Layout lm tf 2 (#10636)
* Added embeddings layer * Added layoutlm layers, main model, maskedlm and token classification classes * Added model classes to tf auto models * Added model to PT to TF conversion script * Added model to doc README * Added tests * Removed unused imports * Added layoutlm model, test, and doc for sequence classification, and fix imports in __init__.py * Made tests pass! * Fixed typos in imports and docs * Fixed a typo in embeddings layer * Removed imports * Fixed formatting issues, imports, tests * Added layoutlm layers, main model, maskedlm and token classification classes * Added model classes to tf auto models * Added model to PT to TF conversion script * Removed unused imports * Added layoutlm model, test, and doc for sequence classification, and fix imports in __init__.py * Made tests pass! * Fixed typos in imports and docs * Removed imports * Fixed small formatting issues * Removed duplicates import from main __init__.py * Chnaged deafult arg to true for adding pooling layer to tf layoutlm * Fixed formatting issues * Style * Added copied from to classes copied from bert * Fixed doc strings examples to work with layoutlm inputs * Removed PyTorch reference in doc strings example * Added integration tests * Cleaned up initialization file * Updated model checkpoint identifiers * Fixed imports Co-authored-by: Amir Tahmasbi <amir@ehsai.ca> Co-authored-by: Lysandre <lysandre.debut@reseau.eseo.fr>
This commit is contained in:
Amir Tahmasbi
324
tests/test_modeling_tf_layoutlm.py
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324
tests/test_modeling_tf_layoutlm.py
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# coding=utf-8
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# Copyright 2018 The Microsoft Research Asia LayoutLM Team Authors, The Hugging Face Team.
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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 numpy as np
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from transformers import LayoutLMConfig, is_tf_available
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from transformers.testing_utils import require_tf, slow
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from .test_configuration_common import ConfigTester
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from .test_modeling_tf_common import TFModelTesterMixin, ids_tensor
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if is_tf_available():
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import tensorflow as tf
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from transformers.models.layoutlm.modeling_tf_layoutlm import (
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TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
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TFLayoutLMForMaskedLM,
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TFLayoutLMForSequenceClassification,
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TFLayoutLMForTokenClassification,
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TFLayoutLMModel,
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)
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class TFLayoutLMModelTester:
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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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# convert bbox to numpy since TF does not support item assignment
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bbox = ids_tensor([self.batch_size, self.seq_length, 4], self.range_bbox).numpy()
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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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bbox = tf.convert_to_tensor(bbox)
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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 = TFLayoutLMModel(config=config)
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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 = TFLayoutLMForMaskedLM(config=config)
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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_sequence_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 = TFLayoutLMForSequenceClassification(config=config)
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result = model(input_ids, bbox, attention_mask=input_mask, token_type_ids=token_type_ids)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
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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 = TFLayoutLMForTokenClassification(config=config)
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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_tf
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class LayoutLMModelTest(TFModelTesterMixin, unittest.TestCase):
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all_model_classes = (
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(TFLayoutLMModel, TFLayoutLMForMaskedLM, TFLayoutLMForTokenClassification, TFLayoutLMForSequenceClassification)
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if is_tf_available()
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else ()
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)
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test_head_masking = False
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test_onnx = True
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onnx_min_opset = 10
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def setUp(self):
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self.model_tester = TFLayoutLMModelTester(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_model_various_embeddings(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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for type in ["absolute", "relative_key", "relative_key_query"]:
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config_and_inputs[0].position_embedding_type = type
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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_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_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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@slow
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def test_model_from_pretrained(self):
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for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
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model = TFLayoutLMModel.from_pretrained(model_name)
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self.assertIsNotNone(model)
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def prepare_layoutlm_batch_inputs():
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# Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
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# fmt: off
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input_ids = tf.convert_to_tensor([[101,1019,1014,1016,1037,12849,4747,1004,14246,2278,5439,4524,5002,2930,2193,2930,4341,3208,1005,1055,2171,2848,11300,3531,102],[101,4070,4034,7020,1024,3058,1015,1013,2861,1013,6070,19274,2772,6205,27814,16147,16147,4343,2047,10283,10969,14389,1012,2338,102]]) # noqa: E231
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attention_mask = tf.convert_to_tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],]) # noqa: E231
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bbox = tf.convert_to_tensor([[[0,0,0,0],[423,237,440,251],[427,272,441,287],[419,115,437,129],[961,885,992,912],[256,38,330,58],[256,38,330,58],[336,42,353,57],[360,39,401,56],[360,39,401,56],[411,39,471,59],[479,41,528,59],[533,39,630,60],[67,113,134,131],[141,115,209,132],[68,149,133,166],[141,149,187,164],[195,148,287,165],[195,148,287,165],[195,148,287,165],[295,148,349,165],[441,149,492,166],[497,149,546,164],[64,201,125,218],[1000,1000,1000,1000]],[[0,0,0,0],[662,150,754,166],[665,199,742,211],[519,213,554,228],[519,213,554,228],[134,433,187,454],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[130,467,204,480],[314,469,376,482],[504,684,582,706],[941,825,973,900],[941,825,973,900],[941,825,973,900],[941,825,973,900],[610,749,652,765],[130,659,168,672],[176,657,237,672],[238,657,312,672],[443,653,628,672],[443,653,628,672],[716,301,825,317],[1000,1000,1000,1000]]]) # noqa: E231
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token_type_ids = tf.convert_to_tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]]) # noqa: E231
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# these are sequence labels (i.e. at the token level)
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labels = tf.convert_to_tensor([[-100,10,10,10,9,1,-100,7,7,-100,7,7,4,2,5,2,8,8,-100,-100,5,0,3,2,-100],[-100,12,12,12,-100,12,10,-100,-100,-100,-100,10,12,9,-100,-100,-100,10,10,10,9,12,-100,10,-100]]) # noqa: E231
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# fmt: on
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return input_ids, attention_mask, bbox, token_type_ids, labels
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@require_tf
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class TFLayoutLMModelIntegrationTest(unittest.TestCase):
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@slow
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def test_forward_pass_no_head(self):
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model = TFLayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased")
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input_ids, attention_mask, bbox, token_type_ids, labels = prepare_layoutlm_batch_inputs()
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# forward pass
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outputs = model(input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids)
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# test the sequence output on [0, :3, :3]
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expected_slice = tf.convert_to_tensor(
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[[0.1785, -0.1947, -0.0425], [-0.3254, -0.2807, 0.2553], [-0.5391, -0.3322, 0.3364]],
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)
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self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3], expected_slice, atol=1e-3))
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# test the pooled output on [1, :3]
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expected_slice = tf.convert_to_tensor([-0.6580, -0.0214, 0.8552])
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self.assertTrue(np.allclose(outputs.pooler_output[1, :3], expected_slice, atol=1e-3))
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@slow
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def test_forward_pass_sequence_classification(self):
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# initialize model with randomly initialized sequence classification head
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model = TFLayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased", num_labels=2)
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input_ids, attention_mask, bbox, token_type_ids, _ = prepare_layoutlm_batch_inputs()
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# forward pass
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outputs = model(
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input_ids=input_ids,
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bbox=bbox,
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attention_mask=attention_mask,
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token_type_ids=token_type_ids,
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labels=tf.convert_to_tensor([1, 1]),
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)
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# test whether we get a loss as a scalar
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loss = outputs.loss
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expected_shape = (2,)
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self.assertEqual(loss.shape, expected_shape)
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# test the shape of the logits
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logits = outputs.logits
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expected_shape = (2, 2)
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self.assertEqual(logits.shape, expected_shape)
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@slow
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def test_forward_pass_token_classification(self):
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# initialize model with randomly initialized token classification head
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model = TFLayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased", num_labels=13)
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input_ids, attention_mask, bbox, token_type_ids, labels = prepare_layoutlm_batch_inputs()
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# forward pass
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outputs = model(
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input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids, labels=labels
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)
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# test the shape of the logits
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logits = outputs.logits
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expected_shape = tf.convert_to_tensor((2, 25, 13))
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self.assertEqual(logits.shape, expected_shape)
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