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import torch
import torch.nn as nn
import src.utils as utils
# from src.models.common.film import FiLM
class FilmLayer(nn.Module):
def __init__(self, D, C, nF, groups = 1):
super().__init__()
self.D = D
self.C = C
self.nF = nF
self.weight = nn.Conv1d(self.D, self.C * nF, 1, groups = groups)
self.bias = nn.Conv1d(self.D, self.C * nF, 1, groups = groups)
def forward(self, x: torch.Tensor, embedding: torch.Tensor):
"""
x: (B, D, F, T)
embedding: (B, D, F)
"""
B, D, _F, T = x.shape
w = self.weight(embedding).reshape(B, self.C, _F, 1) # (B, C, F, 1)
b = self.bias(embedding).reshape(B, self.C, _F, 1) # (B, C, F, 1)
return x * w + b
class LayerNormPermuted(nn.LayerNorm):
def __init__(self, *args, **kwargs):
super(LayerNormPermuted, self).__init__(*args, **kwargs)
def forward(self, x):
"""
Args:
x: [B, C, T, F]
"""
x = x.permute(0, 2, 3, 1) # [B, T, F, C]
x = super().forward(x)
x = x.permute(0, 3, 1, 2) # [B, C, T, F]
return x
class TSH(nn.Module):
def __init__(
self,
block_model_name,
block_model_params,
spk_dim=256,
latent_dim=48,
n_srcs=1,
n_fft=128,
num_inputs=1,
n_layers=6,
use_first_ln=True,
n_imics=1,
lstm_fold_chunk=400,
stft_chunk_size=200,
latent_dim_model1=16,
use_speaker_emb=True,
use_self_speech_model2=True
):
super().__init__()
self.n_srcs = n_srcs
self.n_layers = n_layers
self.num_inputs = num_inputs
assert n_fft % 2 == 0
n_freqs = n_fft // 2 + 1
self.n_freqs = n_freqs
self.latent_dim = latent_dim
self.lstm_fold_chunk=lstm_fold_chunk
self.stft_chunk_size=stft_chunk_size
self.n_fft = n_fft
self.eps=1.0e-5
t_ksize = 3
self.t_ksize = t_ksize
ks, padding = (t_ksize, t_ksize), (0, 1)
self.n_imics=n_imics
self.use_self_speech_model2=use_self_speech_model2
if not use_speaker_emb and use_self_speech_model2:
self.n_imics=self.n_imics+1
module_list = [nn.Conv2d(2*self.n_imics, latent_dim, ks, padding=padding)]
if use_first_ln:
module_list.append(LayerNormPermuted(latent_dim))
self.conv = nn.Sequential(
*module_list
)
# FiLM layer
self.embeds = nn.ModuleList([])
# Process through a stack of blocks
self.blocks = nn.ModuleList([])
for _i in range(n_layers):
self.blocks.append(utils.import_attr(block_model_name)(emb_dim=latent_dim, n_freqs=n_freqs, **block_model_params))
# Project back to TF-Domain
self.deconv = nn.ConvTranspose2d(latent_dim, n_srcs * 2, ks, padding=( self.t_ksize - 1, 1))
self.latent_dim_model1=latent_dim_model1
if latent_dim_model1!=latent_dim:
self.projection_layer = nn.Conv2d(latent_dim_model1, latent_dim, kernel_size=1)
def init_buffers(self, batch_size, device):
conv_buf = torch.zeros(batch_size, 2*self.n_imics, self.t_ksize - 1, self.n_freqs,
device=device)
deconv_buf = torch.zeros(batch_size, self.latent_dim, self.t_ksize - 1, self.n_freqs,
device=device)
block_buffers = {}
for i in range(len(self.blocks)):
block_buffers[f'buf{i}'] = self.blocks[i].init_buffers(batch_size, device)
return dict(conv_buf=conv_buf, deconv_buf=deconv_buf,
block_bufs=block_buffers)
def forward(self, current_input: torch.Tensor, embedding: torch.Tensor, input_state, quantized=False) -> torch.Tensor:
"""
B: batch, M: mic, F: freq bin, C: real/imag, T: time frame
D: dimension of the embedding vector
current_input: (B, CM, T, F)
embedding: (B, D, F)
output: (B, S, T, C*F)
"""
n_batch, _, n_frames, n_freqs = current_input.shape
batch = current_input
if input_state is None:
input_state = self.init_buffers(current_input.shape[0], current_input.device)
conv_buf = input_state['conv_buf']
gridnet_buf = input_state['block_bufs']
if quantized:
batch = nn.functional.pad(batch, (0, 0, self.t_ksize - 1, 0))
else:
batch = torch.cat((conv_buf, batch), dim=2)
conv_buf = batch[:, :, -(self.t_ksize - 1):, :]
batch = self.conv(batch) # [B, D, T, F]
embedding=embedding.transpose(1, 3)
for ii in range(self.n_layers):
if ii==1:
batch=batch*embedding
batch, gridnet_buf[f'buf{ii}'] = self.blocks[ii](batch, gridnet_buf[f'buf{ii}'])
deconv_buf = torch.zeros(n_batch, self.latent_dim, self.t_ksize - 1, self.n_freqs,
device=current_input.device)
if quantized:
batch = nn.functional.pad(batch, (0, 0, self.t_ksize - 1, 0))
else:
batch = torch.cat(( deconv_buf, batch), dim=2)
batch = self.deconv(batch) # [B, n_srcs*C, T, F]
batch = batch.view([n_batch, self.n_srcs, 2, n_frames, n_freqs]) # [B, n_srcs, 2, n_frames, n_freqs]
batch = batch.transpose(2, 3).reshape(n_batch, self.n_srcs, n_frames, 2 * n_freqs) # [B, S, T, F]
input_state['conv_buf'] = conv_buf
input_state['block_bufs'] = gridnet_buf
return batch, input_state
def edge_mode(self):
for i in range(len(self.blocks)):
self.blocks[i].edge_mode()
if __name__ == "__main__":
pass |