# 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 MobileViTV2 model.""" import unittest from functools import cached_property from transformers import MobileViTV2Config from transformers.testing_utils import ( Expectations, require_torch, require_torch_multi_gpu, 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 MobileViTV2ForImageClassification, MobileViTV2ForSemanticSegmentation, MobileViTV2Model from transformers.models.mobilevitv2.modeling_mobilevitv2 import ( make_divisible, ) if is_vision_available(): from PIL import Image from transformers import MobileViTImageProcessor class MobileViTV2ConfigTester(ConfigTester): def create_and_test_config_common_properties(self): config = self.config_class(**self.inputs_dict) self.parent.assertTrue(hasattr(config, "width_multiplier")) class MobileViTV2ModelTester: def __init__( self, parent, batch_size=13, image_size=64, patch_size=2, num_channels=3, hidden_act="swish", conv_kernel_size=3, output_stride=32, classifier_dropout_prob=0.1, initializer_range=0.02, is_training=True, use_labels=True, num_labels=10, scope=None, width_multiplier=0.25, ffn_dropout=0.0, attn_dropout=0.0, ): self.parent = parent self.batch_size = batch_size self.image_size = image_size self.patch_size = patch_size self.num_channels = num_channels self.last_hidden_size = make_divisible(512 * width_multiplier, divisor=8) self.hidden_act = hidden_act self.conv_kernel_size = conv_kernel_size self.output_stride = output_stride 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 self.width_multiplier = width_multiplier self.ffn_dropout_prob = ffn_dropout self.attn_dropout_prob = attn_dropout 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 MobileViTV2Config( image_size=self.image_size, patch_size=self.patch_size, num_channels=self.num_channels, hidden_act=self.hidden_act, conv_kernel_size=self.conv_kernel_size, output_stride=self.output_stride, classifier_dropout_prob=self.classifier_dropout_prob, initializer_range=self.initializer_range, width_multiplier=self.width_multiplier, ffn_dropout=self.ffn_dropout_prob, attn_dropout=self.attn_dropout_prob, base_attn_unit_dims=[16, 24, 32], n_attn_blocks=[1, 1, 2], aspp_out_channels=32, ) def create_and_check_model(self, config, pixel_values, labels, pixel_labels): model = MobileViTV2Model(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 = MobileViTV2ForImageClassification(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 create_and_check_for_semantic_segmentation(self, config, pixel_values, labels, pixel_labels): config.num_labels = self.num_labels model = MobileViTV2ForSemanticSegmentation(config) model.to(torch_device) model.eval() result = model(pixel_values) self.parent.assertEqual( result.logits.shape, ( self.batch_size, self.num_labels, self.image_size // self.output_stride, self.image_size // self.output_stride, ), ) result = model(pixel_values, labels=pixel_labels) self.parent.assertEqual( result.logits.shape, ( self.batch_size, self.num_labels, self.image_size // self.output_stride, self.image_size // self.output_stride, ), ) 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 MobileViTV2ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as MobileViTV2 does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = ( (MobileViTV2Model, MobileViTV2ForImageClassification, MobileViTV2ForSemanticSegmentation) if is_torch_available() else () ) pipeline_model_mapping = ( { "image-feature-extraction": MobileViTV2Model, "image-classification": MobileViTV2ForImageClassification, "image-segmentation": MobileViTV2ForSemanticSegmentation, } if is_torch_available() else {} ) test_resize_embeddings = False has_attentions = False test_torch_exportable = True def setUp(self): self.model_tester = MobileViTV2ModelTester(self) self.config_tester = MobileViTV2ConfigTester(self, config_class=MobileViTV2Config, has_text_modality=False) def test_config(self): self.config_tester.run_common_tests() @unittest.skip(reason="MobileViTV2 does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="MobileViTV2 does not support input and output embeddings") def test_model_get_set_embeddings(self): pass @unittest.skip(reason="MobileViTV2 does not output attentions") def test_attention_outputs(self): pass @require_torch_multi_gpu @unittest.skip(reason="Got `CUDA error: misaligned address` for tests after this one being run.") def test_multi_gpu_data_parallel_forward(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 = 5 self.assertEqual(len(hidden_states), expected_num_stages) # MobileViTV2's feature maps are of shape (batch_size, num_channels, height, width) # with the width and height being successively divided by 2. divisor = 2 for i in range(len(hidden_states)): self.assertListEqual( list(hidden_states[i].shape[-2:]), [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor], ) divisor *= 2 self.assertEqual(self.model_tester.output_stride, divisor // 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) def test_for_semantic_segmentation(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_semantic_segmentation(*config_and_inputs) @slow def test_model_from_pretrained(self): model_name = "apple/mobilevitv2-1.0-imagenet1k-256" model = MobileViTV2Model.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 MobileViTV2ModelIntegrationTest(unittest.TestCase): @cached_property def default_image_processor(self): return ( MobileViTImageProcessor.from_pretrained("apple/mobilevitv2-1.0-imagenet1k-256") if is_vision_available() else None ) @slow def test_inference_image_classification_head(self): model = MobileViTV2ForImageClassification.from_pretrained("apple/mobilevitv2-1.0-imagenet1k-256").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): [-1.6336e00, -7.3204e-02, -5.1883e-01], ("cuda", 8): [-1.6336, -0.0732, -0.5188], } ) 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) @slow def test_inference_semantic_segmentation(self): model = MobileViTV2ForSemanticSegmentation.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3") model = model.to(torch_device) image_processor = MobileViTImageProcessor.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3") image = prepare_img() inputs = image_processor(images=image, return_tensors="pt").to(torch_device) # forward pass with torch.no_grad(): outputs = model(**inputs) logits = outputs.logits # verify the logits expected_shape = torch.Size((1, 21, 32, 32)) self.assertEqual(logits.shape, expected_shape) expectations = Expectations( { (None, None): [ [[7.0863, 7.1525, 6.8201], [6.6931, 6.8770, 6.8933], [6.2978, 7.0366, 6.9636]], [[-3.7134, -3.6712, -3.6675], [-3.5825, -3.3549, -3.4777], [-3.3435, -3.3979, -3.2857]], [[-2.9329, -2.8003, -2.7369], [-3.0564, -2.4780, -2.0207], [-2.6889, -1.9298, -1.7640]], ], ("cuda", 8): [ [[7.0863, 7.1525, 6.8201], [6.6931, 6.8770, 6.8933], [6.2978, 7.0366, 6.9636]], [[-3.7134, -3.6712, -3.6675], [-3.5825, -3.3549, -3.4777], [-3.3435, -3.3979, -3.2857]], [[-2.9329, -2.8003, -2.7369], [-3.0564, -2.4780, -2.0207], [-2.6889, -1.9298, -1.7640]], ], } ) expected_slice = torch.tensor(expectations.get_expectation()).to(torch_device) torch.testing.assert_close(logits[0, :3, :3, :3], expected_slice, rtol=2e-4, atol=2e-4) @slow def test_post_processing_semantic_segmentation(self): model = MobileViTV2ForSemanticSegmentation.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3") model = model.to(torch_device) image_processor = MobileViTImageProcessor.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3") image = prepare_img() inputs = image_processor(images=image, return_tensors="pt").to(torch_device) # forward pass with torch.no_grad(): outputs = model(**inputs) outputs.logits = outputs.logits.detach().cpu() segmentation = image_processor.post_process_semantic_segmentation(outputs=outputs, target_sizes=[(50, 60)]) expected_shape = torch.Size((50, 60)) self.assertEqual(segmentation[0].shape, expected_shape) segmentation = image_processor.post_process_semantic_segmentation(outputs=outputs) expected_shape = torch.Size((32, 32)) self.assertEqual(segmentation[0].shape, expected_shape)