Add Fast Image Processor for mobileViT (#37143)
* Add image_processing_mobilevit_fast.py * Fix copies * update _preprocess for channel_flip * Update for batched image processing * Resolve merge conflicts with main * Fix import order and remove trailing whitespace (ruff clean-up) * Fix copy inconsistencies * Add NotImplementedError for post_process_semantic_segmentation to satisfy repo checks * Add auto_docstring * Adjust style * Update docs/source/en/model_doc/mobilevit.md Co-authored-by: Yoni Gozlan <74535834+yonigozlan@users.noreply.github.com> * Update src/transformers/models/mobilevit/image_processing_mobilevit_fast.py Co-authored-by: Yoni Gozlan <74535834+yonigozlan@users.noreply.github.com> * Update src/transformers/models/mobilevit/image_processing_mobilevit_fast.py Co-authored-by: Yoni Gozlan <74535834+yonigozlan@users.noreply.github.com> * Delete not used function * test: add missing tests for and * Add post_process_semantic_segmentation to mobilevit_fast.py * Add preprocess function to image_processing_mobilebit_fast.py * ruff check for formatting * fix: modify preprocess method to handle BatchFeature correctly * Remove logic for default value assignment Co-authored-by: Yoni Gozlan <74535834+yonigozlan@users.noreply.github.com> * Remove normalization adn RGB conversion logic not used in slow processor Co-authored-by: Yoni Gozlan <74535834+yonigozlan@users.noreply.github.com> * Simplify return_tensors logic using one-liner conditional expression Co-authored-by: Yoni Gozlan <74535834+yonigozlan@users.noreply.github.com> * Remove unused normalization and format parameters Co-authored-by: Yoni Gozlan <74535834+yonigozlan@users.noreply.github.com> * add **kwargs and remove default values in _preprocess * add slow_fast equivalence tests for segmentation * style: autoformat code with ruff * Fix slow_fast equivalence test * merge + remove skipped test --------- Co-authored-by: Yoni Gozlan <74535834+yonigozlan@users.noreply.github.com> Co-authored-by: yonigozlan <yoni.gozlan@huggingface.co>
This commit is contained in:
MinJu-Ha
@@ -15,10 +15,11 @@
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import unittest
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import requests
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from datasets import load_dataset
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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 ImageProcessingTestMixin, prepare_image_inputs
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@@ -27,8 +28,13 @@ if is_torch_available():
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import torch
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if is_vision_available():
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from PIL import Image
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from transformers import MobileViTImageProcessor
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if is_torchvision_available():
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from transformers import MobileViTImageProcessorFast
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class MobileViTImageProcessingTester:
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def __init__(
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@@ -98,6 +104,7 @@ def prepare_semantic_batch_inputs():
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@require_vision
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class MobileViTImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
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image_processing_class = MobileViTImageProcessor if is_vision_available() else None
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fast_image_processing_class = MobileViTImageProcessorFast if is_torchvision_available() else None
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def setUp(self):
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super().setUp()
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@@ -108,124 +115,155 @@ class MobileViTImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase):
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return self.image_processor_tester.prepare_image_processor_dict()
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def test_image_processor_properties(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_center_crop"))
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self.assertTrue(hasattr(image_processing, "center_crop"))
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self.assertTrue(hasattr(image_processing, "do_flip_channel_order"))
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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_center_crop"))
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self.assertTrue(hasattr(image_processing, "center_crop"))
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self.assertTrue(hasattr(image_processing, "do_flip_channel_order"))
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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, {"shortest_edge": 20})
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self.assertEqual(image_processor.crop_size, {"height": 18, "width": 18})
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for image_processing_class in self.image_processor_list:
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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, {"shortest_edge": 20})
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self.assertEqual(image_processor.crop_size, {"height": 18, "width": 18})
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image_processor = self.image_processing_class.from_dict(self.image_processor_dict, size=42, crop_size=84)
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self.assertEqual(image_processor.size, {"shortest_edge": 42})
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self.assertEqual(image_processor.crop_size, {"height": 84, "width": 84})
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image_processor = self.image_processing_class.from_dict(self.image_processor_dict, size=42, crop_size=84)
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self.assertEqual(image_processor.size, {"shortest_edge": 42})
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self.assertEqual(image_processor.crop_size, {"height": 84, "width": 84})
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def test_call_segmentation_maps(self):
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# Initialize image_processing
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image_processing = self.image_processing_class(**self.image_processor_dict)
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# create random PyTorch tensors
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image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)
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maps = []
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for image in image_inputs:
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self.assertIsInstance(image, torch.Tensor)
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maps.append(torch.zeros(image.shape[-2:]).long())
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for image_processing_class in self.image_processor_list:
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# Initialize image_processing
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image_processing = self.image_processing_class(**self.image_processor_dict)
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# create random PyTorch tensors
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image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, torchify=True)
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maps = []
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for image in image_inputs:
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self.assertIsInstance(image, torch.Tensor)
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maps.append(torch.zeros(image.shape[-2:]).long())
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# Test not batched input
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encoding = image_processing(image_inputs[0], maps[0], return_tensors="pt")
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self.assertEqual(
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encoding["pixel_values"].shape,
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(
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1,
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self.image_processor_tester.num_channels,
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self.image_processor_tester.crop_size["height"],
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self.image_processor_tester.crop_size["width"],
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),
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)
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self.assertEqual(
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encoding["labels"].shape,
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(
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1,
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self.image_processor_tester.crop_size["height"],
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self.image_processor_tester.crop_size["width"],
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),
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)
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self.assertEqual(encoding["labels"].dtype, torch.long)
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self.assertTrue(encoding["labels"].min().item() >= 0)
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self.assertTrue(encoding["labels"].max().item() <= 255)
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# Test not batched input
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encoding = image_processing(image_inputs[0], maps[0], return_tensors="pt")
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self.assertEqual(
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encoding["pixel_values"].shape,
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(
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1,
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self.image_processor_tester.num_channels,
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self.image_processor_tester.crop_size["height"],
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self.image_processor_tester.crop_size["width"],
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),
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)
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self.assertEqual(
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encoding["labels"].shape,
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(
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1,
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self.image_processor_tester.crop_size["height"],
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self.image_processor_tester.crop_size["width"],
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),
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)
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self.assertEqual(encoding["labels"].dtype, torch.long)
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self.assertTrue(encoding["labels"].min().item() >= 0)
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self.assertTrue(encoding["labels"].max().item() <= 255)
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# Test batched
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encoding = image_processing(image_inputs, maps, return_tensors="pt")
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self.assertEqual(
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encoding["pixel_values"].shape,
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(
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self.image_processor_tester.batch_size,
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self.image_processor_tester.num_channels,
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self.image_processor_tester.crop_size["height"],
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self.image_processor_tester.crop_size["width"],
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),
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)
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self.assertEqual(
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encoding["labels"].shape,
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(
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self.image_processor_tester.batch_size,
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self.image_processor_tester.crop_size["height"],
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self.image_processor_tester.crop_size["width"],
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),
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)
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self.assertEqual(encoding["labels"].dtype, torch.long)
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self.assertTrue(encoding["labels"].min().item() >= 0)
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self.assertTrue(encoding["labels"].max().item() <= 255)
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# Test batched
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encoding = image_processing(image_inputs, maps, return_tensors="pt")
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self.assertEqual(
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encoding["pixel_values"].shape,
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(
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self.image_processor_tester.batch_size,
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self.image_processor_tester.num_channels,
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self.image_processor_tester.crop_size["height"],
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self.image_processor_tester.crop_size["width"],
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),
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)
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self.assertEqual(
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encoding["labels"].shape,
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(
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self.image_processor_tester.batch_size,
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self.image_processor_tester.crop_size["height"],
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self.image_processor_tester.crop_size["width"],
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),
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)
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self.assertEqual(encoding["labels"].dtype, torch.long)
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self.assertTrue(encoding["labels"].min().item() >= 0)
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self.assertTrue(encoding["labels"].max().item() <= 255)
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# Test not batched input (PIL images)
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# Test not batched input (PIL images)
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image, segmentation_map = prepare_semantic_single_inputs()
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encoding = image_processing(image, segmentation_map, return_tensors="pt")
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self.assertEqual(
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encoding["pixel_values"].shape,
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(
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1,
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self.image_processor_tester.num_channels,
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self.image_processor_tester.crop_size["height"],
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self.image_processor_tester.crop_size["width"],
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),
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)
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self.assertEqual(
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encoding["labels"].shape,
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(
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1,
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self.image_processor_tester.crop_size["height"],
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self.image_processor_tester.crop_size["width"],
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),
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)
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self.assertEqual(encoding["labels"].dtype, torch.long)
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self.assertTrue(encoding["labels"].min().item() >= 0)
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self.assertTrue(encoding["labels"].max().item() <= 255)
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# Test batched input (PIL images)
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images, segmentation_maps = prepare_semantic_batch_inputs()
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encoding = image_processing(images, segmentation_maps, return_tensors="pt")
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self.assertEqual(
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encoding["pixel_values"].shape,
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(
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2,
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self.image_processor_tester.num_channels,
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self.image_processor_tester.crop_size["height"],
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self.image_processor_tester.crop_size["width"],
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),
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)
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self.assertEqual(
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encoding["labels"].shape,
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(
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2,
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self.image_processor_tester.crop_size["height"],
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self.image_processor_tester.crop_size["width"],
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),
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)
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self.assertEqual(encoding["labels"].dtype, torch.long)
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self.assertTrue(encoding["labels"].min().item() >= 0)
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self.assertTrue(encoding["labels"].max().item() <= 255)
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@require_vision
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@require_torch
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def test_slow_fast_equivalence(self):
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if not self.test_slow_image_processor or not self.test_fast_image_processor:
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self.skipTest(reason="Skipping slow/fast equivalence test")
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if self.image_processing_class is None or self.fast_image_processing_class is None:
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self.skipTest(reason="Skipping slow/fast equivalence test as one of the image processors is not defined")
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# Test with single image
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dummy_image = Image.open(
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requests.get("http://images.cocodataset.org/val2017/000000039769.jpg", stream=True).raw
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)
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image_processor_slow = self.image_processing_class(**self.image_processor_dict)
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image_processor_fast = self.fast_image_processing_class(**self.image_processor_dict)
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encoding_slow = image_processor_slow(dummy_image, return_tensors="pt")
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encoding_fast = image_processor_fast(dummy_image, return_tensors="pt")
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self._assert_slow_fast_tensors_equivalence(encoding_slow.pixel_values, encoding_fast.pixel_values)
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# Test with single image and segmentation map
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image, segmentation_map = prepare_semantic_single_inputs()
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encoding = image_processing(image, segmentation_map, return_tensors="pt")
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self.assertEqual(
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encoding["pixel_values"].shape,
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(
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1,
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self.image_processor_tester.num_channels,
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self.image_processor_tester.crop_size["height"],
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self.image_processor_tester.crop_size["width"],
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),
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)
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self.assertEqual(
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encoding["labels"].shape,
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(
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1,
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self.image_processor_tester.crop_size["height"],
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self.image_processor_tester.crop_size["width"],
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),
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)
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self.assertEqual(encoding["labels"].dtype, torch.long)
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self.assertTrue(encoding["labels"].min().item() >= 0)
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self.assertTrue(encoding["labels"].max().item() <= 255)
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# Test batched input (PIL images)
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images, segmentation_maps = prepare_semantic_batch_inputs()
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encoding = image_processing(images, segmentation_maps, return_tensors="pt")
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self.assertEqual(
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encoding["pixel_values"].shape,
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(
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2,
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self.image_processor_tester.num_channels,
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self.image_processor_tester.crop_size["height"],
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self.image_processor_tester.crop_size["width"],
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),
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)
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self.assertEqual(
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encoding["labels"].shape,
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(
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2,
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self.image_processor_tester.crop_size["height"],
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self.image_processor_tester.crop_size["width"],
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),
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)
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self.assertEqual(encoding["labels"].dtype, torch.long)
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self.assertTrue(encoding["labels"].min().item() >= 0)
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self.assertTrue(encoding["labels"].max().item() <= 255)
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encoding_slow = image_processor_slow(image, segmentation_map, return_tensors="pt")
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encoding_fast = image_processor_fast(image, segmentation_map, return_tensors="pt")
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self._assert_slow_fast_tensors_equivalence(encoding_slow.pixel_values, encoding_fast.pixel_values)
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torch.testing.assert_close(encoding_slow.labels, encoding_fast.labels, atol=1e-1, rtol=1e-3)
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