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"""
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
COCO dataset which returns image_id for evaluation.
Mostly copy-paste from https://github.com/pytorch/vision/blob/13b35ff/references/detection/coco_utils.py
"""
import torch
import torch.utils.data
import torchvision
torchvision.disable_beta_transforms_warning()
from torchvision import datapoints
from pycocotools import mask as coco_mask
from src.core import register
__all__ = ['CocoDetection']
@register
class CocoDetection(torchvision.datasets.CocoDetection):
__inject__ = ['transforms']
__share__ = ['remap_mscoco_category']
def __init__(self, img_folder, ann_file, transforms, return_masks, remap_mscoco_category=False):
super(CocoDetection, self).__init__(img_folder, ann_file)
self._transforms = transforms
self.prepare = ConvertCocoPolysToMask(return_masks, remap_mscoco_category)
self.img_folder = img_folder
self.ann_file = ann_file
self.return_masks = return_masks
self.remap_mscoco_category = remap_mscoco_category
def __getitem__(self, idx):
img, target = super(CocoDetection, self).__getitem__(idx)
image_id = self.ids[idx]
target = {'image_id': image_id, 'annotations': target}
img, target = self.prepare(img, target)
# ['boxes', 'masks', 'labels']:
if 'boxes' in target:
target['boxes'] = datapoints.BoundingBox(
target['boxes'],
format=datapoints.BoundingBoxFormat.XYXY,
spatial_size=img.size[::-1]) # h w
if 'masks' in target:
target['masks'] = datapoints.Mask(target['masks'])
if self._transforms is not None:
img, target = self._transforms(img, target)
return img, target
def extra_repr(self) -> str:
s = f' img_folder: {self.img_folder}\n ann_file: {self.ann_file}\n'
s += f' return_masks: {self.return_masks}\n'
if hasattr(self, '_transforms') and self._transforms is not None:
s += f' transforms:\n {repr(self._transforms)}'
return s
def convert_coco_poly_to_mask(segmentations, height, width):
masks = []
for polygons in segmentations:
rles = coco_mask.frPyObjects(polygons, height, width)
mask = coco_mask.decode(rles)
if len(mask.shape) < 3:
mask = mask[..., None]
mask = torch.as_tensor(mask, dtype=torch.uint8)
mask = mask.any(dim=2)
masks.append(mask)
if masks:
masks = torch.stack(masks, dim=0)
else:
masks = torch.zeros((0, height, width), dtype=torch.uint8)
return masks
class ConvertCocoPolysToMask(object):
def __init__(self, return_masks=False, remap_mscoco_category=False):
self.return_masks = return_masks
self.remap_mscoco_category = remap_mscoco_category
def __call__(self, image, target):
w, h = image.size
image_id = target["image_id"]
image_id = torch.tensor([image_id])
anno = target["annotations"]
anno = [obj for obj in anno if 'iscrowd' not in obj or obj['iscrowd'] == 0]
boxes = [obj["bbox"] for obj in anno]
# guard against no boxes via resizing
boxes = torch.as_tensor(boxes, dtype=torch.float32).reshape(-1, 4)
boxes[:, 2:] += boxes[:, :2]
boxes[:, 0::2].clamp_(min=0, max=w)
boxes[:, 1::2].clamp_(min=0, max=h)
if self.remap_mscoco_category:
classes = [mscoco_category2label[obj["category_id"]] for obj in anno]
else:
classes = [obj["category_id"] for obj in anno]
classes = torch.tensor(classes, dtype=torch.int64)
if self.return_masks:
segmentations = [obj["segmentation"] for obj in anno]
masks = convert_coco_poly_to_mask(segmentations, h, w)
keypoints = None
if anno and "keypoints" in anno[0]:
keypoints = [obj["keypoints"] for obj in anno]
keypoints = torch.as_tensor(keypoints, dtype=torch.float32)
num_keypoints = keypoints.shape[0]
if num_keypoints:
keypoints = keypoints.view(num_keypoints, -1, 3)
keep = (boxes[:, 3] > boxes[:, 1]) & (boxes[:, 2] > boxes[:, 0])
boxes = boxes[keep]
classes = classes[keep]
if self.return_masks:
masks = masks[keep]
if keypoints is not None:
keypoints = keypoints[keep]
target = {}
target["boxes"] = boxes
target["labels"] = classes
if self.return_masks:
target["masks"] = masks
target["image_id"] = image_id
if keypoints is not None:
target["keypoints"] = keypoints
# for conversion to coco api
area = torch.tensor([obj["area"] for obj in anno])
iscrowd = torch.tensor([obj["iscrowd"] if "iscrowd" in obj else 0 for obj in anno])
target["area"] = area[keep]
target["iscrowd"] = iscrowd[keep]
target["orig_size"] = torch.as_tensor([int(w), int(h)])
target["size"] = torch.as_tensor([int(w), int(h)])
return image, target
mscoco_category2name = {
1: 'person',
2: 'bicycle',
3: 'car',
4: 'motorcycle',
5: 'airplane',
6: 'bus',
7: 'train',
8: 'truck',
9: 'boat',
10: 'traffic light',
11: 'fire hydrant',
13: 'stop sign',
14: 'parking meter',
15: 'bench',
16: 'bird',
17: 'cat',
18: 'dog',
19: 'horse',
20: 'sheep',
21: 'cow',
22: 'elephant',
23: 'bear',
24: 'zebra',
25: 'giraffe',
27: 'backpack',
28: 'umbrella',
31: 'handbag',
32: 'tie',
33: 'suitcase',
34: 'frisbee',
35: 'skis',
36: 'snowboard',
37: 'sports ball',
38: 'kite',
39: 'baseball bat',
40: 'baseball glove',
41: 'skateboard',
42: 'surfboard',
43: 'tennis racket',
44: 'bottle',
46: 'wine glass',
47: 'cup',
48: 'fork',
49: 'knife',
50: 'spoon',
51: 'bowl',
52: 'banana',
53: 'apple',
54: 'sandwich',
55: 'orange',
56: 'broccoli',
57: 'carrot',
58: 'hot dog',
59: 'pizza',
60: 'donut',
61: 'cake',
62: 'chair',
63: 'couch',
64: 'potted plant',
65: 'bed',
67: 'dining table',
70: 'toilet',
72: 'tv',
73: 'laptop',
74: 'mouse',
75: 'remote',
76: 'keyboard',
77: 'cell phone',
78: 'microwave',
79: 'oven',
80: 'toaster',
81: 'sink',
82: 'refrigerator',
84: 'book',
85: 'clock',
86: 'vase',
87: 'scissors',
88: 'teddy bear',
89: 'hair drier',
90: 'toothbrush'
}
mscoco_category2label = {k: i for i, k in enumerate(mscoco_category2name.keys())}
mscoco_label2category = {v: k for k, v in mscoco_category2label.items()} |