# 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 EfficientFormer model. """ import inspect import unittest import warnings from transformers import EfficientFormerConfig from transformers.models.auto import get_values from transformers.testing_utils import require_torch, 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, floats_tensor, ids_tensor if is_torch_available(): import torch from transformers import ( MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING, MODEL_MAPPING, EfficientFormerForImageClassification, EfficientFormerForImageClassificationWithTeacher, EfficientFormerModel, ) from transformers.models.efficientformer.modeling_efficientformer import ( EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST, ) if is_vision_available(): from PIL import Image from transformers import EfficientFormerImageProcessor class EfficientFormerModelTester: def __init__( self, parent, batch_size: int = 13, image_size: int = 224, patch_size: int = 2, embed_dim: int = 48, # last embed dim of stem num_channels: int = 3, is_training: bool = True, use_labels: bool = True, hidden_size: int = 448, num_hidden_layers: int = 7, # For the l1 num_attention_heads: int = 8, intermediate_size: int = 37, hidden_act: str = "gelu", hidden_dropout_prob: float = 0.1, attention_probs_dropout_prob: float = 0.1, type_sequence_label_size: int = 10, initializer_range: float = 0.02, encoder_stride: int = 2, num_attention_outputs: int = 1, # For l1 ): 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.encoder_stride = encoder_stride self.num_attention_outputs = num_attention_outputs self.embed_dim = embed_dim self.seq_length = embed_dim + 1 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 EfficientFormerConfig( 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, ) def create_and_check_model(self, config, pixel_values, labels): model = EfficientFormerModel(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_image_classification(self, config, pixel_values, labels): config.num_labels = self.type_sequence_label_size model = EfficientFormerForImageClassification(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 = EfficientFormerForImageClassification(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.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 EfficientFormerModelTest(ModelTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as EfficientFormer does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = ( ( EfficientFormerModel, EfficientFormerForImageClassificationWithTeacher, EfficientFormerForImageClassification, ) if is_torch_available() else () ) fx_compatible = False test_pruning = False test_resize_embeddings = False test_head_masking = False def setUp(self): self.model_tester = EfficientFormerModelTester(self) self.config_tester = ConfigTester( self, config_class=EfficientFormerConfig, has_text_modality=False, hidden_size=37 ) def test_config(self): self.config_tester.run_common_tests() @unittest.skip(reason="EfficientFormer does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="EfficientFormer does not support input and output embeddings") def test_model_common_attributes(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.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states expected_num_layers = getattr( self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1 ) self.assertEqual(len(hidden_states), expected_num_layers) if hasattr(self.model_tester, "encoder_seq_length"): seq_length = self.model_tester.encoder_seq_length if hasattr(self.model_tester, "chunk_length") and self.model_tester.chunk_length > 1: seq_length = seq_length * self.model_tester.chunk_length else: seq_length = self.model_tester.seq_length self.assertListEqual( list(hidden_states[-1].shape[-2:]), [seq_length, self.model_tester.hidden_size], ) if config.is_encoder_decoder: hidden_states = outputs.decoder_hidden_states self.assertIsInstance(hidden_states, (list, tuple)) self.assertEqual(len(hidden_states), expected_num_layers) seq_len = getattr(self.model_tester, "seq_length", None) decoder_seq_length = getattr(self.model_tester, "decoder_seq_length", seq_len) self.assertListEqual( list(hidden_states[-1].shape[-2:]), [decoder_seq_length, self.model_tester.hidden_size], ) 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__ == "EfficientFormerForImageClassificationWithTeacher": 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) @unittest.skip(reason="EfficientFormer does not implement masked image modeling yet") 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 EfficientFormerForImageClassificationWithTeacher 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: # EfficientFormerForImageClassificationWithTeacher supports inference-only if ( model_class in get_values(MODEL_MAPPING) or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher" ): 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_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 not in [ *get_values(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING), ] or model_class.__name__ == "EfficientFormerForImageClassificationWithTeacher" ): 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 EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = EfficientFormerModel.from_pretrained(model_name) self.assertIsNotNone(model) def test_attention_outputs(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True seq_len = getattr(self.model_tester, "seq_length", None) encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len) encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length) chunk_length = getattr(self.model_tester, "chunk_length", None) if chunk_length is not None and hasattr(self.model_tester, "num_hashes"): encoder_seq_length = encoder_seq_length * self.model_tester.num_hashes for model_class in self.all_model_classes: inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = False config.return_dict = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions self.assertEqual(len(attentions), self.model_tester.num_attention_outputs) # check that output_attentions also work using config del inputs_dict["output_attentions"] config.output_attentions = True model = model_class(config) model.to(torch_device) model.eval() with torch.no_grad(): outputs = model(**self._prepare_for_class(inputs_dict, model_class)) attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions self.assertEqual(len(attentions), self.model_tester.num_attention_outputs) if chunk_length is not None: self.assertListEqual( list(attentions[0].shape[-4:]), [self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length], ) else: self.assertListEqual( list(attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length], ) # 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 EfficientFormerModelIntegrationTest(unittest.TestCase): @cached_property def default_feature_extractor(self): return ( EfficientFormerImageProcessor.from_pretrained("snap-research/efficientformer-l1-300") if is_vision_available() else None ) @slow def test_inference_image_classification_head(self): model = EfficientFormerForImageClassification.from_pretrained("snap-research/efficientformer-l1-300").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 = (1, 1000) self.assertEqual(outputs.logits.shape, expected_shape) expected_slice = torch.tensor([-0.0555, 0.4825, -0.0852]).to(torch_device) self.assertTrue(torch.allclose(outputs.logits[0][:3], expected_slice, atol=1e-4)) @slow def test_inference_image_classification_head_with_teacher(self): model = EfficientFormerForImageClassificationWithTeacher.from_pretrained( "snap-research/efficientformer-l1-300" ).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 = (1, 1000) self.assertEqual(outputs.logits.shape, expected_shape) expected_slice = torch.tensor([-0.1312, 0.4353, -1.0499]).to(torch_device) self.assertTrue(torch.allclose(outputs.logits[0][:3], expected_slice, atol=1e-4))