data = dataset.TurbDataset(prop, shuffle=1)
trainLoader = DataLoader(data,
batch_size=batch_size,
shuffle=True,
drop_last=True)
print("Training batches: {}".format(len(trainLoader)))
dataValidation = dataset.ValiDataset(data)
valiLoader = DataLoader(dataValidation,
batch_size=batch_size,
shuffle=False,
drop_last=True)
print("Validation batches: {}".format(len(valiLoader)))
# setup training
epochs = int(iterations / len(trainLoader) + 0.5)
netG = TurbNetG(channelExponent=expo, dropout=dropout)
print(netG) # print full net
model_parameters = filter(lambda p: p.requires_grad, netG.parameters())
params = sum([np.prod(p.size()) for p in model_parameters])
print("Initialized TurbNet with {} trainable params ".format(params))
netG.apply(weights_init)
if len(doLoad) > 0:
netG.load_state_dict(torch.load(doLoad))
print("Loaded model " + doLoad)
netG.cuda()
criterionL1 = nn.L1Loss()
criterionL1.cuda()
optimizerG = optim.Adam(netG.parameters(),
targets = torch.FloatTensor(1, 3, 128, 128)
targets = Variable(targets)
targets = targets.to(device)
inputs = torch.FloatTensor(1, 3, 128, 128)
inputs = Variable(inputs)
inputs = inputs.to(device)
targets_dn = torch.FloatTensor(1, 3, 128, 128)
targets_dn = Variable(targets_dn)
targets_dn = targets_dn.to(device)
outputs_dn = torch.FloatTensor(1, 3, 128, 128)
outputs_dn = Variable(outputs_dn)
outputs_dn = outputs_dn.to(device)
netG = TurbNetG(channelExponent=expo)
lf = "./" + prefix + "testout{}.txt".format(suffix)
utils.makeDirs(["results_test"])
# loop over different trained models
avgLoss = 0.
losses = []
models = []
# for si in range(25):
for si in range(1):
s = chr(96 + si)
if (si == 0):
s = "" # check modelG, and modelG + char
# modelFn = "./" + prefix + "modelG{}{}".format(suffix,s)
modelFn = path
targets = torch.FloatTensor(1, 3, res, res)
targets = Variable(targets)
targets = targets.cuda()
inputs = torch.FloatTensor(1, 3, res, res)
inputs = Variable(inputs)
inputs = inputs.cuda()
targets_dn = torch.FloatTensor(1, 3, res, res)
targets_dn = Variable(targets_dn)
targets_dn = targets_dn.cuda()
outputs_dn = torch.FloatTensor(1, 3, res, res)
outputs_dn = Variable(outputs_dn)
outputs_dn = outputs_dn.cuda()
netG = TurbNetG(channelExponent=expo)
lf = "./" + prefix + "testout{}.txt".format(suffix)
utils.makeDirs(["results_test"])
# loop over different trained models
avgLoss = 0.
losses = []
models = []
loss_p_list = []
loss_v_list = []
accum_list = []
for si in range(25):
s = chr(96 + si)
if (si == 0):
s = "" # check modelG, and modelG + char
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