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TCNN_model_paper.py
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TCNN_model_paper.py
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# -*- coding:utf-8 -*-
# Author:凌逆战 | Never
# Date: 2021/12/6
"""
"""
import torch
import torch.nn.functional as F
import torch.nn as nn
class Chomp1d(nn.Module):
def __init__(self, chomp_size):
super(Chomp1d, self).__init__()
self.chomp_size = chomp_size
def forward(self, x):
return x[:, :, :-self.chomp_size].contiguous()
class DepthwiseSeparableConv(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride, padding, dilation, causal=False):
super(DepthwiseSeparableConv, self).__init__()
# Use `groups` option to implement depthwise convolution
depthwise_conv = nn.Conv1d(in_channels, in_channels, kernel_size, stride=stride, padding=padding,
dilation=dilation, groups=in_channels, bias=False)
pointwise_conv = nn.Conv1d(in_channels, out_channels, kernel_size=1, bias=False)
if causal:
self.net = nn.Sequential(depthwise_conv,
Chomp1d(padding),
nn.PReLU(),
nn.BatchNorm1d(in_channels),
pointwise_conv)
else:
self.net = nn.Sequential(depthwise_conv,
nn.PReLU(),
nn.BatchNorm1d(in_channels),
pointwise_conv)
def forward(self, x):
return self.net(x)
class ResBlock(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, dilation):
super(ResBlock, self).__init__()
self.TCM_net = nn.Sequential(
nn.Conv1d(in_channels=in_channels, out_channels=out_channels, kernel_size=1),
nn.PReLU(num_parameters=1),
nn.BatchNorm1d(num_features=out_channels),
DepthwiseSeparableConv(in_channels=out_channels, out_channels=in_channels, kernel_size=kernel_size,
stride=1,
padding=(kernel_size - 1) * dilation, dilation=dilation, causal=True)
)
def forward(self, input):
x = self.TCM_net(input)
return x + input
class TCNN_Block(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=3, init_dilation=3, num_layers=6):
super(TCNN_Block, self).__init__()
layers = []
for i in range(num_layers):
dilation_size = init_dilation ** i
# in_channels = in_channels if i == 0 else out_channels
layers += [ResBlock(in_channels, out_channels,
kernel_size, dilation=dilation_size)]
self.network = nn.Sequential(*layers)
def forward(self, x):
return self.network(x)
class DConv2d_block(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride, padding, output_padding):
super(DConv2d_block, self).__init__()
self.DConv2d = nn.Sequential(
nn.ConvTranspose2d(in_channels=in_channels, out_channels=out_channels,
kernel_size=kernel_size, stride=stride, padding=padding,
output_padding=output_padding),
nn.BatchNorm2d(num_features=out_channels),
nn.PReLU()
)
self.drop = nn.Dropout(0.2)
def forward(self, encode, decode):
encode = self.drop(encode)
skip_connection = torch.cat((encode, decode), dim=1)
DConv2d = self.DConv2d(skip_connection)
return DConv2d
# input: (B, 1, T, 320) T为帧数，320为帧长
class TCNN(nn.Module):
def __init__(self):
super(TCNN, self).__init__()
self.Conv2d_1 = nn.Conv2d(in_channels=1, out_channels=16, kernel_size=(2, 5), stride=(1, 1), padding=(1, 2))
self.bn1 = nn.BatchNorm2d(num_features=16)
self.prelu1 = nn.PReLU()
self.Conv2d_2 = nn.Conv2d(in_channels=16, out_channels=16, kernel_size=(2, 5), stride=(1, 2), padding=(1, 2))
self.bn2 = nn.BatchNorm2d(num_features=16)
self.prelu2 = nn.PReLU()
self.Conv2d_3 = nn.Conv2d(in_channels=16, out_channels=16, kernel_size=(2, 5), stride=(1, 2), padding=(1, 1))
self.bn3 = nn.BatchNorm2d(num_features=16)
self.prelu3 = nn.PReLU()
self.Conv2d_4 = nn.Conv2d(in_channels=16, out_channels=32, kernel_size=(2, 5), stride=(1, 2), padding=(1, 1))
self.bn4 = nn.BatchNorm2d(num_features=32)
self.prelu4 = nn.PReLU()
self.Conv2d_5 = nn.Conv2d(in_channels=32, out_channels=32, kernel_size=(2, 5), stride=(1, 2), padding=(1, 1))
self.bn5 = nn.BatchNorm2d(num_features=32)
self.prelu5 = nn.PReLU()
self.Conv2d_6 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=(2, 5), stride=(1, 2), padding=(1, 1))
self.bn6 = nn.BatchNorm2d(num_features=64)
self.prelu6 = nn.PReLU()
self.Conv2d_7 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=(2, 5), stride=(1, 2), padding=(1, 1))
self.bn7 = nn.BatchNorm2d(num_features=64)
self.prelu7 = nn.PReLU()
self.TCNN_Block_1 = TCNN_Block(in_channels=256, out_channels=512, kernel_size=3, init_dilation=2, num_layers=6)
self.TCNN_Block_2 = TCNN_Block(in_channels=256, out_channels=512, kernel_size=3, init_dilation=2, num_layers=6)
self.TCNN_Block_3 = TCNN_Block(in_channels=256, out_channels=512, kernel_size=3, init_dilation=2, num_layers=6)
self.DConv2d_7 = nn.ConvTranspose2d(in_channels=128, out_channels=64, kernel_size=(2, 5), stride=(1, 2),
padding=(1, 1),
output_padding=(0, 0))
self.bn7_t = nn.BatchNorm2d(num_features=64)
self.prelu7_t = nn.PReLU()
self.DConv2d_6 = nn.ConvTranspose2d(in_channels=128, out_channels=32, kernel_size=(2, 5), stride=(1, 2),
padding=(1, 1),
output_padding=(0, 0))
self.bn6_t = nn.BatchNorm2d(num_features=32)
self.prelu6_t = nn.PReLU()
self.DConv2d_5 = nn.ConvTranspose2d(in_channels=64, out_channels=32, kernel_size=(2, 5), stride=(1, 2),
padding=(1, 1),
output_padding=(0, 0))
self.bn5_t = nn.BatchNorm2d(num_features=32)
self.prelu5_t = nn.PReLU()
self.DConv2d_4 = nn.ConvTranspose2d(in_channels=64, out_channels=16, kernel_size=(2, 5), stride=(1, 2),
padding=(1, 1),
output_padding=(0, 0))
self.bn4_t = nn.BatchNorm2d(num_features=16)
self.prelu4_t = nn.PReLU()
self.DConv2d_3 = nn.ConvTranspose2d(in_channels=32, out_channels=16, kernel_size=(2, 5), stride=(1, 2),
padding=(1, 1),
output_padding=(0, 1))
self.bn3_t = nn.BatchNorm2d(num_features=16)
self.prelu3_t = nn.PReLU()
self.DConv2d_2 = nn.ConvTranspose2d(in_channels=32, out_channels=16, kernel_size=(2, 5), stride=(1, 2),
padding=(1, 2),
output_padding=(0, 1))
self.bn2_t = nn.BatchNorm2d(num_features=16)
self.prelu2_t = nn.PReLU()
self.DConv2d_1 = nn.ConvTranspose2d(in_channels=32, out_channels=1, kernel_size=(2, 5), stride=(1, 1),
padding=(1, 2),
output_padding=(0, 0))
self.bn1_t = nn.BatchNorm2d(num_features=1)
self.prelu1_t = nn.PReLU()
self.dropout = nn.Dropout(p=0.3, inplace=True)
def forward(self, input):
Conv2d_1 = self.prelu1(self.bn1(self.Conv2d_1(input)[:, :, :-1, :].contiguous()))
print("Conv2d_1", Conv2d_1.shape) # [64, 16, 5, 320]
Conv2d_2 = self.prelu2(self.bn2(self.Conv2d_2(Conv2d_1)[:, :, :-1, :].contiguous()))
print("Conv2d_2", Conv2d_2.shape) # [64, 16, 5, 160]
Conv2d_3 = self.prelu3(self.bn3(self.Conv2d_3(Conv2d_2)[:, :, :-1, :].contiguous()))
print("Conv2d_3", Conv2d_3.shape) # [64, 16, 5, 79]
Conv2d_4 = self.prelu4(self.bn4(self.Conv2d_4(Conv2d_3)[:, :, :-1, :].contiguous()))
print("Conv2d_4", Conv2d_4.shape) # [64, 32, 5, 39]
Conv2d_5 = self.prelu5(self.bn5(self.Conv2d_5(Conv2d_4)[:, :, :-1, :].contiguous()))
print("Conv2d_5", Conv2d_5.shape) # [64, 32, 5, 19]
Conv2d_6 = self.prelu6(self.bn6(self.Conv2d_6(Conv2d_5)[:, :, :-1, :].contiguous()))
print("Conv2d_6", Conv2d_6.shape) # [64, 64, 5, 9]
Conv2d_7 = self.prelu7(self.bn7(self.Conv2d_7(Conv2d_6)[:, :, :-1, :].contiguous()))
print("Conv2d_7", Conv2d_7.shape) # [64, 64, 5, 4] (B, 1, T, 320)
reshape_1 = Conv2d_7.permute(0, 1, 3, 2) # [64, 64, 4, 5] (B,C,帧长,帧数)
batch_size, C, frame_len, frame_num = reshape_1.shape
reshape_1 = reshape_1.reshape(batch_size, C * frame_len, frame_num)
# print("reshape_1", reshape_1.shape) # [64, 256, 5]
TCNN_Block_1 = self.TCNN_Block_1(reshape_1)
TCNN_Block_2 = self.TCNN_Block_2(TCNN_Block_1)
TCNN_Block_3 = self.TCNN_Block_3(TCNN_Block_2)
reshape_2 = TCNN_Block_3.reshape(batch_size, C, frame_len, frame_num)
reshape_2 = reshape_2.permute(0, 1, 3, 2)
print("reshape_2", reshape_2.shape) # [64, 64, 5, 4]
DConv2d_7 = self.prelu7_t(self.bn7_t(F.pad(self.DConv2d_7(torch.cat((self.dropout(Conv2d_7), reshape_2), dim=1)),[0, 0, 1, 0]).contiguous()))
print("DConv2d_7", DConv2d_7.shape) # [64, 64, 5, 9]
DConv2d_6 = self.prelu6_t(self.bn6_t(F.pad(self.DConv2d_6(torch.cat((self.dropout(Conv2d_6), DConv2d_7), dim=1)),[0, 0, 1, 0]).contiguous()))
print("DConv2d_6", DConv2d_6.shape) # [64, 32, 5, 19]
DConv2d_5 = self.prelu5_t(self.bn5_t(F.pad(self.DConv2d_5(torch.cat((self.dropout(Conv2d_5), DConv2d_6), dim=1)),[0, 0, 1, 0]).contiguous()))
print("DConv2d_5", DConv2d_5.shape) # [64, 32, 5, 39]
DConv2d_4 = self.prelu4_t(self.bn4_t(F.pad(self.DConv2d_4(torch.cat((self.dropout(Conv2d_4), DConv2d_5), dim=1)),[0, 0, 1, 0]).contiguous()))
print("DConv2d_4", DConv2d_4.shape) # [64, 16, 5, 79]
DConv2d_3 = self.prelu3_t(self.bn3_t(F.pad(self.DConv2d_3(torch.cat((self.dropout(Conv2d_3), DConv2d_4), dim=1)),[0, 0, 1, 0]).contiguous()))
print("DConv2d_3", DConv2d_3.shape) # [64, 16, 5, 160]
DConv2d_2 = self.prelu2_t(self.bn2_t(F.pad(self.DConv2d_2(torch.cat((self.dropout(Conv2d_2), DConv2d_3), dim=1)),[0, 0, 1, 0]).contiguous()))
print("DConv2d_2", DConv2d_2.shape) # [64, 16, 5, 320]
DConv2d_1 = self.prelu1_t(self.bn1_t(F.pad(self.DConv2d_1(torch.cat((self.dropout(Conv2d_1), DConv2d_2), dim=1)),[0, 0, 1, 0]).contiguous()))
print("DConv2d_1", DConv2d_1.shape) # [64, 1, 5, 320]
return DConv2d_1
if __name__ == "__main__":
# x = torch.randn(64, 32, 8192)
# model = TCNN_Block(in_channels=32, out_channels=64, kernel_size=3, init_dilation=3, num_layers=6) # 输出 (64, 1, 8192)
x = torch.randn(64, 1, 5, 320)
model = TCNN()
# x = torch.randn(64, 32, 256)
# model = DepthwiseSeparableConv(in_channels=32, out_channels=64, kernel_size=3, stride=1, padding=4, dilation=2,
# causal=True)
y = model(x)
print("output", y.shape) # output torch.Size([64, 1, 8192])