801 lines
30 KiB
Python
801 lines
30 KiB
Python
# coding=utf-8
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# Copyright 2018 LXMERT 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 copy
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import unittest
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import numpy as np
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from transformers import LxmertConfig, is_tf_available, is_torch_available
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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 ...test_configuration_common import ConfigTester
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from ...test_modeling_common import ModelTesterMixin, ids_tensor
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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_PRETRAINING_MAPPING,
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MODEL_FOR_QUESTION_ANSWERING_MAPPING,
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LxmertForPreTraining,
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LxmertForQuestionAnswering,
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LxmertModel,
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)
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if is_tf_available():
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import tensorflow as tf
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class LxmertModelTester:
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def __init__(
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self,
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parent,
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vocab_size=300,
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hidden_size=28,
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num_attention_heads=2,
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num_labels=2,
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intermediate_size=64,
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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=2,
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initializer_range=0.02,
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layer_norm_eps=1e-12,
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pad_token_id=0,
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num_qa_labels=30,
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num_object_labels=16,
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num_attr_labels=4,
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num_visual_features=10,
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l_layers=2,
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x_layers=1,
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r_layers=1,
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visual_feat_dim=128,
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visual_pos_dim=4,
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visual_loss_normalizer=6.67,
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seq_length=20,
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batch_size=4,
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is_training=True,
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task_matched=True,
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task_mask_lm=True,
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task_obj_predict=True,
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task_qa=True,
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visual_obj_loss=True,
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visual_attr_loss=True,
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visual_feat_loss=True,
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use_token_type_ids=True,
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use_lang_mask=True,
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output_attentions=False,
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output_hidden_states=False,
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scope=None,
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):
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self.parent = parent
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.num_attention_heads = num_attention_heads
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self.num_labels = num_labels
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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.initializer_range = initializer_range
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self.layer_norm_eps = layer_norm_eps
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self.pad_token_id = pad_token_id
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self.num_qa_labels = num_qa_labels
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self.num_object_labels = num_object_labels
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self.num_attr_labels = num_attr_labels
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self.l_layers = l_layers
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self.x_layers = x_layers
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self.r_layers = r_layers
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self.visual_feat_dim = visual_feat_dim
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self.visual_pos_dim = visual_pos_dim
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self.visual_loss_normalizer = visual_loss_normalizer
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self.seq_length = seq_length
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self.batch_size = batch_size
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self.is_training = is_training
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self.use_lang_mask = use_lang_mask
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self.task_matched = task_matched
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self.task_mask_lm = task_mask_lm
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self.task_obj_predict = task_obj_predict
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self.task_qa = task_qa
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self.visual_obj_loss = visual_obj_loss
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self.visual_attr_loss = visual_attr_loss
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self.visual_feat_loss = visual_feat_loss
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self.num_visual_features = num_visual_features
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self.use_token_type_ids = use_token_type_ids
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self.output_attentions = output_attentions
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self.output_hidden_states = output_hidden_states
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self.scope = scope
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self.num_hidden_layers = {"vision": r_layers, "cross_encoder": x_layers, "language": l_layers}
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def prepare_config_and_inputs(self):
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output_attentions = self.output_attentions
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input_ids = ids_tensor([self.batch_size, self.seq_length], vocab_size=self.vocab_size)
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visual_feats = torch.rand(self.batch_size, self.num_visual_features, self.visual_feat_dim, device=torch_device)
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bounding_boxes = torch.rand(self.batch_size, self.num_visual_features, 4, device=torch_device)
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input_mask = None
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if self.use_lang_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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obj_labels = None
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if self.task_obj_predict:
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obj_labels = {}
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if self.visual_attr_loss and self.task_obj_predict:
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obj_labels["attr"] = (
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ids_tensor([self.batch_size, self.num_visual_features], self.num_attr_labels),
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ids_tensor([self.batch_size, self.num_visual_features], self.num_attr_labels),
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)
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if self.visual_feat_loss and self.task_obj_predict:
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obj_labels["feat"] = (
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ids_tensor(
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[self.batch_size, self.num_visual_features, self.visual_feat_dim], self.num_visual_features
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),
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ids_tensor([self.batch_size, self.num_visual_features], self.num_visual_features),
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)
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if self.visual_obj_loss and self.task_obj_predict:
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obj_labels["obj"] = (
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ids_tensor([self.batch_size, self.num_visual_features], self.num_object_labels),
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ids_tensor([self.batch_size, self.num_visual_features], self.num_object_labels),
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)
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ans = None
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if self.task_qa:
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ans = ids_tensor([self.batch_size], self.num_qa_labels)
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masked_lm_labels = None
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if self.task_mask_lm:
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masked_lm_labels = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
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matched_label = None
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if self.task_matched:
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matched_label = ids_tensor([self.batch_size], self.num_labels)
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config = self.get_config()
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return (
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config,
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input_ids,
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visual_feats,
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bounding_boxes,
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token_type_ids,
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input_mask,
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obj_labels,
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masked_lm_labels,
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matched_label,
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ans,
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output_attentions,
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)
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def get_config(self):
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return LxmertConfig(
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vocab_size=self.vocab_size,
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hidden_size=self.hidden_size,
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num_attention_heads=self.num_attention_heads,
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num_labels=self.num_labels,
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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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layer_norm_eps=self.layer_norm_eps,
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pad_token_id=self.pad_token_id,
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num_qa_labels=self.num_qa_labels,
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num_object_labels=self.num_object_labels,
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num_attr_labels=self.num_attr_labels,
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l_layers=self.l_layers,
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x_layers=self.x_layers,
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r_layers=self.r_layers,
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visual_feat_dim=self.visual_feat_dim,
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visual_pos_dim=self.visual_pos_dim,
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visual_loss_normalizer=self.visual_loss_normalizer,
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task_matched=self.task_matched,
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task_mask_lm=self.task_mask_lm,
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task_obj_predict=self.task_obj_predict,
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task_qa=self.task_qa,
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visual_obj_loss=self.visual_obj_loss,
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visual_attr_loss=self.visual_attr_loss,
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visual_feat_loss=self.visual_feat_loss,
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output_attentions=self.output_attentions,
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output_hidden_states=self.output_hidden_states,
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)
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def create_and_check_lxmert_model(
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self,
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config,
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input_ids,
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visual_feats,
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bounding_boxes,
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token_type_ids,
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input_mask,
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obj_labels,
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masked_lm_labels,
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matched_label,
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ans,
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output_attentions,
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):
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model = LxmertModel(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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visual_feats,
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bounding_boxes,
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token_type_ids=token_type_ids,
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attention_mask=input_mask,
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output_attentions=output_attentions,
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)
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result = model(
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input_ids,
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visual_feats,
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bounding_boxes,
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token_type_ids=token_type_ids,
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attention_mask=input_mask,
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output_attentions=not output_attentions,
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)
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result = model(input_ids, visual_feats, bounding_boxes, return_dict=False)
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result = model(input_ids, visual_feats, bounding_boxes, return_dict=True)
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self.parent.assertEqual(result.language_output.shape, (self.batch_size, self.seq_length, self.hidden_size))
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self.parent.assertEqual(
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result.vision_output.shape, (self.batch_size, self.num_visual_features, self.hidden_size)
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)
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self.parent.assertEqual(result.pooled_output.shape, (self.batch_size, self.hidden_size))
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def create_and_check_lxmert_for_question_answering(
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self,
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config,
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input_ids,
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visual_feats,
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bounding_boxes,
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token_type_ids,
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input_mask,
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obj_labels,
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masked_lm_labels,
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matched_label,
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ans,
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output_attentions,
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):
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model = LxmertForQuestionAnswering(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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visual_feats,
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bounding_boxes,
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token_type_ids=token_type_ids,
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attention_mask=input_mask,
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labels=ans,
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output_attentions=output_attentions,
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)
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result = model(input_ids, visual_feats, bounding_boxes, labels=ans)
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result = model(
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input_ids,
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visual_feats,
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bounding_boxes,
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labels=ans,
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token_type_ids=token_type_ids,
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attention_mask=input_mask,
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output_attentions=output_attentions,
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)
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result = model(
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input_ids,
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visual_feats,
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bounding_boxes,
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token_type_ids=token_type_ids,
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attention_mask=input_mask,
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labels=ans,
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output_attentions=not output_attentions,
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)
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self.parent.assertEqual(result.question_answering_score.shape, (self.batch_size, self.num_qa_labels))
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def create_and_check_lxmert_for_pretraining(
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self,
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config,
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input_ids,
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visual_feats,
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bounding_boxes,
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token_type_ids,
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input_mask,
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obj_labels,
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masked_lm_labels,
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matched_label,
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ans,
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output_attentions,
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):
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model = LxmertForPreTraining(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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visual_feats,
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bounding_boxes,
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token_type_ids=token_type_ids,
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attention_mask=input_mask,
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masked_lm_labels=masked_lm_labels,
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obj_labels=obj_labels,
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matched_label=matched_label,
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ans=ans,
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output_attentions=output_attentions,
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)
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result = model(
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input_ids,
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visual_feats,
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bounding_boxes,
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token_type_ids=token_type_ids,
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attention_mask=input_mask,
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masked_lm_labels=masked_lm_labels,
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output_attentions=not output_attentions,
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return_dict=False,
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)
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result = model(
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input_ids,
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visual_feats,
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bounding_boxes,
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token_type_ids=token_type_ids,
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attention_mask=input_mask,
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masked_lm_labels=masked_lm_labels,
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)
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result = model(
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input_ids,
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visual_feats,
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bounding_boxes,
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token_type_ids=token_type_ids,
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attention_mask=input_mask,
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obj_labels=obj_labels,
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)
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result = model(
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input_ids,
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visual_feats,
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bounding_boxes,
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token_type_ids=token_type_ids,
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attention_mask=input_mask,
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matched_label=matched_label,
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)
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result = model(
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input_ids,
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visual_feats,
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bounding_boxes,
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token_type_ids=token_type_ids,
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attention_mask=input_mask,
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ans=ans,
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)
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result = model(
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input_ids,
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visual_feats,
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bounding_boxes,
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token_type_ids=token_type_ids,
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attention_mask=input_mask,
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masked_lm_labels=masked_lm_labels,
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obj_labels=obj_labels,
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matched_label=matched_label,
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ans=ans,
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output_attentions=not output_attentions,
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)
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self.parent.assertEqual(result.prediction_logits.shape, (self.batch_size, self.seq_length, self.vocab_size))
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def resize_lxmert_num_qa_labels(
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self,
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config,
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input_ids,
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visual_feats,
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bounding_boxes,
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token_type_ids,
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input_mask,
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obj_labels,
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masked_lm_labels,
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matched_label,
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ans,
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output_attentions,
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):
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start_labels = config.num_qa_labels
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num_large_labels = config.num_qa_labels * 2
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num_small_labels = int(config.num_qa_labels * 2)
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less_labels_ans = ids_tensor([self.batch_size], num_small_labels)
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more_labels_ans = ids_tensor([self.batch_size], num_large_labels)
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model_pretrain = LxmertForPreTraining(config=config).to(torch_device)
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model_qa = LxmertForQuestionAnswering(config=config).to(torch_device)
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config.num_labels = num_small_labels
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end_labels = config.num_labels
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result_pretrain = model_pretrain(
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input_ids,
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visual_feats,
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bounding_boxes,
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token_type_ids=token_type_ids,
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attention_mask=input_mask,
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ans=ans,
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)
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result_qa = model_qa(
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input_ids,
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visual_feats,
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bounding_boxes,
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labels=ans,
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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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model_pretrain.resize_num_qa_labels(num_small_labels)
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model_qa.resize_num_qa_labels(num_small_labels)
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result_pretrain_less = model_pretrain(
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input_ids,
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visual_feats,
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bounding_boxes,
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token_type_ids=token_type_ids,
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attention_mask=input_mask,
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ans=less_labels_ans,
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)
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result_qa_less = model_qa(
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input_ids,
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visual_feats,
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bounding_boxes,
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labels=less_labels_ans,
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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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model_pretrain.resize_num_qa_labels(num_large_labels)
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model_qa.resize_num_qa_labels(num_large_labels)
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result_pretrain_more = model_pretrain(
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input_ids,
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visual_feats,
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bounding_boxes,
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token_type_ids=token_type_ids,
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attention_mask=input_mask,
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ans=more_labels_ans,
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)
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result_qa_more = model_qa(
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input_ids,
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visual_feats,
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bounding_boxes,
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labels=more_labels_ans,
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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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model_qa_labels = model_qa.num_qa_labels
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self.parent.assertNotEqual(start_labels, end_labels)
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self.parent.assertNotEqual(model_qa_labels, start_labels)
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self.parent.assertEqual(result_qa.question_answering_score.shape, (self.batch_size, start_labels))
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self.parent.assertEqual(result_pretrain.question_answering_score.shape, (self.batch_size, start_labels))
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self.parent.assertEqual(result_qa_less.question_answering_score.shape, (self.batch_size, num_small_labels))
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self.parent.assertEqual(
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result_pretrain_less.question_answering_score.shape, (self.batch_size, num_small_labels)
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)
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self.parent.assertEqual(result_qa_more.question_answering_score.shape, (self.batch_size, num_large_labels))
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self.parent.assertEqual(
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result_pretrain_more.question_answering_score.shape, (self.batch_size, num_large_labels)
|
|
)
|
|
|
|
def prepare_config_and_inputs_for_common(self, return_obj_labels=False):
|
|
config_and_inputs = self.prepare_config_and_inputs()
|
|
(
|
|
config,
|
|
input_ids,
|
|
visual_feats,
|
|
bounding_boxes,
|
|
token_type_ids,
|
|
input_mask,
|
|
obj_labels,
|
|
masked_lm_labels,
|
|
matched_label,
|
|
ans,
|
|
output_attentions,
|
|
) = config_and_inputs
|
|
|
|
inputs_dict = {
|
|
"input_ids": input_ids,
|
|
"visual_feats": visual_feats,
|
|
"visual_pos": bounding_boxes,
|
|
"token_type_ids": token_type_ids,
|
|
"attention_mask": input_mask,
|
|
}
|
|
|
|
if return_obj_labels:
|
|
inputs_dict["obj_labels"] = obj_labels
|
|
else:
|
|
config.task_obj_predict = False
|
|
|
|
return config, inputs_dict
|
|
|
|
|
|
@require_torch
|
|
class LxmertModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
|
|
all_model_classes = (LxmertModel, LxmertForPreTraining, LxmertForQuestionAnswering) if is_torch_available() else ()
|
|
pipeline_model_mapping = (
|
|
{"feature-extraction": LxmertModel, "question-answering": LxmertForQuestionAnswering}
|
|
if is_torch_available()
|
|
else {}
|
|
)
|
|
|
|
fx_compatible = True
|
|
test_head_masking = False
|
|
test_pruning = False
|
|
test_torchscript = False
|
|
|
|
# overwrite function because qa models takes different input label shape
|
|
def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
|
|
inputs_dict = copy.deepcopy(inputs_dict)
|
|
|
|
if return_labels:
|
|
if model_class in get_values(MODEL_FOR_QUESTION_ANSWERING_MAPPING):
|
|
inputs_dict["labels"] = torch.zeros(
|
|
self.model_tester.batch_size, dtype=torch.long, device=torch_device
|
|
)
|
|
elif model_class in get_values(MODEL_FOR_PRETRAINING_MAPPING):
|
|
# special case for models like BERT that use multi-loss training for PreTraining
|
|
inputs_dict["labels"] = torch.zeros(
|
|
(self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
|
|
)
|
|
return inputs_dict
|
|
|
|
def setUp(self):
|
|
self.model_tester = LxmertModelTester(self)
|
|
self.config_tester = ConfigTester(self, config_class=LxmertConfig, hidden_size=37)
|
|
|
|
def test_config(self):
|
|
self.config_tester.run_common_tests()
|
|
|
|
def test_lxmert_model(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_lxmert_model(*config_and_inputs)
|
|
|
|
def test_lxmert_question_answering(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_lxmert_for_question_answering(*config_and_inputs)
|
|
|
|
def test_lxmert_pretraining(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.create_and_check_lxmert_for_pretraining(*config_and_inputs)
|
|
|
|
def test_lxmert_question_answering_labels_resize(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs()
|
|
self.model_tester.resize_lxmert_num_qa_labels(*config_and_inputs)
|
|
|
|
@slow
|
|
def test_model_from_pretrained(self):
|
|
model_name = "unc-nlp/lxmert-base-uncased"
|
|
model = LxmertModel.from_pretrained(model_name)
|
|
model.to(torch_device)
|
|
self.assertIsNotNone(model)
|
|
|
|
def test_attention_outputs(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
seq_len = getattr(self.model_tester, "seq_length", None)
|
|
encoder_seq_length = getattr(self.model_tester, "encoder_seq_length", seq_len)
|
|
encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)
|
|
chunk_length = getattr(self.model_tester, "chunk_length", None)
|
|
if chunk_length is not None and hasattr(self.model_tester, "num_hashes"):
|
|
encoder_seq_length = encoder_seq_length * self.model_tester.num_hashes
|
|
|
|
for model_class in self.all_model_classes:
|
|
inputs_dict["output_attentions"] = True
|
|
inputs_dict["output_hidden_states"] = False
|
|
model = model_class(config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
with torch.no_grad():
|
|
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
|
|
|
|
language_attentions, vision_attentions, cross_encoder_attentions = (outputs[-3], outputs[-2], outputs[-1])
|
|
|
|
self.assertEqual(len(language_attentions), self.model_tester.num_hidden_layers["language"])
|
|
self.assertEqual(len(vision_attentions), self.model_tester.num_hidden_layers["vision"])
|
|
self.assertEqual(len(cross_encoder_attentions), self.model_tester.num_hidden_layers["cross_encoder"])
|
|
|
|
# check that output_attentions also work using config
|
|
del inputs_dict["output_attentions"]
|
|
config.output_attentions = True
|
|
model = model_class(config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
with torch.no_grad():
|
|
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
|
|
|
|
language_attentions, vision_attentions, cross_encoder_attentions = (outputs[-3], outputs[-2], outputs[-1])
|
|
self.assertEqual(len(language_attentions), self.model_tester.num_hidden_layers["language"])
|
|
self.assertEqual(len(vision_attentions), self.model_tester.num_hidden_layers["vision"])
|
|
self.assertEqual(len(cross_encoder_attentions), self.model_tester.num_hidden_layers["cross_encoder"])
|
|
|
|
attentions = [language_attentions, vision_attentions, cross_encoder_attentions]
|
|
attention_shapes = [
|
|
[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
|
|
[
|
|
self.model_tester.num_attention_heads,
|
|
self.model_tester.num_visual_features,
|
|
self.model_tester.num_visual_features,
|
|
],
|
|
[self.model_tester.num_attention_heads, encoder_key_length, self.model_tester.num_visual_features],
|
|
]
|
|
|
|
for attention, attention_shape in zip(attentions, attention_shapes):
|
|
self.assertListEqual(list(attention[0].shape[-3:]), attention_shape)
|
|
out_len = len(outputs)
|
|
|
|
# Check attention is always last and order is fine
|
|
inputs_dict["output_attentions"] = True
|
|
inputs_dict["output_hidden_states"] = True
|
|
model = model_class(config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
with torch.no_grad():
|
|
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
|
|
|
|
# 2 hidden states were added
|
|
self.assertEqual(out_len + 2, len(outputs))
|
|
|
|
language_attentions, vision_attentions, cross_encoder_attentions = (outputs[-3], outputs[-2], outputs[-1])
|
|
self.assertEqual(len(language_attentions), self.model_tester.num_hidden_layers["language"])
|
|
self.assertEqual(len(vision_attentions), self.model_tester.num_hidden_layers["vision"])
|
|
self.assertEqual(len(cross_encoder_attentions), self.model_tester.num_hidden_layers["cross_encoder"])
|
|
|
|
attentions = [language_attentions, vision_attentions, cross_encoder_attentions]
|
|
attention_shapes = [
|
|
[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
|
|
[
|
|
self.model_tester.num_attention_heads,
|
|
self.model_tester.num_visual_features,
|
|
self.model_tester.num_visual_features,
|
|
],
|
|
[self.model_tester.num_attention_heads, encoder_key_length, self.model_tester.num_visual_features],
|
|
]
|
|
|
|
for attention, attention_shape in zip(attentions, attention_shapes):
|
|
self.assertListEqual(list(attention[0].shape[-3:]), attention_shape)
|
|
|
|
def test_hidden_states_output(self):
|
|
def check_hidden_states_output(inputs_dict, config, model_class):
|
|
model = model_class(config)
|
|
model.to(torch_device)
|
|
model.eval()
|
|
|
|
with torch.no_grad():
|
|
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
|
|
language_hidden_states, vision_hidden_states = outputs[-2], outputs[-1]
|
|
|
|
self.assertEqual(len(language_hidden_states), self.model_tester.num_hidden_layers["language"] + 1)
|
|
self.assertEqual(len(vision_hidden_states), self.model_tester.num_hidden_layers["vision"] + 1)
|
|
|
|
seq_length = self.model_tester.seq_length
|
|
num_visual_features = self.model_tester.num_visual_features
|
|
|
|
self.assertListEqual(
|
|
list(language_hidden_states[0].shape[-2:]),
|
|
[seq_length, self.model_tester.hidden_size],
|
|
)
|
|
self.assertListEqual(
|
|
list(vision_hidden_states[0].shape[-2:]),
|
|
[num_visual_features, self.model_tester.hidden_size],
|
|
)
|
|
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
|
|
for model_class in self.all_model_classes:
|
|
inputs_dict["output_hidden_states"] = True
|
|
check_hidden_states_output(inputs_dict, config, model_class)
|
|
|
|
# check that output_hidden_states also work using config
|
|
del inputs_dict["output_hidden_states"]
|
|
config.output_hidden_states = True
|
|
|
|
check_hidden_states_output(inputs_dict, config, model_class)
|
|
|
|
def test_retain_grad_hidden_states_attentions(self):
|
|
config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
|
|
config.output_hidden_states = True
|
|
config.output_attentions = True
|
|
|
|
# no need to test all models as different heads yield the same functionality
|
|
model_class = self.all_model_classes[0]
|
|
model = model_class(config)
|
|
model.to(torch_device)
|
|
|
|
inputs = self._prepare_for_class(inputs_dict, model_class)
|
|
|
|
outputs = model(**inputs)
|
|
|
|
hidden_states_lang = outputs.language_hidden_states[0]
|
|
attentions_lang = outputs.language_attentions[0]
|
|
|
|
hidden_states_vision = outputs.vision_hidden_states[0]
|
|
attentions_vision = outputs.vision_attentions[0]
|
|
|
|
hidden_states_lang.retain_grad()
|
|
attentions_lang.retain_grad()
|
|
hidden_states_vision.retain_grad()
|
|
attentions_vision.retain_grad()
|
|
|
|
outputs.language_output.flatten()[0].backward(retain_graph=True)
|
|
outputs.vision_output.flatten()[0].backward(retain_graph=True)
|
|
|
|
self.assertIsNotNone(hidden_states_lang.grad)
|
|
self.assertIsNotNone(attentions_vision.grad)
|
|
self.assertIsNotNone(hidden_states_vision.grad)
|
|
self.assertIsNotNone(attentions_vision.grad)
|
|
|
|
def prepare_tf_inputs_from_pt_inputs(self, pt_inputs_dict):
|
|
tf_inputs_dict = {}
|
|
for key, value in pt_inputs_dict.items():
|
|
# skip key that does not exist in tf
|
|
if isinstance(value, dict):
|
|
tf_inputs_dict[key] = self.prepare_pt_inputs_from_tf_inputs(value)
|
|
elif isinstance(value, (list, tuple)):
|
|
tf_inputs_dict[key] = (self.prepare_pt_inputs_from_tf_inputs(iter_value) for iter_value in value)
|
|
elif isinstance(value, bool):
|
|
tf_inputs_dict[key] = value
|
|
elif key == "input_values":
|
|
tf_inputs_dict[key] = tf.convert_to_tensor(value.cpu().numpy(), dtype=tf.float32)
|
|
elif key == "pixel_values":
|
|
tf_inputs_dict[key] = tf.convert_to_tensor(value.cpu().numpy(), dtype=tf.float32)
|
|
elif key == "input_features":
|
|
tf_inputs_dict[key] = tf.convert_to_tensor(value.cpu().numpy(), dtype=tf.float32)
|
|
# other general float inputs
|
|
elif value.is_floating_point():
|
|
tf_inputs_dict[key] = tf.convert_to_tensor(value.cpu().numpy(), dtype=tf.float32)
|
|
else:
|
|
tf_inputs_dict[key] = tf.convert_to_tensor(value.cpu().numpy(), dtype=tf.int32)
|
|
|
|
return tf_inputs_dict
|
|
|
|
@unittest.skip("No support for low_cpu_mem_usage=True.")
|
|
def test_save_load_low_cpu_mem_usage(self):
|
|
pass
|
|
|
|
@unittest.skip("No support for low_cpu_mem_usage=True.")
|
|
def test_save_load_low_cpu_mem_usage_checkpoints(self):
|
|
pass
|
|
|
|
@unittest.skip("No support for low_cpu_mem_usage=True.")
|
|
def test_save_load_low_cpu_mem_usage_no_safetensors(self):
|
|
pass
|
|
|
|
|
|
@require_torch
|
|
class LxmertModelIntegrationTest(unittest.TestCase):
|
|
@slow
|
|
def test_inference_no_head_absolute_embedding(self):
|
|
model = LxmertModel.from_pretrained("unc-nlp/lxmert-base-uncased")
|
|
input_ids = torch.tensor([[101, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 102]])
|
|
num_visual_features = 10
|
|
_, visual_feats = np.random.seed(0), np.random.rand(1, num_visual_features, model.config.visual_feat_dim)
|
|
_, visual_pos = np.random.seed(0), np.random.rand(1, num_visual_features, 4)
|
|
visual_feats = torch.as_tensor(visual_feats, dtype=torch.float32)
|
|
visual_pos = torch.as_tensor(visual_pos, dtype=torch.float32)
|
|
output = model(input_ids, visual_feats=visual_feats, visual_pos=visual_pos)[0]
|
|
expected_shape = torch.Size([1, 11, 768])
|
|
self.assertEqual(expected_shape, output.shape)
|
|
expected_slice = torch.tensor(
|
|
[[[0.2417, -0.9807, 0.1480], [1.2541, -0.8320, 0.5112], [1.4070, -1.1052, 0.6990]]]
|
|
)
|
|
|
|
self.assertTrue(torch.allclose(output[:, :3, :3], expected_slice, atol=1e-4))
|