Create image_list_dataset.py

3rdparty/densepose/data/image_list_dataset.py

@@ -0,0 +1,72 @@
+# -*- coding: utf-8 -*-
+# Copyright (c) Facebook, Inc. and its affiliates.
+
+import logging
+import numpy as np
+from typing import Any, Callable, Dict, List, Optional, Union
+import torch
+from torch.utils.data.dataset import Dataset
+
+from detectron2.data.detection_utils import read_image
+
+ImageTransform = Callable[[torch.Tensor], torch.Tensor]
+
+
+class ImageListDataset(Dataset):
+    """
+    Dataset that provides images from a list.
+    """
+
+    _EMPTY_IMAGE = torch.empty((0, 3, 1, 1))
+
+    def __init__(
+        self,
+        image_list: List[str],
+        category_list: Union[str, List[str], None] = None,
+        transform: Optional[ImageTransform] = None,
+    ):
+        """
+        Args:
+            image_list (List[str]): list of paths to image files
+            category_list (Union[str, List[str], None]): list of animal categories for
+                each image. If it is a string, or None, this applies to all images
+        """
+        if type(category_list) == list:
+            self.category_list = category_list
+        else:
+            self.category_list = [category_list] * len(image_list)
+        assert len(image_list) == len(
+            self.category_list
+        ), "length of image and category lists must be equal"
+        self.image_list = image_list
+        self.transform = transform
+
+    def __getitem__(self, idx: int) -> Dict[str, Any]:
+        """
+        Gets selected images from the list
+        
+        Args:
+            idx (int): video index in the video list file
+        Returns:
+            A dictionary containing two keys:
+                images (torch.Tensor): tensor of size [N, 3, H, W] (N = 1, or 0 for _EMPTY_IMAGE)
+                categories (List[str]): categories of the frames
+        """
+        categories = [self.category_list[idx]]
+        fpath = self.image_list[idx]
+        transform = self.transform
+
+        try:
+            image = torch.from_numpy(np.ascontiguousarray(read_image(fpath, format="BGR")))
+            image = image.permute(2, 0, 1).unsqueeze(0).float()  # HWC -> NCHW
+            if transform is not None:
+                image = transform(image)
+            return {"images": image, "categories": categories}
+        except (OSError, RuntimeError) as e:
+            logger = logging.getLogger(__name__)
+            logger.warning(f"Error opening image file container {fpath}: {e}")
+
+        return {"images": self._EMPTY_IMAGE, "categories": []}
+
+    def __len__(self):
+        return len(self.image_list)