exp_name = None # the previous experiment name in TensorBoard
# ------------
rand_seed = 64678
if rand_seed is not None:
np.random.seed(rand_seed)
torch.manual_seed(rand_seed)
torch.cuda.manual_seed(rand_seed)
# load net
net = CrowdCounter()
network.weights_normal_init(net, dev=0.01)
net.cuda()
net.train()
params = list(net.parameters())
optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad,
net.parameters()),
lr=lr)
if not os.path.exists(output_dir):
os.mkdir(output_dir)
# tensorboad
use_tensorboard = use_tensorboard and CrayonClient is not None
if use_tensorboard:
cc = CrayonClient(hostname='127.0.0.1')
if remove_all_log:
cc.remove_all_experiments()
if exp_name is None:
exp_name = save_exp_name
np.random.seed(rand_seed)
torch.manual_seed(rand_seed)
torch.cuda.manual_seed(rand_seed)
#loadt training and validation data
data_loader = ImageDataLoader(train_path, train_gt_path, shuffle=True, gt_downsample=False, pre_load=True)
class_wts = data_loader.get_classifier_weights()
data_loader_val = ImageDataLoader(val_path, val_gt_path, shuffle=False, gt_downsample=False, pre_load=True)
#load net and initialize it
net = CrowdCounter(ce_weights=class_wts)
network.weights_normal_init(net, dev=0.01)
net.cuda()
net.train()
params = list(net.parameters())
optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, net.parameters()), lr=lr) #优化算法
if not os.path.exists(output_dir):
os.mkdir(output_dir)
# tensorboad
use_tensorboard = use_tensorboard and CrayonClient is not None
if use_tensorboard:
cc = CrayonClient(hostname='127.0.0.1')
if remove_all_log:
cc.remove_all_experiments()
if exp_name is None:
exp_name = datetime.now().strftime('vgg16_%m-%d_%H-%M')
exp_name = save_exp_name
exp = cc.create_experiment(exp_name)