def forward(self, xr, xi):
# xr = x[:, :, :, :, 0]
# # imaginary part to zero
# xi = x[:, :, :, :, 1]
xr, xi = self.conv1(xr, xi)
xr, xi = complex_relu(xr, xi)
# xr, xi = complex_max_pool2d(xr, xi, 2, 2)
xr, xi = self.bn1(xr, xi)
xr, xi = self.conv2(xr, xi)
xr, xi = complex_relu(xr, xi)
# xr, xi = complex_max_pool2d(xr, xi, 2, 2)
xr, xi = self.bn2(xr, xi)
xr, xi = self.conv3(xr, xi)
xr, xi = complex_relu(xr, xi)
# xr, xi = complex_max_pool2d(xr, xi, 2, 2)
xr = xr.view(-1, 2 * 2 * self.c3)
xi = xi.view(-1, 2 * 2 * self.c3)
xr, xi = self.fc1(xr, xi)
xr, xi = complex_relu(xr, xi)
xr, xi = self.fc2(xr, xi)
# take the absolute value as output
# x = torch.sqrt(torch.pow(xr, 2) + torch.pow(xi, 2))
return xr, xi
def forward(self, xr, xi):
# inputs shaped (batch_size, 100)
xr, xi = self.dense1(xr, xi)
xr, xi = self.batchnorm1(xr, xi)
xr, xi = complex_relu(xr, xi)
xr, xi = self.dense2(xr, xi)
xr, xi = self.batchnorm2(xr, xi)
xr, xi = complex_relu(xr, xi)
xr, xi = self.dense3(xr, xi)
xr, xi = self.batchnorm3(xr, xi)
xr, xi = complex_relu(xr, xi)
xr, xi = self.dense4(xr, xi)
xr, xi = complex_tanh(xr, xi)
xr, xi = xr.view(-1, 1, 128, 128), xi.view(-1, 1, 128, 128)
return xr, xi
def forward(self, xr, xi):
xr, xi = self.conv1(xr, xi)
xr, xi = complex_relu(xr, xi)
# xr, xi = complex_max_pool2d(xr, xi, 2, 2)
xr, xi = self.bn(xr, xi)
xr, xi = self.conv2(xr, xi)
xr, xi = complex_relu(xr, xi)
# xr, xi = complex_max_pool2d(xr, xi, 2, 2)
xr = xr.view(-1, 2 * 2 * self.c2)
xi = xi.view(-1, 2 * 2 * self.c2)
xr, xi = self.fc1(xr, xi)
xr, xi = complex_relu(xr, xi)
xr, xi = self.fc2(xr, xi)
# take the absolute value as output
x = torch.sqrt(torch.pow(xr, 2) + torch.pow(xi, 2))
return x
def forward(self, xr, xi):
# inputs shaped (batch_size, 100)
xr, xi = self.dense(xr, xi)
xr = xr.view(xr.size(0), 1024, 4, 4)
xi = xi.view(xi.size(0), 1024, 4, 4)
xr, xi = complex_relu(xr, xi) # (batch_size, 1024, 4, 4)
xr, xi = self.deconv1(xr, xi) # (batch_size, 512, 8, 8)
xr, xi = complex_relu(xr, xi)
xr, xi = self.deconv2(xr, xi) # (batch_size, 256, 16, 16)
xr, xi = complex_relu(xr, xi)
xr, xi = self.deconv3(xr, xi) # (batch_size, 128 ,32, 32)
xr, xi = complex_relu(xr, xi)
xr, xi = self.deconv4(xr, xi) # (batch_size, 64, 64, 64)
xr, xi = complex_relu(xr, xi)
xr, xi = self.deconv5(xr, xi) # (batch_size, 1, 128, 128)
return xr, xi # (batch_size, 1, 128, 128)
def forward(self, xr, xi):
# imaginary part to zero
xr, xi = self.fc2(xr, xi)
xr, xi = complex_relu(xr, xi)
# xr, xi = complex_max_pool2d(xr, xi, 2, 2)
xr, xi = self.fc1(xr, xi)
xr = xr.view(-1, self.c2, 2, 2)
xi = xi.view(-1, self.c2, 2, 2)
xr, xi = complex_relu(xr, xi)
xr, xi = self.convt2(xr, xi)
xr, xi = self.bn(xr, xi)
# xr, xi = complex_max_pool2d(xr, xi, 2, 2)
xr, xi = complex_relu(xr, xi)
xr, xi = self.convt1(xr, xi)
return xr, xi
def forward(self, x):
xr = x
# imaginary part to zero
xi = torch.zeros(xr.shape, dtype=xr.dtype, device=xr.device)
xr, xi = self.conv1(xr, xi)
xr, xi = complex_relu(xr, xi)
xr, xi = complex_max_pool2d(xr, xi, 2, 2)
xr, xi = self.bn(xr, xi)
xr, xi = self.conv2(xr, xi)
xr, xi = complex_relu(xr, xi)
xr, xi = complex_max_pool2d(xr, xi, 2, 2)
xr = xr.view(-1, 4 * 4 * 50)
xi = xi.view(-1, 4 * 4 * 50)
xr, xi = self.fc1(xr, xi)
xr, xi = complex_relu(xr, xi)
xr, xi = self.fc2(xr, xi)
# take the absolute value as output
x = torch.sqrt(torch.pow(xr, 2) + torch.pow(xi, 2))
return F.log_softmax(x, dim=1)
def forward(self, x):
xr1 = x[:, :, :, 0::2] # 4维 从第一维每隔2个取一维
# imaginary part to zero
xi1 = x[:, :, :, 1::2] # 4维 从第二维每隔2个取一维
xr, xi = self.conv1(xr1, xi1)
x_br = xr.detach().cpu()
x_bi = xi.detach().cpu()
xr, xi = self.conv2(xr.to(device), xi.to(device))
BN = cl.ComplexBatchNorm2d(1)
xr, xi = BN(xr, xi)
xr, xi = cf.complex_relu(xr, xi)
xr, xi = self.conv3(xr.to(device), xi.to(device))
xr, xi = BN(xr, xi)
xr = xr.detach().cpu()
xi = xi.detach().cpu()
xr = xr + x_br
xi = xi + x_bi
xr, xi = cf.complex_relu(xr.to(device), xi.to(device))
xr, xi = self.conv2(xr.to(device), xi.to(device))
xr = xr.detach().cpu().numpy()
# 修改数据类型 CUDA tensor在这里应该是指变量而不是张量格式的数据改成numpy
xi = xi.detach().cpu().numpy()
xr = xr.reshape(40000) # 变成一个一维 400*100
xi = xi.reshape(40000)
x = np.zeros(80000) # 100个sample，每个sample800个点
for i in range(40000):
x[2 * i] = xr[i] # 按行out输入到一行的out_oneline中
x[2 * i + 1] = xi[i]
x = x.reshape(100, 800)
x = torch.FloatTensor(x).to(device)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
# x = self.dropout(x)
x = F.sigmoid(self.fc4(x))
return x
def forward(self, input_r, input_i):
return complex_relu(input_r, input_i)
