forked from mrq/DL-Art-School
Mods to support contrastive learning on audio files
This commit is contained in:
James Betker
parent
341f28dd82
commit
5037220ac7
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@ -74,6 +74,8 @@ def create_dataset(dataset_opt, return_collate=False):
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from data.audio.gpt_tts_dataset import GptTtsDataset as D
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from data.audio.gpt_tts_dataset import GptTtsCollater as C
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collate = C(dataset_opt)
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elif mode == 'wavfile_clips':
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from data.audio.wavfile_dataset import WavfileDataset as D
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else:
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raise NotImplementedError('Dataset [{:s}] is not recognized.'.format(mode))
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dataset = D(dataset_opt)
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81
codes/data/audio/wavfile_dataset.py
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81
codes/data/audio/wavfile_dataset.py
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@ -0,0 +1,81 @@
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import os
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import random
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import torch
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import torch.utils.data
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from tqdm import tqdm
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from data.util import get_image_paths, is_wav_file
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from models.tacotron2.taco_utils import load_wav_to_torch
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class WavfileDataset(torch.utils.data.Dataset):
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def __init__(self, opt):
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self.path = os.path.dirname(opt['path'])
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cache_path = os.path.join(self.path, 'cache.pth')
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if os.path.exists(cache_path):
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self.audiopaths = torch.load(cache_path)
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else:
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print("Building cache..")
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self.audiopaths = get_image_paths('img', opt['path'], qualifier=is_wav_file)[0]
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torch.save(self.audiopaths, cache_path)
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self.max_wav_value = 32768.0
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self.sampling_rate = 24000
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self.window = 2 * self.sampling_rate
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def get_audio_for_index(self, index):
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audiopath = self.audiopaths[index]
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filename = os.path.join(self.path, audiopath)
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audio, sampling_rate = load_wav_to_torch(filename)
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if sampling_rate != self.sampling_rate:
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raise ValueError(f"Input sampling rate does not match specified rate {self.sampling_rate}")
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audio_norm = audio / self.max_wav_value
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audio_norm = torch.autograd.Variable(audio_norm, requires_grad=False)
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return audio_norm, audiopath
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def __getitem__(self, index):
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clip1, clip2 = None, None
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while clip1 is None and clip2 is None:
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# Split audio_norm into two tensors of equal size.
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audio_norm, filename = self.get_audio_for_index(index)
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if audio_norm.shape[0] < self.window * 2:
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# Try next index. This adds a bit of bias and ideally we'd filter the dataset rather than do this.
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index = (index + 1) % len(self)
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continue
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j = random.randint(0, audio_norm.shape[0] - self.window)
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clip1 = audio_norm[j:j+self.window]
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j = random.randint(0, audio_norm.shape[0]-self.window)
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clip2 = audio_norm[j:j+self.window]
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return {
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'clip1': clip1.unsqueeze(0),
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'clip2': clip2.unsqueeze(0),
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'path': filename,
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}
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def __len__(self):
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return len(self.audiopaths)
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if __name__ == '__main__':
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params = {
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'mode': 'wavfile_clips',
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'path': 'E:\\audio\\LibriTTS\\train-other-500',
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'phase': 'train',
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'n_workers': 0,
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'batch_size': 16,
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}
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from data import create_dataset, create_dataloader, util
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ds, c = create_dataset(params, return_collate=True)
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dl = create_dataloader(ds, params, collate_fn=c)
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i = 0
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m = []
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max_text = 0
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max_mel = 0
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for b in tqdm(dl):
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pass
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m=torch.stack(m)
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print(m.mean(), m.std())
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@ -39,14 +39,17 @@ def cv2torch(cv, batchify=True):
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def is_image_file(filename):
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return any(filename.endswith(extension) for extension in IMG_EXTENSIONS)
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def is_wav_file(filename):
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return filename.endswith('.wav')
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def _get_paths_from_images(path):
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def _get_paths_from_images(path, qualifier=is_image_file):
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"""get image path list from image folder"""
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assert os.path.isdir(path), '{:s} is not a valid directory'.format(path)
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images = []
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for dirpath, _, fnames in sorted(os.walk(path)):
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for fname in sorted(fnames):
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if is_image_file(fname) and 'ref.jpg' not in fname:
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if qualifier(fname) and 'ref.jpg' not in fname:
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img_path = os.path.join(dirpath, fname)
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images.append(img_path)
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if not images:
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@ -64,7 +67,7 @@ def _get_paths_from_lmdb(dataroot):
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return paths, sizes
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def get_image_paths(data_type, dataroot, weights=[]):
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def get_image_paths(data_type, dataroot, weights=[], qualifier=is_image_file):
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"""get image path list
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support lmdb or image files"""
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paths, sizes = None, None
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@ -82,11 +85,11 @@ def get_image_paths(data_type, dataroot, weights=[]):
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if weights:
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extends = weights[i]
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for j in range(extends):
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paths.extend(_get_paths_from_images(r))
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paths.extend(_get_paths_from_images(r, qualifier))
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paths = sorted(paths)
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sizes = len(paths)
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else:
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paths = sorted(_get_paths_from_images(dataroot))
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paths = sorted(_get_paths_from_images(dataroot, qualifier))
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sizes = len(paths)
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else:
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raise NotImplementedError('data_type [{:s}] is not recognized.'.format(data_type))
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@ -117,9 +120,9 @@ def read_img(env, path, size=None, rgb=False):
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stream = open(path, "rb")
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bytes = bytearray(stream.read())
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img = cv2.imdecode(np.asarray(bytes, dtype=np.uint8), cv2.IMREAD_UNCHANGED)
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elif env is 'lmdb':
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elif env == 'lmdb':
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img = _read_img_lmdb(env, path, size)
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elif env is 'buffer':
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elif env == 'buffer':
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img = cv2.imdecode(path, cv2.IMREAD_UNCHANGED)
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else:
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raise NotImplementedError("Unsupported env: %s" % (env,))
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387
codes/models/audio_resnet.py
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387
codes/models/audio_resnet.py
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@ -0,0 +1,387 @@
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import torch
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from torch import Tensor
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import torch.nn as nn
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from trainer.networks import register_model
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from utils.util import opt_get
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from typing import Type, Any, Callable, Union, List, Optional
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__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
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'resnet152', 'resnext50_32x4d', 'resnext101_32x8d',
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'wide_resnet50_2', 'wide_resnet101_2']
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def conv3x3(in_planes: int, out_planes: int, stride: int = 1, groups: int = 1, dilation: int = 1) -> nn.Conv1d:
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"""3x3 convolution with padding"""
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return nn.Conv1d(in_planes, out_planes, kernel_size=5, stride=stride,
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padding=2, groups=groups, bias=False, dilation=dilation)
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def conv1x1(in_planes: int, out_planes: int, stride: int = 1) -> nn.Conv1d:
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"""1x1 convolution"""
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return nn.Conv1d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
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class BasicBlock(nn.Module):
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expansion: int = 1
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def __init__(
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self,
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inplanes: int,
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planes: int,
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stride: int = 1,
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downsample: Optional[nn.Module] = None,
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groups: int = 1,
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base_width: int = 64,
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dilation: int = 1,
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norm_layer: Optional[Callable[..., nn.Module]] = None
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) -> None:
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super(BasicBlock, self).__init__()
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if norm_layer is None:
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norm_layer = nn.BatchNorm1d
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if groups != 1 or base_width != 64:
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raise ValueError('BasicBlock only supports groups=1 and base_width=64')
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if dilation > 1:
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raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
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# Both self.conv1 and self.downsample layers downsample the input when stride != 1
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self.conv1 = conv3x3(inplanes, planes, stride)
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self.bn1 = norm_layer(planes)
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self.relu = nn.ReLU(inplace=True)
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self.conv2 = conv3x3(planes, planes)
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self.bn2 = norm_layer(planes)
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self.downsample = downsample
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self.stride = stride
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def forward(self, x: Tensor) -> Tensor:
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identity = x
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out = self.conv1(x)
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out = self.bn1(out)
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out = self.relu(out)
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out = self.conv2(out)
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out = self.bn2(out)
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if self.downsample is not None:
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identity = self.downsample(x)
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out += identity
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out = self.relu(out)
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return out
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class Bottleneck(nn.Module):
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# Bottleneck in torchvision places the stride for downsampling at 3x3 convolution(self.conv2)
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# while original implementation places the stride at the first 1x1 convolution(self.conv1)
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# according to "Deep residual learning for image recognition"https://arxiv.org/abs/1512.03385.
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# This variant is also known as ResNet V1.5 and improves accuracy according to
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# https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch.
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expansion: int = 4
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def __init__(
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self,
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inplanes: int,
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planes: int,
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stride: int = 1,
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downsample: Optional[nn.Module] = None,
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groups: int = 1,
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base_width: int = 64,
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dilation: int = 1,
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norm_layer: Optional[Callable[..., nn.Module]] = None
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) -> None:
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super(Bottleneck, self).__init__()
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if norm_layer is None:
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norm_layer = nn.BatchNorm1d
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width = int(planes * (base_width / 64.)) * groups
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# Both self.conv2 and self.downsample layers downsample the input when stride != 1
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self.conv1 = conv1x1(inplanes, width)
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self.bn1 = norm_layer(width)
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self.conv2 = conv3x3(width, width, stride, groups, dilation)
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self.bn2 = norm_layer(width)
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self.conv3 = conv1x1(width, planes * self.expansion)
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self.bn3 = norm_layer(planes * self.expansion)
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self.relu = nn.ReLU(inplace=True)
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self.downsample = downsample
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self.stride = stride
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def forward(self, x: Tensor) -> Tensor:
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identity = x
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out = self.conv1(x)
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out = self.bn1(out)
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out = self.relu(out)
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out = self.conv2(out)
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out = self.bn2(out)
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out = self.relu(out)
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out = self.conv3(out)
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out = self.bn3(out)
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if self.downsample is not None:
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identity = self.downsample(x)
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out += identity
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out = self.relu(out)
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return out
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class ResNet(nn.Module):
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def __init__(
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self,
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block: Type[Union[BasicBlock, Bottleneck]],
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layers: List[int],
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num_classes: int = 1000,
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zero_init_residual: bool = False,
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groups: int = 1,
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width_per_group: int = 64,
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replace_stride_with_dilation: Optional[List[bool]] = None,
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norm_layer: Optional[Callable[..., nn.Module]] = None
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) -> None:
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super(ResNet, self).__init__()
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if norm_layer is None:
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norm_layer = nn.BatchNorm1d
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self._norm_layer = norm_layer
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self.inplanes = 64
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self.dilation = 1
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if replace_stride_with_dilation is None:
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# each element in the tuple indicates if we should replace
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# the 2x2 stride with a dilated convolution instead
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replace_stride_with_dilation = [False, False, False]
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if len(replace_stride_with_dilation) != 3:
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raise ValueError("replace_stride_with_dilation should be None "
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"or a 3-element tuple, got {}".format(replace_stride_with_dilation))
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self.groups = groups
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self.base_width = width_per_group
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self.conv1 = nn.Conv1d(1, self.inplanes, kernel_size=7, stride=4, padding=3,
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bias=False)
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self.bn1 = norm_layer(self.inplanes)
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self.relu = nn.ReLU(inplace=True)
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self.maxpool = nn.MaxPool1d(kernel_size=5, stride=4, padding=2)
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self.layer1 = self._make_layer(block, 64, layers[0])
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self.layer2 = self._make_layer(block, 128, layers[1], stride=4,
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dilate=replace_stride_with_dilation[0])
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self.layer3 = self._make_layer(block, 256, layers[2], stride=4,
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dilate=replace_stride_with_dilation[1])
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self.layer4 = self._make_layer(block, 512, layers[3], stride=4,
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dilate=replace_stride_with_dilation[2])
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self.avgpool = nn.AdaptiveAvgPool1d(1)
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self.fc = nn.Linear(512 * block.expansion, num_classes)
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for m in self.modules():
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if isinstance(m, nn.Conv1d):
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nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
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elif isinstance(m, (nn.BatchNorm1d, nn.GroupNorm)):
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nn.init.constant_(m.weight, 1)
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nn.init.constant_(m.bias, 0)
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# Zero-initialize the last BN in each residual branch,
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# so that the residual branch starts with zeros, and each residual block behaves like an identity.
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# This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
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if zero_init_residual:
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for m in self.modules():
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if isinstance(m, Bottleneck):
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nn.init.constant_(m.bn3.weight, 0) # type: ignore[arg-type]
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elif isinstance(m, BasicBlock):
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nn.init.constant_(m.bn2.weight, 0) # type: ignore[arg-type]
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def _make_layer(self, block: Type[Union[BasicBlock, Bottleneck]], planes: int, blocks: int,
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stride: int = 1, dilate: bool = False) -> nn.Sequential:
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norm_layer = self._norm_layer
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downsample = None
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previous_dilation = self.dilation
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if dilate:
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self.dilation *= stride
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stride = 1
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if stride != 1 or self.inplanes != planes * block.expansion:
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downsample = nn.Sequential(
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conv1x1(self.inplanes, planes * block.expansion, stride),
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norm_layer(planes * block.expansion),
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)
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layers = []
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layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
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self.base_width, previous_dilation, norm_layer))
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self.inplanes = planes * block.expansion
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for _ in range(1, blocks):
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layers.append(block(self.inplanes, planes, groups=self.groups,
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base_width=self.base_width, dilation=self.dilation,
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norm_layer=norm_layer))
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return nn.Sequential(*layers)
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def _forward_impl(self, x: Tensor) -> Tensor:
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# See note [TorchScript super()]
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x = self.conv1(x)
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x = self.bn1(x)
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x = self.relu(x)
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x = self.maxpool(x)
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x = self.layer1(x)
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x = self.layer2(x)
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x = self.layer3(x)
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x = self.layer4(x)
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x = self.avgpool(x)
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x = torch.flatten(x, 1)
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x = self.fc(x)
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return x
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def forward(self, x: Tensor) -> Tensor:
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return self._forward_impl(x)
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def _resnet(
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arch: str,
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block: Type[Union[BasicBlock, Bottleneck]],
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layers: List[int],
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pretrained: bool,
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progress: bool,
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**kwargs: Any
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) -> ResNet:
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model = ResNet(block, layers, **kwargs)
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return model
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def resnet18(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
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r"""ResNet-18 model from
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`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
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Args:
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pretrained (bool): If True, returns a model pre-trained on ImageNet
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progress (bool): If True, displays a progress bar of the download to stderr
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"""
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return _resnet('resnet18', BasicBlock, [2, 2, 2, 2], pretrained, progress,
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**kwargs)
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def resnet34(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
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r"""ResNet-34 model from
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`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
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Args:
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pretrained (bool): If True, returns a model pre-trained on ImageNet
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progress (bool): If True, displays a progress bar of the download to stderr
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"""
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return _resnet('resnet34', BasicBlock, [3, 4, 6, 3], pretrained, progress,
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**kwargs)
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def resnet50(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
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r"""ResNet-50 model from
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`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
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Args:
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pretrained (bool): If True, returns a model pre-trained on ImageNet
|
||||
progress (bool): If True, displays a progress bar of the download to stderr
|
||||
"""
|
||||
return _resnet('resnet50', Bottleneck, [3, 4, 6, 3], pretrained, progress,
|
||||
**kwargs)
|
||||
|
||||
|
||||
def resnet101(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
|
||||
r"""ResNet-101 model from
|
||||
`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
|
||||
|
||||
Args:
|
||||
pretrained (bool): If True, returns a model pre-trained on ImageNet
|
||||
progress (bool): If True, displays a progress bar of the download to stderr
|
||||
"""
|
||||
return _resnet('resnet101', Bottleneck, [3, 4, 23, 3], pretrained, progress,
|
||||
**kwargs)
|
||||
|
||||
|
||||
def resnet152(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
|
||||
r"""ResNet-152 model from
|
||||
`"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.
|
||||
|
||||
Args:
|
||||
pretrained (bool): If True, returns a model pre-trained on ImageNet
|
||||
progress (bool): If True, displays a progress bar of the download to stderr
|
||||
"""
|
||||
return _resnet('resnet152', Bottleneck, [3, 8, 36, 3], pretrained, progress,
|
||||
**kwargs)
|
||||
|
||||
|
||||
def resnext50_32x4d(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
|
||||
r"""ResNeXt-50 32x4d model from
|
||||
`"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_.
|
||||
|
||||
Args:
|
||||
pretrained (bool): If True, returns a model pre-trained on ImageNet
|
||||
progress (bool): If True, displays a progress bar of the download to stderr
|
||||
"""
|
||||
kwargs['groups'] = 32
|
||||
kwargs['width_per_group'] = 4
|
||||
return _resnet('resnext50_32x4d', Bottleneck, [3, 4, 6, 3],
|
||||
pretrained, progress, **kwargs)
|
||||
|
||||
|
||||
def resnext101_32x8d(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
|
||||
r"""ResNeXt-101 32x8d model from
|
||||
`"Aggregated Residual Transformation for Deep Neural Networks" <https://arxiv.org/pdf/1611.05431.pdf>`_.
|
||||
|
||||
Args:
|
||||
pretrained (bool): If True, returns a model pre-trained on ImageNet
|
||||
progress (bool): If True, displays a progress bar of the download to stderr
|
||||
"""
|
||||
kwargs['groups'] = 32
|
||||
kwargs['width_per_group'] = 8
|
||||
return _resnet('resnext101_32x8d', Bottleneck, [3, 4, 23, 3],
|
||||
pretrained, progress, **kwargs)
|
||||
|
||||
|
||||
def wide_resnet50_2(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
|
||||
r"""Wide ResNet-50-2 model from
|
||||
`"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_.
|
||||
|
||||
The model is the same as ResNet except for the bottleneck number of channels
|
||||
which is twice larger in every block. The number of channels in outer 1x1
|
||||
convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048
|
||||
channels, and in Wide ResNet-50-2 has 2048-1024-2048.
|
||||
|
||||
Args:
|
||||
pretrained (bool): If True, returns a model pre-trained on ImageNet
|
||||
progress (bool): If True, displays a progress bar of the download to stderr
|
||||
"""
|
||||
kwargs['width_per_group'] = 64 * 2
|
||||
return _resnet('wide_resnet50_2', Bottleneck, [3, 4, 6, 3],
|
||||
pretrained, progress, **kwargs)
|
||||
|
||||
|
||||
def wide_resnet101_2(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
|
||||
r"""Wide ResNet-101-2 model from
|
||||
`"Wide Residual Networks" <https://arxiv.org/pdf/1605.07146.pdf>`_.
|
||||
|
||||
The model is the same as ResNet except for the bottleneck number of channels
|
||||
which is twice larger in every block. The number of channels in outer 1x1
|
||||
convolutions is the same, e.g. last block in ResNet-50 has 2048-512-2048
|
||||
channels, and in Wide ResNet-50-2 has 2048-1024-2048.
|
||||
|
||||
Args:
|
||||
pretrained (bool): If True, returns a model pre-trained on ImageNet
|
||||
progress (bool): If True, displays a progress bar of the download to stderr
|
||||
"""
|
||||
kwargs['width_per_group'] = 64 * 2
|
||||
return _resnet('wide_resnet101_2', Bottleneck, [3, 4, 23, 3],
|
||||
pretrained, progress, **kwargs)
|
||||
|
||||
|
||||
@register_model
|
||||
def register_audio_resnet(opt_net, opt):
|
||||
type = opt_net['type']
|
||||
fn = globals()[type]
|
||||
return fn(**opt_get(opt_net, ['kwargs'], {}))
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
m = resnet34()
|
||||
o = m(torch.randn((1,1,48000)))
|
||||
print(o.shape)
|
||||
|
|
@ -300,7 +300,7 @@ class Trainer:
|
|||
|
||||
if __name__ == '__main__':
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_gpt_tts_lj.yml')
|
||||
parser.add_argument('-opt', type=str, help='Path to option YAML file.', default='../options/train_byol_audio_clips.yml')
|
||||
parser.add_argument('--launcher', choices=['none', 'pytorch'], default='none', help='job launcher')
|
||||
parser.add_argument('--local_rank', type=int, default=0)
|
||||
args = parser.parse_args()
|
||||
|
|
|
|||
|
|
@ -4,6 +4,8 @@ from collections import defaultdict
|
|||
import torch
|
||||
from torch.optim.lr_scheduler import _LRScheduler
|
||||
|
||||
from utils.util import opt_get
|
||||
|
||||
|
||||
def get_scheduler_for_name(name, optimizers, scheduler_opt):
|
||||
schedulers = []
|
||||
|
|
@ -19,7 +21,7 @@ def get_scheduler_for_name(name, optimizers, scheduler_opt):
|
|||
gamma=scheduler_opt['lr_gamma'],
|
||||
clear_state=scheduler_opt['clear_state'],
|
||||
force_lr=scheduler_opt['force_lr'],
|
||||
warmup_steps=scheduler_opt['warmup_steps'])
|
||||
warmup_steps=opt_get(scheduler_opt, ['warmup_steps'], 0))
|
||||
elif name == 'ProgressiveMultiStepLR':
|
||||
sched = ProgressiveMultiStepLR(o, scheduler_opt['gen_lr_steps'],
|
||||
scheduler_opt['progressive_starts'],
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user