# coding=utf-8 # Copyright 2023 The HuggingFace Inc. team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ Testing suite for the PyTorch TVLT model. """ import copy import inspect import unittest import numpy as np from huggingface_hub import hf_hub_download from transformers import ( TvltConfig, is_datasets_available, is_speech_available, is_torch_available, is_vision_available, ) from transformers.testing_utils import require_torch, require_vision, slow, torch_device from transformers.utils import cached_property from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, floats_tensor from ...test_pipeline_mixin import PipelineTesterMixin if is_torch_available(): import torch import torch.nn as nn from transformers import TvltForAudioVisualClassification, TvltForPreTraining, TvltModel from transformers.models.tvlt.modeling_tvlt import TVLT_PRETRAINED_MODEL_ARCHIVE_LIST if is_datasets_available(): from datasets import load_dataset if is_vision_available(): from transformers import TvltImageProcessor if is_speech_available(): from transformers import TvltFeatureExtractor class TvltModelTester: def __init__( self, parent, batch_size=2, image_size=32, spectrogram_length=32, frequency_length=16, image_patch_size=[2, 2], audio_patch_size=[2, 2], num_image_channels=3, num_audio_channels=1, num_frames=2, hidden_size=32, num_hidden_layers=2, num_attention_heads=4, intermediate_size=128, hidden_act="gelu", hidden_dropout_prob=0.0, attention_probs_dropout_prob=0.0, initializer_range=0.02, layer_norm_eps=1e-12, qkv_bias=True, use_mean_pooling=True, decoder_num_attention_heads=4, decoder_hidden_size=32, decoder_num_hidden_layers=2, decoder_intermediate_size=128, image_mask_ratio=0.75, audio_mask_ratio=0.15, audio_mask_type="frame-level", task_matching=True, task_mae=True, num_labels=1, is_training=True, ): self.parent = parent self.batch_size = batch_size self.image_size = image_size self.spectrogram_length = spectrogram_length self.frequency_length = frequency_length self.image_patch_size = image_patch_size self.audio_patch_size = audio_patch_size self.num_image_channels = num_image_channels self.num_audio_channels = num_audio_channels self.num_frames = num_frames self.hidden_size = hidden_size self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.intermediate_size = intermediate_size self.hidden_act = hidden_act self.hidden_dropout_prob = hidden_dropout_prob self.attention_probs_dropout_prob = attention_probs_dropout_prob self.initializer_range = initializer_range self.layer_norm_eps = layer_norm_eps self.qkv_bias = qkv_bias self.use_mean_pooling = use_mean_pooling self.decoder_num_attention_heads = decoder_num_attention_heads self.decoder_hidden_size = decoder_hidden_size self.decoder_num_hidden_layers = decoder_num_hidden_layers self.decoder_intermediate_size = decoder_intermediate_size self.image_mask_ratio = image_mask_ratio self.audio_mask_ratio = audio_mask_ratio self.task_matching = task_matching self.task_mae = task_mae self.num_labels = num_labels self.expected_pixel_seq_len = (self.image_size // self.image_patch_size[0]) ** 2 * self.num_frames self.expected_audio_seq_len = (self.spectrogram_length // self.audio_patch_size[0]) * ( self.frequency_length // self.audio_patch_size[1] ) # we set the expected sequence length (which is used in several tests) # this is equal to the seq length of number of image/video patches + number of audio patches self.expected_seq_len = self.expected_pixel_seq_len + self.expected_audio_seq_len + 1 self.image_mae_output_dim = image_patch_size[0] ** 2 * num_image_channels self.audio_mae_output_dim = audio_patch_size[0] * audio_patch_size[1] * num_audio_channels self.is_training = is_training def prepare_config_and_inputs(self): pixel_values = floats_tensor( [self.batch_size, self.num_frames, self.num_image_channels, self.image_size, self.image_size] ) audio_values = floats_tensor( [self.batch_size, self.num_audio_channels, self.spectrogram_length, self.frequency_length] ) pixel_mask = floats_tensor([self.batch_size, self.expected_pixel_seq_len]) audio_mask = floats_tensor([self.batch_size, self.expected_audio_seq_len]) config = self.get_config() return (config, pixel_values, audio_values, pixel_mask, audio_mask) def prepare_config_and_inputs_for_pretraining(self): pixel_values = floats_tensor( [self.batch_size, self.num_frames, self.num_image_channels, self.image_size, self.image_size] ) audio_values = floats_tensor( [self.batch_size, self.num_audio_channels, self.spectrogram_length, self.frequency_length] ) pixel_mask = floats_tensor([self.batch_size, self.expected_pixel_seq_len]) audio_mask = floats_tensor([self.batch_size, self.expected_audio_seq_len]) pixel_values_mixed = floats_tensor( [self.batch_size, self.num_frames, self.num_image_channels, self.image_size, self.image_size] ) pixel_mask_mixed = floats_tensor([self.batch_size, self.expected_pixel_seq_len]) labels = floats_tensor([self.batch_size]) config = self.get_config() return ( config, pixel_values, audio_values, pixel_mask, audio_mask, pixel_values_mixed, pixel_mask_mixed, labels, ) def get_config(self): return TvltConfig( image_size=self.image_size, spectrogram_length=self.spectrogram_length, frequency_length=self.frequency_length, image_patch_size=self.image_patch_size, audio_patch_size=self.audio_patch_size, num_image_channels=self.num_image_channels, num_audio_channels=self.num_audio_channels, num_frames=self.num_frames, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, initializer_range=self.initializer_range, layer_norm_eps=self.layer_norm_eps, qkv_bias=self.qkv_bias, use_mean_pooling=self.use_mean_pooling, decoder_num_attention_heads=self.decoder_num_attention_heads, decoder_hidden_size=self.decoder_hidden_size, decoder_num_hidden_layers=self.decoder_num_hidden_layers, decoder_intermediate_size=self.decoder_intermediate_size, image_mask_ratio=self.image_mask_ratio, audio_mask_ratio=self.audio_mask_ratio, task_matching=self.task_matching, task_mae=self.task_mae, num_labels=self.num_labels, ) def create_and_check_model(self, config, pixel_values, audio_values, pixel_mask, audio_mask): model = TvltModel(config=config) model.to(torch_device) model.eval() result = model(pixel_values, audio_values, pixel_mask=pixel_mask, audio_mask=audio_mask) result = model(pixel_values, audio_values) self.parent.assertEqual( result.last_hidden_state.shape, (self.batch_size, self.expected_seq_len, self.hidden_size) ) def create_and_check_for_audiovisual_classification( self, config, pixel_values, audio_values, pixel_mask, audio_mask ): model = TvltForAudioVisualClassification(config=config) model.to(torch_device) model.eval() result = model(pixel_values, audio_values, pixel_mask=pixel_mask, audio_mask=audio_mask) result = model(pixel_values, audio_values) self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels)) def create_and_check_for_pretraining( self, config, pixel_values, audio_values, pixel_mask, audio_mask, pixel_values_mixed, pixel_mask_mixed, labels, ): model = TvltForPreTraining(config=config) model.to(torch_device) model.train() result = model( pixel_values, audio_values, pixel_mask, audio_mask, pixel_values_mixed=pixel_values_mixed, pixel_mask_mixed=pixel_mask_mixed, labels=labels, ) self.parent.assertEqual( result.pixel_logits.shape, (self.batch_size, self.expected_pixel_seq_len, self.image_mae_output_dim) ) self.parent.assertEqual( result.audio_logits.shape, (self.batch_size, self.expected_audio_seq_len, self.audio_mae_output_dim) ) self.parent.assertEqual(result.matching_logits.shape, (self.batch_size, self.num_labels)) def create_and_check_for_pretraining_inference( self, config, pixel_values, audio_values, pixel_mask, audio_mask, pixel_values_mixed, pixel_mask_mixed, labels, ): model = TvltForPreTraining(config=config) model.to(torch_device) model.eval() result = model( pixel_values, audio_values, pixel_mask, audio_mask, pixel_values_mixed=pixel_values_mixed, pixel_mask_mixed=pixel_mask_mixed, labels=labels, ) if result.pixel_logits is not None: self.parent.assertEqual( result.pixel_logits.shape, (self.batch_size, self.expected_pixel_seq_len, self.image_mae_output_dim) ) if result.audio_logits is not None: self.parent.assertEqual( result.audio_logits.shape, (self.batch_size, self.expected_audio_seq_len, self.audio_mae_output_dim) ) self.parent.assertEqual(result.matching_logits.shape, (self.batch_size, self.num_labels)) def prepare_config_and_inputs_for_common(self): config_and_inputs = self.prepare_config_and_inputs() (config, pixel_values, audio_values, pixel_mask, audio_mask) = config_and_inputs inputs_dict = { "pixel_values": pixel_values, "audio_values": audio_values, "pixel_mask": pixel_mask, "audio_mask": audio_mask, } return config, inputs_dict def prepare_pixel_values(self): return floats_tensor( [self.batch_size, self.num_frames, self.num_image_channels, self.image_size, self.image_size] ) def prepare_audio_values(self): return floats_tensor( [self.batch_size, self.num_audio_channels, self.spectrogram_length, self.frequency_length] ) @require_torch class TvltModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase): all_model_classes = ( (TvltModel, TvltForPreTraining, TvltForAudioVisualClassification) if is_torch_available() else () ) pipeline_model_mapping = {"feature-extraction": TvltModel} if is_torch_available() else {} fx_compatible = False test_pruning = False test_headmasking = False test_torchscript = False test_resize_embeddings = False main_input_name = "pixel_values" # TvltForAudioVisualClassification and TvltForPreTraining require special treatment def _prepare_for_class(self, inputs_dict, model_class, return_labels=True): inputs_dict = copy.deepcopy(inputs_dict) if return_labels: if model_class.__name__ == "TvltForAudioVisualClassification": inputs_dict["labels"] = torch.zeros( (self.model_tester.batch_size,), dtype=torch.long, device=torch_device ) elif model_class.__name__ == "TvltForPreTraining": inputs_dict["labels"] = torch.zeros( (self.model_tester.batch_size,), dtype=torch.float, device=torch_device ) inputs_dict["pixel_values_mixed"] = torch.zeros( ( self.model_tester.batch_size, self.model_tester.num_frames, self.model_tester.num_image_channels, self.model_tester.image_size, self.model_tester.image_size, ), dtype=torch.float, device=torch_device, ) inputs_dict["pixel_mask_mixed"] = torch.zeros( (self.model_tester.batch_size, self.model_tester.expected_pixel_seq_len), dtype=torch.float, device=torch_device, ) return inputs_dict def setUp(self): self.model_tester = TvltModelTester(self) self.config_tester = ConfigTester(self, config_class=TvltConfig, has_text_modality=False, hidden_size=37) def test_config(self): self.config_tester.run_common_tests() @unittest.skip(reason="TVLT does not use inputs_embeds") def test_inputs_embeds(self): pass def test_model_common_attributes(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) input_embeddings = model.get_input_embeddings() self.assertIsInstance(input_embeddings, (tuple)) for embedding in input_embeddings: self.assertIsInstance(embedding, (nn.Module)) x = model.get_output_embeddings() self.assertTrue(x is None or isinstance(x, nn.Linear)) def test_forward_signature(self): config, _ = self.model_tester.prepare_config_and_inputs_for_common() for model_class in self.all_model_classes: model = model_class(config) signature = inspect.signature(model.forward) # signature.parameters is an OrderedDict => so arg_names order is deterministic arg_names = [*signature.parameters.keys()] expected_arg_names = ["pixel_values", "audio_values"] self.assertListEqual(arg_names[:2], expected_arg_names) def test_model(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_model(*config_and_inputs) def test_for_audiovisual_classification(self): config_and_inputs = self.model_tester.prepare_config_and_inputs() self.model_tester.create_and_check_for_audiovisual_classification(*config_and_inputs) def test_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) self.model_tester.create_and_check_for_pretraining_inference(*config_and_inputs) @slow def test_model_from_pretrained(self): for model_name in TVLT_PRETRAINED_MODEL_ARCHIVE_LIST: model = TvltModel.from_pretrained(model_name) self.assertIsNotNone(model) def test_training(self): if not self.model_tester.is_training: return for model_class in self.all_model_classes[1:]: config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True model = model_class(config) model.to(torch_device) model.train() inputs = self._prepare_for_class(inputs_dict, model_class) for k, v in inputs.items(): print(k, v.shape) loss = model(**inputs).loss loss.backward() def test_training_gradient_checkpointing(self): if not self.model_tester.is_training: return for model_class in self.all_model_classes[1:]: config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.use_cache = False config.return_dict = True model = model_class(config) model.to(torch_device) model.gradient_checkpointing_enable() model.train() inputs = self._prepare_for_class(inputs_dict, model_class) loss = model(**inputs).loss loss.backward() def test_attention_outputs(self): if not self.has_attentions: pass else: config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common() config.return_dict = True for model_class in self.all_model_classes[2:]: seq_len = self.model_tester.expected_seq_len inputs_dict["output_attentions"] = True inputs_dict["output_hidden_states"] = False config.return_dict = 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) # 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)