# Copyright 2023 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 EfficientNet model.""" import unittest from functools import cached_property from transformers import EfficientNetConfig from transformers.testing_utils import is_pipeline_test, require_torch, 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 transformers import EfficientNetForImageClassification, EfficientNetModel if is_vision_available(): from PIL import Image from transformers import AutoImageProcessor class EfficientNetModelTester: def __init__( self, parent, batch_size=13, image_size=32, num_channels=3, kernel_sizes=[3, 3, 5], in_channels=[32, 16, 24], out_channels=[16, 24, 20], strides=[1, 1, 2], num_block_repeats=[1, 1, 2], expand_ratios=[1, 6, 6], is_training=True, use_labels=True, intermediate_size=37, hidden_act="gelu", num_labels=10, ): self.parent = parent self.batch_size = batch_size self.image_size = image_size self.num_channels = num_channels self.kernel_sizes = kernel_sizes self.in_channels = in_channels self.out_channels = out_channels self.strides = strides self.num_block_repeats = num_block_repeats self.expand_ratios = expand_ratios self.is_training = is_training self.hidden_act = hidden_act self.num_labels = num_labels self.use_labels = use_labels 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 EfficientNetConfig( image_size=self.image_size, num_channels=self.num_channels, kernel_sizes=self.kernel_sizes, in_channels=self.in_channels, out_channels=self.out_channels, strides=self.strides, num_block_repeats=self.num_block_repeats, expand_ratios=self.expand_ratios, hidden_act=self.hidden_act, num_labels=self.num_labels, ) def create_and_check_model(self, config, pixel_values, labels): model = EfficientNetModel(config=config) model.to(torch_device) model.eval() result = model(pixel_values) # expected last hidden states: B, C, H // 4, W // 4 self.parent.assertEqual( result.last_hidden_state.shape, (self.batch_size, config.hidden_dim, self.image_size // 4, self.image_size // 4), ) def create_and_check_for_image_classification(self, config, pixel_values, labels): model = EfficientNetForImageClassification(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 EfficientNetModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as EfficientNet does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = (EfficientNetModel, EfficientNetForImageClassification) if is_torch_available() else () pipeline_model_mapping = ( {"image-feature-extraction": EfficientNetModel, "image-classification": EfficientNetForImageClassification} if is_torch_available() else {} ) test_resize_embeddings = False has_attentions = False test_torch_exportable = True def setUp(self): self.model_tester = EfficientNetModelTester(self) self.config_tester = ConfigTester( self, config_class=EfficientNetConfig, has_text_modality=False, hidden_size=37, common_properties=["num_channels", "image_size", "hidden_dim"], ) def test_config(self): self.config_tester.run_common_tests() @unittest.skip(reason="EfficientNet does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="EfficientNet does not support input and output embeddings") def test_model_get_set_embeddings(self): pass @unittest.skip(reason="EfficientNet does not use feedforward chunking") def test_feed_forward_chunking(self): pass 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_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 num_blocks = sum(config.num_block_repeats) * 4 self.assertEqual(len(hidden_states), num_blocks) # EfficientNet's feature maps are of shape (batch_size, num_channels, height, width) self.assertListEqual( list(hidden_states[0].shape[-2:]), [self.model_tester.image_size // 2, self.model_tester.image_size // 2], ) 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 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 = "google/efficientnet-b7" model = EfficientNetModel.from_pretrained(model_name) self.assertIsNotNone(model) @is_pipeline_test @require_vision @slow def test_pipeline_image_feature_extraction(self): super().test_pipeline_image_feature_extraction() @is_pipeline_test @require_vision @slow def test_pipeline_image_feature_extraction_fp16(self): super().test_pipeline_image_feature_extraction_fp16() @is_pipeline_test @require_vision @slow def test_pipeline_image_classification(self): super().test_pipeline_image_classification() # 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 EfficientNetModelIntegrationTest(unittest.TestCase): @cached_property def default_image_processor(self): return AutoImageProcessor.from_pretrained("google/efficientnet-b7") if is_vision_available() else None @slow def test_inference_image_classification_head(self): model = EfficientNetForImageClassification.from_pretrained("google/efficientnet-b7").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([-0.2962, 0.4487, 0.4499]).to(torch_device) torch.testing.assert_close(outputs.logits[0, :3], expected_slice, rtol=1e-4, atol=1e-4)