start = time.clock()
with torch.no_grad():
#try:
if eps <= 0:
input_data = picts
else:
input_data = picts[idxlist]
if error_bound <= 0 and args.split == 0:
outputs = test(torch.from_numpy(input_data).to(device))
totaltime += time.clock() - start
zs = outputs[2].cpu().detach().numpy()
predict = outputs[0].cpu().detach().numpy()
else:
if args.split == 0:
outputs = test.encode(torch.from_numpy(input_data).to(device))
totaltime += time.clock() - start
zs = outputs.cpu().detach().numpy()
else:
split = args.split
len_input = input_data.shape[0]
start = 0
zs = None
while (start < len_input):
end = min(len_input, start + split)
input_split = input_data[start:end]
zs_split = test.encode(
torch.from_numpy(input_split).to(
device)).cpu().detach().numpy()
if start == 0:
zs = zs_split
picts = np.array(picts)
start = time.clock()
with torch.no_grad():
#try:
if eps <= 0:
input_data = picts
else:
input_data = picts[idxlist]
if error_bound <= 0 and args.split == 0:
outputs = test(torch.from_numpy(input_data).to(device))
totaltime += time.clock() - start
zs = outputs[2].cpu().detach().numpy()
predict = outputs[0].cpu().detach().numpy()
else:
outputs = test.encode(torch.from_numpy(input_data).to(device))
totaltime += time.clock() - start
zs = outputs.cpu().detach().numpy()
latent_size = zs.shape[1]
zs = zs.flatten()
print(zs.shape[0])
recon = np.zeros(array_size, dtype=np.float32)
latents = np.array(zs)
if args.transpose:
latents = latents.reshape((-1, latent_size)).transpose().flatten()
if args.gpu:
