# 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 json import pathlib 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, slow from .test_feature_extraction_common import FeatureExtractionSavingTestMixin, prepare_image_inputs if is_torch_available(): import torch if is_vision_available(): from PIL import Image from transformers import DetrFeatureExtractor class DetrFeatureExtractionTester(unittest.TestCase): def __init__( self, parent, batch_size=7, num_channels=3, min_resolution=30, max_resolution=400, do_resize=True, size=18, max_size=1333, # by setting max_size > max_resolution we're effectively not testing this :p do_normalize=True, image_mean=[0.5, 0.5, 0.5], image_std=[0.5, 0.5, 0.5], ): self.parent = parent self.batch_size = batch_size self.num_channels = num_channels self.min_resolution = min_resolution self.max_resolution = max_resolution self.do_resize = do_resize self.size = size self.max_size = max_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, "max_size": self.max_size, "do_normalize": self.do_normalize, "image_mean": self.image_mean, "image_std": self.image_std, } def get_expected_values(self, image_inputs, batched=False): """ This function computes the expected height and width when providing images to DetrFeatureExtractor, assuming do_resize is set to True with a scalar size. """ if not batched: image = image_inputs[0] if isinstance(image, Image.Image): w, h = image.size else: h, w = image.shape[1], image.shape[2] if w < h: expected_height = int(self.size * h / w) expected_width = self.size elif w > h: expected_height = self.size expected_width = int(self.size * w / h) else: expected_height = self.size expected_width = self.size 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 @require_torch @require_vision class DetrFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.TestCase): feature_extraction_class = DetrFeatureExtractor if is_vision_available() else None def setUp(self): self.feature_extract_tester = DetrFeatureExtractionTester(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, "image_mean")) self.assertTrue(hasattr(feature_extractor, "image_std")) self.assertTrue(hasattr(feature_extractor, "do_normalize")) self.assertTrue(hasattr(feature_extractor, "do_resize")) self.assertTrue(hasattr(feature_extractor, "size")) self.assertTrue(hasattr(feature_extractor, "max_size")) 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 = prepare_image_inputs(self.feature_extract_tester, 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 expected_height, expected_width = self.feature_extract_tester.get_expected_values(image_inputs) self.assertEqual( encoded_images.shape, (1, self.feature_extract_tester.num_channels, expected_height, expected_width), ) # Test batched expected_height, expected_width = self.feature_extract_tester.get_expected_values(image_inputs, batched=True) 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, expected_height, expected_width, ), ) def test_call_numpy(self): # Initialize feature_extractor feature_extractor = self.feature_extraction_class(**self.feat_extract_dict) # create random numpy tensors image_inputs = prepare_image_inputs(self.feature_extract_tester, 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 expected_height, expected_width = self.feature_extract_tester.get_expected_values(image_inputs) self.assertEqual( encoded_images.shape, (1, self.feature_extract_tester.num_channels, expected_height, expected_width), ) # Test batched encoded_images = feature_extractor(image_inputs, return_tensors="pt").pixel_values expected_height, expected_width = self.feature_extract_tester.get_expected_values(image_inputs, batched=True) self.assertEqual( encoded_images.shape, ( self.feature_extract_tester.batch_size, self.feature_extract_tester.num_channels, expected_height, expected_width, ), ) def test_call_pytorch(self): # Initialize feature_extractor feature_extractor = self.feature_extraction_class(**self.feat_extract_dict) # create random PyTorch tensors image_inputs = prepare_image_inputs(self.feature_extract_tester, 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 expected_height, expected_width = self.feature_extract_tester.get_expected_values(image_inputs) self.assertEqual( encoded_images.shape, (1, self.feature_extract_tester.num_channels, expected_height, expected_width), ) # Test batched encoded_images = feature_extractor(image_inputs, return_tensors="pt").pixel_values expected_height, expected_width = self.feature_extract_tester.get_expected_values(image_inputs, batched=True) self.assertEqual( encoded_images.shape, ( self.feature_extract_tester.batch_size, self.feature_extract_tester.num_channels, expected_height, expected_width, ), ) def test_equivalence_pad_and_create_pixel_mask(self): # Initialize feature_extractors feature_extractor_1 = self.feature_extraction_class(**self.feat_extract_dict) feature_extractor_2 = self.feature_extraction_class(do_resize=False, do_normalize=False) # create random PyTorch tensors image_inputs = prepare_image_inputs(self.feature_extract_tester, equal_resolution=False, torchify=True) for image in image_inputs: self.assertIsInstance(image, torch.Tensor) # Test whether the method "pad_and_return_pixel_mask" and calling the feature extractor return the same tensors encoded_images_with_method = feature_extractor_1.pad_and_create_pixel_mask(image_inputs, return_tensors="pt") encoded_images = feature_extractor_2(image_inputs, return_tensors="pt") assert torch.allclose(encoded_images_with_method["pixel_values"], encoded_images["pixel_values"], atol=1e-4) assert torch.allclose(encoded_images_with_method["pixel_mask"], encoded_images["pixel_mask"], atol=1e-4) @slow def test_call_pytorch_with_coco_detection_annotations(self): # prepare image and target image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt", "r") as f: target = json.loads(f.read()) target = {"image_id": 39769, "annotations": target} # encode them # TODO replace by facebook/detr-resnet-50 feature_extractor = DetrFeatureExtractor.from_pretrained("nielsr/detr-resnet-50") encoding = feature_extractor(images=image, annotations=target, return_tensors="pt") # verify pixel values expected_shape = torch.Size([1, 3, 800, 1066]) self.assertEqual(encoding["pixel_values"].shape, expected_shape) expected_slice = torch.tensor([0.2796, 0.3138, 0.3481]) assert torch.allclose(encoding["pixel_values"][0, 0, 0, :3], expected_slice, atol=1e-4) # verify area expected_area = torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438]) assert torch.allclose(encoding["target"][0]["area"], expected_area) # verify boxes expected_boxes_shape = torch.Size([6, 4]) self.assertEqual(encoding["target"][0]["boxes"].shape, expected_boxes_shape) expected_boxes_slice = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]) assert torch.allclose(encoding["target"][0]["boxes"][0], expected_boxes_slice, atol=1e-3) # verify image_id expected_image_id = torch.tensor([39769]) assert torch.allclose(encoding["target"][0]["image_id"], expected_image_id) # verify is_crowd expected_is_crowd = torch.tensor([0, 0, 0, 0, 0, 0]) assert torch.allclose(encoding["target"][0]["iscrowd"], expected_is_crowd) # verify class_labels expected_class_labels = torch.tensor([75, 75, 63, 65, 17, 17]) assert torch.allclose(encoding["target"][0]["class_labels"], expected_class_labels) # verify orig_size expected_orig_size = torch.tensor([480, 640]) assert torch.allclose(encoding["target"][0]["orig_size"], expected_orig_size) # verify size expected_size = torch.tensor([800, 1066]) assert torch.allclose(encoding["target"][0]["size"], expected_size) @slow def test_call_pytorch_with_coco_panoptic_annotations(self): # prepare image, target and masks_path image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt", "r") as f: target = json.loads(f.read()) target = {"file_name": "000000039769.png", "image_id": 39769, "segments_info": target} masks_path = pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic") # encode them # TODO replace by .from_pretrained facebook/detr-resnet-50-panoptic feature_extractor = DetrFeatureExtractor(format="coco_panoptic") encoding = feature_extractor(images=image, annotations=target, masks_path=masks_path, return_tensors="pt") # verify pixel values expected_shape = torch.Size([1, 3, 800, 1066]) self.assertEqual(encoding["pixel_values"].shape, expected_shape) expected_slice = torch.tensor([0.2796, 0.3138, 0.3481]) assert torch.allclose(encoding["pixel_values"][0, 0, 0, :3], expected_slice, atol=1e-4) # verify area expected_area = torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147]) assert torch.allclose(encoding["target"][0]["area"], expected_area) # verify boxes expected_boxes_shape = torch.Size([6, 4]) self.assertEqual(encoding["target"][0]["boxes"].shape, expected_boxes_shape) expected_boxes_slice = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]) assert torch.allclose(encoding["target"][0]["boxes"][0], expected_boxes_slice, atol=1e-3) # verify image_id expected_image_id = torch.tensor([39769]) assert torch.allclose(encoding["target"][0]["image_id"], expected_image_id) # verify is_crowd expected_is_crowd = torch.tensor([0, 0, 0, 0, 0, 0]) assert torch.allclose(encoding["target"][0]["iscrowd"], expected_is_crowd) # verify class_labels expected_class_labels = torch.tensor([17, 17, 63, 75, 75, 93]) assert torch.allclose(encoding["target"][0]["class_labels"], expected_class_labels) # verify masks expected_masks_sum = 822338 self.assertEqual(encoding["target"][0]["masks"].sum().item(), expected_masks_sum) # verify orig_size expected_orig_size = torch.tensor([480, 640]) assert torch.allclose(encoding["target"][0]["orig_size"], expected_orig_size) # verify size expected_size = torch.tensor([800, 1066]) assert torch.allclose(encoding["target"][0]["size"], expected_size)