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Running
on
Zero
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| from torchlibrosa.stft import Spectrogram, LogmelFilterBank | |
| def get_audio_encoder(name: str): | |
| if name == "Cnn14": | |
| return Cnn14 | |
| else: | |
| raise Exception('The audio encoder name {} is incorrect or not supported'.format(name)) | |
| class ConvBlock(nn.Module): | |
| def __init__(self, in_channels, out_channels): | |
| super(ConvBlock, self).__init__() | |
| self.conv1 = nn.Conv2d(in_channels=in_channels, | |
| out_channels=out_channels, | |
| kernel_size=(3, 3), stride=(1, 1), | |
| padding=(1, 1), bias=False) | |
| self.conv2 = nn.Conv2d(in_channels=out_channels, | |
| out_channels=out_channels, | |
| kernel_size=(3, 3), stride=(1, 1), | |
| padding=(1, 1), bias=False) | |
| self.bn1 = nn.BatchNorm2d(out_channels) | |
| self.bn2 = nn.BatchNorm2d(out_channels) | |
| def forward(self, input, pool_size=(2, 2), pool_type='avg'): | |
| x = input | |
| x = F.relu_(self.bn1(self.conv1(x))) | |
| x = F.relu_(self.bn2(self.conv2(x))) | |
| if pool_type == 'max': | |
| x = F.max_pool2d(x, kernel_size=pool_size) | |
| elif pool_type == 'avg': | |
| x = F.avg_pool2d(x, kernel_size=pool_size) | |
| elif pool_type == 'avg+max': | |
| x1 = F.avg_pool2d(x, kernel_size=pool_size) | |
| x2 = F.max_pool2d(x, kernel_size=pool_size) | |
| x = x1 + x2 | |
| else: | |
| raise Exception('Incorrect argument!') | |
| return x | |
| class ConvBlock5x5(nn.Module): | |
| def __init__(self, in_channels, out_channels): | |
| super(ConvBlock5x5, self).__init__() | |
| self.conv1 = nn.Conv2d(in_channels=in_channels, | |
| out_channels=out_channels, | |
| kernel_size=(5, 5), stride=(1, 1), | |
| padding=(2, 2), bias=False) | |
| self.bn1 = nn.BatchNorm2d(out_channels) | |
| def forward(self, input, pool_size=(2, 2), pool_type='avg'): | |
| x = input | |
| x = F.relu_(self.bn1(self.conv1(x))) | |
| if pool_type == 'max': | |
| x = F.max_pool2d(x, kernel_size=pool_size) | |
| elif pool_type == 'avg': | |
| x = F.avg_pool2d(x, kernel_size=pool_size) | |
| elif pool_type == 'avg+max': | |
| x1 = F.avg_pool2d(x, kernel_size=pool_size) | |
| x2 = F.max_pool2d(x, kernel_size=pool_size) | |
| x = x1 + x2 | |
| else: | |
| raise Exception('Incorrect argument!') | |
| return x | |
| class AttBlock(nn.Module): | |
| def __init__(self, n_in, n_out, activation='linear', temperature=1.): | |
| super(AttBlock, self).__init__() | |
| self.activation = activation | |
| self.temperature = temperature | |
| self.att = nn.Conv1d(in_channels=n_in, out_channels=n_out, kernel_size=1, stride=1, padding=0, bias=True) | |
| self.cla = nn.Conv1d(in_channels=n_in, out_channels=n_out, kernel_size=1, stride=1, padding=0, bias=True) | |
| self.bn_att = nn.BatchNorm1d(n_out) | |
| def forward(self, x): | |
| # x: (n_samples, n_in, n_time) | |
| norm_att = torch.softmax(torch.clamp(self.att(x), -10, 10), dim=-1) | |
| cla = self.nonlinear_transform(self.cla(x)) | |
| x = torch.sum(norm_att * cla, dim=2) | |
| return x, norm_att, cla | |
| def nonlinear_transform(self, x): | |
| if self.activation == 'linear': | |
| return x | |
| elif self.activation == 'sigmoid': | |
| return torch.sigmoid(x) | |
| class Cnn14(nn.Module): | |
| def __init__(self, sample_rate, window_size, hop_size, mel_bins, fmin, | |
| fmax, classes_num, out_emb): | |
| super(Cnn14, self).__init__() | |
| window = 'hann' | |
| center = True | |
| pad_mode = 'reflect' | |
| ref = 1.0 | |
| amin = 1e-10 | |
| top_db = None | |
| # Spectrogram extractor | |
| self.spectrogram_extractor = Spectrogram(n_fft=window_size, hop_length=hop_size, | |
| win_length=window_size, window=window, center=center, pad_mode=pad_mode, | |
| freeze_parameters=True) | |
| # Logmel feature extractor | |
| self.logmel_extractor = LogmelFilterBank(sr=sample_rate, n_fft=window_size, | |
| n_mels=mel_bins, fmin=fmin, fmax=fmax, ref=ref, amin=amin, top_db=top_db, | |
| freeze_parameters=True) | |
| self.bn0 = nn.BatchNorm2d(64) | |
| self.conv_block1 = ConvBlock(in_channels=1, out_channels=64) | |
| self.conv_block2 = ConvBlock(in_channels=64, out_channels=128) | |
| self.conv_block3 = ConvBlock(in_channels=128, out_channels=256) | |
| self.conv_block4 = ConvBlock(in_channels=256, out_channels=512) | |
| self.conv_block5 = ConvBlock(in_channels=512, out_channels=1024) | |
| self.conv_block6 = ConvBlock(in_channels=1024, out_channels=2048) | |
| # out_emb is 2048 for best Cnn14 | |
| self.fc1 = nn.Linear(2048, out_emb, bias=True) | |
| self.fc_audioset = nn.Linear(out_emb, classes_num, bias=True) | |
| def forward(self, input, mixup_lambda=None): | |
| """ | |
| Input: (batch_size, data_length) | |
| """ | |
| x = self.spectrogram_extractor(input) # (batch_size, 1, time_steps, freq_bins) | |
| x = self.logmel_extractor(x) # (batch_size, 1, time_steps, mel_bins) | |
| x = x.transpose(1, 3) | |
| x = self.bn0(x) | |
| x = x.transpose(1, 3) | |
| x = self.conv_block1(x, pool_size=(2, 2), pool_type='avg') | |
| x = F.dropout(x, p=0.2, training=self.training) | |
| x = self.conv_block2(x, pool_size=(2, 2), pool_type='avg') | |
| x = F.dropout(x, p=0.2, training=self.training) | |
| x = self.conv_block3(x, pool_size=(2, 2), pool_type='avg') | |
| x = F.dropout(x, p=0.2, training=self.training) | |
| x = self.conv_block4(x, pool_size=(2, 2), pool_type='avg') | |
| x = F.dropout(x, p=0.2, training=self.training) | |
| x = self.conv_block5(x, pool_size=(2, 2), pool_type='avg') | |
| x = F.dropout(x, p=0.2, training=self.training) | |
| x = self.conv_block6(x, pool_size=(1, 1), pool_type='avg') | |
| x = F.dropout(x, p=0.2, training=self.training) | |
| x = torch.mean(x, dim=3) | |
| (x1, _) = torch.max(x, dim=2) | |
| x2 = torch.mean(x, dim=2) | |
| x = x1 + x2 | |
| x = F.dropout(x, p=0.5, training=self.training) | |
| x = F.relu_(self.fc1(x)) | |
| embedding = F.dropout(x, p=0.5, training=self.training) | |
| clipwise_output = torch.sigmoid(self.fc_audioset(x)) | |
| output_dict = {'clipwise_output': clipwise_output, 'embedding': embedding} | |
| return output_dict |