709 lines
29 KiB
Python
709 lines
29 KiB
Python
# coding=utf-8
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# Copyright 2021 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 VisualBERT model."""
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import copy
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import unittest
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from transformers import VisualBertConfig, is_torch_available
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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, floats_tensor, 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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VisualBertForMultipleChoice,
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VisualBertForPreTraining,
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VisualBertForQuestionAnswering,
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VisualBertForRegionToPhraseAlignment,
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VisualBertForVisualReasoning,
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VisualBertModel,
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)
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class VisualBertModelTester:
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def __init__(
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self,
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parent,
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batch_size=13,
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seq_length=7,
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visual_seq_length=5,
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is_training=True,
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use_attention_mask=True,
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use_visual_attention_mask=True,
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use_token_type_ids=True,
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use_visual_token_type_ids=True,
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use_labels=True,
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vocab_size=99,
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hidden_size=32,
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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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visual_embedding_dim=20,
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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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num_labels=3,
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num_choices=4,
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scope=None,
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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.seq_length = seq_length
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self.visual_seq_length = visual_seq_length
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self.is_training = is_training
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self.use_attention_mask = use_attention_mask
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self.use_visual_attention_mask = use_visual_attention_mask
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self.use_token_type_ids = use_token_type_ids
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self.use_visual_token_type_ids = use_visual_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.visual_embedding_dim = visual_embedding_dim
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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.num_labels = num_labels
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self.num_choices = num_choices
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self.scope = scope
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def get_config(self):
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return VisualBertConfig(
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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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visual_embedding_dim=self.visual_embedding_dim,
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num_labels=self.num_labels,
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is_decoder=False,
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initializer_range=self.initializer_range,
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)
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def prepare_config_and_inputs_for_common(self):
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input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
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visual_embeds = floats_tensor([self.batch_size, self.visual_seq_length, self.visual_embedding_dim])
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attention_mask = None
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if self.use_attention_mask:
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attention_mask = torch.ones((self.batch_size, self.seq_length), dtype=torch.long, device=torch_device)
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visual_attention_mask = None
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if self.use_visual_attention_mask:
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visual_attention_mask = torch.ones(
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(self.batch_size, self.visual_seq_length), dtype=torch.long, device=torch_device
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)
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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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visual_token_type_ids = None
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if self.use_visual_token_type_ids:
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visual_token_type_ids = ids_tensor([self.batch_size, self.visual_seq_length], self.type_vocab_size)
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config = self.get_config()
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return config, {
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"input_ids": input_ids,
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"token_type_ids": token_type_ids,
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"attention_mask": attention_mask,
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"visual_embeds": visual_embeds,
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"visual_token_type_ids": visual_token_type_ids,
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"visual_attention_mask": visual_attention_mask,
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}
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def prepare_config_and_inputs_for_pretraining(self):
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masked_lm_labels = None
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sentence_image_labels = None
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if self.use_labels:
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masked_lm_labels = ids_tensor([self.batch_size, self.seq_length + self.visual_seq_length], self.vocab_size)
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sentence_image_labels = ids_tensor(
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[self.batch_size],
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self.type_sequence_label_size,
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)
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config, input_dict = self.prepare_config_and_inputs_for_common()
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input_dict.update({"labels": masked_lm_labels, "sentence_image_labels": sentence_image_labels})
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return config, input_dict
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def prepare_config_and_inputs_for_multiple_choice(self):
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input_ids = ids_tensor([self.batch_size, self.num_choices, self.seq_length], self.vocab_size)
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visual_embeds = floats_tensor(
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[self.batch_size, self.num_choices, self.visual_seq_length, self.visual_embedding_dim]
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)
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attention_mask = None
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if self.use_attention_mask:
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attention_mask = torch.ones(
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(self.batch_size, self.num_choices, self.seq_length), dtype=torch.long, device=torch_device
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)
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visual_attention_mask = None
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if self.use_visual_attention_mask:
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visual_attention_mask = torch.ones(
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(self.batch_size, self.num_choices, self.visual_seq_length), dtype=torch.long, device=torch_device
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)
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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.num_choices, self.seq_length], self.type_vocab_size)
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visual_token_type_ids = None
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if self.use_visual_token_type_ids:
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visual_token_type_ids = ids_tensor(
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[self.batch_size, self.num_choices, self.visual_seq_length], self.type_vocab_size
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)
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labels = None
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if self.use_labels:
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labels = ids_tensor([self.batch_size], self.num_choices)
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config = self.get_config()
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return config, {
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"input_ids": input_ids,
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"token_type_ids": token_type_ids,
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"attention_mask": attention_mask,
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"visual_embeds": visual_embeds,
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"visual_token_type_ids": visual_token_type_ids,
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"visual_attention_mask": visual_attention_mask,
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"labels": labels,
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}
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def prepare_config_and_inputs_for_vqa(self):
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vqa_labels = None
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if self.use_labels:
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vqa_labels = floats_tensor([self.batch_size, self.num_labels])
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config, input_dict = self.prepare_config_and_inputs_for_common()
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input_dict.update({"labels": vqa_labels})
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return config, input_dict
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def prepare_config_and_inputs_for_nlvr(self):
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nlvr_labels = None
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if self.use_labels:
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nlvr_labels = ids_tensor([self.batch_size], self.num_labels)
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config, input_dict = self.prepare_config_and_inputs_for_common()
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input_dict.update({"labels": nlvr_labels})
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return config, input_dict
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def prepare_config_and_inputs_for_flickr(self):
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region_to_phrase_position = torch.cat(
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(
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ids_tensor([self.batch_size, self.seq_length], self.visual_seq_length),
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torch.ones(self.batch_size, self.visual_seq_length, dtype=torch.long, device=torch_device) * -1,
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),
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dim=-1,
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)
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flickr_labels = None
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if self.use_labels:
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flickr_labels = floats_tensor(
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[self.batch_size, self.seq_length + self.visual_seq_length, self.visual_seq_length]
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)
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config, input_dict = self.prepare_config_and_inputs_for_common()
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input_dict.update({"region_to_phrase_position": region_to_phrase_position, "labels": flickr_labels})
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return config, input_dict
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def create_and_check_model(self, config, input_dict):
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model = VisualBertModel(config=config)
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model.to(torch_device)
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model.eval()
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result = model(**input_dict)
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self.parent.assertEqual(
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result.last_hidden_state.shape,
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(self.batch_size, self.seq_length + self.visual_seq_length, self.hidden_size),
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)
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def create_and_check_for_pretraining(self, config, input_dict):
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model = VisualBertForPreTraining(config=config)
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model.to(torch_device)
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model.eval()
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result = model(**input_dict)
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self.parent.assertEqual(
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result.prediction_logits.shape,
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(self.batch_size, self.seq_length + self.visual_seq_length, self.vocab_size),
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)
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def create_and_check_for_vqa(self, config, input_dict):
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model = VisualBertForQuestionAnswering(config=config)
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model.to(torch_device)
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model.eval()
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result = model(**input_dict)
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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_multiple_choice(self, config, input_dict):
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model = VisualBertForMultipleChoice(config=config)
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model.to(torch_device)
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model.eval()
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result = model(**input_dict)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices))
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def create_and_check_for_nlvr(self, config, input_dict):
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model = VisualBertForVisualReasoning(config=config)
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model.to(torch_device)
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model.eval()
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result = model(**input_dict)
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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_flickr(self, config, input_dict):
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model = VisualBertForRegionToPhraseAlignment(config=config)
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model.to(torch_device)
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model.eval()
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result = model(**input_dict)
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self.parent.assertEqual(
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result.logits.shape, (self.batch_size, self.seq_length + self.visual_seq_length, self.visual_seq_length)
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)
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@require_torch
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class VisualBertModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
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all_model_classes = (
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(
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VisualBertModel,
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VisualBertForMultipleChoice,
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VisualBertForVisualReasoning,
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VisualBertForRegionToPhraseAlignment,
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VisualBertForQuestionAnswering,
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VisualBertForPreTraining,
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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 = {"feature-extraction": VisualBertModel} if is_torch_available() else {}
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test_torchscript = False
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test_pruning = False
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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 == VisualBertForMultipleChoice:
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for key in inputs_dict.keys():
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value = inputs_dict[key]
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if isinstance(value, torch.Tensor) and value.ndim > 1:
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if key != "visual_embeds":
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inputs_dict[key] = (
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inputs_dict[key].unsqueeze(1).expand(-1, self.model_tester.num_choices, -1).contiguous()
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)
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else:
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inputs_dict[key] = (
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inputs_dict[key]
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.unsqueeze(1)
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.expand(-1, self.model_tester.num_choices, -1, self.model_tester.visual_embedding_dim)
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.contiguous()
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)
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elif model_class == VisualBertForRegionToPhraseAlignment:
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total_length = self.model_tester.seq_length + self.model_tester.visual_seq_length
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batch_size = self.model_tester.batch_size
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inputs_dict["region_to_phrase_position"] = torch.zeros(
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(batch_size, total_length),
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dtype=torch.long,
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device=torch_device,
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)
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if return_labels:
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if model_class == VisualBertForMultipleChoice:
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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 == VisualBertForPreTraining:
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total_length = self.model_tester.seq_length + self.model_tester.visual_seq_length
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batch_size = self.model_tester.batch_size
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inputs_dict["labels"] = torch.zeros(
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(batch_size, total_length),
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dtype=torch.long,
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device=torch_device,
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)
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inputs_dict["sentence_image_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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# Flickr expects float labels
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elif model_class == VisualBertForRegionToPhraseAlignment:
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batch_size = self.model_tester.batch_size
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total_length = self.model_tester.seq_length + self.model_tester.visual_seq_length
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inputs_dict["labels"] = torch.ones(
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(
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batch_size,
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total_length,
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self.model_tester.visual_seq_length,
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),
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dtype=torch.float,
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device=torch_device,
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)
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# VQA expects float labels
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elif model_class == VisualBertForQuestionAnswering:
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inputs_dict["labels"] = torch.ones(
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(self.model_tester.batch_size, self.model_tester.num_labels),
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dtype=torch.float,
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device=torch_device,
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)
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elif model_class == VisualBertForVisualReasoning:
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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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return inputs_dict
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def setUp(self):
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self.model_tester = VisualBertModelTester(self)
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self.config_tester = ConfigTester(self, config_class=VisualBertConfig, hidden_size=37)
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def test_attention_outputs(self):
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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config.return_dict = True
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seq_len = getattr(self.model_tester, "seq_length", None)
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visual_seq_len = getattr(self.model_tester, "visual_seq_length", None)
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encoder_seq_length = (seq_len if seq_len is not None else 0) + (
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visual_seq_len if visual_seq_len is not None else 0
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)
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encoder_key_length = getattr(self.model_tester, "key_length", encoder_seq_length)
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chunk_length = getattr(self.model_tester, "chunk_length", None)
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if chunk_length is not None and hasattr(self.model_tester, "num_hashes"):
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encoder_seq_length = encoder_seq_length * self.model_tester.num_hashes
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for model_class in self.all_model_classes:
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inputs_dict["output_attentions"] = True
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inputs_dict["output_hidden_states"] = False
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config.return_dict = True
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
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self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
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# check that output_attentions also work using config
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del inputs_dict["output_attentions"]
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config.output_attentions = True
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
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self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
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if chunk_length is not None:
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self.assertListEqual(
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list(attentions[0].shape[-4:]),
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[self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length],
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)
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else:
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self.assertListEqual(
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list(attentions[0].shape[-3:]),
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[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
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)
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out_len = len(outputs)
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# Check attention is always last and order is fine
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inputs_dict["output_attentions"] = True
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inputs_dict["output_hidden_states"] = True
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model = model_class(config)
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model.to(torch_device)
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model.eval()
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with torch.no_grad():
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outputs = model(**self._prepare_for_class(inputs_dict, model_class))
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if hasattr(self.model_tester, "num_hidden_states_types"):
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added_hidden_states = self.model_tester.num_hidden_states_types
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elif self.is_encoder_decoder:
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added_hidden_states = 2
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else:
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added_hidden_states = 1
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self.assertEqual(out_len + added_hidden_states, len(outputs))
|
|
|
|
self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
|
|
|
|
self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
|
|
if chunk_length is not None:
|
|
self.assertListEqual(
|
|
list(self_attentions[0].shape[-4:]),
|
|
[self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length],
|
|
)
|
|
else:
|
|
self.assertListEqual(
|
|
list(self_attentions[0].shape[-3:]),
|
|
[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length],
|
|
)
|
|
|
|
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))
|
|
|
|
hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
|
|
|
|
expected_num_layers = getattr(
|
|
self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
|
|
)
|
|
self.assertEqual(len(hidden_states), expected_num_layers)
|
|
|
|
if hasattr(self.model_tester, "encoder_seq_length"):
|
|
seq_length = self.model_tester.encoder_seq_length
|
|
if hasattr(self.model_tester, "chunk_length") and self.model_tester.chunk_length > 1:
|
|
seq_length = seq_length * self.model_tester.chunk_length
|
|
else:
|
|
seq_length = self.model_tester.seq_length + self.model_tester.visual_seq_length
|
|
|
|
self.assertListEqual(
|
|
list(hidden_states[0].shape[-2:]),
|
|
[seq_length, 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_config(self):
|
|
self.config_tester.run_common_tests()
|
|
|
|
def test_model(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_common()
|
|
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_common()
|
|
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_model_for_pretraining(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_pretraining()
|
|
self.model_tester.create_and_check_for_pretraining(*config_and_inputs)
|
|
|
|
def test_model_for_vqa(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_vqa()
|
|
self.model_tester.create_and_check_for_vqa(*config_and_inputs)
|
|
|
|
def test_model_for_nlvr(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_nlvr()
|
|
self.model_tester.create_and_check_for_nlvr(*config_and_inputs)
|
|
|
|
def test_model_for_multiple_choice(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_multiple_choice()
|
|
self.model_tester.create_and_check_for_multiple_choice(*config_and_inputs)
|
|
|
|
def test_model_for_flickr(self):
|
|
config_and_inputs = self.model_tester.prepare_config_and_inputs_for_flickr()
|
|
self.model_tester.create_and_check_for_flickr(*config_and_inputs)
|
|
|
|
@slow
|
|
def test_model_from_pretrained(self):
|
|
model_name = "uclanlp/visualbert-vqa"
|
|
model = VisualBertModel.from_pretrained(model_name)
|
|
self.assertIsNotNone(model)
|
|
|
|
@unittest.skip(
|
|
reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
|
|
)
|
|
def test_training_gradient_checkpointing(self):
|
|
pass
|
|
|
|
@unittest.skip(
|
|
reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
|
|
)
|
|
def test_training_gradient_checkpointing_use_reentrant(self):
|
|
pass
|
|
|
|
@unittest.skip(
|
|
reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
|
|
)
|
|
def test_training_gradient_checkpointing_use_reentrant_false(self):
|
|
pass
|
|
|
|
|
|
@require_torch
|
|
class VisualBertModelIntegrationTest(unittest.TestCase):
|
|
@slow
|
|
def test_inference_vqa_coco_pre(self):
|
|
model = VisualBertForPreTraining.from_pretrained("uclanlp/visualbert-vqa-coco-pre")
|
|
|
|
input_ids = torch.tensor([1, 2, 3, 4, 5, 6], dtype=torch.long).reshape(1, -1)
|
|
token_type_ids = torch.tensor([0, 0, 0, 1, 1, 1], dtype=torch.long).reshape(1, -1)
|
|
visual_embeds = torch.ones(size=(1, 10, 2048), dtype=torch.float32) * 0.5
|
|
visual_token_type_ids = torch.ones(size=(1, 10), dtype=torch.long)
|
|
attention_mask = torch.tensor([1] * 6).reshape(1, -1)
|
|
visual_attention_mask = torch.tensor([1] * 10).reshape(1, -1)
|
|
|
|
with torch.no_grad():
|
|
output = model(
|
|
input_ids=input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
visual_embeds=visual_embeds,
|
|
visual_attention_mask=visual_attention_mask,
|
|
visual_token_type_ids=visual_token_type_ids,
|
|
)
|
|
|
|
vocab_size = 30522
|
|
|
|
expected_shape = torch.Size((1, 16, vocab_size))
|
|
self.assertEqual(output.prediction_logits.shape, expected_shape)
|
|
|
|
expected_slice = torch.tensor(
|
|
[[[-5.1858, -5.1903, -4.9142], [-6.2214, -5.9238, -5.8381], [-6.3027, -5.9939, -5.9297]]]
|
|
)
|
|
|
|
self.assertTrue(torch.allclose(output.prediction_logits[:, :3, :3], expected_slice, atol=1e-4))
|
|
|
|
expected_shape_2 = torch.Size((1, 2))
|
|
self.assertEqual(output.seq_relationship_logits.shape, expected_shape_2)
|
|
|
|
expected_slice_2 = torch.tensor([[0.7393, 0.1754]])
|
|
|
|
self.assertTrue(torch.allclose(output.seq_relationship_logits, expected_slice_2, atol=1e-4))
|
|
|
|
@slow
|
|
def test_inference_vqa(self):
|
|
model = VisualBertForQuestionAnswering.from_pretrained("uclanlp/visualbert-vqa")
|
|
|
|
input_ids = torch.tensor([1, 2, 3, 4, 5, 6], dtype=torch.long).reshape(1, -1)
|
|
token_type_ids = torch.tensor([0, 0, 0, 1, 1, 1], dtype=torch.long).reshape(1, -1)
|
|
visual_embeds = torch.ones(size=(1, 10, 2048), dtype=torch.float32) * 0.5
|
|
visual_token_type_ids = torch.ones(size=(1, 10), dtype=torch.long)
|
|
attention_mask = torch.tensor([1] * 6).reshape(1, -1)
|
|
visual_attention_mask = torch.tensor([1] * 10).reshape(1, -1)
|
|
|
|
with torch.no_grad():
|
|
output = model(
|
|
input_ids=input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
visual_embeds=visual_embeds,
|
|
visual_attention_mask=visual_attention_mask,
|
|
visual_token_type_ids=visual_token_type_ids,
|
|
)
|
|
|
|
# vocab_size = 30522
|
|
|
|
expected_shape = torch.Size((1, 3129))
|
|
self.assertEqual(output.logits.shape, expected_shape)
|
|
|
|
expected_slice = torch.tensor(
|
|
[[-8.9898, 3.0803, -1.8016, 2.4542, -8.3420, -2.0224, -3.3124, -4.4139, -3.1491, -3.8997]]
|
|
)
|
|
|
|
self.assertTrue(torch.allclose(output.logits[:, :10], expected_slice, atol=1e-4))
|
|
|
|
@slow
|
|
def test_inference_nlvr(self):
|
|
model = VisualBertForVisualReasoning.from_pretrained("uclanlp/visualbert-nlvr2")
|
|
|
|
input_ids = torch.tensor([1, 2, 3, 4, 5, 6], dtype=torch.long).reshape(1, -1)
|
|
token_type_ids = torch.tensor([0, 0, 0, 1, 1, 1], dtype=torch.long).reshape(1, -1)
|
|
visual_embeds = torch.ones(size=(1, 10, 1024), dtype=torch.float32) * 0.5
|
|
visual_token_type_ids = torch.ones(size=(1, 10), dtype=torch.long)
|
|
attention_mask = torch.tensor([1] * 6).reshape(1, -1)
|
|
visual_attention_mask = torch.tensor([1] * 10).reshape(1, -1)
|
|
|
|
with torch.no_grad():
|
|
output = model(
|
|
input_ids=input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
visual_embeds=visual_embeds,
|
|
visual_attention_mask=visual_attention_mask,
|
|
visual_token_type_ids=visual_token_type_ids,
|
|
)
|
|
|
|
# vocab_size = 30522
|
|
|
|
expected_shape = torch.Size((1, 2))
|
|
self.assertEqual(output.logits.shape, expected_shape)
|
|
|
|
expected_slice = torch.tensor([[-1.1436, 0.8900]])
|
|
|
|
self.assertTrue(torch.allclose(output.logits, expected_slice, atol=1e-4))
|
|
|
|
@slow
|
|
def test_inference_vcr(self):
|
|
model = VisualBertForMultipleChoice.from_pretrained("uclanlp/visualbert-vcr")
|
|
|
|
input_ids = torch.tensor([[[1, 2, 3, 4, 5, 6] for i in range(4)]], dtype=torch.long)
|
|
attention_mask = torch.ones_like(input_ids)
|
|
token_type_ids = torch.ones_like(input_ids)
|
|
|
|
visual_embeds = torch.ones(size=(1, 4, 10, 512), dtype=torch.float32) * 0.5
|
|
visual_token_type_ids = torch.ones(size=(1, 4, 10), dtype=torch.long)
|
|
visual_attention_mask = torch.ones_like(visual_token_type_ids)
|
|
|
|
with torch.no_grad():
|
|
output = model(
|
|
input_ids=input_ids,
|
|
attention_mask=attention_mask,
|
|
token_type_ids=token_type_ids,
|
|
visual_embeds=visual_embeds,
|
|
visual_attention_mask=visual_attention_mask,
|
|
visual_token_type_ids=visual_token_type_ids,
|
|
)
|
|
|
|
# vocab_size = 30522
|
|
|
|
expected_shape = torch.Size((1, 4))
|
|
self.assertEqual(output.logits.shape, expected_shape)
|
|
|
|
expected_slice = torch.tensor([[-7.7697, -7.7697, -7.7697, -7.7697]])
|
|
|
|
self.assertTrue(torch.allclose(output.logits, expected_slice, atol=1e-4))
|