195 lines
7.0 KiB
Python
195 lines
7.0 KiB
Python
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# Modified by Lu He
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# ------------------------------------------------------------------------
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# Deformable DETR
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# Copyright (c) 2020 SenseTime. All Rights Reserved.
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# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
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# ------------------------------------------------------------------------
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# Modified from DETR (https://github.com/facebookresearch/detr)
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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
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# ------------------------------------------------------------------------
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"""
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COCO dataset which returns image_id for evaluation.
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Mostly copy-paste from https://github.com/pytorch/vision/blob/13b35ff/references/detection/coco_utils.py
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"""
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from pathlib import Path
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import torch
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import torch.utils.data
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from pycocotools import mask as coco_mask
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from .torchvision_datasets import CocoDetection as TvCocoDetection
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from util.misc import get_local_rank, get_local_size
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import datasets.transforms_single as T
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from torch.utils.data.dataset import ConcatDataset
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class CocoDetection(TvCocoDetection):
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def __init__(self, img_folder, ann_file, transforms, return_masks, cache_mode=False, local_rank=0, local_size=1):
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super(CocoDetection, self).__init__(img_folder, ann_file,
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cache_mode=cache_mode, local_rank=local_rank, local_size=local_size)
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self._transforms = transforms
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self.prepare = ConvertCocoPolysToMask(return_masks)
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def __getitem__(self, idx):
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"""
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Args:
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index (int): Index
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Returns:
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tuple: Tuple (image, target). target is the object returned by ``coco.loadAnns``.
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"""
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# idx若为675834，则img_id为675835(img_id=idx+1)
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coco = self.coco
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img_id = self.ids[idx]
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ann_ids = coco.getAnnIds(imgIds=img_id)
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target = coco.loadAnns(ann_ids)
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path = coco.loadImgs(img_id)[0]['file_name']
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img = self.get_image(path)
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image_id = img_id
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target = {'image_id': image_id, 'annotations': target}
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# import cv2
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# import numpy as np
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# image = np.array(img)
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# image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
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# box = target['annotations'][0]['bbox']
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# image = cv2.rectangle(image, (box[0], box[1]), (box[0]+box[2], box[1]+box[3]), color=(0, 0, 255), thickness=1)
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# cv2.imshow('img', image)
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# cv2.waitKey(0)
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img, target = self.prepare(img, target)
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if self._transforms is not None:
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img, target = self._transforms(img, target)
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return img, target
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def convert_coco_poly_to_mask(segmentations, height, width):
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masks = []
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for polygons in segmentations:
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rles = coco_mask.frPyObjects(polygons, height, width)
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mask = coco_mask.decode(rles)
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if len(mask.shape) < 3:
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mask = mask[..., None]
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mask = torch.as_tensor(mask, dtype=torch.uint8)
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mask = mask.any(dim=2)
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masks.append(mask)
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if masks:
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masks = torch.stack(masks, dim=0)
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else:
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masks = torch.zeros((0, height, width), dtype=torch.uint8)
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return masks
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class ConvertCocoPolysToMask(object):
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def __init__(self, return_masks=False):
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self.return_masks = return_masks
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def __call__(self, image, target):
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w, h = image.size
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image_id = target["image_id"]
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image_id = torch.tensor([image_id])
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anno = target["annotations"]
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anno = [obj for obj in anno if 'iscrowd' not in obj or obj['iscrowd'] == 0]
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boxes = [obj["bbox"] for obj in anno]
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# guard against no boxes via resizing
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boxes = torch.as_tensor(boxes, dtype=torch.float32).reshape(-1, 4)
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boxes[:, 2:] += boxes[:, :2]
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boxes[:, 0::2].clamp_(min=0, max=w)
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boxes[:, 1::2].clamp_(min=0, max=h)
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classes = [obj["category_id"] for obj in anno]
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classes = torch.tensor(classes, dtype=torch.int64)
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if self.return_masks:
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segmentations = [obj["segmentation"] for obj in anno]
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masks = convert_coco_poly_to_mask(segmentations, h, w)
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keypoints = None
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if anno and "keypoints" in anno[0]:
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keypoints = [obj["keypoints"] for obj in anno]
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keypoints = torch.as_tensor(keypoints, dtype=torch.float32)
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num_keypoints = keypoints.shape[0]
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if num_keypoints:
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keypoints = keypoints.view(num_keypoints, -1, 3)
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keep = (boxes[:, 3] > boxes[:, 1]) & (boxes[:, 2] > boxes[:, 0])
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boxes = boxes[keep]
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classes = classes[keep]
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if self.return_masks:
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masks = masks[keep]
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if keypoints is not None:
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keypoints = keypoints[keep]
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target = {}
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target["boxes"] = boxes
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target["labels"] = classes
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if self.return_masks:
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target["masks"] = masks
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target["image_id"] = image_id
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if keypoints is not None:
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target["keypoints"] = keypoints
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# for conversion to coco api
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area = torch.tensor([obj["area"] for obj in anno])
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iscrowd = torch.tensor([obj["iscrowd"] if "iscrowd" in obj else 0 for obj in anno])
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target["area"] = area[keep]
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target["iscrowd"] = iscrowd[keep]
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target["orig_size"] = torch.as_tensor([int(h), int(w)])
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target["size"] = torch.as_tensor([int(h), int(w)])
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return image, target
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def make_coco_transforms(image_set):
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normalize = T.Compose([
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T.ToTensor(),
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T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
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])
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if image_set == 'train_vid' or image_set == "train_det" or image_set == "train_joint":
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return T.Compose([
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T.RandomHorizontalFlip(),
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T.RandomResize([600], max_size=1000),
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normalize,
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])
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if image_set == 'val':
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return T.Compose([
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T.RandomResize([600], max_size=1000),
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normalize,
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])
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raise ValueError(f'unknown {image_set}')
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def build(image_set, args):
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root = Path(args.vid_path)
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assert root.exists(), f'provided COCO path {root} does not exist'
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mode = 'instances'
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PATHS = {
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# "train_joint": [(root / "Data" / "DET", root / "annotations" / 'imagenet_det_30plus1cls_vid_train.json'), (root / "Data" / "VID", root / "annotations_10true" / 'imagenet_vid_train.json')],
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"train_det": [(root / "Data" / "DET", root / "annotations" / 'imagenet_det_30plus1cls_vid_train.json')],
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"train_vid": [(root / "Data" / "VID", root / "annotations" / 'imagenet_vid_train.json')],
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"train_joint": [(root / "Data" , root / "annotations" / 'imagenet_vid_train_joint_30.json')],
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"val": [(root / "Data" / "VID", root / "annotations" / 'imagenet_vid_val.json')],
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}
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datasets = []
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for (img_folder, ann_file) in PATHS[image_set]:
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dataset = CocoDetection(img_folder, ann_file, transforms=make_coco_transforms(image_set), return_masks=args.masks, cache_mode=args.cache_mode, local_rank=get_local_rank(), local_size=get_local_size())
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datasets.append(dataset)
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if len(datasets) == 1:
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return datasets[0]
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return ConcatDataset(datasets)
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