def predict(params):
inverter = PrectNormalizer(xr.open_dataset(params['norm_fn']),
params['output_vars'],
params['input_transform'][0],
params['input_transform'][1],
params['var_cut_off'], params['model_type'])
model = CVAE(params)
optimizer = optim.Adam(model.parameters(), lr=0.00001, weight_decay=0.001)
### Load model
checkpoint = torch.load('./runs/VAE_model_DNN_classifier_exp_VAE_Exp04.pt')
model.load_state_dict(checkpoint['model_state_dict'])
optimizer.load_state_dict(checkpoint['optimizer_state_dict'])
epoch = checkpoint['epoch']
loss = checkpoint['loss']
model.cuda()
model.eval()
valid_dataset = spcamDataset(params, phase="validation")
valid_loader = DataLoader(valid_dataset,
sampler=SubsetSampler(valid_dataset.indices))
result_predicted, result_actual = [], []
with torch.no_grad():
for batch_idx, (data, target) in enumerate(valid_loader):
target = target.squeeze(0).type(torch.float32).to(params['device'])
z = torch.randn(data.shape[1], 16)
z = torch.cat((z, data.squeeze(0)), dim=1).cuda()
predPrecit = model.decoder(z)
print("Batch MSE {}".format(
metrics.mean_squared_error(
predPrecit.detach().cpu().numpy(),
target.squeeze(0).detach().cpu().numpy())))
#val_loss = compute_loss(target, sampled_precit, mean, log_var) #.type(torch.FloatTensor).to(params['device']))
#assert val_loss.requires_grad == False
result_predicted.extend(predPrecit.cpu().detach().numpy())
result_actual.extend(target.squeeze(0).cpu().detach().numpy())
mse = metrics.mean_squared_error(np.array(result_actual),
np.array(result_predicted))
print("MSE {}".format(mse))
testDataset = CELEBA(root=opts.root,
train=False,
transform=transforms.ToTensor())
testLoader = torch.utils.data.DataLoader(testDataset,
batch_size=opts.batchSize,
shuffle=False)
print 'Data loaders ready.'
####### Create model #######
cvae = CVAE(nz=opts.nz, imSize=64, fSize=opts.fSize)
dis = DISCRIMINATOR(imSize=64, fSize=opts.fSize)
if cvae.useCUDA:
print 'using CUDA'
cvae.cuda()
dis.cuda()
else:
print '\n *** NOT USING CUDA ***\n'
print cvae
print dis
####### Define optimizer #######
optimizerCVAE = optim.RMSprop(
cvae.parameters(),
lr=opts.lr) #specify the params that are being upated
optimizerDIS = optim.RMSprop(dis.parameters(), lr=opts.lr, alpha=opts.mom)
####### Create a new folder to save results and model info #######
exDir = make_new_folder(opts.outDir)