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# ------------------------------------------------------------------------
# RF-DETR
# Copyright (c) 2025 Roboflow. All Rights Reserved.
# Licensed under the Apache License, Version 2.0 [see LICENSE for details]
# ------------------------------------------------------------------------
# Modified from LW-DETR (https://github.com/Atten4Vis/LW-DETR)
# Copyright (c) 2024 Baidu. All Rights Reserved.
# ------------------------------------------------------------------------
# Modified from Conditional DETR (https://github.com/Atten4Vis/ConditionalDETR)
# Copyright (c) 2021 Microsoft. All Rights Reserved.
# ------------------------------------------------------------------------
# Copied 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
"""
from pathlib import Path
import torch
import torch.utils.data
import torchvision
import rfdetr.datasets.transforms as T
def compute_multi_scale_scales(resolution, expanded_scales=False):
if resolution == 640:
# assume we're doing the original 640x640 and therefore patch_size is 16
patch_size = 16
elif resolution % (14 * 4) == 0:
# assume we're doing some dinov2 resolution variant and therefore patch_size is 14
patch_size = 14
elif resolution % (16 * 4) == 0:
# assume we're doing some other resolution and therefore patch_size is 16
patch_size = 16
else:
raise ValueError(f"Resolution {resolution} is not divisible by 16*4 or 14*4")
# round to the nearest multiple of 4*patch_size to enable both patching and windowing
base_num_patches_per_window = resolution // (patch_size * 4)
offsets = [-3, -2, -1, 0, 1, 2, 3, 4] if not expanded_scales else [-5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5]
scales = [base_num_patches_per_window + offset for offset in offsets]
proposed_scales = [scale * patch_size * 4 for scale in scales]
proposed_scales = [scale for scale in proposed_scales if scale >= patch_size * 4] # ensure minimum image size
return proposed_scales
class CocoDetection(torchvision.datasets.CocoDetection):
def __init__(self, img_folder, ann_file, transforms):
super(CocoDetection, self).__init__(img_folder, ann_file)
self._transforms = transforms
self.prepare = ConvertCoco()
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)
return img, target
class ConvertCoco(object):
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)
keep = (boxes[:, 3] > boxes[:, 1]) & (boxes[:, 2] > boxes[:, 0])
boxes = boxes[keep]
classes = classes[keep]
target = {}
target["boxes"] = boxes
target["labels"] = classes
target["image_id"] = image_id
# 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, resolution, multi_scale=False, expanded_scales=False):
normalize = T.Compose([
T.ToTensor(),
T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
scales = [resolution]
if multi_scale:
# scales = [448, 512, 576, 640, 704, 768, 832, 896]
scales = compute_multi_scale_scales(resolution, expanded_scales)
print(scales)
if image_set == 'train':
return T.Compose([
T.RandomHorizontalFlip(),
T.RandomSelect(
T.RandomResize(scales, max_size=1333),
T.Compose([
T.RandomResize([400, 500, 600]),
T.RandomSizeCrop(384, 600),
T.RandomResize(scales, max_size=1333),
])
),
normalize,
])
if image_set == 'val':
return T.Compose([
T.RandomResize([resolution], max_size=1333),
normalize,
])
if image_set == 'val_speed':
return T.Compose([
T.SquareResize([resolution]),
normalize,
])
raise ValueError(f'unknown {image_set}')
def make_coco_transforms_square_div_64(image_set, resolution, multi_scale=False, expanded_scales=False):
"""
"""
normalize = T.Compose([
T.ToTensor(),
T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
scales = [resolution]
if multi_scale:
# scales = [448, 512, 576, 640, 704, 768, 832, 896]
scales = compute_multi_scale_scales(resolution, expanded_scales)
print(scales)
if image_set == 'train':
return T.Compose([
T.RandomHorizontalFlip(),
T.RandomSelect(
T.SquareResize(scales),
T.Compose([
T.RandomResize([400, 500, 600]),
T.RandomSizeCrop(384, 600),
T.SquareResize(scales),
]),
),
normalize,
])
if image_set == 'val':
return T.Compose([
T.SquareResize([resolution]),
normalize,
])
if image_set == 'val_speed':
return T.Compose([
T.SquareResize([resolution]),
normalize,
])
raise ValueError(f'unknown {image_set}')
def build(image_set, args, resolution):
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'),
"test": (root / "test2017", root / "annotations" / f'image_info_test-dev2017.json'),
}
img_folder, ann_file = PATHS[image_set.split("_")[0]]
try:
square_resize = args.square_resize
except:
square_resize = False
try:
square_resize_div_64 = args.square_resize_div_64
except:
square_resize_div_64 = False
if square_resize_div_64:
dataset = CocoDetection(img_folder, ann_file, transforms=make_coco_transforms_square_div_64(image_set, resolution, multi_scale=args.multi_scale, expanded_scales=args.expanded_scales))
else:
dataset = CocoDetection(img_folder, ann_file, transforms=make_coco_transforms(image_set, resolution, multi_scale=args.multi_scale, expanded_scales=args.expanded_scales))
return dataset
def build_roboflow(image_set, args, resolution):
root = Path(args.dataset_dir)
assert root.exists(), f'provided Roboflow path {root} does not exist'
mode = 'instances'
PATHS = {
"train": (root / "train", root / "train" / "_annotations.coco.json"),
"val": (root / "valid", root / "valid" / "_annotations.coco.json"),
"test": (root / "test", root / "test" / "_annotations.coco.json"),
}
img_folder, ann_file = PATHS[image_set.split("_")[0]]
try:
square_resize = args.square_resize
except:
square_resize = False
try:
square_resize_div_64 = args.square_resize_div_64
except:
square_resize_div_64 = False
if square_resize_div_64:
dataset = CocoDetection(img_folder, ann_file, transforms=make_coco_transforms_square_div_64(image_set, resolution, multi_scale=args.multi_scale))
else:
dataset = CocoDetection(img_folder, ann_file, transforms=make_coco_transforms(image_set, resolution, multi_scale=args.multi_scale))
return dataset
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