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import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
import numpy as np
class EncoderBlock(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=(3, 3)):
super(EncoderBlock, self).__init__()
self.pool_size = 2
self.conv_block = ConvBlock(in_channels, out_channels, kernel_size)
def forward(self, x):
latent = self.conv_block(x)
output = F.avg_pool2d(latent, kernel_size=self.pool_size)
return output, latent
class DecoderBlock(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=(3, 3)):
super(DecoderBlock, self).__init__()
stride = 2
self.upsample = nn.ConvTranspose2d(
in_channels=in_channels,
out_channels=in_channels,
kernel_size=stride,
stride=stride,
padding=(0, 0),
bias=False,
)
self.conv_block = ConvBlock(in_channels * 2, out_channels, kernel_size)
def forward(self, x, latent):
x = self.upsample(x)
x = torch.cat((x, latent), dim=1)
output = self.conv_block(x)
return output
class UNet(nn.Module):
def __init__(self,freq_dim=1281,out_channel=1024):
super(UNet, self).__init__()
self.downsample_ratio = 16
in_channels = 1 #self.audio_channels * self.cmplx_num
self.encoder_block1 = EncoderBlock(in_channels, 16)
self.encoder_block2 = EncoderBlock(16, 64)
self.encoder_block3 = EncoderBlock(64, 256)
self.encoder_block4 = EncoderBlock(256, 1024)
self.middle = EncoderBlock(1024, 1024)
self.decoder_block1 = DecoderBlock(1024, 256)
self.decoder_block2 = DecoderBlock(256, 64)
self.decoder_block3 = DecoderBlock(64, 16)
self.decoder_block4 = DecoderBlock(16, 16)
self.fc = nn.Linear(freq_dim*16, out_channel)
def forward(self, x_ori):
"""
Args:
complex_sp: (batch_size, channels_num, time_steps, freq_bins)，复数张量
Returns:
output: (batch_size, channels_num, time_steps, freq_bins)，复数张量
"""
x= self.process_image(x_ori)
x1, latent1 = self.encoder_block1(x)
x2, latent2 = self.encoder_block2(x1)
x3, latent3 = self.encoder_block3(x2)
x4, latent4 = self.encoder_block4(x3)
_, h = self.middle(x4)
x5 = self.decoder_block1(h, latent4)
x6 = self.decoder_block2(x5, latent3)
x7 = self.decoder_block3(x6, latent2)
x8 = self.decoder_block4(x7, latent1)
x= self.unprocess_image(x8,x_ori.shape[2])
x = x.permute(0, 2, 1, 3).contiguous() # 将形状变为 [6, 256, 16, 1024]
x = x.view(x.size(0), x.size(1), -1)
x= self.fc(x)
return x
def process_image(self, x):
"""
处理频谱以便可以被 downsample_ratio 整除。
Args:
x: (B, C, T, F)
Returns:
output: (B, C, T_padded, F_reduced)
"""
B, C, T, Freq = x.shape
pad_len = (
int(np.ceil(T / self.downsample_ratio)) * self.downsample_ratio
- T
)
x = F.pad(x, pad=(0, 0, 0, pad_len))
output = x[:, :, :, 0 : Freq - 1]
return output
def unprocess_image(self, x,time_steps):
"""
恢复频谱到原始形状。
Args:
x: (B, C, T_padded, F_reduced)
Returns:
output: (B, C, T_original, F_original)
"""
x = F.pad(x, pad=(0, 1))
output = x[:, :,0:time_steps, :]
return output
class ConvBlock(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=(3, 3)):
super(ConvBlock, self).__init__()
padding = [kernel_size[0] // 2, kernel_size[1] // 2]
self.bn1 = nn.BatchNorm2d(in_channels)
self.bn2 = nn.BatchNorm2d(out_channels)
self.conv1 = nn.Conv2d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
padding=padding,
bias=False,
)
self.conv2 = nn.Conv2d(
in_channels=out_channels,
out_channels=out_channels,
kernel_size=kernel_size,
padding=padding,
bias=False,
)
if in_channels != out_channels:
self.shortcut = nn.Conv2d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=(1, 1),
padding=(0, 0),
)
self.is_shortcut = True
else:
self.is_shortcut = False
def forward(self, x):
h = self.conv1(F.leaky_relu_(self.bn1(x)))
h = self.conv2(F.leaky_relu_(self.bn2(h)))
if self.is_shortcut:
return self.shortcut(x) + h
else:
return x + h
def test_unet():
# 定义输入参数
batch_size = 6
channels = 1 # 音频通道数
time_steps = 256 # 时间步数
freq_bins = 1024 # 频率 bins 数
# 创建一个随机的复数张量作为输入
real_part = torch.randn(batch_size, channels, time_steps, freq_bins)
imag_part = torch.randn(batch_size, channels, time_steps, freq_bins)
complex_sp = real_part #torch.complex(real_part, imag_part)
# 实例化 UNet 模型
model = UNet()
# 前向传播
output = model(complex_sp)
# 输出输入和输出的形状
print("输入形状:", complex_sp.shape)
print("输出形状:", output.shape)
# 检查输出是否为复数张量
assert torch.is_complex(output), "输出不是复数张量"
# 检查输出形状是否与输入形状一致
assert output.shape == complex_sp.shape, "输出形状与输入形状不一致"
print("测试通过，模型正常工作。")
# 运行测试函数
if __name__ == "__main__":
test_unet() |