# 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 MobileNetV1 model.""" import unittest from functools import cached_property from transformers import MobileNetV1Config from transformers.testing_utils import Expectations, 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 MobileNetV1ForImageClassification, MobileNetV1Model if is_vision_available(): from PIL import Image from transformers import MobileNetV1ImageProcessor class MobileNetV1ConfigTester(ConfigTester): def create_and_test_config_common_properties(self): config = self.config_class(**self.inputs_dict) self.parent.assertTrue(hasattr(config, "tf_padding")) self.parent.assertTrue(hasattr(config, "depth_multiplier")) class MobileNetV1ModelTester: def __init__( self, parent, batch_size=13, num_channels=3, image_size=32, depth_multiplier=0.25, min_depth=8, tf_padding=True, last_hidden_size=1024, output_stride=32, hidden_act="relu6", classifier_dropout_prob=0.1, initializer_range=0.02, is_training=True, use_labels=True, num_labels=10, scope=None, ): self.parent = parent self.batch_size = batch_size self.num_channels = num_channels self.image_size = image_size self.depth_multiplier = depth_multiplier self.min_depth = min_depth self.tf_padding = tf_padding self.last_hidden_size = int(last_hidden_size * depth_multiplier) self.output_stride = output_stride self.hidden_act = hidden_act self.classifier_dropout_prob = classifier_dropout_prob self.use_labels = use_labels self.is_training = is_training self.num_labels = num_labels self.initializer_range = initializer_range self.scope = scope 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.num_labels) 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 MobileNetV1Config( num_channels=self.num_channels, image_size=self.image_size, depth_multiplier=self.depth_multiplier, min_depth=self.min_depth, tf_padding=self.tf_padding, hidden_act=self.hidden_act, classifier_dropout_prob=self.classifier_dropout_prob, initializer_range=self.initializer_range, ) def create_and_check_model(self, config, pixel_values, labels, pixel_labels): model = MobileNetV1Model(config=config) model.to(torch_device) model.eval() result = model(pixel_values) self.parent.assertEqual( result.last_hidden_state.shape, ( self.batch_size, self.last_hidden_size, self.image_size // self.output_stride, self.image_size // self.output_stride, ), ) def create_and_check_for_image_classification(self, config, pixel_values, labels, pixel_labels): config.num_labels = self.num_labels model = MobileNetV1ForImageClassification(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, pixel_labels = config_and_inputs inputs_dict = {"pixel_values": pixel_values} return config, inputs_dict @require_torch class MobileNetV1ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as MobileNetV1 does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = (MobileNetV1Model, MobileNetV1ForImageClassification) if is_torch_available() else () pipeline_model_mapping = ( {"image-feature-extraction": MobileNetV1Model, "image-classification": MobileNetV1ForImageClassification} if is_torch_available() else {} ) test_resize_embeddings = False has_attentions = False test_torch_exportable = True def setUp(self): self.model_tester = MobileNetV1ModelTester(self) self.config_tester = MobileNetV1ConfigTester(self, config_class=MobileNetV1Config, has_text_modality=False) def test_config(self): self.config_tester.run_common_tests() @unittest.skip(reason="MobileNetV1 does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="MobileNetV1 does not support input and output embeddings") def test_model_get_set_embeddings(self): pass @unittest.skip(reason="MobileNetV1 does not output attentions") def test_attention_outputs(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.hidden_states expected_num_stages = 26 self.assertEqual(len(hidden_states), expected_num_stages) 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/mobilenet_v1_1.0_224" model = MobileNetV1Model.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 MobileNetV1ModelIntegrationTest(unittest.TestCase): @cached_property def default_image_processor(self): return ( MobileNetV1ImageProcessor.from_pretrained("google/mobilenet_v1_1.0_224") if is_vision_available() else None ) @slow def test_inference_image_classification_head(self): model = MobileNetV1ForImageClassification.from_pretrained("google/mobilenet_v1_1.0_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, 1001)) self.assertEqual(outputs.logits.shape, expected_shape) expectations = Expectations( { (None, None): [-4.1739, -1.1233, 3.1205], ("cuda", 8): [-4.1739, -1.1233, 3.1205], } ) expected_slice = torch.tensor(expectations.get_expectation()).to(torch_device) torch.testing.assert_close(outputs.logits[0, :3], expected_slice, rtol=2e-4, atol=2e-4)