# coding=utf-8 # Copyright 2022 The HuggingFace Inc. team. All rights reserved. # # 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. """Testing suite for the PyTorch Data2VecVision model.""" import unittest from transformers import Data2VecVisionConfig from transformers.testing_utils import ( require_torch, require_torch_multi_gpu, require_vision, slow, torch_device, ) from transformers.utils import ( cached_property, is_torch_available, is_vision_available, ) from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from torch import nn from transformers import ( Data2VecVisionForImageClassification, Data2VecVisionForSemanticSegmentation, Data2VecVisionModel, ) from transformers.models.auto.modeling_auto import MODEL_MAPPING_NAMES if is_vision_available(): from PIL import Image from transformers import BeitImageProcessor class Data2VecVisionModelTester: def __init__( self, parent, vocab_size=100, batch_size=13, image_size=30, patch_size=2, num_channels=3, is_training=True, use_labels=True, hidden_size=32, num_hidden_layers=2, num_attention_heads=4, intermediate_size=37, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, type_sequence_label_size=10, initializer_range=0.02, num_labels=3, scope=None, out_indices=[0, 1, 2, 3], attn_implementation="eager", mask_ratio=0.5, ): self.parent = parent self.vocab_size = 100 self.batch_size = batch_size self.image_size = image_size self.patch_size = patch_size self.num_channels = num_channels self.is_training = is_training self.use_labels = use_labels self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.type_sequence_label_size = type_sequence_label_size self.initializer_range = initializer_range self.scope = scope self.out_indices = out_indices self.num_labels = num_labels # in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token) num_patches = (image_size // patch_size) ** 2 self.seq_length = num_patches + 1 self.mask_length = self.seq_length - 1 self.num_masks = int(mask_ratio * self.seq_length) self.attn_implementation = attn_implementation def prepare_config_and_inputs(self): pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]) labels = None pixel_labels = None if self.use_labels: labels = ids_tensor([self.batch_size], self.type_sequence_label_size) pixel_labels = ids_tensor([self.batch_size, self.image_size, self.image_size], self.num_labels) config = self.get_config() return config, pixel_values, labels, pixel_labels def get_config(self): return Data2VecVisionConfig( vocab_size=self.vocab_size, image_size=self.image_size, patch_size=self.patch_size, num_channels=self.num_channels, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, is_decoder=False, initializer_range=self.initializer_range, out_indices=self.out_indices, attn_implementation=self.attn_implementation, ) def create_and_check_model(self, config, pixel_values, labels, pixel_labels): model = Data2VecVisionModel(config=config) model.to(torch_device) model.eval() result = model(pixel_values) # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) num_patches = (self.image_size // self.patch_size) ** 2 self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, num_patches + 1, self.hidden_size)) def create_and_check_for_image_classification(self, config, pixel_values, labels, pixel_labels): config.num_labels = self.type_sequence_label_size model = Data2VecVisionForImageClassification(config) model.to(torch_device) model.eval() result = model(pixel_values, labels=labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size)) def create_and_check_for_image_segmentation(self, config, pixel_values, labels, pixel_labels): config.num_labels = self.num_labels model = Data2VecVisionForSemanticSegmentation(config) model.to(torch_device) model.eval() result = model(pixel_values) self.parent.assertEqual( result.logits.shape, (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) ) result = model(pixel_values, labels=pixel_labels) self.parent.assertEqual( result.logits.shape, (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) ) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() config, pixel_values, labels, pixel_labels = config_and_inputs inputs_dict = {"pixel_values": pixel_values} return config, inputs_dict @require_torch class Data2VecVisionModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as Data2VecVision does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = ( (Data2VecVisionModel, Data2VecVisionForImageClassification, Data2VecVisionForSemanticSegmentation) if is_torch_available() else () ) pipeline_model_mapping = ( { "image-feature-extraction": Data2VecVisionModel, "image-classification": Data2VecVisionForImageClassification, "image-segmentation": Data2VecVisionForSemanticSegmentation, } if is_torch_available() else {} ) test_pruning = False test_resize_embeddings = False test_head_masking = False def setUp(self): self.model_tester = Data2VecVisionModelTester(self) self.config_tester = ConfigTester( self, config_class=Data2VecVisionConfig, has_text_modality=False, hidden_size=37 ) def test_config(self): self.config_tester.run_common_tests() @unittest.skip(reason="Data2VecVision does not use inputs_embeds") def test_inputs_embeds(self): pass @require_torch_multi_gpu @unittest.skip( reason="Data2VecVision has some layers using `add_module` which doesn't work well with `nn.DataParallel`" ) def test_multi_gpu_data_parallel_forward(self): pass def test_model_get_set_embeddings(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) self.assertIsInstance(model.get_input_embeddings(), (nn.Module)) x = model.get_output_embeddings() self.assertTrue(x is None or isinstance(x, nn.Linear)) def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_for_image_segmentation(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_image_segmentation(*config_and_inputs) def test_training(self): if not self.model_tester.is_training: self.skipTest(reason="model_tester.is_training is set to False") config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True for model_class in self.all_model_classes: if model_class.__name__ in MODEL_MAPPING_NAMES.values(): continue model = model_class(config) model.to(torch_device) model.train() inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) loss = model(**inputs).loss loss.backward() def test_training_gradient_checkpointing(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() if not self.model_tester.is_training: self.skipTest(reason="model_tester.is_training is set to False") config.use_cache = False config.return_dict = True for model_class in self.all_model_classes: if model_class.__name__ in MODEL_MAPPING_NAMES.values() or not model_class.supports_gradient_checkpointing: continue # TODO: remove the following 3 lines once we have a MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING # this can then be incorporated into _prepare_for_class in test_modeling_common.py elif model_class.__name__ == "Data2VecVisionForSemanticSegmentation": batch_size, num_channels, height, width = inputs_dict["pixel_values"].shape inputs_dict["labels"] = torch.zeros( [self.model_tester.batch_size, height, width], device=torch_device ).long() model = model_class(config) model.gradient_checkpointing_enable() model.to(torch_device) model.train() inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) loss = model(**inputs).loss loss.backward() def test_initialization(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() configs_no_init = _config_zero_init(config) for model_class in self.all_model_classes: model = model_class(config=configs_no_init) for name, param in model.named_parameters(): # we skip lambda parameters as these require special initial values # determined by config.layer_scale_init_value if "lambda" in name: continue if param.requires_grad: self.assertIn( ((param.data.mean() * 1e9).round() / 1e9).item(), [0.0, 1.0], msg=f"Parameter {name} of model {model_class} seems not properly initialized", ) def test_for_image_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_image_classification(*config_and_inputs) @slow def test_model_from_pretrained(self): model_name = "facebook/data2vec-vision-base-ft1k" model = Data2VecVisionModel.from_pretrained(model_name) self.assertIsNotNone(model) # We will verify our results on an image of cute cats def prepare_img(): image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") return image @require_torch @require_vision class Data2VecVisionModelIntegrationTest(unittest.TestCase): @cached_property def default_image_processor(self): return ( BeitImageProcessor.from_pretrained("facebook/data2vec-vision-base-ft1k") if is_vision_available() else None ) @slow def test_inference_image_classification_head_imagenet_1k(self): model = Data2VecVisionForImageClassification.from_pretrained("facebook/data2vec-vision-base-ft1k").to( torch_device ) image_processor = self.default_image_processor image = prepare_img() inputs = image_processor(images=image, return_tensors="pt").to(torch_device) # forward pass with torch.no_grad(): outputs = model(**inputs) logits = outputs.logits # verify the logits expected_shape = torch.Size((1, 1000)) self.assertEqual(logits.shape, expected_shape) expected_slice = torch.tensor([0.3277, -0.1395, 0.0911]).to(torch_device) torch.testing.assert_close(logits[0, :3], expected_slice, rtol=1e-4, atol=1e-4) expected_top2 = [model.config.label2id[i] for i in ["remote control, remote", "tabby, tabby cat"]] self.assertEqual(logits[0].topk(2).indices.tolist(), expected_top2) @slow def test_inference_interpolate_pos_encoding(self): model_name = "facebook/data2vec-vision-base-ft1k" model = Data2VecVisionModel.from_pretrained(model_name, **{"use_absolute_position_embeddings": True}).to( torch_device ) image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png") processor = BeitImageProcessor.from_pretrained("facebook/data2vec-vision-base-ft1k") inputs = processor(images=image, return_tensors="pt", size={"height": 480, "width": 480}) pixel_values = inputs.pixel_values.to(torch_device) # with interpolate_pos_encoding being True the model should process the higher resolution image # successfully and produce the expected output. with torch.no_grad(): outputs = model(pixel_values, interpolate_pos_encoding=True) # num_cls_tokens + (height / patch_size) * (width / patch_size) # 1 + (480 / 16) * (480 / 16) = 901 expected_shape = torch.Size((1, 901, 768)) self.assertEqual(outputs.last_hidden_state.shape, expected_shape)