256 lines
10 KiB
Python
256 lines
10 KiB
Python
# 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 .coco_video_parser import CocoVID
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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_multi as T
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from torch.utils.data.dataset import ConcatDataset
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import random
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class CocoDetection(TvCocoDetection):
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def __init__(self, img_folder, ann_file, transforms, return_masks, interval1, interval2, num_ref_frames= 3,
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is_train = True, filter_key_img=True, 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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self.ann_file = ann_file
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self.frame_range = [-2, 2]
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self.num_ref_frames = num_ref_frames
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self.cocovid = CocoVID(self.ann_file)
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self.is_train = is_train
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self.filter_key_img = filter_key_img
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self.interval1 = interval1
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self.interval2 = interval2
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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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imgs = []
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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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img_info = coco.loadImgs(img_id)[0]
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path = img_info['file_name']
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video_id = img_info['video_id']
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img = self.get_image(path)
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target = {'image_id': img_id, 'annotations': target}
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img, target = self.prepare(img, target)
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imgs.append(img)
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if video_id == -1: # imgnet_det
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for i in range(self.num_ref_frames):
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imgs.append(img)
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else: # imgnet_vid
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img_ids = self.cocovid.get_img_ids_from_vid(video_id)
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ref_img_ids = []
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if self.is_train: # Train
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interval = self.num_ref_frames + 2 # *20
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left = max(img_ids[0], img_id - interval)
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right = min(img_ids[-1], img_id + interval)
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sample_range = list(range(left, right+1))
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if self.num_ref_frames >= 10:
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sample_range=img_ids
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if self.filter_key_img and img_id in sample_range:
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sample_range.remove(img_id)
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while len(sample_range) < self.num_ref_frames:
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print("sample_range", sample_range)
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sample_range.extend(sample_range)
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ref_img_ids = random.sample(sample_range, self.num_ref_frames)
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else: # Eval
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ref_img_ids = []
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Len = len(img_ids)
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interval = max(int(Len // 16), 1)
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if self.num_ref_frames < 8:
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left_indexs = int((img_id - img_ids[0]) // interval)
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right_indexs = int((img_ids[-1] - img_id) // interval)
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if left_indexs < self.num_ref_frames:
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for i in range(self.num_ref_frames):
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ref_img_ids.append(min(img_id + (i+1)*interval, img_ids[-1]))
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else:
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for i in range(self.num_ref_frames):
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ref_img_ids.append(max(img_id - (i+1)* interval, img_ids[0]))
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sample_range = []
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if self.num_ref_frames >= 8:
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left_indexs = int((img_ids[0] - img_id) // interval)
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right_indexs = int((img_ids[-1] - img_id) // interval)
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for i in range(left_indexs, right_indexs):
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if i < 0:
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index = max(img_id + i*interval, img_ids[0])
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sample_range.append(index)
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elif i > 0:
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index = min(img_id + i * interval, img_ids[-1])
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sample_range.append(index)
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if self.filter_key_img and img_id in sample_range:
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sample_range.remove(img_id)
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while len(sample_range) < self.num_ref_frames:
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print("sample_range", sample_range)
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sample_range.extend(sample_range)
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ref_img_ids = sample_range[:self.num_ref_frames]
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for ref_img_id in ref_img_ids:
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ref_ann_ids = coco.getAnnIds(imgIds=ref_img_id)
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ref_img_info = coco.loadImgs(ref_img_id)[0]
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ref_img_path = ref_img_info['file_name']
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ref_img = self.get_image(ref_img_path)
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imgs.append(ref_img)
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if self._transforms is not None:
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imgs, target = self._transforms(imgs, target)
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return torch.cat(imgs, dim=0), 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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scales = [480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800]
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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_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), is_train =(not args.eval), interval1=args.interval1,
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interval2=args.interval2, num_ref_frames = args.num_ref_frames, return_masks=args.masks, cache_mode=args.cache_mode,
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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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