outputs_cpu = outputs.data.cpu().numpy()
lossL1 = criterionL1(outputs, targets)
L1val_accum += lossL1.item()
if i == 0:
input_ndarray = inputs_cpu.cpu().numpy()[0]
v_norm = (np.max(np.abs(input_ndarray[0, :, :]))**2 +
np.max(np.abs(input_ndarray[1, :, :]))**2)**0.5
outputs_denormalized = data.denormalize(outputs_cpu[0], v_norm)
targets_denormalized = data.denormalize(
targets_cpu.cpu().numpy()[0], v_norm)
utils.makeDirs(["results_train"])
utils.imageOut("results_train/epoch{}_{}".format(epoch, i),
outputs_denormalized,
targets_denormalized,
saveTargets=True)
# data for graph plotting
L1_accum /= len(trainLoader)
L1val_accum /= len(valiLoader)
if saveL1:
if epoch == 0:
utils.resetLog(prefix + "L1.txt")
utils.resetLog(prefix + "L1val.txt")
utils.log(prefix + "L1.txt", "{} ".format(L1_accum), False)
utils.log(prefix + "L1val.txt", "{} ".format(L1val_accum), False)
torch.save(netG.state_dict(), prefix + "modelG")
if i==0:
input_ndarray = inputs_cpu.cpu().numpy()[0]
v_norm = ( np.max(np.abs(input_ndarray[0,:,:]))**2 + np.max(np.abs(input_ndarray[1,:,:]))**2 )**0.5
outputs_denormalized = data.denormalize(outputs_cpu[0], v_norm)
targets_denormalized = data.denormalize(targets_cpu.cpu().numpy()[0], v_norm)
utils.makeDirs(["results_train"])
utils.imageOut("results_train/epoch{}_{}".format(epoch, i), outputs_denormalized, targets_denormalized, saveTargets=True)
# data for graph plotting
Loss_accum /= len(trainLoader)
Lossval_accum /= len(valiLoader)
if saveL1:
if epoch==0:
utils.resetLog(prefix + "L.txt" )
utils.resetLog(prefix + "Lval.txt")
utils.log(prefix + "L.txt" , "{} ".format(Loss_accum), False)
utils.log(prefix + "Lval.txt", "{} ".format(Lossval_accum), False)
torch.save(netG.state_dict(), prefix + "modelG" )
plt.figure()
plt.plot(range(epochs), L_list, 'r-', label='train_loss')
plt.legend()
plt.xlabel('epoch')
plt.ylabel('Loss')
plt.savefig('train.png')
