coco.py

#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. 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
"""
import os
from pathlib import Path
from PIL import Image

import torch
import torch.utils.data
import torchvision
from pycocotools import mask as coco_mask

from detectron2.utils.file_io import PathManager
import detr.datasets.transforms as T


class CocoDetection(torchvision.datasets.CocoDetection):
    def __init__(self, img_folder, ann_file, transforms, return_masks):
        ann_file = PathManager.get_local_path(ann_file)
        super(CocoDetection, self).__init__(img_folder, ann_file)
        self._transforms = transforms
        self.prepare = ConvertCocoPolysToMask(return_masks)

    def _load_image(self, id: int) -> Image.Image:
        path = self.coco.loadImgs(id)[0]["file_name"]
        with PathManager.open(os.path.join(self.root, path), "rb") as f:
            image = Image.open(f).convert("RGB")
        return image

    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


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):

    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, 800]

    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([800], max_size=1333),
            normalize,
        ])

    raise ValueError(f'unknown {image_set}')


def build(image_set, args):
    if "manifold" in args.coco_path:
        root = args.coco_path
        PATHS = {
            "train": (os.path.join(root, "coco_train2017"), "manifold://fair_vision_data/tree/detectron2/json_dataset_annotations/coco/instances_train2017.json"),
            "val": (os.path.join(root, "coco_val2017"), "manifold://fair_vision_data/tree/detectron2/json_dataset_annotations/coco/instances_val2017.json"),
        }
    else:
        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]
    dataset = CocoDetection(img_folder, ann_file, transforms=make_coco_transforms(image_set), return_masks=args.masks)
    return dataset