Superpoint fast image processor (#37804)
* feat: superpoint fast image processor * fix: reran fast cli command to generate fast config * feat: updated test cases * fix: removed old model add * fix: format fix * Update src/transformers/models/superpoint/image_processing_superpoint_fast.py Co-authored-by: Yoni Gozlan <74535834+yonigozlan@users.noreply.github.com> * fix: ported to torch and made requested changes * fix: removed changes to init * fix: init fix * fix: init format fix * fixed testcases and ported to torch * fix: format fixes * failed test case fix * fix superpoint fast * fix docstring --------- Co-authored-by: Yoni Gozlan <74535834+yonigozlan@users.noreply.github.com> Co-authored-by: yonigozlan <yoni.gozlan@huggingface.co>
This commit is contained in:
Avigyan Sinha
@@ -16,12 +16,9 @@ import unittest
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import numpy as np
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from transformers.testing_utils import require_torch, require_vision
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from transformers.utils import is_torch_available, is_vision_available
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from transformers.utils import is_torch_available, is_torchvision_available, is_vision_available
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from ...test_image_processing_common import (
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ImageProcessingTestMixin,
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prepare_image_inputs,
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)
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from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs
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if is_torch_available():
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@@ -32,6 +29,9 @@ if is_torch_available():
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if is_vision_available():
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from transformers import SuperPointImageProcessor
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if is_torchvision_available():
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from transformers import SuperPointImageProcessorFast
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class SuperPointImageProcessingTester:
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def __init__(
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@@ -100,6 +100,7 @@ class SuperPointImageProcessingTester:
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@require_vision
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class SuperPointImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
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image_processing_class = SuperPointImageProcessor if is_vision_available() else None
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fast_image_processing_class = SuperPointImageProcessorFast if is_torchvision_available() else None
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def setUp(self) -> None:
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super().setUp()
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@@ -110,40 +111,44 @@ class SuperPointImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase)
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return self.image_processor_tester.prepare_image_processor_dict()
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def test_image_processing(self):
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image_processing = self.image_processing_class(**self.image_processor_dict)
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self.assertTrue(hasattr(image_processing, "do_resize"))
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self.assertTrue(hasattr(image_processing, "size"))
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self.assertTrue(hasattr(image_processing, "do_rescale"))
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self.assertTrue(hasattr(image_processing, "rescale_factor"))
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self.assertTrue(hasattr(image_processing, "do_grayscale"))
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for image_processing_class in self.image_processor_list:
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image_processing = image_processing_class(**self.image_processor_dict)
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self.assertTrue(hasattr(image_processing, "do_resize"))
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self.assertTrue(hasattr(image_processing, "size"))
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self.assertTrue(hasattr(image_processing, "do_rescale"))
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self.assertTrue(hasattr(image_processing, "rescale_factor"))
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self.assertTrue(hasattr(image_processing, "do_grayscale"))
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def test_image_processor_from_dict_with_kwargs(self):
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image_processor = self.image_processing_class.from_dict(self.image_processor_dict)
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self.assertEqual(image_processor.size, {"height": 480, "width": 640})
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for image_processing_class in self.image_processor_list:
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image_processor = image_processing_class.from_dict(self.image_processor_dict)
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self.assertEqual(image_processor.size, {"height": 480, "width": 640})
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image_processor = self.image_processing_class.from_dict(
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self.image_processor_dict, size={"height": 42, "width": 42}
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)
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self.assertEqual(image_processor.size, {"height": 42, "width": 42})
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image_processor = self.image_processing_class.from_dict(
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self.image_processor_dict, size={"height": 42, "width": 42}
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)
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self.assertEqual(image_processor.size, {"height": 42, "width": 42})
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@unittest.skip(reason="SuperPointImageProcessor is always supposed to return a grayscaled image")
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def test_call_numpy_4_channels(self):
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pass
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def test_input_image_properly_converted_to_grayscale(self):
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image_processor = self.image_processing_class.from_dict(self.image_processor_dict)
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image_inputs = self.image_processor_tester.prepare_image_inputs()
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pre_processed_images = image_processor.preprocess(image_inputs)
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for image in pre_processed_images["pixel_values"]:
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self.assertTrue(np.all(image[0, ...] == image[1, ...]) and np.all(image[1, ...] == image[2, ...]))
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for image_processing_class in self.image_processor_list:
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image_processor = image_processing_class.from_dict(self.image_processor_dict)
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image_inputs = self.image_processor_tester.prepare_image_inputs()
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pre_processed_images = image_processor.preprocess(image_inputs)
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for image in pre_processed_images["pixel_values"]:
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if isinstance(image, torch.Tensor):
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self.assertTrue(
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torch.all(image[0, ...] == image[1, ...]).item()
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and torch.all(image[1, ...] == image[2, ...]).item()
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)
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else:
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self.assertTrue(np.all(image[0, ...] == image[1, ...]) and np.all(image[1, ...] == image[2, ...]))
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@require_torch
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def test_post_processing_keypoint_detection(self):
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image_processor = self.image_processing_class.from_dict(self.image_processor_dict)
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image_inputs = self.image_processor_tester.prepare_image_inputs()
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pre_processed_images = image_processor.preprocess(image_inputs, return_tensors="pt")
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outputs = self.image_processor_tester.prepare_keypoint_detection_output(**pre_processed_images)
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def check_post_processed_output(post_processed_output, image_size):
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for post_processed_output, image_size in zip(post_processed_output, image_size):
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self.assertTrue("keypoints" in post_processed_output)
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@@ -157,12 +162,20 @@ class SuperPointImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase)
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self.assertTrue(all_below_image_size)
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self.assertTrue(all_above_zero)
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tuple_image_sizes = [(image.size[0], image.size[1]) for image in image_inputs]
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tuple_post_processed_outputs = image_processor.post_process_keypoint_detection(outputs, tuple_image_sizes)
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for image_processing_class in self.image_processor_list:
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image_processor = image_processing_class.from_dict(self.image_processor_dict)
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image_inputs = self.image_processor_tester.prepare_image_inputs()
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pre_processed_images = image_processor.preprocess(image_inputs, return_tensors="pt")
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outputs = self.image_processor_tester.prepare_keypoint_detection_output(**pre_processed_images)
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check_post_processed_output(tuple_post_processed_outputs, tuple_image_sizes)
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tuple_image_sizes = [(image.size[0], image.size[1]) for image in image_inputs]
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tuple_post_processed_outputs = image_processor.post_process_keypoint_detection(outputs, tuple_image_sizes)
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tensor_image_sizes = torch.tensor([image.size for image in image_inputs]).flip(1)
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tensor_post_processed_outputs = image_processor.post_process_keypoint_detection(outputs, tensor_image_sizes)
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check_post_processed_output(tuple_post_processed_outputs, tuple_image_sizes)
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check_post_processed_output(tensor_post_processed_outputs, tensor_image_sizes)
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tensor_image_sizes = torch.tensor([image.size for image in image_inputs]).flip(1)
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tensor_post_processed_outputs = image_processor.post_process_keypoint_detection(
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outputs, tensor_image_sizes
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)
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check_post_processed_output(tensor_post_processed_outputs, tensor_image_sizes)
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