# 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 RegNet model.""" import unittest from functools import cached_property from transformers import RegNetConfig from transformers.file_utils import is_torch_available, is_vision_available from transformers.testing_utils import Expectations, is_flaky, require_torch, require_vision, slow, torch_device 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 RegNetForImageClassification, RegNetModel if is_vision_available(): from PIL import Image from transformers import AutoImageProcessor class RegNetModelTester: def __init__( self, parent, batch_size=3, image_size=32, num_channels=3, embeddings_size=10, hidden_sizes=[10, 20, 30, 40], depths=[1, 1, 2, 1], is_training=True, use_labels=True, hidden_act="relu", num_labels=3, scope=None, ): self.parent = parent self.batch_size = batch_size self.image_size = image_size self.num_channels = num_channels self.embeddings_size = embeddings_size self.hidden_sizes = hidden_sizes self.depths = depths self.is_training = is_training self.use_labels = use_labels self.hidden_act = hidden_act self.num_labels = num_labels self.scope = scope self.num_stages = len(hidden_sizes) 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 RegNetConfig( num_channels=self.num_channels, embeddings_size=self.embeddings_size, hidden_sizes=self.hidden_sizes, depths=self.depths, hidden_act=self.hidden_act, num_labels=self.num_labels, ) def create_and_check_model(self, config, pixel_values, labels): model = RegNetModel(config=config) model.to(torch_device) model.eval() result = model(pixel_values) # expected last hidden states: B, C, H // 32, W // 32 self.parent.assertEqual( result.last_hidden_state.shape, (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32), ) def create_and_check_for_image_classification(self, config, pixel_values, labels): config.num_labels = self.num_labels model = RegNetForImageClassification(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 RegNetModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as RegNet does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = (RegNetModel, RegNetForImageClassification) if is_torch_available() else () pipeline_model_mapping = ( {"image-feature-extraction": RegNetModel, "image-classification": RegNetForImageClassification} if is_torch_available() else {} ) test_resize_embeddings = False has_attentions = False test_torch_exportable = True def setUp(self): self.model_tester = RegNetModelTester(self) self.config_tester = ConfigTester( self, config_class=RegNetConfig, has_text_modality=False, common_properties=["num_channels", "hidden_sizes"], ) def test_config(self): self.config_tester.run_common_tests() @is_flaky(description="Larger difference with A10. Still flaky after setting larger tolerance") def test_batching_equivalence(self, atol=3e-5, rtol=3e-5): super().test_batching_equivalence(atol=atol, rtol=rtol) @unittest.skip(reason="RegNet does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="RegNet does not support input and output embeddings") def test_model_get_set_embeddings(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 expected_num_stages = self.model_tester.num_stages self.assertEqual(len(hidden_states), expected_num_stages + 1) # RegNet'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() layers_type = ["basic", "bottleneck"] for model_class in self.all_model_classes: for layer_type in layers_type: config.layer_type = layer_type 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 = "facebook/regnet-y-040" model = RegNetModel.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 RegNetModelIntegrationTest(unittest.TestCase): @cached_property def default_image_processor(self): return AutoImageProcessor.from_pretrained("facebook/regnet-y-040") if is_vision_available() else None @slow def test_inference_image_classification_head(self): model = RegNetForImageClassification.from_pretrained("facebook/regnet-y-040").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) expectations = Expectations( { (None, None): [-0.4180, -1.5051, -3.4836], ("cuda", 8): [-0.4180, -1.5051, -3.4836], } ) 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)