Hi, I am new to pytorch. And I have a problem. In a deep learning network,  I split a convolution into two GPU operations, but GPU memory consumption increases while training, and then it reports an error  “RuntimeError: CUDA error: out of memory”
I tried to use del loss and torch.cuda.empty_cache() but it never works…Could you please help me?
This is the code snippet related to the conv part
class Conv2dBlock_Multi(nn.Module):
def init(self, input_dim ,output_dim, kernel_size, stride,
padding=0, norm=‘none’, activation=‘relu’, pad_type=‘zero’):
super(Conv2dBlock_Multi, self).init()
self.conv = Conv2d_Multi(input_dim, output_dim, kernel_size, stride, bias=True)
def forward(self, x):
    x = self.conv(self.pad(x))
    return x

class Conv2d_Multi(nn.Module):
def init(self,input_dim, output_dim, kernel_size, stride, bias=True):
super(Conv2d_Multi, self).init()
self.conv1 = nn.Conv2d(input_dim//2, output_dim//2, kernel_size, stride, bias=True)
self.conv2 = nn.Conv2d(input_dim - input_dim//2,output_dim - output_dim//2, kernel_size, stride, bias=True)
def forward(self, x):
    x1, x2 = x.split([x.size()[1]//2, x.size()[1]-x.size()[1]//2], dim=1)
    self.conv1 = nn.DataParallel(self.conv1, device_ids=[0]).to('cuda:0')
    self.conv2 = nn.DataParallel(self.conv2, device_ids=[1]).to('cuda:1')
    x1_out = self.conv1(x1)
    x2_out = self.conv2(x2)
    x2_out = x2_out.to('cuda:0')
    x = torch.cat([x1_out,x2_out],dim = 1)

    return x