Implement multi-crop dataset wrapper

1 files changed, 50 insertions, 0 deletions

diff --git a/libs/datautils.py b/libs/datautils.py
index 843f669..feae481 100644
--- a/libs/datautils.py
+++ b/libs/datautils.py
@@ -1,5 +1,8 @@
+from typing import Optional
+
 import numpy as np
 import torch
+from torch.utils.data import Dataset
 from torchvision.transforms import transforms
 
 
@@ -65,3 +68,50 @@ class RandomGaussianBlur(object):
                     img, sigma=np.random.uniform(*self.sigma_range)
                 )
             return img
+
+
+class MultiCropDatasetWrapper(Dataset):
+    """
+    Modified from Facebook SwAV at: https://github.com/facebookresearch/swav/blob/06b1b7cbaf6ba2a792300d79c7299db98b93b7f9/src/multicropdataset.py#L18
+    """
+
+    def __init__(
+            self,
+            dataset: Dataset,
+            n_crops: list[int],
+            crop_sizes: list[tuple[int, int]],
+            crop_scale_ranges: list[tuple[float, float]],
+            other_transforms: Optional[transforms.Compose] = None,
+    ):
+        assert len(crop_sizes) == len(n_crops)
+        assert len(crop_scale_ranges) == len(n_crops)
+
+        if hasattr(dataset, 'transform') and dataset.transform is not None:
+            raise AttributeError('Please pass the transform to wrapper.')
+
+        self.dataset = dataset
+
+        trans = []
+        for crop_size, crop_scale_range, n_crop in zip(
+                crop_sizes, crop_scale_ranges, n_crops
+        ):
+            rand_resize_crop = transforms.RandomResizedCrop(
+                crop_size, scale=crop_scale_range
+            )
+            if other_transforms is not None:
+                trans_i = transforms.Compose([
+                    rand_resize_crop, other_transforms
+                ])
+            else:
+                trans_i = rand_resize_crop
+            trans += [trans_i] * n_crop
+        self.transform = trans
+
+    def __len__(self):
+        return len(self.dataset)
+
+    def __getitem__(self, index):
+        img, target = self.dataset[index]
+        multi_crops = list(map(lambda trans: trans(img), self.transform))
+
+        return multi_crops, target