414 lines
16 KiB
Python
414 lines
16 KiB
Python
# coding=utf-8
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# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
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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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"""Testing suite for the PyTorch LayoutLMv3 model."""
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import copy
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import unittest
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from transformers.models.auto import get_values
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from transformers.testing_utils import require_torch, slow, torch_device
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from transformers.utils import cached_property, is_torch_available, is_vision_available
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from ...test_configuration_common import ConfigTester
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from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
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from ...test_pipeline_mixin import PipelineTesterMixin
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if is_torch_available():
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import torch
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from transformers import (
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MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
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MODEL_FOR_QUESTION_ANSWERING_MAPPING,
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MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
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MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
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LayoutLMv3Config,
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LayoutLMv3ForQuestionAnswering,
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LayoutLMv3ForSequenceClassification,
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LayoutLMv3ForTokenClassification,
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LayoutLMv3Model,
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)
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if is_vision_available():
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from PIL import Image
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from transformers import LayoutLMv3ImageProcessor
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class LayoutLMv3ModelTester:
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def __init__(
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self,
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parent,
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batch_size=2,
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num_channels=3,
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image_size=4,
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patch_size=2,
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text_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=36,
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num_hidden_layers=2,
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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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coordinate_size=6,
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shape_size=6,
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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.num_channels = num_channels
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self.image_size = image_size
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self.patch_size = patch_size
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self.text_seq_length = text_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.coordinate_size = coordinate_size
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self.shape_size = shape_size
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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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# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
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self.text_seq_length = text_seq_length
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self.image_seq_length = (image_size // patch_size) ** 2 + 1
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self.seq_length = self.text_seq_length + self.image_seq_length
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def prepare_config_and_inputs(self):
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input_ids = ids_tensor([self.batch_size, self.text_seq_length], self.vocab_size)
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bbox = ids_tensor([self.batch_size, self.text_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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pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
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input_mask = None
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if self.use_input_mask:
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input_mask = random_attention_mask([self.batch_size, self.text_seq_length])
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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.text_seq_length], self.type_vocab_size)
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sequence_labels = None
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token_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.text_seq_length], self.num_labels)
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config = LayoutLMv3Config(
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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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coordinate_size=self.coordinate_size,
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shape_size=self.shape_size,
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input_size=self.image_size,
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patch_size=self.patch_size,
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)
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return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels
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def create_and_check_model(
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self, config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels
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):
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model = LayoutLMv3Model(config=config)
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model.to(torch_device)
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model.eval()
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# text + image
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result = model(input_ids, pixel_values=pixel_values)
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result = model(
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input_ids, bbox=bbox, pixel_values=pixel_values, attention_mask=input_mask, token_type_ids=token_type_ids
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)
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result = model(input_ids, bbox=bbox, pixel_values=pixel_values, token_type_ids=token_type_ids)
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result = model(input_ids, bbox=bbox, pixel_values=pixel_values)
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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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# text only
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result = model(input_ids)
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self.parent.assertEqual(
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result.last_hidden_state.shape, (self.batch_size, self.text_seq_length, self.hidden_size)
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)
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# image only
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result = model(pixel_values=pixel_values)
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self.parent.assertEqual(
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result.last_hidden_state.shape, (self.batch_size, self.image_seq_length, self.hidden_size)
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)
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def create_and_check_for_sequence_classification(
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self, config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels
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):
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config.num_labels = self.num_labels
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model = LayoutLMv3ForSequenceClassification(config)
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model.to(torch_device)
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model.eval()
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result = model(
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input_ids,
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bbox=bbox,
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pixel_values=pixel_values,
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attention_mask=input_mask,
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token_type_ids=token_type_ids,
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labels=sequence_labels,
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)
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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, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels
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):
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config.num_labels = self.num_labels
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model = LayoutLMv3ForTokenClassification(config=config)
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model.to(torch_device)
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model.eval()
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result = model(
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input_ids,
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bbox=bbox,
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pixel_values=pixel_values,
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attention_mask=input_mask,
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token_type_ids=token_type_ids,
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labels=token_labels,
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)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.text_seq_length, self.num_labels))
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def create_and_check_for_question_answering(
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self, config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels
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):
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model = LayoutLMv3ForQuestionAnswering(config=config)
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model.to(torch_device)
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model.eval()
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result = model(
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input_ids,
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bbox=bbox,
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pixel_values=pixel_values,
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attention_mask=input_mask,
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token_type_ids=token_type_ids,
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start_positions=sequence_labels,
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end_positions=sequence_labels,
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)
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self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length))
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self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length))
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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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pixel_values,
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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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) = 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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"pixel_values": pixel_values,
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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 LayoutLMv3ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
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test_pruning = False
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test_torchscript = False
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test_mismatched_shapes = False
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all_model_classes = (
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(
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LayoutLMv3Model,
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LayoutLMv3ForSequenceClassification,
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LayoutLMv3ForTokenClassification,
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LayoutLMv3ForQuestionAnswering,
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)
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if is_torch_available()
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else ()
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)
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pipeline_model_mapping = (
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{"document-question-answering": LayoutLMv3ForQuestionAnswering, "feature-extraction": LayoutLMv3Model}
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if is_torch_available()
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else {}
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)
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# TODO: Fix the failed tests
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def is_pipeline_test_to_skip(
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self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
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):
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# `DocumentQuestionAnsweringPipeline` is expected to work with this model, but it combines the text and visual
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# embedding along the sequence dimension (dim 1), which causes an error during post-processing as `p_mask` has
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# the sequence dimension of the text embedding only.
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# (see the line `embedding_output = torch.cat([embedding_output, visual_embeddings], dim=1)`)
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return True
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def setUp(self):
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self.model_tester = LayoutLMv3ModelTester(self)
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self.config_tester = ConfigTester(self, config_class=LayoutLMv3Config, hidden_size=37)
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def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
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inputs_dict = copy.deepcopy(inputs_dict)
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if model_class in get_values(MODEL_FOR_MULTIPLE_CHOICE_MAPPING):
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inputs_dict = {
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k: v.unsqueeze(1).expand(-1, self.model_tester.num_choices, -1).contiguous()
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if isinstance(v, torch.Tensor) and v.ndim > 1
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else v
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for k, v in inputs_dict.items()
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}
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if return_labels:
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if model_class in get_values(MODEL_FOR_MULTIPLE_CHOICE_MAPPING):
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inputs_dict["labels"] = torch.ones(self.model_tester.batch_size, dtype=torch.long, device=torch_device)
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elif model_class in get_values(MODEL_FOR_QUESTION_ANSWERING_MAPPING):
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inputs_dict["start_positions"] = torch.zeros(
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self.model_tester.batch_size, dtype=torch.long, device=torch_device
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)
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inputs_dict["end_positions"] = torch.zeros(
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self.model_tester.batch_size, dtype=torch.long, device=torch_device
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)
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elif model_class in [
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*get_values(MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING),
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]:
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inputs_dict["labels"] = torch.zeros(
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self.model_tester.batch_size, dtype=torch.long, device=torch_device
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)
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elif model_class in [
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*get_values(MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING),
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]:
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inputs_dict["labels"] = torch.zeros(
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(self.model_tester.batch_size, self.model_tester.text_seq_length),
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dtype=torch.long,
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device=torch_device,
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)
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return inputs_dict
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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_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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def test_for_question_answering(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_question_answering(*config_and_inputs)
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@slow
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def test_model_from_pretrained(self):
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model_name = "microsoft/layoutlmv3-base"
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model = LayoutLMv3Model.from_pretrained(model_name)
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self.assertIsNotNone(model)
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# We will verify our results on an image of cute cats
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def prepare_img():
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image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
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return image
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@require_torch
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class LayoutLMv3ModelIntegrationTest(unittest.TestCase):
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@cached_property
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def default_image_processor(self):
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return LayoutLMv3ImageProcessor(apply_ocr=False) if is_vision_available() else None
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@slow
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def test_inference_no_head(self):
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model = LayoutLMv3Model.from_pretrained("microsoft/layoutlmv3-base").to(torch_device)
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image_processor = self.default_image_processor
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image = prepare_img()
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pixel_values = image_processor(images=image, return_tensors="pt").pixel_values.to(torch_device)
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input_ids = torch.tensor([[1, 2]])
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bbox = torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]]).unsqueeze(0)
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# forward pass
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outputs = model(
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input_ids=input_ids.to(torch_device),
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bbox=bbox.to(torch_device),
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pixel_values=pixel_values.to(torch_device),
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)
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# verify the logits
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expected_shape = torch.Size((1, 199, 768))
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self.assertEqual(outputs.last_hidden_state.shape, expected_shape)
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expected_slice = torch.tensor(
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[[-0.0529, 0.3618, 0.1632], [-0.1587, -0.1667, -0.0400], [-0.1557, -0.1671, -0.0505]]
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).to(torch_device)
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self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :3, :3], expected_slice, atol=1e-4))
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