# 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 LayoutLMv3 model. """ from __future__ import annotations import copy import inspect import unittest import numpy as np from transformers import is_tf_available, is_vision_available from transformers.models.auto import get_values from transformers.testing_utils import require_tf, slow from transformers.utils import cached_property from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_tf_available(): import tensorflow as tf from transformers import ( TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST, TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING, TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING, TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, LayoutLMv3Config, TFLayoutLMv3ForQuestionAnswering, TFLayoutLMv3ForSequenceClassification, TFLayoutLMv3ForTokenClassification, TFLayoutLMv3Model, ) if is_vision_available(): from PIL import Image from transformers import LayoutLMv3ImageProcessor class TFLayoutLMv3ModelTester: def __init__( self, parent, batch_size=2, num_channels=3, image_size=4, patch_size=2, text_seq_length=7, is_training=True, use_input_mask=True, use_token_type_ids=True, use_labels=True, vocab_size=99, hidden_size=36, 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, max_position_embeddings=512, type_vocab_size=16, type_sequence_label_size=2, initializer_range=0.02, coordinate_size=6, shape_size=6, num_labels=3, num_choices=4, scope=None, range_bbox=1000, ): self.parent = parent self.batch_size = batch_size self.num_channels = num_channels self.image_size = image_size self.patch_size = patch_size self.is_training = is_training self.use_input_mask = use_input_mask self.use_token_type_ids = use_token_type_ids self.use_labels = use_labels self.vocab_size = vocab_size 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.max_position_embeddings = max_position_embeddings self.type_vocab_size = type_vocab_size self.type_sequence_label_size = type_sequence_label_size self.initializer_range = initializer_range self.coordinate_size = coordinate_size self.shape_size = shape_size self.num_labels = num_labels self.num_choices = num_choices self.scope = scope self.range_bbox = range_bbox # LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token) self.text_seq_length = text_seq_length self.image_seq_length = (image_size // patch_size) ** 2 + 1 self.seq_length = self.text_seq_length + self.image_seq_length def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.text_seq_length], self.vocab_size) bbox = ids_tensor([self.batch_size, self.text_seq_length, 4], self.range_bbox) bbox = bbox.numpy() # Ensure that bbox is legal for i in range(bbox.shape[0]): for j in range(bbox.shape[1]): if bbox[i, j, 3] < bbox[i, j, 1]: tmp_coordinate = bbox[i, j, 3] bbox[i, j, 3] = bbox[i, j, 1] bbox[i, j, 1] = tmp_coordinate if bbox[i, j, 2] < bbox[i, j, 0]: tmp_coordinate = bbox[i, j, 2] bbox[i, j, 2] = bbox[i, j, 0] bbox[i, j, 0] = tmp_coordinate bbox = tf.constant(bbox) pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]) input_mask = None if self.use_input_mask: input_mask = random_attention_mask([self.batch_size, self.text_seq_length]) token_type_ids = None if self.use_token_type_ids: token_type_ids = ids_tensor([self.batch_size, self.text_seq_length], self.type_vocab_size) sequence_labels = None token_labels = None if self.use_labels: sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size) token_labels = ids_tensor([self.batch_size, self.text_seq_length], self.num_labels) config = LayoutLMv3Config( vocab_size=self.vocab_size, 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, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, initializer_range=self.initializer_range, coordinate_size=self.coordinate_size, shape_size=self.shape_size, input_size=self.image_size, patch_size=self.patch_size, ) return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels def create_and_check_model(self, config, input_ids, bbox, pixel_values, token_type_ids, input_mask): model = TFLayoutLMv3Model(config=config) # text + image result = model(input_ids, pixel_values=pixel_values, training=False) result = model( input_ids, bbox=bbox, pixel_values=pixel_values, attention_mask=input_mask, token_type_ids=token_type_ids, training=False, ) result = model(input_ids, bbox=bbox, pixel_values=pixel_values, training=False) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) # text only result = model(input_ids, training=False) self.parent.assertEqual( result.last_hidden_state.shape, (self.batch_size, self.text_seq_length, self.hidden_size) ) # image only result = model({"pixel_values": pixel_values}, training=False) self.parent.assertEqual( result.last_hidden_state.shape, (self.batch_size, self.image_seq_length, self.hidden_size) ) def create_and_check_for_sequence_classification( self, config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels ): config.num_labels = self.num_labels model = TFLayoutLMv3ForSequenceClassification(config=config) result = model( input_ids, bbox=bbox, pixel_values=pixel_values, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels, training=False, ) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels)) def create_and_check_for_token_classification( self, config, input_ids, bbox, pixel_values, token_type_ids, input_mask, token_labels ): config.num_labels = self.num_labels model = TFLayoutLMv3ForTokenClassification(config=config) result = model( input_ids, bbox=bbox, pixel_values=pixel_values, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels, training=False, ) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.text_seq_length, self.num_labels)) def create_and_check_for_question_answering( self, config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels ): config.num_labels = 2 model = TFLayoutLMv3ForQuestionAnswering(config=config) result = model( input_ids, bbox=bbox, pixel_values=pixel_values, attention_mask=input_mask, token_type_ids=token_type_ids, start_positions=sequence_labels, end_positions=sequence_labels, training=False, ) self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length)) self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() (config, input_ids, bbox, pixel_values, token_type_ids, input_mask, _, _) = config_and_inputs inputs_dict = { "input_ids": input_ids, "bbox": bbox, "pixel_values": pixel_values, "token_type_ids": token_type_ids, "attention_mask": input_mask, } return config, inputs_dict @require_tf class TFLayoutLMv3ModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( ( TFLayoutLMv3Model, TFLayoutLMv3ForQuestionAnswering, TFLayoutLMv3ForSequenceClassification, TFLayoutLMv3ForTokenClassification, ) if is_tf_available() else () ) pipeline_model_mapping = ( {"document-question-answering": TFLayoutLMv3ForQuestionAnswering, "feature-extraction": TFLayoutLMv3Model} if is_tf_available() else {} ) test_pruning = False test_resize_embeddings = False test_onnx = False # TODO: Fix the failed tests def is_pipeline_test_to_skip( self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name ): return True def _prepare_for_class(self, inputs_dict, model_class, return_labels=False) -> dict: inputs_dict = copy.deepcopy(inputs_dict) if model_class in get_values(TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING): inputs_dict = { k: tf.tile(tf.expand_dims(v, 1), (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)) if isinstance(v, tf.Tensor) and v.ndim > 0 else v for k, v in inputs_dict.items() } if return_labels: if model_class in get_values(TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING): inputs_dict["labels"] = tf.ones(self.model_tester.batch_size, dtype=tf.int32) elif model_class in get_values(TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING): inputs_dict["start_positions"] = tf.zeros(self.model_tester.batch_size, dtype=tf.int32) inputs_dict["end_positions"] = tf.zeros(self.model_tester.batch_size, dtype=tf.int32) elif model_class in get_values(TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING): inputs_dict["labels"] = tf.zeros(self.model_tester.batch_size, dtype=tf.int32) elif model_class in get_values(TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING): inputs_dict["labels"] = tf.zeros( (self.model_tester.batch_size, self.model_tester.text_seq_length), dtype=tf.int32 ) return inputs_dict def setUp(self): self.model_tester = TFLayoutLMv3ModelTester(self) self.config_tester = ConfigTester(self, config_class=LayoutLMv3Config, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() 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: 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) input_ids = prepared_for_class.pop("input_ids") loss = model(input_ids, **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) input_ids = prepared_for_class.pop("input_ids") 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(input_ids, **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_ids"} 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]) def test_model(self): ( config, input_ids, bbox, pixel_values, token_type_ids, input_mask, _, _, ) = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(config, input_ids, bbox, pixel_values, token_type_ids, input_mask) def test_model_various_embeddings(self): ( config, input_ids, bbox, pixel_values, token_type_ids, input_mask, _, _, ) = self.model_tester.prepare_config_and_inputs() for type in ["absolute", "relative_key", "relative_key_query"]: config.position_embedding_type = type self.model_tester.create_and_check_model(config, input_ids, bbox, pixel_values, token_type_ids, input_mask) def test_for_sequence_classification(self): ( config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, _, ) = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_sequence_classification( config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels ) def test_for_token_classification(self): ( config, input_ids, bbox, pixel_values, token_type_ids, input_mask, _, token_labels, ) = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_token_classification( config, input_ids, bbox, pixel_values, token_type_ids, input_mask, token_labels ) def test_for_question_answering(self): ( config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, _, ) = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_question_answering( config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels ) @slow def test_model_from_pretrained(self): for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]: model = TFLayoutLMv3Model.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 TFLayoutLMv3ModelIntegrationTest(unittest.TestCase): @cached_property def default_image_processor(self): return LayoutLMv3ImageProcessor(apply_ocr=False) if is_vision_available() else None @slow def test_inference_no_head(self): model = TFLayoutLMv3Model.from_pretrained("microsoft/layoutlmv3-base") image_processor = self.default_image_processor image = prepare_img() pixel_values = image_processor(images=image, return_tensors="tf").pixel_values input_ids = tf.constant([[1, 2]]) bbox = tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]]), axis=0) # forward pass outputs = model(input_ids=input_ids, bbox=bbox, pixel_values=pixel_values, training=False) # verify the logits expected_shape = (1, 199, 768) self.assertEqual(outputs.last_hidden_state.shape, expected_shape) expected_slice = tf.constant( [[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]] ) self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3], expected_slice, atol=1e-4))