# Copyright 2024 HuggingFace Inc. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import unittest import numpy as np from transformers.testing_utils import require_torch, require_vision from transformers.utils import is_torchvision_available, is_vision_available from ...test_image_processing_common import ImageProcessingTestMixin, prepare_image_inputs if is_vision_available(): from transformers import PromptDepthAnythingImageProcessor if is_torchvision_available(): from transformers import PromptDepthAnythingImageProcessorFast class PromptDepthAnythingImageProcessingTester(unittest.TestCase): def __init__( self, parent, batch_size=7, num_channels=3, image_size=18, min_resolution=30, max_resolution=400, do_resize=True, size=None, do_normalize=True, image_mean=[0.5, 0.5, 0.5], image_std=[0.5, 0.5, 0.5], ): super().__init__() size = size if size is not None else {"height": 18, "width": 18} self.parent = parent self.batch_size = batch_size self.num_channels = num_channels self.image_size = image_size self.min_resolution = min_resolution self.max_resolution = max_resolution self.do_resize = do_resize self.size = size self.do_normalize = do_normalize self.image_mean = image_mean self.image_std = image_std def prepare_image_processor_dict(self): return { "image_mean": self.image_mean, "image_std": self.image_std, "do_normalize": self.do_normalize, "do_resize": self.do_resize, "size": self.size, } def expected_output_image_shape(self, images): return self.num_channels, self.size["height"], self.size["width"] def prepare_image_inputs(self, equal_resolution=False, numpify=False, torchify=False): return prepare_image_inputs( batch_size=self.batch_size, num_channels=self.num_channels, min_resolution=self.min_resolution, max_resolution=self.max_resolution, equal_resolution=equal_resolution, numpify=numpify, torchify=torchify, ) @require_torch @require_vision class PromptDepthAnythingImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase): image_processing_class = PromptDepthAnythingImageProcessor if is_vision_available() else None fast_image_processing_class = PromptDepthAnythingImageProcessorFast if is_torchvision_available() else None def setUp(self): super().setUp() self.image_processor_tester = PromptDepthAnythingImageProcessingTester(self) @property def image_processor_dict(self): return self.image_processor_tester.prepare_image_processor_dict() def test_image_processor_properties(self): for image_processing_class in self.image_processor_list: image_processing = image_processing_class(**self.image_processor_dict) self.assertTrue(hasattr(image_processing, "image_mean")) self.assertTrue(hasattr(image_processing, "image_std")) self.assertTrue(hasattr(image_processing, "do_normalize")) self.assertTrue(hasattr(image_processing, "do_resize")) self.assertTrue(hasattr(image_processing, "size")) self.assertTrue(hasattr(image_processing, "do_rescale")) self.assertTrue(hasattr(image_processing, "rescale_factor")) self.assertTrue(hasattr(image_processing, "do_pad")) self.assertTrue(hasattr(image_processing, "size_divisor")) self.assertTrue(hasattr(image_processing, "prompt_scale_to_meter")) def test_image_processor_from_dict_with_kwargs(self): for image_processing_class in self.image_processor_list: image_processor = image_processing_class.from_dict(self.image_processor_dict) self.assertEqual(image_processor.size, {"height": 18, "width": 18}) image_processor = image_processing_class.from_dict(self.image_processor_dict, size=42) self.assertEqual(image_processor.size, {"height": 42, "width": 42}) def test_keep_aspect_ratio(self): size = {"height": 512, "width": 512} for image_processing_class in self.image_processor_list: image_processor = image_processing_class(size=size, keep_aspect_ratio=True, ensure_multiple_of=32) image = np.zeros((489, 640, 3)) pixel_values = image_processor(image, return_tensors="pt").pixel_values self.assertEqual(list(pixel_values.shape), [1, 3, 512, 672]) def test_prompt_depth_processing(self): size = {"height": 756, "width": 756} for image_processing_class in self.image_processor_list: image_processor = image_processing_class(size=size, keep_aspect_ratio=True, ensure_multiple_of=32) image = np.zeros((756, 1008, 3)) prompt_depth = np.random.random((192, 256)) outputs = image_processor(image, prompt_depth=prompt_depth, return_tensors="pt") pixel_values = outputs.pixel_values prompt_depth_values = outputs.prompt_depth self.assertEqual(list(pixel_values.shape), [1, 3, 768, 1024]) self.assertEqual(list(prompt_depth_values.shape), [1, 1, 192, 256]) @require_torch @require_vision def test_slow_fast_equivalence(self): if not self.test_slow_image_processor or not self.test_fast_image_processor: self.skipTest(reason="Skipping slow/fast equivalence test") if self.image_processing_class is None or self.fast_image_processing_class is None: self.skipTest(reason="Skipping slow/fast equivalence test as one of the image processors is not defined") image = np.zeros((756, 1008, 3)) prompt_depth = np.random.random((192, 256)) size = {"height": 756, "width": 756} image_processor_slow = self.image_processing_class( size=size, keep_aspect_ratio=True, ensure_multiple_of=32, do_pad=True, size_divisor=51 ) image_processor_fast = self.fast_image_processing_class( size=size, keep_aspect_ratio=True, ensure_multiple_of=32, do_pad=True, size_divisor=51 ) encoding_slow = image_processor_slow(image, prompt_depth=prompt_depth, return_tensors="pt") encoding_fast = image_processor_fast(image, prompt_depth=prompt_depth, return_tensors="pt") self._assert_slow_fast_tensors_equivalence(encoding_slow.pixel_values, encoding_fast.pixel_values) self.assertEqual(encoding_slow.prompt_depth.dtype, encoding_fast.prompt_depth.dtype) self._assert_slow_fast_tensors_equivalence(encoding_slow.prompt_depth, encoding_fast.prompt_depth) @require_torch @require_vision def test_slow_fast_equivalence_batched(self): if not self.test_slow_image_processor or not self.test_fast_image_processor: self.skipTest(reason="Skipping slow/fast equivalence test") if self.image_processing_class is None or self.fast_image_processing_class is None: self.skipTest(reason="Skipping slow/fast equivalence test as one of the image processors is not defined") if hasattr(self.image_processor_tester, "do_center_crop") and self.image_processor_tester.do_center_crop: self.skipTest( reason="Skipping as do_center_crop is True and center_crop functions are not equivalent for fast and slow processors" ) batch_size = self.image_processor_tester.batch_size images = self.image_processor_tester.prepare_image_inputs(equal_resolution=True, torchify=True) prompt_depths = [np.random.random((192, 256)) for _ in range(batch_size)] size = {"height": 756, "width": 756} image_processor_slow = self.image_processing_class(size=size, keep_aspect_ratio=False, ensure_multiple_of=32) image_processor_fast = self.fast_image_processing_class( size=size, keep_aspect_ratio=False, ensure_multiple_of=32 ) encoding_slow = image_processor_slow(images, prompt_depth=prompt_depths, return_tensors="pt") encoding_fast = image_processor_fast(images, prompt_depth=prompt_depths, return_tensors="pt") self._assert_slow_fast_tensors_equivalence(encoding_slow.pixel_values, encoding_fast.pixel_values) self.assertEqual(encoding_slow.prompt_depth.dtype, encoding_fast.prompt_depth.dtype) self._assert_slow_fast_tensors_equivalence(encoding_slow.prompt_depth, encoding_fast.prompt_depth)