SUMIN/HuggingFace_transformer
1
0
Fork 0
Code Issues Pull Requests Actions 7 Packages Projects Releases Wiki Activity

Layout lm tf 2 (#10636)

Browse Source 
* 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
2021-03-25 09:32:38 -07:00
committed by GitHub
parent 1a3e0c4fe6
commit 4684bfc757
10 changed files with 1782 additions and 2 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
docs/source/index.rst
View File

@@ -281,7 +281,7 @@ TensorFlow and/or Flax.
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
| LXMERT | ✅ | ✅ | ✅ | ✅ | ❌ |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
| LayoutLM | ✅ | ✅ | ✅ | ❌ | ❌ |
| LayoutLM | ✅ | ✅ | ✅ | ✅ | ❌ |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+
| Longformer | ✅ | ✅ | ✅ | ✅ | ❌ |
+-----------------------------+----------------+----------------+-----------------+--------------------+--------------+

28
docs/source/model_doc/layoutlm.rst
View File

@@ -130,3 +130,31 @@ LayoutLMForTokenClassification
.. autoclass:: transformers.LayoutLMForTokenClassification
:members:
TFLayoutLMModel
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.TFLayoutLMModel
:members:
TFLayoutLMForMaskedLM
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.TFLayoutLMForMaskedLM
:members:
TFLayoutLMForSequenceClassification
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.TFLayoutLMForSequenceClassification
:members:
TFLayoutLMForTokenClassification
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
.. autoclass:: transformers.TFLayoutLMForTokenClassification
:members:

20
src/transformers/__init__.py
View File

@@ -1214,6 +1214,17 @@ if is_tf_available():
"TFXLMRobertaModel",
]
)
_import_structure["models.layoutlm"].extend(
[
"TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFLayoutLMForMaskedLM",
"TFLayoutLMForSequenceClassification",
"TFLayoutLMForTokenClassification",
"TFLaoutLMMainLayer",
"TFLayoutLMModel",
"TFLayoutLMPreTrainedModel",
]
)
_import_structure["models.xlnet"].extend(
[
"TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST",
@@ -2010,6 +2021,15 @@ if TYPE_CHECKING:
# Benchmarks
from .benchmark.benchmark_tf import TensorFlowBenchmark
from .generation_tf_utils import tf_top_k_top_p_filtering
from .modeling_tf_layoutlm import (
TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLayoutLMForMaskedLM,
TFLayoutLMForSequenceClassification,
TFLayoutLMForTokenClassification,
TFLayoutLMMainLayer,
TFLayoutLMModel,
TFLayoutLMPreTrainedModel,
)
from .modeling_tf_utils import TFPreTrainedModel, TFSequenceSummary, TFSharedEmbeddings, shape_list
from .models.albert import (
TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,

10
src/transformers/convert_pytorch_checkpoint_to_tf2.py
View File

@@ -31,6 +31,7 @@ from . import (
ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP,
FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP,
LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP,
ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
@@ -50,6 +51,7 @@ from . import (
ElectraConfig,
FlaubertConfig,
GPT2Config,
LayoutLMConfig,
LxmertConfig,
OpenAIGPTConfig,
RobertaConfig,
@@ -69,6 +71,7 @@ from . import (
TFElectraForPreTraining,
TFFlaubertWithLMHeadModel,
TFGPT2LMHeadModel,
TFLayoutLMForMaskedLM,
TFLxmertForPreTraining,
TFLxmertVisualFeatureEncoder,
TFOpenAIGPTLMHeadModel,
@@ -111,6 +114,7 @@ if is_torch_available():
ElectraForPreTraining,
FlaubertWithLMHeadModel,
GPT2LMHeadModel,
LayoutLMForMaskedLM,
LxmertForPreTraining,
LxmertVisualFeatureEncoder,
OpenAIGPTLMHeadModel,
@@ -211,6 +215,12 @@ MODEL_CLASSES = {
RobertaForMaskedLM,
ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
),
"layoutlm": (
LayoutLMConfig,
TFLayoutLMForMaskedLM,
LayoutLMForMaskedLM,
LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
),
"roberta-large-mnli": (
RobertaConfig,
TFRobertaForSequenceClassification,

1
src/transformers/modeling_tf_pytorch_utils.py
View File

@@ -333,6 +333,7 @@ def load_tf2_weights_in_pytorch_model(pt_model, tf_weights, allow_missing_keys=F
all_tf_weights = set(list(tf_weights_map.keys()))
loaded_pt_weights_data_ptr = {}
missing_keys_pt = []
for pt_weight_name, pt_weight in current_pt_params_dict.items():
# Handle PyTorch shared weight ()not duplicated in TF 2.0
if pt_weight.data_ptr() in loaded_pt_weights_data_ptr:

13
src/transformers/models/auto/modeling_tf_auto.py
View File

@@ -102,6 +102,12 @@ from ..funnel.modeling_tf_funnel import (
TFFunnelModel,
)
from ..gpt2.modeling_tf_gpt2 import TFGPT2ForSequenceClassification, TFGPT2LMHeadModel, TFGPT2Model
from ..layoutlm.modeling_tf_layoutlm import (
TFLayoutLMForMaskedLM,
TFLayoutLMForSequenceClassification,
TFLayoutLMForTokenClassification,
TFLayoutLMModel,
)
from ..led.modeling_tf_led import TFLEDForConditionalGeneration, TFLEDModel
from ..longformer.modeling_tf_longformer import (
TFLongformerForMaskedLM,
@@ -189,6 +195,7 @@ from .configuration_auto import (
FlaubertConfig,
FunnelConfig,
GPT2Config,
LayoutLMConfig,
LEDConfig,
LongformerConfig,
LxmertConfig,
@@ -227,6 +234,7 @@ TF_MODEL_MAPPING = OrderedDict(
(XLMRobertaConfig, TFXLMRobertaModel),
(LongformerConfig, TFLongformerModel),
(RobertaConfig, TFRobertaModel),
(LayoutLMConfig, TFLayoutLMModel),
(BertConfig, TFBertModel),
(OpenAIGPTConfig, TFOpenAIGPTModel),
(GPT2Config, TFGPT2Model),
@@ -260,6 +268,7 @@ TF_MODEL_FOR_PRETRAINING_MAPPING = OrderedDict(
(CamembertConfig, TFCamembertForMaskedLM),
(XLMRobertaConfig, TFXLMRobertaForMaskedLM),
(RobertaConfig, TFRobertaForMaskedLM),
(LayoutLMConfig, TFLayoutLMForMaskedLM),
(BertConfig, TFBertForPreTraining),
(OpenAIGPTConfig, TFOpenAIGPTLMHeadModel),
(GPT2Config, TFGPT2LMHeadModel),
@@ -289,6 +298,7 @@ TF_MODEL_WITH_LM_HEAD_MAPPING = OrderedDict(
(XLMRobertaConfig, TFXLMRobertaForMaskedLM),
(LongformerConfig, TFLongformerForMaskedLM),
(RobertaConfig, TFRobertaForMaskedLM),
(LayoutLMConfig, TFLayoutLMForMaskedLM),
(BertConfig, TFBertForMaskedLM),
(OpenAIGPTConfig, TFOpenAIGPTLMHeadModel),
(GPT2Config, TFGPT2LMHeadModel),
@@ -330,6 +340,7 @@ TF_MODEL_FOR_MASKED_LM_MAPPING = OrderedDict(
(XLMRobertaConfig, TFXLMRobertaForMaskedLM),
(LongformerConfig, TFLongformerForMaskedLM),
(RobertaConfig, TFRobertaForMaskedLM),
(LayoutLMConfig, TFLayoutLMForMaskedLM),
(BertConfig, TFBertForMaskedLM),
(MobileBertConfig, TFMobileBertForMaskedLM),
(FlaubertConfig, TFFlaubertWithLMHeadModel),
@@ -366,6 +377,7 @@ TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING = OrderedDict(
(XLMRobertaConfig, TFXLMRobertaForSequenceClassification),
(LongformerConfig, TFLongformerForSequenceClassification),
(RobertaConfig, TFRobertaForSequenceClassification),
(LayoutLMConfig, TFLayoutLMForSequenceClassification),
(BertConfig, TFBertForSequenceClassification),
(XLNetConfig, TFXLNetForSequenceClassification),
(MobileBertConfig, TFMobileBertForSequenceClassification),
@@ -414,6 +426,7 @@ TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING = OrderedDict(
(XLMRobertaConfig, TFXLMRobertaForTokenClassification),
(LongformerConfig, TFLongformerForTokenClassification),
(RobertaConfig, TFRobertaForTokenClassification),
(LayoutLMConfig, TFLayoutLMForTokenClassification),
(BertConfig, TFBertForTokenClassification),
(MobileBertConfig, TFMobileBertForTokenClassification),
(XLNetConfig, TFXLNetForTokenClassification),

25
src/transformers/models/layoutlm/__init__.py
View File

@@ -18,7 +18,9 @@
from typing import TYPE_CHECKING
from ...file_utils import _BaseLazyModule, is_tokenizers_available, is_torch_available
from ...file_utils import _BaseLazyModule, is_tf_available, is_tokenizers_available, is_torch_available
from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig
from .tokenization_layoutlm import LayoutLMTokenizer
_import_structure = {
@@ -38,6 +40,17 @@ if is_torch_available():
"LayoutLMModel",
]
if is_tf_available():
_import_structure["modeling_tf_layoutlm"] = [
"TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFLayoutLMForMaskedLM",
"TFLayoutLMForTokenClassification",
"TFLayoutLMForSequenceClassification",
"TFLayoutLMMainLayer",
"TFLayoutLMModel",
"TFLayoutLMPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_layoutlm import LAYOUTLM_PRETRAINED_CONFIG_ARCHIVE_MAP, LayoutLMConfig
@@ -54,6 +67,16 @@ if TYPE_CHECKING:
LayoutLMForTokenClassification,
LayoutLMModel,
)
if is_tf_available():
from .modeling_tf_layoutlm import (
TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLayoutLMForMaskedLM,
TFLayoutLMForSequenceClassification,
TFLayoutLMForTokenClassification,
TFLayoutLMMainLayer,
TFLayoutLMModel,
TFLayoutLMPreTrainedModel,
)
else:
import importlib

1308
src/transformers/models/layoutlm/modeling_tf_layoutlm.py Normal file
View File

File diff suppressed because it is too large Load Diff

53
src/transformers/utils/dummy_tf_objects.py
View File

@@ -16,6 +16,59 @@ def tf_top_k_top_p_filtering(*args, **kwargs):
requires_tf(tf_top_k_top_p_filtering)
TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST = None
class TFLayoutLMForMaskedLM:
def __init__(self, *args, **kwargs):
requires_tf(self)
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_tf(self)
class TFLayoutLMForSequenceClassification:
def __init__(self, *args, **kwargs):
requires_tf(self)
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_tf(self)
class TFLayoutLMForTokenClassification:
def __init__(self, *args, **kwargs):
requires_tf(self)
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_tf(self)
class TFLayoutLMMainLayer:
def __init__(self, *args, **kwargs):
requires_tf(self)
class TFLayoutLMModel:
def __init__(self, *args, **kwargs):
requires_tf(self)
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_tf(self)
class TFLayoutLMPreTrainedModel:
def __init__(self, *args, **kwargs):
requires_tf(self)
@classmethod
def from_pretrained(self, *args, **kwargs):
requires_tf(self)
class TFPreTrainedModel:
def __init__(self, *args, **kwargs):
requires_tf(self)

324
tests/test_modeling_tf_layoutlm.py Normal file
View File

@@ -0,0 +1,324 @@
# 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)