239 lines
9.2 KiB
Python
239 lines
9.2 KiB
Python
# coding=utf-8
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# Copyright 2018 The Microsoft Research Asia LayoutLM Team Authors.
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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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from transformers import is_torch_available
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from transformers.file_utils import cached_property
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from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
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from .test_configuration_common import ConfigTester
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from .test_modeling_common import ModelTesterMixin, ids_tensor
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if is_torch_available():
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from transformers import LayoutLMConfig, LayoutLMForMaskedLM, LayoutLMForTokenClassification, LayoutLMModel
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class LayoutLMModelTester:
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"""You can also import this e.g from .test_modeling_bart import BartModelTester """
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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_labels=True,
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vocab_size=99,
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hidden_size=32,
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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=16,
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type_sequence_label_size=2,
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initializer_range=0.02,
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num_labels=3,
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num_choices=4,
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scope=None,
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range_bbox=1000,
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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_labels = use_labels
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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.type_sequence_label_size = type_sequence_label_size
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self.initializer_range = initializer_range
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self.num_labels = num_labels
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self.num_choices = num_choices
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self.scope = scope
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self.range_bbox = range_bbox
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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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bbox = ids_tensor([self.batch_size, self.seq_length, 4], self.range_bbox)
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# Ensure that bbox is legal
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for i in range(bbox.shape[0]):
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for j in range(bbox.shape[1]):
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if bbox[i, j, 3] < bbox[i, j, 1]:
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t = bbox[i, j, 3]
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bbox[i, j, 3] = bbox[i, j, 1]
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bbox[i, j, 1] = t
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if bbox[i, j, 2] < bbox[i, j, 0]:
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t = bbox[i, j, 2]
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bbox[i, j, 2] = bbox[i, j, 0]
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bbox[i, j, 0] = t
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input_mask = None
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if self.use_input_mask:
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input_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
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token_type_ids = None
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if self.use_token_type_ids:
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token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
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sequence_labels = None
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token_labels = None
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choice_labels = None
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if self.use_labels:
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sequence_labels = ids_tensor([self.batch_size], self.type_sequence_label_size)
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token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
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choice_labels = ids_tensor([self.batch_size], self.num_choices)
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config = 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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)
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return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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def create_and_check_model(
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self, config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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model = LayoutLMModel(config=config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, bbox, attention_mask=input_mask, token_type_ids=token_type_ids)
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result = model(input_ids, bbox, token_type_ids=token_type_ids)
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result = model(input_ids, bbox)
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self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
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self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
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def create_and_check_for_masked_lm(
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self, config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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model = LayoutLMForMaskedLM(config=config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, bbox, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
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def create_and_check_for_token_classification(
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self, config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
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):
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config.num_labels = self.num_labels
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model = LayoutLMForTokenClassification(config=config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, bbox, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
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def prepare_config_and_inputs_for_common(self):
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config_and_inputs = self.prepare_config_and_inputs()
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(
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config,
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input_ids,
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bbox,
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token_type_ids,
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input_mask,
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sequence_labels,
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token_labels,
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choice_labels,
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) = config_and_inputs
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inputs_dict = {
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"input_ids": input_ids,
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"bbox": bbox,
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"token_type_ids": token_type_ids,
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"attention_mask": input_mask,
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}
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return config, inputs_dict
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@require_torch
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class LayoutLMModelTest(ModelTesterMixin, unittest.TestCase):
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all_model_classes = (
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(LayoutLMModel, LayoutLMForMaskedLM, LayoutLMForTokenClassification) if is_torch_available() else ()
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)
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def setUp(self):
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self.model_tester = LayoutLMModelTester(self)
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self.config_tester = ConfigTester(self, config_class=LayoutLMConfig, hidden_size=37)
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def test_config(self):
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self.config_tester.run_common_tests()
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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_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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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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@cached_property
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def big_model(self):
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"""Cached property means this code will only be executed once."""
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checkpoint_path = "microsoft/layoutlm-large-uncased"
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model = LayoutLMForMaskedLM.from_pretrained(checkpoint_path).to(
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torch_device
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) # test whether AutoModel can determine your model_class from checkpoint name
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if torch_device == "cuda":
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model.half()
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# optional: do more testing! This will save you time later!
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@slow
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def test_that_LayoutLM_can_be_used_in_a_pipeline(self):
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"""We can use self.big_model here without calling __init__ again."""
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pass
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def test_LayoutLM_loss_doesnt_change_if_you_add_padding(self):
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pass
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def test_LayoutLM_bad_args(self):
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pass
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def test_LayoutLM_backward_pass_reduces_loss(self):
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"""Test loss/gradients same as reference implementation, for example."""
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pass
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@require_torch_gpu
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def test_large_inputs_in_fp16_dont_cause_overflow(self):
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pass
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