init

transformers/tests/models/fuyu/test_image_processing_fuyu.py

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+import unittest
+
+import numpy as np
+
+from transformers import is_torch_available, is_vision_available
+from transformers.testing_utils import (
+    require_torch,
+    require_torchvision,
+    require_vision,
+)
+
+
+if is_torch_available() and is_vision_available():
+    import torch
+
+    from transformers import FuyuImageProcessor
+
+if is_vision_available():
+    from PIL import Image
+
+
+@require_torch
+@require_vision
+@require_torchvision
+class TestFuyuImageProcessor(unittest.TestCase):
+    def setUp(self):
+        self.size = {"height": 160, "width": 320}
+        self.processor = FuyuImageProcessor(size=self.size, padding_value=1.0)
+        self.batch_size = 3
+        self.channels = 3
+        self.height = 300
+        self.width = 300
+
+        self.image_input = torch.rand(self.batch_size, self.channels, self.height, self.width)
+
+        self.image_patch_dim_h = 30
+        self.image_patch_dim_w = 30
+        self.sample_image = np.zeros((450, 210, 3), dtype=np.uint8)
+        self.sample_image_pil = Image.fromarray(self.sample_image)
+
+    def test_patches(self):
+        expected_num_patches = self.processor.get_num_patches(image_height=self.height, image_width=self.width)
+
+        patches_final = self.processor.patchify_image(image=self.image_input)
+        assert patches_final.shape[1] == expected_num_patches, (
+            f"Expected {expected_num_patches} patches, got {patches_final.shape[1]}."
+        )
+
+    def test_scale_to_target_aspect_ratio(self):
+        # (h:450, w:210) fitting (160, 320) -> (160, 210*160/450)
+        scaled_image = self.processor.resize(self.sample_image, size=self.size)
+        self.assertEqual(scaled_image.shape[0], 160)
+        self.assertEqual(scaled_image.shape[1], 74)
+
+    def test_apply_transformation_numpy(self):
+        transformed_image = self.processor.preprocess(self.sample_image).images[0][0]
+        self.assertEqual(transformed_image.shape[1], 160)
+        self.assertEqual(transformed_image.shape[2], 320)
+
+    def test_apply_transformation_pil(self):
+        transformed_image = self.processor.preprocess(self.sample_image_pil).images[0][0]
+        self.assertEqual(transformed_image.shape[1], 160)
+        self.assertEqual(transformed_image.shape[2], 320)