626 lines
24 KiB
Python
626 lines
24 KiB
Python
# coding=utf-8
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# Copyright 2023 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 TVLT model. """
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import copy
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import inspect
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import unittest
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import numpy as np
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from huggingface_hub import hf_hub_download
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from transformers import (
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TvltConfig,
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is_datasets_available,
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is_speech_available,
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is_torch_available,
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is_vision_available,
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)
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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
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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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import torch.nn as nn
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from transformers import TvltForAudioVisualClassification, TvltForPreTraining, TvltModel
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if is_datasets_available():
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from datasets import load_dataset
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if is_vision_available():
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from transformers import TvltImageProcessor
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if is_speech_available():
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from transformers import TvltFeatureExtractor
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class TvltModelTester:
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def __init__(
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self,
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parent,
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batch_size=2,
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image_size=32,
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spectrogram_length=32,
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frequency_length=16,
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image_patch_size=[2, 2],
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audio_patch_size=[2, 2],
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num_image_channels=3,
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num_audio_channels=1,
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num_frames=2,
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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=128,
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hidden_act="gelu",
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hidden_dropout_prob=0.0,
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attention_probs_dropout_prob=0.0,
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initializer_range=0.02,
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layer_norm_eps=1e-12,
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qkv_bias=True,
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use_mean_pooling=True,
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decoder_num_attention_heads=4,
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decoder_hidden_size=32,
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decoder_num_hidden_layers=2,
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decoder_intermediate_size=128,
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image_mask_ratio=0.75,
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audio_mask_ratio=0.15,
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audio_mask_type="frame-level",
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task_matching=True,
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task_mae=True,
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num_labels=1,
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is_training=True,
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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.image_size = image_size
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self.spectrogram_length = spectrogram_length
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self.frequency_length = frequency_length
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self.image_patch_size = image_patch_size
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self.audio_patch_size = audio_patch_size
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self.num_image_channels = num_image_channels
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self.num_audio_channels = num_audio_channels
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self.num_frames = num_frames
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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.initializer_range = initializer_range
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self.layer_norm_eps = layer_norm_eps
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self.qkv_bias = qkv_bias
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self.use_mean_pooling = use_mean_pooling
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self.decoder_num_attention_heads = decoder_num_attention_heads
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self.decoder_hidden_size = decoder_hidden_size
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self.decoder_num_hidden_layers = decoder_num_hidden_layers
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self.decoder_intermediate_size = decoder_intermediate_size
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self.image_mask_ratio = image_mask_ratio
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self.audio_mask_ratio = audio_mask_ratio
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self.task_matching = task_matching
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self.task_mae = task_mae
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self.num_labels = num_labels
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self.expected_pixel_seq_len = (self.image_size // self.image_patch_size[0]) ** 2 * self.num_frames
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self.expected_audio_seq_len = (self.spectrogram_length // self.audio_patch_size[0]) * (
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self.frequency_length // self.audio_patch_size[1]
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)
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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 number of image/video patches + number of audio patches
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self.expected_seq_len = self.expected_pixel_seq_len + self.expected_audio_seq_len + 1
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self.image_mae_output_dim = image_patch_size[0] ** 2 * num_image_channels
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self.audio_mae_output_dim = audio_patch_size[0] * audio_patch_size[1] * num_audio_channels
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self.is_training = is_training
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def prepare_config_and_inputs(self):
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pixel_values = floats_tensor(
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[self.batch_size, self.num_frames, self.num_image_channels, self.image_size, self.image_size]
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)
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audio_values = floats_tensor(
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[self.batch_size, self.num_audio_channels, self.spectrogram_length, self.frequency_length]
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)
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pixel_mask = floats_tensor([self.batch_size, self.expected_pixel_seq_len])
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audio_mask = floats_tensor([self.batch_size, self.expected_audio_seq_len])
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config = self.get_config()
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return (config, pixel_values, audio_values, pixel_mask, audio_mask)
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def prepare_config_and_inputs_for_pretraining(self):
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pixel_values = floats_tensor(
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[self.batch_size, self.num_frames, self.num_image_channels, self.image_size, self.image_size]
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)
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audio_values = floats_tensor(
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[self.batch_size, self.num_audio_channels, self.spectrogram_length, self.frequency_length]
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)
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pixel_mask = floats_tensor([self.batch_size, self.expected_pixel_seq_len])
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audio_mask = floats_tensor([self.batch_size, self.expected_audio_seq_len])
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pixel_values_mixed = floats_tensor(
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[self.batch_size, self.num_frames, self.num_image_channels, self.image_size, self.image_size]
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)
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pixel_mask_mixed = floats_tensor([self.batch_size, self.expected_pixel_seq_len])
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labels = floats_tensor([self.batch_size])
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config = self.get_config()
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return (
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config,
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pixel_values,
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audio_values,
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pixel_mask,
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audio_mask,
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pixel_values_mixed,
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pixel_mask_mixed,
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labels,
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)
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def get_config(self):
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return TvltConfig(
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image_size=self.image_size,
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spectrogram_length=self.spectrogram_length,
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frequency_length=self.frequency_length,
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image_patch_size=self.image_patch_size,
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audio_patch_size=self.audio_patch_size,
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num_image_channels=self.num_image_channels,
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num_audio_channels=self.num_audio_channels,
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num_frames=self.num_frames,
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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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initializer_range=self.initializer_range,
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layer_norm_eps=self.layer_norm_eps,
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qkv_bias=self.qkv_bias,
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use_mean_pooling=self.use_mean_pooling,
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decoder_num_attention_heads=self.decoder_num_attention_heads,
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decoder_hidden_size=self.decoder_hidden_size,
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decoder_num_hidden_layers=self.decoder_num_hidden_layers,
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decoder_intermediate_size=self.decoder_intermediate_size,
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image_mask_ratio=self.image_mask_ratio,
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audio_mask_ratio=self.audio_mask_ratio,
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task_matching=self.task_matching,
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task_mae=self.task_mae,
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num_labels=self.num_labels,
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)
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def create_and_check_model(self, config, pixel_values, audio_values, pixel_mask, audio_mask):
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model = TvltModel(config=config)
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model.to(torch_device)
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model.eval()
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result = model(pixel_values, audio_values, pixel_mask=pixel_mask, audio_mask=audio_mask)
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result = model(pixel_values, audio_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_audiovisual_classification(
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self, config, pixel_values, audio_values, pixel_mask, audio_mask
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):
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model = TvltForAudioVisualClassification(config=config)
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model.to(torch_device)
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model.eval()
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result = model(pixel_values, audio_values, pixel_mask=pixel_mask, audio_mask=audio_mask)
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result = model(pixel_values, audio_values)
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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_pretraining(
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self,
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config,
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pixel_values,
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audio_values,
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pixel_mask,
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audio_mask,
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pixel_values_mixed,
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pixel_mask_mixed,
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labels,
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):
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model = TvltForPreTraining(config=config)
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model.to(torch_device)
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model.train()
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result = model(
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pixel_values,
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audio_values,
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pixel_mask,
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audio_mask,
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pixel_values_mixed=pixel_values_mixed,
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pixel_mask_mixed=pixel_mask_mixed,
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labels=labels,
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)
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self.parent.assertEqual(
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result.pixel_logits.shape, (self.batch_size, self.expected_pixel_seq_len, self.image_mae_output_dim)
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)
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self.parent.assertEqual(
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result.audio_logits.shape, (self.batch_size, self.expected_audio_seq_len, self.audio_mae_output_dim)
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)
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self.parent.assertEqual(result.matching_logits.shape, (self.batch_size, self.num_labels))
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def create_and_check_for_pretraining_inference(
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self,
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config,
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pixel_values,
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audio_values,
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pixel_mask,
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audio_mask,
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pixel_values_mixed,
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pixel_mask_mixed,
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labels,
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):
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model = TvltForPreTraining(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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pixel_values,
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audio_values,
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pixel_mask,
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audio_mask,
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pixel_values_mixed=pixel_values_mixed,
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pixel_mask_mixed=pixel_mask_mixed,
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labels=labels,
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)
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if result.pixel_logits is not None:
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self.parent.assertEqual(
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result.pixel_logits.shape, (self.batch_size, self.expected_pixel_seq_len, self.image_mae_output_dim)
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)
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if result.audio_logits is not None:
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self.parent.assertEqual(
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result.audio_logits.shape, (self.batch_size, self.expected_audio_seq_len, self.audio_mae_output_dim)
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)
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self.parent.assertEqual(result.matching_logits.shape, (self.batch_size, 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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(config, pixel_values, audio_values, pixel_mask, audio_mask) = config_and_inputs
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inputs_dict = {
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"pixel_values": pixel_values,
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"audio_values": audio_values,
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"pixel_mask": pixel_mask,
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"audio_mask": audio_mask,
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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(
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[self.batch_size, self.num_frames, self.num_image_channels, self.image_size, self.image_size]
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)
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def prepare_audio_values(self):
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return floats_tensor(
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[self.batch_size, self.num_audio_channels, self.spectrogram_length, self.frequency_length]
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)
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@require_torch
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class TvltModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
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all_model_classes = (
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(TvltModel, TvltForPreTraining, TvltForAudioVisualClassification) if is_torch_available() else ()
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)
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pipeline_model_mapping = {"feature-extraction": TvltModel} if is_torch_available() else {}
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fx_compatible = False
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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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test_resize_embeddings = False
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main_input_name = "pixel_values"
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# TvltForAudioVisualClassification and TvltForPreTraining require special treatment
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def _prepare_for_class(self, inputs_dict, model_class, return_labels=True):
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inputs_dict = copy.deepcopy(inputs_dict)
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if return_labels:
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if model_class.__name__ == "TvltForAudioVisualClassification":
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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.__name__ == "TvltForPreTraining":
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inputs_dict["labels"] = torch.zeros(
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(self.model_tester.batch_size,), dtype=torch.float, device=torch_device
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)
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inputs_dict["pixel_values_mixed"] = torch.zeros(
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(
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self.model_tester.batch_size,
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self.model_tester.num_frames,
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self.model_tester.num_image_channels,
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self.model_tester.image_size,
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self.model_tester.image_size,
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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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inputs_dict["pixel_mask_mixed"] = torch.zeros(
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(self.model_tester.batch_size, self.model_tester.expected_pixel_seq_len),
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dtype=torch.float,
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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 = TvltModelTester(self)
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self.config_tester = ConfigTester(self, config_class=TvltConfig, has_text_modality=False, 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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@unittest.skip(reason="TVLT does not use inputs_embeds")
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def test_inputs_embeds(self):
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pass
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def test_model_common_attributes(self):
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config, _ = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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model = model_class(config)
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input_embeddings = model.get_input_embeddings()
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self.assertIsInstance(input_embeddings, (tuple))
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for embedding in input_embeddings:
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self.assertIsInstance(embedding, (nn.Module))
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x = model.get_output_embeddings()
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self.assertTrue(x is None or isinstance(x, nn.Linear))
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def test_forward_signature(self):
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config, _ = self.model_tester.prepare_config_and_inputs_for_common()
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for model_class in self.all_model_classes:
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model = model_class(config)
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signature = inspect.signature(model.forward)
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# signature.parameters is an OrderedDict => so arg_names order is deterministic
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arg_names = [*signature.parameters.keys()]
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expected_arg_names = ["pixel_values", "audio_values"]
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self.assertListEqual(arg_names[:2], expected_arg_names)
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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_audiovisual_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_audiovisual_classification(*config_and_inputs)
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def test_for_pretraining(self):
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config_and_inputs = self.model_tester.prepare_config_and_inputs_for_pretraining()
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self.model_tester.create_and_check_for_pretraining(*config_and_inputs)
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self.model_tester.create_and_check_for_pretraining_inference(*config_and_inputs)
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@slow
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def test_model_from_pretrained(self):
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model_name = "ZinengTang/tvlt-base"
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model = TvltModel.from_pretrained(model_name)
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self.assertIsNotNone(model)
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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[1:]:
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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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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)
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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[1:]:
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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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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)
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loss = model(**inputs).loss
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loss.backward()
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def test_attention_outputs(self):
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if not self.has_attentions:
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pass
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else:
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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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for model_class in self.all_model_classes[2:]:
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seq_len = self.model_tester.expected_seq_len
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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():
|
|
outputs = model(**self._prepare_for_class(inputs_dict, model_class))
|
|
attentions = outputs.attentions
|
|
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
|
|
|
|
# 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))
|
|
attentions = outputs.attentions
|
|
self.assertEqual(len(attentions), self.model_tester.num_hidden_layers)
|
|
|
|
self.assertListEqual(
|
|
list(attentions[0].shape[-3:]),
|
|
[self.model_tester.num_attention_heads, seq_len, seq_len],
|
|
)
|
|
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))
|
|
|
|
self.assertEqual(out_len + 1, len(outputs))
|
|
|
|
self_attentions = outputs.attentions
|
|
|
|
self.assertEqual(len(self_attentions), self.model_tester.num_hidden_layers)
|
|
self.assertListEqual(
|
|
list(self_attentions[0].shape[-3:]),
|
|
[self.model_tester.num_attention_heads, seq_len, seq_len],
|
|
)
|
|
|
|
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.hidden_states
|
|
expected_num_layers = self.model_tester.num_hidden_layers + 1
|
|
self.assertEqual(len(hidden_states), expected_num_layers)
|
|
|
|
seq_length = self.model_tester.expected_seq_len
|
|
|
|
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[2:]:
|
|
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)
|
|
|
|
|
|
# We will verify our results on a video of eating spaghetti
|
|
# Frame indices used: [164 168 172 176 181 185 189 193 198 202 206 210 215 219 223 227]
|
|
def prepare_video(num_frames=8):
|
|
file = hf_hub_download(
|
|
repo_id="hf-internal-testing/spaghetti-video", filename="eating_spaghetti.npy", repo_type="dataset"
|
|
)
|
|
video = np.load(file)[:num_frames]
|
|
return list(video)
|
|
|
|
|
|
def prepare_audio(num_samples=1):
|
|
ds = load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
|
|
# automatic decoding with librispeech
|
|
speech_samples = ds.sort("id").select(range(num_samples))[:num_samples]["audio"]
|
|
return [x["array"] for x in speech_samples]
|
|
|
|
|
|
@require_torch
|
|
@require_vision
|
|
class TvltModelIntegrationTest(unittest.TestCase):
|
|
@cached_property
|
|
def default_processors(self):
|
|
# logits were tested with a different mean and std, so we use the same here
|
|
return (
|
|
TvltImageProcessor() if is_vision_available() else None,
|
|
TvltFeatureExtractor(),
|
|
)
|
|
|
|
def test_inference_for_base_model(self):
|
|
model = TvltModel.from_pretrained("ZinengTang/tvlt-base").to(torch_device)
|
|
|
|
image_processor, audio_feature_extractor = self.default_processors
|
|
video = prepare_video()
|
|
audio = prepare_audio()
|
|
video_inputs = image_processor(video, return_tensors="pt").to(torch_device)
|
|
audio_inputs = audio_feature_extractor(audio, return_tensors="pt").to(torch_device)
|
|
inputs = {}
|
|
inputs.update(video_inputs)
|
|
inputs.update(audio_inputs)
|
|
|
|
# forward pass
|
|
with torch.no_grad():
|
|
outputs = model(**inputs)
|
|
|
|
# verify the logits
|
|
expected_last_hidden_state_slice = torch.tensor([[-0.0186, -0.0691], [0.0242, -0.0398]], device=torch_device)
|
|
self.assertTrue(
|
|
torch.allclose(outputs.last_hidden_state[:, :2, :2], expected_last_hidden_state_slice, atol=1e-4)
|
|
)
|
|
|
|
def test_inference_for_pretraining(self):
|
|
model = TvltForPreTraining.from_pretrained("ZinengTang/tvlt-base").to(torch_device)
|
|
|
|
image_processor, audio_feature_extractor = self.default_processors
|
|
video = prepare_video()
|
|
video_mixed = prepare_video()
|
|
audio = prepare_audio()
|
|
video_inputs = image_processor(video, return_tensors="pt", mask_pixel=True).to(torch_device)
|
|
video_mixed_inputs = image_processor(video_mixed, is_mixed=True, return_tensors="pt").to(torch_device)
|
|
audio_inputs = audio_feature_extractor(audio, return_tensors="pt", mask_audio=True).to(torch_device)
|
|
labels = torch.tensor([[0.0]], device=torch_device)
|
|
inputs = {}
|
|
inputs.update(video_inputs)
|
|
inputs.update(video_mixed_inputs)
|
|
inputs.update(audio_inputs)
|
|
inputs.update({"labels": labels})
|
|
|
|
# forward pass
|
|
with torch.no_grad():
|
|
outputs = model(**inputs)
|
|
|
|
# verify the logits
|
|
expected_pixel_logits_shape = torch.Size([1, 1568, 768])
|
|
expected_audio_logits_shape = torch.Size([1, 96, 256])
|
|
expected_matching_logits_shape = torch.Size([1, 1])
|
|
|
|
if outputs.pixel_logits is not None:
|
|
self.assertEqual(outputs.pixel_logits.shape, expected_pixel_logits_shape)
|
|
if outputs.audio_logits is not None:
|
|
self.assertEqual(outputs.audio_logits.shape, expected_audio_logits_shape)
|
|
self.assertTrue(outputs.matching_logits.shape, expected_matching_logits_shape)
|