[CI]【Hackathon 9th Sprint Example NO 25】功能模块 fastdeploy/input/ernie4_5_vl_processor/image_preprocessor/image_preprocessor_adaptive.py 单测补充 #5265
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Pull request overview
This PR adds comprehensive unit tests for the image_preprocessor_adaptive.py module as part of Hackathon Stage 9 Task 25. The tests aim to achieve high code coverage for the adaptive image preprocessing functionality used in the ERNIE 4.5 VL processor.
Key Changes:
- Added 799 lines of unit tests covering helper functions (
round_by_factor,ceil_by_factor,floor_by_factor,smart_resize, etc.) - Extensive testing of the
AdaptiveImageProcessorclass and its preprocessing methods - Tests for image and video batching utilities
You can also share your feedback on Copilot code review for a chance to win a $100 gift card. Take the survey.
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| def test_is_scaled_image_uint8(self): | ||
| """Test is_scaled_image for uint8 image""" | ||
| image = np.array([[0, 255], [128, 200]], dtype=np.uint8) | ||
| self.assertFalse(is_scaled_image(image)) | ||
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| def test_is_scaled_image_scaled(self): | ||
| """Test is_scaled_image for scaled image""" | ||
| image = np.array([[0.0, 0.5], [0.3, 1.0]], dtype=np.float32) | ||
| self.assertTrue(is_scaled_image(image)) | ||
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| def test_is_scaled_image_not_scaled(self): | ||
| """Test is_scaled_image for unscaled float image""" | ||
| image = np.array([[0.0, 255.0], [128.0, 300.0]], dtype=np.float32) | ||
| self.assertFalse(is_scaled_image(image)) | ||
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| def test_make_batched_images_single(self): | ||
| """Test make_batched_images handling single image""" | ||
| img = Image.new("RGB", (224, 224)) | ||
| result = make_batched_images(img) | ||
| self.assertEqual(len(result), 1) | ||
| self.assertEqual(result[0], img) | ||
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| def test_make_batched_images_list(self): | ||
| """Test make_batched_images handling image list""" | ||
| imgs = [Image.new("RGB", (224, 224)) for _ in range(3)] | ||
| result = make_batched_images(imgs) | ||
| self.assertEqual(len(result), 3) | ||
| self.assertEqual(result, imgs) | ||
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| def test_make_batched_images_nested_list(self): | ||
| """Test make_batched_images handling nested list""" | ||
| imgs = [[Image.new("RGB", (224, 224)) for _ in range(2)] for _ in range(2)] | ||
| result = make_batched_images(imgs) | ||
| self.assertEqual(len(result), 4) # 2*2 = 4 | ||
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| def test_make_batched_images_invalid(self): | ||
| """Test make_batched_images handling invalid input""" | ||
| with self.assertRaises(ValueError): | ||
| make_batched_images("invalid") | ||
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| def test_make_batched_videos_list_of_images(self): | ||
| """Test make_batched_videos handling image list""" | ||
| imgs = [Image.new("RGB", (224, 224)) for _ in range(3)] | ||
| result = make_batched_videos(imgs) | ||
| self.assertEqual(len(result), 1) | ||
| self.assertEqual(len(result[0]), 3) | ||
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| def test_make_batched_videos_nested_list(self): | ||
| """Test make_batched_videos handling nested list""" | ||
| imgs = [[Image.new("RGB", (224, 224)) for _ in range(2)] for _ in range(2)] | ||
| result = make_batched_videos(imgs) | ||
| self.assertEqual(len(result), 2) | ||
| self.assertEqual(len(result[0]), 2) | ||
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| def test_make_batched_videos_4d_array(self): | ||
| """Test make_batched_videos handling 4D array""" | ||
| video = np.random.rand(3, 224, 224, 3).astype(np.uint8) | ||
| result = make_batched_videos(video) | ||
| self.assertEqual(len(result), 1) | ||
| self.assertIsInstance(result[0], list) | ||
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| def test_make_batched_videos_invalid(self): | ||
| """Test make_batched_videos handling invalid input""" | ||
| with self.assertRaises(ValueError): | ||
| make_batched_videos("invalid") | ||
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| def test_preprocess_images(self): | ||
| """Test preprocess handling images""" | ||
| img = Image.new("RGB", (224, 224)) | ||
| result = self.processor.preprocess(images=img) | ||
| self.assertIn("pixel_values", result) | ||
| self.assertIn("image_grid_thw", result) | ||
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| def test_preprocess_videos(self): | ||
| """Test preprocess handling videos""" | ||
| frames = [Image.new("RGB", (224, 224)) for _ in range(4)] | ||
| result = self.processor.preprocess(images=None, videos=frames) | ||
| self.assertIn("pixel_values_videos", result) | ||
| self.assertIn("video_grid_thw", result) | ||
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| def test_preprocess_both_images_and_videos(self): | ||
| """Test preprocess handling both images and videos""" | ||
| img = Image.new("RGB", (224, 224)) | ||
| frames = [Image.new("RGB", (224, 224)) for _ in range(4)] | ||
| result = self.processor.preprocess(images=img, videos=frames) | ||
| # When both images and videos are provided, may only return videos result | ||
| # According to code logic, if videos is not None, it will overwrite data dict | ||
| self.assertTrue("pixel_values" in result or "pixel_values_videos" in result) | ||
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| def test_preprocess_invalid_images(self): | ||
| """Test preprocess handling invalid image""" | ||
| with self.assertRaises(ValueError): | ||
| self.processor.preprocess(images="invalid") | ||
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| def test_preprocess_with_predetermined_grid_thw(self): | ||
| """Test preprocess using predetermined_grid_thw""" | ||
| img = Image.new("RGB", (224, 224)) | ||
| # predetermined_grid_thw should be (h, w) format, not [1, h, w] | ||
| predetermined_grid_thw = [(16, 16)] # For single image, should be (h, w) tuple | ||
| result = self.processor.preprocess(images=img, predetermined_grid_thw=predetermined_grid_thw) | ||
| self.assertIn("pixel_values", result) | ||
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| def test_preprocess_no_resize(self): | ||
| """Test preprocess without resize""" | ||
| img = Image.new("RGB", (224, 224)) | ||
| result = self.processor.preprocess(images=img, do_resize=False) | ||
| self.assertIn("pixel_values", result) | ||
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| def test_preprocess_no_rescale(self): | ||
| """Test preprocess without rescale""" | ||
| img = Image.new("RGB", (224, 224)) | ||
| result = self.processor.preprocess(images=img, do_rescale=False) | ||
| self.assertIn("pixel_values", result) | ||
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| def test_preprocess_no_normalize(self): | ||
| """Test preprocess without normalize""" | ||
| img = Image.new("RGB", (224, 224)) | ||
| result = self.processor.preprocess(images=img, do_normalize=False) | ||
| self.assertIn("pixel_values", result) | ||
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| def test_preprocess_custom_mean_std(self): | ||
| """Test preprocess using custom mean and std""" | ||
| img = Image.new("RGB", (224, 224)) | ||
| result = self.processor.preprocess(images=img, image_mean=[0.5, 0.5, 0.5], image_std=[0.5, 0.5, 0.5]) | ||
| self.assertIn("pixel_values", result) | ||
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| def test_make_batched_videos_4d_array_in_list(self): | ||
| """Test make_batched_videos handling 4D array in list (lines 119-120)""" | ||
| # Create a list of 4D arrays | ||
| videos = [np.random.rand(3, 224, 224, 3).astype(np.uint8)] | ||
| result = make_batched_videos(videos) | ||
| self.assertEqual(len(result), 1) | ||
| self.assertIsInstance(result[0], list) | ||
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| def test_preprocess_do_convert_rgb(self): | ||
| """Test preprocess with do_convert_rgb=True (line 289)""" | ||
| img = Image.new("L", (224, 224)) # Grayscale image | ||
| result = self.processor.preprocess(images=img, do_convert_rgb=True) | ||
| self.assertIn("pixel_values", result) | ||
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| def test_preprocess_scaled_image_warning(self): | ||
| """Test warning for scaled image in preprocess (line 295)""" | ||
| # Create a scaled image (values between 0-1) | ||
| img_array = np.random.rand(224, 224, 3).astype(np.float32) | ||
| # Use patch to capture warning | ||
| with patch( | ||
| "fastdeploy.input.ernie4_5_vl_processor.image_preprocessor.image_preprocessor_adaptive.data_processor_logger" | ||
| ) as mock_logger: | ||
| # Directly call _preprocess, pass scaled image | ||
| self.processor._preprocess( | ||
| [img_array], # Pass scaled numpy array | ||
| do_rescale=True, | ||
| do_convert_rgb=False, | ||
| ) | ||
| # Verify warning is called (if is_scaled_image returns True) | ||
| # mock_logger.warning should be called | ||
| if is_scaled_image(img_array): | ||
| # If image is indeed scaled, warning should be called | ||
| mock_logger.warning.assert_called() | ||
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| def test_preprocess_data_format_last(self): | ||
| """Test preprocess with data_format=LAST (line 351)""" | ||
| img = Image.new("RGB", (224, 224)) | ||
| from paddleformers.transformers.image_utils import ChannelDimension | ||
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| result = self.processor.preprocess(images=img, data_format=ChannelDimension.LAST) | ||
| self.assertIn("pixel_values", result) | ||
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| def test_preprocess_invalid_images_check(self): | ||
| """Test invalid image check in preprocess (line 464)""" | ||
| # Test invalid image type - need to ensure valid_images returns False | ||
| # Use patch to make valid_images return False, but make_batched_images succeeds | ||
| with patch( | ||
| "fastdeploy.input.ernie4_5_vl_processor.image_preprocessor.image_preprocessor_adaptive.valid_images" | ||
| ) as mock_valid: | ||
| mock_valid.return_value = False | ||
| valid_images_list = [Image.new("RGB", (224, 224))] # Valid image, but valid_images returns False | ||
| with self.assertRaises(ValueError) as context: | ||
| self.processor.preprocess(images=valid_images_list) | ||
| self.assertIn("Invalid image type", str(context.exception)) | ||
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| def test_smart_resize_high_aspect_ratio_height_gt_width(self): | ||
| """Test smart_resize when aspect ratio exceeds MAX_RATIO, height > width case (lines 558-560)""" | ||
| height, width = 10000, 10 # height > width, aspect ratio = 1000 | ||
| new_h, new_w = smart_resize(height, width, factor=28, min_pixels=56 * 56, max_pixels=28 * 28 * 1280) | ||
| self.assertIsInstance(new_h, int) | ||
| self.assertIsInstance(new_w, int) | ||
| self.assertLessEqual(max(new_h, new_w) / min(new_h, new_w), 200) | ||
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| def test_smart_resize_high_aspect_ratio_width_gt_height(self): | ||
| """Test smart_resize when aspect ratio exceeds MAX_RATIO, width > height case (lines 561-563)""" | ||
| height, width = 10, 10000 # width > height, aspect ratio = 1000 | ||
| new_h, new_w = smart_resize(height, width, factor=28, min_pixels=56 * 56, max_pixels=28 * 28 * 1280) | ||
| self.assertIsInstance(new_h, int) | ||
| self.assertIsInstance(new_w, int) | ||
| self.assertLessEqual(max(new_h, new_w) / min(new_h, new_w), 200) | ||
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| def test_is_scaled_image_edge_cases(self): | ||
| """Test is_scaled_image edge cases (lines 80-84)""" | ||
| # Test with values exactly at boundaries | ||
| image1 = np.array([[0.0, 1.0]], dtype=np.float32) | ||
| self.assertTrue(is_scaled_image(image1)) | ||
| image2 = np.array([[0.0, 1.1]], dtype=np.float32) | ||
| self.assertFalse(is_scaled_image(image2)) | ||
| image3 = np.array([[-0.1, 1.0]], dtype=np.float32) | ||
| self.assertFalse(is_scaled_image(image3)) | ||
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| def test_make_batched_images_nested_list_edge_case(self): | ||
| """Test make_batched_images with nested list edge case (lines 98-107)""" | ||
| # Test with nested list where first element is a list of images | ||
| imgs = [[Image.new("RGB", (224, 224)) for _ in range(2)] for _ in range(2)] | ||
| result = make_batched_images(imgs) | ||
| self.assertEqual(len(result), 4) | ||
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| def test_make_batched_videos_edge_cases(self): | ||
| """Test make_batched_videos edge cases (lines 113-125)""" | ||
| # Test with single Image.Image in list | ||
| img = Image.new("RGB", (224, 224)) | ||
| result = make_batched_videos([img]) | ||
| self.assertEqual(len(result), 1) | ||
| self.assertEqual(len(result[0]), 1) | ||
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| # Test with 4D array (video) | ||
| video = np.random.rand(3, 224, 224, 3).astype(np.uint8) | ||
| result = make_batched_videos(video) | ||
| self.assertEqual(len(result), 1) | ||
| self.assertIsInstance(result[0], list) | ||
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| def test_preprocess_predetermined_grid_thw_multiple_images(self): | ||
| """Test preprocess with predetermined_grid_thw for multiple images (lines 307-310)""" | ||
| imgs = [Image.new("RGB", (224, 224)) for _ in range(2)] | ||
| predetermined_grid_thw = [(16, 16), (20, 20)] | ||
| result = self.processor.preprocess(images=imgs, predetermined_grid_thw=predetermined_grid_thw) | ||
| self.assertIn("pixel_values", result) | ||
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| def test_preprocess_predetermined_grid_thw_length_mismatch(self): | ||
| """Test preprocess with predetermined_grid_thw length mismatch (lines 308-310) | ||
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| Note: The implementation raises IndexError when predetermined_grid_thw length | ||
| doesn't match images length, because it accesses predetermined_grid_thw[img_idx] | ||
| directly without checking bounds first. | ||
| """ | ||
| imgs = [Image.new("RGB", (224, 224)) for _ in range(2)] | ||
| predetermined_grid_thw = [(16, 16)] # Length mismatch - only 1 element for 2 images | ||
| # The function raises IndexError when accessing predetermined_grid_thw[1] | ||
| with self.assertRaises(IndexError): | ||
| self.processor.preprocess(images=imgs, predetermined_grid_thw=predetermined_grid_thw) | ||
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| def test_preprocess_with_input_data_format(self): | ||
| """Test preprocess with input_data_format parameter (lines 299-301)""" | ||
| img = Image.new("RGB", (224, 224)) | ||
| from paddleformers.transformers.image_utils import ChannelDimension | ||
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| result = self.processor.preprocess(images=img, input_data_format=ChannelDimension.FIRST) | ||
| self.assertIn("pixel_values", result) | ||
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| def test_preprocess_do_resize_with_predetermined_grid_thw(self): | ||
| """Test preprocess with do_resize=True and predetermined_grid_thw (lines 314-317)""" | ||
| img = Image.new("RGB", (224, 224)) | ||
| predetermined_grid_thw = [(16, 16)] | ||
| result = self.processor.preprocess(images=img, predetermined_grid_thw=predetermined_grid_thw, do_resize=True) | ||
| self.assertIn("pixel_values", result) | ||
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| def test_preprocess_videos_with_predetermined_grid_thw(self): | ||
| """Test preprocess videos with predetermined_grid_thw (lines 511)""" | ||
| frames = [Image.new("RGB", (224, 224)) for _ in range(4)] | ||
| predetermined_grid_thw = [(16, 16)] * 4 | ||
| result = self.processor.preprocess(images=None, videos=frames, predetermined_grid_thw=predetermined_grid_thw) | ||
| self.assertIn("pixel_values_videos", result) | ||
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| def test_preprocess_multiple_images_loop(self): | ||
| """Test preprocess with multiple images in loop (lines 468-488)""" | ||
| imgs = [Image.new("RGB", (224, 224)) for _ in range(3)] | ||
| result = self.processor.preprocess(images=imgs) | ||
| self.assertIn("pixel_values", result) | ||
| self.assertIn("image_grid_thw", result) | ||
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| def test_preprocess_videos_loop(self): | ||
| """Test preprocess with videos in loop (lines 496-521)""" | ||
| videos = [[Image.new("RGB", (224, 224)) for _ in range(4)] for _ in range(2)] | ||
| result = self.processor.preprocess(images=None, videos=videos) | ||
| self.assertIn("pixel_values_videos", result) | ||
| self.assertIn("video_grid_thw", result) | ||
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| def test_preprocess_return_tensors(self): | ||
| """Test preprocess with return_tensors parameter (lines 396, 523)""" | ||
| img = Image.new("RGB", (224, 224)) | ||
| # Use string instead of TensorType enum which may not be available | ||
| result = self.processor.preprocess(images=img, return_tensors="np") | ||
| self.assertIn("pixel_values", result) | ||
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| def test_preprocess_channel_dimension_none(self): | ||
| """Test preprocess with input_data_format=None (lines 299-301)""" | ||
| img = Image.new("RGB", (224, 224)) | ||
| result = self.processor.preprocess(images=img, input_data_format=None) | ||
| self.assertIn("pixel_values", result) | ||
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| def test_preprocess_do_rescale_false_with_scaled_image(self): | ||
| """Test preprocess with do_rescale=False and scaled image (line 335)""" | ||
| # Create a scaled image | ||
| img_array = np.random.rand(224, 224, 3).astype(np.float32) * 0.5 # Values in [0, 0.5] | ||
| img = Image.fromarray((img_array * 255).astype(np.uint8)) | ||
| result = self.processor.preprocess(images=img, do_rescale=False) | ||
| self.assertIn("pixel_values", result) | ||
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| def test_preprocess_do_normalize_false(self): | ||
| """Test preprocess with do_normalize=False (lines 338-344)""" | ||
| img = Image.new("RGB", (224, 224)) | ||
| result = self.processor.preprocess(images=img, do_normalize=False) | ||
| self.assertIn("pixel_values", result) | ||
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| def test_preprocess_custom_resample(self): | ||
| """Test preprocess with custom resample parameter (line 332)""" | ||
| img = Image.new("RGB", (224, 224)) | ||
| from PIL import Image as PILImage | ||
|
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| result = self.processor.preprocess(images=img, resample=PILImage.BILINEAR) | ||
| self.assertIn("pixel_values", result) | ||
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| def test_preprocess_custom_rescale_factor(self): | ||
| """Test preprocess with custom rescale_factor (line 336)""" | ||
| img = Image.new("RGB", (224, 224)) | ||
| result = self.processor.preprocess(images=img, rescale_factor=1.0 / 128.0) | ||
| self.assertIn("pixel_values", result) | ||
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| def test_preprocess_custom_image_mean_std(self): | ||
| """Test preprocess with custom image_mean and image_std (lines 339-344)""" | ||
| img = Image.new("RGB", (224, 224)) | ||
| result = self.processor.preprocess( | ||
| images=img, image_mean=[0.485, 0.456, 0.406], image_std=[0.229, 0.224, 0.225] | ||
| ) | ||
| self.assertIn("pixel_values", result) | ||
|
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| def test_preprocess_data_format_channels_first(self): | ||
| """Test preprocess with data_format=FIRST (line 346)""" | ||
| img = Image.new("RGB", (224, 224)) | ||
| from paddleformers.transformers.image_utils import ChannelDimension | ||
|
|
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| result = self.processor.preprocess(images=img, data_format=ChannelDimension.FIRST) | ||
| self.assertIn("pixel_values", result) | ||
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| def test_preprocess_data_format_channels_last(self): | ||
| """Test preprocess with data_format=LAST (line 350)""" | ||
| img = Image.new("RGB", (224, 224)) | ||
| from paddleformers.transformers.image_utils import ChannelDimension | ||
|
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| result = self.processor.preprocess(images=img, data_format=ChannelDimension.LAST) | ||
| self.assertIn("pixel_values", result) | ||
|
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| def test_preprocess_patches_reshape(self): | ||
| """Test preprocess patches reshape logic (lines 349-381)""" | ||
| img = Image.new("RGB", (224, 224)) | ||
| result = self.processor.preprocess(images=img) | ||
| self.assertIn("pixel_values", result) | ||
| # Verify pixel_values shape | ||
| pixel_values = result["pixel_values"] | ||
| self.assertIsInstance(pixel_values, np.ndarray) | ||
|
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| def test_preprocess_videos_multiple(self): | ||
| """Test preprocess with multiple videos (lines 496-521)""" | ||
| videos = [ | ||
| [Image.new("RGB", (224, 224)) for _ in range(4)], | ||
| [Image.new("RGB", (224, 224)) for _ in range(4)], | ||
| ] | ||
| result = self.processor.preprocess(images=None, videos=videos) | ||
| self.assertIn("pixel_values_videos", result) | ||
| self.assertIn("video_grid_thw", result) | ||
|
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| def test_make_batched_images_invalid_nested_list(self): | ||
| """Test make_batched_images with invalid nested list (line 98)""" | ||
| # Test with nested list but first element is not an image | ||
| invalid_input = [[1, 2, 3], [4, 5, 6]] | ||
| with self.assertRaises(ValueError) as context: | ||
| make_batched_images(invalid_input) | ||
| self.assertIn("Could not make batched images", str(context.exception)) | ||
|
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| def test_make_batched_images_invalid_single(self): | ||
| """Test make_batched_images with invalid single input (line 107)""" | ||
| invalid_input = "not an image" | ||
| with self.assertRaises(ValueError) as context: | ||
| make_batched_images(invalid_input) | ||
| self.assertIn("Could not make batched images", str(context.exception)) | ||
|
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| def test_make_batched_videos_nested_list_of_images(self): | ||
| """Test make_batched_videos with nested list of images (line 113)""" | ||
| images = [[Image.new("RGB", (224, 224)) for _ in range(2)]] | ||
| result = make_batched_videos(images) | ||
| self.assertEqual(result, images) | ||
|
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| def test_make_batched_videos_list_of_images_nested_output(self): | ||
| """Test make_batched_videos with list of images (line 117)""" | ||
| images = [Image.new("RGB", (224, 224)) for _ in range(2)] | ||
| result = make_batched_videos(images) | ||
| self.assertEqual(result, [images]) | ||
|
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| def test_make_batched_videos_4d_array_in_list_variant(self): | ||
| """Test make_batched_videos with 4D array in list (line 119) | ||
|
|
||
| Note: make_batched_videos expects 4D array (time, height, width, channels), | ||
| not 5D array (batch, time, height, width, channels). | ||
| """ | ||
| # Create a 4D numpy array (time, height, width, channels) | ||
| video_array = np.random.rand(4, 224, 224, 3).astype(np.uint8) | ||
| result = make_batched_videos([video_array]) | ||
| self.assertIsInstance(result, list) | ||
|
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| def test_make_batched_videos_4d_array_single(self): | ||
| """Test make_batched_videos with single 4D array (line 122) | ||
|
|
||
| Note: make_batched_videos expects 4D array (time, height, width, channels), | ||
| not 5D array (batch, time, height, width, channels). | ||
| """ | ||
| # Create a 4D numpy array (time, height, width, channels) | ||
| video_array = np.random.rand(4, 224, 224, 3).astype(np.uint8) | ||
| result = make_batched_videos(video_array) | ||
| self.assertIsInstance(result, list) | ||
|
|
||
| def test_make_batched_videos_invalid_input(self): | ||
| """Test make_batched_videos with invalid input (line 125)""" | ||
| invalid_input = "not a video" | ||
| with self.assertRaises(ValueError) as context: | ||
| make_batched_videos(invalid_input) | ||
| self.assertIn("Could not make batched video", str(context.exception)) | ||
|
|
||
| def test_is_scaled_image_uint8_false(self): | ||
| """Test is_scaled_image with uint8 image (line 80)""" | ||
| image = np.random.rand(224, 224, 3).astype(np.uint8) * 255 | ||
| result = is_scaled_image(image) | ||
| self.assertFalse(result) | ||
|
|
||
| def test_is_scaled_image_scaled_true(self): | ||
| """Test is_scaled_image with scaled float image (line 84)""" | ||
| image = np.random.rand(224, 224, 3).astype(np.float32) * 0.5 # Values in [0, 0.5] | ||
| result = is_scaled_image(image) | ||
| self.assertTrue(result) | ||
|
|
||
| def test_is_scaled_image_not_scaled_false(self): | ||
| """Test is_scaled_image with non-scaled float image (line 84)""" | ||
| image = np.random.rand(224, 224, 3).astype(np.float32) * 255 # Values > 1 | ||
| result = is_scaled_image(image) | ||
| self.assertFalse(result) |
There was a problem hiding this comment.
Duplicate test coverage: The following test pairs test identical functionality:
test_is_scaled_image_uint8(line 152) andtest_is_scaled_image_uint8_false(line 576)test_is_scaled_image_scaled(line 157) andtest_is_scaled_image_scaled_true(line 582)test_is_scaled_image_not_scaled(line 162) andtest_is_scaled_image_not_scaled_false(line 588)test_make_batched_images_invalid(line 187) andtest_make_batched_images_invalid_single(line 528)test_make_batched_videos_invalid(line 213) andtest_make_batched_videos_invalid_input(line 569)test_make_batched_videos_4d_array(line 206) andtest_make_batched_videos_4d_array_single(line 558)
Recommendation: Remove the duplicate tests to reduce maintenance overhead and improve test suite clarity.
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| def test_is_scaled_image_uint8_false(self): | ||
| """Test is_scaled_image with uint8 image (line 80)""" | ||
| image = np.random.rand(224, 224, 3).astype(np.uint8) * 255 | ||
| result = is_scaled_image(image) | ||
| self.assertFalse(result) | ||
|
|
||
| def test_is_scaled_image_scaled_true(self): | ||
| """Test is_scaled_image with scaled float image (line 84)""" | ||
| image = np.random.rand(224, 224, 3).astype(np.float32) * 0.5 # Values in [0, 0.5] | ||
| result = is_scaled_image(image) | ||
| self.assertTrue(result) | ||
|
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||
| def test_is_scaled_image_not_scaled_false(self): | ||
| """Test is_scaled_image with non-scaled float image (line 84)""" | ||
| image = np.random.rand(224, 224, 3).astype(np.float32) * 255 # Values > 1 | ||
| result = is_scaled_image(image) | ||
| self.assertFalse(result) |
There was a problem hiding this comment.
[nitpick] Inconsistent test naming convention. Some test names use descriptive suffixes (e.g., test_is_scaled_image_uint8_false, test_is_scaled_image_scaled_true) while others don't (e.g., test_is_scaled_image_uint8, test_is_scaled_image_scaled).
Recommendation: Use consistent naming. Either:
- Remove the redundant
_true/_falsesuffixes (the test name already implies the expected result) - Or use them consistently across all tests
The first option is preferred as it's more concise and the assertion in the test body already makes the expected behavior clear.
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| class TestImagePreprocessorAdaptive(unittest.TestCase): | ||
| def setUp(self): | ||
| """Set up test environment""" | ||
| self.processor = AdaptiveImageProcessor( | ||
| min_pixels=56 * 56, | ||
| max_pixels=28 * 28 * 1280, | ||
| patch_size=14, | ||
| temporal_conv_size=2, | ||
| merge_size=2, | ||
| ) | ||
|
|
||
| def test_init(self): | ||
| """Test initialization""" | ||
| self.assertEqual(self.processor.min_pixels, 56 * 56) | ||
| self.assertEqual(self.processor.max_pixels, 28 * 28 * 1280) | ||
| self.assertEqual(self.processor.patch_size, 14) | ||
| self.assertEqual(self.processor.temporal_conv_size, 2) | ||
| self.assertEqual(self.processor.merge_size, 2) | ||
|
|
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| def test_set_pixels(self): | ||
| """Test setting pixels""" | ||
| self.processor.set_pixels(min_pixels=100, max_pixels=200, msg="test") | ||
| self.assertEqual(self.processor.min_pixels, 100) | ||
| self.assertEqual(self.processor.max_pixels, 200) | ||
| self.assertEqual(self.processor.size["min_pixels"], 100) | ||
| self.assertEqual(self.processor.size["max_pixels"], 200) | ||
|
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| def test_set_pixels_negative_min(self): | ||
| """Test setting negative min_pixels should raise error""" | ||
| with self.assertRaises(AssertionError): | ||
| self.processor.set_pixels(min_pixels=-1) | ||
|
|
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| def test_set_pixels_zero_max(self): | ||
| """Test setting 0 or negative max_pixels should raise error""" | ||
| with self.assertRaises(AssertionError): | ||
| self.processor.set_pixels(max_pixels=0) | ||
|
|
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| def test_get_smarted_resize(self): | ||
| """Test get_smarted_resize""" | ||
| height, width = 224, 224 | ||
| (resized_h, resized_w), (patches_h, patches_w) = self.processor.get_smarted_resize(height, width) | ||
| self.assertIsInstance(resized_h, int) | ||
| self.assertIsInstance(resized_w, int) | ||
| self.assertIsInstance(patches_h, int) | ||
| self.assertIsInstance(patches_w, int) | ||
|
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| def test_get_smarted_resize_with_custom_pixels(self): | ||
| """Test get_smarted_resize with custom pixels""" | ||
| height, width = 224, 224 | ||
| (resized_h, resized_w), (_, _) = self.processor.get_smarted_resize( | ||
| height, width, min_pixels=100, max_pixels=10000 | ||
| ) | ||
| self.assertIsInstance(resized_h, int) | ||
| self.assertIsInstance(resized_w, int) | ||
|
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| def test_round_by_factor(self): | ||
| """Test round_by_factor""" | ||
| self.assertEqual(round_by_factor(100, 28), 112) # 100/28 ≈ 3.57, round(3.57) = 4, 4*28 = 112 | ||
| self.assertEqual(round_by_factor(50, 10), 50) | ||
| self.assertEqual(round_by_factor(55, 10), 60) | ||
|
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| def test_ceil_by_factor(self): | ||
| """Test ceil_by_factor""" | ||
| self.assertEqual(ceil_by_factor(100, 28), 112) # ceil(100/28)*28 = ceil(3.57)*28 = 4*28 = 112 | ||
| self.assertEqual(ceil_by_factor(50, 10), 50) | ||
| self.assertEqual(ceil_by_factor(55, 10), 60) | ||
|
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| def test_floor_by_factor(self): | ||
| """Test floor_by_factor""" | ||
| self.assertEqual(floor_by_factor(100, 28), 84) # floor(100/28)*28 = floor(3.57)*28 = 3*28 = 84 | ||
| self.assertEqual(floor_by_factor(50, 10), 50) | ||
| self.assertEqual(floor_by_factor(55, 10), 50) | ||
|
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| def test_smart_resize_basic(self): | ||
| """Test smart_resize basic functionality""" | ||
| height, width = 224, 224 | ||
| new_h, new_w = smart_resize(height, width, factor=28, min_pixels=56 * 56, max_pixels=28 * 28 * 1280) | ||
| self.assertIsInstance(new_h, int) | ||
| self.assertIsInstance(new_w, int) | ||
| self.assertEqual(new_h % 28, 0) | ||
| self.assertEqual(new_w % 28, 0) | ||
|
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| def test_smart_resize_high_aspect_ratio(self): | ||
| """Test case when aspect ratio exceeds MAX_RATIO""" | ||
| height, width = 1000, 10 # aspect ratio = 100 | ||
| new_h, new_w = smart_resize(height, width, factor=28, min_pixels=56 * 56, max_pixels=28 * 28 * 1280) | ||
| self.assertIsInstance(new_h, int) | ||
| self.assertIsInstance(new_w, int) | ||
| self.assertLessEqual(max(new_h, new_w) / min(new_h, new_w), 200) | ||
|
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| def test_smart_resize_too_large(self): | ||
| """Test case when pixel count exceeds max_pixels""" | ||
| height, width = 10000, 10000 | ||
| new_h, new_w = smart_resize(height, width, factor=28, min_pixels=56 * 56, max_pixels=28 * 28 * 1280) | ||
| self.assertLessEqual(new_h * new_w, 28 * 28 * 1280) | ||
|
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| def test_smart_resize_too_small(self): | ||
| """Test case when pixel count is less than min_pixels""" | ||
| height, width = 10, 10 | ||
| new_h, new_w = smart_resize(height, width, factor=28, min_pixels=56 * 56, max_pixels=28 * 28 * 1280) | ||
| self.assertGreaterEqual(new_h * new_w, 56 * 56) | ||
|
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| def test_smart_resize_invalid_result(self): | ||
| """Test case when smart_resize returns invalid result""" | ||
| # This case should not happen, but if it does, ValueError will be raised | ||
| # We test by setting extreme parameters | ||
| # Note: This test may not trigger ValueError, as smart_resize logic may not produce invalid results | ||
| # If testing is really needed, try other extreme cases | ||
| try: | ||
| result = smart_resize(1, 1, factor=100000, min_pixels=100, max_pixels=1000) | ||
| # If successful, verify result | ||
| self.assertIsInstance(result, tuple) | ||
| self.assertEqual(len(result), 2) | ||
| except ValueError: | ||
| # If ValueError is raised, this is also expected | ||
| pass | ||
|
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| def test_is_scaled_image_uint8(self): | ||
| """Test is_scaled_image for uint8 image""" | ||
| image = np.array([[0, 255], [128, 200]], dtype=np.uint8) | ||
| self.assertFalse(is_scaled_image(image)) | ||
|
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| def test_is_scaled_image_scaled(self): | ||
| """Test is_scaled_image for scaled image""" | ||
| image = np.array([[0.0, 0.5], [0.3, 1.0]], dtype=np.float32) | ||
| self.assertTrue(is_scaled_image(image)) | ||
|
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| def test_is_scaled_image_not_scaled(self): | ||
| """Test is_scaled_image for unscaled float image""" | ||
| image = np.array([[0.0, 255.0], [128.0, 300.0]], dtype=np.float32) | ||
| self.assertFalse(is_scaled_image(image)) | ||
|
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| def test_make_batched_images_single(self): | ||
| """Test make_batched_images handling single image""" | ||
| img = Image.new("RGB", (224, 224)) | ||
| result = make_batched_images(img) | ||
| self.assertEqual(len(result), 1) | ||
| self.assertEqual(result[0], img) | ||
|
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| def test_make_batched_images_list(self): | ||
| """Test make_batched_images handling image list""" | ||
| imgs = [Image.new("RGB", (224, 224)) for _ in range(3)] | ||
| result = make_batched_images(imgs) | ||
| self.assertEqual(len(result), 3) | ||
| self.assertEqual(result, imgs) | ||
|
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| def test_make_batched_images_nested_list(self): | ||
| """Test make_batched_images handling nested list""" | ||
| imgs = [[Image.new("RGB", (224, 224)) for _ in range(2)] for _ in range(2)] | ||
| result = make_batched_images(imgs) | ||
| self.assertEqual(len(result), 4) # 2*2 = 4 | ||
|
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| def test_make_batched_images_invalid(self): | ||
| """Test make_batched_images handling invalid input""" | ||
| with self.assertRaises(ValueError): | ||
| make_batched_images("invalid") | ||
|
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| def test_make_batched_videos_list_of_images(self): | ||
| """Test make_batched_videos handling image list""" | ||
| imgs = [Image.new("RGB", (224, 224)) for _ in range(3)] | ||
| result = make_batched_videos(imgs) | ||
| self.assertEqual(len(result), 1) | ||
| self.assertEqual(len(result[0]), 3) | ||
|
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| def test_make_batched_videos_nested_list(self): | ||
| """Test make_batched_videos handling nested list""" | ||
| imgs = [[Image.new("RGB", (224, 224)) for _ in range(2)] for _ in range(2)] | ||
| result = make_batched_videos(imgs) | ||
| self.assertEqual(len(result), 2) | ||
| self.assertEqual(len(result[0]), 2) | ||
|
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| def test_make_batched_videos_4d_array(self): | ||
| """Test make_batched_videos handling 4D array""" | ||
| video = np.random.rand(3, 224, 224, 3).astype(np.uint8) | ||
| result = make_batched_videos(video) | ||
| self.assertEqual(len(result), 1) | ||
| self.assertIsInstance(result[0], list) | ||
|
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| def test_make_batched_videos_invalid(self): | ||
| """Test make_batched_videos handling invalid input""" | ||
| with self.assertRaises(ValueError): | ||
| make_batched_videos("invalid") | ||
|
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| def test_preprocess_images(self): | ||
| """Test preprocess handling images""" | ||
| img = Image.new("RGB", (224, 224)) | ||
| result = self.processor.preprocess(images=img) | ||
| self.assertIn("pixel_values", result) | ||
| self.assertIn("image_grid_thw", result) | ||
|
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| def test_preprocess_videos(self): | ||
| """Test preprocess handling videos""" | ||
| frames = [Image.new("RGB", (224, 224)) for _ in range(4)] | ||
| result = self.processor.preprocess(images=None, videos=frames) | ||
| self.assertIn("pixel_values_videos", result) | ||
| self.assertIn("video_grid_thw", result) | ||
|
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| def test_preprocess_both_images_and_videos(self): | ||
| """Test preprocess handling both images and videos""" | ||
| img = Image.new("RGB", (224, 224)) | ||
| frames = [Image.new("RGB", (224, 224)) for _ in range(4)] | ||
| result = self.processor.preprocess(images=img, videos=frames) | ||
| # When both images and videos are provided, may only return videos result | ||
| # According to code logic, if videos is not None, it will overwrite data dict | ||
| self.assertTrue("pixel_values" in result or "pixel_values_videos" in result) | ||
|
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| def test_preprocess_invalid_images(self): | ||
| """Test preprocess handling invalid image""" | ||
| with self.assertRaises(ValueError): | ||
| self.processor.preprocess(images="invalid") | ||
|
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| def test_preprocess_with_predetermined_grid_thw(self): | ||
| """Test preprocess using predetermined_grid_thw""" | ||
| img = Image.new("RGB", (224, 224)) | ||
| # predetermined_grid_thw should be (h, w) format, not [1, h, w] | ||
| predetermined_grid_thw = [(16, 16)] # For single image, should be (h, w) tuple | ||
| result = self.processor.preprocess(images=img, predetermined_grid_thw=predetermined_grid_thw) | ||
| self.assertIn("pixel_values", result) | ||
|
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| def test_preprocess_no_resize(self): | ||
| """Test preprocess without resize""" | ||
| img = Image.new("RGB", (224, 224)) | ||
| result = self.processor.preprocess(images=img, do_resize=False) | ||
| self.assertIn("pixel_values", result) | ||
|
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| def test_preprocess_no_rescale(self): | ||
| """Test preprocess without rescale""" | ||
| img = Image.new("RGB", (224, 224)) | ||
| result = self.processor.preprocess(images=img, do_rescale=False) | ||
| self.assertIn("pixel_values", result) | ||
|
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| def test_preprocess_no_normalize(self): | ||
| """Test preprocess without normalize""" | ||
| img = Image.new("RGB", (224, 224)) | ||
| result = self.processor.preprocess(images=img, do_normalize=False) | ||
| self.assertIn("pixel_values", result) | ||
|
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| def test_preprocess_custom_mean_std(self): | ||
| """Test preprocess using custom mean and std""" | ||
| img = Image.new("RGB", (224, 224)) | ||
| result = self.processor.preprocess(images=img, image_mean=[0.5, 0.5, 0.5], image_std=[0.5, 0.5, 0.5]) | ||
| self.assertIn("pixel_values", result) | ||
|
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| def test_make_batched_videos_4d_array_in_list(self): | ||
| """Test make_batched_videos handling 4D array in list (lines 119-120)""" | ||
| # Create a list of 4D arrays | ||
| videos = [np.random.rand(3, 224, 224, 3).astype(np.uint8)] | ||
| result = make_batched_videos(videos) | ||
| self.assertEqual(len(result), 1) | ||
| self.assertIsInstance(result[0], list) | ||
|
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| def test_preprocess_do_convert_rgb(self): | ||
| """Test preprocess with do_convert_rgb=True (line 289)""" | ||
| img = Image.new("L", (224, 224)) # Grayscale image | ||
| result = self.processor.preprocess(images=img, do_convert_rgb=True) | ||
| self.assertIn("pixel_values", result) | ||
|
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| def test_preprocess_scaled_image_warning(self): | ||
| """Test warning for scaled image in preprocess (line 295)""" | ||
| # Create a scaled image (values between 0-1) | ||
| img_array = np.random.rand(224, 224, 3).astype(np.float32) | ||
| # Use patch to capture warning | ||
| with patch( | ||
| "fastdeploy.input.ernie4_5_vl_processor.image_preprocessor.image_preprocessor_adaptive.data_processor_logger" | ||
| ) as mock_logger: | ||
| # Directly call _preprocess, pass scaled image | ||
| self.processor._preprocess( | ||
| [img_array], # Pass scaled numpy array | ||
| do_rescale=True, | ||
| do_convert_rgb=False, | ||
| ) | ||
| # Verify warning is called (if is_scaled_image returns True) | ||
| # mock_logger.warning should be called | ||
| if is_scaled_image(img_array): | ||
| # If image is indeed scaled, warning should be called | ||
| mock_logger.warning.assert_called() | ||
|
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| def test_preprocess_data_format_last(self): | ||
| """Test preprocess with data_format=LAST (line 351)""" | ||
| img = Image.new("RGB", (224, 224)) | ||
| from paddleformers.transformers.image_utils import ChannelDimension | ||
|
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||
| result = self.processor.preprocess(images=img, data_format=ChannelDimension.LAST) | ||
| self.assertIn("pixel_values", result) | ||
|
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| def test_preprocess_invalid_images_check(self): | ||
| """Test invalid image check in preprocess (line 464)""" | ||
| # Test invalid image type - need to ensure valid_images returns False | ||
| # Use patch to make valid_images return False, but make_batched_images succeeds | ||
| with patch( | ||
| "fastdeploy.input.ernie4_5_vl_processor.image_preprocessor.image_preprocessor_adaptive.valid_images" | ||
| ) as mock_valid: | ||
| mock_valid.return_value = False | ||
| valid_images_list = [Image.new("RGB", (224, 224))] # Valid image, but valid_images returns False | ||
| with self.assertRaises(ValueError) as context: | ||
| self.processor.preprocess(images=valid_images_list) | ||
| self.assertIn("Invalid image type", str(context.exception)) | ||
|
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| def test_smart_resize_high_aspect_ratio_height_gt_width(self): | ||
| """Test smart_resize when aspect ratio exceeds MAX_RATIO, height > width case (lines 558-560)""" | ||
| height, width = 10000, 10 # height > width, aspect ratio = 1000 | ||
| new_h, new_w = smart_resize(height, width, factor=28, min_pixels=56 * 56, max_pixels=28 * 28 * 1280) | ||
| self.assertIsInstance(new_h, int) | ||
| self.assertIsInstance(new_w, int) | ||
| self.assertLessEqual(max(new_h, new_w) / min(new_h, new_w), 200) | ||
|
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| def test_smart_resize_high_aspect_ratio_width_gt_height(self): | ||
| """Test smart_resize when aspect ratio exceeds MAX_RATIO, width > height case (lines 561-563)""" | ||
| height, width = 10, 10000 # width > height, aspect ratio = 1000 | ||
| new_h, new_w = smart_resize(height, width, factor=28, min_pixels=56 * 56, max_pixels=28 * 28 * 1280) | ||
| self.assertIsInstance(new_h, int) | ||
| self.assertIsInstance(new_w, int) | ||
| self.assertLessEqual(max(new_h, new_w) / min(new_h, new_w), 200) | ||
|
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| def test_is_scaled_image_edge_cases(self): | ||
| """Test is_scaled_image edge cases (lines 80-84)""" | ||
| # Test with values exactly at boundaries | ||
| image1 = np.array([[0.0, 1.0]], dtype=np.float32) | ||
| self.assertTrue(is_scaled_image(image1)) | ||
| image2 = np.array([[0.0, 1.1]], dtype=np.float32) | ||
| self.assertFalse(is_scaled_image(image2)) | ||
| image3 = np.array([[-0.1, 1.0]], dtype=np.float32) | ||
| self.assertFalse(is_scaled_image(image3)) | ||
|
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| def test_make_batched_images_nested_list_edge_case(self): | ||
| """Test make_batched_images with nested list edge case (lines 98-107)""" | ||
| # Test with nested list where first element is a list of images | ||
| imgs = [[Image.new("RGB", (224, 224)) for _ in range(2)] for _ in range(2)] | ||
| result = make_batched_images(imgs) | ||
| self.assertEqual(len(result), 4) | ||
|
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| def test_make_batched_videos_edge_cases(self): | ||
| """Test make_batched_videos edge cases (lines 113-125)""" | ||
| # Test with single Image.Image in list | ||
| img = Image.new("RGB", (224, 224)) | ||
| result = make_batched_videos([img]) | ||
| self.assertEqual(len(result), 1) | ||
| self.assertEqual(len(result[0]), 1) | ||
|
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| # Test with 4D array (video) | ||
| video = np.random.rand(3, 224, 224, 3).astype(np.uint8) | ||
| result = make_batched_videos(video) | ||
| self.assertEqual(len(result), 1) | ||
| self.assertIsInstance(result[0], list) | ||
|
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| def test_preprocess_predetermined_grid_thw_multiple_images(self): | ||
| """Test preprocess with predetermined_grid_thw for multiple images (lines 307-310)""" | ||
| imgs = [Image.new("RGB", (224, 224)) for _ in range(2)] | ||
| predetermined_grid_thw = [(16, 16), (20, 20)] | ||
| result = self.processor.preprocess(images=imgs, predetermined_grid_thw=predetermined_grid_thw) | ||
| self.assertIn("pixel_values", result) | ||
|
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| def test_preprocess_predetermined_grid_thw_length_mismatch(self): | ||
| """Test preprocess with predetermined_grid_thw length mismatch (lines 308-310) | ||
|
|
||
| Note: The implementation raises IndexError when predetermined_grid_thw length | ||
| doesn't match images length, because it accesses predetermined_grid_thw[img_idx] | ||
| directly without checking bounds first. | ||
| """ | ||
| imgs = [Image.new("RGB", (224, 224)) for _ in range(2)] | ||
| predetermined_grid_thw = [(16, 16)] # Length mismatch - only 1 element for 2 images | ||
| # The function raises IndexError when accessing predetermined_grid_thw[1] | ||
| with self.assertRaises(IndexError): | ||
| self.processor.preprocess(images=imgs, predetermined_grid_thw=predetermined_grid_thw) | ||
|
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| def test_preprocess_with_input_data_format(self): | ||
| """Test preprocess with input_data_format parameter (lines 299-301)""" | ||
| img = Image.new("RGB", (224, 224)) | ||
| from paddleformers.transformers.image_utils import ChannelDimension | ||
|
|
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| result = self.processor.preprocess(images=img, input_data_format=ChannelDimension.FIRST) | ||
| self.assertIn("pixel_values", result) | ||
|
|
||
| def test_preprocess_do_resize_with_predetermined_grid_thw(self): | ||
| """Test preprocess with do_resize=True and predetermined_grid_thw (lines 314-317)""" | ||
| img = Image.new("RGB", (224, 224)) | ||
| predetermined_grid_thw = [(16, 16)] | ||
| result = self.processor.preprocess(images=img, predetermined_grid_thw=predetermined_grid_thw, do_resize=True) | ||
| self.assertIn("pixel_values", result) | ||
|
|
||
| def test_preprocess_videos_with_predetermined_grid_thw(self): | ||
| """Test preprocess videos with predetermined_grid_thw (lines 511)""" | ||
| frames = [Image.new("RGB", (224, 224)) for _ in range(4)] | ||
| predetermined_grid_thw = [(16, 16)] * 4 | ||
| result = self.processor.preprocess(images=None, videos=frames, predetermined_grid_thw=predetermined_grid_thw) | ||
| self.assertIn("pixel_values_videos", result) | ||
|
|
||
| def test_preprocess_multiple_images_loop(self): | ||
| """Test preprocess with multiple images in loop (lines 468-488)""" | ||
| imgs = [Image.new("RGB", (224, 224)) for _ in range(3)] | ||
| result = self.processor.preprocess(images=imgs) | ||
| self.assertIn("pixel_values", result) | ||
| self.assertIn("image_grid_thw", result) | ||
|
|
||
| def test_preprocess_videos_loop(self): | ||
| """Test preprocess with videos in loop (lines 496-521)""" | ||
| videos = [[Image.new("RGB", (224, 224)) for _ in range(4)] for _ in range(2)] | ||
| result = self.processor.preprocess(images=None, videos=videos) | ||
| self.assertIn("pixel_values_videos", result) | ||
| self.assertIn("video_grid_thw", result) | ||
|
|
||
| def test_preprocess_return_tensors(self): | ||
| """Test preprocess with return_tensors parameter (lines 396, 523)""" | ||
| img = Image.new("RGB", (224, 224)) | ||
| # Use string instead of TensorType enum which may not be available | ||
| result = self.processor.preprocess(images=img, return_tensors="np") | ||
| self.assertIn("pixel_values", result) | ||
|
|
||
| def test_preprocess_channel_dimension_none(self): | ||
| """Test preprocess with input_data_format=None (lines 299-301)""" | ||
| img = Image.new("RGB", (224, 224)) | ||
| result = self.processor.preprocess(images=img, input_data_format=None) | ||
| self.assertIn("pixel_values", result) | ||
|
|
||
| def test_preprocess_do_rescale_false_with_scaled_image(self): | ||
| """Test preprocess with do_rescale=False and scaled image (line 335)""" | ||
| # Create a scaled image | ||
| img_array = np.random.rand(224, 224, 3).astype(np.float32) * 0.5 # Values in [0, 0.5] | ||
| img = Image.fromarray((img_array * 255).astype(np.uint8)) | ||
| result = self.processor.preprocess(images=img, do_rescale=False) | ||
| self.assertIn("pixel_values", result) | ||
|
|
||
| def test_preprocess_do_normalize_false(self): | ||
| """Test preprocess with do_normalize=False (lines 338-344)""" | ||
| img = Image.new("RGB", (224, 224)) | ||
| result = self.processor.preprocess(images=img, do_normalize=False) | ||
| self.assertIn("pixel_values", result) | ||
|
|
||
| def test_preprocess_custom_resample(self): | ||
| """Test preprocess with custom resample parameter (line 332)""" | ||
| img = Image.new("RGB", (224, 224)) | ||
| from PIL import Image as PILImage | ||
|
|
||
| result = self.processor.preprocess(images=img, resample=PILImage.BILINEAR) | ||
| self.assertIn("pixel_values", result) | ||
|
|
||
| def test_preprocess_custom_rescale_factor(self): | ||
| """Test preprocess with custom rescale_factor (line 336)""" | ||
| img = Image.new("RGB", (224, 224)) | ||
| result = self.processor.preprocess(images=img, rescale_factor=1.0 / 128.0) | ||
| self.assertIn("pixel_values", result) | ||
|
|
||
| def test_preprocess_custom_image_mean_std(self): | ||
| """Test preprocess with custom image_mean and image_std (lines 339-344)""" | ||
| img = Image.new("RGB", (224, 224)) | ||
| result = self.processor.preprocess( | ||
| images=img, image_mean=[0.485, 0.456, 0.406], image_std=[0.229, 0.224, 0.225] | ||
| ) | ||
| self.assertIn("pixel_values", result) | ||
|
|
||
| def test_preprocess_data_format_channels_first(self): | ||
| """Test preprocess with data_format=FIRST (line 346)""" | ||
| img = Image.new("RGB", (224, 224)) | ||
| from paddleformers.transformers.image_utils import ChannelDimension | ||
|
|
||
| result = self.processor.preprocess(images=img, data_format=ChannelDimension.FIRST) | ||
| self.assertIn("pixel_values", result) | ||
|
|
||
| def test_preprocess_data_format_channels_last(self): | ||
| """Test preprocess with data_format=LAST (line 350)""" | ||
| img = Image.new("RGB", (224, 224)) | ||
| from paddleformers.transformers.image_utils import ChannelDimension | ||
|
|
||
| result = self.processor.preprocess(images=img, data_format=ChannelDimension.LAST) | ||
| self.assertIn("pixel_values", result) | ||
|
|
||
| def test_preprocess_patches_reshape(self): | ||
| """Test preprocess patches reshape logic (lines 349-381)""" | ||
| img = Image.new("RGB", (224, 224)) | ||
| result = self.processor.preprocess(images=img) | ||
| self.assertIn("pixel_values", result) | ||
| # Verify pixel_values shape | ||
| pixel_values = result["pixel_values"] | ||
| self.assertIsInstance(pixel_values, np.ndarray) | ||
|
|
||
| def test_preprocess_videos_multiple(self): | ||
| """Test preprocess with multiple videos (lines 496-521)""" | ||
| videos = [ | ||
| [Image.new("RGB", (224, 224)) for _ in range(4)], | ||
| [Image.new("RGB", (224, 224)) for _ in range(4)], | ||
| ] | ||
| result = self.processor.preprocess(images=None, videos=videos) | ||
| self.assertIn("pixel_values_videos", result) | ||
| self.assertIn("video_grid_thw", result) | ||
|
|
||
| def test_make_batched_images_invalid_nested_list(self): | ||
| """Test make_batched_images with invalid nested list (line 98)""" | ||
| # Test with nested list but first element is not an image | ||
| invalid_input = [[1, 2, 3], [4, 5, 6]] | ||
| with self.assertRaises(ValueError) as context: | ||
| make_batched_images(invalid_input) | ||
| self.assertIn("Could not make batched images", str(context.exception)) | ||
|
|
||
| def test_make_batched_images_invalid_single(self): | ||
| """Test make_batched_images with invalid single input (line 107)""" | ||
| invalid_input = "not an image" | ||
| with self.assertRaises(ValueError) as context: | ||
| make_batched_images(invalid_input) | ||
| self.assertIn("Could not make batched images", str(context.exception)) | ||
|
|
||
| def test_make_batched_videos_nested_list_of_images(self): | ||
| """Test make_batched_videos with nested list of images (line 113)""" | ||
| images = [[Image.new("RGB", (224, 224)) for _ in range(2)]] | ||
| result = make_batched_videos(images) | ||
| self.assertEqual(result, images) | ||
|
|
||
| def test_make_batched_videos_list_of_images_nested_output(self): | ||
| """Test make_batched_videos with list of images (line 117)""" | ||
| images = [Image.new("RGB", (224, 224)) for _ in range(2)] | ||
| result = make_batched_videos(images) | ||
| self.assertEqual(result, [images]) | ||
|
|
||
| def test_make_batched_videos_4d_array_in_list_variant(self): | ||
| """Test make_batched_videos with 4D array in list (line 119) | ||
|
|
||
| Note: make_batched_videos expects 4D array (time, height, width, channels), | ||
| not 5D array (batch, time, height, width, channels). | ||
| """ | ||
| # Create a 4D numpy array (time, height, width, channels) | ||
| video_array = np.random.rand(4, 224, 224, 3).astype(np.uint8) | ||
| result = make_batched_videos([video_array]) | ||
| self.assertIsInstance(result, list) | ||
|
|
||
| def test_make_batched_videos_4d_array_single(self): | ||
| """Test make_batched_videos with single 4D array (line 122) | ||
|
|
||
| Note: make_batched_videos expects 4D array (time, height, width, channels), | ||
| not 5D array (batch, time, height, width, channels). | ||
| """ | ||
| # Create a 4D numpy array (time, height, width, channels) | ||
| video_array = np.random.rand(4, 224, 224, 3).astype(np.uint8) | ||
| result = make_batched_videos(video_array) | ||
| self.assertIsInstance(result, list) | ||
|
|
||
| def test_make_batched_videos_invalid_input(self): | ||
| """Test make_batched_videos with invalid input (line 125)""" | ||
| invalid_input = "not a video" | ||
| with self.assertRaises(ValueError) as context: | ||
| make_batched_videos(invalid_input) | ||
| self.assertIn("Could not make batched video", str(context.exception)) | ||
|
|
||
| def test_is_scaled_image_uint8_false(self): | ||
| """Test is_scaled_image with uint8 image (line 80)""" | ||
| image = np.random.rand(224, 224, 3).astype(np.uint8) * 255 | ||
| result = is_scaled_image(image) | ||
| self.assertFalse(result) | ||
|
|
||
| def test_is_scaled_image_scaled_true(self): | ||
| """Test is_scaled_image with scaled float image (line 84)""" | ||
| image = np.random.rand(224, 224, 3).astype(np.float32) * 0.5 # Values in [0, 0.5] | ||
| result = is_scaled_image(image) | ||
| self.assertTrue(result) | ||
|
|
||
| def test_is_scaled_image_not_scaled_false(self): | ||
| """Test is_scaled_image with non-scaled float image (line 84)""" | ||
| image = np.random.rand(224, 224, 3).astype(np.float32) * 255 # Values > 1 | ||
| result = is_scaled_image(image) | ||
| self.assertFalse(result) | ||
|
|
||
| def test_preprocess_with_scaled_image_warning(self): | ||
| """Test preprocess with scaled image triggers warning (lines 294-298) | ||
|
|
||
| Note: The warning is only triggered when is_scaled_image() returns True, | ||
| which requires float images with values in [0, 1]. Converting to PIL Image | ||
| and back converts to uint8, so the warning won't be triggered. | ||
| This test verifies the preprocess works without errors. | ||
| """ | ||
| # Create a scaled image (values in [0, 1]) | ||
| scaled_image = np.random.rand(224, 224, 3).astype(np.float32) * 0.5 | ||
| scaled_image = Image.fromarray((scaled_image * 255).astype(np.uint8)) | ||
|
|
||
| # The image is now uint8, so is_scaled_image returns False and no warning is triggered | ||
| result = self.processor.preprocess(images=[scaled_image], do_rescale=True) | ||
| self.assertIn("pixel_values", result) | ||
|
|
||
| def test_preprocess_predetermined_grid_thw_length_mismatch_assert(self): | ||
| """Test preprocess with predetermined_grid_thw length mismatch (line 310) | ||
|
|
||
| Note: The source code expects predetermined_grid_thw elements to be (height, width) tuples, | ||
| but when 3-element arrays like [1, 16, 16] are passed, it raises ValueError when unpacking. | ||
| """ | ||
| images = [Image.new("RGB", (224, 224)) for _ in range(2)] | ||
| predetermined_grid_thw = np.array([[1, 16, 16]]) # Only 1, but 2 images | ||
|
|
||
| # First fails because of unpacking 3 values into 2 variables | ||
| with self.assertRaises(ValueError) as context: | ||
| self.processor.preprocess(images=images, predetermined_grid_thw=predetermined_grid_thw, do_resize=True) | ||
| self.assertIn("too many values to unpack", str(context.exception)) | ||
|
|
||
| def test_preprocess_loop_multiple_images(self): | ||
| """Test preprocess loop with multiple images (lines 312-348)""" | ||
| images = [Image.new("RGB", (224, 224)) for _ in range(3)] | ||
| result = self.processor.preprocess(images=images) | ||
| self.assertIn("pixel_values", result) | ||
| pixel_values = result["pixel_values"] | ||
| self.assertIsInstance(pixel_values, np.ndarray) | ||
|
|
||
| def test_preprocess_with_predetermined_grid_thw_in_loop(self): | ||
| """Test preprocess with predetermined_grid_thw in loop (lines 314-317) | ||
|
|
||
| Note: predetermined_grid_thw expects (height, width) tuples, not (t, h, w). | ||
| The values are grid dimensions that get multiplied by patch_size. | ||
| """ | ||
| images = [Image.new("RGB", (224, 224)) for _ in range(2)] | ||
| # Use 2D grid (h, w) format | ||
| predetermined_grid_thw = [(16, 16), (16, 16)] | ||
|
|
||
| result = self.processor.preprocess( | ||
| images=images, predetermined_grid_thw=predetermined_grid_thw, do_resize=True | ||
| ) | ||
| self.assertIn("pixel_values", result) | ||
|
|
||
| def test_preprocess_patches_reshape_multiple_inputs(self): | ||
| """Test preprocess patches reshape logic (lines 349-381)""" | ||
| images = [Image.new("RGB", (224, 224))] | ||
| result = self.processor.preprocess(images=images) | ||
| self.assertIn("pixel_values", result) | ||
| pixel_values = result["pixel_values"] | ||
| # Verify shape is correct after reshape | ||
| self.assertEqual(len(pixel_values.shape), 2) # Should be [grid_t * grid_h * grid_w, C * psz * psz] | ||
|
|
||
| def test_smart_resize_high_aspect_ratio_height_gt_width_case(self): | ||
| """Test smart_resize with high aspect ratio, height > width (lines 557-563)""" | ||
| # Create image with very high aspect ratio | ||
| height, width = 1000, 50 # Aspect ratio = 20 | ||
| factor = 14 | ||
| min_pixels = 1000 | ||
| max_pixels = 100000 | ||
|
|
||
| new_h, new_w = smart_resize(height, width, factor, min_pixels, max_pixels) | ||
| self.assertIsInstance(new_h, int) | ||
| self.assertIsInstance(new_w, int) | ||
| self.assertGreater(new_h, 0) | ||
| self.assertGreater(new_w, 0) | ||
|
|
||
| def test_smart_resize_high_aspect_ratio_width_gt_height_case(self): | ||
| """Test smart_resize with high aspect ratio, width > height (lines 562-563)""" | ||
| # Create image with very high aspect ratio (wide) | ||
| height, width = 50, 1000 # Aspect ratio = 20 | ||
| factor = 14 | ||
| min_pixels = 1000 | ||
| max_pixels = 100000 | ||
|
|
||
| new_h, new_w = smart_resize(height, width, factor, min_pixels, max_pixels) | ||
| self.assertIsInstance(new_h, int) | ||
| self.assertIsInstance(new_w, int) | ||
|
|
||
| def test_smart_resize_exceeds_max_pixels(self): | ||
| """Test smart_resize when h_bar * w_bar > max_pixels (lines 575-578)""" | ||
| height, width = 10000, 10000 # Very large image | ||
| factor = 14 | ||
| min_pixels = 1000 | ||
| max_pixels = 10000 # Small max_pixels | ||
|
|
||
| new_h, new_w = smart_resize(height, width, factor, min_pixels, max_pixels) | ||
| self.assertLessEqual(new_h * new_w, max_pixels) | ||
| self.assertGreaterEqual(new_h * new_w, min_pixels) | ||
|
|
||
| def test_smart_resize_below_min_pixels(self): | ||
| """Test smart_resize when h_bar * w_bar < min_pixels (lines 579-582)""" | ||
| height, width = 10, 10 # Very small image | ||
| factor = 14 | ||
| min_pixels = 10000 # Large min_pixels | ||
| max_pixels = 100000 | ||
|
|
||
| new_h, new_w = smart_resize(height, width, factor, min_pixels, max_pixels) | ||
| self.assertGreaterEqual(new_h * new_w, min_pixels) | ||
| self.assertLessEqual(new_h * new_w, max_pixels) | ||
|
|
||
| def test_smart_resize_invalid_result_constraints(self): | ||
| """Test smart_resize with invalid result (line 585)""" | ||
| # This is hard to trigger, but we can test the validation | ||
| height, width = 100, 100 | ||
| factor = 14 | ||
| min_pixels = 10000 | ||
| max_pixels = 1000 # max < min, which is invalid but should be caught | ||
|
|
||
| # This should raise an error or return valid values | ||
| try: | ||
| new_h, new_w = smart_resize(height, width, factor, min_pixels, max_pixels) | ||
| # If it doesn't raise, verify the result is valid | ||
| self.assertGreaterEqual(new_h * new_w, min_pixels) | ||
| self.assertLessEqual(new_h * new_w, max_pixels) | ||
| except ValueError: | ||
| # Expected if validation catches the issue | ||
| pass | ||
|
|
||
| def test_preprocess_videos_loop_numpy_output(self): | ||
| """Test preprocess videos loop (lines 496-521)""" | ||
| videos = [ | ||
| [Image.new("RGB", (224, 224)) for _ in range(4)], | ||
| [Image.new("RGB", (224, 224)) for _ in range(4)], | ||
| ] | ||
| result = self.processor.preprocess(images=None, videos=videos) | ||
| self.assertIn("pixel_values_videos", result) | ||
| self.assertIn("video_grid_thw", result) | ||
| self.assertIsInstance(result["pixel_values_videos"], np.ndarray) | ||
|
|
||
| def test_preprocess_both_images_and_videos_full_outputs(self): | ||
| """Test preprocess with both images and videos (lines 458-523) | ||
|
|
||
| Note: Current implementation has a known issue where the data dict is overwritten | ||
| when processing both images and videos. The video processing overwrites the image | ||
| results, so only video outputs are returned. | ||
| """ | ||
| images = [Image.new("RGB", (224, 224))] | ||
| videos = [[Image.new("RGB", (224, 224)) for _ in range(4)]] | ||
|
|
||
| result = self.processor.preprocess(images=images, videos=videos) | ||
| # Due to implementation, only video results are returned when both are provided | ||
| self.assertIn("pixel_values_videos", result) | ||
| self.assertIn("video_grid_thw", result) | ||
|
|
||
| def test_preprocess_images_loop_with_predetermined_grid_thw(self): | ||
| """Test preprocess images loop with predetermined_grid_thw (lines 468-486) | ||
|
|
||
| Note: predetermined_grid_thw expects (height, width) tuples, not (t, h, w). | ||
| """ | ||
| images = [Image.new("RGB", (224, 224)) for _ in range(2)] | ||
| # Use 2D grid (h, w) format | ||
| predetermined_grid_thw = [(16, 16), (16, 16)] | ||
|
|
||
| result = self.processor.preprocess( | ||
| images=images, predetermined_grid_thw=predetermined_grid_thw, do_resize=True | ||
| ) | ||
| self.assertIn("pixel_values", result) | ||
| self.assertEqual(len(result["image_grid_thw"]), 2) | ||
|
|
||
| def test_preprocess_invalid_images_check_list_input(self): | ||
| """Test preprocess with invalid images check (line 464) | ||
|
|
||
| Note: The error is raised by make_batched_images before valid_images check, | ||
| so the error message is different. | ||
| """ | ||
| invalid_images = ["not an image", "also not an image"] | ||
|
|
||
| with self.assertRaises(ValueError) as context: | ||
| self.processor.preprocess(images=invalid_images) | ||
| self.assertIn("Could not make batched images", str(context.exception)) | ||
|
|
||
| def test_round_by_factor_edge_cases(self): | ||
| """Test round_by_factor with edge cases (lines 526-530)""" | ||
| self.assertEqual(round_by_factor(0, 14), 0) | ||
| self.assertEqual(round_by_factor(14, 14), 14) | ||
| self.assertEqual(round_by_factor(13, 14), 14) # Round up | ||
| self.assertEqual(round_by_factor(15, 14), 14) # Round down | ||
|
|
||
| def test_ceil_by_factor_edge_cases(self): | ||
| """Test ceil_by_factor with edge cases (lines 532-536)""" | ||
| self.assertEqual(ceil_by_factor(0, 14), 0) | ||
| self.assertEqual(ceil_by_factor(14, 14), 14) | ||
| self.assertEqual(ceil_by_factor(13, 14), 14) # Ceil up | ||
| self.assertEqual(ceil_by_factor(15, 14), 28) # Ceil up to next multiple | ||
|
|
||
| def test_floor_by_factor_edge_cases(self): | ||
| """Test floor_by_factor with edge cases (lines 538-542)""" | ||
| self.assertEqual(floor_by_factor(0, 14), 0) | ||
| self.assertEqual(floor_by_factor(14, 14), 14) | ||
| self.assertEqual(floor_by_factor(13, 14), 0) # Floor down | ||
| self.assertEqual(floor_by_factor(15, 14), 14) # Floor down to multiple | ||
| self.assertEqual(floor_by_factor(28, 14), 28) # Exact multiple | ||
|
|
||
|
|
||
| if __name__ == "__main__": | ||
| unittest.main() |
There was a problem hiding this comment.
Magic numbers used throughout tests without explanation. Values like 224, 28, 56 * 56, 28 * 28 * 1280 appear repeatedly without constants or documentation explaining their significance.
Recommendation: Define test constants at the class level or module level with descriptive names:
TEST_IMAGE_SIZE = 224
PATCH_SIZE = 28
MIN_PIXELS = 56 * 56
MAX_PIXELS = 28 * 28 * 1280This improves test maintainability and makes the test logic clearer, especially for someone unfamiliar with the image processing requirements.
Comment on lines
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+150
| def test_smart_resize_invalid_result(self): | ||
| """Test case when smart_resize returns invalid result""" | ||
| # This case should not happen, but if it does, ValueError will be raised | ||
| # We test by setting extreme parameters | ||
| # Note: This test may not trigger ValueError, as smart_resize logic may not produce invalid results | ||
| # If testing is really needed, try other extreme cases | ||
| try: | ||
| result = smart_resize(1, 1, factor=100000, min_pixels=100, max_pixels=1000) | ||
| # If successful, verify result | ||
| self.assertIsInstance(result, tuple) | ||
| self.assertEqual(len(result), 2) | ||
| except ValueError: | ||
| # If ValueError is raised, this is also expected | ||
| pass |
There was a problem hiding this comment.
Test accepts both success and failure without clear expectation. The test comments acknowledge "This test may not trigger ValueError" and accepts both outcomes with a try-except pass. This makes the test non-deterministic and unclear about what behavior is correct.
Recommendation: Either:
- Remove this test if the invalid case cannot be reliably triggered
- Mock the function to force the error path if testing error handling is important
- Document which outcome is expected for the given inputs and only accept that outcome
Tests that pass regardless of the result provide no value and can mask real issues.
Suggested change
| def test_smart_resize_invalid_result(self): | |
| """Test case when smart_resize returns invalid result""" | |
| # This case should not happen, but if it does, ValueError will be raised | |
| # We test by setting extreme parameters | |
| # Note: This test may not trigger ValueError, as smart_resize logic may not produce invalid results | |
| # If testing is really needed, try other extreme cases | |
| try: | |
| result = smart_resize(1, 1, factor=100000, min_pixels=100, max_pixels=1000) | |
| # If successful, verify result | |
| self.assertIsInstance(result, tuple) | |
| self.assertEqual(len(result), 2) | |
| except ValueError: | |
| # If ValueError is raised, this is also expected | |
| pass |
| @@ -0,0 +1,799 @@ | |||
| """ | |||
There was a problem hiding this comment.
The PR description is incomplete. According to the coding guidelines, the description should at a minimum explain:
- Why these modifications are being made
- What problem is being solved
The current description only has section headers but no actual content explaining the motivation, modifications, or usage. Please provide:
- The motivation for adding these tests
- What specific functionality is being tested
- Any context about the Hackathon task requirements
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| def test_preprocess_scaled_image_warning(self): | ||
| """Test warning for scaled image in preprocess (line 295)""" | ||
| # Create a scaled image (values between 0-1) | ||
| img_array = np.random.rand(224, 224, 3).astype(np.float32) | ||
| # Use patch to capture warning | ||
| with patch( | ||
| "fastdeploy.input.ernie4_5_vl_processor.image_preprocessor.image_preprocessor_adaptive.data_processor_logger" | ||
| ) as mock_logger: | ||
| # Directly call _preprocess, pass scaled image | ||
| self.processor._preprocess( | ||
| [img_array], # Pass scaled numpy array | ||
| do_rescale=True, | ||
| do_convert_rgb=False, | ||
| ) | ||
| # Verify warning is called (if is_scaled_image returns True) | ||
| # mock_logger.warning should be called | ||
| if is_scaled_image(img_array): | ||
| # If image is indeed scaled, warning should be called | ||
| mock_logger.warning.assert_called() |
There was a problem hiding this comment.
Mock assertion is conditionally executed, making the test unreliable. The test only asserts mock_logger.warning.assert_called() if is_scaled_image(img_array) returns True, but there's no assertion if it returns False.
This means:
- If the image isn't scaled (due to random values), the mock assertion is skipped and the test passes without validating anything
- The test doesn't verify the actual behavior it claims to test
Recommendation: Create a deterministic scaled image and unconditionally verify the warning is called:
img_array = np.array([[[0.5, 0.5, 0.5]]] * 224 * 224).reshape(224, 224, 3).astype(np.float32)
# ... test code ...
mock_logger.warning.assert_called_once()| @@ -0,0 +1,799 @@ | |||
| """ | |||
There was a problem hiding this comment.
The PR title does not follow the required format. According to the coding guidelines, the title should follow the format [TAG]Title. The current title has Chinese characters in the middle and doesn't follow a clear format.
Suggested format: [CI] Add unit tests for image_preprocessor_adaptive module (Hackathon Stage 9 Task 25)
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| """ | ||
| # Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved. |
There was a problem hiding this comment.
[nitpick] Inconsistent comment style: The docstring uses triple quotes but has an extra quote on line 1. The opening """ should be on its own line or the content should start on the same line, not have a standalone quote.
Suggested fix:
"""
# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
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| def test_preprocess_both_images_and_videos_full_outputs(self): | ||
| """Test preprocess with both images and videos (lines 458-523) | ||
|
|
||
| Note: Current implementation has a known issue where the data dict is overwritten | ||
| when processing both images and videos. The video processing overwrites the image | ||
| results, so only video outputs are returned. | ||
| """ | ||
| images = [Image.new("RGB", (224, 224))] | ||
| videos = [[Image.new("RGB", (224, 224)) for _ in range(4)]] | ||
|
|
||
| result = self.processor.preprocess(images=images, videos=videos) | ||
| # Due to implementation, only video results are returned when both are provided | ||
| self.assertIn("pixel_values_videos", result) | ||
| self.assertIn("video_grid_thw", result) | ||
|
|
There was a problem hiding this comment.
The test comment contains a note about a "known issue" where the data dict is overwritten when processing both images and videos. If this is truly a known bug in the implementation, this should be:
- Documented as a bug report or tracked issue
- The test should use
self.fail()orself.skipTest()to indicate this is testing buggy behavior - The comment should reference the issue tracking number
Testing buggy behavior without clear indication makes it unclear whether the test validates correct or incorrect behavior.
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| class TestImagePreprocessorAdaptive(unittest.TestCase): | ||
| def setUp(self): | ||
| """Set up test environment""" | ||
| self.processor = AdaptiveImageProcessor( | ||
| min_pixels=56 * 56, | ||
| max_pixels=28 * 28 * 1280, | ||
| patch_size=14, | ||
| temporal_conv_size=2, | ||
| merge_size=2, | ||
| ) | ||
|
|
||
| def test_init(self): | ||
| """Test initialization""" | ||
| self.assertEqual(self.processor.min_pixels, 56 * 56) | ||
| self.assertEqual(self.processor.max_pixels, 28 * 28 * 1280) | ||
| self.assertEqual(self.processor.patch_size, 14) | ||
| self.assertEqual(self.processor.temporal_conv_size, 2) | ||
| self.assertEqual(self.processor.merge_size, 2) | ||
|
|
||
| def test_set_pixels(self): | ||
| """Test setting pixels""" | ||
| self.processor.set_pixels(min_pixels=100, max_pixels=200, msg="test") | ||
| self.assertEqual(self.processor.min_pixels, 100) | ||
| self.assertEqual(self.processor.max_pixels, 200) | ||
| self.assertEqual(self.processor.size["min_pixels"], 100) | ||
| self.assertEqual(self.processor.size["max_pixels"], 200) | ||
|
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||
| def test_set_pixels_negative_min(self): | ||
| """Test setting negative min_pixels should raise error""" | ||
| with self.assertRaises(AssertionError): | ||
| self.processor.set_pixels(min_pixels=-1) | ||
|
|
||
| def test_set_pixels_zero_max(self): | ||
| """Test setting 0 or negative max_pixels should raise error""" | ||
| with self.assertRaises(AssertionError): | ||
| self.processor.set_pixels(max_pixels=0) | ||
|
|
||
| def test_get_smarted_resize(self): | ||
| """Test get_smarted_resize""" | ||
| height, width = 224, 224 | ||
| (resized_h, resized_w), (patches_h, patches_w) = self.processor.get_smarted_resize(height, width) | ||
| self.assertIsInstance(resized_h, int) | ||
| self.assertIsInstance(resized_w, int) | ||
| self.assertIsInstance(patches_h, int) | ||
| self.assertIsInstance(patches_w, int) | ||
|
|
||
| def test_get_smarted_resize_with_custom_pixels(self): | ||
| """Test get_smarted_resize with custom pixels""" | ||
| height, width = 224, 224 | ||
| (resized_h, resized_w), (_, _) = self.processor.get_smarted_resize( | ||
| height, width, min_pixels=100, max_pixels=10000 | ||
| ) | ||
| self.assertIsInstance(resized_h, int) | ||
| self.assertIsInstance(resized_w, int) | ||
|
|
||
| def test_round_by_factor(self): | ||
| """Test round_by_factor""" | ||
| self.assertEqual(round_by_factor(100, 28), 112) # 100/28 ≈ 3.57, round(3.57) = 4, 4*28 = 112 | ||
| self.assertEqual(round_by_factor(50, 10), 50) | ||
| self.assertEqual(round_by_factor(55, 10), 60) | ||
|
|
||
| def test_ceil_by_factor(self): | ||
| """Test ceil_by_factor""" | ||
| self.assertEqual(ceil_by_factor(100, 28), 112) # ceil(100/28)*28 = ceil(3.57)*28 = 4*28 = 112 | ||
| self.assertEqual(ceil_by_factor(50, 10), 50) | ||
| self.assertEqual(ceil_by_factor(55, 10), 60) | ||
|
|
||
| def test_floor_by_factor(self): | ||
| """Test floor_by_factor""" | ||
| self.assertEqual(floor_by_factor(100, 28), 84) # floor(100/28)*28 = floor(3.57)*28 = 3*28 = 84 | ||
| self.assertEqual(floor_by_factor(50, 10), 50) | ||
| self.assertEqual(floor_by_factor(55, 10), 50) | ||
|
|
||
| def test_smart_resize_basic(self): | ||
| """Test smart_resize basic functionality""" | ||
| height, width = 224, 224 | ||
| new_h, new_w = smart_resize(height, width, factor=28, min_pixels=56 * 56, max_pixels=28 * 28 * 1280) | ||
| self.assertIsInstance(new_h, int) | ||
| self.assertIsInstance(new_w, int) | ||
| self.assertEqual(new_h % 28, 0) | ||
| self.assertEqual(new_w % 28, 0) | ||
|
|
||
| def test_smart_resize_high_aspect_ratio(self): | ||
| """Test case when aspect ratio exceeds MAX_RATIO""" | ||
| height, width = 1000, 10 # aspect ratio = 100 | ||
| new_h, new_w = smart_resize(height, width, factor=28, min_pixels=56 * 56, max_pixels=28 * 28 * 1280) | ||
| self.assertIsInstance(new_h, int) | ||
| self.assertIsInstance(new_w, int) | ||
| self.assertLessEqual(max(new_h, new_w) / min(new_h, new_w), 200) | ||
|
|
||
| def test_smart_resize_too_large(self): | ||
| """Test case when pixel count exceeds max_pixels""" | ||
| height, width = 10000, 10000 | ||
| new_h, new_w = smart_resize(height, width, factor=28, min_pixels=56 * 56, max_pixels=28 * 28 * 1280) | ||
| self.assertLessEqual(new_h * new_w, 28 * 28 * 1280) | ||
|
|
||
| def test_smart_resize_too_small(self): | ||
| """Test case when pixel count is less than min_pixels""" | ||
| height, width = 10, 10 | ||
| new_h, new_w = smart_resize(height, width, factor=28, min_pixels=56 * 56, max_pixels=28 * 28 * 1280) | ||
| self.assertGreaterEqual(new_h * new_w, 56 * 56) | ||
|
|
||
| def test_smart_resize_invalid_result(self): | ||
| """Test case when smart_resize returns invalid result""" | ||
| # This case should not happen, but if it does, ValueError will be raised | ||
| # We test by setting extreme parameters | ||
| # Note: This test may not trigger ValueError, as smart_resize logic may not produce invalid results | ||
| # If testing is really needed, try other extreme cases | ||
| try: | ||
| result = smart_resize(1, 1, factor=100000, min_pixels=100, max_pixels=1000) | ||
| # If successful, verify result | ||
| self.assertIsInstance(result, tuple) | ||
| self.assertEqual(len(result), 2) | ||
| except ValueError: | ||
| # If ValueError is raised, this is also expected | ||
| pass | ||
|
|
||
| def test_is_scaled_image_uint8(self): | ||
| """Test is_scaled_image for uint8 image""" | ||
| image = np.array([[0, 255], [128, 200]], dtype=np.uint8) | ||
| self.assertFalse(is_scaled_image(image)) | ||
|
|
||
| def test_is_scaled_image_scaled(self): | ||
| """Test is_scaled_image for scaled image""" | ||
| image = np.array([[0.0, 0.5], [0.3, 1.0]], dtype=np.float32) | ||
| self.assertTrue(is_scaled_image(image)) | ||
|
|
||
| def test_is_scaled_image_not_scaled(self): | ||
| """Test is_scaled_image for unscaled float image""" | ||
| image = np.array([[0.0, 255.0], [128.0, 300.0]], dtype=np.float32) | ||
| self.assertFalse(is_scaled_image(image)) | ||
|
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||
| def test_make_batched_images_single(self): | ||
| """Test make_batched_images handling single image""" | ||
| img = Image.new("RGB", (224, 224)) | ||
| result = make_batched_images(img) | ||
| self.assertEqual(len(result), 1) | ||
| self.assertEqual(result[0], img) | ||
|
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||
| def test_make_batched_images_list(self): | ||
| """Test make_batched_images handling image list""" | ||
| imgs = [Image.new("RGB", (224, 224)) for _ in range(3)] | ||
| result = make_batched_images(imgs) | ||
| self.assertEqual(len(result), 3) | ||
| self.assertEqual(result, imgs) | ||
|
|
||
| def test_make_batched_images_nested_list(self): | ||
| """Test make_batched_images handling nested list""" | ||
| imgs = [[Image.new("RGB", (224, 224)) for _ in range(2)] for _ in range(2)] | ||
| result = make_batched_images(imgs) | ||
| self.assertEqual(len(result), 4) # 2*2 = 4 | ||
|
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||
| def test_make_batched_images_invalid(self): | ||
| """Test make_batched_images handling invalid input""" | ||
| with self.assertRaises(ValueError): | ||
| make_batched_images("invalid") | ||
|
|
||
| def test_make_batched_videos_list_of_images(self): | ||
| """Test make_batched_videos handling image list""" | ||
| imgs = [Image.new("RGB", (224, 224)) for _ in range(3)] | ||
| result = make_batched_videos(imgs) | ||
| self.assertEqual(len(result), 1) | ||
| self.assertEqual(len(result[0]), 3) | ||
|
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| def test_make_batched_videos_nested_list(self): | ||
| """Test make_batched_videos handling nested list""" | ||
| imgs = [[Image.new("RGB", (224, 224)) for _ in range(2)] for _ in range(2)] | ||
| result = make_batched_videos(imgs) | ||
| self.assertEqual(len(result), 2) | ||
| self.assertEqual(len(result[0]), 2) | ||
|
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||
| def test_make_batched_videos_4d_array(self): | ||
| """Test make_batched_videos handling 4D array""" | ||
| video = np.random.rand(3, 224, 224, 3).astype(np.uint8) | ||
| result = make_batched_videos(video) | ||
| self.assertEqual(len(result), 1) | ||
| self.assertIsInstance(result[0], list) | ||
|
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| def test_make_batched_videos_invalid(self): | ||
| """Test make_batched_videos handling invalid input""" | ||
| with self.assertRaises(ValueError): | ||
| make_batched_videos("invalid") | ||
|
|
||
| def test_preprocess_images(self): | ||
| """Test preprocess handling images""" | ||
| img = Image.new("RGB", (224, 224)) | ||
| result = self.processor.preprocess(images=img) | ||
| self.assertIn("pixel_values", result) | ||
| self.assertIn("image_grid_thw", result) | ||
|
|
||
| def test_preprocess_videos(self): | ||
| """Test preprocess handling videos""" | ||
| frames = [Image.new("RGB", (224, 224)) for _ in range(4)] | ||
| result = self.processor.preprocess(images=None, videos=frames) | ||
| self.assertIn("pixel_values_videos", result) | ||
| self.assertIn("video_grid_thw", result) | ||
|
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||
| def test_preprocess_both_images_and_videos(self): | ||
| """Test preprocess handling both images and videos""" | ||
| img = Image.new("RGB", (224, 224)) | ||
| frames = [Image.new("RGB", (224, 224)) for _ in range(4)] | ||
| result = self.processor.preprocess(images=img, videos=frames) | ||
| # When both images and videos are provided, may only return videos result | ||
| # According to code logic, if videos is not None, it will overwrite data dict | ||
| self.assertTrue("pixel_values" in result or "pixel_values_videos" in result) | ||
|
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| def test_preprocess_invalid_images(self): | ||
| """Test preprocess handling invalid image""" | ||
| with self.assertRaises(ValueError): | ||
| self.processor.preprocess(images="invalid") | ||
|
|
||
| def test_preprocess_with_predetermined_grid_thw(self): | ||
| """Test preprocess using predetermined_grid_thw""" | ||
| img = Image.new("RGB", (224, 224)) | ||
| # predetermined_grid_thw should be (h, w) format, not [1, h, w] | ||
| predetermined_grid_thw = [(16, 16)] # For single image, should be (h, w) tuple | ||
| result = self.processor.preprocess(images=img, predetermined_grid_thw=predetermined_grid_thw) | ||
| self.assertIn("pixel_values", result) | ||
|
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| def test_preprocess_no_resize(self): | ||
| """Test preprocess without resize""" | ||
| img = Image.new("RGB", (224, 224)) | ||
| result = self.processor.preprocess(images=img, do_resize=False) | ||
| self.assertIn("pixel_values", result) | ||
|
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||
| def test_preprocess_no_rescale(self): | ||
| """Test preprocess without rescale""" | ||
| img = Image.new("RGB", (224, 224)) | ||
| result = self.processor.preprocess(images=img, do_rescale=False) | ||
| self.assertIn("pixel_values", result) | ||
|
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| def test_preprocess_no_normalize(self): | ||
| """Test preprocess without normalize""" | ||
| img = Image.new("RGB", (224, 224)) | ||
| result = self.processor.preprocess(images=img, do_normalize=False) | ||
| self.assertIn("pixel_values", result) | ||
|
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| def test_preprocess_custom_mean_std(self): | ||
| """Test preprocess using custom mean and std""" | ||
| img = Image.new("RGB", (224, 224)) | ||
| result = self.processor.preprocess(images=img, image_mean=[0.5, 0.5, 0.5], image_std=[0.5, 0.5, 0.5]) | ||
| self.assertIn("pixel_values", result) | ||
|
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||
| def test_make_batched_videos_4d_array_in_list(self): | ||
| """Test make_batched_videos handling 4D array in list (lines 119-120)""" | ||
| # Create a list of 4D arrays | ||
| videos = [np.random.rand(3, 224, 224, 3).astype(np.uint8)] | ||
| result = make_batched_videos(videos) | ||
| self.assertEqual(len(result), 1) | ||
| self.assertIsInstance(result[0], list) | ||
|
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| def test_preprocess_do_convert_rgb(self): | ||
| """Test preprocess with do_convert_rgb=True (line 289)""" | ||
| img = Image.new("L", (224, 224)) # Grayscale image | ||
| result = self.processor.preprocess(images=img, do_convert_rgb=True) | ||
| self.assertIn("pixel_values", result) | ||
|
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| def test_preprocess_scaled_image_warning(self): | ||
| """Test warning for scaled image in preprocess (line 295)""" | ||
| # Create a scaled image (values between 0-1) | ||
| img_array = np.random.rand(224, 224, 3).astype(np.float32) | ||
| # Use patch to capture warning | ||
| with patch( | ||
| "fastdeploy.input.ernie4_5_vl_processor.image_preprocessor.image_preprocessor_adaptive.data_processor_logger" | ||
| ) as mock_logger: | ||
| # Directly call _preprocess, pass scaled image | ||
| self.processor._preprocess( | ||
| [img_array], # Pass scaled numpy array | ||
| do_rescale=True, | ||
| do_convert_rgb=False, | ||
| ) | ||
| # Verify warning is called (if is_scaled_image returns True) | ||
| # mock_logger.warning should be called | ||
| if is_scaled_image(img_array): | ||
| # If image is indeed scaled, warning should be called | ||
| mock_logger.warning.assert_called() | ||
|
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| def test_preprocess_data_format_last(self): | ||
| """Test preprocess with data_format=LAST (line 351)""" | ||
| img = Image.new("RGB", (224, 224)) | ||
| from paddleformers.transformers.image_utils import ChannelDimension | ||
|
|
||
| result = self.processor.preprocess(images=img, data_format=ChannelDimension.LAST) | ||
| self.assertIn("pixel_values", result) | ||
|
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| def test_preprocess_invalid_images_check(self): | ||
| """Test invalid image check in preprocess (line 464)""" | ||
| # Test invalid image type - need to ensure valid_images returns False | ||
| # Use patch to make valid_images return False, but make_batched_images succeeds | ||
| with patch( | ||
| "fastdeploy.input.ernie4_5_vl_processor.image_preprocessor.image_preprocessor_adaptive.valid_images" | ||
| ) as mock_valid: | ||
| mock_valid.return_value = False | ||
| valid_images_list = [Image.new("RGB", (224, 224))] # Valid image, but valid_images returns False | ||
| with self.assertRaises(ValueError) as context: | ||
| self.processor.preprocess(images=valid_images_list) | ||
| self.assertIn("Invalid image type", str(context.exception)) | ||
|
|
||
| def test_smart_resize_high_aspect_ratio_height_gt_width(self): | ||
| """Test smart_resize when aspect ratio exceeds MAX_RATIO, height > width case (lines 558-560)""" | ||
| height, width = 10000, 10 # height > width, aspect ratio = 1000 | ||
| new_h, new_w = smart_resize(height, width, factor=28, min_pixels=56 * 56, max_pixels=28 * 28 * 1280) | ||
| self.assertIsInstance(new_h, int) | ||
| self.assertIsInstance(new_w, int) | ||
| self.assertLessEqual(max(new_h, new_w) / min(new_h, new_w), 200) | ||
|
|
||
| def test_smart_resize_high_aspect_ratio_width_gt_height(self): | ||
| """Test smart_resize when aspect ratio exceeds MAX_RATIO, width > height case (lines 561-563)""" | ||
| height, width = 10, 10000 # width > height, aspect ratio = 1000 | ||
| new_h, new_w = smart_resize(height, width, factor=28, min_pixels=56 * 56, max_pixels=28 * 28 * 1280) | ||
| self.assertIsInstance(new_h, int) | ||
| self.assertIsInstance(new_w, int) | ||
| self.assertLessEqual(max(new_h, new_w) / min(new_h, new_w), 200) | ||
|
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| def test_is_scaled_image_edge_cases(self): | ||
| """Test is_scaled_image edge cases (lines 80-84)""" | ||
| # Test with values exactly at boundaries | ||
| image1 = np.array([[0.0, 1.0]], dtype=np.float32) | ||
| self.assertTrue(is_scaled_image(image1)) | ||
| image2 = np.array([[0.0, 1.1]], dtype=np.float32) | ||
| self.assertFalse(is_scaled_image(image2)) | ||
| image3 = np.array([[-0.1, 1.0]], dtype=np.float32) | ||
| self.assertFalse(is_scaled_image(image3)) | ||
|
|
||
| def test_make_batched_images_nested_list_edge_case(self): | ||
| """Test make_batched_images with nested list edge case (lines 98-107)""" | ||
| # Test with nested list where first element is a list of images | ||
| imgs = [[Image.new("RGB", (224, 224)) for _ in range(2)] for _ in range(2)] | ||
| result = make_batched_images(imgs) | ||
| self.assertEqual(len(result), 4) | ||
|
|
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| def test_make_batched_videos_edge_cases(self): | ||
| """Test make_batched_videos edge cases (lines 113-125)""" | ||
| # Test with single Image.Image in list | ||
| img = Image.new("RGB", (224, 224)) | ||
| result = make_batched_videos([img]) | ||
| self.assertEqual(len(result), 1) | ||
| self.assertEqual(len(result[0]), 1) | ||
|
|
||
| # Test with 4D array (video) | ||
| video = np.random.rand(3, 224, 224, 3).astype(np.uint8) | ||
| result = make_batched_videos(video) | ||
| self.assertEqual(len(result), 1) | ||
| self.assertIsInstance(result[0], list) | ||
|
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||
| def test_preprocess_predetermined_grid_thw_multiple_images(self): | ||
| """Test preprocess with predetermined_grid_thw for multiple images (lines 307-310)""" | ||
| imgs = [Image.new("RGB", (224, 224)) for _ in range(2)] | ||
| predetermined_grid_thw = [(16, 16), (20, 20)] | ||
| result = self.processor.preprocess(images=imgs, predetermined_grid_thw=predetermined_grid_thw) | ||
| self.assertIn("pixel_values", result) | ||
|
|
||
| def test_preprocess_predetermined_grid_thw_length_mismatch(self): | ||
| """Test preprocess with predetermined_grid_thw length mismatch (lines 308-310) | ||
|
|
||
| Note: The implementation raises IndexError when predetermined_grid_thw length | ||
| doesn't match images length, because it accesses predetermined_grid_thw[img_idx] | ||
| directly without checking bounds first. | ||
| """ | ||
| imgs = [Image.new("RGB", (224, 224)) for _ in range(2)] | ||
| predetermined_grid_thw = [(16, 16)] # Length mismatch - only 1 element for 2 images | ||
| # The function raises IndexError when accessing predetermined_grid_thw[1] | ||
| with self.assertRaises(IndexError): | ||
| self.processor.preprocess(images=imgs, predetermined_grid_thw=predetermined_grid_thw) | ||
|
|
||
| def test_preprocess_with_input_data_format(self): | ||
| """Test preprocess with input_data_format parameter (lines 299-301)""" | ||
| img = Image.new("RGB", (224, 224)) | ||
| from paddleformers.transformers.image_utils import ChannelDimension | ||
|
|
||
| result = self.processor.preprocess(images=img, input_data_format=ChannelDimension.FIRST) | ||
| self.assertIn("pixel_values", result) | ||
|
|
||
| def test_preprocess_do_resize_with_predetermined_grid_thw(self): | ||
| """Test preprocess with do_resize=True and predetermined_grid_thw (lines 314-317)""" | ||
| img = Image.new("RGB", (224, 224)) | ||
| predetermined_grid_thw = [(16, 16)] | ||
| result = self.processor.preprocess(images=img, predetermined_grid_thw=predetermined_grid_thw, do_resize=True) | ||
| self.assertIn("pixel_values", result) | ||
|
|
||
| def test_preprocess_videos_with_predetermined_grid_thw(self): | ||
| """Test preprocess videos with predetermined_grid_thw (lines 511)""" | ||
| frames = [Image.new("RGB", (224, 224)) for _ in range(4)] | ||
| predetermined_grid_thw = [(16, 16)] * 4 | ||
| result = self.processor.preprocess(images=None, videos=frames, predetermined_grid_thw=predetermined_grid_thw) | ||
| self.assertIn("pixel_values_videos", result) | ||
|
|
||
| def test_preprocess_multiple_images_loop(self): | ||
| """Test preprocess with multiple images in loop (lines 468-488)""" | ||
| imgs = [Image.new("RGB", (224, 224)) for _ in range(3)] | ||
| result = self.processor.preprocess(images=imgs) | ||
| self.assertIn("pixel_values", result) | ||
| self.assertIn("image_grid_thw", result) | ||
|
|
||
| def test_preprocess_videos_loop(self): | ||
| """Test preprocess with videos in loop (lines 496-521)""" | ||
| videos = [[Image.new("RGB", (224, 224)) for _ in range(4)] for _ in range(2)] | ||
| result = self.processor.preprocess(images=None, videos=videos) | ||
| self.assertIn("pixel_values_videos", result) | ||
| self.assertIn("video_grid_thw", result) | ||
|
|
||
| def test_preprocess_return_tensors(self): | ||
| """Test preprocess with return_tensors parameter (lines 396, 523)""" | ||
| img = Image.new("RGB", (224, 224)) | ||
| # Use string instead of TensorType enum which may not be available | ||
| result = self.processor.preprocess(images=img, return_tensors="np") | ||
| self.assertIn("pixel_values", result) | ||
|
|
||
| def test_preprocess_channel_dimension_none(self): | ||
| """Test preprocess with input_data_format=None (lines 299-301)""" | ||
| img = Image.new("RGB", (224, 224)) | ||
| result = self.processor.preprocess(images=img, input_data_format=None) | ||
| self.assertIn("pixel_values", result) | ||
|
|
||
| def test_preprocess_do_rescale_false_with_scaled_image(self): | ||
| """Test preprocess with do_rescale=False and scaled image (line 335)""" | ||
| # Create a scaled image | ||
| img_array = np.random.rand(224, 224, 3).astype(np.float32) * 0.5 # Values in [0, 0.5] | ||
| img = Image.fromarray((img_array * 255).astype(np.uint8)) | ||
| result = self.processor.preprocess(images=img, do_rescale=False) | ||
| self.assertIn("pixel_values", result) | ||
|
|
||
| def test_preprocess_do_normalize_false(self): | ||
| """Test preprocess with do_normalize=False (lines 338-344)""" | ||
| img = Image.new("RGB", (224, 224)) | ||
| result = self.processor.preprocess(images=img, do_normalize=False) | ||
| self.assertIn("pixel_values", result) | ||
|
|
||
| def test_preprocess_custom_resample(self): | ||
| """Test preprocess with custom resample parameter (line 332)""" | ||
| img = Image.new("RGB", (224, 224)) | ||
| from PIL import Image as PILImage | ||
|
|
||
| result = self.processor.preprocess(images=img, resample=PILImage.BILINEAR) | ||
| self.assertIn("pixel_values", result) | ||
|
|
||
| def test_preprocess_custom_rescale_factor(self): | ||
| """Test preprocess with custom rescale_factor (line 336)""" | ||
| img = Image.new("RGB", (224, 224)) | ||
| result = self.processor.preprocess(images=img, rescale_factor=1.0 / 128.0) | ||
| self.assertIn("pixel_values", result) | ||
|
|
||
| def test_preprocess_custom_image_mean_std(self): | ||
| """Test preprocess with custom image_mean and image_std (lines 339-344)""" | ||
| img = Image.new("RGB", (224, 224)) | ||
| result = self.processor.preprocess( | ||
| images=img, image_mean=[0.485, 0.456, 0.406], image_std=[0.229, 0.224, 0.225] | ||
| ) | ||
| self.assertIn("pixel_values", result) | ||
|
|
||
| def test_preprocess_data_format_channels_first(self): | ||
| """Test preprocess with data_format=FIRST (line 346)""" | ||
| img = Image.new("RGB", (224, 224)) | ||
| from paddleformers.transformers.image_utils import ChannelDimension | ||
|
|
||
| result = self.processor.preprocess(images=img, data_format=ChannelDimension.FIRST) | ||
| self.assertIn("pixel_values", result) | ||
|
|
||
| def test_preprocess_data_format_channels_last(self): | ||
| """Test preprocess with data_format=LAST (line 350)""" | ||
| img = Image.new("RGB", (224, 224)) | ||
| from paddleformers.transformers.image_utils import ChannelDimension | ||
|
|
||
| result = self.processor.preprocess(images=img, data_format=ChannelDimension.LAST) | ||
| self.assertIn("pixel_values", result) | ||
|
|
||
| def test_preprocess_patches_reshape(self): | ||
| """Test preprocess patches reshape logic (lines 349-381)""" | ||
| img = Image.new("RGB", (224, 224)) | ||
| result = self.processor.preprocess(images=img) | ||
| self.assertIn("pixel_values", result) | ||
| # Verify pixel_values shape | ||
| pixel_values = result["pixel_values"] | ||
| self.assertIsInstance(pixel_values, np.ndarray) | ||
|
|
||
| def test_preprocess_videos_multiple(self): | ||
| """Test preprocess with multiple videos (lines 496-521)""" | ||
| videos = [ | ||
| [Image.new("RGB", (224, 224)) for _ in range(4)], | ||
| [Image.new("RGB", (224, 224)) for _ in range(4)], | ||
| ] | ||
| result = self.processor.preprocess(images=None, videos=videos) | ||
| self.assertIn("pixel_values_videos", result) | ||
| self.assertIn("video_grid_thw", result) | ||
|
|
||
| def test_make_batched_images_invalid_nested_list(self): | ||
| """Test make_batched_images with invalid nested list (line 98)""" | ||
| # Test with nested list but first element is not an image | ||
| invalid_input = [[1, 2, 3], [4, 5, 6]] | ||
| with self.assertRaises(ValueError) as context: | ||
| make_batched_images(invalid_input) | ||
| self.assertIn("Could not make batched images", str(context.exception)) | ||
|
|
||
| def test_make_batched_images_invalid_single(self): | ||
| """Test make_batched_images with invalid single input (line 107)""" | ||
| invalid_input = "not an image" | ||
| with self.assertRaises(ValueError) as context: | ||
| make_batched_images(invalid_input) | ||
| self.assertIn("Could not make batched images", str(context.exception)) | ||
|
|
||
| def test_make_batched_videos_nested_list_of_images(self): | ||
| """Test make_batched_videos with nested list of images (line 113)""" | ||
| images = [[Image.new("RGB", (224, 224)) for _ in range(2)]] | ||
| result = make_batched_videos(images) | ||
| self.assertEqual(result, images) | ||
|
|
||
| def test_make_batched_videos_list_of_images_nested_output(self): | ||
| """Test make_batched_videos with list of images (line 117)""" | ||
| images = [Image.new("RGB", (224, 224)) for _ in range(2)] | ||
| result = make_batched_videos(images) | ||
| self.assertEqual(result, [images]) | ||
|
|
||
| def test_make_batched_videos_4d_array_in_list_variant(self): | ||
| """Test make_batched_videos with 4D array in list (line 119) | ||
|
|
||
| Note: make_batched_videos expects 4D array (time, height, width, channels), | ||
| not 5D array (batch, time, height, width, channels). | ||
| """ | ||
| # Create a 4D numpy array (time, height, width, channels) | ||
| video_array = np.random.rand(4, 224, 224, 3).astype(np.uint8) | ||
| result = make_batched_videos([video_array]) | ||
| self.assertIsInstance(result, list) | ||
|
|
||
| def test_make_batched_videos_4d_array_single(self): | ||
| """Test make_batched_videos with single 4D array (line 122) | ||
|
|
||
| Note: make_batched_videos expects 4D array (time, height, width, channels), | ||
| not 5D array (batch, time, height, width, channels). | ||
| """ | ||
| # Create a 4D numpy array (time, height, width, channels) | ||
| video_array = np.random.rand(4, 224, 224, 3).astype(np.uint8) | ||
| result = make_batched_videos(video_array) | ||
| self.assertIsInstance(result, list) | ||
|
|
||
| def test_make_batched_videos_invalid_input(self): | ||
| """Test make_batched_videos with invalid input (line 125)""" | ||
| invalid_input = "not a video" | ||
| with self.assertRaises(ValueError) as context: | ||
| make_batched_videos(invalid_input) | ||
| self.assertIn("Could not make batched video", str(context.exception)) | ||
|
|
||
| def test_is_scaled_image_uint8_false(self): | ||
| """Test is_scaled_image with uint8 image (line 80)""" | ||
| image = np.random.rand(224, 224, 3).astype(np.uint8) * 255 | ||
| result = is_scaled_image(image) | ||
| self.assertFalse(result) | ||
|
|
||
| def test_is_scaled_image_scaled_true(self): | ||
| """Test is_scaled_image with scaled float image (line 84)""" | ||
| image = np.random.rand(224, 224, 3).astype(np.float32) * 0.5 # Values in [0, 0.5] | ||
| result = is_scaled_image(image) | ||
| self.assertTrue(result) | ||
|
|
||
| def test_is_scaled_image_not_scaled_false(self): | ||
| """Test is_scaled_image with non-scaled float image (line 84)""" | ||
| image = np.random.rand(224, 224, 3).astype(np.float32) * 255 # Values > 1 | ||
| result = is_scaled_image(image) | ||
| self.assertFalse(result) | ||
|
|
||
| def test_preprocess_with_scaled_image_warning(self): | ||
| """Test preprocess with scaled image triggers warning (lines 294-298) | ||
|
|
||
| Note: The warning is only triggered when is_scaled_image() returns True, | ||
| which requires float images with values in [0, 1]. Converting to PIL Image | ||
| and back converts to uint8, so the warning won't be triggered. | ||
| This test verifies the preprocess works without errors. | ||
| """ | ||
| # Create a scaled image (values in [0, 1]) | ||
| scaled_image = np.random.rand(224, 224, 3).astype(np.float32) * 0.5 | ||
| scaled_image = Image.fromarray((scaled_image * 255).astype(np.uint8)) | ||
|
|
||
| # The image is now uint8, so is_scaled_image returns False and no warning is triggered | ||
| result = self.processor.preprocess(images=[scaled_image], do_rescale=True) | ||
| self.assertIn("pixel_values", result) | ||
|
|
||
| def test_preprocess_predetermined_grid_thw_length_mismatch_assert(self): | ||
| """Test preprocess with predetermined_grid_thw length mismatch (line 310) | ||
|
|
||
| Note: The source code expects predetermined_grid_thw elements to be (height, width) tuples, | ||
| but when 3-element arrays like [1, 16, 16] are passed, it raises ValueError when unpacking. | ||
| """ | ||
| images = [Image.new("RGB", (224, 224)) for _ in range(2)] | ||
| predetermined_grid_thw = np.array([[1, 16, 16]]) # Only 1, but 2 images | ||
|
|
||
| # First fails because of unpacking 3 values into 2 variables | ||
| with self.assertRaises(ValueError) as context: | ||
| self.processor.preprocess(images=images, predetermined_grid_thw=predetermined_grid_thw, do_resize=True) | ||
| self.assertIn("too many values to unpack", str(context.exception)) | ||
|
|
||
| def test_preprocess_loop_multiple_images(self): | ||
| """Test preprocess loop with multiple images (lines 312-348)""" | ||
| images = [Image.new("RGB", (224, 224)) for _ in range(3)] | ||
| result = self.processor.preprocess(images=images) | ||
| self.assertIn("pixel_values", result) | ||
| pixel_values = result["pixel_values"] | ||
| self.assertIsInstance(pixel_values, np.ndarray) | ||
|
|
||
| def test_preprocess_with_predetermined_grid_thw_in_loop(self): | ||
| """Test preprocess with predetermined_grid_thw in loop (lines 314-317) | ||
|
|
||
| Note: predetermined_grid_thw expects (height, width) tuples, not (t, h, w). | ||
| The values are grid dimensions that get multiplied by patch_size. | ||
| """ | ||
| images = [Image.new("RGB", (224, 224)) for _ in range(2)] | ||
| # Use 2D grid (h, w) format | ||
| predetermined_grid_thw = [(16, 16), (16, 16)] | ||
|
|
||
| result = self.processor.preprocess( | ||
| images=images, predetermined_grid_thw=predetermined_grid_thw, do_resize=True | ||
| ) | ||
| self.assertIn("pixel_values", result) | ||
|
|
||
| def test_preprocess_patches_reshape_multiple_inputs(self): | ||
| """Test preprocess patches reshape logic (lines 349-381)""" | ||
| images = [Image.new("RGB", (224, 224))] | ||
| result = self.processor.preprocess(images=images) | ||
| self.assertIn("pixel_values", result) | ||
| pixel_values = result["pixel_values"] | ||
| # Verify shape is correct after reshape | ||
| self.assertEqual(len(pixel_values.shape), 2) # Should be [grid_t * grid_h * grid_w, C * psz * psz] | ||
|
|
||
| def test_smart_resize_high_aspect_ratio_height_gt_width_case(self): | ||
| """Test smart_resize with high aspect ratio, height > width (lines 557-563)""" | ||
| # Create image with very high aspect ratio | ||
| height, width = 1000, 50 # Aspect ratio = 20 | ||
| factor = 14 | ||
| min_pixels = 1000 | ||
| max_pixels = 100000 | ||
|
|
||
| new_h, new_w = smart_resize(height, width, factor, min_pixels, max_pixels) | ||
| self.assertIsInstance(new_h, int) | ||
| self.assertIsInstance(new_w, int) | ||
| self.assertGreater(new_h, 0) | ||
| self.assertGreater(new_w, 0) | ||
|
|
||
| def test_smart_resize_high_aspect_ratio_width_gt_height_case(self): | ||
| """Test smart_resize with high aspect ratio, width > height (lines 562-563)""" | ||
| # Create image with very high aspect ratio (wide) | ||
| height, width = 50, 1000 # Aspect ratio = 20 | ||
| factor = 14 | ||
| min_pixels = 1000 | ||
| max_pixels = 100000 | ||
|
|
||
| new_h, new_w = smart_resize(height, width, factor, min_pixels, max_pixels) | ||
| self.assertIsInstance(new_h, int) | ||
| self.assertIsInstance(new_w, int) | ||
|
|
||
| def test_smart_resize_exceeds_max_pixels(self): | ||
| """Test smart_resize when h_bar * w_bar > max_pixels (lines 575-578)""" | ||
| height, width = 10000, 10000 # Very large image | ||
| factor = 14 | ||
| min_pixels = 1000 | ||
| max_pixels = 10000 # Small max_pixels | ||
|
|
||
| new_h, new_w = smart_resize(height, width, factor, min_pixels, max_pixels) | ||
| self.assertLessEqual(new_h * new_w, max_pixels) | ||
| self.assertGreaterEqual(new_h * new_w, min_pixels) | ||
|
|
||
| def test_smart_resize_below_min_pixels(self): | ||
| """Test smart_resize when h_bar * w_bar < min_pixels (lines 579-582)""" | ||
| height, width = 10, 10 # Very small image | ||
| factor = 14 | ||
| min_pixels = 10000 # Large min_pixels | ||
| max_pixels = 100000 | ||
|
|
||
| new_h, new_w = smart_resize(height, width, factor, min_pixels, max_pixels) | ||
| self.assertGreaterEqual(new_h * new_w, min_pixels) | ||
| self.assertLessEqual(new_h * new_w, max_pixels) | ||
|
|
||
| def test_smart_resize_invalid_result_constraints(self): | ||
| """Test smart_resize with invalid result (line 585)""" | ||
| # This is hard to trigger, but we can test the validation | ||
| height, width = 100, 100 | ||
| factor = 14 | ||
| min_pixels = 10000 | ||
| max_pixels = 1000 # max < min, which is invalid but should be caught | ||
|
|
||
| # This should raise an error or return valid values | ||
| try: | ||
| new_h, new_w = smart_resize(height, width, factor, min_pixels, max_pixels) | ||
| # If it doesn't raise, verify the result is valid | ||
| self.assertGreaterEqual(new_h * new_w, min_pixels) | ||
| self.assertLessEqual(new_h * new_w, max_pixels) | ||
| except ValueError: | ||
| # Expected if validation catches the issue | ||
| pass | ||
|
|
||
| def test_preprocess_videos_loop_numpy_output(self): | ||
| """Test preprocess videos loop (lines 496-521)""" | ||
| videos = [ | ||
| [Image.new("RGB", (224, 224)) for _ in range(4)], | ||
| [Image.new("RGB", (224, 224)) for _ in range(4)], | ||
| ] | ||
| result = self.processor.preprocess(images=None, videos=videos) | ||
| self.assertIn("pixel_values_videos", result) | ||
| self.assertIn("video_grid_thw", result) | ||
| self.assertIsInstance(result["pixel_values_videos"], np.ndarray) | ||
|
|
||
| def test_preprocess_both_images_and_videos_full_outputs(self): | ||
| """Test preprocess with both images and videos (lines 458-523) | ||
|
|
||
| Note: Current implementation has a known issue where the data dict is overwritten | ||
| when processing both images and videos. The video processing overwrites the image | ||
| results, so only video outputs are returned. | ||
| """ | ||
| images = [Image.new("RGB", (224, 224))] | ||
| videos = [[Image.new("RGB", (224, 224)) for _ in range(4)]] | ||
|
|
||
| result = self.processor.preprocess(images=images, videos=videos) | ||
| # Due to implementation, only video results are returned when both are provided | ||
| self.assertIn("pixel_values_videos", result) | ||
| self.assertIn("video_grid_thw", result) | ||
|
|
||
| def test_preprocess_images_loop_with_predetermined_grid_thw(self): | ||
| """Test preprocess images loop with predetermined_grid_thw (lines 468-486) | ||
|
|
||
| Note: predetermined_grid_thw expects (height, width) tuples, not (t, h, w). | ||
| """ | ||
| images = [Image.new("RGB", (224, 224)) for _ in range(2)] | ||
| # Use 2D grid (h, w) format | ||
| predetermined_grid_thw = [(16, 16), (16, 16)] | ||
|
|
||
| result = self.processor.preprocess( | ||
| images=images, predetermined_grid_thw=predetermined_grid_thw, do_resize=True | ||
| ) | ||
| self.assertIn("pixel_values", result) | ||
| self.assertEqual(len(result["image_grid_thw"]), 2) | ||
|
|
||
| def test_preprocess_invalid_images_check_list_input(self): | ||
| """Test preprocess with invalid images check (line 464) | ||
|
|
||
| Note: The error is raised by make_batched_images before valid_images check, | ||
| so the error message is different. | ||
| """ | ||
| invalid_images = ["not an image", "also not an image"] | ||
|
|
||
| with self.assertRaises(ValueError) as context: | ||
| self.processor.preprocess(images=invalid_images) | ||
| self.assertIn("Could not make batched images", str(context.exception)) | ||
|
|
||
| def test_round_by_factor_edge_cases(self): | ||
| """Test round_by_factor with edge cases (lines 526-530)""" | ||
| self.assertEqual(round_by_factor(0, 14), 0) | ||
| self.assertEqual(round_by_factor(14, 14), 14) | ||
| self.assertEqual(round_by_factor(13, 14), 14) # Round up | ||
| self.assertEqual(round_by_factor(15, 14), 14) # Round down | ||
|
|
||
| def test_ceil_by_factor_edge_cases(self): | ||
| """Test ceil_by_factor with edge cases (lines 532-536)""" | ||
| self.assertEqual(ceil_by_factor(0, 14), 0) | ||
| self.assertEqual(ceil_by_factor(14, 14), 14) | ||
| self.assertEqual(ceil_by_factor(13, 14), 14) # Ceil up | ||
| self.assertEqual(ceil_by_factor(15, 14), 28) # Ceil up to next multiple | ||
|
|
||
| def test_floor_by_factor_edge_cases(self): | ||
| """Test floor_by_factor with edge cases (lines 538-542)""" | ||
| self.assertEqual(floor_by_factor(0, 14), 0) | ||
| self.assertEqual(floor_by_factor(14, 14), 14) | ||
| self.assertEqual(floor_by_factor(13, 14), 0) # Floor down | ||
| self.assertEqual(floor_by_factor(15, 14), 14) # Floor down to multiple | ||
| self.assertEqual(floor_by_factor(28, 14), 28) # Exact multiple | ||
|
|
||
|
|
||
| if __name__ == "__main__": | ||
| unittest.main() |
There was a problem hiding this comment.
Multiple test docstrings contain hardcoded line number references (e.g., "line 289", "line 295", etc.). These line numbers will become outdated if the source code changes, making the comments misleading.
Recommendation: Remove the line number references from test docstrings. The test method names and their implementation should be self-documenting. If you need to track which code paths are tested, use code coverage tools instead of manual line number tracking.
kesmeey
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Motivation
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Checklist
[FDConfig],[APIServer],[Engine],[Scheduler],[PD Disaggregation],[Executor],[Graph Optimization],[Speculative Decoding],[RL],[Models],[Quantization],[Loader],[OP],[KVCache],[DataProcessor],[BugFix],[Docs],[CI],[Optimization],[Feature],[Benchmark],[Others],[XPU],[HPU],[GCU],[DCU],[Iluvatar],[Metax]]pre-commitbefore commit.releasebranch, make sure the PR has been submitted to thedevelopbranch, then cherry-pick it to thereleasebranch with the[Cherry-Pick]PR tag.