# Copyright 2021 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 import torch 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 PIL import Image from transformers import ViltImageProcessor if is_torchvision_available(): from transformers import ViltImageProcessorFast class ViltImageProcessingTester: def __init__( self, parent, batch_size=7, num_channels=3, image_size=18, min_resolution=30, max_resolution=400, do_resize=True, size=None, size_divisor=2, do_normalize=True, image_mean=[0.5, 0.5, 0.5], image_std=[0.5, 0.5, 0.5], ): size = size if size is not None else {"shortest_edge": 30} 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.size_divisor = size_divisor 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, "size_divisor": self.size_divisor, } def get_expected_values(self, image_inputs, batched=False): """ This function computes the expected height and width when providing images to ViltImageProcessor, assuming do_resize is set to True with a scalar size and size_divisor. """ if not batched: size = self.size["shortest_edge"] image = image_inputs[0] if isinstance(image, Image.Image): w, h = image.size elif isinstance(image, np.ndarray): h, w = image.shape[0], image.shape[1] else: h, w = image.shape[1], image.shape[2] scale = size / min(w, h) if h < w: newh, neww = size, scale * w else: newh, neww = scale * h, size max_size = int((1333 / 800) * size) if max(newh, neww) > max_size: scale = max_size / max(newh, neww) newh = newh * scale neww = neww * scale newh, neww = int(newh + 0.5), int(neww + 0.5) expected_height, expected_width = ( newh // self.size_divisor * self.size_divisor, neww // self.size_divisor * self.size_divisor, ) else: expected_values = [] for image in image_inputs: expected_height, expected_width = self.get_expected_values([image]) expected_values.append((expected_height, expected_width)) expected_height = max(expected_values, key=lambda item: item[0])[0] expected_width = max(expected_values, key=lambda item: item[1])[1] return expected_height, expected_width def expected_output_image_shape(self, images): height, width = self.get_expected_values(images, batched=True) return (self.num_channels, height, 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 ViltImageProcessingTest(ImageProcessingTestMixin, unittest.TestCase): image_processing_class = ViltImageProcessor if is_vision_available() else None fast_image_processing_class = ViltImageProcessorFast if is_torchvision_available() else None def setUp(self): super().setUp() self.image_processor_tester = ViltImageProcessingTester(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, "size_divisor")) self.assertTrue(hasattr(image_processing, "do_pad")) self.assertTrue(hasattr(image_processing, "resample")) self.assertTrue(hasattr(image_processing, "do_rescale")) self.assertTrue(hasattr(image_processing, "model_input_names")) 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, {"shortest_edge": 30}) image_processor = image_processing_class.from_dict(self.image_processor_dict, size=42) self.assertEqual(image_processor.size, {"shortest_edge": 42}) def test_slow_fast_equivalence(self): image_inputs = self.image_processor_tester.prepare_image_inputs(equal_resolution=False) image_processor_slow = self.image_processing_class(**self.image_processor_dict, do_pad=True) image_processor_fast = self.fast_image_processing_class(**self.image_processor_dict, do_pad=True) slow_outputs = image_processor_slow(image_inputs, return_tensors="pt") slow_pixel_values = slow_outputs.pixel_values slow_pixel_mask = slow_outputs.pixel_mask fast_outputs = image_processor_fast(image_inputs, return_tensors="pt") fast_pixel_values = fast_outputs.pixel_values fast_pixel_mask = fast_outputs.pixel_mask self.assertEqual(slow_pixel_values.shape, fast_pixel_values.shape) self.assertTrue(torch.allclose(slow_pixel_values, fast_pixel_values, atol=1e-2)) self.assertEqual(slow_pixel_mask.shape, fast_pixel_mask.shape) self.assertTrue(torch.equal(slow_pixel_mask, fast_pixel_mask))