def run_check_net():
batch_size = 32
C, H, W = 3, 128, 128
num_class = 2
input = np.random.uniform(0, 1, (batch_size, C, H, W)).astype(np.float32)
truth = np.random.choice(num_class, batch_size).astype(np.float32)
#------------
input = torch.from_numpy(input).float().cuda()
truth = torch.from_numpy(truth).long().cuda()
input = to_var(input)
truth = to_var(truth)
#---
criterion = softmax_cross_entropy_criterion
net = Net(num_class).cuda()
net.set_mode('backup')
print(net)
logit = net.forward(input)
loss = criterion(logit, truth)
def run_check_net():
num_class = 2
x = torch.rand(36, 9, 48, 48)
net = Net(num_class)
output = net.forward(x)