 1\n", + " \n", + " total_loss += loss.item()\n", + " train_pbar.set_description(f\"🏋️> Epoch [{str(epoch).zfill(3)}/{str(cfg.total_epochs).zfill(3)}] | Loss {loss.item():.4f} | LR {optimizer.param_groups[0]['lr']:.6f} | Step\")\n", + " scheduler.step()\n", + " \n", + " avg_loss_train = total_loss/len(dataloader)\n", + " logger.info(f\"[Summary-train] Epoch [{str(epoch).zfill(3)}/{str(cfg.total_epochs).zfill(3)}] | Average Loss (train) {avg_loss_train:.4f} --- {time()-tic:.5f} sec. elapsed\")\n", + " ckpt_name = f\"epoch{str(epoch).zfill(3)}.pth\"\n", + " ckpt_path = osp.join(cfg.work_dir, ckpt_name)\n", + " torch.save(model.module.state_dict(), ckpt_path)\n", + "\n", + " # validation\n", + " if validate:\n", + " tic2 = time()\n", + " avg_loss_valid = valid_model(model, dataloaders_valid, criterion, cfg)\n", + " logger.info(f\"[Summary-valid] Epoch [{str(epoch).zfill(3)}/{str(cfg.total_epochs).zfill(3)}] | Average Loss (valid) {avg_loss_valid:.4f} --- {time()-tic2:.5f} sec. elapsed\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5c3aaa51-efa5-414c-a0a8-9f3b475ca67e", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c2f0dd2c-ceaa-40c1-943f-3392c4bb1b3d", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cc8f7cda-227e-4a03-b694-97af713b73f6", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "91348ce9-12d7-4882-b222-b9b60cedebec", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c1e63562-cc6c-460d-aab5-f2f9bb5724cc", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "6b2dd91c-9830-41a3-bddd-6c2f09254a7c", + "metadata": {}, + "outputs": [], + "source": [ + "device = torch.device('cuda')\n", + "# Move model to device\n", + "model = model.to(device)\n", + "\n", + "# Move inputs to device\n", + "images = images.to(device)\n" + ] + }, + { + "cell_type": "markdown", + "id": "faf1e322-2867-47d7-8778-09e83ead56ef", + "metadata": {}, + "source": [ + "## Define my own training process" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4cbc9686-7e2f-4fcb-af2b-b260fb8051f5", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b4ed1ef2-58d7-4d18-9966-1ebb95adc529", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5e568149-21ff-47c5-93f1-35d7290b837f", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f0a0a470-7c51-4027-b1cb-85d5a52bb28e", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "430f8a82-9c8f-4014-b3ff-ac9548213294", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c4b00355-74b1-4f87-abd1-2e2dc31846e6", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2de2c69e-ebc5-4e96-aa0e-5aab0cc88c31", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "raw", + "id": "867a6faa-7419-4a01-9719-2739639be673", + "metadata": { + "jupyter": { + "source_hidden": true + } + }, + "source": [ + "import torch\n", + "import torch.nn.functional as F\n", + "\n", + "def generate_heatmaps(keypoints, output_size):\n", + " \"\"\"\n", + " Generate heatmaps from keypoints for training.\n", + " Args:\n", + " - keypoints: Tensor of shape (batch_size, num_keypoints, 3) containing (x, y, visibility)\n", + " - output_size: (height, width) of the heatmaps\n", + " Returns:\n", + " - heatmaps: Tensor of shape (batch_size, num_keypoints, height, width)\n", + " \"\"\"\n", + " batch_size, num_keypoints, _ = keypoints.shape\n", + " height, width = output_size\n", + " heatmaps = torch.zeros(batch_size, num_keypoints, height, width, device=keypoints.device)\n", + "\n", + " #print(\"heatmaps: \", heatmaps)\n", + " for i in range(batch_size):\n", + " for j in range(num_keypoints):\n", + " x, y, visibility = keypoints[i, j, 0], keypoints[i, j, 1], keypoints[i, j, 2]\n", + " if visibility > 0:\n", + " # Create a Gaussian heatmap for each keypoint\n", + " gaussian = generate_gaussian(x, y, height, width)\n", + " print(\"gaussian max: \", gaussian.max())\n", + " print(\"gaussian min: \", gaussian.min())\n", + " heatmaps[i, j] = gaussian\n", + "\n", + " return heatmaps\n", + "\n", + "def generate_gaussian(x, y, height, width, sigma=1):\n", + " \"\"\"\n", + " Generate a Gaussian heatmap centered at (x, y) with standard deviation sigma.\n", + " \"\"\"\n", + " grid_x, grid_y = torch.meshgrid(torch.arange(0, width), torch.arange(0, height))\n", + " grid = torch.stack([grid_x, grid_y], dim=-1).float()\n", + " \n", + " mean = torch.tensor([x, y], dtype=torch.float32)\n", + " variance = sigma ** 2\n", + " diff = grid - mean\n", + " dist = torch.sum(diff ** 2, dim=-1)\n", + " gaussian = torch.exp(-dist / (2 * variance))\n", + "\n", + " return gaussian\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bdf3b94a-370e-481b-a1a6-9c165bc06e34", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "3e554ac4-3b6f-4621-8bde-a358e68e8e25", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "images.shape: torch.Size([4, 3, 208, 208])\n", + "labels.shape: torch.Size([4, 17, 3])\n" + ] + } + ], + "source": [ + "for images, labels in dataloader:\n", + " print(\"images.shape: \", images.shape)\n", + " print(\"labels.shape: \", labels.shape)\n", + " break" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "f2e9ad48-4a8e-4bdd-91a2-68f810e859c4", + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'plt' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[32], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241m.\u001b[39mimshow(outputs[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mcpu()\u001b[38;5;241m.\u001b[39mdetach()\u001b[38;5;241m.\u001b[39mnumpy())\n\u001b[0;32m 2\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n", + "\u001b[1;31mNameError\u001b[0m: name 'plt' is not defined" + ] + } + ], + "source": [ + "plt.imshow(outputs[0][0].cpu().detach().numpy())\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "38308db6-5e0c-4d08-91c4-b7408adcb81a", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bf122f87-f911-4c39-a6fc-4f004d1988ce", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "9c5386a0-b07b-47f5-be1e-9af65dcb6de2", + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "import numpy as np\n", + "\n", + "def generate_heatmaps(keypoints, output_size, sigma=2):\n", + " \"\"\"\n", + " Generate ground truth heatmaps for keypoints.\n", + " \n", + " Args:\n", + " keypoints: Tensor of shape (batch_size, num_keypoints, 3) with (x, y, confidence).\n", + " output_size: Tuple (height, width) of the heatmap.\n", + " sigma: Standard deviation of the Gaussian.\n", + " \n", + " Returns:\n", + " heatmaps: Tensor of shape (batch_size, num_keypoints, height, width).\n", + " \"\"\"\n", + " batch_size, num_keypoints, _ = keypoints.shape\n", + " height, width = output_size\n", + " heatmaps = torch.zeros((batch_size, num_keypoints, height, width), device=keypoints.device)\n", + "\n", + " for b in range(batch_size):\n", + " for k in range(num_keypoints):\n", + " x, y, confidence = keypoints[b, k]\n", + " \n", + " # Skip keypoints with zero confidence\n", + " if confidence <= 0 or x < 0 or y < 0:\n", + " continue\n", + " \n", + " # Create a meshgrid for Gaussian generation\n", + " xx, yy = torch.meshgrid(torch.arange(width, device=keypoints.device), \n", + " torch.arange(height, device=keypoints.device), \n", + " indexing='xy')\n", + " \n", + " # Calculate the 2D Gaussian heatmap\n", + " heatmap = torch.exp(-((xx - x)**2 + (yy - y)**2) / (2 * sigma**2))\n", + " heatmaps[b, k] = heatmap\n", + "\n", + " return heatmaps" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0b738642-c839-410a-8ba0-aa9698e70aa0", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "6ca3122b-2019-4bc1-a8cd-39e7230d52de", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Generated heatmaps shape: torch.Size([2, 17, 255, 255])\n" + ] + } + ], + "source": [ + "# Example usage\n", + "keypoints = torch.tensor([[[226., 129., 2.], [228., 127., 2.], [225., 127., 2.], [0., 0., 0.], [0., 0., 0.],\n", + " [233., 128., 2.], [218., 130., 2.], [239., 135., 2.], [213., 136., 2.], [243., 139., 2.],\n", + " [211., 137., 2.], [232., 149., 2.], [222., 148., 2.], [232., 169., 2.], [222., 169., 2.],\n", + " [233., 188., 2.], [221., 182., 2.]],\n", + " [[584., 101., 2.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [587., 137., 2.],\n", + " [637., 137., 2.], [567., 196., 2.], [0., 0., 0.], [561., 235., 2.], [619., 214., 2.],\n", + " [589., 222., 2.], [630., 224., 2.], [579., 317., 2.], [614., 309., 2.], [586., 400., 2.],\n", + " [611., 399., 2.]]], device='cuda:0')\n", + "\n", + "heatmaps = generate_heatmaps(keypoints, output_size=(255, 255), sigma=2)\n", + "\n", + "print(\"Generated heatmaps shape:\", heatmaps.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "3615cb01-7df8-43d9-9bd1-16f644df0125", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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StM/Jkye1ePFi+f1+ZWRkaOnSpWptbb2sAwEADC49DlhbW5umTZumdevWnXefkpISNTY2JpaXX345afvixYt18OBBbd++XVu3btXu3bu1fPnyno8eADBo9fgvMs+ZM0dz5sy54D4+n0/BYLDbbR9//LG2bdum999/XzfffLMk6YUXXtDcuXP1q1/9Snl5eT0dEgBgEOqX98AqKyuVnZ2t66+/XitWrNCJEycS26qqqpSRkZGIlyQVFRXJ5XKpurq6P4YDABiAenwGdjElJSVasGCB8vPzVV9fryeffFJz5sxRVVWV3G63QqGQsrOzkwfh8SgzM1OhUKjbx4xGo4pGo4nbkUikr4cNALBMnwds0aJFiX9PmTJFU6dO1YQJE1RZWalZs2b16jErKiq0Zs2avhoiAGAA6PfL6MePH6+srCzV1dVJkoLBoI4dO5a0TywW08mTJ8/7vll5ebnC4XBiOXz4cH8PGwDwFdfvATty5IhOnDih3NxcSVJhYaGam5tVU1OT2Gfnzp2Kx+MqKCjo9jF8Pp/8fn/SAgAY3Hr8EmJra2vibEqSDh06pH379ikzM1OZmZlas2aNFi5cqGAwqPr6ej3++OO65pprVFxcLEm64YYbVFJSomXLlmn9+vXq7OxUWVmZFi1axBWIAIBL1uMzsL179+qmm27STTfdJElauXKlbrrpJq1atUput1v79+/X9773PV133XVaunSppk+frr///e/y+XyJx3jppZc0ceJEzZo1S3PnztXtt9+uP/7xj313VACAAc8xxphUD6KnIpGIAoGA7tCd8jhpqR4OAKCHYqZTldqicDjc67eF+C5EAICVCBgAwEoEDABgJQIGALASAQMAWImAAQCsRMAAAFYiYAAAKxEwAICVCBgAwEoEDABgJQIGALASAQMAWImAAQCsRMAAAFYiYAAAKxEwAICVCBgAwEoEDABgJQIGALASAQMAWImAAQCsRMAAAFYiYAAAKxEwAICVCBgAwEoEDABgJQIGALASAQMAWImAAQCsRMAAAFYiYAAAKxEwAICVCBgAwEoEDABgJQIGALASAQMAWImAAQCsRMAAAFYiYAAAKxEwAICVCBgAwEoEDABgJQIGALASAQMAWImAAQCsRMAAAFYiYAAAKxEwAICVCBgAwEoEDABgJQIGALASAQMAWImAAQCsRMAAAFYiYAAAKxEwAICVCBgAwEoEDABgJQIGALASAQMAWImAAQCs1KOAVVRUaMaMGRoxYoSys7N11113qba2Nmmf9vZ2lZaWauTIkRo+fLgWLlyopqampH0aGho0b948DR06VNnZ2XrssccUi8Uu/2gAAINGjwK2a9culZaWas+ePdq+fbs6Ozs1e/ZstbW1JfZ5+OGH9cYbb2jTpk3atWuXjh49qgULFiS2d3V1ad68eero6NC7776rP//5z9qwYYNWrVrVd0cFABjwHGOM6e2djx8/ruzsbO3atUszZ85UOBzWqFGjtHHjRn3/+9+XJH3yySe64YYbVFVVpVtvvVVvvvmmvvvd7+ro0aPKycmRJK1fv15PPPGEjh8/Lq/Xe9GfG4lEFAgEdIfulMdJ6+3wAQApEjOdqtQWhcNh+f3+Xj3GZb0HFg6HJUmZmZmSpJqaGnV2dqqoqCixz8SJEzV27FhVVVVJkqqqqjRlypREvCSpuLhYkUhEBw8e7PbnRKNRRSKRpAUAMLj1OmDxeFwPPfSQbrvtNk2ePFmSFAqF5PV6lZGRkbRvTk6OQqFQYp//jtfZ7We3daeiokKBQCCxjBkzprfDBgAMEL0OWGlpqQ4cOKBXXnmlL8fTrfLycoXD4cRy+PDhfv+ZAICvNk9v7lRWVqatW7dq9+7dGj16dGJ9MBhUR0eHmpubk87CmpqaFAwGE/u89957SY939irFs/t8mc/nk8/n681QAQADVI/OwIwxKisr0+bNm7Vz507l5+cnbZ8+fbrS0tK0Y8eOxLra2lo1NDSosLBQklRYWKgPP/xQx44dS+yzfft2+f1+TZo06XKOBQAwiPToDKy0tFQbN27Uli1bNGLEiMR7VoFAQOnp6QoEAlq6dKlWrlypzMxM+f1+PfjggyosLNStt94qSZo9e7YmTZqkH/7wh1q7dq1CoZCeeuoplZaWcpYFALhkPbqM3nGcbte/+OKLuv/++yWd+SDzI488opdfflnRaFTFxcX6/e9/n/Ty4GeffaYVK1aosrJSw4YN05IlS/Tcc8/J47m0nnIZPQDYrS8uo7+sz4GlCgEDALul/HNgAACkCgEDAFiJgAEArETAAABWImAAACsRMACAlQgYAMBKBAwAYCUCBgCwEgEDAFiJgAEArETAAABWImAAACsRMACAlQgYAMBKBAwAYCUCBgCwEgEDAFiJgAEArETAAABWImAAACsRMACAlQgYAMBKBAwAYCUCBgCwEgEDAFiJgAEArETAAABWImAAACsRMACAlQgYAMBKBAwAYCUCBgCwEgEDAFiJgAEArETAAABWImAAACsRMACAlQgYAMBKBAwAYCUCBgCwEgEDAFiJgAEArETAAABWImAAACsRMACAlQgYAMBKBAwAYCUCBgCwEgEDAFiJgAEArETAAABWImAAACsRMACAlQgYAMBKBAwAYCUCBgCwEgEDAFiJgAEArETAAABWImAAACv1KGAVFRWaMWOGRowYoezsbN11112qra1N2ueOO+6Q4zhJywMPPJC0T0NDg+bNm6ehQ4cqOztbjz32mGKx2OUfDQBg0PD0ZOddu3aptLRUM2bMUCwW05NPPqnZs2fro48+0rBhwxL7LVu2TM8++2zi9tChQxP/7urq0rx58xQMBvXuu++qsbFRP/rRj5SWlqb/+Z//6YNDAgAMBj0K2LZt25Jub9iwQdnZ2aqpqdHMmTMT64cOHapgMNjtY/ztb3/TRx99pLfeeks5OTm68cYb9fOf/1xPPPGEnnnmGXm93l4cBgBgsLms98DC4bAkKTMzM2n9Sy+9pKysLE2ePFnl5eU6depUYltVVZWmTJminJycxLri4mJFIhEdPHjwcoYDABhEenQG9t/i8bgeeugh3XbbbZo8eXJi/X333adx48YpLy9P+/fv1xNPPKHa2lq99tprkqRQKJQUL0mJ26FQqNufFY1GFY1GE7cjkUhvhw0AGCB6HbDS0lIdOHBA77zzTtL65cuXJ/49ZcoU5ebmatasWaqvr9eECRN69bMqKiq0Zs2a3g4VADAA9eolxLKyMm3dulVvv/22Ro8efcF9CwoKJEl1dXWSpGAwqKampqR9zt4+3/tm5eXlCofDieXw4cO9GTYAYADpUcCMMSorK9PmzZu1c+dO5efnX/Q++/btkyTl5uZKkgoLC/Xhhx/q2LFjiX22b98uv9+vSZMmdfsYPp9Pfr8/aQEADG49egmxtLRUGzdu1JYtWzRixIjEe1aBQEDp6emqr6/Xxo0bNXfuXI0cOVL79+/Xww8/rJkzZ2rq1KmSpNmzZ2vSpEn64Q9/qLVr1yoUCumpp55SaWmpfD5f3x8hAGBAcowx5pJ3dpxu17/44ou6//77dfjwYf3gBz/QgQMH1NbWpjFjxujuu+/WU089lXTW9Nlnn2nFihWqrKzUsGHDtGTJEj333HPyeC6tp5FIRIFAQHfoTnmctEsdPgDgKyJmOlWpLQqHw71+Va1HAfuqCIfDysjI0O2aK48IGADYJqZOvaO/qrm5WYFAoFeP0eurEFOppaVFkvSO/prikQAALkdLS0uvA2blGVg8Hldtba0mTZqkw4cPc1FHNyKRiMaMGcP8XABzdGHMz8UxRxd2ofkxxqilpUV5eXlyuXr3nRpWnoG5XC5dddVVksRViRfB/Fwcc3RhzM/FMUcXdr756e2Z11n8ORUAgJUIGADAStYGzOfzafXq1Xx27DyYn4tjji6M+bk45ujC+nt+rLyIAwAAa8/AAACDGwEDAFiJgAEArETAAABWsjJg69at09VXX60hQ4aooKBA7733XqqHlDLPPPOMHMdJWiZOnJjY3t7ertLSUo0cOVLDhw/XwoULz/l7bAPJ7t27NX/+fOXl5clxHL3++utJ240xWrVqlXJzc5Wenq6ioiJ9+umnSfucPHlSixcvlt/vV0ZGhpYuXarW1tYreBT962JzdP/995/zO1VSUpK0z0Ceo4qKCs2YMUMjRoxQdna27rrrLtXW1ibtcynPq4aGBs2bN09Dhw5Vdna2HnvsMcVisSt5KP3iUubnjjvuOOd36IEHHkjapy/mx7qAvfrqq1q5cqVWr16tf/7zn5o2bZqKi4uT/r7YYPP1r39djY2NieW//0r2ww8/rDfeeEObNm3Srl27dPToUS1YsCCFo+1fbW1tmjZtmtatW9ft9rVr1+r555/X+vXrVV1drWHDhqm4uFjt7e2JfRYvXqyDBw9q+/bt2rp1q3bv3p30l8Ztd7E5kqSSkpKk36mXX345aftAnqNdu3aptLRUe/bs0fbt29XZ2anZs2erra0tsc/FnlddXV2aN2+eOjo69O677+rPf/6zNmzYoFWrVqXikPrUpcyPJC1btizpd2jt2rWJbX02P8Yyt9xyiyktLU3c7urqMnl5eaaioiKFo0qd1atXm2nTpnW7rbm52aSlpZlNmzYl1n388cdGkqmqqrpCI0wdSWbz5s2J2/F43ASDQfPLX/4ysa65udn4fD7z8ssvG2OM+eijj4wk8/777yf2efPNN43jOObf//73FRv7lfLlOTLGmCVLlpg777zzvPcZbHN07NgxI8ns2rXLGHNpz6u//vWvxuVymVAolNjnD3/4g/H7/SYajV7ZA+hnX54fY4z51re+ZX7605+e9z59NT9WnYF1dHSopqZGRUVFiXUul0tFRUWqqqpK4chS69NPP1VeXp7Gjx+vxYsXq6GhQZJUU1Ojzs7OpPmaOHGixo4dOyjn69ChQwqFQknzEQgEVFBQkJiPqqoqZWRk6Oabb07sU1RUJJfLperq6is+5lSprKxUdna2rr/+eq1YsUInTpxIbBtscxQOhyVJmZmZki7teVVVVaUpU6YoJycnsU9xcbEikYgOHjx4BUff/748P2e99NJLysrK0uTJk1VeXq5Tp04ltvXV/Fj1Zb6ff/65urq6kg5aknJycvTJJ5+kaFSpVVBQoA0bNuj6669XY2Oj1qxZo29+85s6cOCAQqGQvF6vMjIyku6Tk5OT+Gvag8nZY+7u9+fstlAopOzs7KTtHo9HmZmZg2bOSkpKtGDBAuXn56u+vl5PPvmk5syZo6qqKrnd7kE1R/F4XA899JBuu+02TZ48WZIu6XkVCoW6/T07u22g6G5+JOm+++7TuHHjlJeXp/379+uJJ55QbW2tXnvtNUl9Nz9WBQznmjNnTuLfU6dOVUFBgcaNG6e//OUvSk9PT+HIYKtFixYl/j1lyhRNnTpVEyZMUGVlpWbNmpXCkV15paWlOnDgQNL7yviP883Pf78fOmXKFOXm5mrWrFmqr6/XhAkT+uznW/USYlZWltxu9zlX+zQ1NSkYDKZoVF8tGRkZuu6661RXV6dgMKiOjg41Nzcn7TNY5+vsMV/o9ycYDJ5zQVAsFtPJkycH5ZxJ0vjx45WVlaW6ujpJg2eOysrKtHXrVr399tsaPXp0Yv2lPK+CwWC3v2dntw0E55uf7hQUFEhS0u9QX8yPVQHzer2aPn26duzYkVgXj8e1Y8cOFRYWpnBkXx2tra2qr69Xbm6upk+frrS0tKT5qq2tVUNDw6Ccr/z8fAWDwaT5iEQiqq6uTsxHYWGhmpubVVNTk9hn586disfjiSfhYHPkyBGdOHFCubm5kgb+HBljVFZWps2bN2vnzp3Kz89P2n4pz6vCwkJ9+OGHSaHfvn27/H6/Jk2adGUOpJ9cbH66s2/fPklK+h3qk/npxUUnKfXKK68Yn89nNmzYYD766COzfPlyk5GRkXQ1y2DyyCOPmMrKSnPo0CHzj3/8wxQVFZmsrCxz7NgxY4wxDzzwgBk7dqzZuXOn2bt3ryksLDSFhYUpHnX/aWlpMR988IH54IMPjCTz61//2nzwwQfms88+M8YY89xzz5mMjAyzZcsWs3//fnPnnXea/Px8c/r06cRjlJSUmJtuuslUV1ebd955x1x77bXm3nvvTdUh9bkLzVFLS4t59NFHTVVVlTl06JB56623zDe+8Q1z7bXXmvb29sRjDOQ5WrFihQkEAqaystI0NjYmllOnTiX2udjzKhaLmcmTJ5vZs2ebffv2mW3btplRo0aZ8vLyVBxSn7rY/NTV1Zlnn33W7N271xw6dMhs2bLFjB8/3sycOTPxGH01P9YFzBhjXnjhBTN27Fjj9XrNLbfcYvbs2ZPqIaXMPffcY3Jzc43X6zVXXXWVueeee0xdXV1i++nTp81PfvIT87Wvfc0MHTrU3H333aaxsTGFI+5fb7/9tpF0zrJkyRJjzJlL6Z9++mmTk5NjfD6fmTVrlqmtrU16jBMnTph7773XDB8+3Pj9fvPjH//YtLS0pOBo+seF5ujUqVNm9uzZZtSoUSYtLc2MGzfOLFu27Jz/QRzIc9Td3EgyL774YmKfS3le/etf/zJz5swx6enpJisryzzyyCOms7PzCh9N37vY/DQ0NJiZM2eazMxM4/P5zDXXXGMee+wxEw6Hkx6nL+aHP6cCALCSVe+BAQBwFgEDAFiJgAEArETAAABWImAAACsRMACAlQgYAMBKBAwAYCUCBgCwEgEDAFiJgAEArETAAABW+v8d0V2BGpROHwAAAABJRU5ErkJggg==", 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", 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", 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", 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", 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", 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", 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", 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", 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NEQD6NhM/dhl8R1SuI+3yHoxoULqjWKZbH2u0PvWNUEZmRG2hDKU1ezS4yVHGtx1yt7TLORpRPBb7/hM54qk+k1Pq9U+jHzVqlIYOHaq6ujpJUiAQ0IEDB5LGxGIxHTp06KSvm1VUVCgUCiW2xsbG3p42ANits/PYp2q0R5QWjsh7KKqBTXEN/I9b7roMtf+PXwPqvRq4z9HAYFye/4vIaWuXiXTIdHb+4DMSL1y9/j6wffv26eDBg8rLy5MkFRUVqaWlRTU1NZoyZYokafPmzYrH4yosLOzyMbxe7wlXMgIATsKYYwGKxuQcbZcr1CqPMRpsjNztHsUGOIqnO3K3x5XeGpenpUNp/9cmp/WI4u3tx+JnwWcinnXAWltbE8+mJGnv3r3asWOHsrOzlZ2dreXLl2vOnDkKBAKqr6/XI488oksvvVQlJSWSpHHjxqm0tFQLFy7UypUrFY1GVV5errlz53IFIgD0FBOX6ZTikYhcobB09Kg8rZlK/zZDxu2SSUuTE+2U0xGVczQi09qmeEeHTEfUik/hkLoRsE8++UQ33nhj4vaSJUskSfPnz9eLL76onTt36qWXXlJLS4vy8/M1Y8YMPf3000nPoF5++WWVl5dr+vTpcrlcmjNnjp599tkeOB0AgKTvAxSXicYUl+R8HyUn0nHsPWIulxSPH/sU+o6oTCQiE41ZEy9JcoyxZKY/EA6H5ff7dYNukdtJT/V0AODCdfzvgKWlHftzKt/Hy3EcGWP+++nzx1/3Ok+/OoyZqKq0TqFQSD6fr1uPwWchAkBfZo59moYxcZmYjn1C/Qlj4v8daxECBgD9gfnBX17ucr99CBgA9CcWB+vHev19YAAA9AYCBgCwEgEDAFiJgAEArETAAABWImAAACsRMACAlQgYAMBKBAwAYCUCBgCwEgEDAFiJgAEArETAAABWImAAACsRMACAlQgYAMBKBAwAYCUCBgCwEgEDAFiJgAEArETAAABWImAAACsRMACAlQgYAMBKBAwAYCUCBgCwEgEDAFiJgAEArETAAABWImAAACsRMACAlQgYAMBKBAwAYCUCBgCwEgEDAFiJgAEArETAAABWImAAACsRMACAlQgYAMBKBAwAYCUCBgCwEgEDAFiJgAEArETAAABWImAAACsRMACAlQgYAMBKBAwAYCUCBgCwEgEDAFiJgAEArETAAABWImAAACsRMACAlQgYAMBKBAwAYCUCBgCwEgEDAFjprAJWWVmpqVOnavDgwcrJydGtt96q2trapDHt7e0qKyvTkCFDNGjQIM2ZM0fNzc1JYxoaGjRr1ixlZmYqJydHDz/8sGKx2LmfDQCg3zirgG3ZskVlZWXatm2bNm7cqGg0qhkzZqitrS0x5oEHHtDbb7+tN954Q1u2bNH+/fs1e/bsxPHOzk7NmjVLHR0d+vDDD/XSSy9p9erVWrp0ac+dFQCgz3OMMaa7d/7222+Vk5OjLVu2aNq0aQqFQho2bJjWrFmjX/3qV5Kkr776SuPGjVN1dbWuueYavfvuu/rlL3+p/fv3Kzc3V5K0cuVKPfroo/r222/l8XhO+3XD4bD8fr9u0C1yO+ndnT4AIEViJqoqrVMoFJLP5+vWY5zTa2ChUEiSlJ2dLUmqqalRNBpVcXFxYszYsWM1YsQIVVdXS5Kqq6s1ceLERLwkqaSkROFwWLt37+7y60QiEYXD4aQNANC/dTtg8Xhc999/v6699lpNmDBBkhQMBuXxeJSVlZU0Njc3V8FgMDHmh/E6fvz4sa5UVlbK7/cntuHDh3d32gCAPqLbASsrK9OuXbv06quv9uR8ulRRUaFQKJTYGhsbe/1rAgAubO7u3Km8vFzr16/X1q1bdfHFFyf2BwIBdXR0qKWlJelZWHNzswKBQGLMRx99lPR4x69SPD7mx7xer7xeb3emCgDoo87qGZgxRuXl5Vq7dq02b96sgoKCpONTpkxRenq6Nm3alNhXW1urhoYGFRUVSZKKior0+eef68CBA4kxGzdulM/n0/jx48/lXAAA/chZPQMrKyvTmjVrtG7dOg0ePDjxmpXf71dGRob8fr8WLFigJUuWKDs7Wz6fT/fdd5+Kiop0zTXXSJJmzJih8ePH66677tKKFSsUDAb1+OOPq6ysjGdZAIAzdlaX0TuO0+X+VatW6e6775Z07I3MDz74oF555RVFIhGVlJTohRdeSPr14Ndff63FixerqqpKAwcO1Pz58/XMM8/I7T6znnIZPQDYrScuoz+n94GlCgEDALul/H1gAACkCgEDAFiJgAEArETAAABWImAAACsRMACAlQgYAMBKBAwAYCUCBgCwEgEDAFiJgAEArETAAABWImAAACsRMACAlQgYAMBKBAwAYCUCBgCwEgEDAFiJgAEArETAAABWImAAACsRMACAlQgYAMBKBAwAYCUCBgCwEgEDAFiJgAEArETAAABWImAAACsRMACAlQgYAMBKBAwAYCUCBgCwEgEDAFiJgAEArETAAABWImAAACsRMACAlQgYAMBKBAwAYCUCBgCwEgEDAFiJgAEArETAAABWImAAACsRMACAlQgYAMBKBAwAYCUCBgCwEgEDAFiJgAEArETAAABWImAAACsRMACAlQgYAMBKBAwAYCUCBgCwEgEDAFiJgAEArETAAABWImAAACudVcAqKys1depUDR48WDk5Obr11ltVW1ubNOaGG26Q4zhJ27333ps0pqGhQbNmzVJmZqZycnL08MMPKxaLnfvZAAD6DffZDN6yZYvKyso0depUxWIxPfbYY5oxY4a++OILDRw4MDFu4cKFeuqppxK3MzMzE//u7OzUrFmzFAgE9OGHH6qpqUm/+c1vlJ6erj/96U89cEoAgP7grAK2YcOGpNurV69WTk6OampqNG3atMT+zMxMBQKBLh/jX//6l7744gu99957ys3N1ZVXXqmnn35ajz76qJ588kl5PJ5unAYAoL85p9fAQqGQJCk7Oztp/8svv6yhQ4dqwoQJqqio0JEjRxLHqqurNXHiROXm5ib2lZSUKBwOa/fu3ecyHQBAP3JWz8B+KB6P6/7779e1116rCRMmJPbfeeedGjlypPLz87Vz5049+uijqq2t1ZtvvilJCgaDSfGSlLgdDAa7/FqRSESRSCRxOxwOd3faAIA+otsBKysr065du/TBBx8k7V+0aFHi3xMnTlReXp6mT5+u+vp6jR49ultfq7KyUsuXL+/uVAEAfVC3foVYXl6u9evX6/3339fFF198yrGFhYWSpLq6OklSIBBQc3Nz0pjjt0/2ullFRYVCoVBia2xs7M60AQB9yFkFzBij8vJyrV27Vps3b1ZBQcFp77Njxw5JUl5eniSpqKhIn3/+uQ4cOJAYs3HjRvl8Po0fP77Lx/B6vfL5fEkbAKB/O6tfIZaVlWnNmjVat26dBg8enHjNyu/3KyMjQ/X19VqzZo1uuukmDRkyRDt37tQDDzygadOmadKkSZKkGTNmaPz48brrrru0YsUKBYNBPf744yorK5PX6+35MwQA9EmOMcac8WDH6XL/qlWrdPfdd6uxsVG//vWvtWvXLrW1tWn48OG67bbb9Pjjjyc9a/r666+1ePFiVVVVaeDAgZo/f76eeeYZud1n1tNwOCy/368bdIvcTvqZTh8AcIGImaiqtE6hUKjbv1U7q4BdKEKhkLKysnSdbpJbBAwAbBNTVB/oHbW0tMjv93frMbp9FWIqHT58WJL0gd5J8UwAAOfi8OHD3Q6Ylc/A4vG4amtrNX78eDU2NnJRRxfC4bCGDx/O+pwCa3RqrM/psUandqr1Mcbo8OHDys/Pl8vVvc/UsPIZmMvl0kUXXSRJXJV4GqzP6bFGp8b6nB5rdGonW5/uPvM6jj+nAgCwEgEDAFjJ2oB5vV4tW7aM946dBOtzeqzRqbE+p8canVpvr4+VF3EAAGDtMzAAQP9GwAAAViJgAAArETAAgJWsDNjzzz+vSy65RAMGDFBhYaE++uijVE8pZZ588kk5jpO0jR07NnG8vb1dZWVlGjJkiAYNGqQ5c+ac8PfY+pKtW7fq5ptvVn5+vhzH0VtvvZV03BijpUuXKi8vTxkZGSouLtaePXuSxhw6dEjz5s2Tz+dTVlaWFixYoNbW1vN4Fr3rdGt09913n/A9VVpamjSmL69RZWWlpk6dqsGDBysnJ0e33nqramtrk8acyc9VQ0ODZs2apczMTOXk5Ojhhx9WLBY7n6fSK85kfW644YYTvofuvffepDE9sT7WBey1117TkiVLtGzZMn366aeaPHmySkpKkv6+WH9zxRVXqKmpKbH98K9kP/DAA3r77bf1xhtvaMuWLdq/f79mz56dwtn2rra2Nk2ePFnPP/98l8dXrFihZ599VitXrtT27ds1cOBAlZSUqL29PTFm3rx52r17tzZu3Kj169dr69atSX9p3HanWyNJKi0tTfqeeuWVV5KO9+U12rJli8rKyrRt2zZt3LhR0WhUM2bMUFtbW2LM6X6uOjs7NWvWLHV0dOjDDz/USy+9pNWrV2vp0qWpOKUedSbrI0kLFy5M+h5asWJF4liPrY+xzNVXX23KysoStzs7O01+fr6prKxM4axSZ9myZWby5MldHmtpaTHp6enmjTfeSOz78ssvjSRTXV19nmaYOpLM2rVrE7fj8bgJBALmz3/+c2JfS0uL8Xq95pVXXjHGGPPFF18YSebjjz9OjHn33XeN4zjmm2++OW9zP19+vEbGGDN//nxzyy23nPQ+/W2NDhw4YCSZLVu2GGPO7OfqnXfeMS6XywSDwcSYF1980fh8PhOJRM7vCfSyH6+PMcb8/Oc/N7///e9Pep+eWh+rnoF1dHSopqZGxcXFiX0ul0vFxcWqrq5O4cxSa8+ePcrPz9eoUaM0b948NTQ0SJJqamoUjUaT1mvs2LEaMWJEv1yvvXv3KhgMJq2H3+9XYWFhYj2qq6uVlZWlq666KjGmuLhYLpdL27dvP+9zTpWqqirl5ORozJgxWrx4sQ4ePJg41t/WKBQKSZKys7MlndnPVXV1tSZOnKjc3NzEmJKSEoXDYe3evfs8zr73/Xh9jnv55Zc1dOhQTZgwQRUVFTpy5EjiWE+tj1Uf5vvdd9+ps7Mz6aQlKTc3V1999VWKZpVahYWFWr16tcaMGaOmpiYtX75c119/vXbt2qVgMCiPx6OsrKyk++Tm5ib+mnZ/cvycu/r+OX4sGAwqJycn6bjb7VZ2dna/WbPS0lLNnj1bBQUFqq+v12OPPaaZM2equrpaaWlp/WqN4vG47r//fl177bWaMGGCJJ3Rz1UwGOzy++z4sb6iq/WRpDvvvFMjR45Ufn6+du7cqUcffVS1tbV68803JfXc+lgVMJxo5syZiX9PmjRJhYWFGjlypF5//XVlZGSkcGaw1dy5cxP/njhxoiZNmqTRo0erqqpK06dPT+HMzr+ysjLt2rUr6XVl/NfJ1ueHr4dOnDhReXl5mj59uurr6zV69Oge+/pW/Qpx6NChSktLO+Fqn+bmZgUCgRTN6sKSlZWlyy+/XHV1dQoEAuro6FBLS0vSmP66XsfP+VTfP4FA4IQLgmKxmA4dOtQv10ySRo0apaFDh6qurk5S/1mj8vJyrV+/Xu+//74uvvjixP4z+bkKBAJdfp8dP9YXnGx9ulJYWChJSd9DPbE+VgXM4/FoypQp2rRpU2JfPB7Xpk2bVFRUlMKZXThaW1tVX1+vvLw8TZkyRenp6UnrVVtbq4aGhn65XgUFBQoEAknrEQ6HtX379sR6FBUVqaWlRTU1NYkxmzdvVjweT/wQ9jf79u3TwYMHlZeXJ6nvr5ExRuXl5Vq7dq02b96sgoKCpONn8nNVVFSkzz//PCn0GzdulM/n0/jx48/PifSS061PV3bs2CFJSd9DPbI+3bjoJKVeffVV4/V6zerVq80XX3xhFi1aZLKyspKuZulPHnzwQVNVVWX27t1r/v3vf5vi4mIzdOhQc+DAAWOMMffee68ZMWKE2bx5s/nkk09MUVGRKSoqSvGse8/hw4fNZ599Zj777DMjyfz1r381n332mfn666+NMcY888wzJisry6xbt87s3LnT3HLLLaagoMAcPXo08RilpaXmpz/9qdm+fbv54IMPzGWXXWbuuOOOVJ1SjzvVGh0+fNg89NBDprq62uzdu9e899575mc/+5m57LLLTHt7e+Ix+vIaLV682Pj9flNVVWWampoS25EjRxJjTvdzFYvFzIQJE8yMGTPMjh07zIYNG8ywYcNMRUVFKk6pR51uferq6sxTTz1lPvnkE7N3716zbt06M2rUKDNt2rTEY/TU+lgXMGOMee6558yIESOMx+MxV199tdm2bVuqp5Qyt99+u8nLyzMej8dcdNFF5vbbbzd1dXWJ40ePHjW/+93vzE9+8hOTmZlpbrvtNtPU1JTCGfeu999/30g6YZs/f74x5til9E888YTJzc01Xq/XTJ8+3dTW1iY9xsGDB80dd9xhBg0aZHw+n7nnnnvM4cOHU3A2veNUa3TkyBEzY8YMM2zYMJOenm5GjhxpFi5ceMJ/EPvyGnW1NpLMqlWrEmPO5OfqP//5j5k5c6bJyMgwQ4cONQ8++KCJRqPn+Wx63unWp6GhwUybNs1kZ2cbr9drLr30UvPwww+bUCiU9Dg9sT78ORUAgJWseg0MAIDjCBgAwEoEDABgJQIGALASAQMAWImAAQCsRMAAAFYiYAAAKxEwAICVCBgAwEoEDABgJQIGALDS/wfJIlOkuPPImQAAAABJRU5ErkJggg==", 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", 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", 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", 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#plt.imshow(outputs[0][0].cpu().detach().numpy())\n", + "import matplotlib.pyplot as plt\n", + "for i in range(0, 17):\n", + " plt.imshow(heatmaps[0][i].cpu().detach().numpy())\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "f238c110-8e4f-4865-bc1c-f8aef0127ef3", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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nDLxcDZdjAABcdihAAIAoKEAAgCgoQACAKChAAIAoKEAAgCjKtg3bJzhy3mp7TWuJti6PYF3mwGC2flttv0OXFnM+bj7r+FOi+k0TXDJjVGDLtNkybhyHGzIuKxF4P128XeMxF9hObI4LbLUt6nIM1pzm5RgyOEbrsgEZrE3qnKGXVcjiGAPb5Yuak8sxAAAuNxQgAEAUFCAAQBQUIABAFBQgAEAUFCAAQBTl24ZdUSEl49s4c7Nm+cc4o+3Xagk1WnulItKwLYGp3pK/RdtK5s0Zib5Wa/PFKY3jLxjt3Waqr5EibLShOyO1Oqk22teNJGBJZhq2rPs/tL3dGmccR3D6clqLrjWn1YZcV+cfF5CinDoug2Tq1DmzSLXOIg07JbXePBUjNA2dNGwAwHREAQIAREEBAgBEQQECAERBAQIAREEBAgBEUb5t2CMjEyYtWy2hVqu1jLZfc5wkZ7XwWmOt9mZrm5HcnCRGirTFSpG20qclJdaaG6z2TXNOY72dlbA95N9m3odKSQo2xpqJz6EpylY7cVZp2Nac1r729weNKwwYa2O1BAc+/9PSsIPHZpH4nUG7tJTy2DGvFuAfRxo2AGBaogABAKKgAAEAoqAAAQCioAABAKKgAAEAoijbNuykskJJMn73kln+9F0ZLbp2o3E4q0VRVsu0ldxsMdo+k/4B/zgjRTgxEq0lSVYLr9VqXFtrjDNaW435EgWOS2u1N9rCg1OtreRmq7U7NJk4cL6i5rSSm421yRlp8JkkTKe1oVv3R2iLfmiqtZVMHvi4kdJarcPa0H3jEleQjDMm3sY7IABAFBQgAEAUFCAAQBQUIABAFBQgAEAUFCAAQBRl24btRpxcMkEfn9FqbLYhWunTRQhu774QliKsnNFOm/O3fbsLF4xx9t8hSWCqtTt33rxdLyt9ODBFPDUpOLQN2UynNhKmy2i+oua0xoUeo5kUHtZqbaaoS3aqtTE2ONU6tNXa3E/7Nc5K0g++P3z76i6hB1u8AwIAREIBAgBEQQECAERBAQIAREEBAgBEQQECAEQxqTbs1tZWfetb39JPf/pT1dXV6cMf/rD+5m/+RkuXLh39NwMDA3rggQe0b98+DQ4Oas2aNXr88cfV0NAwqR1LKhIlyfj6mMyZ7R9UMBKNzclS6rDV3mi1t1qtv3Pn+LdZycVWi7bRhpsz5nNWMrVkJncnRhq2avxpwNa+mu3kVst4wWiXTUsft9KwQ1tmsxgX2tpttbariJTp0MTnwH2NkoZd6lRrYz4zfT3lGM2x1v1hvf752sJdQbLPfJA0yXdAbW1tamlp0ZEjR/Tss89qeHhYn/jEJ9TX1zf6b7Zs2aKnnnpK+/fvV1tbm06fPq1169ZNZhoAwAwwqXdAzzzzzJj/37Nnj6655hq1t7frd37nd9TT06MnnnhCe/fu1erVqyVJu3fv1o033qgjR47o1ltvnbo9BwBMa0V9B9TT0yNJmj9/viSpvb1dw8PDam5uHv03y5Yt0+LFi3X48OEJb2NwcFC9vb1jfgAAl7/gAlQoFLR582bddtttuvnmmyVJnZ2dqq6uVn19/Zh/29DQoM7Ozglvp7W1Vfl8fvRn0aJFobsEAJhGggtQS0uLXnnlFe3bt6+oHdi2bZt6enpGfzo6Ooq6PQDA9BAURrpx40Z95zvf0XPPPaeFCxeO/r6xsVFDQ0Pq7u4e8y6oq6tLjY2NE95WTU2Naoxr2QMALk+TKkDOOW3atEkHDhzQoUOHtGTJkjHbV65cqaqqKh08eFDr16+XJJ04cUKnTp1SU1PTlOyws1KkrdReKw13xGgJliQjRTY0nVmhSdEWKynXasM1WpAl2enU5jj/uppJwSHpu0pJ+01JQ7bbgo3WZ+sxZ7So24nPgfNZ95Px2JCkwoB/X3NmW3hgOnUGadjm4986tSFtztD25cBUaw1bpyiEpaFfnDPssZMYbxC8j3F3aVcfmFQBamlp0d69e/Xtb39bc+fOHf1eJ5/Pq66uTvl8Xvfee6+2bt2q+fPna968edq0aZOamprogAMAjDGpArRr1y5J0sc+9rExv9+9e7f++I//WJL06KOPKpfLaf369WNORAUA4LdN+iO4NLW1tdq5c6d27twZvFMAgMsfWXAAgCgoQACAKChAAIAoKEAAgCiCTkQtiaoqKRl//kFu9izvEKtJIukf8M+VFtVvCbw8gmbV+bdZ56xY5ysM+I8xmeVfN/PyD5J9eQTj/BErjj4pBJ53YZ0/UczlGELP9bDOZ7HOn8jgsgqhlw2QpFytta+B54iU+hizuhyDdYzWOGtO8xIvYa9H1nlwaXOa62q9PpTycgwAAEwVChAAIAoKEAAgCgoQACAKChAAIAoKEAAgivJtwx4envAyCNblGC4lq27CcecvmNuTOqNlesi/P2YE/vm+sHFGxL912Qh37px/XBorAt5o7zXbNwtGy3xo27MZN58SVV/iOYPnC7xUQWobcuhlFUIvOVHiY0y9HENgG7J5WoDVap3B5RjS5Orz3m0jv+72brOO0Xt/XOLlGHgHBACIggIEAIiCAgQAiIICBACIggIEAIiCAgQAiKJs27Cdk5zGt+paLYFm4rGRhp1ccYW9L0bKdG7e3KBxyWx/S6Q1TlVGu6g1n5V2bCRaSymtplYCr5n4a7R2hiZTB6YoSyltwaVObjbGFYz7KjTROm3OUqdTm23PGeynlHL/W68roanWZnK7cYzGqSZvfeB6/3ySql76WdCcZls4adgAgOmIAgQAiIICBACIggIEAIiCAgQAiIICBACIomzbsDUyIiXj66OVvhucFN3ba+6K1TJa6DOStI1WSzcUmE5ttItaLaHOaKVMTRE3tpst09aah7bohqYoW+umlFTrUic3G+NyRktwlDRss33ZaCfO4H4MbdFOHZtFGrb13DDb0P33f8WR1/zzSXLWYyA0udv3mksaNgCgnFGAAABRUIAAAFFQgAAAUVCAAABRUIAAAFGUbRt2UlmhJBm/e2YybY2RBny+z7stN3eOvTNDRhqsmZTsH2ceR8Foi8752zcT4zgKv/q1cZNGorVSWq2NFk1zXGiLrtUuHZiinDpnFmnYoW3IGSRFx5gzeJz1HDefb+H3f8W7Gr3bRs50+cct8I8rdP3SvzPmc8p/akfa65gbtB8D/oHG69EEp8r8Zm8u6aZ5BwQAiIICBACIggIEAIiCAgQAiIICBACIggIEAIiibNuw3YiTSyZoORwwEq+NFkUz0dlo0ZYk5Yw6bbUoWgnUxjaT0aJZ6D3v3Wa1LxeMNlsprfU1gxRlq+05qzTsEs9ptpMbj+Ms0rczmzO0Dd1s0Q47tcFM0U+Zc+Q/O/3jjMfNyGljnNVqba2p9fzvsRP2zcd4aKq9Z5wjDRsAUM4oQACAKChAAIAoKEAAgCgoQACAKChAAIAoyrYNO6lIlEyUtFrlb7VMamv9N2i1YRptxpLkBgb8c9YYLaPGbQanD1vJ1Vbgr9EubrVZS5KySLUObXvOIJm6mLEF47SAXG1g+3qJE6YzmzOL+zGrNGxjztyc2d5thX7jtcFqfbeej9a+Fox2eWNNL/4DI0nf2FcrZdt5UvYTV5CMs2JGbzv9nwAAMPUoQACAKChAAIAoKEAAgCgoQACAKChAAIAoyrYN2701IpdM0MZptS9aqa1WarXRZi3JbH2U0fppjbPSea1kWquV0jxGizWfyqzVOjhFOaUNOTDVOmclMFvt66Ep2qHj0tqQs5gzi4Rxq9U6sLVbSmm1N9LyzVMYrOeVlYZttPaHrmnqWGN9RozTQkjDBgBMSxQgAEAUFCAAQBQUIABAFBQgAEAUFCAAQBSTasPetWuXdu3apf/4j/+QJN1000166KGHtHbtWknSwMCAHnjgAe3bt0+Dg4Nas2aNHn/8cTU0NEx6x5LKCiXJ+N0z20mtlkgr0dpK0ZbsJG2jnVJG+2JoMq0lsdqwrVbyItKwc3X+tTOTgq32ZSsN3GolDUztllLagiv8f6eZ+1riVOvM2tCzOMbQ+aw0bON5WlQaujWnsa9mirTV2m2kqNvJ9P7nVPrYwFMtPK9/maRhL1y4UDt27FB7e7teeOEFrV69WnfeeadeffVVSdKWLVv01FNPaf/+/Wpra9Pp06e1bt26yUwBAJghJvWn9h133DHm///6r/9au3bt0pEjR7Rw4UI98cQT2rt3r1avXi1J2r17t2688UYdOXJEt95669TtNQBg2gv+DmhkZET79u1TX1+fmpqa1N7eruHhYTU3N4/+m2XLlmnx4sU6fPiw93YGBwfV29s75gcAcPmbdAF6+eWXNWfOHNXU1Ohzn/ucDhw4oPe9733q7OxUdXW16uvrx/z7hoYGdXZ2em+vtbVV+Xx+9GfRokWTPggAwPQz6QK0dOlSHT9+XEePHtV9992nDRs26LXXXgvegW3btqmnp2f0p6OjI/i2AADTx6Tbraqrq3X99ddLklauXKljx47pK1/5iu6++24NDQ2pu7t7zLugrq4uNTY2em+vpqZGNWldWACAy07R5wEVCgUNDg5q5cqVqqqq0sGDB0e3nThxQqdOnVJTU1Ox0wAALjOTege0bds2rV27VosXL9a5c+e0d+9eHTp0SN/73veUz+d17733auvWrZo/f77mzZunTZs2qampKagDzjnJaYLzWqx+/Zz/3BoN+2PcUy9iYMWqG+femPHo1r4afH33Usp5MNZlHKxLSsg+D8Y818c6fyY0Vj/0fJ1izoMZ8Z/QYF86IPA8mOBLFYTNJ2V0jKHnlljzWef6BK5p6ljrsWo9N3rOBY0LvhyDdd5hmsDXKt+6XurlGCZVgM6ePas/+qM/0pkzZ5TP57V8+XJ973vf0+/93u9Jkh599FHlcjmtX79+zImoAAC8U+KCr2KWjd7eXuXzeX285g9UmYw/s9f6S946g9j6q0JV4WcQh15YqqzeARl//V6c0/hrzfrLOXRcFu+AUv46DH4HFJqSUEbzZTZn8Nn1pV3T1DmzeIxn8dxIubCkqWCUgYB3QG+5YR0qfEs9PT2aN2+e/6YveQcBAJhCFCAAQBQUIABAFBQgAEAUYbn/JeC9HIN1+QNjmxlxbt2mJI0EfmFoxaNbvR9Wo4HV9modh3E5BusLUck+RvNyDBcu+OfMIFa/YDR95Kz7X+V1eYQol2OYJvuaxbgYc5pNGKFNOMY4KaVJKbjxp4SXYwAAYKpQgAAAUVCAAABRUIAAAFFQgAAAUVCAAABRlG0btkZGpGR826DZSmjcnJmTlJKUa+ZPhWazBWbIRclJs1qfrTTswFTj4Db0wLWRMkqnLnWqtdVqn5YFF5rcbT3Gp0lSeJQ5rcd46PM4JQvOzINMeX5Mds5LTcPmHRAAIAoKEAAgCgoQACAKChAAIAoKEAAgCgoQACCK8m3D9jBTra3Lylq3mZKGHdxqbbW+mi3aRqtlFim6aUnBWcxpta9mcSlrK5lckhsa9o+1jjGLFOXQtSkmDduYs2CsjZUyHnqMpZ4vsznr6vzj+vv94zJI0ZaKSMMOSOAnDRsAUNYoQACAKChAAIAoKEAAgCgoQACAKChAAIAoyrYN2znJaYK2aiMpWlX+Vtu0xGdzX0LTsIMTho124tC252KSokucwB28NtZ8RputlFE7eRb3fxYp2ilz5owW9iyO0Zwv9L4w5itqTqtl2kq1t8ZZ7dLWfqal2puPHeM0BKtl3pNq75z9fHsb74AAAFFQgAAAUVCAAABRUIAAAFFQgAAAUVCAAABRlG0btgpOSiZow642WgmNtsdcXa1/Kk8r4duC2xdD05BrrPTdDFKbjfkkyRnrYx1jwbo/QhOGM1hTKeUYS5xOXepx0uV/jKmJ71k8H832dWOc9dwwW7szSnw3E/8nnjNxki6hE5t3QACAKChAAIAoKEAAgCgoQACAKChAAIAoKEAAgCjKtw07l0hJMu7XZkuo0UpotVpbidbFzGmm6FqtnWbba1gartm+mtKGrtDEb2tcaKpx6Li0VntrfYITmK1U4wzmCxwnpSQpZ5H4Hjoug2Ty9LEZtFpbrxtWq7V5qkU2ie/KjX8dHh331sRzkoYNAChrFCAAQBQUIABAFBQgAEAUFCAAQBQUIABAFOXbhu1Jww5uewxsX7441miZzaBlMjgNNzQNu5ik4CxSlK1W2xjHWEaJ31nMJ9n3Y8F4rAYfYxnNV9ScRnt/btYs/3wXLni3madoBM6XOqf1/LBOUylMcMUCkYYNAChzFCAAQBQUIABAFBQgAEAUFCAAQBQUIABAFOXbhh2Shp1BMq2U0vpc4a/hmbQTh6ZvZ5UUPFIIGxe6NlkdY2BbeM56zFn7msV8RbQhm/dj6GM19HETOJ/ZLpwi+H602sn7B/zzzZntH9fXP+XzpY0NXVfSsAEA0xIFCAAQBQUIABAFBQgAEAUFCAAQBQUIABBFUQVox44dSpJEmzdvHv3dwMCAWlpadOWVV2rOnDlav369urq6it1PAMBlJvg8oGPHjunrX/+6li9fPub3W7Zs0dNPP639+/crn89r48aNWrdunZ5//vnJTeC7HEPopQqsPn8j4lwKj0fPIlbfHGccY2jcvBR+6YDpMi7GnDP+cgylPsZi7n/r+R94fqF5rk/ga1VWl1Wx7v+kcuJ9zfRyDOfPn9c999yjb3zjG7riiitGf9/T06MnnnhCX/rSl7R69WqtXLlSu3fv1o9+9CMdOXIkZCoAwGUqqAC1tLTok5/8pJqbm8f8vr29XcPDw2N+v2zZMi1evFiHDx8ubk8BAJeVSX8Et2/fPr344os6duzYuG2dnZ2qrq5WfX39mN83NDSos7NzwtsbHBzU4G+9rezt7Z3sLgEApqFJvQPq6OjQ/fffr3/8x39UbW3tlOxAa2ur8vn86M+iRYum5HYBAOVtUgWovb1dZ8+e1Qc+8AFVVlaqsrJSbW1teuyxx1RZWamGhgYNDQ2pu7t7zLiuri41NjZOeJvbtm1TT0/P6E9HR0fwwQAApo9JfQR3++236+WXXx7zu09/+tNatmyZ/uIv/kKLFi1SVVWVDh48qPXr10uSTpw4oVOnTqmpqWnC26ypqVGN0WUCALg8TaoAzZ07VzfffPOY382ePVtXXnnl6O/vvfdebd26VfPnz9e8efO0adMmNTU16dZbb53cnvkuxxB6qQKzlTAlqt66BETgJQeC49+tY7T2MzTiXuHHaMa4h15WIYNxqWNDLzmQwb5mcdkAKZvHTuiaZnGMRd3/1tpYl2MZGfGPC30eW639xnxSEccYcP9f6uUYpvx6QI8++qhyuZzWr1+vwcFBrVmzRo8//vhUTwMAmOYS59z4sz0j6u3tVT6f18dr/kCVif8voYkkoX9xG3/FSJK1RMkE79JGx1kX3TL/cvKPU84/n6y/gKyLdRXzl1PovhaMNQ1cm+A1TRsb+g44g33NYpxkH2PoYyf4L+4Sr2nq2NDnvzXOeq2y1jRwnJSyr4H3v+8d0FtuWIfck+rp6dG8efO848mCAwBEQQECAERBAQIAREEBAgBEMeVdcFNmqtOwi2lDziANO5MU5dAvfdNSdAMbOILbSadRGnYWyc0zIg07dL46fwKL+Vysq/Nuk1JeOwJfc6x9LYS+bgS+xknhj7nclfO920Y6z058e1mmYQMAUCwKEAAgCgoQACAKChAAIAoKEAAgCgoQACCK8m3D9qVhh7ZaW63EaS26oe3Eocm9pW7RLiYN28rmKqeE6YwSv83k5gyOMbM0bON+zCSdOnS+wFT71PvfOp3Auh+NcWartZXLFjhfVonvhV++6R/nyZ9zzs6lexvvgAAAUVCAAABRUIAAAFFQgAAAUVCAAABRUIAAAFGUbxu2Lw07tEWzmDTs0HRiY18zSRgus6To6TIuszkzSCfPYj9jzFlO46S00zSM15zQVOvQy24HPt6k8MugW69Vvn1NXEGyr4IuiXdAAIBIKEAAgCgoQACAKChAAIAoKEAAgCgoQACAKMq3DduThi2rfdFqlwxsQZRSWjSt9m5rzsC28EzSt4tJ0c1gzkxStItJw7bGWi2zgY+54PmKadENnbPU92NWj3EzDdvfhmyujfVaZaVhW/tqzmf3PQefFhBwlQHSsAEAZY0CBACIggIEAIiCAgQAiIICBACIggIEAIiifNuwPcxkaiuZ1mpBNMalzWmn6AYm99ZYadhTn/hddmnYWaxpMccYmBRspaFnkUwcmqIeY87ghPnQ+YznlCQV+gemfs4ySlGXMnpekYYNAJiOKEAAgCgoQACAKChAAIAoKEAAgCgoQACAKKZdG3Zwq7XVvpxVGnZwUnBgq3UGycSpY8so1TqzYzRaX7NotQ6eL/C+kOwk5eA5A1vtszjGtFMtrDkLFy745zTauwv9/f5xoaeTBLZLS9k8r3wt+qRhAwDKGgUIABAFBQgAEAUFCAAQBQUIABAFBQgAEAUFCAAQRfmeBzQyIiXj66N5qYLA/nkr/l2ScnW1/jlLfHmETC4bYMTNp85Z6lj9MrscQ2g8fiZrGnhOUtqcJb8cR4mfG1La88P//DfHWecIBY4zH291dd5tkuSs85ICXzu4HAMAYFqiAAEAoqAAAQCioAABAKKgAAEAoqAAAQCiKN82bA8zVt3TEiilxLgb4zKbM4tLDlgtmoEt4alzWvsaOqe1plmMSxsbelmFDPbVXFOr1TrtMR76WDXGZfLcCB2X1oZurKvZhhx4fwTPZ62p0WYtKfwxbo0bmfiyC1yOAQBQ1ihAAIAoKEAAgCgoQACAKChAAIAoJlWAvvCFLyhJkjE/y5YtG90+MDCglpYWXXnllZozZ47Wr1+vrq6uKd9pAMD0N+k27Jtuukn/9m//9t838Futj1u2bNHTTz+t/fv3K5/Pa+PGjVq3bp2ef/75Se+YKzi5xI37fWjib2jaq1REcnOpU5QzmE8KT2AOTeCOkfhd6mPMZL7AcUXNWepjzGC+1DlDU8YD29DT9tXPfj+RxWOn2DTsSRegyspKNTY2jvt9T0+PnnjiCe3du1erV6+WJO3evVs33nijjhw5oltvvXWyUwEALmOT/g7o9ddf14IFC/Se97xH99xzj06dOiVJam9v1/DwsJqbm0f/7bJly7R48WIdPnx46vYYAHBZmNQ7oFWrVmnPnj1aunSpzpw5o4cfflgf/ehH9corr6izs1PV1dWqr68fM6ahoUGdnZ3e2xwcHNTgbyUN9Pb2Tu4IAADT0qQK0Nq1a0f/e/ny5Vq1apWuu+46ffOb31RdytX4fFpbW/Xwww8HjQUATF9FtWHX19frve99r9544w01NjZqaGhI3d3dY/5NV1fXhN8ZvW3btm3q6ekZ/eno6ChmlwAA00RRBej8+fP62c9+pmuvvVYrV65UVVWVDh48OLr9xIkTOnXqlJqamry3UVNTo3nz5o35AQBc/ib1Edyf/dmf6Y477tB1112n06dPa/v27aqoqNCnPvUp5fN53Xvvvdq6davmz5+vefPmadOmTWpqagrrgHMFTdTHF5oUGzpOSkk1Tvw1PDRF12onzSK115pPKmJdQ48xg/sxs2MMnDOT+Yp4jAcnMFst8xk8brKYr5ix5toEJvAHz+dJps50zrcmHuecvd5vm1QB+sUvfqFPfepT+tWvfqWrr75aH/nIR3TkyBFdffXVkqRHH31UuVxO69ev1+DgoNasWaPHH398MlMAAGaIxDk3/mzPiHp7e5XP5/Wx5C5VJhOcHGW84wj96yDtr8OL78Z8g/37Y85p/ZVn/eVU4nFSEesauq8Z3I/F/HWYxZzlNK6osSV+bsi6HwPnk8rr/phWj3HPO6C33LAOuSfV09Njfq1CFhwAIAoKEAAgCgoQACCKSWfBZe3tr6Te8nZRGN8BGZ9HW9cot8ZdHGxtNz53Nuf0f/XmnPH9SInHXRwbuq6h+zr192PaNepLPWc5jStqbImfGzLvx7D5Ls5ZPvfH9HqM+78DurjdbjEouwJ07tw5SdIP9bQ00b5bx3MJ6atTOq4Ydldw+YyTwtcndM4Y92Op55wu44odG6KYx2qo6XJ/TKfHuC6+nufzee/2suuCKxQKOn36tObOnaskSdTb26tFixapo6ODk1TfgbXxY238WBs/1sZvMmvjnNO5c+e0YMEC5XL+d6Zl9w4ol8tp4cKF435PSoIfa+PH2vixNn6sjd+lro31zudtNCEAAKKgAAEAoij7AlRTU6Pt27erpsa+pPJMxNr4sTZ+rI0fa+OXxdqUXRMCAGBmKPt3QACAyxMFCAAQBQUIABAFBQgAEEVZF6CdO3fq3e9+t2pra7Vq1Sr9+Mc/jr1LUTz33HO64447tGDBAiVJoieffHLMduecHnroIV177bWqq6tTc3OzXn/99Tg7W0Ktra360Ic+pLlz5+qaa67RXXfdpRMnToz5NwMDA2ppadGVV16pOXPmaP369erq6oq0x6Wza9cuLV++fPSkwaamJn33u98d3T5T12UiO3bsUJIk2rx58+jvZvL6fOELX1CSJGN+li1bNrp9KtembAvQP//zP2vr1q3avn27XnzxRa1YsUJr1qzR2bNnY+9ayfX19WnFihXauXPnhNu/+MUv6rHHHtPXvvY1HT16VLNnz9aaNWs0MDBQ4j0trba2NrW0tOjIkSN69tlnNTw8rE984hPq6+sb/TdbtmzRU089pf3796utrU2nT5/WunXrIu51aSxcuFA7duxQe3u7XnjhBa1evVp33nmnXn31VUkzd13e6dixY/r617+u5cuXj/n9TF+fm266SWfOnBn9+eEPfzi6bUrXxpWpW265xbW0tIz+/8jIiFuwYIFrbW2NuFfxSXIHDhwY/f9CoeAaGxvdI488Mvq77u5uV1NT4/7pn/4pwh7Gc/bsWSfJtbW1OecurkNVVZXbv3//6L/593//dyfJHT58ONZuRnPFFVe4v/u7v2NdfuPcuXPuhhtucM8++6z73d/9XXf//fc753jcbN++3a1YsWLCbVO9NmX5DmhoaEjt7e1qbm4e/V0ul1Nzc7MOHz4ccc/Kz8mTJ9XZ2TlmrfL5vFatWjXj1qqnp0eSNH/+fElSe3u7hoeHx6zNsmXLtHjx4hm1NiMjI9q3b5/6+vrU1NTEuvxGS0uLPvnJT45ZB4nHjSS9/vrrWrBggd7znvfonnvu0alTpyRN/dqUXRipJL355psaGRlRQ0PDmN83NDTopz/9aaS9Kk+dnZ2SNOFavb1tJigUCtq8ebNuu+023XzzzZIurk11dbXq6+vH/NuZsjYvv/yympqaNDAwoDlz5ujAgQN63/vep+PHj8/odZGkffv26cUXX9SxY8fGbZvpj5tVq1Zpz549Wrp0qc6cOaOHH35YH/3oR/XKK69M+dqUZQECJqulpUWvvPLKmM+qZ7qlS5fq+PHj6unp0b/8y79ow4YNamtri71b0XV0dOj+++/Xs88+q9ra2ti7U3bWrl07+t/Lly/XqlWrdN111+mb3/ym6urqpnSusvwI7qqrrlJFRcW4zoquri41NjZG2qvy9PZ6zOS12rhxo77zne/oBz/4wZhLeTQ2NmpoaEjd3d1j/v1MWZvq6mpdf/31WrlypVpbW7VixQp95StfmfHr0t7errNnz+oDH/iAKisrVVlZqba2Nj322GOqrKxUQ0PDjF6fd6qvr9d73/tevfHGG1P+2CnLAlRdXa2VK1fq4MGDo78rFAo6ePCgmpqaIu5Z+VmyZIkaGxvHrFVvb6+OHj162a+Vc04bN27UgQMH9P3vf19LliwZs33lypWqqqoaszYnTpzQqVOnLvu1mUihUNDg4OCMX5fbb79dL7/8so4fPz7688EPflD33HPP6H/P5PV5p/Pnz+tnP/uZrr322ql/7AQ2SmRu3759rqamxu3Zs8e99tpr7rOf/ayrr693nZ2dsXet5M6dO+deeukl99JLLzlJ7ktf+pJ76aWX3M9//nPnnHM7duxw9fX17tvf/rb7yU9+4u688063ZMkS19/fH3nPs3Xfffe5fD7vDh065M6cOTP6c+HChdF/87nPfc4tXrzYff/733cvvPCCa2pqck1NTRH3ujQefPBB19bW5k6ePOl+8pOfuAcffNAlSeL+9V//1Tk3c9fF57e74Jyb2evzwAMPuEOHDrmTJ0+6559/3jU3N7urrrrKnT171jk3tWtTtgXIOee++tWvusWLF7vq6mp3yy23uCNHjsTepSh+8IMfOEnjfjZs2OCcu9iK/fnPf941NDS4mpoad/vtt7sTJ07E3ekSmGhNJLndu3eP/pv+/n73p3/6p+6KK65ws2bNcr//+7/vzpw5E2+nS+RP/uRP3HXXXeeqq6vd1Vdf7W6//fbR4uPczF0Xn3cWoJm8Pnfffbe79tprXXV1tXvXu97l7r77bvfGG2+Mbp/KteFyDACAKMryOyAAwOWPAgQAiIICBACIggIEAIiCAgQAiIICBACIggIEAIiCAgQAiIICBACIggIEAIiCAgQAiIICBACI4v8D59AfRnfXM+oAAAAASUVORK5CYII=", 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w49Kw58Sn75qpxVYLopGE+85Yo05HxmO+7ctmGnb8fG7YMyk5KQ3batM2ttU31TqqMlq0rbWx0oBTSjw3j7F1zlkpyr4p2lZqs9USrAJSra1xVou27zjPfbTGFTI2lVRrz8TvxH00U8395iQNGwAwI1GAAABBUIAAAEFQgAAAQVCAAABBUIAAAEGUbht2LidFE9uq3ZDRomklzBaSlGyl+ia0cMeJPNt3rXZia/8LSsMeNfbRSKe2Wq0t1jE2Ge273onWkmQd45yR+G0d44zRTm+tt3WOXzDW20hRlhJa8a1tHTzvNWfu3GDsY2aKtrWPvsnUkn8CtzXOOo6eLfpB9tF6P4pLricNGwBQyihAAIAgKEAAgCAoQACAIChAAIAgKEAAgCBKtg07qihXFE2yeVbiq9VqPGa0Ehop2pLdam22jFrtu9Z+WPMZCdM5oyU2qjLShxPapTPz58WPtdrijfZus33XSEq2Wq2thHGz7V1KSCD3S1L3TSA307CN1l7vRGMVkmrtN2dmnnFOWYnPvknRVlK6EtKprde4b8K0Na7SSIM3xyXso3XueCZ3k4YNAJiRKEAAgCAoQACAIChAAIAgKEAAgCAoQACAIEq2DduNjMpNkt5q5D3bjDbbpKTkqMyo01brt5U+a7Q+m22f1jhjO33nSxprjjNara01j3xTra1E5yRG4rOsdGKDd8K0mT5ujPNNNFbCtvomMBc78dmcz2glluykaOtyCiPx29xHz+Not4QXsI/W8beS1EnDBgDMRBQgAEAQFCAAQBAUIABAEBQgAEAQFCAAQBBTasNuaWnRP//zP+v111/X3Llz9du//dv667/+a61YsSL/M0NDQ3rggQfU2tqq4eFhrV+/Xo8//rhqa2untmVlZVI0sa3SSpiV0aLr4toFdQmt3VZbrJVAayXsGqnWstowjXFWu3Rmwfz4cVYyteSfTm0kfkejfq3NkXW0jDZTZeyjbKYhW0nBVsKwlQZupWH7jjPWO7U0bM85iz6ukDRsz6Roc019x1nH33qdJs3pmcAd26LtnGS8NbxnSp+A2tvb1dzcrI6ODj3zzDMaHR3VZz7zGQ0ODuZ/Zvv27Xrqqad08OBBtbe36/Tp09q4ceNUpgEAzAJT+gT09NNPj/v3/v37dc0116izs1O/8zu/o76+Pj3xxBM6cOCA1q1bJ0nat2+fbrjhBnV0dOjWW2+dvi0HAMxoBf0NqK+vT5K0aNEiSVJnZ6dGR0fV2NiY/5mVK1eqvr5ehw8fnvQ5hoeH1d/fP+4LAHD58y5AuVxO27Zt02233aabbrpJktTd3a3KykrV1NSM+9na2lp1d3dP+jwtLS3KZrP5r2XLlvluEgBgBvEuQM3NzXr11VfV2tpa0Abs3LlTfX19+a+urq6Cng8AMDN4hZFu2bJFP/zhD/XCCy9o6dKl+e/X1dVpZGREvb294z4F9fT0qK6ubtLnqqqqUpXR2QIAuDxNqQA557R161YdOnRIzz//vJYvXz7u8TVr1qiiokJtbW1qamqSJJ04cUKnTp1SQ0PD1LYsl5Oi3MRtuGAkM0d+qdWJadhWAq3VolphjDs3GPuY1RKZM1qmrTTsnDVfQhq22WptSWj99eGM42gPTPiwbyUpW2341pS+adieqd5mq7XVoq6EVnzfdGrfhGlrnO98SUnRvsffN2HakNp5k0aSemwadsJ6v2tKBai5uVkHDhzQD37wAy1cuDD/d51sNqu5c+cqm83q3nvv1Y4dO7Ro0SJVV1dr69atamhooAMOADDOlArQ3r17JUm/93u/N+77+/bt0x/90R9Jkh599FFlMhk1NTWNuxAVAID3m/Kv4JLMmTNHe/bs0Z49e7w3CgBw+SMLDgAQBAUIABAEBQgAEAQFCAAQhNeFqMUQVVYqiiZeD2NG1ecmXjeUf+xcfKS49ZxSwrU+CxfEPpbr7Yt9LFOTjZ/PuGYn4zlflK2On8+4jYMkRVVGVLu1Nsb1THLxx0q5+GaXyLpew2iSSbrWyTfK34y4T+HWAWnMJ9m3OUnl9gjWtXVprM1Muh2Dta3mrWGM15vsa698bzlS1NsxAAAwXShAAIAgKEAAgCAoQACAIChAAIAgKEAAgCBKtg3bjYzITZZ0nkL7rnWrAknKWO2UVsu01aI6cC72Masl0rodQ2xLpOzttGLaJbvV2hw3Et/6LitX0Ip/t27HYLVoW9uihLZgK8rfd8404v8LuR2D77Z6xvib8/neViHE7RjMfby0WxJMGGdd2mCepwnn+IeWxj7mTp2OH+dzHC/xdgx8AgIABEEBAgAEQQECAARBAQIABEEBAgAEQQECAARRsm3YUVmZomiS9r/IqJlzjTZko+05Ux2fFC1Jbii+LTJTvTD2Mau92xpnpVNH8+JTi2W1S8+bF/9YQlKyKozTxGh9N9Opy+KPoxsy0pCtNHTPpGhJcheMNfdNirba961WW6t9P4XtTJwzjTTsEhqXONYzKTqyXjcWq9XaSLTuvme1+bSLv/d67GPm69HnMgRnXErxPnwCAgAEQQECAARBAQIABEEBAgAEQQECAARBAQIABFGybdhubEwumph8bLY2+qbIDtpp2Fbrs9kWa7Q25qw0bKvV2GrRttKAjX201kaS3JiVQG48Zj2nNS6NpOiEpGAzSdw3KdpoJ08lYbqQNGzPsd5r4zvOeL2ZxzDpUgNrTuv8N5LkzRZtK/Fd8Ynv1mu87u+OG88p+zhOdzq99XzvwycgAEAQFCAAQBAUIABAEBQgAEAQFCAAQBAUIABAECXbhh2bhm2NMdqlc/0D8ePmzzef150/Hz92QfxYK4HbTLU2U50rYx8bvaE+9rGK//NW/HwZu0U3sjqtrfZVI7nXTBj2TcO2xhlJ0VKJJTdba+OZ+J2Yhu07Z5G3NY1kcslOJ3crlsc/73/+Ivax3I3XxT6W+Xn8OKvV2nqtZmpq4sdJcr/pjZ/TSsO22tALxCcgAEAQFCAAQBAUIABAEBQgAEAQFCAAQBAUIABAECXbhh2bhm20Ibq4ZFbJbhc22qylhORa3+Rio+3TStjVcHyKcPnL/xk/X/wzWuG7kuz9915zK9XaSjX2HZeUhp1GcrPvOGsfU0jRTpzTTMOOb7VOZW3SeL0ljI3ePBU/znitWq3WFjOZeiz+Mffr/2c/sZVAf9GYMyFJvRB8AgIABEEBAgAEQQECAARBAQIABEEBAgAEQQECAARRsm3YsWnYkVEzjWRenRuMn8tItJYkd/5C/Njy+DkjGa2t1rYarc1WMm1UEX84zXEJbZbeqdZGa29UGZ/qXexk6oLmvGCcG1Zys++4FNZGCpBq7Z2GHZ9qbbVaF3L8M9nq2MfMlH0juV2+rfZG+7r1+n/nB4x2eqP1OzN/Xuxj1v5fCj4BAQCCoAABAIKgAAEAgqAAAQCCoAABAIKgAAEAgijZNmznnNwkGc5WGraVTG22KCYk5VotqrKSa3PWY0YbprUtViul0WpttnYnpd36JjcbScFWa3fRk6mlhMTnIqdhFzkp+p05jVRra21806mtcdZ8QynMlzDWbDW2znFjW82EfSth3prPei0mMZ43N3DO/3mTpk3tmQEAMFCAAABBUIAAAEFQgAAAQVCAAABBUIAAAEFMqQ1779692rt3r37xi19Ikm688UY99NBD2rBhgyRpaGhIDzzwgFpbWzU8PKz169fr8ccfV21t7ZQ3LIqiyVuOjRbNaE58Um5u8LzXOElme7esVN+LRjpvuV8HvJVaa7ZoG22fSdtiJvAaib++6cTeqdZW+2pCUrCZ+FzspGjf+axxVvuyEo6j1U5sjUthW9PYzoLmNFqfM9dcFftY7u3fxM9nvB4j6xxP2kfreHgmZce/Hydc2vGuKX0CWrp0qXbv3q3Ozk699NJLWrdune6880699tprkqTt27frqaee0sGDB9Xe3q7Tp09r48aNU5kCADBLTOl/w++4445x//6rv/or7d27Vx0dHVq6dKmeeOIJHThwQOvWrZMk7du3TzfccIM6Ojp06623Tt9WAwBmPO+/AY2Njam1tVWDg4NqaGhQZ2enRkdH1djYmP+ZlStXqr6+XocPH459nuHhYfX394/7AgBc/qZcgI4fP64FCxaoqqpKX/ziF3Xo0CF99KMfVXd3tyorK1VTUzPu52tra9Xd3R37fC0tLcpms/mvZcuWTXknAAAzz5QL0IoVK3Ts2DEdOXJE9913nzZv3qyf/exn3huwc+dO9fX15b+6urq8nwsAMHNMuRWrsrJSH/7whyVJa9as0dGjR/WNb3xDd999t0ZGRtTb2zvuU1BPT4/q6upin6+qqkpVCfdrBwBcfgq+DiiXy2l4eFhr1qxRRUWF2tra8o+dOHFCp06dUkNDQ6HTAAAuM1P6BLRz505t2LBB9fX1GhgY0IEDB/T888/rxz/+sbLZrO69917t2LFDixYtUnV1tbZu3aqGhgavDrjY2zEYPelu1OhzLzNqbVJUu8U3Vt7q57f67q3Ied8Y96RbFRi84/HTuq1CnIvGmkr2sbJi7lO4PYI5n3nbCOOWChXGLUWSxprH4zIYV8hY43jkzr4dP8739WiMS7qtjCXh1REv7j3HGe9F7zOlAnT27Fn94R/+oc6cOaNsNqtVq1bpxz/+sX7/939fkvToo48qk8moqalp3IWoAAB8UOTMy1yLr7+/X9lsVusWblJ5VDnhcfPmcBbr04F147gCmJ9IzIF+n4CUM25IZ/2fkzWuEL5zprGtSad50k354ljbah1/3+No8T1vJHtbrbXxnTON+XzHpTWnJY3Xhu/7TSFitvWiG9GzQ0+qr69P1dXVscPJggMABEEBAgAEQQECAARBAQIABOF3T4AiiL0dQ8b4w6bz/ONtQlS/yWiZjazntRofPNuQo3nz4p/TaglOulWBFVW/YH78uPMX4selcDsGd8GYr5A4/rlz48f5bqvv7Rh8t9NYm8SxaexjCY1LHJvG7Tis17HxerRa9JMatNxY/PujdZmKV+u7S2h6eRefgAAAQVCAAABBUIAAAEFQgAAAQVCAAABBUIAAAEGUbBt2rItGUrAvK324AGbLZBopykOeabgjCftvJCk7K4HXSm72TRH3TtFOSEO29tF3Tt/j6Jv47Dlf4tg0jmMpjUsam0Yauu98Vop20vtYIWNjB8ZcTnKJEaN8AgIABEEBAgAEQQECAARBAQIABEEBAgAEQQECAARRsm3YbmxMLpp4i1nvhGlLJuG2upHRvmi0hUeVE28pfinjMlcuin0s9+vfxM+XVlKwlTLtm6LsmzCcQjKxlFI6tZHAXUrjQsxZSuMKmtM3Ddt6TVnvG8Ztt61xUkICt2cCf3wb+qXdqpxPQACAIChAAIAgKEAAgCAoQACAIChAAIAgKEAAgCBKtg07jtkuabRo+yZTS5IbM9oQrYRZq33RYLVa2ynKvknR9naabaHWWDPV2kh8LnYycdJY3zmtfbTSt9OYL+kc903g9h1X7DVNSkNPYx+t14a1rdalHeXGe1zS+431XmW0d1vjFDfOGc/3/qe+pJ8CAGCaUYAAAEFQgAAAQVCAAABBUIAAAEFQgAAAQcy4NmzvNNx58+LHnT9vz2m1IVspynPnxI+ztjWFVOuC0rBLKSnaN317Ju1jGmtjpC+HmDON4+g7X0FzpvH69x1XSOK3cQlL7sJQ/Li4tnBnt/2/h09AAIAgKEAAgCAoQACAIChAAIAgKEAAgCAoQACAIEq3DTuTkaKJ9dFqJTTTXo1W68Q0bN+Uad9tNebzTsMtJCnaN53a3McUEoatcQltyN5JysUe53scjflCzGmOs863NOYrYE6zRd9oX07lfcMalzTWOP7m+yNp2ACAmYgCBAAIggIEAAiCAgQACIICBAAIggIEAAiidNuwx8akaGIrn5kUa6W2eibaJo4tduLvTErDtlo7fbfVd76kpGBr7ExJw/YcJyUkvluXBcyQfbT2T0ppH+cbCfyDxmUh1nzWeWq8N0oJ74/W5R1xrdZSfGu3u7TPNnwCAgAEQQECAARBAQIABEEBAgAEQQECAARBAQIABFG6bdgxzLbHFJKiE8eaqcaeyc1pjPNNGE6a00rD9p0zjfmstOdC5kwjDduazzz+fvNJCa22vnOW0Lik13ga6+qdau17/K30bcl+fVw05rTkcjFPGPP9D+ATEAAgCAoQACAIChAAIAgKEAAgCAoQACAIChAAIIiCCtDu3bsVRZG2bduW/97Q0JCam5t15ZVXasGCBWpqalJPT0+h2wkAuMx4Xwd09OhRffvb39aqVavGfX/79u360Y9+pIMHDyqbzWrLli3auHGjXnzxxYI3VkqIKrfixj1v4yCldDsGa1wJRdwnjvXdxxIalzjWd11LaB+tcSHmLPo+ZuKvgXlnsIuf0/fWIdZ7juetI0LcViXncxsHl3Dd1bu8PgGdO3dOmzZt0ne+8x1dccUV+e/39fXpiSee0Ne+9jWtW7dOa9as0b59+/STn/xEHR0dPlMBAC5TXgWoublZn/3sZ9XY2Dju+52dnRodHR33/ZUrV6q+vl6HDx8ubEsBAJeVKf8KrrW1VS+//LKOHj064bHu7m5VVlaqpqZm3Pdra2vV3d096fMNDw9r+H0fDfv7+6e6SQCAGWhKn4C6urp0//336x/+4R80Z459+9dL1dLSomw2m/9atmzZtDwvAKC0TakAdXZ26uzZs/r4xz+u8vJylZeXq729XY899pjKy8tVW1urkZER9fb2jhvX09Ojurq6SZ9z586d6uvry391dXV57wwAYOaY0q/gbr/9dh0/fnzc9+655x6tXLlSf/7nf65ly5apoqJCbW1tampqkiSdOHFCp06dUkNDw6TPWVVVpaqE7g0AwOVnSgVo4cKFuummm8Z9b/78+bryyivz37/33nu1Y8cOLVq0SNXV1dq6dasaGhp06623Tm3LysqkaGIrnxlx7htVXnK3YzDi360Y9zQi7hPHeu6j7+0YfI9F0i0nfG+r4LuPvtuawnwh5iz6fEm3KkjjHLdarYv9mkoaa5zj1rYq7jYezri9x/tM+/2AHn30UWUyGTU1NWl4eFjr16/X448/Pt3TAABmuMg54wqsAPr7+5XNZrVu4SaVR5NckBV3AyTJ/HQQW6ml5JtV+c5pjbOkcUiMm1ElzmeN9WXN6Tuf7/GX7HPAGuu7rqU0X4g5S2m+pLEpvP7NT0DWtoY4xy0x+3jRjejZoSfV19en6urq2OFkwQEAgqAAAQCCoAABAIKgAAEAgpj2Lrhpk8tJ0cQ/cJnJxIPnYx8LkhRcQqnWvqnNkn+Krve2Wi2hKcwXYs5SGhdizlTGWcfQSKaWEpL0fdOw5xnvG+eN9w3f+ZLSsK33KiOB26v1213aZxs+AQEAgqAAAQCCoAABAIKgAAEAgqAAAQCCoAABAIIo3TbsGGaqrW8ydVJSsG8C8+jF+HG+6dS+8xWSFG1taxr7WOz5CpnTN0W7hM6bgub0XZs05vNMpk4c651O7ZuGn8I4yT4eF411tR6Ly7tzl5aDyScgAEAQFCAAQBAUIABAEBQgAEAQFCAAQBAUIABAEKXbhj02JkUTbxPrnUw9f54xzmjtVkI6rWcads6YM2Mk9/qmWpvbWUgatu/aWNvqO18hScFpzOmbol3k9PUQcxb9+CelYVvbaiRF+86ZynyFnOOe52psW7hLuAX8u/gEBAAIggIEAAiCAgQACIICBAAIggIEAAiCAgQACKJ027DLyqRoYiufdzK11UoYReammGnRnunEUXn80nsnRfuuTSFp2N5Jwb5rasxnHMeC9tEaa83pexxTON8KSnxPIw3bd019xyVcamEfR89zzpozjfMt6Rz3PVczxueUsYmXyrzzhDHf/+BTX9JPAQAwzShAAIAgKEAAgCAoQACAIChAAIAgKEAAgCBKtw07hpkwe95I7fVM0S1kbNGTon3Tl41EYymhnTyNxOfLZR99k6KLPC7EnN7jrKRoo106MfHdej2mMGexU9TTmpM0bADAjEQBAgAEQQECAARBAQIABEEBAgAEQQECAARRum3YzklyE79ttFqnkaIspZTc7JsU7JtM7JuinNacs2EfPdOpiz1f4py+25rGPl5MIX07Yc6c1b6cRqq1T9uzEt5vkuYkDRsAMJtQgAAAQVCAAABBUIAAAEFQgAAAQVCAAABBUIAAAEGU7nVAMVKJ/zei2BPH+t6OIY3bOPhG3BcSVe+5rkVfU+O8kUrs1hFFnk+Sovnz4sdeGJr2OYt+G4cCzvGMdc6lcjsGz9s/GMdQSuc4xl4j5C7tsw2fgAAAQVCAAABBUIAAAEFQgAAAQVCAAABBUIAAAEGUbht2LidFuQnfTmonjZMYx57CWO84dt9bFfjGuBcQVe+9rWmsjW/cvGRGzqcyZxpr4zmflHDupLGPxR6XdI5bx9+6BYTveWPwns9os06c06fVehrwCQgAEAQFCAAQBAUIABAEBQgAEAQFCAAQxJQK0Fe+8hVFUTTua+XKlfnHh4aG1NzcrCuvvFILFixQU1OTenp6pn2jAQAz35TbsG+88Ub927/9238/Qfl/P8X27dv1ox/9SAcPHlQ2m9WWLVu0ceNGvfjii1PfskxGiibWR+/03XlGMu25QXNTZkzibwrjQsxZcvvom07su62+83mOCzFnKY1LHFtKx3FsLH5chf12nsa2xre+x7fuv9+UC1B5ebnq6uomfL+vr09PPPGEDhw4oHXr1kmS9u3bpxtuuEEdHR269dZbpzoVAOAyNuW/Ab3xxhtasmSJrrvuOm3atEmnTp2SJHV2dmp0dFSNjY35n125cqXq6+t1+PDh6dtiAMBlYUqfgNauXav9+/drxYoVOnPmjB5++GF96lOf0quvvqru7m5VVlaqpqZm3Jja2lp1d3fHPufw8LCG33fFcH9//9T2AAAwI02pAG3YsCH/36tWrdLatWt17bXX6sknn9TchDsOxmlpadHDDz/sNRYAMHMV1IZdU1Ojj3zkI3rzzTdVV1enkZER9fb2jvuZnp6eSf9m9J6dO3eqr68v/9XV1VXIJgEAZoiCCtC5c+f01ltvafHixVqzZo0qKirU1taWf/zEiRM6deqUGhoaYp+jqqpK1dXV474AAJe/Kf0K7ktf+pLuuOMOXXvttTp9+rR27dqlsrIyff7zn1c2m9W9996rHTt2aNGiRaqurtbWrVvV0NDg1QHnRkblJuvky01MyM6zkmLPX/Aa9962xCqlxN80xoWY0zqOae2jbxpyGvvom4bsOS5xrLWPaWxrsedLGltKx9GQ2jlunTvOTe37HzClAvSrX/1Kn//85/XrX/9aV199tT75yU+qo6NDV199tSTp0UcfVSaTUVNTk4aHh7V+/Xo9/vjjU5kCADBLRM5dYqkqkv7+fmWzWX26vEnl0cQLQKNyo2ameN+KWMa9Usz/C5gp40LN6aPY84Was9hmw3HEtLroRvTs+Vb19fWZf1YhCw4AEAQFCAAQBAUIABDElLPg0vben6Quusk7OiJndMG5EPXU9/fVM2VcqDl9hPjbwWz4e8VsOI6YTu+9fye1GJRcARoYGJAk/e+x/zX5D/h1KAIAimxgYEDZbDb28ZLrgsvlcjp9+rQWLlyoKIrU39+vZcuWqauri4tUP4C1icfaxGNt4rE28aayNs45DQwMaMmSJcoY3ckl9wkok8lo6dKlE75PSkI81iYeaxOPtYnH2sS71LWxPvm8hyYEAEAQFCAAQBAlX4Cqqqq0a9cuVVXZt1SejVibeKxNPNYmHmsTL421KbkmBADA7FDyn4AAAJcnChAAIAgKEAAgCAoQACCIki5Ae/bs0Yc+9CHNmTNHa9eu1X/8x3+E3qQgXnjhBd1xxx1asmSJoijS97///XGPO+f00EMPafHixZo7d64aGxv1xhtvhNnYImppadEnPvEJLVy4UNdcc43uuusunThxYtzPDA0Nqbm5WVdeeaUWLFigpqYm9fT0BNri4tm7d69WrVqVv2iwoaFB//Iv/5J/fLauy2R2796tKIq0bdu2/Pdm8/p85StfURRF475WrlyZf3w616ZkC9A//uM/aseOHdq1a5defvllrV69WuvXr9fZs2dDb1rRDQ4OavXq1dqzZ8+kj3/1q1/VY489pm9961s6cuSI5s+fr/Xr12toaKjIW1pc7e3tam5uVkdHh5555hmNjo7qM5/5jAYHB/M/s337dj311FM6ePCg2tvbdfr0aW3cuDHgVhfH0qVLtXv3bnV2duqll17SunXrdOedd+q1116TNHvX5YOOHj2qb3/721q1atW478/29bnxxht15syZ/Ne///u/5x+b1rVxJeqWW25xzc3N+X+PjY25JUuWuJaWloBbFZ4kd+jQofy/c7mcq6urc4888kj+e729va6qqsp973vfC7CF4Zw9e9ZJcu3t7c65d9ahoqLCHTx4MP8zP//5z50kd/jw4VCbGcwVV1zhvvvd77Iu7xoYGHDXX3+9e+aZZ9zv/u7vuvvvv985x3mza9cut3r16kkfm+61KclPQCMjI+rs7FRjY2P+e5lMRo2NjTp8+HDALSs9J0+eVHd397i1ymazWrt27axbq76+PknSokWLJEmdnZ0aHR0dtzYrV65UfX39rFqbsbExtba2anBwUA0NDazLu5qbm/XZz3523DpInDeS9MYbb2jJkiW67rrrtGnTJp06dUrS9K9NyYWRStLbb7+tsbEx1dbWjvt+bW2tXn/99UBbVZq6u7sladK1eu+x2SCXy2nbtm267bbbdNNNN0l6Z20qKytVU1Mz7mdny9ocP35cDQ0NGhoa0oIFC3To0CF99KMf1bFjx2b1ukhSa2urXn75ZR09enTCY7P9vFm7dq3279+vFStW6MyZM3r44Yf1qU99Sq+++uq0r01JFiBgqpqbm/Xqq6+O+131bLdixQodO3ZMfX19+qd/+idt3rxZ7e3toTcruK6uLt1///165plnNGfOnNCbU3I2bNiQ/+9Vq1Zp7dq1uvbaa/Xkk09q7ty50zpXSf4K7qqrrlJZWdmEzoqenh7V1dUF2qrS9N56zOa12rJli374wx/queeeG3crj7q6Oo2MjKi3t3fcz8+WtamsrNSHP/xhrVmzRi0tLVq9erW+8Y1vzPp16ezs1NmzZ/Xxj39c5eXlKi8vV3t7ux577DGVl5ertrZ2Vq/PB9XU1OgjH/mI3nzzzWk/d0qyAFVWVmrNmjVqa2vLfy+Xy6mtrU0NDQ0Bt6z0LF++XHV1dePWqr+/X0eOHLns18o5py1btujQoUN69tlntXz58nGPr1mzRhUVFePW5sSJEzp16tRlvzaTyeVyGh4envXrcvvtt+v48eM6duxY/uvmm2/Wpk2b8v89m9fng86dO6e33npLixcvnv5zx7NRInWtra2uqqrK7d+/3/3sZz9zX/jCF1xNTY3r7u4OvWlFNzAw4F555RX3yiuvOEnua1/7mnvllVfcL3/5S+ecc7t373Y1NTXuBz/4gfvpT3/q7rzzTrd8+XJ34cKFwFuervvuu89ls1n3/PPPuzNnzuS/zp8/n/+ZL37xi66+vt49++yz7qWXXnINDQ2uoaEh4FYXx4MPPuja29vdyZMn3U9/+lP34IMPuiiK3L/+678652bvusR5fxecc7N7fR544AH3/PPPu5MnT7oXX3zRNTY2uquuusqdPXvWOTe9a1OyBcg55775zW+6+vp6V1lZ6W655RbX0dERepOCeO6555ykCV+bN292zr3Tiv3lL3/Z1dbWuqqqKnf77be7EydOhN3oIphsTSS5ffv25X/mwoUL7k//9E/dFVdc4ebNm+f+4A/+wJ05cybcRhfJH//xH7trr73WVVZWuquvvtrdfvvt+eLj3OxdlzgfLECzeX3uvvtut3jxYldZWel+67d+y919993uzTffzD8+nWvD7RgAAEGU5N+AAACXPwoQACAIChAAIAgKEAAgCAoQACAIChAAIAgKEAAgCAoQACAIChAAIAgKEAAgCAoQACAIChAAIIj/D/54vfs2AUSkAAAAAElFTkSuQmCC", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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", 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TOp1WTU2N7/0CfQeUTqclSTNnzpQkdXV1aXh4WM3Nzbn7LFq0SA0NDers7BzzMTKZjPr6+kZtAICJzzoBZbNZrVu3TpdcconOO+88SVIqlVJ1dbVqa2tH3beurk6pVGrMx2lra1Mymcxt8+fPt50SAKCMWCeg1tZW7d+/X9u2bQs0gY0bNyqdTue27u7uQI8HACgPVs1I16xZoxdeeEFvvvmm5s2bl7u9vr5eQ0ND6u3tHXUU1NPTo/r6+jEfK5FIKJEo0Xc9AIDIKCgBeZ6ntWvXaseOHdq5c6cWLlw4an9jY6OqqqrU3t6ulpYWSdKBAwd06NAhNTU1FTYzv/JZ0xeCpi/SgpQwZg1jVlT477PtMm2KC2O8fGFhdPwOQ1hf0EapG7Jt1+a8hSaGfcZCkxDiTCZKN2wXBQolLRgZ35oVlIBaW1u1detWPffcc5oxY0bue51kMqmpU6cqmUxq9erV2rBhg2bOnKmamhqtXbtWTU1N46qAAwBMHgUloMcee0yS9L3vfW/U7U899ZR+/OMfS5IefPBBxeNxtbS0KJPJaPny5dqyZUtRJgsAmDgK/hNcPlOmTNHmzZu1efNm60kBACY+esEBAJwgAQEAnCABAQCcIAEBAJywOhG1JGzaj5tq2U3NBvM1zawyLJPpvKRKwzk7prhSjzcZmpEGEaVGnbZx+c67sY2NUlyQZqRReh1NgpzrFsZc/T7jYVyOAQCAYiEBAQCcIAEBAJwgAQEAnCABAQCcIAEBAJyIbhl21pM0Rimf7SUXvADli8PH/feZShRLHXd8xH+fSZBLKti21S+1sMpXJ0KcizHLJS6IiJV2e1984R960kl2Ywa8HANHQAAAJ0hAAAAnSEAAACdIQAAAJ0hAAAAnSEAAACeiW4btx1RqaNsN29QpOl+sqeurbXdq25JROlPbxQWJLXWc7fvYFBckNkpxYb3+YZRwh/C+Od79qXHIynnfyjergsekGzYAoCyRgAAATpCAAABOkIAAAE6QgAAATpCAAABORLcMOx4bu6zY1PE5jE7RkjRk6E5dYSi1tu1qbSrDjAcoJ7flopOwHzpFFz8urDFNpbhhxJkEWZt8JdwRYV1m7RBHQAAAJ0hAAAAnSEAAACdIQAAAJ0hAAAAnSEAAACeiW4ad9SSNUf5o6jBtKt80ddjNV6KdqPbfF0ZXX8vn6E3xn2csM+z/mOVksnfDNlXa28bli1WZrE1Ir7837P/ZiVX7f+a8wYx/3JSEeT6+D2p4HiN5fo5VGn7c265rQBwBAQCcIAEBAJwgAQEAnCABAQCcIAEBAJwgAQEAnIhuGbZfN2xT+bKxDDVAp2hTV2sT05C2nbsNcbHBofxzKrZSd8qeKJ2ioxSXLzZK3akdrE2sqsouznT6holt921TZ/4gjxtiiTZHQAAAJ0hAAAAnSEAAACdIQAAAJ0hAAAAnSEAAACeiW4bt1w3b1GE6rG7YtmPaKnVpcxBR6mocpBuybefyiRAX1pi2r0epxwsSa/tZHesUk6+NuOlM7QJHQAAAJ0hAAAAnSEAAACdIQAAAJ0hAAAAnSEAAACeiW4YdpW7Ylt2prbvPhlFqGVb5Zrl0Nc4XN9E7Puc7XSBK3bCj9FpIUtzwe7rtZ9z29A3b8aTSl5qPA0dAAAAnSEAAACdIQAAAJ0hAAAAnSEAAACdIQAAAJwpKQI899pgWL16smpoa1dTUqKmpSS+99FJu/+DgoFpbWzVr1ixNnz5dLS0t6unpsZtZ1ht7i8XstnjcfwsSa2I7Zql5nnmzjS113Nel+2NtE+U5hhFXTnMNIy5oabPfNjLiv4UxXr7N9nFDVFACmjdvnjZt2qSuri69/fbbuvzyy3XNNdfo/ffflyStX79ezz//vLZv366Ojg4dPnxYK1euDGXiAIDyFvO8IOlfmjlzpu6//35dd911mj17trZu3arrrrtOkvThhx/q7LPPVmdnpy6++OJxPV5fX5+SyaSa592qyniisMmEda0YF9enKbZyev6lvo6MizFLHZfvN9kovY4uPm9h/KZ//Lj/vkpDDwBHJ4UW0/FsRq99+pjS6bRqamp872f9HdDIyIi2bdumgYEBNTU1qaurS8PDw2pubs7dZ9GiRWpoaFBnZ6fv42QyGfX19Y3aAAATX8EJaN++fZo+fboSiYRuueUW7dixQ+ecc45SqZSqq6tVW1s76v51dXVKpVK+j9fW1qZkMpnb5s+fX/CTAACUn4IT0FlnnaW9e/dq9+7duvXWW7Vq1Sp98MEH1hPYuHGj0ul0buvu7rZ+LABA+Si4GWl1dbVOP/10SVJjY6P27Nmjhx9+WNdff72GhobU29s76iiop6dH9fX1vo+XSCSUSBT4XQ8AoOwFPg8om80qk8mosbFRVVVVam9vz+07cOCADh06pKampqDDAAAmmIKOgDZu3KgVK1aooaFB/f392rp1q3bu3KlXXnlFyWRSq1ev1oYNGzRz5kzV1NRo7dq1ampqGncF3CjFvhxDkKqzUles2XLRbr1cLjkQ1nO0HbPUcflE6XWMUlwQpko3E1fnAvoxzWesn9FfBY1r2IJW6OjRo/rRj36kI0eOKJlMavHixXrllVf0/e9/X5L04IMPKh6Pq6WlRZlMRsuXL9eWLVsKGQIAMEkEPg+o2HLnATXcNvZ5QLZHQC6U+jeZCF5wCkAZKPIR0PFsRq8d2hLeeUAAAARBAgIAOEECAgA4QQICADhhWSdYAllP0hhfjJm+EJsoX6aXumDARaPGqDUjLZemomHEuRiz1A1X873HSz3mRGm46ndagOl0gf/CERAAwAkSEADACRIQAMAJEhAAwAkSEADACRIQAMCJ6JZh+7Ht9xakT1ypS7/L5TGDsOqwKylPNbXVeEFio9TV2nY8F2NGrRt2Oc3VNi6C/SA5AgIAOEECAgA4QQICADhBAgIAOEECAgA4QQICADhRfmXYtuKGXBukRNs21roM2TCeiy66YXSnNpVaG+ca4Dnaxpa647PtPIOU6IYxZqTGCxAbpdMbyvA5cgQEAHCCBAQAcIIEBABwggQEAHCCBAQAcIIEBABwIrpl2PHY2OXI1iW6DtiWL+brXFzs8aLWKdp2vCBxpe5qHaUu2i7GjNJ4YcYWW1jl0raxvqeMjO/xOAICADhBAgIAOEECAgA4QQICADhBAgIAOEECAgA4Ed0y7KwnaYySw6yhDtu243WQTsFhdJiNUlyQWNuOz6XuoiyVvqt1qeOCvMdL/TqW/H0TILZc4oLEVlb47/N7PcZ5KglHQAAAJ0hAAAAnSEAAACdIQAAAJ0hAAAAnSEAAACeiW4bt1w3bNme66BQ70ePCGtO3w67M3dCDdFEul67WdMMu/nhhxZZLXL5YU+l7QBwBAQCcIAEBAJwgAQEAnCABAQCcIAEBAJwgAQEAnIhuGTbdsIsbZ9thOMiYtt2pTdOJ2nOcCHEuxoxSXFhj2ir1ePnGNH2uxtn12g9HQAAAJ0hAAAAnSEAAACdIQAAAJ0hAAAAnSEAAACeiW4ZNN+zixtFF2W68ILHlEpcv1rY7ue14YcQFEcaYQU4ZCIPt58r3vTG+NeMICADgBAkIAOAECQgA4AQJCADgBAkIAOAECQgA4ESgBLRp0ybFYjGtW7cud9vg4KBaW1s1a9YsTZ8+XS0tLerp6Qk6TwDABGN9HtCePXv0xBNPaPHixaNuX79+vV588UVt375dyWRSa9as0cqVK/XWW28Fnqyk6F2Owbaev9SXVQjSqr7UY5bTJScmwuufb0zby2pE6XIM+ZjOdQrjdTQ9ZjldcsLvHKFxXqbB6gjo2LFjuvHGG/Xb3/5WJ598cu72dDqtJ598Ug888IAuv/xyNTY26qmnntLf//537dq1y2YoAMAEZZWAWltbddVVV6m5uXnU7V1dXRoeHh51+6JFi9TQ0KDOzs5gMwUATCgF/wlu27Zteuedd7Rnz54T9qVSKVVXV6u2tnbU7XV1dUqlUmM+XiaTUSaTyf2/r6+v0CkBAMpQQUdA3d3duv322/XHP/5RU6ZMKcoE2tralEwmc9v8+fOL8rgAgGgrKAF1dXXp6NGjuuCCC1RZWanKykp1dHTokUceUWVlperq6jQ0NKTe3t5RcT09Paqvrx/zMTdu3Kh0Op3buru7rZ8MAKB8FPQnuCuuuEL79u0bddtNN92kRYsW6Ze//KXmz5+vqqoqtbe3q6WlRZJ04MABHTp0SE1NTWM+ZiKRUCKRsJw+AKBcFZSAZsyYofPOO2/UbdOmTdOsWbNyt69evVobNmzQzJkzVVNTo7Vr16qpqUkXX3xxcWZsKrU2mSiXHLCNC6Olfr4xSx3HJSfsxnMxZtTiJsNzDCs2gKJfD+jBBx9UPB5XS0uLMpmMli9fri1bthR7GABAmYt5Xr6zl0qrr69PyWRSzQ23qTI+xp/mxnmC04RlPJKxuXBUnrigsQAmnePZjF779DGl02nV1NT43o9ecAAAJ0hAAAAnSEAAACdIQAAAJ4peBVc0WU/SGF9wR60bdqk7/ppKpsPoaBwktly6aLsYM0pxLsaMUpwUXpdxG1F7j1cZ0sTxEfOYeXAEBABwggQEAHCCBAQAcIIEBABwggQEAHCCBAQAcCK6ZdjxmE8Psoh1wy6XuCDdfm1jJ0KHYRdj8hxLHxdWl3EbUXv9TaXWvn0ixzcXjoAAAE6QgAAATpCAAABOkIAAAE6QgAAATpCAAABORLcM289k7xRs27XXNs7FmHTDLm2cizGjFBckttSfjSCf4zDW1a983VTW/l84AgIAOEECAgA4QQICADhBAgIAOEECAgA4QQICADhRfmXYUesUaxvn20VWUp5qSqvxXHTDNimXuHyxpX4dw4hzMWa5xOWLLXWn+LC6dtvG0g0bAFCOSEAAACdIQAAAJ0hAAAAnSEAAACdIQAAAJ8qvDDtrqG2NG/KpbUdbKZzOzaYyxTDiXHTRtZ1rOXVDNi1dlJ7jZO+GHUS5PEcXHd/phg0AKEckIACAEyQgAIATJCAAgBMkIACAEyQgAIAT5VeGbSq1NrHtMJsvNowxoxSXT6nHLHVnahdjRq3j80SIc6Gc1oZu2ACAyYQEBABwggQEAHCCBAQAcIIEBABwggQEAHCCBAQAcKL8zgMKo1V5kMsx2F7moFziXIxZ8ktcKMD6lLitfhhrmm/MUl86IGrvcdvzuUq9NuO8BELBY3I5BgDAREMCAgA4QQICADhBAgIAOEECAgA4QQICADhRfmXYtmWIE6VVvYvLMZT6sgou4ibDc4zSmOX0HjeJ0uU48gljrlyOAQBQjkhAAAAnSEAAACdIQAAAJ0hAAAAnCkpA99xzj2Kx2Kht0aJFuf2Dg4NqbW3VrFmzNH36dLW0tKinp6fokwYAlL+Cy7DPPfdcvfbaa/95gMr/PMT69ev14osvavv27Uomk1qzZo1Wrlypt956qzizlew7/prKt/N1SjbtN5UvhtG5udSdiYPE2q5NqcfLN6aJqXw3Ss+xnLphh1GGXE7vcRNjXPl1wy44AVVWVqq+vv6E29PptJ588klt3bpVl19+uSTpqaee0tlnn61du3bp4osvLnQoAMAEVvB3QB999JHmzp2r0047TTfeeKMOHTokSerq6tLw8LCam5tz9120aJEaGhrU2dlZvBkDACaEgo6Ali1bpqefflpnnXWWjhw5onvvvVeXXXaZ9u/fr1QqperqatXW1o6KqaurUyqV8n3MTCajTCaT+39fX19hzwAAUJYKSkArVqzI/Xvx4sVatmyZFixYoGeeeUZTp061mkBbW5vuvfdeq1gAQPkKVIZdW1urM888Ux9//LHq6+s1NDSk3t7eUffp6ekZ8zujr23cuFHpdDq3dXd3B5kSAKBMBEpAx44d0yeffKI5c+aosbFRVVVVam9vz+0/cOCADh06pKamJt/HSCQSqqmpGbUBACa+gv4E9/Of/1xXX321FixYoMOHD+vuu+9WRUWFbrjhBiWTSa1evVobNmzQzJkzVVNTo7Vr16qpqcmuAi4WG7v8z1RObZKvDNPEdsxxdoQ9McwQZ+poaztevk64YYxZNnFBlNNzDOG9E8p71fA5DuV9qtI/RxPTzzHjXPKw/hnnZ3xzKSgBffrpp7rhhhv073//W7Nnz9all16qXbt2afbs2ZKkBx98UPF4XC0tLcpkMlq+fLm2bNlS+NwBABNezPOCHBoUX19fn5LJpJoXtKoynjjxDtGabjisf8sLYbywxsTEEKX3aljvUxdjlrnj2YxeO7RF6XTa+LUKveAAAE6QgAAATpCAAABOFNwLLmxffyV1PDvkd4cSzsYR43cyDr4DclJBhrIQqfeqg++A+GyM6euf3/lKDCKXgPr7+yVJO7t/63gmAIAg+vv7lUwmffdHrgoum83q8OHDmjFjhmKxmPr6+jR//nx1d3dzkuo3sDb+WBt/rI0/1sZfIWvjeZ76+/s1d+5cxeP+3/RE7ggoHo9r3rx5J9xOlwR/rI0/1sYfa+OPtfE33rUxHfl8jSIEAIATJCAAgBORT0CJREJ33323EokxuiJMcqyNP9bGH2vjj7XxF8baRK4IAQAwOUT+CAgAMDGRgAAATpCAAABOkIAAAE5EOgFt3rxZ3/72tzVlyhQtW7ZM//jHP1xPyYk333xTV199tebOnatYLKZnn3121H7P83TXXXdpzpw5mjp1qpqbm/XRRx+5mWwJtbW16aKLLtKMGTN06qmn6tprr9WBAwdG3WdwcFCtra2aNWuWpk+frpaWFvX09Diacek89thjWrx4ce6kwaamJr300ku5/ZN1XcayadMmxWIxrVu3LnfbZF6fe+65R7FYbNS2aNGi3P5irk1kE9Cf//xnbdiwQXfffbfeeecdLVmyRMuXL9fRo0ddT63kBgYGtGTJEm3evHnM/ffdd58eeeQRPf7449q9e7emTZum5cuXa3BwsMQzLa2Ojg61trZq165devXVVzU8PKwrr7xSAwMDufusX79ezz//vLZv366Ojg4dPnxYK1eudDjr0pg3b542bdqkrq4uvf3227r88st1zTXX6P3335c0edflm/bs2aMnnnhCixcvHnX7ZF+fc889V0eOHMltf/vb33L7iro2XkQtXbrUa21tzf1/ZGTEmzt3rtfW1uZwVu5J8nbs2JH7fzab9err6737778/d1tvb6+XSCS8P/3pTw5m6M7Ro0c9SV5HR4fneV+tQ1VVlbd9+/bcff75z396krzOzk5X03Tm5JNP9n73u9+xLv+nv7/fO+OMM7xXX33V++53v+vdfvvtnufxvrn77ru9JUuWjLmv2GsTySOgoaEhdXV1qbm5OXdbPB5Xc3OzOjs7Hc4seg4ePKhUKjVqrZLJpJYtWzbp1iqdTkuSZs6cKUnq6urS8PDwqLVZtGiRGhoaJtXajIyMaNu2bRoYGFBTUxPr8n9aW1t11VVXjVoHifeNJH300UeaO3euTjvtNN144406dOiQpOKvTeSakUrS559/rpGREdXV1Y26va6uTh9++KGjWUVTKpWSpDHX6ut9k0E2m9W6det0ySWX6LzzzpP01dpUV1ertrZ21H0ny9rs27dPTU1NGhwc1PTp07Vjxw6dc8452rt376ReF0natm2b3nnnHe3Zs+eEfZP9fbNs2TI9/fTTOuuss3TkyBHde++9uuyyy7R///6ir00kExBQqNbWVu3fv3/U36onu7POOkt79+5VOp3WX/7yF61atUodHR2up+Vcd3e3br/9dr366quaMmWK6+lEzooVK3L/Xrx4sZYtW6YFCxbomWee0dSpU4s6ViT/BHfKKaeooqLihMqKnp4e1dfXO5pVNH29HpN5rdasWaMXXnhBb7zxxqhLedTX12toaEi9vb2j7j9Z1qa6ulqnn366Ghsb1dbWpiVLlujhhx+e9OvS1dWlo0eP6oILLlBlZaUqKyvV0dGhRx55RJWVlaqrq5vU6/NNtbW1OvPMM/Xxxx8X/b0TyQRUXV2txsZGtbe3527LZrNqb29XU1OTw5lFz8KFC1VfXz9qrfr6+rR79+4Jv1ae52nNmjXasWOHXn/9dS1cuHDU/sbGRlVVVY1amwMHDujQoUMTfm3Gks1mlclkJv26XHHFFdq3b5/27t2b2y688ELdeOONuX9P5vX5pmPHjumTTz7RnDlziv/esSyUCN22bdu8RCLhPf30094HH3zg3XzzzV5tba2XSqVcT63k+vv7vXfffdd79913PUneAw884L377rvev/71L8/zPG/Tpk1ebW2t99xzz3nvvfeed80113gLFy70vvzyS8czD9ett97qJZNJb+fOnd6RI0dy2xdffJG7zy233OI1NDR4r7/+uvf22297TU1NXlNTk8NZl8Ydd9zhdXR0eAcPHvTee+8974477vBisZj317/+1fO8ybsufv67Cs7zJvf6/OxnP/N27tzpHTx40Hvrrbe85uZm75RTTvGOHj3qeV5x1yayCcjzPO/RRx/1GhoavOrqam/p0qXerl27XE/JiTfeeMOTdMK2atUqz/O+KsW+8847vbq6Oi+RSHhXXHGFd+DAAbeTLoGx1kSS99RTT+Xu8+WXX3q33Xabd/LJJ3snnXSS94Mf/MA7cuSIu0mXyE9+8hNvwYIFXnV1tTd79mzviiuuyCUfz5u86+LnmwloMq/P9ddf782ZM8errq72vvWtb3nXX3+99/HHH+f2F3NtuBwDAMCJSH4HBACY+EhAAAAnSEAAACdIQAAAJ0hAAAAnSEAAACdIQAAAJ0hAAAAnSEAAACdIQAAAJ0hAAAAnSEAAACf+P6xbpA/HFrIuAAAAAElFTkSuQmCC", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#plt.imshow(outputs[0][0].cpu().detach().numpy())\n", + "import matplotlib.pyplot as plt\n", + "for i in range(0, 17):\n", + " plt.imshow(outputs[0][i].cpu().detach().numpy())\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "89ae76ec-92b7-4773-8893-fa1028f1c0a2", + "metadata": {}, + "outputs": [], + "source": [ + "#plt.imshow(outputs[0][0].cpu().detach().numpy())\n", + "import matplotlib.pyplot as plt\n", + "plt.imshow(gt_heatmaps[0][5].cpu().detach().numpy())\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "d55e9df0-d46a-445a-8032-6ff5bd566ea7", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "d55944fe-f7bd-463c-bda9-848d3ac29275", + "metadata": {}, + "source": [ + "## convert keypoint" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "83242f5c-71ae-4c95-a8fa-d385904d00d7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor([[[0., 0., 2.],\n", + " [0., 0., 2.],\n", + " [0., 0., 2.],\n", + " [0., 0., 2.],\n", + " [0., 0., 2.],\n", + " [0., 0., 2.],\n", + " [0., 0., 2.],\n", + " [0., 0., 2.],\n", + " [0., 0., 1.],\n", + " [0., 0., 1.],\n", + " [0., 0., 0.],\n", + " [0., 0., 1.],\n", + " [0., 0., 1.],\n", + " [0., 0., 0.],\n", + " [0., 0., 0.],\n", + " [0., 0., 0.],\n", + " [0., 0., 0.]],\n", + "\n", + " [[0., 0., 2.],\n", + " [0., 0., 2.],\n", + " [0., 0., 2.],\n", + " [0., 0., 2.],\n", + " [0., 0., 0.],\n", + " [0., 0., 2.],\n", + " [0., 0., 2.],\n", + " [0., 0., 2.],\n", + " [0., 0., 2.],\n", + " [0., 0., 2.],\n", + " [0., 0., 2.],\n", + " [0., 0., 2.],\n", + " [0., 0., 2.],\n", + " [0., 0., 2.],\n", + " [0., 0., 2.],\n", + " [0., 0., 2.],\n", + " [0., 0., 2.]],\n", + "\n", + " [[0., 0., 0.],\n", + " [0., 0., 0.],\n", + " [0., 0., 0.],\n", + " [0., 0., 0.],\n", + " [0., 0., 0.],\n", + " [0., 0., 2.],\n", + " [0., 0., 2.],\n", + " [0., 0., 0.],\n", + " [0., 0., 2.],\n", + " [0., 0., 0.],\n", + " [0., 0., 2.],\n", + " [0., 0., 0.],\n", + " [0., 0., 0.],\n", + " [0., 0., 0.],\n", + " [0., 0., 0.],\n", + " [0., 0., 0.],\n", + " [0., 0., 0.]],\n", + "\n", + " [[0., 0., 2.],\n", + " [0., 0., 2.],\n", + " [0., 0., 2.],\n", + " [0., 0., 2.],\n", + " [0., 0., 2.],\n", + " [0., 0., 2.],\n", + " [0., 0., 2.],\n", + " [0., 0., 2.],\n", + " [0., 0., 2.],\n", + " [0., 0., 2.],\n", + " [0., 0., 2.],\n", + " [0., 0., 2.],\n", + " [0., 0., 2.],\n", + " [0., 0., 2.],\n", + " [0., 0., 2.],\n", + " [0., 0., 2.],\n", + " [0., 0., 2.]]])\n" + ] + } + ], + "source": [ + "import torch\n", + "\n", + "\n", + "# Original and target sizes\n", + "original_size = (208, 208) # Replace with actual dimensions\n", + "target_size = (52, 52)\n", + "\n", + "# Resizing function\n", + "def resize_keypoints_new(keypoints, original_size, target_size):\n", + " original_height, original_width = original_size\n", + " target_height, target_width = target_size\n", + " \n", + " scale_x = int(target_width / original_width)\n", + " scale_y = int(target_height / original_height)\n", + " \n", + " resized_keypoints = keypoints.clone()\n", + " resized_keypoints[..., 0] *= scale_x\n", + " resized_keypoints[..., 1] *= scale_y\n", + " \n", + " return resized_keypoints\n", + "\n", + "# Resized keypoints\n", + "resized_keypoints = resize_keypoints_new(keypoints, original_size, target_size)\n", + "print(resized_keypoints)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "718c212b-ebb5-4ddb-8b7c-88537ea6f85f", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9f4ef8c2-e3d2-4e7d-8388-d4b4ddc18587", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "e2e1b2cd-c9ce-4149-a89c-ddd210196fb7", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#plt.imshow(outputs[0][0].cpu().detach().numpy())\n", + "import matplotlib.pyplot as plt\n", + "plt.imshow(gt_heatmaps[0][2].cpu().detach().numpy())\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c9f12e02-03b0-47bb-85fb-d9723e50e0e5", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "3b414522-0a5e-4a11-83e6-20aa0fc04d2b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([4, 3, 208, 208])" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "images.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "5dc276a4-7c8b-4972-90d0-e3c77fb9c199", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([4, 17, 52, 52])" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gt_heatmaps.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "b1c06c2b-8708-4a24-9abe-33901f646d90", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch [1/10]: 100%|██████████████████████████████████████████████████| 2751/2751 [03:28<00:00, 13.18batch/s, loss=4.61]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch [1/10], Loss: 4.6097\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch [2/10]: 100%|██████████████████████████████████████████████████| 2751/2751 [03:32<00:00, 12.96batch/s, loss=3.91]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch [2/10], Loss: 3.9105\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch [3/10]: 100%|██████████████████████████████████████████████████| 2751/2751 [03:28<00:00, 13.17batch/s, loss=3.79]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch [3/10], Loss: 3.7892\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch [4/10]: 100%|███████████████████████████████████████████████████| 2751/2751 [03:30<00:00, 13.08batch/s, loss=3.7]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch [4/10], Loss: 3.7024\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch [5/10]: 100%|██████████████████████████████████████████████████| 2751/2751 [03:30<00:00, 13.09batch/s, loss=3.63]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epoch [5/10], Loss: 3.6335\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epoch [6/10]: 1%|▌ | 30/2751 [00:02<03:28, 13.04batch/s, loss=3.77]\n", + "\n", + "KeyboardInterrupt\n", + "\n" + ] + } + ], + "source": [ + "import torch\n", + "import torch.optim as optim\n", + "from torch.utils.data import DataLoader\n", + "from tqdm import tqdm # Import tqdm\n", + "\n", + "optimizer = optim.Adam(model.parameters(), lr=1e-4)\n", + "criterion = torch.nn.MSELoss()\n", + "\n", + "# Define training loop\n", + "num_epochs = 10 # Number of epochs\n", + "for epoch in range(num_epochs):\n", + " model.train() # Set model to training mode\n", + " running_loss = 0.0\n", + "\n", + " # Wrap the DataLoader with tqdm to show the progress\n", + " with tqdm(dataloader, desc=f\"Epoch [{epoch + 1}/{num_epochs}]\", unit='batch') as pbar:\n", + " for images, labels in pbar:\n", + " # Send images and labels to GPU if needed\n", + " images = images.cuda()\n", + " labels = labels.cuda()\n", + "\n", + " optimizer.zero_grad() # Zero the gradients\n", + "\n", + " # Forward pass\n", + " outputs = model(images) # The model outputs keypoint heatmaps\n", + " #print(outputs.shape)\n", + "\n", + " original_size = (208, 208)\n", + " target_size = (52, 52)\n", + " labels = resize_keypoints_new(labels, original_size, target_size)\n", + " gt_heatmaps = generate_heatmaps(labels, output_size=outputs.shape[2:]) # Implement this\n", + " loss = criterion(outputs*100, gt_heatmaps*100)\n", + "\n", + " # Backward pass and optimization\n", + " loss.backward()\n", + " optimizer.step()\n", + "\n", + " running_loss += loss.item()\n", + "\n", + " # Update the tqdm progress bar with the current loss\n", + " pbar.set_postfix(loss=running_loss / (pbar.n + 1)) # Display the average loss\n", + "\n", + " # Print loss for the current epoch\n", + " avg_loss = running_loss / len(dataloader)\n", + " print(f'Epoch [{epoch + 1}/{num_epochs}], Loss: {avg_loss:.4f}')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cac52986-3416-4280-8092-9b197ac006ad", + "metadata": {}, + "outputs": [], + "source": [ + "input_tensor.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f5dbc351-bb56-450f-85c7-225902b2a9b8", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "12157857-e9bf-4645-a137-3e5ddf0d3141", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1a27d991-83b5-485f-8e50-9273601dedab", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fdd034a0-c159-4b2f-8f49-abdbd49844bd", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "88eab6b5-72f5-4407-a2d1-c9f74dfb2f8b", + "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", + "pygments_lexer": "ipython3", + "version": "3.9.19" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}