Fast image processor (#28847)
* Draft fast image processors * Draft working fast version * py3.8 compatible cache * Enable loading fast image processors through auto * Tidy up; rescale behaviour based on input type * Enable tests for fast image processors * Smarter rescaling * Don't default to Fast * Safer imports * Add necessary Pillow requirement * Woops * Add AutoImageProcessor test * Fix up * Fix test for imagegpt * Fix test * Review comments * Add warning for TF and JAX input types * Rearrange * Return transforms * NumpyToTensor transformation * Rebase - include changes from upstream in ImageProcessingMixin * Safe typing * Fix up * convert mean/std to tesnor to rescale * Don't store transforms in state * Fix up * Update src/transformers/image_processing_utils_fast.py Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * Update src/transformers/models/auto/image_processing_auto.py Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * Update src/transformers/models/auto/image_processing_auto.py Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * Update src/transformers/models/auto/image_processing_auto.py Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> * Warn if fast image processor available * Update src/transformers/models/vit/image_processing_vit_fast.py * Transpose incoming numpy images to be in CHW format * Update mapping names based on packages, auto set fast to None * Fix up * Fix * Add AutoImageProcessor.from_pretrained(checkpoint, use_fast=True) test * Update src/transformers/models/vit/image_processing_vit_fast.py Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com> * Add equivalence and speed tests * Fix up --------- Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com> Co-authored-by: Pavel Iakubovskii <qubvel@gmail.com>
This commit is contained in:
amyeroberts
@@ -19,7 +19,9 @@ import os
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import pathlib
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import tempfile
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from transformers import BatchFeature
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import requests
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from transformers import AutoImageProcessor, BatchFeature
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from transformers.image_utils import AnnotationFormat, AnnotionFormat
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from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_vision
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from transformers.utils import is_torch_available, is_vision_available
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@@ -129,176 +131,263 @@ def prepare_video_inputs(
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class ImageProcessingTestMixin:
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test_cast_dtype = None
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image_processing_class = None
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fast_image_processing_class = None
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image_processors_list = None
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test_slow_image_processor = True
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test_fast_image_processor = True
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def setUp(self):
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image_processor_list = []
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if self.test_slow_image_processor and self.image_processing_class:
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image_processor_list.append(self.image_processing_class)
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if self.test_fast_image_processor and self.fast_image_processing_class:
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image_processor_list.append(self.fast_image_processing_class)
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self.image_processor_list = image_processor_list
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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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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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if not self.test_slow_image_processor or not self.test_fast_image_processor:
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self.skipTest("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("Skipping slow/fast equivalence test as one of the image processors is not defined")
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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.assertTrue(torch.allclose(encoding_slow.pixel_values, encoding_fast.pixel_values, atol=1e-3))
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@require_vision
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@require_torch
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def test_fast_is_faster_than_slow(self):
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import time
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def measure_time(self, image_processor, dummy_image):
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start = time.time()
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_ = image_processor(dummy_image, return_tensors="pt")
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return time.time() - start
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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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if not self.test_slow_image_processor or not self.test_fast_image_processor:
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self.skipTest("Skipping speed 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("Skipping speed test as one of the image processors is not defined")
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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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slow_time = self.measure_time(image_processor_slow, dummy_image)
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fast_time = self.measure_time(image_processor_fast, dummy_image)
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self.assertLessEqual(fast_time, slow_time)
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def test_image_processor_to_json_string(self):
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image_processor = self.image_processing_class(**self.image_processor_dict)
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obj = json.loads(image_processor.to_json_string())
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for key, value in self.image_processor_dict.items():
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self.assertEqual(obj[key], value)
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for image_processing_class in self.image_processor_list:
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image_processor = image_processing_class(**self.image_processor_dict)
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obj = json.loads(image_processor.to_json_string())
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for key, value in self.image_processor_dict.items():
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self.assertEqual(obj[key], value)
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def test_image_processor_to_json_file(self):
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image_processor_first = self.image_processing_class(**self.image_processor_dict)
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for image_processing_class in self.image_processor_list:
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image_processor_first = image_processing_class(**self.image_processor_dict)
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with tempfile.TemporaryDirectory() as tmpdirname:
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json_file_path = os.path.join(tmpdirname, "image_processor.json")
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image_processor_first.to_json_file(json_file_path)
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image_processor_second = self.image_processing_class.from_json_file(json_file_path)
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with tempfile.TemporaryDirectory() as tmpdirname:
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json_file_path = os.path.join(tmpdirname, "image_processor.json")
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image_processor_first.to_json_file(json_file_path)
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image_processor_second = image_processing_class.from_json_file(json_file_path)
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self.assertEqual(image_processor_second.to_dict(), image_processor_first.to_dict())
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self.assertEqual(image_processor_second.to_dict(), image_processor_first.to_dict())
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def test_image_processor_from_and_save_pretrained(self):
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image_processor_first = self.image_processing_class(**self.image_processor_dict)
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for image_processing_class in self.image_processor_list:
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image_processor_first = image_processing_class(**self.image_processor_dict)
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with tempfile.TemporaryDirectory() as tmpdirname:
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saved_file = image_processor_first.save_pretrained(tmpdirname)[0]
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check_json_file_has_correct_format(saved_file)
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image_processor_second = self.image_processing_class.from_pretrained(tmpdirname)
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with tempfile.TemporaryDirectory() as tmpdirname:
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saved_file = image_processor_first.save_pretrained(tmpdirname)[0]
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check_json_file_has_correct_format(saved_file)
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image_processor_second = image_processing_class.from_pretrained(tmpdirname)
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self.assertEqual(image_processor_second.to_dict(), image_processor_first.to_dict())
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self.assertEqual(image_processor_second.to_dict(), image_processor_first.to_dict())
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def test_image_processor_save_load_with_autoimageprocessor(self):
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for image_processing_class in self.image_processor_list:
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image_processor_first = image_processing_class(**self.image_processor_dict)
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with tempfile.TemporaryDirectory() as tmpdirname:
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saved_file = image_processor_first.save_pretrained(tmpdirname)[0]
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check_json_file_has_correct_format(saved_file)
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image_processor_second = AutoImageProcessor.from_pretrained(tmpdirname)
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self.assertEqual(image_processor_second.to_dict(), image_processor_first.to_dict())
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def test_init_without_params(self):
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image_processor = self.image_processing_class()
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self.assertIsNotNone(image_processor)
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for image_processing_class in self.image_processor_list:
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image_processor = image_processing_class()
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self.assertIsNotNone(image_processor)
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@require_torch
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@require_vision
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def test_cast_dtype_device(self):
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if self.test_cast_dtype is not None:
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# Initialize image_processor
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image_processor = self.image_processing_class(**self.image_processor_dict)
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for image_processing_class in self.image_processor_list:
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if self.test_cast_dtype is not None:
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# Initialize image_processor
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image_processor = 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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encoding = image_processor(image_inputs, return_tensors="pt")
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# for layoutLM compatiblity
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self.assertEqual(encoding.pixel_values.device, torch.device("cpu"))
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self.assertEqual(encoding.pixel_values.dtype, torch.float32)
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encoding = image_processor(image_inputs, return_tensors="pt").to(torch.float16)
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self.assertEqual(encoding.pixel_values.device, torch.device("cpu"))
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self.assertEqual(encoding.pixel_values.dtype, torch.float16)
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encoding = image_processor(image_inputs, return_tensors="pt").to("cpu", torch.bfloat16)
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self.assertEqual(encoding.pixel_values.device, torch.device("cpu"))
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self.assertEqual(encoding.pixel_values.dtype, torch.bfloat16)
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with self.assertRaises(TypeError):
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_ = image_processor(image_inputs, return_tensors="pt").to(torch.bfloat16, "cpu")
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# Try with text + image feature
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encoding = image_processor(image_inputs, return_tensors="pt")
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encoding.update({"input_ids": torch.LongTensor([[1, 2, 3], [4, 5, 6]])})
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encoding = encoding.to(torch.float16)
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self.assertEqual(encoding.pixel_values.device, torch.device("cpu"))
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self.assertEqual(encoding.pixel_values.dtype, torch.float16)
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self.assertEqual(encoding.input_ids.dtype, torch.long)
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def test_call_pil(self):
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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 = image_processing_class(**self.image_processor_dict)
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# create random PIL images
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image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False)
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for image in image_inputs:
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self.assertIsInstance(image, Image.Image)
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# Test not batched input
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encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
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self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
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# Test batched
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encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
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self.assertEqual(
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tuple(encoded_images.shape), (self.image_processor_tester.batch_size, *expected_output_image_shape)
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)
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def test_call_numpy(self):
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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 = image_processing_class(**self.image_processor_dict)
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# create random numpy tensors
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image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, numpify=True)
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for image in image_inputs:
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self.assertIsInstance(image, np.ndarray)
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# Test not batched input
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encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
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self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
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# Test batched
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encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
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self.assertEqual(
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tuple(encoded_images.shape), (self.image_processor_tester.batch_size, *expected_output_image_shape)
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)
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def test_call_pytorch(self):
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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 = 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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encoding = image_processor(image_inputs, return_tensors="pt")
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# for layoutLM compatiblity
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self.assertEqual(encoding.pixel_values.device, torch.device("cpu"))
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self.assertEqual(encoding.pixel_values.dtype, torch.float32)
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for image in image_inputs:
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self.assertIsInstance(image, torch.Tensor)
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encoding = image_processor(image_inputs, return_tensors="pt").to(torch.float16)
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self.assertEqual(encoding.pixel_values.device, torch.device("cpu"))
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self.assertEqual(encoding.pixel_values.dtype, torch.float16)
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# Test not batched input
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encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
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self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
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encoding = image_processor(image_inputs, return_tensors="pt").to("cpu", torch.bfloat16)
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self.assertEqual(encoding.pixel_values.device, torch.device("cpu"))
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self.assertEqual(encoding.pixel_values.dtype, torch.bfloat16)
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with self.assertRaises(TypeError):
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_ = image_processor(image_inputs, return_tensors="pt").to(torch.bfloat16, "cpu")
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# Try with text + image feature
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encoding = image_processor(image_inputs, return_tensors="pt")
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encoding.update({"input_ids": torch.LongTensor([[1, 2, 3], [4, 5, 6]])})
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encoding = encoding.to(torch.float16)
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self.assertEqual(encoding.pixel_values.device, torch.device("cpu"))
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self.assertEqual(encoding.pixel_values.dtype, torch.float16)
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self.assertEqual(encoding.input_ids.dtype, torch.long)
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def test_call_pil(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 PIL images
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image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False)
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for image in image_inputs:
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self.assertIsInstance(image, Image.Image)
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# Test not batched input
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encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
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self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
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# Test batched
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encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
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self.assertEqual(
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tuple(encoded_images.shape), (self.image_processor_tester.batch_size, *expected_output_image_shape)
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)
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def test_call_numpy(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 numpy tensors
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image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, numpify=True)
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for image in image_inputs:
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self.assertIsInstance(image, np.ndarray)
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# Test not batched input
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encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
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self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
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# Test batched
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encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
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self.assertEqual(
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tuple(encoded_images.shape), (self.image_processor_tester.batch_size, *expected_output_image_shape)
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)
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def test_call_pytorch(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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for image in image_inputs:
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self.assertIsInstance(image, torch.Tensor)
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# Test not batched input
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encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values
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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
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self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
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# Test batched
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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
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encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
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self.assertEqual(
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tuple(encoded_images.shape),
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(self.image_processor_tester.batch_size, *expected_output_image_shape),
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)
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# Test batched
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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
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encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values
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self.assertEqual(
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tuple(encoded_images.shape),
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(self.image_processor_tester.batch_size, *expected_output_image_shape),
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)
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def test_call_numpy_4_channels(self):
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# Test that can process images which have an arbitrary number of channels
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# Initialize image_processing
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image_processor = self.image_processing_class(**self.image_processor_dict)
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for image_processing_class in self.image_processor_list:
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# Test that can process images which have an arbitrary number of channels
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# Initialize image_processing
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image_processor = image_processing_class(**self.image_processor_dict)
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# create random numpy tensors
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self.image_processor_tester.num_channels = 4
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image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, numpify=True)
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# create random numpy tensors
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self.image_processor_tester.num_channels = 4
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image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False, numpify=True)
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# Test not batched input
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encoded_images = image_processor(
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image_inputs[0],
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return_tensors="pt",
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input_data_format="channels_first",
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image_mean=0,
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image_std=1,
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).pixel_values
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expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
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self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
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# Test not batched input
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encoded_images = image_processor(
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image_inputs[0],
|
||||
return_tensors="pt",
|
||||
input_data_format="channels_first",
|
||||
image_mean=0,
|
||||
image_std=1,
|
||||
).pixel_values
|
||||
expected_output_image_shape = self.image_processor_tester.expected_output_image_shape([image_inputs[0]])
|
||||
self.assertEqual(tuple(encoded_images.shape), (1, *expected_output_image_shape))
|
||||
|
||||
# Test batched
|
||||
encoded_images = image_processor(
|
||||
image_inputs,
|
||||
return_tensors="pt",
|
||||
input_data_format="channels_first",
|
||||
image_mean=0,
|
||||
image_std=1,
|
||||
).pixel_values
|
||||
expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
|
||||
self.assertEqual(
|
||||
tuple(encoded_images.shape), (self.image_processor_tester.batch_size, *expected_output_image_shape)
|
||||
)
|
||||
# Test batched
|
||||
encoded_images = image_processor(
|
||||
image_inputs,
|
||||
return_tensors="pt",
|
||||
input_data_format="channels_first",
|
||||
image_mean=0,
|
||||
image_std=1,
|
||||
).pixel_values
|
||||
expected_output_image_shape = self.image_processor_tester.expected_output_image_shape(image_inputs)
|
||||
self.assertEqual(
|
||||
tuple(encoded_images.shape), (self.image_processor_tester.batch_size, *expected_output_image_shape)
|
||||
)
|
||||
|
||||
def test_image_processor_preprocess_arguments(self):
|
||||
image_processor = self.image_processing_class(**self.image_processor_dict)
|
||||
if hasattr(image_processor, "_valid_processor_keys") and hasattr(image_processor, "preprocess"):
|
||||
preprocess_parameter_names = inspect.getfullargspec(image_processor.preprocess).args
|
||||
preprocess_parameter_names.remove("self")
|
||||
preprocess_parameter_names.sort()
|
||||
valid_processor_keys = image_processor._valid_processor_keys
|
||||
valid_processor_keys.sort()
|
||||
self.assertEqual(preprocess_parameter_names, valid_processor_keys)
|
||||
for image_processing_class in self.image_processor_list:
|
||||
image_processor = image_processing_class(**self.image_processor_dict)
|
||||
if hasattr(image_processor, "_valid_processor_keys") and hasattr(image_processor, "preprocess"):
|
||||
preprocess_parameter_names = inspect.getfullargspec(image_processor.preprocess).args
|
||||
preprocess_parameter_names.remove("self")
|
||||
preprocess_parameter_names.sort()
|
||||
valid_processor_keys = image_processor._valid_processor_keys
|
||||
valid_processor_keys.sort()
|
||||
self.assertEqual(preprocess_parameter_names, valid_processor_keys)
|
||||
|
||||
|
||||
class AnnotationFormatTestMixin:
|
||||
|
||||
Reference in New Issue
Block a user