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)