From 1b6b82dbc9370b64063b18751898864789d2dc4d Mon Sep 17 00:00:00 2001 From: Hokuto Munakata Date: Mon, 14 Oct 2024 16:47:08 +0900 Subject: [PATCH 01/19] Add clap text encoder --- lighthouse/feature_extractor/text_encoder.py | 5 ++- .../feature_extractor/text_encoders/clap_t.py | 43 +++++++++++++++++++ 2 files changed, 47 insertions(+), 1 deletion(-) create mode 100644 lighthouse/feature_extractor/text_encoders/clap_t.py diff --git a/lighthouse/feature_extractor/text_encoder.py b/lighthouse/feature_extractor/text_encoder.py index 779daf2..80e3cf3 100644 --- a/lighthouse/feature_extractor/text_encoder.py +++ b/lighthouse/feature_extractor/text_encoder.py @@ -3,6 +3,7 @@ from lighthouse.feature_extractor.base_encoder import BaseEncoder from lighthouse.feature_extractor.text_encoders.clip_t import CLIPText from lighthouse.feature_extractor.text_encoders.glove import GloVe +from lighthouse.feature_extractor.text_encoders.clap_t import CLAPText from typing import Tuple @@ -37,6 +38,7 @@ def _select_text_encoders(self): 'clip': [CLIPText], 'clip_slowfast': [CLIPText], 'clip_slowfast_pann': [CLIPText], + 'clap': [CLAPText], } model_path_dict = { @@ -44,6 +46,7 @@ def _select_text_encoders(self): 'clip': ['ViT-B/32'], 'clip_slowfast': ['ViT-B/32'], 'clip_slowfast_pann': ['ViT-B/32'], + 'clap': ['2023'], } text_encoders = [encoder(self._device, model_path) @@ -56,4 +59,4 @@ def encode( outputs = [encoder(query) for encoder in self._text_encoders] text_features = torch.cat([o[0] for o in outputs]) text_masks = torch.cat([o[1] for o in outputs]) - return text_features, text_masks \ No newline at end of file + return text_features, text_masks diff --git a/lighthouse/feature_extractor/text_encoders/clap_t.py b/lighthouse/feature_extractor/text_encoders/clap_t.py new file mode 100644 index 0000000..737602a --- /dev/null +++ b/lighthouse/feature_extractor/text_encoders/clap_t.py @@ -0,0 +1,43 @@ +from typing import Tuple + +import torch +from msclap import CLAP + +""" +Copyright $today.year LY Corporation + +LY Corporation licenses this file to you 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: + + https://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. +""" + + +class CLAPText: + def __init__( + self, + device: str, + model_path: str, + ) -> None: + self._model_path: str = model_path + self._device: str = device + use_cuda = True if self._device == 'cuda' else False + self._clap_extractor = CLAP(use_cuda=use_cuda, version=model_path) + self._preprocessor = self._clap_extractor.preprocess_text + self._text_encoder = self._clap_extractor.clap.caption_encoder + + def __call__(self, query: str) -> Tuple[torch.Tensor, torch.Tensor]: + preprocessed = self._preprocessor([query]) + mask = preprocessed['attention_mask'] + + out = self._text_encoder.base(**preprocessed) + x = out[0] # out[1] is pooled output + + return x, mask From f821d267e17110348c13c3ce427e429cd257f8a0 Mon Sep 17 00:00:00 2001 From: Hokuto Munakata Date: Mon, 14 Oct 2024 23:34:22 +0900 Subject: [PATCH 02/19] Add CLAPAudio encoder and refactor AudioEncoder --- lighthouse/feature_extractor/audio_encoder.py | 63 ++++++--------- .../audio_encoders/clap_a.py | 64 +++++++++++++++ .../feature_extractor/audio_encoders/pann.py | 80 ++++++++++++++++++- 3 files changed, 164 insertions(+), 43 deletions(-) create mode 100644 lighthouse/feature_extractor/audio_encoders/clap_a.py diff --git a/lighthouse/feature_extractor/audio_encoder.py b/lighthouse/feature_extractor/audio_encoder.py index e099d18..dfa2b65 100644 --- a/lighthouse/feature_extractor/audio_encoder.py +++ b/lighthouse/feature_extractor/audio_encoder.py @@ -3,7 +3,8 @@ import numpy as np from lighthouse.feature_extractor.base_encoder import BaseEncoder -from lighthouse.feature_extractor.audio_encoders.pann import Cnn14 +from lighthouse.feature_extractor.audio_encoders.pann import PANN, PANNConfig +from lighthouse.feature_extractor.audio_encoders.clap_a import CLAPAudio, CLAPAudioConfig from typing import Optional, Tuple @@ -25,14 +26,6 @@ class AudioEncoder(BaseEncoder): - SAMPLE_RATE: int = 32000 - WINDOW_SIZE: int = 1024 - HOP_SIZE: int = 320 - MEL_BINS: int = 64 - FMIN: int = 50 - FMAX: int = 14000 - CLASSES_NUM: int = 527 - def __init__( self, feature_name: str, @@ -41,27 +34,22 @@ def __init__( self._feature_name = feature_name self._device = device self._pann_path = pann_path + self._audio_encoders = self._select_audio_encoders() - self._model = Cnn14(sample_rate=self.SAMPLE_RATE, window_size=self.WINDOW_SIZE, - hop_size=self.HOP_SIZE, mel_bins=self.MEL_BINS, - fmin=self.FMIN, fmax=self.FMAX, classes_num=self.CLASSES_NUM) - - if pann_path is not None: - checkpoint = torch.load(pann_path, map_location=device) - self._model.load_state_dict(checkpoint['model']) - self._model.eval() - else: - raise TypeError('pann_path should not be None when using AudioEncoder.') - - def _move_data_to_device( - self, - x: np.ndarray) -> torch.Tensor: - if 'float' in str(x.dtype): - return torch.Tensor(x).to(self._device) - elif 'int' in str(x.dtype): - return torch.LongTensor(x).to(self._device) - else: - raise ValueError('The input x cannot be cast into float or int.') + def _select_audio_encoders(self): + audio_encoders = { + 'pann': [PANN], + 'clap': [CLAPAudio] + } + + config_dict = { + 'pann': [PANNConfig(dict(model_path=self._pann_path))], + 'clap': [CLAPAudioConfig()], + } + + audio_encoders = [encoder(self._device, cfg) + for encoder, cfg in zip(audio_encoders[self._feature_name], config_dict[self._feature_name])] + return audio_encoders @torch.no_grad() def encode( @@ -69,14 +57,9 @@ def encode( video_path: str, feature_time: int = 2, ) -> Tuple[torch.Tensor, torch.Tensor]: - (audio, _) = librosa.core.load(video_path, sr=self.SAMPLE_RATE, mono=True) - time = audio.shape[-1] / self.SAMPLE_RATE - batches = int(time // feature_time) - clip_sr = round(self.SAMPLE_RATE * feature_time) - assert clip_sr >= 9920, 'clip_sr = round(sampling_rate * feature_time) should be larger than 9920.' - audio = audio[:batches * clip_sr] - audio_clips = np.reshape(audio, [batches, clip_sr]) - audio_clip_tensor = self._move_data_to_device(audio_clips) - output_dict = self._model(audio_clip_tensor, None) - audio_mask = torch.ones(1, len(output_dict['embedding'])).to(self._device) - return output_dict['embedding'].unsqueeze(0), audio_mask \ No newline at end of file + audio, sr = librosa.core.load(video_path, sr=None, mono=True) + + outputs = [encoder(audio, sr, feature_time) for encoder in self._audio_encoders] + audio_features = torch.cat([o[0] for o in outputs]) + audio_masks = torch.cat([o[1] for o in outputs]) + return audio_features, audio_masks diff --git a/lighthouse/feature_extractor/audio_encoders/clap_a.py b/lighthouse/feature_extractor/audio_encoders/clap_a.py new file mode 100644 index 0000000..3e0a0f7 --- /dev/null +++ b/lighthouse/feature_extractor/audio_encoders/clap_a.py @@ -0,0 +1,64 @@ +import argparse +from pathlib import Path + +import numpy as np +import librosa +from msclap import CLAP +import torch +from torch.nn import functional as F +from tqdm import tqdm + + +class CLAPAudioConfig: + def __init__(self, cfg: dict = None): + self.sample_rate: int = 32000 + self.window_sec: float = 1.0 + self.version: str = '2023' + + if cfg is not None: + self.update(cfg) + + def update(self, cfg: dict): + self.__dict__.update(cfg) + + +class CLAPAudio(torch.nn.Module): + def __init__(self, device: str, cfg: CLAPAudioConfig): + super(CLAPAudio, self).__init__() + use_cuda = True if device == 'cuda' else False + self.clap = CLAP(use_cuda=use_cuda, version=cfg.version) + self.sample_rate = cfg.sample_rate + self.window_sec = cfg.window_sec + self._device = device + + def _preprocess(self, audio: np.ndarray, sr: int, feature_time: float): + audio = librosa.resample(audio, orig_sr=sr, target_sr=self.sample_rate) + audio = self._move_data_to_device(audio) + + win_length = int(round(self.window_sec * self.sample_rate)) + hop_length = int(round(feature_time * self.sample_rate)) + + time = audio.shape[-1] / self.sample_rate + batches = int(time // feature_time) + clip_sr = round(self.sample_rate * feature_time) + audio = audio[:batches * clip_sr] + audio_clip = audio.unfold(0, win_length, hop_length) + + return audio_clip + + def _move_data_to_device( + self, + x: np.ndarray) -> torch.Tensor: + if 'float' in str(x.dtype): + return torch.Tensor(x).to(self._device) + elif 'int' in str(x.dtype): + return torch.LongTensor(x).to(self._device) + else: + raise ValueError('The input x cannot be cast into float or int.') + + def forward(self, audio: np.ndarray, sr: int, feature_time: float): + audio_clip = self._preprocess(audio, sr, feature_time) + output_dict = self.clap.clap.audio_encoder.base(audio_clip) + audio_mask = torch.ones(1, len(output_dict['embedding'])).to(self._device) + x = output_dict['embedding'].unsqueeze(0) + return x, audio_mask diff --git a/lighthouse/feature_extractor/audio_encoders/pann.py b/lighthouse/feature_extractor/audio_encoders/pann.py index 40870f2..ad36f92 100644 --- a/lighthouse/feature_extractor/audio_encoders/pann.py +++ b/lighthouse/feature_extractor/audio_encoders/pann.py @@ -22,10 +22,85 @@ import torch import torch.nn as nn import torch.nn.functional as F +import librosa +import numpy as np from torchlibrosa.stft import Spectrogram, LogmelFilterBank from torchlibrosa.augmentation import SpecAugmentation - + + +class PANNConfig: + def __init__(self, cfg: dict = None): + self.sample_rate: int = 32000 + self.window_size: int = 1024 + self.hop_size: int = 320 + self.mel_bins: int = 64 + self.fmin: int = 50 + self.fmax: int = 14000 + self.classes_num: int = 527 + self.model_path: str = None + + if cfg is not None: + self.update(cfg) + + def update(self, cfg: dict): + self.__dict__.update(cfg) + + +class PANN(torch.nn.Module): + def __init__(self, device: str, cfg: PANNConfig): + super(PANN, self).__init__() + self._device = device + self.sample_rate = cfg.sample_rate + self._model = Cnn14( + sample_rate=cfg.sample_rate, + window_size=cfg.window_size, + hop_size=cfg.hop_size, + mel_bins=cfg.mel_bins, + fmin=cfg.fmin, + fmax=cfg.fmax, + classes_num=cfg.classes_num, + ) + + if cfg.model_path is not None: + checkpoint = torch.load(cfg.model_path, map_location=device) + self._model.load_state_dict(checkpoint['model']) + self._model.eval() + self._model.to(device) + else: + raise TypeError('pann_path should not be None when using AudioEncoder.') + + def _preprocess(self, audio: np.ndarray, sr: int, feature_time: float): + audio = librosa.resample(audio, orig_sr=sr, target_sr=self.sample_rate) + audio = self._move_data_to_device(audio) + + time = audio.shape[-1] / self.sample_rate + batches = int(time // feature_time) + clip_sr = round(self.sample_rate * feature_time) + assert clip_sr >= 9920, 'clip_sr = round(sampling_rate * feature_time) should be larger than 9920.' + audio = audio[:batches * clip_sr] # Truncate audio to fit the clip_sr + audio_clip = audio.reshape([batches, clip_sr]) + + return audio_clip + + def _move_data_to_device( + self, + x: np.ndarray) -> torch.Tensor: + if 'float' in str(x.dtype): + return torch.Tensor(x).to(self._device) + elif 'int' in str(x.dtype): + return torch.LongTensor(x).to(self._device) + else: + raise ValueError('The input x cannot be cast into float or int.') + + def forward(self, audio: np.ndarray, sr: int, feature_time: float): + audio_clip = self._preprocess(audio, sr, feature_time) + output_dict = self._model(audio_clip, None) # audio_clip: (batch_size, clip_samples) + audio_mask = torch.ones(1, len(output_dict['embedding'])).to(self._device) + x = output_dict['embedding'].unsqueeze(0) + return x, audio_mask + + def do_mixup(x, mixup_lambda): out = x[0::2].transpose(0, -1) * mixup_lambda[0::2] + \ @@ -36,7 +111,6 @@ def do_mixup(x, mixup_lambda): def init_layer(layer): """Initialize a Linear or Convolutional layer. """ nn.init.xavier_uniform_(layer.weight) - if hasattr(layer, 'bias'): if layer.bias is not None: layer.bias.data.fill_(0.) @@ -182,4 +256,4 @@ def forward(self, input, mixup_lambda=None): output_dict = {'clipwise_output': clipwise_output, 'embedding': embedding} - return output_dict \ No newline at end of file + return output_dict From 6c330193ad88af932ebadc6ac5f34ff24c63fda1 Mon Sep 17 00:00:00 2001 From: Hokuto Munakata Date: Mon, 14 Oct 2024 23:52:38 +0900 Subject: [PATCH 03/19] Move feature_time to cfg --- lighthouse/feature_extractor/audio_encoder.py | 3 +-- .../feature_extractor/audio_encoders/clap_a.py | 14 ++++++++------ .../feature_extractor/audio_encoders/pann.py | 14 ++++++++------ 3 files changed, 17 insertions(+), 14 deletions(-) diff --git a/lighthouse/feature_extractor/audio_encoder.py b/lighthouse/feature_extractor/audio_encoder.py index dfa2b65..1a796a4 100644 --- a/lighthouse/feature_extractor/audio_encoder.py +++ b/lighthouse/feature_extractor/audio_encoder.py @@ -55,11 +55,10 @@ def _select_audio_encoders(self): def encode( self, video_path: str, - feature_time: int = 2, ) -> Tuple[torch.Tensor, torch.Tensor]: audio, sr = librosa.core.load(video_path, sr=None, mono=True) - outputs = [encoder(audio, sr, feature_time) for encoder in self._audio_encoders] + outputs = [encoder(audio, sr) for encoder in self._audio_encoders] audio_features = torch.cat([o[0] for o in outputs]) audio_masks = torch.cat([o[1] for o in outputs]) return audio_features, audio_masks diff --git a/lighthouse/feature_extractor/audio_encoders/clap_a.py b/lighthouse/feature_extractor/audio_encoders/clap_a.py index 3e0a0f7..76af95a 100644 --- a/lighthouse/feature_extractor/audio_encoders/clap_a.py +++ b/lighthouse/feature_extractor/audio_encoders/clap_a.py @@ -14,6 +14,7 @@ def __init__(self, cfg: dict = None): self.sample_rate: int = 32000 self.window_sec: float = 1.0 self.version: str = '2023' + self.feature_time: float = 1.0 if cfg is not None: self.update(cfg) @@ -29,18 +30,19 @@ def __init__(self, device: str, cfg: CLAPAudioConfig): self.clap = CLAP(use_cuda=use_cuda, version=cfg.version) self.sample_rate = cfg.sample_rate self.window_sec = cfg.window_sec + self.feature_time = cfg.feature_time self._device = device - def _preprocess(self, audio: np.ndarray, sr: int, feature_time: float): + def _preprocess(self, audio: np.ndarray, sr: int): audio = librosa.resample(audio, orig_sr=sr, target_sr=self.sample_rate) audio = self._move_data_to_device(audio) win_length = int(round(self.window_sec * self.sample_rate)) - hop_length = int(round(feature_time * self.sample_rate)) + hop_length = int(round(self.feature_time * self.sample_rate)) time = audio.shape[-1] / self.sample_rate - batches = int(time // feature_time) - clip_sr = round(self.sample_rate * feature_time) + batches = int(time // self.feature_time) + clip_sr = round(self.sample_rate * self.feature_time) audio = audio[:batches * clip_sr] audio_clip = audio.unfold(0, win_length, hop_length) @@ -56,8 +58,8 @@ def _move_data_to_device( else: raise ValueError('The input x cannot be cast into float or int.') - def forward(self, audio: np.ndarray, sr: int, feature_time: float): - audio_clip = self._preprocess(audio, sr, feature_time) + def forward(self, audio: np.ndarray, sr: int): + audio_clip = self._preprocess(audio, sr) output_dict = self.clap.clap.audio_encoder.base(audio_clip) audio_mask = torch.ones(1, len(output_dict['embedding'])).to(self._device) x = output_dict['embedding'].unsqueeze(0) diff --git a/lighthouse/feature_extractor/audio_encoders/pann.py b/lighthouse/feature_extractor/audio_encoders/pann.py index ad36f92..4fccfa6 100644 --- a/lighthouse/feature_extractor/audio_encoders/pann.py +++ b/lighthouse/feature_extractor/audio_encoders/pann.py @@ -39,6 +39,7 @@ def __init__(self, cfg: dict = None): self.fmax: int = 14000 self.classes_num: int = 527 self.model_path: str = None + self.feature_time: float = 2.0 if cfg is not None: self.update(cfg) @@ -52,6 +53,7 @@ def __init__(self, device: str, cfg: PANNConfig): super(PANN, self).__init__() self._device = device self.sample_rate = cfg.sample_rate + self.feature_time = cfg.feature_time self._model = Cnn14( sample_rate=cfg.sample_rate, window_size=cfg.window_size, @@ -70,17 +72,17 @@ def __init__(self, device: str, cfg: PANNConfig): else: raise TypeError('pann_path should not be None when using AudioEncoder.') - def _preprocess(self, audio: np.ndarray, sr: int, feature_time: float): + def _preprocess(self, audio: np.ndarray, sr: int): audio = librosa.resample(audio, orig_sr=sr, target_sr=self.sample_rate) - audio = self._move_data_to_device(audio) time = audio.shape[-1] / self.sample_rate - batches = int(time // feature_time) - clip_sr = round(self.sample_rate * feature_time) + batches = int(time // self.feature_time) + clip_sr = round(self.sample_rate * self.feature_time) assert clip_sr >= 9920, 'clip_sr = round(sampling_rate * feature_time) should be larger than 9920.' audio = audio[:batches * clip_sr] # Truncate audio to fit the clip_sr audio_clip = audio.reshape([batches, clip_sr]) + audio_clip = self._move_data_to_device(audio_clip) return audio_clip def _move_data_to_device( @@ -93,8 +95,8 @@ def _move_data_to_device( else: raise ValueError('The input x cannot be cast into float or int.') - def forward(self, audio: np.ndarray, sr: int, feature_time: float): - audio_clip = self._preprocess(audio, sr, feature_time) + def forward(self, audio: np.ndarray, sr: int): + audio_clip = self._preprocess(audio, sr) output_dict = self._model(audio_clip, None) # audio_clip: (batch_size, clip_samples) audio_mask = torch.ones(1, len(output_dict['embedding'])).to(self._device) x = output_dict['embedding'].unsqueeze(0) From 3e29b16c8c9003603969d74181651f500b3d9845 Mon Sep 17 00:00:00 2001 From: Hokuto Munakata Date: Mon, 21 Oct 2024 17:25:59 +0900 Subject: [PATCH 04/19] Fix a bug in evaluate.py --- training/evaluate.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/training/evaluate.py b/training/evaluate.py index 8265257..3560a92 100755 --- a/training/evaluate.py +++ b/training/evaluate.py @@ -439,7 +439,7 @@ def check_valid_combination(dataset, feature, domain): is_valid = check_valid_combination(args.dataset, args.feature, args.domain) if is_valid: - option_manager = BaseOptions(args.model, args.dataset, args.feature, args.domain) + option_manager = BaseOptions(args.model, args.dataset, args.feature, False, args.domain) option_manager.parse() opt = option_manager.option os.makedirs(opt.results_dir, exist_ok=True) From 025a610a66fa183383cc22ab056d3f88dec24f80 Mon Sep 17 00:00:00 2001 From: Hokuto Munakata Date: Mon, 21 Oct 2024 17:26:13 +0900 Subject: [PATCH 05/19] Fix clap feature extractor --- .../feature_extractor/audio_encoders/clap_a.py | 16 +++++++++------- 1 file changed, 9 insertions(+), 7 deletions(-) diff --git a/lighthouse/feature_extractor/audio_encoders/clap_a.py b/lighthouse/feature_extractor/audio_encoders/clap_a.py index 76af95a..3a654a8 100644 --- a/lighthouse/feature_extractor/audio_encoders/clap_a.py +++ b/lighthouse/feature_extractor/audio_encoders/clap_a.py @@ -3,15 +3,16 @@ import numpy as np import librosa -from msclap import CLAP import torch +import torchaudio.transforms as T +from msclap import CLAP from torch.nn import functional as F from tqdm import tqdm class CLAPAudioConfig: def __init__(self, cfg: dict = None): - self.sample_rate: int = 32000 + self.sample_rate: int = 44100 self.window_sec: float = 1.0 self.version: str = '2023' self.feature_time: float = 1.0 @@ -34,16 +35,17 @@ def __init__(self, device: str, cfg: CLAPAudioConfig): self._device = device def _preprocess(self, audio: np.ndarray, sr: int): - audio = librosa.resample(audio, orig_sr=sr, target_sr=self.sample_rate) audio = self._move_data_to_device(audio) + audio = T.Resample(sr, self.sample_rate)(audio) # original implementation in msclap win_length = int(round(self.window_sec * self.sample_rate)) hop_length = int(round(self.feature_time * self.sample_rate)) - time = audio.shape[-1] / self.sample_rate - batches = int(time // self.feature_time) - clip_sr = round(self.sample_rate * self.feature_time) - audio = audio[:batches * clip_sr] + # Truncate audio to fit the feature_time + # Note that this implementation is different from PANNs + half_win = win_length // 2 + audio = F.pad(audio, (half_win, half_win), mode="constant", value=0) + audio_clip = audio.unfold(0, win_length, hop_length) return audio_clip From fcb3d537fbe60a4c27eb396bbd4465ece4c6961f Mon Sep 17 00:00:00 2001 From: Hokuto Munakata Date: Mon, 21 Oct 2024 17:26:39 +0900 Subject: [PATCH 06/19] Add clap feature --- lighthouse/models.py | 36 ++++++++++++++++++++++++++++++------ 1 file changed, 30 insertions(+), 6 deletions(-) diff --git a/lighthouse/models.py b/lighthouse/models.py index 56cf637..124df20 100644 --- a/lighthouse/models.py +++ b/lighthouse/models.py @@ -72,9 +72,15 @@ def __init__( self._size = 224 self._moment_num = 10 - self._vision_encoder: VisionEncoder = self._initialize_vision_encoder(feature_name, slowfast_path) + if feature_name in ['clip', 'clip_slowfast', 'clip_slowfast_pann', 'i3d_clip', 'resnet_glove']: + self._vision_encoder: VisionEncoder = self._initialize_vision_encoder(feature_name, slowfast_path) + else: + self._vision_encoder = None self._text_encoder: TextEncoder = self._initialize_text_encoder(feature_name) - self._audio_encoder: Optional[AudioEncoder] = self._initialize_audio_encoder(feature_name, pann_path) + if feature_name in ['clip_slowfast_pann', 'clap']: + self._audio_encoder: AudioEncoder = self._initialize_audio_encoder(feature_name, pann_path) + else: + self._audio_encoder = None self._model: torch.nn.Module = self._initialize_model(args, model_name) self._load_weights(ckpt['model']) @@ -125,7 +131,7 @@ def _initialize_audio_encoder( self, feature_name: str, pann_path: Optional[str]) -> Optional[AudioEncoder]: - return AudioEncoder(feature_name, self._device, pann_path) if feature_name == 'clip_slowfast_pann' else None + return AudioEncoder(feature_name, self._device, pann_path) def _load_weights( self, @@ -148,9 +154,9 @@ def _normalize_and_concat_with_timestamps( def _is_predictable( self, ) -> bool: - if self._video_feats is None or self._video_mask is None or self._video_path is None: + if (self._video_feats is None or self._video_mask is None or self._video_path is None) and self._feature_name != 'clap': return False - if self._feature_name == 'clip_slowfast_pann' and self._audio_feats is None: + if (self._feature_name == 'clip_slowfast_pann' or self._feature_name != 'clap') and self._audio_feats is None: return False return True @@ -241,6 +247,24 @@ def encode_video( self._video_path = video_path self._audio_feats = self._encode_audio(video_path) + @torch.no_grad() + def encode_audio( + self, + audio_path: str) -> None: + if self._audio_encoder is None: + raise ValueError('The audio encoder is not initialized.') + audio_feats: torch.Tensor + audio_mask: torch.Tensor + audio_feats, audio_mask = self._audio_encoder.encode(audio_path) + self._audio_feats = audio_feats + + n_frames = torch.sum(audio_mask, dtype=torch.int) + tef_st = torch.arange(0, n_frames, 1.0) / n_frames + tef_ed = tef_st + 1.0 / n_frames + tef = torch.stack([tef_st, tef_ed], dim=1).to(self._device) + self._video_feats = tef.unsqueeze(0) + self._video_mask = audio_mask + @torch.no_grad() def predict( self, @@ -357,4 +381,4 @@ def __init__( pann_path: Optional[str] = None ) -> None: super().__init__('taskweave', ckpt_path, device, - feature_name, slowfast_path, pann_path) \ No newline at end of file + feature_name, slowfast_path, pann_path) From 8b51819508d5398bba3a6b7406c5949a9a21da99 Mon Sep 17 00:00:00 2001 From: Hokuto Munakata Date: Mon, 21 Oct 2024 17:26:47 +0900 Subject: [PATCH 07/19] Add msclap dependency --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 7581611..e1beba3 100644 --- a/setup.py +++ b/setup.py @@ -5,6 +5,6 @@ version='0.1', install_requires=['easydict', 'pandas', 'tqdm', 'pyyaml', 'scikit-learn', 'ffmpeg-python', 'ftfy', 'regex', 'einops', 'fvcore', 'gradio', 'torchlibrosa', 'librosa', - 'clip@git+https://github.com/openai/CLIP.git'], + 'clip@git+https://github.com/openai/CLIP.git', 'msclap'], packages=find_packages(exclude=['training']), ) From e7f0111652f1a077b155a53ead3e68f37d031aa1 Mon Sep 17 00:00:00 2001 From: Hokuto Munakata Date: Mon, 21 Oct 2024 17:50:54 +0900 Subject: [PATCH 08/19] Add audio sample for API --- api_example/1a-ODBWMUAE.wav | Bin 0 -> 3201056 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 api_example/1a-ODBWMUAE.wav diff --git a/api_example/1a-ODBWMUAE.wav b/api_example/1a-ODBWMUAE.wav new file mode 100644 index 0000000000000000000000000000000000000000..ab616bdf55b8db3cac1caad89f854e3ab88b4d4c GIT binary patch literal 3201056 zcmXWk3Aj$x_XqHEUNSV0AykG28B@kk<`AJ2Dno=a^%IIhks&H&uB4Qq43#lM6f!iB 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.../feature_extractor/audio_encoders/pann.py | 18 +++++++++------- lighthouse/models.py | 21 +++++++++++-------- mypy.ini | 8 ++++++- 4 files changed, 36 insertions(+), 24 deletions(-) diff --git a/lighthouse/feature_extractor/audio_encoders/clap_a.py b/lighthouse/feature_extractor/audio_encoders/clap_a.py index 3a654a8..0d9f0ad 100644 --- a/lighthouse/feature_extractor/audio_encoders/clap_a.py +++ b/lighthouse/feature_extractor/audio_encoders/clap_a.py @@ -1,5 +1,6 @@ import argparse from pathlib import Path +from typing import Optional import numpy as np import librosa @@ -11,7 +12,7 @@ class CLAPAudioConfig: - def __init__(self, cfg: dict = None): + def __init__(self, cfg: Optional[dict] = None): self.sample_rate: int = 44100 self.window_sec: float = 1.0 self.version: str = '2023' @@ -34,9 +35,9 @@ def __init__(self, device: str, cfg: CLAPAudioConfig): self.feature_time = cfg.feature_time self._device = device - def _preprocess(self, audio: np.ndarray, sr: int): - audio = self._move_data_to_device(audio) - audio = T.Resample(sr, self.sample_rate)(audio) # original implementation in msclap + def _preprocess(self, audio: np.ndarray, sr: int) -> torch.Tensor: + audio_tensor = self._move_data_to_device(audio) + audio_tensor = T.Resample(sr, self.sample_rate)(audio_tensor) # original implementation in msclap win_length = int(round(self.window_sec * self.sample_rate)) hop_length = int(round(self.feature_time * self.sample_rate)) @@ -44,9 +45,9 @@ def _preprocess(self, audio: np.ndarray, sr: int): # Truncate audio to fit the feature_time # Note that this implementation is different from PANNs half_win = win_length // 2 - audio = F.pad(audio, (half_win, half_win), mode="constant", value=0) + audio_tensor = F.pad(audio_tensor, (half_win, half_win), mode="constant", value=0) - audio_clip = audio.unfold(0, win_length, hop_length) + audio_clip = audio_tensor.unfold(0, win_length, hop_length) return audio_clip diff --git a/lighthouse/feature_extractor/audio_encoders/pann.py b/lighthouse/feature_extractor/audio_encoders/pann.py index 4fccfa6..a754f1d 100644 --- a/lighthouse/feature_extractor/audio_encoders/pann.py +++ b/lighthouse/feature_extractor/audio_encoders/pann.py @@ -19,6 +19,8 @@ https://github.com/qiuqiangkong/audioset_tagging_cnn/blob/master/pytorch/models.py """ +from typing import Optional + import torch import torch.nn as nn import torch.nn.functional as F @@ -30,7 +32,7 @@ class PANNConfig: - def __init__(self, cfg: dict = None): + def __init__(self, cfg: Optional[dict] = None): self.sample_rate: int = 32000 self.window_size: int = 1024 self.hop_size: int = 320 @@ -38,7 +40,7 @@ def __init__(self, cfg: dict = None): self.fmin: int = 50 self.fmax: int = 14000 self.classes_num: int = 527 - self.model_path: str = None + self.model_path: Optional[str] = None self.feature_time: float = 2.0 if cfg is not None: @@ -51,9 +53,9 @@ def update(self, cfg: dict): class PANN(torch.nn.Module): def __init__(self, device: str, cfg: PANNConfig): super(PANN, self).__init__() - self._device = device - self.sample_rate = cfg.sample_rate - self.feature_time = cfg.feature_time + self._device: str = device + self.sample_rate: int = cfg.sample_rate + self.feature_time: float = cfg.feature_time self._model = Cnn14( sample_rate=cfg.sample_rate, window_size=cfg.window_size, @@ -72,7 +74,7 @@ def __init__(self, device: str, cfg: PANNConfig): else: raise TypeError('pann_path should not be None when using AudioEncoder.') - def _preprocess(self, audio: np.ndarray, sr: int): + def _preprocess(self, audio: np.ndarray, sr: int) -> torch.Tensor: audio = librosa.resample(audio, orig_sr=sr, target_sr=self.sample_rate) time = audio.shape[-1] / self.sample_rate @@ -82,8 +84,8 @@ def _preprocess(self, audio: np.ndarray, sr: int): audio = audio[:batches * clip_sr] # Truncate audio to fit the clip_sr audio_clip = audio.reshape([batches, clip_sr]) - audio_clip = self._move_data_to_device(audio_clip) - return audio_clip + audio_clip_tensor = self._move_data_to_device(audio_clip) + return audio_clip_tensor def _move_data_to_device( self, diff --git a/lighthouse/models.py b/lighthouse/models.py index 124df20..deb75ad 100644 --- a/lighthouse/models.py +++ b/lighthouse/models.py @@ -72,15 +72,15 @@ def __init__( self._size = 224 self._moment_num = 10 + self._vision_encoder: Optional[VisionEncoder] = None if feature_name in ['clip', 'clip_slowfast', 'clip_slowfast_pann', 'i3d_clip', 'resnet_glove']: - self._vision_encoder: VisionEncoder = self._initialize_vision_encoder(feature_name, slowfast_path) - else: - self._vision_encoder = None - self._text_encoder: TextEncoder = self._initialize_text_encoder(feature_name) + self._vision_encoder = self._initialize_vision_encoder(feature_name, slowfast_path) + + self._audio_encoder: Optional[AudioEncoder] = None if feature_name in ['clip_slowfast_pann', 'clap']: - self._audio_encoder: AudioEncoder = self._initialize_audio_encoder(feature_name, pann_path) - else: - self._audio_encoder = None + self._audio_encoder = self._initialize_audio_encoder(feature_name, pann_path) + + self._text_encoder: TextEncoder = self._initialize_text_encoder(feature_name) self._model: torch.nn.Module = self._initialize_model(args, model_name) self._load_weights(ckpt['model']) @@ -236,7 +236,10 @@ def encode_video( video_path: str) -> None: video_feats: torch.Tensor video_mask: torch.Tensor - video_feats, video_mask = self._vision_encoder.encode(video_path) + if self._vision_encoder is not None: + video_feats, video_mask = self._vision_encoder.encode(video_path) + else: + raise ValueError('The vision encoder is not initialized.') timestamed_video_feats: torch.Tensor = self._normalize_and_concat_with_timestamps(video_feats) n_frames: int = len(timestamed_video_feats) if n_frames > 75: @@ -258,7 +261,7 @@ def encode_audio( audio_feats, audio_mask = self._audio_encoder.encode(audio_path) self._audio_feats = audio_feats - n_frames = torch.sum(audio_mask, dtype=torch.int) + n_frames = int(torch.sum(audio_mask, dtype=torch.int)) tef_st = torch.arange(0, n_frames, 1.0) / n_frames tef_ed = tef_st + 1.0 / n_frames tef = torch.stack([tef_st, tef_ed], dim=1).to(self._device) diff --git a/mypy.ini b/mypy.ini index 93d1eca..54eecc0 100644 --- a/mypy.ini +++ b/mypy.ini @@ -29,4 +29,10 @@ ignore_missing_imports = True ignore_missing_imports = True [mypy-torchvision.*] -ignore_missing_imports = True \ No newline at end of file +ignore_missing_imports = True + +[mypy-torchaudio.*] +ignore_missing_imports = True + +[mypy-msclap.*] +ignore_missing_imports = True From f77078daf647cb547474b2c60e4bb855c3ae3d93 Mon Sep 17 00:00:00 2001 From: Hokuto Munakata Date: Mon, 21 Oct 2024 18:46:27 +0900 Subject: [PATCH 10/19] Remove unnecessary imports --- lighthouse/feature_extractor/audio_encoder.py | 1 - lighthouse/feature_extractor/audio_encoders/clap_a.py | 4 ---- 2 files changed, 5 deletions(-) diff --git a/lighthouse/feature_extractor/audio_encoder.py b/lighthouse/feature_extractor/audio_encoder.py index 1a796a4..8480324 100644 --- a/lighthouse/feature_extractor/audio_encoder.py +++ b/lighthouse/feature_extractor/audio_encoder.py @@ -1,6 +1,5 @@ import torch import librosa -import numpy as np from lighthouse.feature_extractor.base_encoder import BaseEncoder from lighthouse.feature_extractor.audio_encoders.pann import PANN, PANNConfig diff --git a/lighthouse/feature_extractor/audio_encoders/clap_a.py b/lighthouse/feature_extractor/audio_encoders/clap_a.py index 0d9f0ad..bb5c225 100644 --- a/lighthouse/feature_extractor/audio_encoders/clap_a.py +++ b/lighthouse/feature_extractor/audio_encoders/clap_a.py @@ -1,14 +1,10 @@ -import argparse -from pathlib import Path from typing import Optional import numpy as np -import librosa import torch import torchaudio.transforms as T from msclap import CLAP from torch.nn import functional as F -from tqdm import tqdm class CLAPAudioConfig: From 56cfce1d6a9b6e44c65746ad38ec39a4f2adf2a7 Mon Sep 17 00:00:00 2001 From: Hokuto Munakata Date: Mon, 21 Oct 2024 20:40:41 +0900 Subject: [PATCH 11/19] Fix import order --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index e1beba3..327310e 100644 --- a/setup.py +++ b/setup.py @@ -5,6 +5,6 @@ version='0.1', install_requires=['easydict', 'pandas', 'tqdm', 'pyyaml', 'scikit-learn', 'ffmpeg-python', 'ftfy', 'regex', 'einops', 'fvcore', 'gradio', 'torchlibrosa', 'librosa', - 'clip@git+https://github.com/openai/CLIP.git', 'msclap'], + 'msclap', 'clip@git+https://github.com/openai/CLIP.git'], packages=find_packages(exclude=['training']), ) From 57465fd9eb197900a514e418e80ca1592cc393da Mon Sep 17 00:00:00 2001 From: Hokuto Munakata Date: Mon, 21 Oct 2024 20:43:45 +0900 Subject: [PATCH 12/19] Replace args for inference --- training/evaluate.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/training/evaluate.py b/training/evaluate.py index 3560a92..67779b7 100755 --- a/training/evaluate.py +++ b/training/evaluate.py @@ -439,7 +439,8 @@ def check_valid_combination(dataset, feature, domain): is_valid = check_valid_combination(args.dataset, args.feature, args.domain) if is_valid: - option_manager = BaseOptions(args.model, args.dataset, args.feature, False, args.domain) + resume = False + option_manager = BaseOptions(args.model, args.dataset, args.feature, resume, args.domain) option_manager.parse() opt = option_manager.option os.makedirs(opt.results_dir, exist_ok=True) From 6b9c297c7668a07644e38b4c68c45bfc20312ff2 Mon Sep 17 00:00:00 2001 From: Hokuto Munakata Date: Mon, 21 Oct 2024 22:39:04 +0900 Subject: [PATCH 13/19] Fix a bug in _is_predictable --- lighthouse/models.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/lighthouse/models.py b/lighthouse/models.py index deb75ad..85c5bce 100644 --- a/lighthouse/models.py +++ b/lighthouse/models.py @@ -156,7 +156,7 @@ def _is_predictable( ) -> bool: if (self._video_feats is None or self._video_mask is None or self._video_path is None) and self._feature_name != 'clap': return False - if (self._feature_name == 'clip_slowfast_pann' or self._feature_name != 'clap') and self._audio_feats is None: + if (self._feature_name == 'clip_slowfast_pann' or self._feature_name == 'clap') and self._audio_feats is None: return False return True From 8518776e5c78b36cee7e8b9067cf513f150d127a Mon Sep 17 00:00:00 2001 From: Hokuto Munakata Date: Mon, 21 Oct 2024 23:17:38 +0900 Subject: [PATCH 14/19] Fix CLAP feature extractor because of test --- lighthouse/feature_extractor/audio_encoders/clap_a.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/lighthouse/feature_extractor/audio_encoders/clap_a.py b/lighthouse/feature_extractor/audio_encoders/clap_a.py index bb5c225..82905d6 100644 --- a/lighthouse/feature_extractor/audio_encoders/clap_a.py +++ b/lighthouse/feature_extractor/audio_encoders/clap_a.py @@ -38,10 +38,10 @@ def _preprocess(self, audio: np.ndarray, sr: int) -> torch.Tensor: win_length = int(round(self.window_sec * self.sample_rate)) hop_length = int(round(self.feature_time * self.sample_rate)) - # Truncate audio to fit the feature_time - # Note that this implementation is different from PANNs - half_win = win_length // 2 - audio_tensor = F.pad(audio_tensor, (half_win, half_win), mode="constant", value=0) + time = audio_tensor.shape[-1] / self.sample_rate + batches = int(time // self.feature_time) + clip_sr = round(self.sample_rate * self.feature_time) + audio_tensor = audio_tensor[:batches * clip_sr] # Truncate audio to fit the clip_sr audio_clip = audio_tensor.unfold(0, win_length, hop_length) From e2f61ad06aa50b466ed101520502a4f5ffc7609b Mon Sep 17 00:00:00 2001 From: Hokuto Munakata Date: Mon, 21 Oct 2024 23:37:53 +0900 Subject: [PATCH 15/19] Add test for AMR model --- .gitignore | 1 + tests/test_audios/.gitkeep | 0 tests/test_models.py | 78 ++++++++++++++++++++++++++++++++++++-- 3 files changed, 76 insertions(+), 3 deletions(-) create mode 100644 tests/test_audios/.gitkeep diff --git a/.gitignore b/.gitignore index 733ed7d..7607f38 100644 --- a/.gitignore +++ b/.gitignore @@ -9,6 +9,7 @@ highlight_*.png SLOWFAST_8x8_R50.pkl Cnn14_mAP=0.431.pth tests/test_videos/video_duration_* +tests/test_audios/audio_duration_* tests/weights/*.ckpt # Mac diff --git a/tests/test_audios/.gitkeep b/tests/test_audios/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/tests/test_models.py b/tests/test_models.py index 4599ba2..d173003 100644 --- a/tests/test_models.py +++ b/tests/test_models.py @@ -8,10 +8,13 @@ FEATURES = ['clip', 'clip_slowfast'] +AUDIO_FEATURES = ['clap'] MODELS = ['moment_detr', 'qd_detr', 'eatr', 'cg_detr', 'uvcom'] DATASETS = ['qvhighlight'] MIN_DURATION = 10 MAX_DURATION = 151 +MIN_DURATION_AUDIO = 10 +MAX_DURATION_AUDIO = 50 MOMENT_NUM = 10 SAMPLE_NUM = 10 @@ -27,6 +30,17 @@ def test_generate_multiple_duration_videos(): for return_code in return_codes: assert return_code == 0, '[ffmpeg conversion] return_code should be set 0.' +@pytest.mark.dependency() +def test_generate_multiple_duration_audios(): + durations = [i for i in range(MIN_DURATION_AUDIO, MAX_DURATION_AUDIO)] + return_codes = [] + for duration in durations: + cmd = f'ffmpeg -y -i api_example/1a-ODBWMUAE.wav -t {duration} -c copy tests/test_audios/audio_duration_{duration}.wav' + result = subprocess.run(cmd, shell=True) + return_codes.append(result.returncode) + for return_code in return_codes: + assert return_code == 0, '[ffmpeg conversion] return_code should be set 0.' + @pytest.mark.dependency() def test_save_model_weights(): return_codes = [] @@ -37,6 +51,14 @@ def test_save_model_weights(): cmd = f'wget -P tests/weights/ https://zenodo.org/records/13363606/files/{feature}_{model}_{dataset}.ckpt' result = subprocess.run(cmd, shell=True) return_codes.append(result.returncode) + + for feature in AUDIO_FEATURES: + for model in ['qd_detr']: + for dataset in ['clotho-moment']: + if not os.path.exists(f'tests/weights/{feature}_{model}_{dataset}.ckpt'): + cmd = f'wget -P tests/weights/ https://zenodo.org/records/13961029/files/{feature}_{model}_{dataset}.ckpt' + result = subprocess.run(cmd, shell=True) + return_codes.append(result.returncode) for return_code in return_codes: assert return_code == 0, '[save model weights] return_code should be set 0.' @@ -53,9 +75,9 @@ def test_load_slowfast_pann_weights(): @pytest.mark.dependency(depends=['test_generate_multiple_duration_videos', 'test_save_model_weights', 'test_load_slowfast_pann_weights']) -def test_model_prediction(): +def test_video_model_prediction(): """ - Test all of the trained models, except for resnet_glove features and taskweave + Test all of the trained video moment retrieval models, except for resnet_glove features and taskweave Untested features: - ResNet+GloVe is skipped due to their low performance. - CLIP+Slowfast+PANNs is skipped due to their low latency. @@ -72,6 +94,7 @@ def test_model_prediction(): 'uvcom': UVCOMPredictor, } + # test video features for feature in FEATURES: for model_name in MODELS: for dataset in DATASETS: @@ -90,4 +113,53 @@ def test_model_prediction(): assert len(prediction['pred_relevant_windows']) == MOMENT_NUM, \ f'The number of moments from {feature}_{model_name}_{dataset} is expected {MOMENT_NUM}, but got {len(prediction["pred_relevant_windows"])}.' assert len(prediction['pred_saliency_scores']) == math.ceil(second / model._clip_len), \ - f'The number of saliency scores from {feature}_{model_name}_{dataset} is expected {math.ceil(second / model._clip_len)}, but got {len(prediction["pred_saliency_scores"])}.' \ No newline at end of file + f'The number of saliency scores from {feature}_{model_name}_{dataset} is expected {math.ceil(second / model._clip_len)}, but got {len(prediction["pred_saliency_scores"])}.' + + # test audio features + for feature in AUDIO_FEATURES: + for model_name in ['qd_detr']: + for dataset in ['clotho-moment']: + model_weight = os.path.join('tests/weights/', f'{feature}_{model_name}_{dataset}.ckpt') + model = QDDETRPredictor(model_weight, device='cpu', feature_name=feature, + slowfast_path=None, + pann_path=None) + + # test 10 duration samples + durations = random.sample([i for i in range(MIN_DURATION_AUDIO, MAX_DURATION_AUDIO)], SAMPLE_NUM) + for second in durations: + audio_path = f'tests/test_audios/audio_duration_{second}.wav' + model.encode_audio(audio_path) + query = 'Water cascades down from a waterfall.' + prediction = model.predict(query) + assert len(prediction['pred_relevant_windows']) == MOMENT_NUM, \ + f'The number of moments from {feature}_{model_name}_{dataset} is expected {MOMENT_NUM}, but got {len(prediction["pred_relevant_windows"])}.' + assert len(prediction['pred_saliency_scores']) == math.ceil(second / model._clip_len), \ + f'The number of saliency scores from {feature}_{model_name}_{dataset} is expected {math.ceil(second / model._clip_len)}, but got {len(prediction["pred_saliency_scores"])}.' + + +@pytest.mark.dependency(depends=['test_generate_multiple_duration_audios', + 'test_save_model_weights']) +def test_audio_model_prediction(): + """ + Test all of the trained audio moment retrieval models (now only supports QD-DETR with CLAP features.) + """ + # test audio features + for feature in AUDIO_FEATURES: + for model_name in ['qd_detr']: + for dataset in ['clotho-moment']: + model_weight = os.path.join('tests/weights/', f'{feature}_{model_name}_{dataset}.ckpt') + model = QDDETRPredictor(model_weight, device='cpu', feature_name=feature, + slowfast_path=None, + pann_path=None) + + # test 10 duration samples + durations = random.sample([i for i in range(MIN_DURATION_AUDIO, MAX_DURATION_AUDIO)], SAMPLE_NUM) + for second in durations: + audio_path = f'tests/test_audios/audio_duration_{second}.wav' + model.encode_audio(audio_path) + query = 'Water cascades down from a waterfall.' + prediction = model.predict(query) + assert len(prediction['pred_relevant_windows']) == MOMENT_NUM, \ + f'The number of moments from {feature}_{model_name}_{dataset} is expected {MOMENT_NUM}, but got {len(prediction["pred_relevant_windows"])}.' + assert len(prediction['pred_saliency_scores']) == math.ceil(second / model._clip_len), \ + f'The number of saliency scores from {feature}_{model_name}_{dataset} is expected {math.ceil(second / model._clip_len)}, but got {len(prediction["pred_saliency_scores"])}.' From 017d30a1df5105918d9ff028ebdd3919ecd0ae8e Mon Sep 17 00:00:00 2001 From: Hokuto Munakata Date: Tue, 22 Oct 2024 00:06:12 +0900 Subject: [PATCH 16/19] Remove unnecessary import --- lighthouse/feature_extractor/audio_encoders/clap_a.py | 1 - 1 file changed, 1 deletion(-) diff --git a/lighthouse/feature_extractor/audio_encoders/clap_a.py b/lighthouse/feature_extractor/audio_encoders/clap_a.py index 82905d6..9a98631 100644 --- a/lighthouse/feature_extractor/audio_encoders/clap_a.py +++ b/lighthouse/feature_extractor/audio_encoders/clap_a.py @@ -4,7 +4,6 @@ import torch import torchaudio.transforms as T from msclap import CLAP -from torch.nn import functional as F class CLAPAudioConfig: From e0b4aca81d34768b0eecedfa89761d2779737b9c Mon Sep 17 00:00:00 2001 From: Hokuto Munakata Date: Tue, 22 Oct 2024 01:03:26 +0900 Subject: [PATCH 17/19] Remove duplicated test --- tests/test_models.py | 22 ---------------------- 1 file changed, 22 deletions(-) diff --git a/tests/test_models.py b/tests/test_models.py index d173003..0e9bdda 100644 --- a/tests/test_models.py +++ b/tests/test_models.py @@ -115,28 +115,6 @@ def test_video_model_prediction(): assert len(prediction['pred_saliency_scores']) == math.ceil(second / model._clip_len), \ f'The number of saliency scores from {feature}_{model_name}_{dataset} is expected {math.ceil(second / model._clip_len)}, but got {len(prediction["pred_saliency_scores"])}.' - # test audio features - for feature in AUDIO_FEATURES: - for model_name in ['qd_detr']: - for dataset in ['clotho-moment']: - model_weight = os.path.join('tests/weights/', f'{feature}_{model_name}_{dataset}.ckpt') - model = QDDETRPredictor(model_weight, device='cpu', feature_name=feature, - slowfast_path=None, - pann_path=None) - - # test 10 duration samples - durations = random.sample([i for i in range(MIN_DURATION_AUDIO, MAX_DURATION_AUDIO)], SAMPLE_NUM) - for second in durations: - audio_path = f'tests/test_audios/audio_duration_{second}.wav' - model.encode_audio(audio_path) - query = 'Water cascades down from a waterfall.' - prediction = model.predict(query) - assert len(prediction['pred_relevant_windows']) == MOMENT_NUM, \ - f'The number of moments from {feature}_{model_name}_{dataset} is expected {MOMENT_NUM}, but got {len(prediction["pred_relevant_windows"])}.' - assert len(prediction['pred_saliency_scores']) == math.ceil(second / model._clip_len), \ - f'The number of saliency scores from {feature}_{model_name}_{dataset} is expected {math.ceil(second / model._clip_len)}, but got {len(prediction["pred_saliency_scores"])}.' - - @pytest.mark.dependency(depends=['test_generate_multiple_duration_audios', 'test_save_model_weights']) def test_audio_model_prediction(): From d216ea3ec11a710a6cf8638d1f75199764df0b7b Mon Sep 17 00:00:00 2001 From: Hokuto Munakata Date: Tue, 22 Oct 2024 10:14:30 +0900 Subject: [PATCH 18/19] Add msclap in test --- .github/workflows/pytest.yml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/.github/workflows/pytest.yml b/.github/workflows/pytest.yml index 61e599f..d12813c 100644 --- a/.github/workflows/pytest.yml +++ b/.github/workflows/pytest.yml @@ -29,6 +29,7 @@ jobs: run: | pip install torch==2.0.0 torchvision==0.15.1 torchaudio==2.0.1 torchtext==0.15.1 pip install easydict pandas tqdm pyyaml scikit-learn ffmpeg-python ftfy regex einops fvcore gradio torchlibrosa librosa + pip insatll msclap pip install 'clip@git+https://github.com/openai/CLIP.git' pip install -e . pip install pytest @@ -37,4 +38,4 @@ jobs: - name: Run pytest - run: pytest tests/test_models.py \ No newline at end of file + run: pytest tests/test_models.py From 6d53e694d534f6807ea8a900c91308a851c6f14f Mon Sep 17 00:00:00 2001 From: Hokuto Munakata Date: Tue, 22 Oct 2024 10:22:40 +0900 Subject: [PATCH 19/19] Fix typo --- .github/workflows/pytest.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/pytest.yml b/.github/workflows/pytest.yml index d12813c..e69071f 100644 --- a/.github/workflows/pytest.yml +++ b/.github/workflows/pytest.yml @@ -29,7 +29,7 @@ jobs: run: | pip install torch==2.0.0 torchvision==0.15.1 torchaudio==2.0.1 torchtext==0.15.1 pip install easydict pandas tqdm pyyaml scikit-learn ffmpeg-python ftfy regex einops fvcore gradio torchlibrosa librosa - pip insatll msclap + pip install msclap pip install 'clip@git+https://github.com/openai/CLIP.git' pip install -e . pip install pytest