# 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 DeiT model. """ from __future__ import annotations import inspect import unittest import numpy as np from transformers import DeiTConfig from transformers.testing_utils import require_tf, require_vision, slow from transformers.utils import cached_property, is_tf_available, is_vision_available 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 tensorflow as tf from transformers import ( TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher, TFDeiTForMaskedImageModeling, TFDeiTModel, ) from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST if is_vision_available(): from PIL import Image from transformers import DeiTImageProcessor class TFDeiTModelTester: def __init__( self, parent, batch_size=13, image_size=30, patch_size=2, num_channels=3, is_training=True, use_labels=True, hidden_size=32, num_hidden_layers=2, num_attention_heads=4, intermediate_size=37, hidden_act="gelu", hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, type_sequence_label_size=10, initializer_range=0.02, num_labels=3, scope=None, encoder_stride=2, ): self.parent = parent self.batch_size = batch_size self.image_size = image_size self.patch_size = patch_size self.num_channels = num_channels self.is_training = is_training self.use_labels = use_labels self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.type_sequence_label_size = type_sequence_label_size self.initializer_range = initializer_range self.scope = scope self.encoder_stride = encoder_stride # in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens) num_patches = (image_size // patch_size) ** 2 self.seq_length = num_patches + 2 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.type_sequence_label_size) config = self.get_config() return config, pixel_values, labels def get_config(self): return DeiTConfig( image_size=self.image_size, patch_size=self.patch_size, num_channels=self.num_channels, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, is_decoder=False, initializer_range=self.initializer_range, encoder_stride=self.encoder_stride, ) def create_and_check_model(self, config, pixel_values, labels): model = TFDeiTModel(config=config) result = model(pixel_values) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) def create_and_check_for_masked_image_modeling(self, config, pixel_values, labels): model = TFDeiTForMaskedImageModeling(config=config) result = model(pixel_values) self.parent.assertEqual( result.reconstruction.shape, (self.batch_size, self.num_channels, self.image_size, self.image_size) ) # test greyscale images config.num_channels = 1 model = TFDeiTForMaskedImageModeling(config) pixel_values = floats_tensor([self.batch_size, 1, self.image_size, self.image_size]) result = model(pixel_values) self.parent.assertEqual(result.reconstruction.shape, (self.batch_size, 1, self.image_size, self.image_size)) def create_and_check_for_image_classification(self, config, pixel_values, labels): config.num_labels = self.type_sequence_label_size model = TFDeiTForImageClassification(config) result = model(pixel_values, labels=labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size)) # test greyscale images config.num_channels = 1 model = TFDeiTForImageClassification(config) pixel_values = floats_tensor([self.batch_size, 1, self.image_size, self.image_size]) result = model(pixel_values, labels=labels) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size)) 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_tf class TFDeiTModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_tf_common.py, as DeiT does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = ( ( TFDeiTModel, TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher, TFDeiTForMaskedImageModeling, ) if is_tf_available() else () ) pipeline_model_mapping = ( { "feature-extraction": TFDeiTModel, "image-classification": (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher), } if is_tf_available() else {} ) test_pruning = False test_resize_embeddings = False test_head_masking = False test_onnx = False def setUp(self): self.model_tester = TFDeiTModelTester(self) self.config_tester = ConfigTester(self, config_class=DeiTConfig, has_text_modality=False, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() @unittest.skip(reason="DeiT does not use inputs_embeds") def test_inputs_embeds(self): pass def test_model_common_attributes(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) self.assertIsInstance(model.get_input_embeddings(), (tf.keras.layers.Layer)) x = model.get_output_embeddings() self.assertTrue(x is None or isinstance(x, tf.keras.layers.Dense)) 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_for_masked_image_modeling(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_masked_image_modeling(*config_and_inputs) 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) # special case for DeiTForImageClassificationWithTeacher model def _prepare_for_class(self, inputs_dict, model_class, return_labels=False): inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels) if return_labels: if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call).parameters: del inputs_dict["labels"] return inputs_dict @slow def test_model_from_pretrained(self): for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = TFDeiTModel.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 @require_vision class DeiTModelIntegrationTest(unittest.TestCase): @cached_property def default_image_processor(self): return ( DeiTImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224") if is_vision_available() else None ) @slow def test_inference_image_classification_head(self): model = TFDeiTForImageClassificationWithTeacher.from_pretrained("facebook/deit-base-distilled-patch16-224") image_processor = self.default_image_processor image = prepare_img() inputs = image_processor(images=image, return_tensors="tf") # forward pass outputs = model(**inputs) # verify the logits expected_shape = tf.TensorShape((1, 1000)) self.assertEqual(outputs.logits.shape, expected_shape) expected_slice = tf.constant([-1.0266, 0.1912, -1.2861]) self.assertTrue(np.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4))