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# Deformable DETR
# Copyright (c) 2020 SenseTime. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------
# Modified from DETR (https://github.com/facebookresearch/detr)
# 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 random
from pathlib import Path
import datasets.transforms as T
import torch
import torch.utils.data
import torchvision.transforms.functional as F
from pycocotools import mask as coco_mask
from util.misc import get_local_rank, get_local_size
from .torchvision_datasets import CocoDetection as TvCocoDetection
class CocoDetection(TvCocoDetection):
def __init__(
self,
img_folder,
ann_file,
transforms,
return_masks,
cache_mode=False,
local_rank=0,
local_size=1,
test_hflip_aug=False,
tta=False,
is_train=False,
lsj_img_size=1824,
):
super(CocoDetection, self).__init__(
img_folder,
ann_file,
cache_mode=cache_mode,
local_rank=local_rank,
local_size=local_size,
)
self._transforms = transforms
self.prepare = ConvertCocoPolysToMask(return_masks)
self.test_hflip_aug = test_hflip_aug
self.tta = tta
if lsj_img_size == 1728: # for back-compatibility
self.tta_image_size = [1536, 1152,]
else:
self.tta_image_size = [1728, 1536, 1344,]
self.is_train = is_train
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)
if self._transforms is not None:
img, target = self._transforms(img, target)
if self.test_hflip_aug:
flipped_img = torch.flip(img, dims=[-1])
new_img = torch.cat([img, flipped_img], dim=0)
return new_img, target
elif self.tta:
tta_images = [img]
flipped_img = torch.flip(img, dims=[-1])
tta_images.append(flipped_img)
_, height, width = img.shape
max_size_len = height if height >= width else width
for new_max_size in self.tta_image_size:
scale = new_max_size / max_size_len
new_height, new_width = int(scale * height), int(scale * width)
new_img = F.resize(img, size=(new_height, new_width))
tta_images.append(new_img)
flipped_img = torch.flip(new_img, dims=[-1])
tta_images.append(flipped_img)
return tta_images, target
else:
return img, target
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):
self.return_masks = return_masks
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)
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(h), int(w)])
target["size"] = torch.as_tensor([int(h), int(w)])
return image, target
def make_coco_transforms(image_set, bigger):
normalize = T.Compose(
[T.ToTensor(), T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]
)
if "train" in image_set:
scales = [480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800]
if "val" in image_set or "test" in image_set:
scales = [800]
max_size = 1333
if bigger:
scales = [int(1.5 * s) for s in scales]
max_size = 2000
if image_set == "train":
augmentation_list = [
T.RandomHorizontalFlip(),
T.RandomSelect(
T.RandomResize(scales, max_size=max_size),
T.Compose(
[
T.RandomResize([400, 500, 600]),
T.RandomSizeCrop(384, 600),
T.RandomResize(scales, max_size=max_size),
]
),
),
normalize,
]
return T.Compose(augmentation_list)
if image_set == "val":
return T.Compose(
[
T.RandomResize(scales, max_size=max_size),
normalize,
]
)
raise ValueError(f"unknown {image_set}")
def make_coco_transforms_lsj(
image_set, image_size, lsj_img_train_min=480, lsj_strong_aug=False
):
"""
Reference: https://github.com/facebookresearch/detectron2/blob/main/projects/ViTDet/configs/common/coco_loader_lsj.py
import detectron2.data.transforms as T
from detectron2 import model_zoo
from detectron2.config import LazyCall as L
# Data using LSJ
image_size = 1024
dataloader = model_zoo.get_config("common/data/coco.py").dataloader
dataloader.train.mapper.augmentations = [
L(T.RandomFlip)(horizontal=True), # flip first
L(T.ResizeScale)(
min_scale=0.1, max_scale=2.0, target_height=image_size, target_width=image_size
),
L(T.FixedSizeCrop)(crop_size=(image_size, image_size), pad=False),
]
dataloader.train.mapper.image_format = "RGB"
dataloader.train.total_batch_size = 64
# recompute boxes due to cropping
dataloader.train.mapper.recompute_boxes = True
dataloader.test.mapper.augmentations = [
L(T.ResizeShortestEdge)(short_edge_length=image_size, max_size=image_size),
]
"""
"""
In our implementation, we simulate lsj data augmentation by:
(1) first the following augmentations
(2) then padding to (image_size, image_size) in collator, see util/misc/collate_fn_lsj.py
"""
normalize = T.Compose(
[T.ToTensor(), T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]
)
if "train" in image_set:
scales = [scale for scale in range(lsj_img_train_min, image_size, 32)]
if "val" in image_set or "test" in image_set or "unlabel" in image_set:
scales = [image_size - 32]
# max_size = 1333
# if bigger:
# scales = [int(1.5 * s) for s in scales]
# max_size = 2000
max_size = image_size - 32 # for some wired bugs
augmentation_list = []
if "train" in image_set:
if lsj_strong_aug:
augmentation_list.extend(
[
T.ColorJitter((0.4, 0.4, 0.4, 0.1), p=0.5),
T.RandomGrayscale(p=0.2),
# T.RandomErasingP05(),
]
)
augmentation_list.extend(
[
T.RandomHorizontalFlip(),
T.RandomSelect(
# similar to (T.ResizeScale)(min_scale=0.1, max_scale=1.0, target_height=image_size, target_width=image_size) and pad
T.RandomResize(scales, max_size=max_size),
# similar to (T.ResizeScale)(min_scale=1.0, max_scale=2.0, target_height=image_size, target_width=image_size) and crop
T.Compose(
[
T.RandomResize([400, 500, 600]),
T.RandomSizeCrop(384, 600),
T.RandomResize([max_size], max_size=max_size),
]
),
),
normalize,
]
)
return T.Compose(augmentation_list)
if image_set == "val":
return T.Compose(
[
T.RandomResize(scales, max_size=max_size),
normalize,
]
)
raise ValueError(f"unknown {image_set}")
def build(image_set, args):
root = Path(args.coco_path)
assert root.exists(), f"provided COCO path {root} does not exist"
mode = "instances"
PATHS = {
"train": (root / "train2017", root / "annotations" / f"{mode}_train2017.json"),
"val": (root / "val2017", root / "annotations" / f"{mode}_val2017.json"),
}
img_folder, ann_file = PATHS[image_set]
if args.lsj:
coco_transform = make_coco_transforms_lsj(
image_set,
args.lsj_img_size,
args.lsj_img_train_min,
args.lsj_strong_aug,
)
else:
coco_transform = make_coco_transforms(image_set, args.bigger)
dataset = CocoDetection(
img_folder,
ann_file,
transforms=coco_transform,
return_masks=args.masks,
cache_mode=args.cache_mode,
local_rank=get_local_rank(),
local_size=get_local_size(),
test_hflip_aug=args.test_hflip_aug,
tta=args.tta,
is_train=("train" in image_set),
lsj_img_size=args.lsj_img_size,
)
return dataset
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