lossL1 = criterionL1(gen_out, targets)
lossL1.backward()
optimizerG.step()
lossL1viz = lossL1.item()
L1_accum += lossL1viz
if i == len(trainLoader) - 1:
logline = "Epoch: {}, batch-idx: {}, L1: {}\n".format(
epoch, i, lossL1viz)
print(logline)
# validation
netG.eval()
L1val_accum = 0.0
for i, validata in enumerate(valiLoader, 0):
inputs_cpu, targets_cpu = validata
targets_cpu, inputs_cpu = targets_cpu.float().cuda(), inputs_cpu.float(
).cuda()
inputs.data.resize_as_(inputs_cpu).copy_(inputs_cpu)
targets.data.resize_as_(targets_cpu).copy_(targets_cpu)
outputs = netG(inputs)
outputs_cpu = outputs.data.cpu().numpy()
lossL1 = criterionL1(outputs, targets)
L1val_accum += lossL1.item()
if i == 0:
def getModel(expo):
netG = TurbNetG(channelExponent=expo).to(device)
netG.load_state_dict(torch.load(f'models/model_w_{expo}', map_location=device))
netG.eval()
return netG