def recon_loss (self, recon_x, x):
if self.reconstruction_dist == "bernouilli":
return helper.BCE_with_digits_loss(recon_x, x)
elif self.reconstruction_dist == "gaussian":
return helper.MSE_loss(recon_x,x)
else:
raise ValueError ("Unkown decoder distribution: {}".format(self.reconstruction_dist))
def valid_losses(self, outputs, inputs):
x = inputs['image']
recon_x = outputs['recon_x']
mu = outputs['mu']
logvar = outputs['logvar']
if self.use_gpu and not x.is_cuda:
x = x.cuda()
if self.use_gpu and not recon_x.is_cuda:
recon_x = recon_x.cuda()
if self.use_gpu and not mu.is_cuda:
mu = mu.cuda()
if self.use_gpu and not logvar.is_cuda:
logvar = logvar.cuda()
BCE_loss = helper.BCE_with_digits_loss(recon_x, x)
MSE_loss = helper.MSE_loss(recon_x,x)
if self.reconstruction_dist == "bernouilli":
recon_loss = BCE_loss
elif self.reconstruction_dist == "gaussian":
recon_loss = MSE_loss
KLD_loss, KLD_per_latent_dim, KLD_var = helper.KLD_loss(mu, logvar)
total_loss = recon_loss + KLD_loss
return {'total': total_loss, 'recon': recon_loss, 'BCE': BCE_loss, 'MSE': MSE_loss, 'KLD': KLD_loss, 'KLD_var': KLD_var}
def valid_losses(self, outputs, inputs):
""" train loss:
recon_x: reconstructed images
x: origin images
mu: latent mean
logvar: latent log variance
"""
x = inputs['image']
recon_x = outputs['recon_x']
mu = outputs['mu']
logvar = outputs['logvar']
if self.use_gpu and not x.is_cuda:
x = x.cuda()
if self.use_gpu and not recon_x.is_cuda:
recon_x = recon_x.cuda()
if self.use_gpu and not mu.is_cuda:
mu = mu.cuda()
if self.use_gpu and not logvar.is_cuda:
logvar = logvar.cuda()
BCE = helper.BCE_with_digits_loss(recon_x, x)
KLD, KLD_per_latent_dim, KLD_var = helper.KLD_loss(mu, logvar)
total = (BCE + self.beta *
(KLD + float(self.add_var_to_KLD_loss) * KLD_var))
return {'total': total, 'BCE': BCE, 'KLD': KLD, 'KLD_var': KLD_var}
def test():
# configuration file
print("Loading the configuration ... \n")
config = configuration.Config()
model_init_params = config.model_init_params
img_size = model_init_params['input_size']
# load datasets
print("Loading the test dataset ... \n")
test_dataset = DatasetHDF5(filepath=config.dataset_filepath,
split='test',
img_size = img_size)
test_loader = DataLoader(test_dataset,
batch_size=config.test_batch_size,
shuffle=True)
# test_npz_filepath = config.test_npz_filepath
# test_dataset_npz = np.load(test_npz_filepath)
# test_batch_size = 1
# test_dataset = Dataset(img_size)
# test_dataset.update(test_dataset_npz['observations'].shape[0], torch.from_numpy(test_dataset_npz['observations']).float(), test_dataset_npz['labels'])
# test_loader = DataLoader(test_dataset, batch_size=test_batch_size, shuffle=True)
# load the trained model
print("Loading the trained model ... \n")
model_path = 'training/models/best_weight_model.pth'
if os.path.exists(model_path):
saved_model = torch.load (model_path, map_location='cpu')
model_cls = getattr(ad.representations.static.pytorchnnrepresentation, saved_model['type'])
model = model_cls (**saved_model['init_params'])
model.load_state_dict(saved_model['state_dict'])
model.eval()
model.use_gpu = False
else:
raise ValueError('The model {!r} does not exist!'.format(model_path))
# output files
output_testing_folder = 'testing'
if os.path.exists(output_testing_folder):
print('WARNING: testing folder already exists')
else:
os.makedirs(output_testing_folder)
output_test_dataset_filename = os.path.join(output_testing_folder, 'output_test_dataset.npz')
if os.path.exists(output_test_dataset_filename):
print('WARNING: the output test dataset already exists, skipping the forward pass on test dataset')
return
# prepare output arrays
input_size = saved_model['init_params']['input_size']
n_latents = saved_model['init_params']['n_latents']
test_x = np.empty((test_loader.__len__(), input_size[0], input_size[1]))
test_recon_x = np.empty((test_loader.__len__(), input_size[0], input_size[1]))
test_y = np.empty(test_loader.__len__())
test_recon_y = np.empty((test_loader.__len__(), 3))
test_mu = np.empty((test_loader.__len__(), n_latents))
test_sigma = np.empty((test_loader.__len__(), n_latents)) # FOR AE: mu=z, sigma=0
test_MSE_loss = np.empty(test_loader.__len__())
test_BCE_loss = np.empty(test_loader.__len__())
test_KLD_loss = np.empty(test_loader.__len__())
test_KLD_loss_per_latent_dim = np.empty((test_loader.__len__(), n_latents))
test_KLD_loss_var = np.empty(test_loader.__len__())
test_CE_loss = np.empty(test_loader.__len__())
# Loop over the test images:
print("Testing the images ... \n")
with torch.no_grad():
idx = 0
for data in test_loader:
# input
input_img = Variable(data['image'])
input_label = Variable(data['label'])
test_x[idx,:,:] = input_img.cpu().data.numpy().reshape((input_size[0], input_size[1]))
test_y[idx] = input_label.cpu().data.numpy()
# forward pass outputs
outputs = model(input_img)
test_mu[idx,:] = outputs['mu'].cpu().data.numpy().reshape(n_latents)
test_sigma[idx,:] = outputs['logvar'].exp().sqrt().cpu().data.numpy().reshape(n_latents)
test_recon_x[idx,:,:] = torch.sigmoid(outputs['recon_x']).cpu().data.numpy().reshape((input_size[0], input_size[1]))
# compute reconstruction losses
MSE_loss = helper.MSE_loss(torch.sigmoid(outputs['recon_x']), input_img).cpu().data.numpy().reshape(1)
BCE_loss = helper.BCE_with_digits_loss(outputs['recon_x'], input_img).cpu().data.numpy().reshape(1)
if 'recon_y' in outputs:
test_recon_y[idx,:] = F.softmax(outputs['recon_y']).cpu().data.numpy().reshape(3)
CE_loss = (F.cross_entropy(outputs['recon_y'], input_label, size_average=False) / input_label.size()[0])
else:
test_recon_y[idx,:] = np.zeros(3)
CE_loss = 0
KLD_loss, KLD_loss_per_latent_dim, KLD_loss_var = helper.KLD_loss(outputs['mu'], outputs['logvar'])
test_MSE_loss[idx] = MSE_loss
test_BCE_loss[idx] = BCE_loss
test_CE_loss[idx] = CE_loss
test_KLD_loss[idx] = KLD_loss.cpu().data.numpy().reshape(1)
test_KLD_loss_per_latent_dim[idx,:] = KLD_loss_per_latent_dim.cpu().data.numpy().reshape(n_latents)
test_KLD_loss_var[idx] = KLD_loss_var.cpu().data.numpy().reshape(1)
idx += 1
# Save in the experiment test folder
print("Saving the results ... \n")
np.savez(output_test_dataset_filename, x = test_x, recon_x = test_recon_x, y = test_y, recon_y = test_recon_y, mu = test_mu, sigma = test_sigma, MSE_loss = test_MSE_loss, BCE_loss = test_BCE_loss, KLD_loss = test_KLD_loss, KLD_loss_per_latent_dim = test_KLD_loss_per_latent_dim, KLD_loss_var = test_KLD_loss_var, CE_loss = test_CE_loss)
return