# coding=utf-8 # 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 from transformers.file_utils import is_torch_available, is_vision_available from transformers.testing_utils import require_torch, require_vision from .test_feature_extraction_common import FeatureExtractionSavingTestMixin if is_torch_available(): import torch if is_vision_available(): from PIL import Image from transformers import CLIPFeatureExtractor class CLIPFeatureExtractionTester(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=20, do_center_crop=True, crop_size=18, do_normalize=True, image_mean=[0.48145466, 0.4578275, 0.40821073], image_std=[0.26862954, 0.26130258, 0.27577711], ): 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_center_crop = do_center_crop self.crop_size = crop_size self.do_normalize = do_normalize self.image_mean = image_mean self.image_std = image_std def prepare_feat_extract_dict(self): return { "do_resize": self.do_resize, "size": self.size, "do_center_crop": self.do_center_crop, "crop_size": self.crop_size, "do_normalize": self.do_normalize, "image_mean": self.image_mean, "image_std": self.image_std, } def prepare_inputs(self, equal_resolution=False, numpify=False, torchify=False): """This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True, or a list of PyTorch tensors if one specifies torchify=True. """ assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time" if equal_resolution: image_inputs = [] for i in range(self.batch_size): image_inputs.append( np.random.randint( 255, size=(self.num_channels, self.max_resolution, self.max_resolution), dtype=np.uint8 ) ) else: image_inputs = [] for i in range(self.batch_size): width, height = np.random.choice(np.arange(self.min_resolution, self.max_resolution), 2) image_inputs.append(np.random.randint(255, size=(self.num_channels, width, height), dtype=np.uint8)) if not numpify and not torchify: # PIL expects the channel dimension as last dimension image_inputs = [Image.fromarray(np.moveaxis(x, 0, -1)) for x in image_inputs] if torchify: image_inputs = [torch.from_numpy(x) for x in image_inputs] return image_inputs @require_torch @require_vision class CLIPFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.TestCase): feature_extraction_class = CLIPFeatureExtractor if is_vision_available() else None def setUp(self): self.feature_extract_tester = CLIPFeatureExtractionTester(self) @property def feat_extract_dict(self): return self.feature_extract_tester.prepare_feat_extract_dict() def test_feat_extract_properties(self): feature_extractor = self.feature_extraction_class(**self.feat_extract_dict) self.assertTrue(hasattr(feature_extractor, "do_resize")) self.assertTrue(hasattr(feature_extractor, "size")) self.assertTrue(hasattr(feature_extractor, "do_center_crop")) self.assertTrue(hasattr(feature_extractor, "center_crop")) self.assertTrue(hasattr(feature_extractor, "do_normalize")) self.assertTrue(hasattr(feature_extractor, "image_mean")) self.assertTrue(hasattr(feature_extractor, "image_std")) def test_batch_feature(self): pass def test_call_pil(self): # Initialize feature_extractor feature_extractor = self.feature_extraction_class(**self.feat_extract_dict) # create random PIL images image_inputs = self.feature_extract_tester.prepare_inputs(equal_resolution=False) for image in image_inputs: self.assertIsInstance(image, Image.Image) # Test not batched input encoded_images = feature_extractor(image_inputs[0], return_tensors="pt").pixel_values self.assertEqual( encoded_images.shape, ( 1, self.feature_extract_tester.num_channels, self.feature_extract_tester.crop_size, self.feature_extract_tester.crop_size, ), ) # Test batched encoded_images = feature_extractor(image_inputs, return_tensors="pt").pixel_values self.assertEqual( encoded_images.shape, ( self.feature_extract_tester.batch_size, self.feature_extract_tester.num_channels, self.feature_extract_tester.crop_size, self.feature_extract_tester.crop_size, ), ) def test_call_numpy(self): # Initialize feature_extractor feature_extractor = self.feature_extraction_class(**self.feat_extract_dict) # create random numpy tensors image_inputs = self.feature_extract_tester.prepare_inputs(equal_resolution=False, numpify=True) for image in image_inputs: self.assertIsInstance(image, np.ndarray) # Test not batched input encoded_images = feature_extractor(image_inputs[0], return_tensors="pt").pixel_values self.assertEqual( encoded_images.shape, ( 1, self.feature_extract_tester.num_channels, self.feature_extract_tester.crop_size, self.feature_extract_tester.crop_size, ), ) # Test batched encoded_images = feature_extractor(image_inputs, return_tensors="pt").pixel_values self.assertEqual( encoded_images.shape, ( self.feature_extract_tester.batch_size, self.feature_extract_tester.num_channels, self.feature_extract_tester.crop_size, self.feature_extract_tester.crop_size, ), ) def test_call_pytorch(self): # Initialize feature_extractor feature_extractor = self.feature_extraction_class(**self.feat_extract_dict) # create random PyTorch tensors image_inputs = self.feature_extract_tester.prepare_inputs(equal_resolution=False, torchify=True) for image in image_inputs: self.assertIsInstance(image, torch.Tensor) # Test not batched input encoded_images = feature_extractor(image_inputs[0], return_tensors="pt").pixel_values self.assertEqual( encoded_images.shape, ( 1, self.feature_extract_tester.num_channels, self.feature_extract_tester.crop_size, self.feature_extract_tester.crop_size, ), ) # Test batched encoded_images = feature_extractor(image_inputs, return_tensors="pt").pixel_values self.assertEqual( encoded_images.shape, ( self.feature_extract_tester.batch_size, self.feature_extract_tester.num_channels, self.feature_extract_tester.crop_size, self.feature_extract_tester.crop_size, ), )