# Copyright (c) 2023 PaddlePaddle Authors. 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. import unittest import paddle from paddle import Tensor from paddlenlp.transformers import ( LayoutLMConfig, LayoutLMForMaskedLM, LayoutLMForSequenceClassification, LayoutLMForTokenClassification, LayoutLMModel, LayoutLMPretrainedModel, ) from ...testing_utils import slow from ..test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask class LayoutLMModelTester: """Base LayoutLM Model tester which can test:""" def __init__( self, parent, batch_size=13, seq_length=7, is_training=True, use_input_mask=True, use_token_type_ids=True, use_position_ids=True, vocab_size=103, hidden_size=24, num_hidden_layers=5, 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=2, initializer_range=0.02, pad_token_id=0, type_sequence_label_size=2, num_labels=3, num_choices=4, num_classes=3, scope=None, ): self.parent = parent self.batch_size = batch_size self.seq_length = 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_position_ids = use_position_ids 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.initializer_range = initializer_range self.pad_token_id = pad_token_id self.type_sequence_label_size = type_sequence_label_size self.num_labels = num_labels self.num_choices = num_choices self.num_classes = num_classes self.scope = scope def prepare_config_and_inputs(self): input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size) attention_mask = None if self.use_input_mask: attention_mask = random_attention_mask([self.batch_size, self.seq_length]) position_ids = None if self.use_position_ids: ones = paddle.ones_like(input_ids, dtype="int64") seq_length = paddle.cumsum(ones, axis=1) position_ids = seq_length - ones bbox = paddle.expand(paddle.to_tensor([0, 0, 0, 0]), [self.batch_size, self.seq_length, 4]) config = self.get_config() return config, input_ids, position_ids, attention_mask, bbox def get_config(self): return LayoutLMConfig( 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, pad_token_id=self.pad_token_id, num_class=self.num_classes, num_labels=self.num_labels, ) def prepare_config_and_inputs_for_common(self): config, input_ids, position_ids, attention_mask, bbox = self.prepare_config_and_inputs() inputs_dict = { "input_ids": input_ids, "position_ids": position_ids, "attention_mask": attention_mask, "bbox": bbox, } return config, inputs_dict def create_and_check_model( self, config: LayoutLMConfig, input_ids: Tensor, position_ids: Tensor, attention_mask: Tensor, bbox: Tensor ): model = LayoutLMModel(config) model.eval() result = model(input_ids, attention_mask=attention_mask, position_ids=position_ids, bbox=bbox) self.parent.assertEqual(result[0].shape, [self.batch_size, self.seq_length, self.hidden_size]) self.parent.assertEqual(result[1].shape, [self.batch_size, self.hidden_size]) def create_and_check_for_token_classification( self, config: LayoutLMConfig, input_ids: Tensor, position_ids: Tensor, attention_mask: Tensor, bbox: Tensor ): model = LayoutLMForTokenClassification(config) model.eval() result = model( input_ids, attention_mask=attention_mask, position_ids=position_ids, bbox=bbox, ) if paddle.is_tensor(result): result = [result] self.parent.assertEqual(result[0].shape, [self.batch_size, self.seq_length, self.num_classes]) def create_and_check_for_sequence_classification( self, config: LayoutLMConfig, input_ids: Tensor, position_ids: Tensor, attention_mask: Tensor, bbox: Tensor ): model = LayoutLMForSequenceClassification(config) model.eval() result = model( input_ids, attention_mask=attention_mask, position_ids=position_ids, bbox=bbox, ) if paddle.is_tensor(result): result = [result] self.parent.assertEqual(result[0].shape, [self.batch_size, self.num_classes]) def create_and_check_for_masked_lm( self, config: LayoutLMConfig, input_ids: Tensor, position_ids: Tensor, attention_mask: Tensor, bbox: Tensor ): model = LayoutLMForMaskedLM(config) model.eval() result = model( input_ids, attention_mask=attention_mask, position_ids=position_ids, bbox=bbox, ) if paddle.is_tensor(result): result = [result] self.parent.assertEqual(result[0].shape, [self.batch_size, self.seq_length, self.vocab_size]) class LayoutLMModelModelTest(ModelTesterMixin, unittest.TestCase): base_model_class = LayoutLMModel use_labels = False return_dict = False all_model_classes = ( LayoutLMModel, LayoutLMForTokenClassification, LayoutLMForSequenceClassification, LayoutLMForMaskedLM, ) def setUp(self): self.model_tester = LayoutLMModelTester(self) # set attribute in setUp to overwrite the static attribute self.test_resize_embeddings = False 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_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_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_masked_lm(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_masked_lm(*config_and_inputs) @slow @unittest.skip("Skip for miss model weight.") def test_model_from_pretrained(self): for model_name in list(LayoutLMPretrainedModel.pretrained_init_configuration)[:1]: model = LayoutLMModel.from_pretrained(model_name) self.assertIsNotNone(model) if __name__ == "__main__": unittest.main()