# 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 LeViT model. """ import inspect import unittest import warnings from math import ceil, floor from packaging import version from transformers import LevitConfig from transformers.file_utils import cached_property, is_torch_available, is_vision_available from transformers.models.auto import get_values from transformers.testing_utils import require_torch, require_vision, slow, torch_device from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor if is_torch_available(): import torch from transformers import ( MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING, MODEL_MAPPING, LevitForImageClassification, LevitForImageClassificationWithTeacher, LevitModel, ) from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST if is_vision_available(): from PIL import Image from transformers import LevitFeatureExtractor class LevitConfigTester(ConfigTester): def create_and_test_config_common_properties(self): config = self.config_class(**self.inputs_dict) self.parent.assertTrue(hasattr(config, "hidden_sizes")) self.parent.assertTrue(hasattr(config, "num_attention_heads")) class LevitModelTester: def __init__( self, parent, batch_size=13, image_size=64, num_channels=3, kernel_size=3, stride=2, padding=1, patch_size=16, hidden_sizes=[128, 256, 384], num_attention_heads=[4, 6, 8], depths=[2, 3, 4], key_dim=[16, 16, 16], drop_path_rate=0, mlp_ratio=[2, 2, 2], attention_ratio=[2, 2, 2], initializer_range=0.02, is_training=True, use_labels=True, num_labels=2, # Check ): self.parent = parent self.batch_size = batch_size self.image_size = image_size self.num_channels = num_channels self.kernel_size = kernel_size self.stride = stride self.padding = padding self.hidden_sizes = hidden_sizes self.num_attention_heads = num_attention_heads self.depths = depths self.key_dim = key_dim self.drop_path_rate = drop_path_rate self.patch_size = patch_size self.attention_ratio = attention_ratio self.mlp_ratio = mlp_ratio self.initializer_range = initializer_range self.down_ops = [ ["Subsample", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2], ["Subsample", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2], ] self.is_training = is_training self.use_labels = use_labels self.num_labels = num_labels self.initializer_range = initializer_range 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.num_labels) config = self.get_config() return config, pixel_values, labels def get_config(self): return LevitConfig( image_size=self.image_size, num_channels=self.num_channels, kernel_size=self.kernel_size, stride=self.stride, padding=self.padding, patch_size=self.patch_size, hidden_sizes=self.hidden_sizes, num_attention_heads=self.num_attention_heads, depths=self.depths, key_dim=self.key_dim, drop_path_rate=self.drop_path_rate, mlp_ratio=self.mlp_ratio, attention_ratio=self.attention_ratio, initializer_range=self.initializer_range, down_ops=self.down_ops, ) def create_and_check_model(self, config, pixel_values, labels): model = LevitModel(config=config) model.to(torch_device) model.eval() result = model(pixel_values) image_size = (self.image_size, self.image_size) height, width = image_size[0], image_size[1] for _ in range(4): height = floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1) width = floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1) self.parent.assertEqual( result.last_hidden_state.shape, (self.batch_size, ceil(height / 4) * ceil(width / 4), self.hidden_sizes[-1]), ) def create_and_check_for_image_classification(self, config, pixel_values, labels): config.num_labels = self.num_labels model = LevitForImageClassification(config) model.to(torch_device) model.eval() result = model(pixel_values, labels=labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels)) 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 LevitModelTest(ModelTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as Levit does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = ( (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher) if is_torch_available() else () ) test_pruning = False test_torchscript = False test_resize_embeddings = False test_head_masking = False has_attentions = False def setUp(self): self.model_tester = LevitModelTester(self) self.config_tester = ConfigTester(self, config_class=LevitConfig, has_text_modality=False, hidden_size=37) def test_config(self): self.create_and_test_config_common_properties() self.config_tester.create_and_test_config_to_json_string() self.config_tester.create_and_test_config_to_json_file() self.config_tester.create_and_test_config_from_and_save_pretrained() self.config_tester.create_and_test_config_with_num_labels() self.config_tester.check_config_can_be_init_without_params() self.config_tester.check_config_arguments_init() def create_and_test_config_common_properties(self): return @unittest.skip(reason="Levit does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="Levit does not support input and output embeddings") def test_model_common_attributes(self): pass @unittest.skip(reason="Levit does not output attentions") def test_attention_outputs(self): pass def test_forward_signature(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) signature = inspect.signature(model.forward) # signature.parameters is an OrderedDict => so arg_names order is deterministic arg_names = [*signature.parameters.keys()] expected_arg_names = ["pixel_values"] self.assertListEqual(arg_names[:1], expected_arg_names) def test_hidden_states_output(self): def check_hidden_states_output(inputs_dict, config, model_class): model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) hidden_states = outputs.hidden_states expected_num_layers = len(self.model_tester.depths) + 1 self.assertEqual(len(hidden_states), expected_num_layers) image_size = (self.model_tester.image_size, self.model_tester.image_size) height, width = image_size[0], image_size[1] for _ in range(4): height = floor( ( (height + 2 * self.model_tester.padding - self.model_tester.kernel_size) / self.model_tester.stride ) + 1 ) width = floor( ( (width + 2 * self.model_tester.padding - self.model_tester.kernel_size) / self.model_tester.stride ) + 1 ) # verify the first hidden states (first block) self.assertListEqual( list(hidden_states[0].shape[-2:]), [ height * width, self.model_tester.hidden_sizes[0], ], ) config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: inputs_dict["output_hidden_states"] = True check_hidden_states_output(inputs_dict, config, model_class) # check that output_hidden_states also work using config del inputs_dict["output_hidden_states"] config.output_hidden_states = True check_hidden_states_output(inputs_dict, config, model_class) 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__ == "LevitForImageClassificationWithTeacher": del inputs_dict["labels"] return inputs_dict 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_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 LevitForImageClassificationWithTeacher model def test_training(self): if not self.model_tester.is_training: return config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True for model_class in self.all_model_classes: # LevitForImageClassificationWithTeacher supports inference-only if ( model_class in get_values(MODEL_MAPPING) or model_class.__name__ == "LevitForImageClassificationWithTeacher" ): 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: return config.use_cache = False config.return_dict = True for model_class in self.all_model_classes: if model_class in get_values(MODEL_MAPPING) or not model_class.supports_gradient_checkpointing: continue # LevitForImageClassificationWithTeacher supports inference-only if model_class.__name__ == "LevitForImageClassificationWithTeacher": 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() def test_problem_types(self): parsed_torch_version_base = version.parse(version.parse(torch.__version__).base_version) if parsed_torch_version_base.base_version.startswith("1.9"): self.skipTest(reason="This test fails with PyTorch 1.9.x: some CUDA issue") 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 not in [ *get_values(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING), ] or model_class.__name__ == "LevitForImageClassificationWithTeacher" ): 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): for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = LevitModel.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 LevitModelIntegrationTest(unittest.TestCase): @cached_property def default_feature_extractor(self): return LevitFeatureExtractor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]) @slow def test_inference_image_classification_head(self): model = LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]).to( torch_device ) feature_extractor = self.default_feature_extractor image = prepare_img() inputs = feature_extractor(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([1.0448, -0.3745, -1.8317]).to(torch_device) self.assertTrue(torch.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4))