# 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 TensorFlow MobileViT model. """ from __future__ import annotations import inspect import unittest from transformers import MobileViTConfig from transformers.file_utils import is_tf_available, is_vision_available from transformers.testing_utils import require_tf, slow from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_tf_available(): import numpy as np import tensorflow as tf from transformers import TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation, TFMobileViTModel from transformers.models.mobilevit.modeling_tf_mobilevit import TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST if is_vision_available(): from PIL import Image from transformers import MobileViTImageProcessor class TFMobileViTConfigTester(ConfigTester): def create_and_test_config_common_properties(self): config = self.config_class(**self.inputs_dict) self.parent.assertTrue(hasattr(config, "hidden_sizes")) self.parent.assertTrue(hasattr(config, "neck_hidden_sizes")) self.parent.assertTrue(hasattr(config, "num_attention_heads")) class TFMobileViTModelTester: def __init__( self, parent, batch_size=13, image_size=32, patch_size=2, num_channels=3, last_hidden_size=32, num_attention_heads=4, hidden_act="silu", conv_kernel_size=3, output_stride=32, hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, 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.image_size = image_size self.patch_size = patch_size self.num_channels = num_channels self.last_hidden_size = last_hidden_size self.num_attention_heads = num_attention_heads self.hidden_act = hidden_act self.conv_kernel_size = conv_kernel_size self.output_stride = output_stride self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob 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 MobileViTConfig( image_size=self.image_size, patch_size=self.patch_size, num_channels=self.num_channels, num_attention_heads=self.num_attention_heads, hidden_act=self.hidden_act, conv_kernel_size=self.conv_kernel_size, output_stride=self.output_stride, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, classifier_dropout_prob=self.classifier_dropout_prob, initializer_range=self.initializer_range, hidden_sizes=[12, 16, 20], neck_hidden_sizes=[8, 8, 16, 16, 32, 32, 32], ) def create_and_check_model(self, config, pixel_values, labels, pixel_labels): model = TFMobileViTModel(config=config) result = model(pixel_values, training=False) expected_height = expected_width = self.image_size // self.output_stride self.parent.assertEqual( result.last_hidden_state.shape, (self.batch_size, self.last_hidden_size, expected_height, expected_width) ) def create_and_check_for_image_classification(self, config, pixel_values, labels, pixel_labels): config.num_labels = self.num_labels model = TFMobileViTForImageClassification(config) result = model(pixel_values, labels=labels, training=False) 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 = TFMobileViTForSemanticSegmentation(config) expected_height = expected_width = self.image_size // self.output_stride result = model(pixel_values, training=False) self.parent.assertEqual( result.logits.shape, (self.batch_size, self.num_labels, expected_height, expected_width) ) result = model(pixel_values, labels=pixel_labels, training=False) self.parent.assertEqual( result.logits.shape, (self.batch_size, self.num_labels, expected_height, expected_width) ) 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_tf class TFMobileViTModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as MobileViT does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = ( (TFMobileViTModel, TFMobileViTForImageClassification, TFMobileViTForSemanticSegmentation) if is_tf_available() else () ) pipeline_model_mapping = ( {"feature-extraction": TFMobileViTModel, "image-classification": TFMobileViTForImageClassification} if is_tf_available() else {} ) test_pruning = False test_resize_embeddings = False test_head_masking = False has_attentions = False test_onnx = False def setUp(self): self.model_tester = TFMobileViTModelTester(self) self.config_tester = TFMobileViTConfigTester(self, config_class=MobileViTConfig, has_text_modality=False) def test_config(self): self.config_tester.run_common_tests() @unittest.skip(reason="MobileViT does not use inputs_embeds") def test_inputs_embeds(self): pass @unittest.skip(reason="MobileViT does not support input and output embeddings") def test_model_common_attributes(self): pass @unittest.skip(reason="MobileViT does not output attentions") def test_attention_outputs(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.call) # 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_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) 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) # MobileViT'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) @unittest.skipIf( not is_tf_available() or len(tf.config.list_physical_devices("GPU")) == 0, reason="TF does not support backprop for grouped convolutions on CPU.", ) def test_dataset_conversion(self): super().test_dataset_conversion() def check_keras_fit_results(self, val_loss1, val_loss2, atol=2e-1, rtol=2e-1): self.assertTrue(np.allclose(val_loss1, val_loss2, atol=atol, rtol=rtol)) @unittest.skipIf( not is_tf_available() or len(tf.config.list_physical_devices("GPU")) == 0, reason="TF does not support backprop for grouped convolutions on CPU.", ) @slow def test_keras_fit(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: # Since `TFMobileViTModel` cannot operate with the default `fit()` method. if model_class.__name__ != "TFMobileViTModel": model = model_class(config) if getattr(model, "hf_compute_loss", None): super().test_keras_fit() # The default test_loss_computation() uses -100 as a proxy ignore_index # to test masked losses. Overridding to avoid -100 since semantic segmentation # models use `semantic_loss_ignore_index` from the config. def test_loss_computation(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: # set an ignore index to correctly test the masked loss used in # `TFMobileViTForSemanticSegmentation`. if model_class.__name__ != "TFMobileViTForSemanticSegmentation": config.semantic_loss_ignore_index = 5 model = model_class(config) if getattr(model, "hf_compute_loss", None): # The number of elements in the loss should be the same as the number of elements in the label prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True) added_label = prepared_for_class[ sorted(prepared_for_class.keys() - inputs_dict.keys(), reverse=True)[0] ] expected_loss_size = added_label.shape.as_list()[:1] # Test that model correctly compute the loss with kwargs prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True) possible_input_names = {"input_ids", "pixel_values", "input_features"} input_name = possible_input_names.intersection(set(prepared_for_class)).pop() model_input = prepared_for_class.pop(input_name) loss = model(model_input, **prepared_for_class)[0] self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]) # Test that model correctly compute the loss when we mask some positions prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True) possible_input_names = {"input_ids", "pixel_values", "input_features"} input_name = possible_input_names.intersection(set(prepared_for_class)).pop() model_input = prepared_for_class.pop(input_name) if "labels" in prepared_for_class: labels = prepared_for_class["labels"].numpy() if len(labels.shape) > 1 and labels.shape[1] != 1: # labels[0] = -100 prepared_for_class["labels"] = tf.convert_to_tensor(labels) loss = model(model_input, **prepared_for_class)[0] self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]) self.assertTrue(not np.any(np.isnan(loss.numpy()))) # Test that model correctly compute the loss with a dict prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True) loss = model(prepared_for_class)[0] self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]) # Test that model correctly compute the loss with a tuple prepared_for_class = self._prepare_for_class(inputs_dict.copy(), model_class, return_labels=True) # Get keys that were added with the _prepare_for_class function label_keys = prepared_for_class.keys() - inputs_dict.keys() signature = inspect.signature(model.call).parameters signature_names = list(signature.keys()) # Create a dictionary holding the location of the tensors in the tuple tuple_index_mapping = {0: input_name} for label_key in label_keys: label_key_index = signature_names.index(label_key) tuple_index_mapping[label_key_index] = label_key sorted_tuple_index_mapping = sorted(tuple_index_mapping.items()) # Initialize a list with their default values, update the values and convert to a tuple list_input = [] for name in signature_names: if name != "kwargs": list_input.append(signature[name].default) for index, value in sorted_tuple_index_mapping: list_input[index] = prepared_for_class[value] tuple_input = tuple(list_input) # Send to model loss = model(tuple_input[:-1])[0] self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1]) @slow def test_model_from_pretrained(self): for model_name in TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = TFMobileViTModel.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_tf class TFMobileViTModelIntegrationTest(unittest.TestCase): @slow def test_inference_image_classification_head(self): model = TFMobileViTForImageClassification.from_pretrained("apple/mobilevit-xx-small") image_processor = MobileViTImageProcessor.from_pretrained("apple/mobilevit-xx-small") image = prepare_img() inputs = image_processor(images=image, return_tensors="tf") # forward pass outputs = model(**inputs, training=False) # verify the logits expected_shape = tf.TensorShape((1, 1000)) self.assertEqual(outputs.logits.shape, expected_shape) expected_slice = tf.constant([-1.9364, -1.2327, -0.4653]) tf.debugging.assert_near(outputs.logits[0, :3], expected_slice, atol=1e-4, rtol=1e-04) @slow def test_inference_semantic_segmentation(self): # `from_pt` will be removed model = TFMobileViTForSemanticSegmentation.from_pretrained("apple/deeplabv3-mobilevit-xx-small") image_processor = MobileViTImageProcessor.from_pretrained("apple/deeplabv3-mobilevit-xx-small") image = prepare_img() inputs = image_processor(images=image, return_tensors="tf") # forward pass outputs = model(inputs.pixel_values, training=False) logits = outputs.logits # verify the logits expected_shape = tf.TensorShape((1, 21, 32, 32)) self.assertEqual(logits.shape, expected_shape) expected_slice = tf.constant( [ [[6.9713, 6.9786, 7.2422], [7.2893, 7.2825, 7.4446], [7.6580, 7.8797, 7.9420]], [[-10.6869, -10.3250, -10.3471], [-10.4228, -9.9868, -9.7132], [-11.0405, -11.0221, -10.7318]], [[-3.3089, -2.8539, -2.6740], [-3.2706, -2.5621, -2.5108], [-3.2534, -2.6615, -2.6651]], ] ) tf.debugging.assert_near(logits[0, :3, :3, :3], expected_slice, rtol=1e-4, atol=1e-4)