Add LayoutLM Model (#7064)

* first version * finish test docs readme model/config/tokenization class * apply make style and make quality * fix layoutlm GitHub link * fix conflict in index.rst and add layoutlm to pretrained_models.rst * fix bug in test_parents_and_children_in_mappings * reformat modeling_auto.py and tokenization_auto.py * fix bug in test_modeling_layoutlm.py * Update docs/source/model_doc/layoutlm.rst Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * Update docs/source/model_doc/layoutlm.rst Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> * remove inh, add tokenizer fast, and update some doc * copy and rename necessary class from modeling_bert to modeling_layoutlm * Update src/transformers/configuration_layoutlm.py Co-authored-by: Lysandre Debut <lysandre@huggingface.co> * Update src/transformers/configuration_layoutlm.py Co-authored-by: Lysandre Debut <lysandre@huggingface.co> * Update src/transformers/configuration_layoutlm.py Co-authored-by: Lysandre Debut <lysandre@huggingface.co> * Update src/transformers/configuration_layoutlm.py Co-authored-by: Lysandre Debut <lysandre@huggingface.co> * Update src/transformers/modeling_layoutlm.py Co-authored-by: Lysandre Debut <lysandre@huggingface.co> * Update src/transformers/modeling_layoutlm.py Co-authored-by: Lysandre Debut <lysandre@huggingface.co> * Update src/transformers/modeling_layoutlm.py Co-authored-by: Lysandre Debut <lysandre@huggingface.co> * add mish to activations.py, import ACT2FN and import logging from utils Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com> Co-authored-by: Lysandre Debut <lysandre@huggingface.co>
2020-09-22 21:28:02 +08:00
parent 244e1b5ba3
commit cd9a0585ea
14 changed files with 1510 additions and 3 deletions
--- a/tests/test_modeling_layoutlm.py
+++ b/tests/test_modeling_layoutlm.py
@@ -0,0 +1,239 @@
+# coding=utf-8
+# Copyright 2018 The Microsoft Research Asia LayoutLM Team Authors.
+#
+# 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
+
+from transformers import is_torch_available
+from transformers.file_utils import cached_property
+from transformers.testing_utils import require_torch, require_torch_and_cuda, slow, torch_device
+
+from .test_configuration_common import ConfigTester
+from .test_modeling_common import ModelTesterMixin, ids_tensor
+
+
+if is_torch_available():
+    from transformers import LayoutLMConfig, LayoutLMForMaskedLM, LayoutLMForTokenClassification, LayoutLMModel
+
+
+class LayoutLMModelTester:
+    """You can also import this e.g from .test_modeling_bart import BartModelTester """
+
+    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)
+
+        bbox = ids_tensor([self.batch_size, self.seq_length, 4], self.range_bbox)
+        # 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
+
+        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_dict=True,
+        )
+
+        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 = LayoutLMModel(config=config)
+        model.to(torch_device)
+        model.eval()
+        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 = LayoutLMForMaskedLM(config=config)
+        model.to(torch_device)
+        model.eval()
+        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_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 = LayoutLMForTokenClassification(config=config)
+        model.to(torch_device)
+        model.eval()
+        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_torch
+class LayoutLMModelTest(ModelTesterMixin, unittest.TestCase):
+
+    all_model_classes = (
+        (LayoutLMModel, LayoutLMForMaskedLM, LayoutLMForTokenClassification) if is_torch_available() else ()
+    )
+
+    def setUp(self):
+        self.model_tester = LayoutLMModelTester(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_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_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)
+
+    @cached_property
+    def big_model(self):
+        """Cached property means this code will only be executed once."""
+        checkpoint_path = "microsoft/layoutlm-large-uncased"
+        model = LayoutLMForMaskedLM.from_pretrained(checkpoint_path).to(
+            torch_device
+        )  # test whether AutoModel can determine your model_class from checkpoint name
+        if torch_device == "cuda":
+            model.half()
+
+    # optional: do more testing! This will save you time later!
+    @slow
+    def test_that_LayoutLM_can_be_used_in_a_pipeline(self):
+        """We can use self.big_model here without calling __init__ again."""
+        pass
+
+    def test_LayoutLM_loss_doesnt_change_if_you_add_padding(self):
+        pass
+
+    def test_LayoutLM_bad_args(self):
+        pass
+
+    def test_LayoutLM_backward_pass_reduces_loss(self):
+        """Test loss/gradients same as reference implementation, for example."""
+        pass
+
+    @require_torch_and_cuda
+    def test_large_inputs_in_fp16_dont_cause_overflow(self):
+        pass