from .modules import * import numpy as np class UNetRes_FiLM(nn.Module): def __init__(self, channels, cond_embedding_dim, nsrc=1): super(UNetRes_FiLM, self).__init__() activation = 'relu' momentum = 0.01 self.nsrc = nsrc self.channels = channels self.downsample_ratio = 2 ** 6 # This number equals 2^{#encoder_blocks} self.encoder_block1 = EncoderBlockRes2BCond(in_channels=channels * nsrc, out_channels=32, downsample=(2, 2), activation=activation, momentum=momentum, cond_embedding_dim=cond_embedding_dim) self.encoder_block2 = EncoderBlockRes2BCond(in_channels=32, out_channels=64, downsample=(2, 2), activation=activation, momentum=momentum, cond_embedding_dim=cond_embedding_dim) self.encoder_block3 = EncoderBlockRes2BCond(in_channels=64, out_channels=128, downsample=(2, 2), activation=activation, momentum=momentum, cond_embedding_dim=cond_embedding_dim) self.encoder_block4 = EncoderBlockRes2BCond(in_channels=128, out_channels=256, downsample=(2, 2), activation=activation, momentum=momentum, cond_embedding_dim=cond_embedding_dim) self.encoder_block5 = EncoderBlockRes2BCond(in_channels=256, out_channels=384, downsample=(2, 2), activation=activation, momentum=momentum, cond_embedding_dim=cond_embedding_dim) self.encoder_block6 = EncoderBlockRes2BCond(in_channels=384, out_channels=384, downsample=(2, 2), activation=activation, momentum=momentum, cond_embedding_dim=cond_embedding_dim) self.conv_block7 = ConvBlockResCond(in_channels=384, out_channels=384, kernel_size=(3, 3), activation=activation, momentum=momentum, cond_embedding_dim=cond_embedding_dim) self.decoder_block1 = DecoderBlockRes2BCond(in_channels=384, out_channels=384, stride=(2, 2), activation=activation, momentum=momentum, cond_embedding_dim=cond_embedding_dim) self.decoder_block2 = DecoderBlockRes2BCond(in_channels=384, out_channels=384, stride=(2, 2), activation=activation, momentum=momentum, cond_embedding_dim=cond_embedding_dim) self.decoder_block3 = DecoderBlockRes2BCond(in_channels=384, out_channels=256, stride=(2, 2), activation=activation, momentum=momentum, cond_embedding_dim=cond_embedding_dim) self.decoder_block4 = DecoderBlockRes2BCond(in_channels=256, out_channels=128, stride=(2, 2), activation=activation, momentum=momentum, cond_embedding_dim=cond_embedding_dim) self.decoder_block5 = DecoderBlockRes2BCond(in_channels=128, out_channels=64, stride=(2, 2), activation=activation, momentum=momentum, cond_embedding_dim=cond_embedding_dim) self.decoder_block6 = DecoderBlockRes2BCond(in_channels=64, out_channels=32, stride=(2, 2), activation=activation, momentum=momentum, cond_embedding_dim=cond_embedding_dim) self.after_conv_block1 = ConvBlockResCond(in_channels=32, out_channels=32, kernel_size=(3, 3), activation=activation, momentum=momentum, cond_embedding_dim=cond_embedding_dim) self.after_conv2 = nn.Conv2d(in_channels=32, out_channels=1, kernel_size=(1, 1), stride=(1, 1), padding=(0, 0), bias=True) self.init_weights() def init_weights(self): init_layer(self.after_conv2) def forward(self, sp, cond_vec, dec_cond_vec): """ Args: input: sp: (batch_size, channels_num, segment_samples) Outputs: output_dict: { 'wav': (batch_size, channels_num, segment_samples), 'sp': (batch_size, channels_num, time_steps, freq_bins)} """ x = sp # Pad spectrogram to be evenly divided by downsample ratio. origin_len = x.shape[2] # time_steps pad_len = int(np.ceil(x.shape[2] / self.downsample_ratio)) * self.downsample_ratio - origin_len x = F.pad(x, pad=(0, 0, 0, pad_len)) x = x[..., 0: x.shape[-1] - 2] # (bs, channels, T, F) # UNet (x1_pool, x1) = self.encoder_block1(x, cond_vec) # x1_pool: (bs, 32, T / 2, F / 2) (x2_pool, x2) = self.encoder_block2(x1_pool, cond_vec) # x2_pool: (bs, 64, T / 4, F / 4) (x3_pool, x3) = self.encoder_block3(x2_pool, cond_vec) # x3_pool: (bs, 128, T / 8, F / 8) (x4_pool, x4) = self.encoder_block4(x3_pool, dec_cond_vec) # x4_pool: (bs, 256, T / 16, F / 16) (x5_pool, x5) = self.encoder_block5(x4_pool, dec_cond_vec) # x5_pool: (bs, 512, T / 32, F / 32) (x6_pool, x6) = self.encoder_block6(x5_pool, dec_cond_vec) # x6_pool: (bs, 1024, T / 64, F / 64) x_center = self.conv_block7(x6_pool, dec_cond_vec) # (bs, 2048, T / 64, F / 64) x7 = self.decoder_block1(x_center, x6, dec_cond_vec) # (bs, 1024, T / 32, F / 32) x8 = self.decoder_block2(x7, x5, dec_cond_vec) # (bs, 512, T / 16, F / 16) x9 = self.decoder_block3(x8, x4, cond_vec) # (bs, 256, T / 8, F / 8) x10 = self.decoder_block4(x9, x3, cond_vec) # (bs, 128, T / 4, F / 4) x11 = self.decoder_block5(x10, x2, cond_vec) # (bs, 64, T / 2, F / 2) x12 = self.decoder_block6(x11, x1, cond_vec) # (bs, 32, T, F) x = self.after_conv_block1(x12, cond_vec) # (bs, 32, T, F) x = self.after_conv2(x) # (bs, channels, T, F) # Recover shape x = F.pad(x, pad=(0, 2)) x = x[:, :, 0: origin_len, :] return x if __name__ == "__main__": model = UNetRes_FiLM(channels=1, cond_embedding_dim=16) cond_vec = torch.randn((1, 16)) dec_vec = cond_vec print(model(torch.randn((1, 1, 1001, 513)), cond_vec, dec_vec).size())