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https://github.com/huggingface/transformers.git
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c030fc8913
* Fix Fuyu image scaling bug It could produce negative padding and hence inference errors for certain image sizes. * Fix aspect ratio scaling test
67 lines
2.3 KiB
Python
67 lines
2.3 KiB
Python
import unittest
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import numpy as np
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from transformers import is_torch_available, is_vision_available
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from transformers.testing_utils import (
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require_torch,
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require_torchvision,
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require_vision,
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)
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if is_torch_available() and is_vision_available():
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import torch
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from transformers import FuyuImageProcessor
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if is_vision_available():
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from PIL import Image
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@require_torch
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@require_vision
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@require_torchvision
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class TestFuyuImageProcessor(unittest.TestCase):
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def setUp(self):
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self.processor = FuyuImageProcessor(target_height=160, target_width=320, padding_value=1.0)
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self.batch_size = 3
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self.channels = 3
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self.height = 300
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self.width = 300
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self.image_input = torch.rand(self.batch_size, self.channels, self.height, self.width)
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self.image_patch_dim_h = 30
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self.image_patch_dim_w = 30
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self.sample_image = np.zeros((450, 210, 3), dtype=np.uint8)
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self.sample_image_pil = Image.fromarray(self.sample_image)
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def test_patches(self):
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expected_num_patches = self.processor.get_num_patches(
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img_h=self.height, img_w=self.width, patch_dim_h=self.image_patch_dim_h, patch_dim_w=self.image_patch_dim_w
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)
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patches_final = self.processor.patchify_image(
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image=self.image_input, patch_dim_h=self.image_patch_dim_h, patch_dim_w=self.image_patch_dim_w
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)
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assert (
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patches_final.shape[1] == expected_num_patches
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), f"Expected {expected_num_patches} patches, got {patches_final.shape[1]}."
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def test_scale_to_target_aspect_ratio(self):
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# (h:450, w:210) fitting (160, 320) -> (160, 210*160/450)
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scaled_image = self.processor._scale_to_target_aspect_ratio(self.sample_image)
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self.assertEqual(scaled_image.shape[0], 160)
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self.assertEqual(scaled_image.shape[1], 74)
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def test_apply_transformation_numpy(self):
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transformed_image = self.processor.apply_transformation(self.sample_image)
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self.assertEqual(transformed_image.shape[0], 160)
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self.assertEqual(transformed_image.shape[1], 320)
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def test_apply_transformation_pil(self):
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transformed_image = self.processor.apply_transformation(self.sample_image_pil)
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self.assertEqual(transformed_image.shape[0], 160)
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self.assertEqual(transformed_image.shape[1], 320)
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