677 lines
26 KiB
Python
677 lines
26 KiB
Python
# coding=utf-8
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# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Testing suite for the PyTorch ViLT model."""
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import unittest
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from datasets import load_dataset
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from packaging import version
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from transformers import ViltConfig, is_torch_available, is_vision_available
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from transformers.testing_utils import require_torch, require_vision, slow, torch_device
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from transformers.utils import cached_property
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from ...test_configuration_common import ConfigTester
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from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
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from ...test_pipeline_mixin import PipelineTesterMixin
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if is_torch_available():
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import torch
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from transformers import (
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ViltForImageAndTextRetrieval,
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ViltForImagesAndTextClassification,
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ViltForMaskedLM,
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ViltForQuestionAnswering,
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ViltForTokenClassification,
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ViltModel,
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)
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from transformers.models.auto.modeling_auto import MODEL_MAPPING_NAMES
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if is_vision_available():
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import PIL
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from PIL import Image
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from transformers import ViltProcessor
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class ViltModelTester:
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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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image_size=30,
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patch_size=2,
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num_channels=3,
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is_training=True,
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use_input_mask=True,
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use_token_type_ids=True,
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use_labels=True,
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vocab_size=99,
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hidden_size=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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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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scope=None,
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modality_type_vocab_size=2,
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add_multiple_images=False,
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num_images=-1,
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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.image_size = image_size
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self.patch_size = patch_size
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self.num_channels = num_channels
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self.is_training = is_training
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self.use_input_mask = use_input_mask
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self.use_token_type_ids = use_token_type_ids
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self.use_labels = use_labels
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.intermediate_size = intermediate_size
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self.hidden_act = hidden_act
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self.hidden_dropout_prob = hidden_dropout_prob
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self.attention_probs_dropout_prob = attention_probs_dropout_prob
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self.max_position_embeddings = max_position_embeddings
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self.type_vocab_size = type_vocab_size
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self.type_sequence_label_size = type_sequence_label_size
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self.initializer_range = initializer_range
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self.num_labels = num_labels
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self.scope = scope
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self.modality_type_vocab_size = modality_type_vocab_size
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self.add_multiple_images = add_multiple_images
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self.num_images = num_images
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# we set the expected sequence length (which is used in several tests)
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# this is equal to the seq length of the text tokens + number of image patches + 1 for the CLS token
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self.expected_seq_len = self.seq_length + (self.image_size // self.patch_size) ** 2 + 1
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def prepare_config_and_inputs(self):
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input_ids = ids_tensor([self.batch_size, self.seq_length], self.vocab_size)
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if self.add_multiple_images:
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pixel_values = floats_tensor([self.batch_size, 2, self.num_channels, self.image_size, self.image_size])
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else:
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pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
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input_mask = None
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if self.use_input_mask:
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input_mask = random_attention_mask([self.batch_size, self.seq_length])
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token_type_ids = None
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if self.use_token_type_ids:
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token_type_ids = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size)
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if self.use_labels:
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token_labels = ids_tensor([self.batch_size, self.seq_length], self.num_labels)
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config = self.get_config()
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return (config, input_ids, token_type_ids, input_mask, pixel_values, token_labels)
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def get_config(self):
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return ViltConfig(
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image_size=self.image_size,
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patch_size=self.patch_size,
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num_channels=self.num_channels,
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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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is_decoder=False,
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initializer_range=self.initializer_range,
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num_labels=self.num_labels,
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modality_type_vocab_size=self.modality_type_vocab_size,
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num_images=self.num_images,
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)
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def create_and_check_model(
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self,
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config,
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input_ids,
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token_type_ids,
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input_mask,
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pixel_values,
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token_labels,
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):
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model = ViltModel(config=config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, pixel_values=pixel_values)
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result = model(input_ids, token_type_ids=token_type_ids, pixel_values=pixel_values)
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result = model(input_ids, pixel_values=pixel_values)
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self.parent.assertEqual(
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result.last_hidden_state.shape, (self.batch_size, self.expected_seq_len, self.hidden_size)
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)
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def create_and_check_for_token_classification(
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self,
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config,
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input_ids,
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token_type_ids,
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input_mask,
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pixel_values,
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token_labels,
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):
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model = ViltForTokenClassification(config=config)
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model.to(torch_device)
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model.eval()
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result = model(input_ids, attention_mask=input_mask, token_type_ids=token_type_ids, pixel_values=pixel_values)
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result = model(input_ids, token_type_ids=token_type_ids, pixel_values=pixel_values)
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result = model(input_ids, pixel_values=pixel_values)
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self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels))
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def prepare_config_and_inputs_for_common(self):
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config_and_inputs = self.prepare_config_and_inputs()
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(
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config,
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input_ids,
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token_type_ids,
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input_mask,
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pixel_values,
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token_labels,
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) = config_and_inputs
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inputs_dict = {
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"input_ids": input_ids,
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"token_type_ids": token_type_ids,
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"attention_mask": input_mask,
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"pixel_values": pixel_values,
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}
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return config, inputs_dict
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def prepare_pixel_values(self):
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return floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
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@require_torch
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class ViltModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
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all_model_classes = (
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(
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ViltModel,
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ViltForQuestionAnswering,
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ViltForImageAndTextRetrieval,
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ViltForMaskedLM,
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ViltForTokenClassification,
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)
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if is_torch_available()
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else ()
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)
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pipeline_model_mapping = (
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{"image-feature-extraction": ViltModel, "visual-question-answering": ViltForQuestionAnswering}
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if is_torch_available()
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else {}
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)
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test_pruning = False
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test_headmasking = False
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test_torchscript = False
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model_split_percents = [0.5, 0.8, 0.9]
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# ViltForMaskedLM, ViltForQuestionAnswering and ViltForImagesAndTextClassification require special treatment
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def _prepare_for_class(self, inputs_dict, model_class, return_labels=False):
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inputs_dict = super()._prepare_for_class(inputs_dict, model_class, return_labels=return_labels)
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if return_labels:
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if model_class.__name__ == "ViltForQuestionAnswering":
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inputs_dict["labels"] = torch.zeros(
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self.model_tester.batch_size, self.model_tester.num_labels, device=torch_device
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)
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elif model_class.__name__ in ["ViltForMaskedLM", "ViltForTokenClassification"]:
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inputs_dict["labels"] = torch.zeros(
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(self.model_tester.batch_size, self.model_tester.seq_length), dtype=torch.long, device=torch_device
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)
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elif model_class.__name__ == "ViltForImagesAndTextClassification":
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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 = ViltModelTester(self)
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self.config_tester = ConfigTester(self, config_class=ViltConfig, hidden_size=37)
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def test_config(self):
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self.config_tester.run_common_tests()
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def test_model(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_model(*config_and_inputs)
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def test_for_token_classification(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs()
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self.model_tester.create_and_check_for_token_classification(*config_and_inputs)
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def test_training(self):
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if not self.model_tester.is_training:
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return
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for model_class in self.all_model_classes:
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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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if model_class.__name__ == "ViltForImagesAndTextClassification":
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config.modality_type_vocab_size = 3
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# ViltForImageAndTextRetrieval doesn't support training for now
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if model_class.__name__ in [*MODEL_MAPPING_NAMES.values(), "ViltForImageAndTextRetrieval"]:
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continue
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model = model_class(config)
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model.to(torch_device)
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model.train()
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inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
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for k, v in inputs.items():
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print(k, v.shape)
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loss = model(**inputs).loss
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loss.backward()
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def test_training_gradient_checkpointing(self):
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if not self.model_tester.is_training:
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return
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for model_class in self.all_model_classes:
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config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()
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config.use_cache = False
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config.return_dict = True
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# ViltForImageAndTextRetrieval doesn't support training for now
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if (
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model_class.__name__ in [*MODEL_MAPPING_NAMES.values(), "ViltForImageAndTextRetrieval"]
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or not model_class.supports_gradient_checkpointing
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):
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continue
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model = model_class(config)
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model.to(torch_device)
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model.gradient_checkpointing_enable()
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model.train()
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inputs = self._prepare_for_class(inputs_dict, model_class, return_labels=True)
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loss = model(**inputs).loss
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loss.backward()
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@unittest.skip(
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reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
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)
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def test_training_gradient_checkpointing_use_reentrant(self):
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pass
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@unittest.skip(
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reason="This architecure seem to not compute gradients properly when using GC, check: https://github.com/huggingface/transformers/pull/27124"
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)
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def test_training_gradient_checkpointing_use_reentrant_false(self):
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pass
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@unittest.skip(
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reason="""VilT samples image tokens from a multinomial distribution, resulting in not deterministic
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hidden states"""
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)
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def test_save_load(self):
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pass
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@unittest.skip(
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reason="""VilT samples image tokens from a multinomial distribution, resulting in not deterministic
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hidden states"""
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)
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def test_determinism(self):
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pass
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@unittest.skip(
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"VilT samples image tokens from a multinomial distribution, resulting in not deterministic hidden states"
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)
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def test_batching_equivalence(self):
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pass
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@unittest.skip(
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reason="""VilT samples image tokens from a multinomial distribution, resulting in not deterministic
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hidden states"""
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)
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def test_model_outputs_equivalence(self):
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pass
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@unittest.skip(
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reason="""VilT samples image tokens from a multinomial distribution, resulting in not deterministic
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hidden states. Cannot test equivalence on logit level"""
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)
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def test_inputs_embeds_matches_input_ids(self):
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pass
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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, "expected_seq_len", None)
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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.attentions
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if model_class.__name__ == "ViltForImagesAndTextClassification":
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# attentions are a list of length num_images
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# each element contains the attentions of a particular image index
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self.assertEqual(len(attentions), self.model_tester.num_images)
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self.assertEqual(len(attentions[0]), self.model_tester.num_hidden_layers)
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else:
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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.attentions
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if model_class.__name__ == "ViltForImagesAndTextClassification":
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# attentions are a list of length num_images
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# each element contains the attentions of a particular image index
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self.assertEqual(len(attentions), self.model_tester.num_images)
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self.assertEqual(len(attentions[0]), self.model_tester.num_hidden_layers)
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else:
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self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
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if model_class.__name__ == "ViltForImagesAndTextClassification":
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self.assertListEqual(
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list(attentions[0][0].shape[-3:]),
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[self.model_tester.num_attention_heads, seq_len, seq_len],
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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, seq_len, seq_len],
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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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self.assertEqual(out_len + 1, len(outputs))
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self_attentions = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
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if model_class.__name__ == "ViltForImagesAndTextClassification":
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self.assertEqual(len(self_attentions), self.model_tester.num_images)
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self.assertEqual(len(self_attentions[0]), self.model_tester.num_hidden_layers)
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self.assertListEqual(
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list(self_attentions[0][0].shape[-3:]),
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[self.model_tester.num_attention_heads, seq_len, seq_len],
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)
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else:
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self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
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self.assertListEqual(
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list(self_attentions[0].shape[-3:]),
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[self.model_tester.num_attention_heads, seq_len, seq_len],
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)
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def test_hidden_states_output(self):
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def check_hidden_states_output(inputs_dict, config, model_class):
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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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hidden_states = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
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expected_num_layers = getattr(
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self.model_tester, "expected_num_hidden_layers", self.model_tester.num_hidden_layers + 1
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)
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if model_class.__name__ == "ViltForImagesAndTextClassification":
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# hidden_states are a list of length num_images
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# each element contains the hidden states of a particular image index
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self.assertEqual(len(hidden_states), self.model_tester.num_images)
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self.assertEqual(len(hidden_states[0]), expected_num_layers)
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else:
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self.assertEqual(len(hidden_states), expected_num_layers)
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|
|
|
seq_length = self.model_tester.expected_seq_len
|
|
|
|
if model_class.__name__ == "ViltForImagesAndTextClassification":
|
|
self.assertListEqual(
|
|
list(hidden_states[0][0].shape[-2:]),
|
|
[seq_length, self.model_tester.hidden_size],
|
|
)
|
|
else:
|
|
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:
|
|
print("Model class:", model_class)
|
|
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)
|
|
|
|
output = outputs[0]
|
|
|
|
# Encoder-/Decoder-only models
|
|
hidden_states = outputs.hidden_states[0]
|
|
attentions = outputs.attentions[0]
|
|
|
|
if model_class.__name__ == "ViltForImagesAndTextClassification":
|
|
# hidden_states are a list of length num_images
|
|
# each element contains the hidden states of a particular image index
|
|
hidden_states[0].retain_grad()
|
|
attentions[0].retain_grad()
|
|
else:
|
|
hidden_states.retain_grad()
|
|
attentions.retain_grad()
|
|
|
|
output.flatten()[0].backward(retain_graph=True)
|
|
|
|
if model_class.__name__ == "ViltForImagesAndTextClassification":
|
|
# hidden_states are a list of length num_images
|
|
# each element contains the hidden states of a particular image index
|
|
self.assertIsNotNone(hidden_states[0].grad)
|
|
self.assertIsNotNone(attentions[0].grad)
|
|
else:
|
|
self.assertIsNotNone(hidden_states.grad)
|
|
self.assertIsNotNone(attentions.grad)
|
|
|
|
@slow
|
|
def test_model_from_pretrained(self):
|
|
model_name = "dandelin/vilt-b32-mlm"
|
|
model = ViltModel.from_pretrained(model_name)
|
|
self.assertIsNotNone(model)
|
|
|
|
|
|
@require_torch
|
|
class ViltForImagesAndTextClassificationModelTest(ViltModelTest, unittest.TestCase):
|
|
all_model_classes = (ViltForImagesAndTextClassification,) if is_torch_available() else ()
|
|
|
|
def setUp(self):
|
|
self.model_tester = ViltModelTester(self, modality_type_vocab_size=3, add_multiple_images=True, num_images=2)
|
|
self.config_tester = ConfigTester(self, config_class=ViltConfig, hidden_size=37)
|
|
|
|
@unittest.skip("We only test the model that takes in multiple images")
|
|
def test_model(self):
|
|
pass
|
|
|
|
@unittest.skip("We only test the model that takes in multiple images")
|
|
def test_for_token_classification(self):
|
|
pass
|
|
|
|
|
|
# We will verify our results on an image of cute cats
|
|
def prepare_img():
|
|
image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
|
|
return image
|
|
|
|
|
|
@require_torch
|
|
@require_vision
|
|
class ViltModelIntegrationTest(unittest.TestCase):
|
|
@cached_property
|
|
def default_processor(self):
|
|
return ViltProcessor.from_pretrained("dandelin/vilt-b32-finetuned-vqa") if is_vision_available() else None
|
|
|
|
@slow
|
|
def test_inference_masked_lm(self):
|
|
model = ViltForMaskedLM.from_pretrained("dandelin/vilt-b32-mlm").to(torch_device)
|
|
|
|
processor = self.default_processor
|
|
image = prepare_img()
|
|
text = "a bunch of [MASK] laying on a [MASK]."
|
|
inputs = processor(image, text, return_tensors="pt").to(torch_device)
|
|
|
|
# forward pass
|
|
with torch.no_grad():
|
|
outputs = model(**inputs)
|
|
|
|
# verify the logits
|
|
expected_shape = torch.Size([1, 11, 30522])
|
|
self.assertEqual(outputs.logits.shape, expected_shape)
|
|
|
|
expected_slice = torch.tensor([-12.5061, -12.5123, -12.5174]).to(torch_device)
|
|
self.assertTrue(torch.allclose(outputs.logits[0, 0, :3], expected_slice, atol=1e-4))
|
|
|
|
# verify masked token prediction equals "cats"
|
|
predicted_id = outputs.logits[0, 4, :].argmax(-1).item()
|
|
assert processor.decode([predicted_id]) == "cats"
|
|
|
|
@slow
|
|
def test_inference_visual_question_answering(self):
|
|
model = ViltForQuestionAnswering.from_pretrained("dandelin/vilt-b32-finetuned-vqa").to(torch_device)
|
|
|
|
processor = self.default_processor
|
|
image = prepare_img()
|
|
text = "How many cats are there?"
|
|
inputs = processor(image, text, return_tensors="pt").to(torch_device)
|
|
|
|
# forward pass
|
|
with torch.no_grad():
|
|
outputs = model(**inputs)
|
|
|
|
# verify the logits
|
|
expected_shape = torch.Size((1, 3129))
|
|
self.assertEqual(outputs.logits.shape, expected_shape)
|
|
|
|
expected_slice = torch.tensor([-15.9495, -18.1472, -10.3041]).to(torch_device)
|
|
|
|
self.assertTrue(torch.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4))
|
|
|
|
# compute loss
|
|
vqa_labels = [[2, 3, 155, 800]]
|
|
vqa_scores = [[1.0, 0.3, 0.3, 0.3]]
|
|
labels = torch.zeros(1, model.config.num_labels).to(torch_device)
|
|
|
|
for i, (labels_example, scores_example) in enumerate(zip(vqa_labels, vqa_scores)):
|
|
for l, s in zip(labels_example, scores_example):
|
|
labels[i, l] = s
|
|
|
|
# forward pass
|
|
outputs = model(**inputs, labels=labels)
|
|
|
|
# verify we have a positive loss
|
|
self.assertTrue(outputs.loss > 0)
|
|
|
|
@slow
|
|
def test_inference_natural_language_visual_reasoning(self):
|
|
model = ViltForImagesAndTextClassification.from_pretrained("dandelin/vilt-b32-finetuned-nlvr2").to(
|
|
torch_device
|
|
)
|
|
|
|
processor = self.default_processor
|
|
|
|
dataset = load_dataset("hf-internal-testing/fixtures_nlvr2", split="test")
|
|
image1 = Image.open(dataset[0]["file"]).convert("RGB")
|
|
image2 = Image.open(dataset[1]["file"]).convert("RGB")
|
|
|
|
text = (
|
|
"The left image contains twice the number of dogs as the right image, and at least two dogs in total are"
|
|
" standing."
|
|
)
|
|
encoding_1 = processor(image1, text, return_tensors="pt")
|
|
encoding_2 = processor(image2, text, return_tensors="pt")
|
|
|
|
pixel_values = torch.stack([encoding_1.pixel_values, encoding_2.pixel_values], dim=1)
|
|
|
|
# forward pass
|
|
outputs = model(
|
|
input_ids=encoding_1.input_ids.to(torch_device),
|
|
pixel_values=pixel_values.to(torch_device),
|
|
)
|
|
|
|
# verify the logits
|
|
expected_shape = torch.Size([1, 2])
|
|
self.assertEqual(outputs.logits.shape, expected_shape)
|
|
|
|
is_pillow_less_than_9 = version.parse(PIL.__version__) < version.parse("9.0.0")
|
|
|
|
if is_pillow_less_than_9:
|
|
expected_slice = torch.tensor(
|
|
[-2.4013, 2.9342],
|
|
device=torch_device,
|
|
)
|
|
else:
|
|
expected_slice = torch.tensor(
|
|
[-2.3713, 2.9168],
|
|
device=torch_device,
|
|
)
|
|
|
|
self.assertTrue(torch.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4))
|