# coding=utf-8 # Copyright 2022s 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_torch_available, is_vision_available from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs if is_torch_available(): import torch if is_vision_available(): from PIL import Image from transformers import ConvNextImageProcessor class ConvNextImageProcessingTester(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, crop_pct=0.875, 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": 20} 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.crop_pct = crop_pct 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, "crop_pct": self.crop_pct, } @require_torch @require_vision class ConvNextImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase): image_processing_class = ConvNextImageProcessor if is_vision_available() else None def setUp(self): self.image_processor_tester = ConvNextImageProcessingTester(self) @property def image_processor_dict(self): return self.image_processor_tester.prepare_image_processor_dict() def test_image_processor_properties(self): image_processing = self.image_processing_class(**self.image_processor_dict) self.assertTrue(hasattr(image_processing, "do_resize")) self.assertTrue(hasattr(image_processing, "size")) self.assertTrue(hasattr(image_processing, "crop_pct")) self.assertTrue(hasattr(image_processing, "do_normalize")) self.assertTrue(hasattr(image_processing, "image_mean")) self.assertTrue(hasattr(image_processing, "image_std")) def test_image_processor_from_dict_with_kwargs(self): image_processor = self.image_processing_class.from_dict(self.image_processor_dict) self.assertEqual(image_processor.size, {"shortest_edge": 20}) image_processor = self.image_processing_class.from_dict(self.image_processor_dict, size=42) self.assertEqual(image_processor.size, {"shortest_edge": 42}) def test_batch_feature(self): pass def test_call_pil(self): # Initialize image_processing image_processing = self.image_processing_class(**self.image_processor_dict) # create random PIL images image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False) for image in image_inputs: self.assertIsInstance(image, Image.Image) # Test not batched input encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values self.assertEqual( encoded_images.shape, ( 1, self.image_processor_tester.num_channels, self.image_processor_tester.size["shortest_edge"], self.image_processor_tester.size["shortest_edge"], ), ) # Test batched encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values self.assertEqual( encoded_images.shape, ( self.image_processor_tester.batch_size, self.image_processor_tester.num_channels, self.image_processor_tester.size["shortest_edge"], self.image_processor_tester.size["shortest_edge"], ), ) def test_call_numpy(self): # Initialize image_processing image_processing = self.image_processing_class(**self.image_processor_dict) # create random numpy tensors image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False, numpify=True) for image in image_inputs: self.assertIsInstance(image, np.ndarray) # Test not batched input encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values self.assertEqual( encoded_images.shape, ( 1, self.image_processor_tester.num_channels, self.image_processor_tester.size["shortest_edge"], self.image_processor_tester.size["shortest_edge"], ), ) # Test batched encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values self.assertEqual( encoded_images.shape, ( self.image_processor_tester.batch_size, self.image_processor_tester.num_channels, self.image_processor_tester.size["shortest_edge"], self.image_processor_tester.size["shortest_edge"], ), ) def test_call_pytorch(self): # Initialize image_processing image_processing = self.image_processing_class(**self.image_processor_dict) # create random PyTorch tensors image_inputs = prepare_image_inputs(self.image_processor_tester, equal_resolution=False, torchify=True) for image in image_inputs: self.assertIsInstance(image, torch.Tensor) # Test not batched input encoded_images = image_processing(image_inputs[0], return_tensors="pt").pixel_values self.assertEqual( encoded_images.shape, ( 1, self.image_processor_tester.num_channels, self.image_processor_tester.size["shortest_edge"], self.image_processor_tester.size["shortest_edge"], ), ) # Test batched encoded_images = image_processing(image_inputs, return_tensors="pt").pixel_values self.assertEqual( encoded_images.shape, ( self.image_processor_tester.batch_size, self.image_processor_tester.num_channels, self.image_processor_tester.size["shortest_edge"], self.image_processor_tester.size["shortest_edge"], ), )