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- # coding=utf-8
- # Copyright 2018 The Microsoft Research Asia LayoutLM Team Authors, The Hugging Face Team.
- #
- # 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 numpy as np
-
- from transformers import LayoutLMConfig, is_tf_available
- from transformers.testing_utils import require_tf, slow
-
- from .test_configuration_common import ConfigTester
- from .test_modeling_tf_common import TFModelTesterMixin, ids_tensor
-
-
- if is_tf_available():
- import tensorflow as tf
-
- from transformers.models.layoutlm.modeling_tf_layoutlm import (
- TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
- TFLayoutLMForMaskedLM,
- TFLayoutLMForSequenceClassification,
- TFLayoutLMForTokenClassification,
- TFLayoutLMModel,
- )
-
-
- class TFLayoutLMModelTester:
- def __init__(
- self,
- parent,
- batch_size=13,
- seq_length=7,
- is_training=True,
- use_input_mask=True,
- use_token_type_ids=True,
- use_labels=True,
- vocab_size=99,
- hidden_size=32,
- 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=16,
- type_sequence_label_size=2,
- initializer_range=0.02,
- num_labels=3,
- num_choices=4,
- scope=None,
- range_bbox=1000,
- ):
- 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_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.num_labels = num_labels
- self.num_choices = num_choices
- self.scope = scope
- self.range_bbox = range_bbox
-
- def prepare_config_and_inputs(self):
- input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
-
- # convert bbox to numpy since TF does not support item assignment
- bbox = ids_tensor([self.batch_size, self.seq_length, 4], self.range_bbox).numpy()
- # 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
- bbox = tf.convert_to_tensor(bbox)
-
- input_mask = None
- if self.use_input_mask:
- input_mask = ids_tensor([self.batch_size, self.seq_length], vocab_size=2)
-
- token_type_ids = None
- if self.use_token_type_ids:
- token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
-
- sequence_labels = None
- token_labels = None
- choice_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.seq_length], self.num_labels)
- choice_labels = ids_tensor([self.batch_size], self.num_choices)
-
- config = 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,
- )
-
- return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
-
- def create_and_check_model(
- self, config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
- ):
- model = TFLayoutLMModel(config=config)
-
- result = model(input_ids, bbox, attention_mask=input_mask, token_type_ids=token_type_ids)
- result = model(input_ids, bbox, token_type_ids=token_type_ids)
- result = model(input_ids, bbox)
- self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
- self.parent.assertEqual(result.pooler_output.shape, (self.batch_size, self.hidden_size))
-
- def create_and_check_for_masked_lm(
- self, config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
- ):
- model = TFLayoutLMForMaskedLM(config=config)
-
- result = model(input_ids, bbox, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
- self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
-
- def create_and_check_for_sequence_classification(
- self, config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
- ):
- config.num_labels = self.num_labels
- model = TFLayoutLMForSequenceClassification(config=config)
-
- result = model(input_ids, bbox, attention_mask=input_mask, token_type_ids=token_type_ids)
- self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels))
-
- def create_and_check_for_token_classification(
- self, config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
- ):
- config.num_labels = self.num_labels
- model = TFLayoutLMForTokenClassification(config=config)
-
- result = model(input_ids, bbox, attention_mask=input_mask, token_type_ids=token_type_ids, labels=token_labels)
- self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
-
- def prepare_config_and_inputs_for_common(self):
- config_and_inputs = self.prepare_config_and_inputs()
- (
- config,
- input_ids,
- bbox,
- token_type_ids,
- input_mask,
- sequence_labels,
- token_labels,
- choice_labels,
- ) = config_and_inputs
- inputs_dict = {
- "input_ids": input_ids,
- "bbox": bbox,
- "token_type_ids": token_type_ids,
- "attention_mask": input_mask,
- }
- return config, inputs_dict
-
-
- @require_tf
- class LayoutLMModelTest(TFModelTesterMixin, unittest.TestCase):
-
- all_model_classes = (
- (TFLayoutLMModel, TFLayoutLMForMaskedLM, TFLayoutLMForTokenClassification, TFLayoutLMForSequenceClassification)
- if is_tf_available()
- else ()
- )
- test_head_masking = False
- test_onnx = True
- onnx_min_opset = 10
-
- def setUp(self):
- self.model_tester = TFLayoutLMModelTester(self)
- self.config_tester = ConfigTester(self, config_class=LayoutLMConfig, hidden_size=37)
-
- 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_model_various_embeddings(self):
- config_and_inputs = self.model_tester.prepare_config_and_inputs()
- for type in ["absolute", "relative_key", "relative_key_query"]:
- config_and_inputs[0].position_embedding_type = type
- self.model_tester.create_and_check_model(*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)
-
- 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)
-
- @slow
- def test_model_from_pretrained(self):
- for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
- model = TFLayoutLMModel.from_pretrained(model_name)
- self.assertIsNotNone(model)
-
-
- def prepare_layoutlm_batch_inputs():
- # Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
- # fmt: off
- 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
- 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
- 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
- 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
- # these are sequence labels (i.e. at the token level)
- 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
- # fmt: on
-
- return input_ids, attention_mask, bbox, token_type_ids, labels
-
-
- @require_tf
- class TFLayoutLMModelIntegrationTest(unittest.TestCase):
- @slow
- def test_forward_pass_no_head(self):
- model = TFLayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased")
-
- input_ids, attention_mask, bbox, token_type_ids, labels = prepare_layoutlm_batch_inputs()
-
- # forward pass
- outputs = model(input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids)
-
- # test the sequence output on [0, :3, :3]
- expected_slice = tf.convert_to_tensor(
- [[0.1785, -0.1947, -0.0425], [-0.3254, -0.2807, 0.2553], [-0.5391, -0.3322, 0.3364]],
- )
-
- self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3], expected_slice, atol=1e-3))
-
- # test the pooled output on [1, :3]
- expected_slice = tf.convert_to_tensor([-0.6580, -0.0214, 0.8552])
-
- self.assertTrue(np.allclose(outputs.pooler_output[1, :3], expected_slice, atol=1e-3))
-
- @slow
- def test_forward_pass_sequence_classification(self):
- # initialize model with randomly initialized sequence classification head
- model = TFLayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased", num_labels=2)
-
- input_ids, attention_mask, bbox, token_type_ids, _ = prepare_layoutlm_batch_inputs()
-
- # forward pass
- outputs = model(
- input_ids=input_ids,
- bbox=bbox,
- attention_mask=attention_mask,
- token_type_ids=token_type_ids,
- labels=tf.convert_to_tensor([1, 1]),
- )
-
- # test whether we get a loss as a scalar
- loss = outputs.loss
- expected_shape = (2,)
- self.assertEqual(loss.shape, expected_shape)
-
- # test the shape of the logits
- logits = outputs.logits
- expected_shape = (2, 2)
- self.assertEqual(logits.shape, expected_shape)
-
- @slow
- def test_forward_pass_token_classification(self):
- # initialize model with randomly initialized token classification head
- model = TFLayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased", num_labels=13)
-
- input_ids, attention_mask, bbox, token_type_ids, labels = prepare_layoutlm_batch_inputs()
-
- # forward pass
- outputs = model(
- input_ids=input_ids, bbox=bbox, attention_mask=attention_mask, token_type_ids=token_type_ids, labels=labels
- )
-
- # test the shape of the logits
- logits = outputs.logits
- expected_shape = tf.convert_to_tensor((2, 25, 13))
- self.assertEqual(logits.shape, expected_shape)
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