# Copyright 2021 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 DeiT model.""" import unittest import warnings from functools import cached_property from transformers import DeiTConfig from transformers.testing_utils import ( require_accelerate, require_torch, require_torch_accelerator, require_torch_fp16, require_vision, slow, torch_device, ) from transformers.utils import is_torch_available, is_vision_available from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from torch import nn from transformers import ( DeiTForImageClassification, DeiTForImageClassificationWithTeacher, DeiTForMaskedImageModeling, DeiTModel, ) from transformers.models.auto.modeling_auto import ( MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES, MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES, MODEL_MAPPING_NAMES, ) if is_vision_available(): from PIL import Image from transformers import DeiTImageProcessor class DeiTModelTester: def __init__( self, parent, 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, encoder_stride=2, mask_ratio=0.5, attn_implementation="eager", ): self.parent = parent 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.encoder_stride = encoder_stride self.attn_implementation = attn_implementation # in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens) num_patches = (image_size // patch_size) ** 2 self.seq_length = num_patches + 2 self.mask_ratio = mask_ratio self.num_masks = int(mask_ratio * self.seq_length) self.mask_length = num_patches def prepare_config_and_inputs(self): pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]) labels = None if self.use_labels: labels = ids_tensor([self.batch_size], self.type_sequence_label_size) config = self.get_config() return config, pixel_values, labels def get_config(self): return DeiTConfig( 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, encoder_stride=self.encoder_stride, attn_implementation=self.attn_implementation, ) def create_and_check_model(self, config, pixel_values, labels): model = DeiTModel(config=config) model.to(torch_device) model.eval() result = model(pixel_values) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) def create_and_check_for_masked_image_modeling(self, config, pixel_values, labels): model = DeiTForMaskedImageModeling(config=config) model.to(torch_device) model.eval() result = model(pixel_values) self.parent.assertEqual( result.reconstruction.shape, (self.batch_size, self.num_channels, self.image_size, self.image_size) ) # test greyscale images config.num_channels = 1 model = DeiTForMaskedImageModeling(config) model.to(torch_device) model.eval() pixel_values = floats_tensor([self.batch_size, 1, self.image_size, self.image_size]) result = model(pixel_values) self.parent.assertEqual(result.reconstruction.shape, (self.batch_size, 1, self.image_size, self.image_size)) def create_and_check_for_image_classification(self, config, pixel_values, labels): config.num_labels = self.type_sequence_label_size model = DeiTForImageClassification(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)) # test greyscale images config.num_channels = 1 model = DeiTForImageClassification(config) model.to(torch_device) model.eval() pixel_values = floats_tensor([self.batch_size, 1, self.image_size, self.image_size]) result = model(pixel_values, labels=labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() ( config, pixel_values, labels, ) = config_and_inputs inputs_dict = {"pixel_values": pixel_values} return config, inputs_dict @require_torch class DeiTModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as DeiT does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = ( ( DeiTModel, DeiTForImageClassification, DeiTForImageClassificationWithTeacher, DeiTForMaskedImageModeling, ) if is_torch_available() else () ) pipeline_model_mapping = ( { "image-feature-extraction": DeiTModel, "image-classification": (DeiTForImageClassification, DeiTForImageClassificationWithTeacher), } if is_torch_available() else {} ) test_resize_embeddings = False test_torch_exportable = True def setUp(self): self.model_tester = DeiTModelTester(self) self.config_tester = ConfigTester(self, config_class=DeiTConfig, has_text_modality=False, hidden_size=37) @unittest.skip( "Since `torch==2.3+cu121`, although this test passes, many subsequent tests have `CUDA error: misaligned address`." "If `nvidia-xxx-cu118` are also installed, no failure (even with `torch==2.3+cu121`)." ) def test_multi_gpu_data_parallel_forward(self): super().test_multi_gpu_data_parallel_forward() def test_config(self): self.config_tester.run_common_tests() @unittest.skip(reason="DeiT does not use inputs_embeds") def test_inputs_embeds(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_masked_image_modeling(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_masked_image_modeling(*config_and_inputs) 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) # special case for DeiTForImageClassificationWithTeacher model def _prepare_for_class(self, inputs_dict, model_class, return_labels=False): inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels) if return_labels: if model_class.__name__ == "DeiTForImageClassificationWithTeacher": del inputs_dict["labels"] return inputs_dict 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: # DeiTForImageClassificationWithTeacher supports inference-only if ( model_class.__name__ in MODEL_MAPPING_NAMES.values() or model_class.__name__ == "DeiTForImageClassificationWithTeacher" ): 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 # DeiTForImageClassificationWithTeacher supports inference-only if model_class.__name__ == "DeiTForImageClassificationWithTeacher": continue 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() @unittest.skip( reason="This architecture seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant(self): pass @unittest.skip( reason="This architecture seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124" ) def test_training_gradient_checkpointing_use_reentrant_false(self): pass def test_problem_types(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() problem_types = [ {"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float}, {"title": "single_label_classification", "num_labels": 1, "dtype": torch.long}, {"title": "regression", "num_labels": 1, "dtype": torch.float}, ] for model_class in self.all_model_classes: if ( model_class.__name__ not in [ *MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES.values(), *MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES.values(), ] or model_class.__name__ == "DeiTForImageClassificationWithTeacher" ): continue for problem_type in problem_types: with self.subTest(msg=f"Testing {model_class} with {problem_type['title']}"): config.problem_type = problem_type["title"] config.num_labels = problem_type["num_labels"] model = model_class(config) model.to(torch_device) model.train() inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True) if problem_type["num_labels"] > 1: inputs["labels"] = inputs["labels"].unsqueeze(1).repeat(1, problem_type["num_labels"]) inputs["labels"] = inputs["labels"].to(problem_type["dtype"]) # This tests that we do not trigger the warning form PyTorch "Using a target size that is different # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure # they have the same size." which is a symptom something in wrong for the regression problem. # See https://github.com/huggingface/transformers/issues/11780 with warnings.catch_warnings(record=True) as warning_list: loss = model(**inputs).loss for w in warning_list: if "Using a target size that is different to the input size" in str(w.message): raise ValueError( f"Something is going wrong in the regression problem: intercepted {w.message}" ) loss.backward() @slow def test_model_from_pretrained(self): model_name = "facebook/deit-base-distilled-patch16-224" model = DeiTModel.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 DeiTModelIntegrationTest(unittest.TestCase): @cached_property def default_image_processor(self): return ( DeiTImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224") if is_vision_available() else None ) @slow def test_inference_image_classification_head(self): model = DeiTForImageClassificationWithTeacher.from_pretrained("facebook/deit-base-distilled-patch16-224").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) # verify the logits expected_shape = torch.Size((1, 1000)) self.assertEqual(outputs.logits.shape, expected_shape) expected_slice = torch.tensor([-1.0266, 0.1912, -1.2861]).to(torch_device) torch.testing.assert_close(outputs.logits[0, :3], expected_slice, rtol=1e-4, atol=1e-4) @slow def test_inference_interpolate_pos_encoding(self): model = DeiTForImageClassificationWithTeacher.from_pretrained("facebook/deit-base-distilled-patch16-224").to( torch_device ) image_processor = self.default_image_processor # image size is {"height": 480, "width": 640} image = prepare_img() image_processor.size = {"height": 480, "width": 640} # center crop set to False so image is not center cropped to 224x224 inputs = image_processor(images=image, return_tensors="pt", do_center_crop=False).to(torch_device) # forward pass with torch.no_grad(): outputs = model(**inputs, interpolate_pos_encoding=True) # verify the logits expected_shape = torch.Size((1, 1000)) self.assertEqual(outputs.logits.shape, expected_shape) @slow @require_accelerate @require_torch_accelerator @require_torch_fp16 def test_inference_fp16(self): r""" A small test to make sure that inference work in half precision without any problem. """ model = DeiTModel.from_pretrained( "facebook/deit-base-distilled-patch16-224", dtype=torch.float16, device_map="auto" ) image_processor = self.default_image_processor image = prepare_img() inputs = image_processor(images=image, return_tensors="pt") pixel_values = inputs.pixel_values.to(torch_device) # forward pass to make sure inference works in fp16 with torch.no_grad(): _ = model(pixel_values)