[Fix]: Support numpy array in random crop resize with two pic

mmselfsup/datasets/pipelines/transforms.py

@@ -4,6 +4,7 @@ import random
 import warnings
 from typing import Dict, List, Optional, Sequence, Tuple, Union
 
+import cv2
 import numpy as np
 import torch
 import torchvision.transforms.functional as F
@@ -265,7 +266,8 @@ class RandomResizedCropAndInterpolationWithTwoPic(BaseTransform):
             tuple: params (i, j, h, w) to be passed to ``crop`` for a random
                 sized crop.
         """
-        area = img.size[0] * img.size[1]
+        img_h, img_w = img.shape[:2]
+        area = img_h * img_w
 
         for _ in range(10):
             target_area = random.uniform(*scale) * area
@@ -275,24 +277,24 @@ class RandomResizedCropAndInterpolationWithTwoPic(BaseTransform):
             w = int(round(math.sqrt(target_area * aspect_ratio)))
             h = int(round(math.sqrt(target_area / aspect_ratio)))
 
-            if w <= img.size[0] and h <= img.size[1]:
-                i = random.randint(0, img.size[1] - h)
-                j = random.randint(0, img.size[0] - w)
+            if w <= img_w and h <= img_h:
+                i = random.randint(0, img_h - h)
+                j = random.randint(0, img_w - w)
                 return i, j, h, w
 
         # Fallback to central crop
-        in_ratio = img.size[0] / img.size[1]
+        in_ratio = img_w / img_h
         if in_ratio < min(ratio):
-            w = img.size[0]
+            w = img_w
             h = int(round(w / min(ratio)))
         elif in_ratio > max(ratio):
-            h = img.size[1]
+            h = img_h
             w = int(round(h * max(ratio)))
         else:  # whole image
-            w = img.size[0]
-            h = img.size[1]
-        i = (img.size[1] - h) // 2
-        j = (img.size[0] - w) // 2
+            w = img_w
+            h = img_h
+        i = (img_h - h) // 2
+        j = (img_w - w) // 2
         return i, j, h, w
 
     def transform(self, results: Dict) -> Dict:
@@ -303,14 +305,17 @@ class RandomResizedCropAndInterpolationWithTwoPic(BaseTransform):
         else:
             interpolation = self.interpolation
         if self.second_size is None:
-            img = F.resized_crop(img, i, j, h, w, self.size, interpolation)
+            img = img[i:i + h, j:j + w]
+            img = cv2.resize(img, self.size, interpolation=interpolation)
             results.update({'img': img})
 
         else:
-            img = F.resized_crop(img, i, j, h, w, self.size, interpolation)
-            img_target = F.resized_crop(img, i, j, h, w, self.second_size,
-                                        self.second_interpolation)
-            results.update({'img': img, 'target_img': img_target})
+            img = img[i:i + h, j:j + w]
+            img_sample = cv2.resize(
+                img, self.size, interpolation=interpolation)
+            img_target = cv2.resize(
+                img, self.second_size, interpolation=self.second_interpolation)
+            results.update({'img': [img_sample, img_target]})
         return results
 
     def __repr__(self) -> str:

tests/test_datasets/test_pipelines/test_transforms.py

@@ -2,7 +2,6 @@
 import numpy as np
 import pytest
 import torch
-from PIL import Image
 
 from mmselfsup.datasets.pipelines import (
     BEiTMaskGenerator, Lighting, RandomGaussianBlur, RandomPatchWithLabels,
@@ -55,14 +54,12 @@ def test_random_resize_crop_with_two_pic():
         scale=(0.08, 1.0))
     module = RandomResizedCropAndInterpolationWithTwoPic(**transform)
     fake_input = torch.rand((224, 224, 3)).numpy().astype(np.uint8)
-    fake_input = Image.fromarray(fake_input)
 
     results = {'img': fake_input}
     results = module(results)
-
     # test transform
-    assert list(results['img'].size) == [224, 224]
-    assert list(results['target_img'].size) == [112, 112]
+    assert list(results['img'][0].shape) == [224, 224, 3]
+    assert list(results['img'][1].shape) == [112, 112, 3]
 
     # test repr
     assert isinstance(str(module), str)