# 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 PyTorch LayoutLMv3 model.""" import copy import unittest from functools import cached_property from transformers.models.auto import get_values from transformers.testing_utils import require_torch, 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, random_attention_mask from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch from transformers import ( MODEL_FOR_MULTIPLE_CHOICE_MAPPING, MODEL_FOR_QUESTION_ANSWERING_MAPPING, MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING, LayoutLMv3Config, LayoutLMv3ForQuestionAnswering, LayoutLMv3ForSequenceClassification, LayoutLMv3ForTokenClassification, LayoutLMv3Model, ) if is_vision_available(): from PIL import Image from transformers import LayoutLMv3ImageProcessor class LayoutLMv3ModelTester: 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.text_seq_length = text_seq_length 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) # 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]: t = bbox[i, j, 3] bbox[i, j, 3] = bbox[i, j, 1] bbox[i, j, 1] = t if bbox[i, j, 2] < bbox[i, j, 0]: t = bbox[i, j, 2] bbox[i, j, 2] = bbox[i, j, 0] bbox[i, j, 0] = t 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, sequence_labels, token_labels ): model = LayoutLMv3Model(config=config) model.to(torch_device) model.eval() # text + image result = model(input_ids, pixel_values=pixel_values) result = model( input_ids, bbox=bbox, pixel_values=pixel_values, attention_mask=input_mask, token_type_ids=token_type_ids ) result = model(input_ids, bbox=bbox, pixel_values=pixel_values, token_type_ids=token_type_ids) result = model(input_ids, bbox=bbox, pixel_values=pixel_values) self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size)) # text only result = model(input_ids) 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) 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, token_labels ): config.num_labels = self.num_labels model = LayoutLMv3ForSequenceClassification(config) model.to(torch_device) model.eval() result = model( input_ids, bbox=bbox, pixel_values=pixel_values, attention_mask=input_mask, token_type_ids=token_type_ids, labels=sequence_labels, ) 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, sequence_labels, token_labels ): config.num_labels = self.num_labels model = LayoutLMv3ForTokenClassification(config=config) model.to(torch_device) model.eval() result = model( input_ids, bbox=bbox, pixel_values=pixel_values, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels, ) 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, token_labels ): model = LayoutLMv3ForQuestionAnswering(config=config) model.to(torch_device) model.eval() 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, ) 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, sequence_labels, token_labels, ) = 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_torch class LayoutLMv3ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): test_mismatched_shapes = False all_model_classes = ( ( LayoutLMv3Model, LayoutLMv3ForSequenceClassification, LayoutLMv3ForTokenClassification, LayoutLMv3ForQuestionAnswering, ) if is_torch_available() else () ) pipeline_model_mapping = ( {"document-question-answering": LayoutLMv3ForQuestionAnswering, "feature-extraction": LayoutLMv3Model} if is_torch_available() else {} ) # TODO: Fix the failed tests def is_pipeline_test_to_skip( self, pipeline_test_case_name, config_class, model_architecture, tokenizer_name, image_processor_name, feature_extractor_name, processor_name, ): # `DocumentQuestionAnsweringPipeline` is expected to work with this model, but it combines the text and visual # embedding along the sequence dimension (dim 1), which causes an error during post-processing as `p_mask` has # the sequence dimension of the text embedding only. # (see the line `embedding_output = torch.cat([embedding_output, visual_embeddings], dim=1)`) return True def setUp(self): self.model_tester = LayoutLMv3ModelTester(self) self.config_tester = ConfigTester(self, config_class=LayoutLMv3Config, hidden_size=37) def _prepare_for_class(self, inputs_dict, model_class, return_labels=False): inputs_dict = copy.deepcopy(inputs_dict) if model_class in get_values(MODEL_FOR_MULTIPLE_CHOICE_MAPPING): inputs_dict = { k: v.unsqueeze(1).expand(-1, self.model_tester.num_choices, -1).contiguous() if isinstance(v, torch.Tensor) and v.ndim > 1 else v for k, v in inputs_dict.items() } if return_labels: if model_class in get_values(MODEL_FOR_MULTIPLE_CHOICE_MAPPING): inputs_dict["labels"] = torch.ones(self.model_tester.batch_size, dtype=torch.long, device=torch_device) elif model_class in get_values(MODEL_FOR_QUESTION_ANSWERING_MAPPING): inputs_dict["start_positions"] = torch.zeros( self.model_tester.batch_size, dtype=torch.long, device=torch_device ) inputs_dict["end_positions"] = torch.zeros( self.model_tester.batch_size, dtype=torch.long, device=torch_device ) elif model_class in [ *get_values(MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING), ]: inputs_dict["labels"] = torch.zeros( self.model_tester.batch_size, dtype=torch.long, device=torch_device ) elif model_class in [ *get_values(MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING), ]: inputs_dict["labels"] = torch.zeros( (self.model_tester.batch_size, self.model_tester.text_seq_length), dtype=torch.long, device=torch_device, ) return inputs_dict def test_config(self): self.config_tester.run_common_tests() 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_sequence_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs) def test_for_token_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_token_classification(*config_and_inputs) def test_for_question_answering(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_question_answering(*config_and_inputs) @slow def test_model_from_pretrained(self): model_name = "microsoft/layoutlmv3-base" model = LayoutLMv3Model.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 class LayoutLMv3ModelIntegrationTest(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 = LayoutLMv3Model.from_pretrained("microsoft/layoutlmv3-base").to(torch_device) image_processor = self.default_image_processor image = prepare_img() pixel_values = image_processor(images=image, return_tensors="pt").pixel_values.to(torch_device) input_ids = torch.tensor([[1, 2]]) bbox = torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]).unsqueeze(0) # forward pass outputs = model( input_ids=input_ids.to(torch_device), bbox=bbox.to(torch_device), pixel_values=pixel_values.to(torch_device), ) # verify the logits expected_shape = torch.Size((1, 199, 768)) self.assertEqual(outputs.last_hidden_state.shape, expected_shape) expected_slice = torch.tensor( [[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]] ).to(torch_device) torch.testing.assert_close(outputs.last_hidden_state[0, :3, :3], expected_slice, rtol=1e-4, atol=1e-4)