wehub-resource-sync
241 lines
8.4 KiB
Python
241 lines
8.4 KiB
Python
# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import paddle
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from paddle import Tensor
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from paddlenlp.transformers import (
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LayoutLMConfig,
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LayoutLMForMaskedLM,
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LayoutLMForSequenceClassification,
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LayoutLMForTokenClassification,
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LayoutLMModel,
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LayoutLMPretrainedModel,
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)
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from ...testing_utils import slow
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from ..test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
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class LayoutLMModelTester:
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"""Base LayoutLM Model tester which can test:"""
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def __init__(
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self,
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parent,
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batch_size=13,
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seq_length=7,
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is_training=True,
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use_input_mask=True,
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use_token_type_ids=True,
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use_position_ids=True,
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vocab_size=103,
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hidden_size=24,
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num_hidden_layers=5,
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num_attention_heads=4,
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intermediate_size=37,
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hidden_act="gelu",
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hidden_dropout_prob=0.1,
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attention_probs_dropout_prob=0.1,
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max_position_embeddings=512,
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type_vocab_size=2,
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initializer_range=0.02,
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pad_token_id=0,
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type_sequence_label_size=2,
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num_labels=3,
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num_choices=4,
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num_classes=3,
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scope=None,
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):
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self.parent = parent
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self.batch_size = batch_size
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self.seq_length = seq_length
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self.is_training = is_training
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self.use_input_mask = use_input_mask
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self.use_token_type_ids = use_token_type_ids
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self.use_position_ids = use_position_ids
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.intermediate_size = intermediate_size
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self.hidden_act = hidden_act
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.max_position_embeddings = max_position_embeddings
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self.type_vocab_size = type_vocab_size
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self.initializer_range = initializer_range
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self.pad_token_id = pad_token_id
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self.type_sequence_label_size = type_sequence_label_size
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self.num_labels = num_labels
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self.num_choices = num_choices
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self.num_classes = num_classes
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self.scope = scope
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def prepare_config_and_inputs(self):
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input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
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attention_mask = None
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if self.use_input_mask:
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attention_mask = random_attention_mask([self.batch_size, self.seq_length])
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position_ids = None
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if self.use_position_ids:
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ones = paddle.ones_like(input_ids, dtype="int64")
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seq_length = paddle.cumsum(ones, axis=1)
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position_ids = seq_length - ones
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bbox = paddle.expand(paddle.to_tensor([0, 0, 0, 0]), [self.batch_size, self.seq_length, 4])
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config = self.get_config()
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return config, input_ids, position_ids, attention_mask, bbox
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def get_config(self):
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return LayoutLMConfig(
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vocab_size=self.vocab_size,
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hidden_size=self.hidden_size,
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num_hidden_layers=self.num_hidden_layers,
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num_attention_heads=self.num_attention_heads,
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intermediate_size=self.intermediate_size,
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hidden_act=self.hidden_act,
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hidden_dropout_prob=self.hidden_dropout_prob,
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attention_probs_dropout_prob=self.attention_probs_dropout_prob,
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max_position_embeddings=self.max_position_embeddings,
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type_vocab_size=self.type_vocab_size,
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initializer_range=self.initializer_range,
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pad_token_id=self.pad_token_id,
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num_class=self.num_classes,
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num_labels=self.num_labels,
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)
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def prepare_config_and_inputs_for_common(self):
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config, input_ids, position_ids, attention_mask, bbox = self.prepare_config_and_inputs()
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inputs_dict = {
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"input_ids": input_ids,
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"position_ids": position_ids,
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"attention_mask": attention_mask,
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"bbox": bbox,
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}
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return config, inputs_dict
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def create_and_check_model(
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self, config: LayoutLMConfig, input_ids: Tensor, position_ids: Tensor, attention_mask: Tensor, bbox: Tensor
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):
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model = LayoutLMModel(config)
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model.eval()
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result = model(input_ids, attention_mask=attention_mask, position_ids=position_ids, bbox=bbox)
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self.parent.assertEqual(result[0].shape, [self.batch_size, self.seq_length, self.hidden_size])
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self.parent.assertEqual(result[1].shape, [self.batch_size, self.hidden_size])
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def create_and_check_for_token_classification(
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self, config: LayoutLMConfig, input_ids: Tensor, position_ids: Tensor, attention_mask: Tensor, bbox: Tensor
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):
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model = LayoutLMForTokenClassification(config)
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model.eval()
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result = model(
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input_ids,
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attention_mask=attention_mask,
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position_ids=position_ids,
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bbox=bbox,
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)
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if paddle.is_tensor(result):
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result = [result]
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self.parent.assertEqual(result[0].shape, [self.batch_size, self.seq_length, self.num_classes])
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def create_and_check_for_sequence_classification(
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self, config: LayoutLMConfig, input_ids: Tensor, position_ids: Tensor, attention_mask: Tensor, bbox: Tensor
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):
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model = LayoutLMForSequenceClassification(config)
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model.eval()
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result = model(
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input_ids,
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attention_mask=attention_mask,
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position_ids=position_ids,
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bbox=bbox,
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)
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if paddle.is_tensor(result):
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result = [result]
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self.parent.assertEqual(result[0].shape, [self.batch_size, self.num_classes])
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def create_and_check_for_masked_lm(
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self, config: LayoutLMConfig, input_ids: Tensor, position_ids: Tensor, attention_mask: Tensor, bbox: Tensor
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):
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model = LayoutLMForMaskedLM(config)
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model.eval()
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result = model(
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input_ids,
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attention_mask=attention_mask,
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position_ids=position_ids,
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bbox=bbox,
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)
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if paddle.is_tensor(result):
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result = [result]
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self.parent.assertEqual(result[0].shape, [self.batch_size, self.seq_length, self.vocab_size])
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class LayoutLMModelModelTest(ModelTesterMixin, unittest.TestCase):
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base_model_class = LayoutLMModel
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use_labels = False
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return_dict = False
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all_model_classes = (
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LayoutLMModel,
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LayoutLMForTokenClassification,
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LayoutLMForSequenceClassification,
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LayoutLMForMaskedLM,
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)
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def setUp(self):
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self.model_tester = LayoutLMModelTester(self)
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# set attribute in setUp to overwrite the static attribute
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self.test_resize_embeddings = False
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def test_model(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_model(*config_and_inputs)
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def test_for_token_classification(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
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def test_for_sequence_classification(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_for_sequence_classification(*config_and_inputs)
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def test_for_masked_lm(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_for_masked_lm(*config_and_inputs)
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@slow
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@unittest.skip("Skip for miss model weight.")
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def test_model_from_pretrained(self):
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for model_name in list(LayoutLMPretrainedModel.pretrained_init_configuration)[:1]:
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model = LayoutLMModel.from_pretrained(model_name)
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self.assertIsNotNone(model)
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if __name__ == "__main__":
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unittest.main()
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