so, what should i do for the correction.
this my Resnet block
class ResNet(torch.nn.Module):
def init(self, block , num_layers, classes=2):
super(ResNet,self).init()
self.input_planes = 64
self.conv1 = torch.nn.Conv1d(1, 64, kernel_size=3, stride=1, padding=1)
self.bn1 = torch.nn.BatchNorm1d(64)
self.layer1 = self._layer(block, 64, num_layers[0], stride=1)
self.layer2 = self._layer(block, 128, num_layers[1], stride=1)
self.layer3 = self._layer(block, 256, num_layers[2], stride=1)
self.layer4 = self._layer(block, 512, num_layers[3], stride=1)
self.averagePool = torch.nn.AvgPool1d(kernel_size = 4, stride = 1)
self.fc = torch.nn.Linear(512*block.expension, classes)
def _layer(self, block, planes, num_layers, stride= 1):
dim_change= None
if stride != 1 or planes != self.input_planes*block.expension:
dim_change = torch.nn.Sequential(torch.nn.Conv1d(self.input_planes, planes*block.expension, kernel_size=1, stride=stride),
torch.nn.BatchNorm1d(planes*block.expension))
netLayers = []
netLayers.append(block(self.input_planes,planes, stride=stride, dim_change= dim_change))
self.input_planes = planes * block.expension
for i in range(1,num_layers):
netLayers.append(block(self.input_planes,planes))
self.input_planes = planes* block.expension
return torch.nn.Sequential(*netLayers)