I work with PySyft, I would like to train a ResNet50 model Here is a part of my code:
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.model = models.resnet50(pretrained=False)
self.fc1 = nn.Linear(2048,2048)
self.fc2 = nn.Linear(2048, 3)
self.dropout = nn.Dropout(0.3)
def forward(self, x):
x = self.model.conv1(x)
x = self.model.bn1(x)
x = self.model.relu(x)
x = self.model.maxpool(x)
...
x = self.model.avgpool(x)
x = x.view(-1,2048*1*1)
x = nn.functional.relu(self.fc1(x))
x = self.dropout(x)
x = nn.functional.softmax(self.fc2(x), dim=1)
return x
def train(args, model, device, train_loader, optimizer, epoch):
model.train()
for batch_idx, (data, target) in enumerate(federated_train_loader): # <-- now it is a distributed dataset
model.send(data.location) # <-- NEW: send the model to the right location
data, target = data.to(device), target.to(device)
optimizer.zero_grad()
output = model(data)
loss = F.nll_loss(output, target)
loss.backward()
optimizer.step()
model.get() # <-- NEW: get the model back
if batch_idx % args.log_interval == 0:
loss = loss.get() # <-- NEW: get the loss back
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, batch_idx * args.batch_size, len(train_loader) * args.batch_size, #batch_idx * len(data), len(train_loader.dataset),
100. * batch_idx / len(train_loader), loss.item()))
The error is in The model.get()
self.set_(tensor.native_type(self.dtype)) :
RuntimeError: Expected object of device type cuda but got device type cpu for argument #1 'self' in call to _th_set_
i can't understand why in function train() below, when I add a pretrained model into a new model class, it cannot get model back
