/
refexp.py
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/
refexp.py
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# Copyright (c) Aishwarya Kamath & Nicolas Carion. Licensed under the Apache License 2.0. All Rights Reserved
# 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
from pycocotools import mask as coco_mask
import datasets.transforms_image as T
class ModulatedDetection(torchvision.datasets.CocoDetection):
def __init__(self, img_folder, ann_file, transforms, return_masks):
super(ModulatedDetection, self).__init__(img_folder, ann_file)
self._transforms = transforms
self.prepare = ConvertCocoPolysToMask(return_masks)
def __getitem__(self, idx):
instance_check = False
while not instance_check:
img, target = super(ModulatedDetection, self).__getitem__(idx)
image_id = self.ids[idx]
coco_img = self.coco.loadImgs(image_id)[0]
caption = coco_img["caption"]
dataset_name = coco_img["dataset_name"] if "dataset_name" in coco_img else None
target = {"image_id": image_id, "annotations": target, "caption": caption}
img, target = self.prepare(img, target)
if self._transforms is not None:
img, target = self._transforms(img, target)
target["dataset_name"] = dataset_name
for extra_key in ["sentence_id", "original_img_id", "original_id", "task_id"]:
if extra_key in coco_img:
target[extra_key] = coco_img[extra_key] # box xyxy -> cxcywh
# FIXME: handle "valid", since some box may be removed due to random crop
target["valid"] = torch.tensor([1]) if len(target["area"]) != 0 else torch.tensor([0])
if torch.any(target['valid'] == 1): # at leatst one instance
instance_check = True
else:
import random
idx = random.randint(0, self.__len__() - 1)
return img.unsqueeze(0), target
# return img: [1, 3, H, W], the first dimension means T = 1.
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"]
caption = target["caption"] if "caption" in target else None
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] # xminyminwh -> xyxy
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)
# keep the valid boxes
keep = (boxes[:, 3] > boxes[:, 1]) & (boxes[:, 2] > boxes[:, 0])
boxes = boxes[keep]
classes = classes[keep]
if self.return_masks:
masks = masks[keep]
target = {}
target["boxes"] = boxes
target["labels"] = classes
if caption is not None:
target["caption"] = caption
if self.return_masks:
target["masks"] = masks
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["valid"] = torch.tensor([1])
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, cautious):
normalize = T.Compose([T.ToTensor(), T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])
scales = [480, 512, 544, 576, 608, 640, 672, 704, 736, 768]
final_scales = [296, 328, 360, 392, 416, 448, 480, 512]
max_size = 800
if image_set == "train":
horizontal = [] if cautious else [T.RandomHorizontalFlip()]
return T.Compose(
horizontal
+ [
T.RandomSelect(
T.RandomResize(scales, max_size=max_size),
T.Compose(
[
T.RandomResize([400, 500, 600]),
T.RandomSizeCrop(384, 600, respect_boxes=cautious),
T.RandomResize(final_scales, max_size=640),
]
),
),
normalize,
]
)
if image_set == "val":
return T.Compose(
[
T.RandomResize([360], max_size=640),
normalize,
]
)
raise ValueError(f"unknown {image_set}")
def build(dataset_file, image_set, args):
root = Path(args.coco_path)
assert root.exists(), f"provided COCO path {root} does not exist"
mode = "instances"
dataset = dataset_file
PATHS = {
"train": (root / "train2014", root / dataset / f"{mode}_{dataset}_train.json"),
"val": (root / "train2014", root / dataset / f"{mode}_{dataset}_val.json"),
}
img_folder, ann_file = PATHS[image_set]
dataset = ModulatedDetection(
img_folder,
ann_file,
transforms=make_coco_transforms(image_set, False),
return_masks=args.masks,
)
return dataset