# 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 Data2VecVision model. """ from __future__ import annotations import collections.abc import inspect import unittest import numpy as np from transformers import Data2VecVisionConfig from transformers.file_utils import cached_property, is_tf_available, is_vision_available from transformers.testing_utils import require_tf, require_vision, 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 tensorflow as tf from transformers import ( TFData2VecVisionForImageClassification, TFData2VecVisionForSemanticSegmentation, TFData2VecVisionModel, ) from transformers.models.data2vec.modeling_tf_data2vec_vision import ( TF_DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST, ) if is_vision_available(): from PIL import Image from transformers import BeitImageProcessor class TFData2VecVisionModelTester: def __init__( self, parent, vocab_size=100, 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, out_indices=[0, 1, 2, 3], ): self.parent = parent self.vocab_size = 100 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.out_indices = out_indices self.num_labels = num_labels 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.type_sequence_label_size) 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 Data2VecVisionConfig( vocab_size=self.vocab_size, 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, out_indices=self.out_indices, ) def create_and_check_model(self, config, pixel_values, labels, pixel_labels): model = TFData2VecVisionModel(config=config) result = model(pixel_values, training=False) # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) image_size = ( self.image_size if isinstance(self.image_size, collections.abc.Iterable) else (self.image_size, self.image_size) ) patch_size = ( self.patch_size if isinstance(self.image_size, collections.abc.Iterable) else (self.patch_size, self.patch_size) ) num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, num_patches + 1, self.hidden_size)) def create_and_check_for_image_classification(self, config, pixel_values, labels, pixel_labels): config.num_labels = self.type_sequence_label_size model = TFData2VecVisionForImageClassification(config) result = model(pixel_values, labels=labels, training=False) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size)) def create_and_check_for_image_segmentation(self, config, pixel_values, labels, pixel_labels): config.num_labels = self.num_labels model = TFData2VecVisionForSemanticSegmentation(config) result = model(pixel_values, training=False) self.parent.assertEqual( result.logits.shape, (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) ) result = model(pixel_values, labels=pixel_labels) self.parent.assertEqual( result.logits.shape, (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) ) 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 def prepare_config_and_inputs_for_keras_fit(self): config_and_inputs = self.prepare_config_and_inputs() config, pixel_values, _, _ = config_and_inputs inputs_dict = {"pixel_values": pixel_values, "labels": tf.zeros((self.batch_size))} return config, inputs_dict @require_tf class TFData2VecVisionModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase): """ Here we also overwrite some of the tests of test_modeling_common.py, as Data2VecVision does not use input_ids, inputs_embeds, attention_mask and seq_length. """ all_model_classes = ( (TFData2VecVisionModel, TFData2VecVisionForImageClassification, TFData2VecVisionForSemanticSegmentation) if is_tf_available() else () ) pipeline_model_mapping = ( {"feature-extraction": TFData2VecVisionModel, "image-classification": TFData2VecVisionForImageClassification} if is_tf_available() else {} ) test_pruning = False test_onnx = False test_resize_embeddings = False test_head_masking = False def setUp(self): self.model_tester = TFData2VecVisionModelTester(self) self.config_tester = ConfigTester( self, config_class=Data2VecVisionConfig, has_text_modality=False, hidden_size=37 ) def test_config(self): self.config_tester.run_common_tests() @unittest.skip(reason="Data2VecVision does not use inputs_embeds") def test_inputs_embeds(self): # Data2VecVision does not use inputs_embeds 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.Layer)) 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_image_segmentation(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_image_segmentation(*config_and_inputs) def test_attention_outputs(self): config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True # in Data2VecVision, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token) image_size = ( self.model_tester.image_size if isinstance(self.model_tester.image_size, collections.abc.Iterable) else (self.model_tester.image_size, self.model_tester.image_size) ) patch_size = ( self.model_tester.patch_size if isinstance(self.model_tester.patch_size, collections.abc.Iterable) else (self.model_tester.patch_size, self.model_tester.patch_size) ) num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) seq_len = num_patches + 1 encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len) encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length) chunk_length = getattr(self.model_tester, "chunk_length", None) if chunk_length is not None and hasattr(self.model_tester, "num_hashes"): encoder_seq_length = encoder_seq_length * self.model_tester.num_hashes for model_class in self.all_model_classes: inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = False config.return_dict = True model = model_class(config) outputs = model(**self._prepare_for_class(inputs_dict, model_class), training=False) attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) # check that output_attentions also work using config del inputs_dict["output_attentions"] config.output_attentions = True model = model_class(config) outputs = model(**self._prepare_for_class(inputs_dict, model_class), training=False) attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions self.assertEqual(len(attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length], ) out_len = len(outputs) # Check attention is always last and order is fine inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = True model = model_class(config) outputs = model(**self._prepare_for_class(inputs_dict, model_class), training=False) self.assertEqual(out_len + 1, len(outputs)) self_attentions = outputs.attentions self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers) self.assertListEqual( list(self_attentions[0].shape[-3:]), [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length], ) 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.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states expected_num_layers = getattr( self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1 ) self.assertEqual(len(hidden_states), expected_num_layers) # Data2VecVision has a different seq_length image_size = ( self.model_tester.image_size if isinstance(self.model_tester.image_size, collections.abc.Iterable) else (self.model_tester.image_size, self.model_tester.image_size) ) patch_size = ( self.model_tester.patch_size if isinstance(self.model_tester.patch_size, collections.abc.Iterable) else (self.model_tester.patch_size, self.model_tester.patch_size) ) num_patches = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0]) seq_length = num_patches + 1 self.assertListEqual( list(hidden_states[0].shape[-2:]), [seq_length, self.model_tester.hidden_size], ) 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) # Overriding this method since the base method won't be compatible with Data2VecVision. @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 `TFData2VecVisionModel` cannot operate with the default `fit()` method. if model_class.__name__ != "TFData2VecVisionModel": model = model_class(config) if getattr(model, "hf_compute_loss", None): # Test that model correctly compute the loss with kwargs _, prepared_for_class = self.model_tester.prepare_config_and_inputs_for_keras_fit() label_names = {"labels"} self.assertGreater(len(label_names), 0, msg="No matching label names found!") labels = {key: val for key, val in prepared_for_class.items() if key in label_names} inputs_minus_labels = { key: val for key, val in prepared_for_class.items() if key not in label_names } self.assertGreater(len(inputs_minus_labels), 0) model.compile(optimizer=tf.keras.optimizers.SGD(0.0), run_eagerly=True) # Make sure the model fits without crashing regardless of where we pass the labels history1 = model.fit( prepared_for_class, validation_data=prepared_for_class, steps_per_epoch=1, validation_steps=1, shuffle=False, ) val_loss1 = history1.history["val_loss"][0] history2 = model.fit( inputs_minus_labels, labels, validation_data=(inputs_minus_labels, labels), steps_per_epoch=1, validation_steps=1, shuffle=False, ) val_loss2 = history2.history["val_loss"][0] self.assertTrue(np.allclose(val_loss1, val_loss2, atol=1e-2, rtol=1e-3)) def check_pt_tf_outputs(self, tf_outputs, pt_outputs, model_class, tol=2e-4, name="outputs", attributes=None): # We override with a slightly higher tol value, as semseg models tend to diverge a bit more super().check_pt_tf_outputs(tf_outputs, pt_outputs, model_class, tol, name, attributes) # Overriding this method since the base method won't be compatible with Data2VecVision. 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: # Since `TFData2VecVisionModel` won't have labels against which we # could compute loss. if model_class.__name__ != "TFData2VecVisionModel": 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.model_tester.prepare_config_and_inputs_for_keras_fit() added_label = prepared_for_class[ sorted(prepared_for_class.keys() - inputs_dict.keys(), reverse=True)[0] ] loss_size = tf.size(added_label) # Test that model correctly compute the loss with kwargs 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.assertEqual(loss.shape, [loss_size]) # Test that model correctly compute the loss with a dict _, prepared_for_class = self.model_tester.prepare_config_and_inputs_for_keras_fit() loss = model(**prepared_for_class)[0] self.assertEqual(loss.shape, [loss_size]) # Test that model correctly compute the loss with a tuple 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.assertEqual(loss.shape, [loss_size]) 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): for model_name in TF_DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = TFData2VecVisionModel.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 TFData2VecVisionModelIntegrationTest(unittest.TestCase): @cached_property def default_image_processor(self): return ( BeitImageProcessor.from_pretrained("facebook/data2vec-vision-base-ft1k") if is_vision_available() else None ) @slow def test_inference_image_classification_head_imagenet_1k(self): model = TFData2VecVisionForImageClassification.from_pretrained("facebook/data2vec-vision-base-ft1k") image_processor = self.default_image_processor image = prepare_img() inputs = image_processor(images=image, return_tensors="tf") # forward pass outputs = model(**inputs) logits = outputs.logits # verify the logits expected_shape = tf.convert_to_tensor([1, 1000]) self.assertEqual(logits.shape, expected_shape) expected_slice = tf.convert_to_tensor([0.3277, -0.1395, 0.0911]) tf.debugging.assert_near(logits[0, :3], expected_slice, atol=1e-4) expected_top2 = [model.config.label2id[i] for i in ["remote control, remote", "tabby, tabby cat"]] self.assertEqual(tf.nn.top_k(outputs.logits[0], 2).indices.numpy().tolist(), expected_top2)