1018 lines
1.2 MiB
Plaintext
1018 lines
1.2 MiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"<a href=\"https://colab.research.google.com/github/PacktPublishing/Hands-On-Computer-Vision-with-PyTorch/blob/master/Chapter09/Instance_Segmentation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\u001b[K |████████████████████████████████| 48 kB 2.6 MB/s \n",
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"\u001b[K |████████████████████████████████| 60 kB 5.9 MB/s \n",
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"\u001b[K |████████████████████████████████| 10.9 MB 30.2 MB/s \n",
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"\u001b[K |████████████████████████████████| 78 kB 6.3 MB/s \n",
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"\u001b[K |████████████████████████████████| 948 kB 29.1 MB/s \n",
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"\u001b[K |████████████████████████████████| 58 kB 5.3 MB/s \n",
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"\u001b[K |████████████████████████████████| 232 kB 48.2 MB/s \n",
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"\u001b[K |████████████████████████████████| 51 kB 6.4 MB/s \n",
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"\u001b[?25h Building wheel for typing (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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"\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
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"albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.4.0 which is incompatible.\u001b[0m\n",
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" Building wheel for pycocotools (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
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]
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}
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],
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"source": [
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"!wget --quiet http://sceneparsing.csail.mit.edu/data/ChallengeData2017/images.tar\n",
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"!wget --quiet http://sceneparsing.csail.mit.edu/data/ChallengeData2017/annotations_instance.tar\n",
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"!tar -xf images.tar\n",
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"!tar -xf annotations_instance.tar\n",
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"!rm images.tar annotations_instance.tar\n",
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"!pip install -qU torch_snippets\n",
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"!wget --quiet https://raw.githubusercontent.com/pytorch/vision/release/0.12/references/detection/engine.py\n",
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"!wget --quiet https://raw.githubusercontent.com/pytorch/vision/release/0.12/references/detection/utils.py\n",
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"!wget --quiet https://raw.githubusercontent.com/pytorch/vision/release/0.12/references/detection/transforms.py\n",
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"!wget --quiet https://raw.githubusercontent.com/pytorch/vision/release/0.12/references/detection/coco_eval.py\n",
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"!wget --quiet https://raw.githubusercontent.com/pytorch/vision/release/0.12/references/detection/coco_utils.py\n",
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"!pip install -q -U 'git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI'"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from torch_snippets import *\n",
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"from torch_snippets.inspector import inspect\n",
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"import torchvision\n",
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"from torchvision.models.detection.faster_rcnn import FastRCNNPredictor\n",
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"from torchvision.models.detection.mask_rcnn import MaskRCNNPredictor\n",
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"\n",
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"from engine import train_one_epoch, evaluate\n",
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"import utils\n",
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"import transforms as T\n",
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"device = 'cuda' if torch.cuda.is_available() else 'cpu'"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"all_images = Glob('images/training')\n",
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"all_annots = Glob('annotations_instance/training')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": "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"text/plain": [
|
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"<Figure size 1440x1440 with 5 Axes>"
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]
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},
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"metadata": {
|
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"needs_background": "light"
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},
|
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"output_type": "display_data"
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}
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],
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"source": [
|
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|
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"f = 'ADE_train_00014301'\n",
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"\n",
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"im = read(find(f, all_images), 1)\n",
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"an = read(find(f, all_annots), 1).transpose(2,0,1)\n",
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"r,g,b = an\n",
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"nzs = np.nonzero(r==4) # 4 stands for person\n",
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"instances = np.unique(g[nzs])\n",
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"masks = np.zeros((len(instances), *r.shape))\n",
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"for ix,_id in enumerate(instances):\n",
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" masks[ix] = g==_id\n",
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"\n",
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"subplots([im, *masks], sz=20)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"100%|██████████| 5000/5000 [00:27<00:00, 179.49it/s]\n"
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]
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}
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],
|
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"source": [
|
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|
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"annots = []\n",
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|
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"for ann in Tqdm(all_annots[:5000]):\n",
|
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|
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" _ann = read(ann, 1).transpose(2,0,1)\n",
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|
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" r,g,b = _ann\n",
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" if 4 not in np.unique(r): continue\n",
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|
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" annots.append(ann)"
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]
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},
|
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{
|
||
|
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"cell_type": "code",
|
||
|
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"execution_count": null,
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|
|
"metadata": {},
|
||
|
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"outputs": [],
|
||
|
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"source": [
|
||
|
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"from sklearn.model_selection import train_test_split\n",
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"_annots = stems(annots)\n",
|
||
|
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"trn_items, val_items = train_test_split(_annots, random_state=2)"
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]
|
||
|
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},
|
||
|
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{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"def get_transform(train):\n",
|
||
|
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" image_transforms = []\n",
|
||
|
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" image_transforms.append(T.PILToTensor())\n",
|
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|
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" if train:\n",
|
||
|
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" image_transforms.append(T.RandomHorizontalFlip(0.5))\n",
|
||
|
|
" return T.Compose(image_transforms)"
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||
|
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]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"class MasksDataset(Dataset):\n",
|
||
|
|
" def __init__(self, items, transforms, N):\n",
|
||
|
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" self.items = items\n",
|
||
|
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" self.transforms = transforms\n",
|
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|
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" self.N = N\n",
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|
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" def get_mask(self, path):\n",
|
||
|
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" an = read(path, 1).transpose(2,0,1)\n",
|
||
|
|
" r,g,b = an\n",
|
||
|
|
" nzs = np.nonzero(r==4)\n",
|
||
|
|
" instances = np.unique(g[nzs])\n",
|
||
|
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" masks = np.zeros((len(instances), *r.shape))\n",
|
||
|
|
" for ix,_id in enumerate(instances):\n",
|
||
|
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" masks[ix] = g==_id\n",
|
||
|
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" return masks\n",
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|
|
" def __getitem__(self, ix):\n",
|
||
|
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" _id = self.items[ix]\n",
|
||
|
|
" img_path = f'images/training/{_id}.jpg'\n",
|
||
|
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" mask_path = f'annotations_instance/training/{_id}.png'\n",
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|
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" masks = self.get_mask(mask_path)\n",
|
||
|
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" obj_ids = np.arange(1, len(masks)+1)\n",
|
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|
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" img = Image.open(img_path).convert(\"RGB\")\n",
|
||
|
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" num_objs = len(obj_ids)\n",
|
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|
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" boxes = []\n",
|
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|
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" for i in range(num_objs):\n",
|
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|
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" obj_pixels = np.where(masks[i])\n",
|
||
|
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" xmin = np.min(obj_pixels[1])\n",
|
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|
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" xmax = np.max(obj_pixels[1])\n",
|
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|
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" ymin = np.min(obj_pixels[0])\n",
|
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|
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" ymax = np.max(obj_pixels[0])\n",
|
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|
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" if (((xmax-xmin)<=10) | (ymax-ymin)<=10):\n",
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|
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" xmax = xmin+10\n",
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|
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" ymax = ymin+10\n",
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|
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" boxes.append([xmin, ymin, xmax, ymax])\n",
|
||
|
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" boxes = torch.as_tensor(boxes, dtype=torch.float32)\n",
|
||
|
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" labels = torch.ones((num_objs,), dtype=torch.int64)\n",
|
||
|
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" masks = torch.as_tensor(masks, dtype=torch.uint8)\n",
|
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|
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" area = (boxes[:, 3] - boxes[:, 1]) * (boxes[:, 2] - boxes[:, 0])\n",
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||
|
|
" iscrowd = torch.zeros((num_objs,), dtype=torch.int64)\n",
|
||
|
|
" image_id = torch.tensor([ix])\n",
|
||
|
|
" target = {}\n",
|
||
|
|
" target[\"boxes\"] = boxes\n",
|
||
|
|
" target[\"labels\"] = labels\n",
|
||
|
|
" target[\"masks\"] = masks\n",
|
||
|
|
" target[\"image_id\"] = image_id\n",
|
||
|
|
" target[\"area\"] = area\n",
|
||
|
|
" target[\"iscrowd\"] = iscrowd\n",
|
||
|
|
" if self.transforms is not None:\n",
|
||
|
|
" img, target = self.transforms(img, target)\n",
|
||
|
|
" if (img.dtype == torch.float32) or (img.dtype == torch.uint8) :\n",
|
||
|
|
" img = img/255.\n",
|
||
|
|
" return img, target\n",
|
||
|
|
" def __len__(self):\n",
|
||
|
|
" return self.N\n",
|
||
|
|
" def choose(self):\n",
|
||
|
|
" return self[randint(len(self))]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
" Tensor\tShape: torch.Size([3, 512, 684])\tMin: 0.000\tMax: 1.000\tMean: 0.486\tdtype: torch.float32\n",
|
||
|
|
" Dict Of 6 items\n",
|
||
|
|
"\tBOXES:\n",
|
||
|
|
"\t Tensor\tShape: torch.Size([3, 4])\tMin: 42.000\tMax: 477.000\tMean: 259.417\tdtype: torch.float32\n",
|
||
|
|
"\tLABELS:\n",
|
||
|
|
"\t Tensor\tShape: torch.Size([3])\tMin: 1.000\tMax: 1.000\tMean: 1.000\tdtype: torch.int64\n",
|
||
|
|
"\tMASKS:\n",
|
||
|
|
"\t Tensor\tShape: torch.Size([3, 512, 684])\tMin: 0.000\tMax: 1.000\tMean: 0.008\tdtype: torch.uint8\n",
|
||
|
|
"\tIMAGE_ID:\n",
|
||
|
|
"\t Tensor\tShape: torch.Size([1])\tMin: 0.000\tMax: 0.000\tMean: 0.000\tdtype: torch.int64\n",
|
||
|
|
"\tAREA:\n",
|
||
|
|
"\t Tensor\tShape: torch.Size([3])\tMin: 1932.000\tMax: 10688.000\tMean: 5270.667\tdtype: torch.float32\n",
|
||
|
|
"... ... 1 more item(s)\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 720x720 with 4 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"x = MasksDataset(trn_items, get_transform(train=True), N=100)\n",
|
||
|
|
"im,targ = x[0]\n",
|
||
|
|
"inspect(im,targ)\n",
|
||
|
|
"subplots([im, *targ['masks']], sz=10)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"def get_model_instance_segmentation(num_classes):\n",
|
||
|
|
" # load an instance segmentation model pre-trained pre-trained on COCO\n",
|
||
|
|
" model = torchvision.models.detection.maskrcnn_resnet50_fpn(pretrained=True)\n",
|
||
|
|
"\n",
|
||
|
|
" # get number of input features for the classifier\n",
|
||
|
|
" in_features = model.roi_heads.box_predictor.cls_score.in_features\n",
|
||
|
|
" # replace the pre-trained head with a new one\n",
|
||
|
|
" model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)\n",
|
||
|
|
"\n",
|
||
|
|
" # now get the number of input features for the mask classifier\n",
|
||
|
|
" in_features_mask = model.roi_heads.mask_predictor.conv5_mask.in_channels\n",
|
||
|
|
" hidden_layer = 256\n",
|
||
|
|
" # and replace the mask predictor with a new one\n",
|
||
|
|
" model.roi_heads.mask_predictor = MaskRCNNPredictor(in_features_mask,\n",
|
||
|
|
" hidden_layer,num_classes)\n",
|
||
|
|
" return model"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"MaskRCNN(\n",
|
||
|
|
" (transform): GeneralizedRCNNTransform(\n",
|
||
|
|
" Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])\n",
|
||
|
|
" Resize(min_size=(800,), max_size=1333, mode='bilinear')\n",
|
||
|
|
" )\n",
|
||
|
|
" (backbone): BackboneWithFPN(\n",
|
||
|
|
" (body): IntermediateLayerGetter(\n",
|
||
|
|
" (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
|
||
|
|
" (bn1): FrozenBatchNorm2d(64, eps=0.0)\n",
|
||
|
|
" (relu): ReLU(inplace=True)\n",
|
||
|
|
" (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
|
||
|
|
" (layer1): Sequential(\n",
|
||
|
|
" (0): Bottleneck(\n",
|
||
|
|
" (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
|
|
" (bn1): FrozenBatchNorm2d(64, eps=0.0)\n",
|
||
|
|
" (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||
|
|
" (bn2): FrozenBatchNorm2d(64, eps=0.0)\n",
|
||
|
|
" (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
|
|
" (bn3): FrozenBatchNorm2d(256, eps=0.0)\n",
|
||
|
|
" (relu): ReLU(inplace=True)\n",
|
||
|
|
" (downsample): Sequential(\n",
|
||
|
|
" (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
|
|
" (1): FrozenBatchNorm2d(256, eps=0.0)\n",
|
||
|
|
" )\n",
|
||
|
|
" )\n",
|
||
|
|
" (1): Bottleneck(\n",
|
||
|
|
" (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
|
|
" (bn1): FrozenBatchNorm2d(64, eps=0.0)\n",
|
||
|
|
" (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||
|
|
" (bn2): FrozenBatchNorm2d(64, eps=0.0)\n",
|
||
|
|
" (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
|
|
" (bn3): FrozenBatchNorm2d(256, eps=0.0)\n",
|
||
|
|
" (relu): ReLU(inplace=True)\n",
|
||
|
|
" )\n",
|
||
|
|
" (2): Bottleneck(\n",
|
||
|
|
" (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
|
|
" (bn1): FrozenBatchNorm2d(64, eps=0.0)\n",
|
||
|
|
" (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||
|
|
" (bn2): FrozenBatchNorm2d(64, eps=0.0)\n",
|
||
|
|
" (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
|
|
" (bn3): FrozenBatchNorm2d(256, eps=0.0)\n",
|
||
|
|
" (relu): ReLU(inplace=True)\n",
|
||
|
|
" )\n",
|
||
|
|
" )\n",
|
||
|
|
" (layer2): Sequential(\n",
|
||
|
|
" (0): Bottleneck(\n",
|
||
|
|
" (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
|
|
" (bn1): FrozenBatchNorm2d(128, eps=0.0)\n",
|
||
|
|
" (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
|
||
|
|
" (bn2): FrozenBatchNorm2d(128, eps=0.0)\n",
|
||
|
|
" (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
|
|
" (bn3): FrozenBatchNorm2d(512, eps=0.0)\n",
|
||
|
|
" (relu): ReLU(inplace=True)\n",
|
||
|
|
" (downsample): Sequential(\n",
|
||
|
|
" (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
|
||
|
|
" (1): FrozenBatchNorm2d(512, eps=0.0)\n",
|
||
|
|
" )\n",
|
||
|
|
" )\n",
|
||
|
|
" (1): Bottleneck(\n",
|
||
|
|
" (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
|
|
" (bn1): FrozenBatchNorm2d(128, eps=0.0)\n",
|
||
|
|
" (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||
|
|
" (bn2): FrozenBatchNorm2d(128, eps=0.0)\n",
|
||
|
|
" (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
|
|
" (bn3): FrozenBatchNorm2d(512, eps=0.0)\n",
|
||
|
|
" (relu): ReLU(inplace=True)\n",
|
||
|
|
" )\n",
|
||
|
|
" (2): Bottleneck(\n",
|
||
|
|
" (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
|
|
" (bn1): FrozenBatchNorm2d(128, eps=0.0)\n",
|
||
|
|
" (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||
|
|
" (bn2): FrozenBatchNorm2d(128, eps=0.0)\n",
|
||
|
|
" (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
|
|
" (bn3): FrozenBatchNorm2d(512, eps=0.0)\n",
|
||
|
|
" (relu): ReLU(inplace=True)\n",
|
||
|
|
" )\n",
|
||
|
|
" (3): Bottleneck(\n",
|
||
|
|
" (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
|
|
" (bn1): FrozenBatchNorm2d(128, eps=0.0)\n",
|
||
|
|
" (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||
|
|
" (bn2): FrozenBatchNorm2d(128, eps=0.0)\n",
|
||
|
|
" (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
|
|
" (bn3): FrozenBatchNorm2d(512, eps=0.0)\n",
|
||
|
|
" (relu): ReLU(inplace=True)\n",
|
||
|
|
" )\n",
|
||
|
|
" )\n",
|
||
|
|
" (layer3): Sequential(\n",
|
||
|
|
" (0): Bottleneck(\n",
|
||
|
|
" (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
|
|
" (bn1): FrozenBatchNorm2d(256, eps=0.0)\n",
|
||
|
|
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
|
||
|
|
" (bn2): FrozenBatchNorm2d(256, eps=0.0)\n",
|
||
|
|
" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
|
|
" (bn3): FrozenBatchNorm2d(1024, eps=0.0)\n",
|
||
|
|
" (relu): ReLU(inplace=True)\n",
|
||
|
|
" (downsample): Sequential(\n",
|
||
|
|
" (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
|
||
|
|
" (1): FrozenBatchNorm2d(1024, eps=0.0)\n",
|
||
|
|
" )\n",
|
||
|
|
" )\n",
|
||
|
|
" (1): Bottleneck(\n",
|
||
|
|
" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
|
|
" (bn1): FrozenBatchNorm2d(256, eps=0.0)\n",
|
||
|
|
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||
|
|
" (bn2): FrozenBatchNorm2d(256, eps=0.0)\n",
|
||
|
|
" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
|
|
" (bn3): FrozenBatchNorm2d(1024, eps=0.0)\n",
|
||
|
|
" (relu): ReLU(inplace=True)\n",
|
||
|
|
" )\n",
|
||
|
|
" (2): Bottleneck(\n",
|
||
|
|
" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
|
|
" (bn1): FrozenBatchNorm2d(256, eps=0.0)\n",
|
||
|
|
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||
|
|
" (bn2): FrozenBatchNorm2d(256, eps=0.0)\n",
|
||
|
|
" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
|
|
" (bn3): FrozenBatchNorm2d(1024, eps=0.0)\n",
|
||
|
|
" (relu): ReLU(inplace=True)\n",
|
||
|
|
" )\n",
|
||
|
|
" (3): Bottleneck(\n",
|
||
|
|
" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
|
|
" (bn1): FrozenBatchNorm2d(256, eps=0.0)\n",
|
||
|
|
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||
|
|
" (bn2): FrozenBatchNorm2d(256, eps=0.0)\n",
|
||
|
|
" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
|
|
" (bn3): FrozenBatchNorm2d(1024, eps=0.0)\n",
|
||
|
|
" (relu): ReLU(inplace=True)\n",
|
||
|
|
" )\n",
|
||
|
|
" (4): Bottleneck(\n",
|
||
|
|
" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
|
|
" (bn1): FrozenBatchNorm2d(256, eps=0.0)\n",
|
||
|
|
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||
|
|
" (bn2): FrozenBatchNorm2d(256, eps=0.0)\n",
|
||
|
|
" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
|
|
" (bn3): FrozenBatchNorm2d(1024, eps=0.0)\n",
|
||
|
|
" (relu): ReLU(inplace=True)\n",
|
||
|
|
" )\n",
|
||
|
|
" (5): Bottleneck(\n",
|
||
|
|
" (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
|
|
" (bn1): FrozenBatchNorm2d(256, eps=0.0)\n",
|
||
|
|
" (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||
|
|
" (bn2): FrozenBatchNorm2d(256, eps=0.0)\n",
|
||
|
|
" (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
|
|
" (bn3): FrozenBatchNorm2d(1024, eps=0.0)\n",
|
||
|
|
" (relu): ReLU(inplace=True)\n",
|
||
|
|
" )\n",
|
||
|
|
" )\n",
|
||
|
|
" (layer4): Sequential(\n",
|
||
|
|
" (0): Bottleneck(\n",
|
||
|
|
" (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
|
|
" (bn1): FrozenBatchNorm2d(512, eps=0.0)\n",
|
||
|
|
" (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
|
||
|
|
" (bn2): FrozenBatchNorm2d(512, eps=0.0)\n",
|
||
|
|
" (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
|
|
" (bn3): FrozenBatchNorm2d(2048, eps=0.0)\n",
|
||
|
|
" (relu): ReLU(inplace=True)\n",
|
||
|
|
" (downsample): Sequential(\n",
|
||
|
|
" (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
|
||
|
|
" (1): FrozenBatchNorm2d(2048, eps=0.0)\n",
|
||
|
|
" )\n",
|
||
|
|
" )\n",
|
||
|
|
" (1): Bottleneck(\n",
|
||
|
|
" (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
|
|
" (bn1): FrozenBatchNorm2d(512, eps=0.0)\n",
|
||
|
|
" (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||
|
|
" (bn2): FrozenBatchNorm2d(512, eps=0.0)\n",
|
||
|
|
" (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
|
|
" (bn3): FrozenBatchNorm2d(2048, eps=0.0)\n",
|
||
|
|
" (relu): ReLU(inplace=True)\n",
|
||
|
|
" )\n",
|
||
|
|
" (2): Bottleneck(\n",
|
||
|
|
" (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
|
|
" (bn1): FrozenBatchNorm2d(512, eps=0.0)\n",
|
||
|
|
" (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
|
||
|
|
" (bn2): FrozenBatchNorm2d(512, eps=0.0)\n",
|
||
|
|
" (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
|
||
|
|
" (bn3): FrozenBatchNorm2d(2048, eps=0.0)\n",
|
||
|
|
" (relu): ReLU(inplace=True)\n",
|
||
|
|
" )\n",
|
||
|
|
" )\n",
|
||
|
|
" )\n",
|
||
|
|
" (fpn): FeaturePyramidNetwork(\n",
|
||
|
|
" (inner_blocks): ModuleList(\n",
|
||
|
|
" (0): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))\n",
|
||
|
|
" (1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))\n",
|
||
|
|
" (2): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1))\n",
|
||
|
|
" (3): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1))\n",
|
||
|
|
" )\n",
|
||
|
|
" (layer_blocks): ModuleList(\n",
|
||
|
|
" (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
|
||
|
|
" (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
|
||
|
|
" (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
|
||
|
|
" (3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
|
||
|
|
" )\n",
|
||
|
|
" (extra_blocks): LastLevelMaxPool()\n",
|
||
|
|
" )\n",
|
||
|
|
" )\n",
|
||
|
|
" (rpn): RegionProposalNetwork(\n",
|
||
|
|
" (anchor_generator): AnchorGenerator()\n",
|
||
|
|
" (head): RPNHead(\n",
|
||
|
|
" (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
|
||
|
|
" (cls_logits): Conv2d(256, 3, kernel_size=(1, 1), stride=(1, 1))\n",
|
||
|
|
" (bbox_pred): Conv2d(256, 12, kernel_size=(1, 1), stride=(1, 1))\n",
|
||
|
|
" )\n",
|
||
|
|
" )\n",
|
||
|
|
" (roi_heads): RoIHeads(\n",
|
||
|
|
" (box_roi_pool): MultiScaleRoIAlign(featmap_names=['0', '1', '2', '3'], output_size=(7, 7), sampling_ratio=2)\n",
|
||
|
|
" (box_head): TwoMLPHead(\n",
|
||
|
|
" (fc6): Linear(in_features=12544, out_features=1024, bias=True)\n",
|
||
|
|
" (fc7): Linear(in_features=1024, out_features=1024, bias=True)\n",
|
||
|
|
" )\n",
|
||
|
|
" (box_predictor): FastRCNNPredictor(\n",
|
||
|
|
" (cls_score): Linear(in_features=1024, out_features=2, bias=True)\n",
|
||
|
|
" (bbox_pred): Linear(in_features=1024, out_features=8, bias=True)\n",
|
||
|
|
" )\n",
|
||
|
|
" (mask_roi_pool): MultiScaleRoIAlign(featmap_names=['0', '1', '2', '3'], output_size=(14, 14), sampling_ratio=2)\n",
|
||
|
|
" (mask_head): MaskRCNNHeads(\n",
|
||
|
|
" (mask_fcn1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
|
||
|
|
" (relu1): ReLU(inplace=True)\n",
|
||
|
|
" (mask_fcn2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
|
||
|
|
" (relu2): ReLU(inplace=True)\n",
|
||
|
|
" (mask_fcn3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
|
||
|
|
" (relu3): ReLU(inplace=True)\n",
|
||
|
|
" (mask_fcn4): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
|
||
|
|
" (relu4): ReLU(inplace=True)\n",
|
||
|
|
" )\n",
|
||
|
|
" (mask_predictor): MaskRCNNPredictor(\n",
|
||
|
|
" (conv5_mask): ConvTranspose2d(256, 256, kernel_size=(2, 2), stride=(2, 2))\n",
|
||
|
|
" (relu): ReLU(inplace=True)\n",
|
||
|
|
" (mask_fcn_logits): Conv2d(256, 2, kernel_size=(1, 1), stride=(1, 1))\n",
|
||
|
|
" )\n",
|
||
|
|
" )\n",
|
||
|
|
")"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"model = get_model_instance_segmentation(2).to(device)\n",
|
||
|
|
"model"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"dataset = MasksDataset(trn_items, get_transform(train=True), N=len(trn_items))\n",
|
||
|
|
"dataset_test = MasksDataset(val_items, get_transform(train=False), N=len(val_items))\n",
|
||
|
|
"\n",
|
||
|
|
"# define training and validation data loaders\n",
|
||
|
|
"data_loader = torch.utils.data.DataLoader(\n",
|
||
|
|
" dataset, batch_size=2, shuffle=True, num_workers=0,\n",
|
||
|
|
" collate_fn=utils.collate_fn)\n",
|
||
|
|
"\n",
|
||
|
|
"data_loader_test = torch.utils.data.DataLoader(\n",
|
||
|
|
" dataset_test, batch_size=1, shuffle=False, num_workers=0,\n",
|
||
|
|
" collate_fn=utils.collate_fn)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"num_classes = 2\n",
|
||
|
|
"model = get_model_instance_segmentation(num_classes).to(device)\n",
|
||
|
|
"params = [p for p in model.parameters() if p.requires_grad]\n",
|
||
|
|
"optimizer = torch.optim.SGD(params, lr=0.005,\n",
|
||
|
|
" momentum=0.9, weight_decay=0.0005)\n",
|
||
|
|
"# and a learning rate scheduler\n",
|
||
|
|
"lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer,\n",
|
||
|
|
" step_size=3,\n",
|
||
|
|
" gamma=0.1)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Epoch: [0] [ 0/482] eta: 0:19:00 lr: 0.000015 loss: 5.5752 (5.5752) loss_classifier: 0.8792 (0.8792) loss_box_reg: 0.8384 (0.8384) loss_mask: 3.7591 (3.7591) loss_objectness: 0.0579 (0.0579) loss_rpn_box_reg: 0.0406 (0.0406) time: 2.3664 data: 0.2396 max mem: 5656\n",
|
||
|
|
"Epoch: [0] [ 10/482] eta: 0:17:21 lr: 0.000119 loss: 4.4057 (4.4962) loss_classifier: 0.8939 (0.8836) loss_box_reg: 0.3374 (0.3255) loss_mask: 3.1220 (3.2297) loss_objectness: 0.0395 (0.0374) loss_rpn_box_reg: 0.0189 (0.0200) time: 2.2061 data: 0.0599 max mem: 7010\n",
|
||
|
|
"Epoch: [0] [ 20/482] eta: 0:16:13 lr: 0.000223 loss: 2.3401 (3.1059) loss_classifier: 0.5854 (0.6268) loss_box_reg: 0.2879 (0.3154) loss_mask: 1.5016 (2.1052) loss_objectness: 0.0275 (0.0391) loss_rpn_box_reg: 0.0097 (0.0193) time: 2.0950 data: 0.0462 max mem: 7010\n",
|
||
|
|
"Epoch: [0] [ 30/482] eta: 0:15:52 lr: 0.000327 loss: 1.3235 (2.5105) loss_classifier: 0.2697 (0.5083) loss_box_reg: 0.3295 (0.3287) loss_mask: 0.6481 (1.6156) loss_objectness: 0.0256 (0.0382) loss_rpn_box_reg: 0.0169 (0.0197) time: 2.0544 data: 0.0498 max mem: 7010\n",
|
||
|
|
"Epoch: [0] [ 40/482] eta: 0:15:16 lr: 0.000431 loss: 1.1525 (2.1411) loss_classifier: 0.2526 (0.4368) loss_box_reg: 0.3490 (0.3244) loss_mask: 0.4338 (1.3189) loss_objectness: 0.0398 (0.0401) loss_rpn_box_reg: 0.0169 (0.0209) time: 2.0381 data: 0.0498 max mem: 7010\n",
|
||
|
|
"Epoch: [0] [ 50/482] eta: 0:14:54 lr: 0.000535 loss: 0.9247 (1.9162) loss_classifier: 0.1698 (0.3886) loss_box_reg: 0.2237 (0.3282) loss_mask: 0.3867 (1.1401) loss_objectness: 0.0281 (0.0392) loss_rpn_box_reg: 0.0114 (0.0202) time: 2.0131 data: 0.0610 max mem: 7010\n",
|
||
|
|
"Epoch: [0] [ 60/482] eta: 0:14:28 lr: 0.000638 loss: 0.8918 (1.7327) loss_classifier: 0.1380 (0.3478) loss_box_reg: 0.2182 (0.3140) loss_mask: 0.3384 (1.0056) loss_objectness: 0.0281 (0.0426) loss_rpn_box_reg: 0.0114 (0.0226) time: 2.0290 data: 0.0806 max mem: 7010\n",
|
||
|
|
"Epoch: [0] [ 70/482] eta: 0:14:18 lr: 0.000742 loss: 0.6852 (1.5877) loss_classifier: 0.1196 (0.3171) loss_box_reg: 0.2182 (0.3067) loss_mask: 0.2703 (0.9032) loss_objectness: 0.0281 (0.0394) loss_rpn_box_reg: 0.0066 (0.0212) time: 2.1122 data: 0.0643 max mem: 7010\n",
|
||
|
|
"Epoch: [0] [ 80/482] eta: 0:13:59 lr: 0.000846 loss: 0.7877 (1.5065) loss_classifier: 0.1280 (0.2988) loss_box_reg: 0.3146 (0.3121) loss_mask: 0.2894 (0.8359) loss_objectness: 0.0127 (0.0377) loss_rpn_box_reg: 0.0114 (0.0220) time: 2.1728 data: 0.0598 max mem: 7397\n",
|
||
|
|
"Epoch: [0] [ 90/482] eta: 0:13:40 lr: 0.000950 loss: 0.9356 (1.4376) loss_classifier: 0.1458 (0.2816) loss_box_reg: 0.3707 (0.3133) loss_mask: 0.3574 (0.7842) loss_objectness: 0.0190 (0.0365) loss_rpn_box_reg: 0.0180 (0.0220) time: 2.1258 data: 0.0642 max mem: 7397\n",
|
||
|
|
"Epoch: [0] [100/482] eta: 0:13:17 lr: 0.001054 loss: 0.9208 (1.3765) loss_classifier: 0.1397 (0.2671) loss_box_reg: 0.3707 (0.3114) loss_mask: 0.3647 (0.7410) loss_objectness: 0.0144 (0.0351) loss_rpn_box_reg: 0.0127 (0.0219) time: 2.0945 data: 0.0482 max mem: 7397\n",
|
||
|
|
"Epoch: [0] [110/482] eta: 0:12:57 lr: 0.001158 loss: 0.7449 (1.3182) loss_classifier: 0.1145 (0.2534) loss_box_reg: 0.3175 (0.3092) loss_mask: 0.3104 (0.7013) loss_objectness: 0.0113 (0.0331) loss_rpn_box_reg: 0.0096 (0.0213) time: 2.0841 data: 0.0597 max mem: 7397\n",
|
||
|
|
"Epoch: [0] [120/482] eta: 0:12:37 lr: 0.001262 loss: 0.8370 (1.2859) loss_classifier: 0.1253 (0.2448) loss_box_reg: 0.3549 (0.3110) loss_mask: 0.3249 (0.6721) loss_objectness: 0.0138 (0.0340) loss_rpn_box_reg: 0.0193 (0.0240) time: 2.1204 data: 0.0764 max mem: 7397\n",
|
||
|
|
"Epoch: [0] [130/482] eta: 0:12:17 lr: 0.001365 loss: 0.7976 (1.2434) loss_classifier: 0.1197 (0.2353) loss_box_reg: 0.2635 (0.3046) loss_mask: 0.3405 (0.6463) loss_objectness: 0.0226 (0.0334) loss_rpn_box_reg: 0.0215 (0.0238) time: 2.1225 data: 0.0722 max mem: 7397\n",
|
||
|
|
"Epoch: [0] [140/482] eta: 0:11:59 lr: 0.001469 loss: 0.7081 (1.2095) loss_classifier: 0.1068 (0.2274) loss_box_reg: 0.2080 (0.3000) loss_mask: 0.3405 (0.6248) loss_objectness: 0.0217 (0.0327) loss_rpn_box_reg: 0.0171 (0.0246) time: 2.1536 data: 0.0611 max mem: 7397\n",
|
||
|
|
"Epoch: [0] [150/482] eta: 0:11:37 lr: 0.001573 loss: 0.7522 (1.1775) loss_classifier: 0.0847 (0.2193) loss_box_reg: 0.1924 (0.2910) loss_mask: 0.3606 (0.6111) loss_objectness: 0.0174 (0.0320) loss_rpn_box_reg: 0.0199 (0.0241) time: 2.1382 data: 0.0556 max mem: 7397\n",
|
||
|
|
"Epoch: [0] [160/482] eta: 0:11:14 lr: 0.001677 loss: 0.7844 (1.1583) loss_classifier: 0.1275 (0.2149) loss_box_reg: 0.1283 (0.2864) loss_mask: 0.3421 (0.5944) loss_objectness: 0.0413 (0.0379) loss_rpn_box_reg: 0.0124 (0.0246) time: 2.0422 data: 0.0565 max mem: 7397\n",
|
||
|
|
"Epoch: [0] [170/482] eta: 0:10:53 lr: 0.001781 loss: 0.7441 (1.1383) loss_classifier: 0.1275 (0.2104) loss_box_reg: 0.1731 (0.2818) loss_mask: 0.3421 (0.5821) loss_objectness: 0.0512 (0.0393) loss_rpn_box_reg: 0.0140 (0.0247) time: 2.0471 data: 0.0580 max mem: 7397\n",
|
||
|
|
"Epoch: [0] [180/482] eta: 0:10:30 lr: 0.001885 loss: 0.7389 (1.1167) loss_classifier: 0.1088 (0.2058) loss_box_reg: 0.1782 (0.2773) loss_mask: 0.3572 (0.5695) loss_objectness: 0.0371 (0.0394) loss_rpn_box_reg: 0.0169 (0.0247) time: 2.0214 data: 0.0548 max mem: 7397\n",
|
||
|
|
"Epoch: [0] [190/482] eta: 0:10:09 lr: 0.001988 loss: 0.7369 (1.0932) loss_classifier: 0.1137 (0.2013) loss_box_reg: 0.1782 (0.2730) loss_mask: 0.3162 (0.5559) loss_objectness: 0.0230 (0.0385) loss_rpn_box_reg: 0.0142 (0.0244) time: 2.0172 data: 0.0557 max mem: 7472\n",
|
||
|
|
"Epoch: [0] [200/482] eta: 0:09:49 lr: 0.002092 loss: 0.6406 (1.0779) loss_classifier: 0.1390 (0.1998) loss_box_reg: 0.1775 (0.2714) loss_mask: 0.3061 (0.5442) loss_objectness: 0.0176 (0.0383) loss_rpn_box_reg: 0.0076 (0.0242) time: 2.1139 data: 0.0526 max mem: 7472\n",
|
||
|
|
"Epoch: [0] [210/482] eta: 0:09:27 lr: 0.002196 loss: 0.6591 (1.0593) loss_classifier: 0.1058 (0.1949) loss_box_reg: 0.1948 (0.2685) loss_mask: 0.3317 (0.5347) loss_objectness: 0.0178 (0.0372) loss_rpn_box_reg: 0.0128 (0.0239) time: 2.0869 data: 0.0533 max mem: 7472\n",
|
||
|
|
"Epoch: [0] [220/482] eta: 0:09:05 lr: 0.002300 loss: 0.5995 (1.0413) loss_classifier: 0.0771 (0.1903) loss_box_reg: 0.1384 (0.2628) loss_mask: 0.3158 (0.5275) loss_objectness: 0.0091 (0.0368) loss_rpn_box_reg: 0.0132 (0.0239) time: 2.0243 data: 0.0517 max mem: 7472\n",
|
||
|
|
"Epoch: [0] [230/482] eta: 0:08:45 lr: 0.002404 loss: 0.5488 (1.0206) loss_classifier: 0.0771 (0.1861) loss_box_reg: 0.1384 (0.2585) loss_mask: 0.3013 (0.5169) loss_objectness: 0.0074 (0.0357) loss_rpn_box_reg: 0.0060 (0.0234) time: 2.0572 data: 0.0482 max mem: 7472\n",
|
||
|
|
"Epoch: [0] [240/482] eta: 0:08:24 lr: 0.002508 loss: 0.5726 (1.0085) loss_classifier: 0.0902 (0.1839) loss_box_reg: 0.1435 (0.2558) loss_mask: 0.3014 (0.5098) loss_objectness: 0.0091 (0.0353) loss_rpn_box_reg: 0.0133 (0.0237) time: 2.1009 data: 0.0742 max mem: 7472\n",
|
||
|
|
"Epoch: [0] [250/482] eta: 0:08:03 lr: 0.002612 loss: 0.5975 (0.9965) loss_classifier: 0.0902 (0.1816) loss_box_reg: 0.1355 (0.2537) loss_mask: 0.3355 (0.5032) loss_objectness: 0.0154 (0.0347) loss_rpn_box_reg: 0.0170 (0.0233) time: 2.0566 data: 0.0683 max mem: 7472\n",
|
||
|
|
"Epoch: [0] [260/482] eta: 0:07:43 lr: 0.002715 loss: 0.6450 (0.9821) loss_classifier: 0.0816 (0.1786) loss_box_reg: 0.1494 (0.2506) loss_mask: 0.3354 (0.4959) loss_objectness: 0.0153 (0.0341) loss_rpn_box_reg: 0.0119 (0.0229) time: 2.1059 data: 0.0468 max mem: 7472\n",
|
||
|
|
"Epoch: [0] [270/482] eta: 0:07:23 lr: 0.002819 loss: 0.6738 (0.9781) loss_classifier: 0.1087 (0.1784) loss_box_reg: 0.1711 (0.2504) loss_mask: 0.3463 (0.4899) loss_objectness: 0.0163 (0.0361) loss_rpn_box_reg: 0.0119 (0.0233) time: 2.1858 data: 0.0666 max mem: 7472\n",
|
||
|
|
"Epoch: [0] [280/482] eta: 0:07:01 lr: 0.002923 loss: 0.6262 (0.9664) loss_classifier: 0.0892 (0.1756) loss_box_reg: 0.1449 (0.2462) loss_mask: 0.3501 (0.4849) loss_objectness: 0.0280 (0.0364) loss_rpn_box_reg: 0.0087 (0.0232) time: 2.1137 data: 0.0605 max mem: 7472\n",
|
||
|
|
"Epoch: [0] [290/482] eta: 0:06:40 lr: 0.003027 loss: 0.6054 (0.9550) loss_classifier: 0.0817 (0.1727) loss_box_reg: 0.0929 (0.2428) loss_mask: 0.3019 (0.4783) loss_objectness: 0.0440 (0.0382) loss_rpn_box_reg: 0.0113 (0.0231) time: 2.0563 data: 0.0434 max mem: 7472\n",
|
||
|
|
"Epoch: [0] [300/482] eta: 0:06:20 lr: 0.003131 loss: 0.6487 (0.9488) loss_classifier: 0.0922 (0.1709) loss_box_reg: 0.1191 (0.2404) loss_mask: 0.3320 (0.4754) loss_objectness: 0.0427 (0.0389) loss_rpn_box_reg: 0.0121 (0.0232) time: 2.0836 data: 0.0515 max mem: 7472\n",
|
||
|
|
"Epoch: [0] [310/482] eta: 0:05:59 lr: 0.003235 loss: 0.7082 (0.9449) loss_classifier: 0.0969 (0.1694) loss_box_reg: 0.1515 (0.2396) loss_mask: 0.3683 (0.4714) loss_objectness: 0.0424 (0.0398) loss_rpn_box_reg: 0.0133 (0.0247) time: 2.0858 data: 0.0690 max mem: 7472\n",
|
||
|
|
"Epoch: [0] [320/482] eta: 0:05:38 lr: 0.003338 loss: 0.7512 (0.9427) loss_classifier: 0.0994 (0.1687) loss_box_reg: 0.1919 (0.2402) loss_mask: 0.3849 (0.4693) loss_objectness: 0.0353 (0.0396) loss_rpn_box_reg: 0.0357 (0.0250) time: 2.1452 data: 0.0762 max mem: 7472\n",
|
||
|
|
"Epoch: [0] [330/482] eta: 0:05:18 lr: 0.003442 loss: 0.8277 (0.9388) loss_classifier: 0.1592 (0.1682) loss_box_reg: 0.2393 (0.2398) loss_mask: 0.3809 (0.4659) loss_objectness: 0.0306 (0.0397) loss_rpn_box_reg: 0.0325 (0.0252) time: 2.2168 data: 0.0701 max mem: 7472\n",
|
||
|
|
"Epoch: [0] [340/482] eta: 0:04:57 lr: 0.003546 loss: 0.6757 (0.9307) loss_classifier: 0.1065 (0.1662) loss_box_reg: 0.1474 (0.2375) loss_mask: 0.3438 (0.4626) loss_objectness: 0.0306 (0.0396) loss_rpn_box_reg: 0.0133 (0.0248) time: 2.1240 data: 0.0569 max mem: 7472\n",
|
||
|
|
"Epoch: [0] [350/482] eta: 0:04:35 lr: 0.003650 loss: 0.5709 (0.9201) loss_classifier: 0.0669 (0.1634) loss_box_reg: 0.1112 (0.2336) loss_mask: 0.3369 (0.4597) loss_objectness: 0.0229 (0.0388) loss_rpn_box_reg: 0.0117 (0.0246) time: 1.9926 data: 0.0469 max mem: 7472\n",
|
||
|
|
"Epoch: [0] [360/482] eta: 0:04:14 lr: 0.003754 loss: 0.6046 (0.9188) loss_classifier: 0.1003 (0.1641) loss_box_reg: 0.1324 (0.2334) loss_mask: 0.3303 (0.4571) loss_objectness: 0.0204 (0.0392) loss_rpn_box_reg: 0.0121 (0.0250) time: 2.0180 data: 0.0626 max mem: 7472\n",
|
||
|
|
"Epoch: [0] [370/482] eta: 0:03:53 lr: 0.003858 loss: 0.6006 (0.9078) loss_classifier: 0.0758 (0.1615) loss_box_reg: 0.0887 (0.2301) loss_mask: 0.3290 (0.4529) loss_objectness: 0.0181 (0.0386) loss_rpn_box_reg: 0.0084 (0.0247) time: 2.0244 data: 0.0655 max mem: 7472\n",
|
||
|
|
"Epoch: [0] [380/482] eta: 0:03:32 lr: 0.003962 loss: 0.5364 (0.9007) loss_classifier: 0.0682 (0.1604) loss_box_reg: 0.0931 (0.2289) loss_mask: 0.2617 (0.4485) loss_objectness: 0.0139 (0.0383) loss_rpn_box_reg: 0.0050 (0.0247) time: 2.0151 data: 0.0667 max mem: 7472\n",
|
||
|
|
"Epoch: [0] [390/482] eta: 0:03:11 lr: 0.004065 loss: 0.7302 (0.9014) loss_classifier: 0.1498 (0.1610) loss_box_reg: 0.2004 (0.2305) loss_mask: 0.3288 (0.4462) loss_objectness: 0.0307 (0.0386) loss_rpn_box_reg: 0.0100 (0.0250) time: 2.1033 data: 0.0753 max mem: 7472\n",
|
||
|
|
"Epoch: [0] [400/482] eta: 0:02:51 lr: 0.004169 loss: 0.7474 (0.8952) loss_classifier: 0.1134 (0.1600) loss_box_reg: 0.2025 (0.2292) loss_mask: 0.3490 (0.4431) loss_objectness: 0.0315 (0.0381) loss_rpn_box_reg: 0.0130 (0.0248) time: 2.1243 data: 0.0560 max mem: 7472\n",
|
||
|
|
"Epoch: [0] [410/482] eta: 0:02:30 lr: 0.004273 loss: 0.5860 (0.8895) loss_classifier: 0.0841 (0.1584) loss_box_reg: 0.1454 (0.2277) loss_mask: 0.3100 (0.4401) loss_objectness: 0.0135 (0.0377) loss_rpn_box_reg: 0.0110 (0.0257) time: 2.1124 data: 0.0537 max mem: 7472\n",
|
||
|
|
"Epoch: [0] [420/482] eta: 0:02:09 lr: 0.004377 loss: 0.6747 (0.8857) loss_classifier: 0.0979 (0.1575) loss_box_reg: 0.1829 (0.2272) loss_mask: 0.3432 (0.4379) loss_objectness: 0.0167 (0.0374) loss_rpn_box_reg: 0.0182 (0.0256) time: 2.0534 data: 0.0636 max mem: 7472\n",
|
||
|
|
"Epoch: [0] [430/482] eta: 0:01:48 lr: 0.004481 loss: 0.6763 (0.8817) loss_classifier: 0.1123 (0.1566) loss_box_reg: 0.2011 (0.2271) loss_mask: 0.3441 (0.4354) loss_objectness: 0.0201 (0.0370) loss_rpn_box_reg: 0.0182 (0.0256) time: 1.9787 data: 0.0590 max mem: 7472\n",
|
||
|
|
"Epoch: [0] [440/482] eta: 0:01:27 lr: 0.004585 loss: 0.5922 (0.8753) loss_classifier: 0.0770 (0.1549) loss_box_reg: 0.1138 (0.2247) loss_mask: 0.3409 (0.4336) loss_objectness: 0.0169 (0.0367) loss_rpn_box_reg: 0.0143 (0.0255) time: 2.0144 data: 0.0529 max mem: 7472\n",
|
||
|
|
"Epoch: [0] [450/482] eta: 0:01:06 lr: 0.004688 loss: 0.5922 (0.8718) loss_classifier: 0.0838 (0.1539) loss_box_reg: 0.1332 (0.2244) loss_mask: 0.3409 (0.4317) loss_objectness: 0.0113 (0.0363) loss_rpn_box_reg: 0.0107 (0.0254) time: 2.0415 data: 0.0510 max mem: 7472\n",
|
||
|
|
"Epoch: [0] [460/482] eta: 0:00:45 lr: 0.004792 loss: 0.7216 (0.8688) loss_classifier: 0.1217 (0.1534) loss_box_reg: 0.2018 (0.2241) loss_mask: 0.3192 (0.4296) loss_objectness: 0.0107 (0.0362) loss_rpn_box_reg: 0.0123 (0.0254) time: 2.0111 data: 0.0544 max mem: 7472\n",
|
||
|
|
"Epoch: [0] [470/482] eta: 0:00:24 lr: 0.004896 loss: 0.6759 (0.8650) loss_classifier: 0.1234 (0.1532) loss_box_reg: 0.1583 (0.2228) loss_mask: 0.3192 (0.4277) loss_objectness: 0.0207 (0.0359) loss_rpn_box_reg: 0.0134 (0.0254) time: 1.9929 data: 0.0480 max mem: 7472\n",
|
||
|
|
"Epoch: [0] [480/482] eta: 0:00:04 lr: 0.005000 loss: 0.6775 (0.8623) loss_classifier: 0.1407 (0.1530) loss_box_reg: 0.1559 (0.2217) loss_mask: 0.3240 (0.4261) loss_objectness: 0.0219 (0.0360) loss_rpn_box_reg: 0.0148 (0.0254) time: 1.9480 data: 0.0479 max mem: 7472\n",
|
||
|
|
"Epoch: [0] [481/482] eta: 0:00:02 lr: 0.005000 loss: 0.6555 (0.8611) loss_classifier: 0.1407 (0.1528) loss_box_reg: 0.1366 (0.2213) loss_mask: 0.3131 (0.4257) loss_objectness: 0.0219 (0.0359) loss_rpn_box_reg: 0.0134 (0.0254) time: 1.9022 data: 0.0456 max mem: 7472\n",
|
||
|
|
"Epoch: [0] Total time: 0:16:38 (2.0720 s / it)\n",
|
||
|
|
"creating index...\n",
|
||
|
|
"index created!\n",
|
||
|
|
"Test: [ 0/321] eta: 0:03:07 model_time: 0.5358 (0.5358) evaluator_time: 0.0336 (0.0336) time: 0.5840 data: 0.0141 max mem: 7472\n",
|
||
|
|
"Test: [100/321] eta: 0:01:56 model_time: 0.4708 (0.4680) evaluator_time: 0.0227 (0.0377) time: 0.5279 data: 0.0188 max mem: 7472\n",
|
||
|
|
"Test: [200/321] eta: 0:01:04 model_time: 0.4536 (0.4670) evaluator_time: 0.0357 (0.0438) time: 0.5308 data: 0.0233 max mem: 7472\n",
|
||
|
|
"Test: [300/321] eta: 0:00:11 model_time: 0.4652 (0.4660) evaluator_time: 0.0366 (0.0433) time: 0.5570 data: 0.0269 max mem: 7472\n",
|
||
|
|
"Test: [320/321] eta: 0:00:00 model_time: 0.4608 (0.4663) evaluator_time: 0.0370 (0.0441) time: 0.5569 data: 0.0280 max mem: 7472\n",
|
||
|
|
"Test: Total time: 0:02:51 (0.5352 s / it)\n",
|
||
|
|
"Averaged stats: model_time: 0.4608 (0.4663) evaluator_time: 0.0370 (0.0441)\n",
|
||
|
|
"Accumulating evaluation results...\n",
|
||
|
|
"DONE (t=0.13s).\n",
|
||
|
|
"Accumulating evaluation results...\n",
|
||
|
|
"DONE (t=0.14s).\n",
|
||
|
|
"IoU metric: bbox\n",
|
||
|
|
" Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.338\n",
|
||
|
|
" Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.665\n",
|
||
|
|
" Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.296\n",
|
||
|
|
" Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.208\n",
|
||
|
|
" Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.402\n",
|
||
|
|
" Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.488\n",
|
||
|
|
" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.140\n",
|
||
|
|
" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.405\n",
|
||
|
|
" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.485\n",
|
||
|
|
" Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.374\n",
|
||
|
|
" Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.545\n",
|
||
|
|
" Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.614\n",
|
||
|
|
"IoU metric: segm\n",
|
||
|
|
" Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.295\n",
|
||
|
|
" Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.630\n",
|
||
|
|
" Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.231\n",
|
||
|
|
" Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.158\n",
|
||
|
|
" Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.355\n",
|
||
|
|
" Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.466\n",
|
||
|
|
" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.126\n",
|
||
|
|
" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.356\n",
|
||
|
|
" Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.422\n",
|
||
|
|
" Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.304\n",
|
||
|
|
" Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.487\n",
|
||
|
|
" Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.553\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"num_epochs = 1\n",
|
||
|
|
"\n",
|
||
|
|
"trn_history = []\n",
|
||
|
|
"for epoch in range(num_epochs):\n",
|
||
|
|
" # train for one epoch, printing every 10 iterations\n",
|
||
|
|
" res = train_one_epoch(model, optimizer, data_loader, device, epoch, print_freq=10)\n",
|
||
|
|
" trn_history.append(res)\n",
|
||
|
|
" # update the learning rate\n",
|
||
|
|
" lr_scheduler.step()\n",
|
||
|
|
" # evaluate on the test dataset \n",
|
||
|
|
" res = evaluate(model, data_loader_test, device=device)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"import matplotlib.pyplot as plt\n",
|
||
|
|
"plt.title('Training Loss') \n",
|
||
|
|
"losses = [np.mean(list(trn_history[i].meters['loss'].deque)) for i in range(len(trn_history))]\n",
|
||
|
|
"plt.plot(losses)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 720x720 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"metadata": {
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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||
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"text/plain": [
|
||
|
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"metadata": {
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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||
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"metadata": {
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"output_type": "display_data"
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{
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"data": {
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"text/plain": [
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||
|
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"metadata": {
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},
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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"output_type": "display_data"
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{
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"data": {
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||
|
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"text/plain": [
|
||
|
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"<Figure size 432x288 with 1 Axes>"
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||
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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||
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"output_type": "display_data"
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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},
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"metadata": {
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{
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"data": {
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"text/plain": [
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"<Figure size 432x288 with 1 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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},
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{
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"data": {
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|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 432x288 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {
|
||
|
|
"needs_background": "light"
|
||
|
|
},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"model.eval()\n",
|
||
|
|
"im = dataset_test[10][0]\n",
|
||
|
|
"show(im)\n",
|
||
|
|
"with torch.no_grad():\n",
|
||
|
|
" prediction = model([im.to(device)])\n",
|
||
|
|
" for i in range(len(prediction[0]['masks'])):\n",
|
||
|
|
" plt.imshow(Image.fromarray(prediction[0]['masks'][i, 0].mul(255).byte().cpu().numpy()))\n",
|
||
|
|
" plt.title('Class: '+str(prediction[0]['labels'][i].cpu().numpy())+' Score:'+str(prediction[0]['scores'][i].cpu().numpy()))\n",
|
||
|
|
" plt.show()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
" "
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "python3",
|
||
|
|
"language": "python",
|
||
|
|
"name": "python3"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 4
|
||
|
|
}
|