save_exp_name = method + '_' + dataset_name + '_' + 'v1' remove_all_log = False # remove all historical experiments in TensorBoard 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()
def main(): parser = argparse.ArgumentParser(description='mcnn worldexp.') parser.add_argument('--preload', type=int, default=1) parser.add_argument('--data', type=str, default="/mnt/m2/mzcc/crowd_data/worldexpo", help='train, test, etc') args = parser.parse_args() method = 'mcnn' dataset_name = 'worldexpo' output_dir = './saved_models/' data_path = args.data train_path = data_path+'/train_frame' train_gt_path = data_path+'/train_dmap' train_mask_path = os.path.join(data_path,'train_roi') val_path = data_path+'/test_frame' val_gt_path = data_path+'/test_dmap' val_mask_path = os.path.join(data_path, 'test_roi') #training configuration start_step = 0 end_step = 3000 lr = 0.000001 momentum = 0.9 disp_interval = 500 log_interval = 250 #Tensorboard config use_tensorboard = False save_exp_name = method + '_' + dataset_name + '_' + 'v1' remove_all_log = False # remove all historical experiments in TensorBoard 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) # network.weights_xavier_init(net, gain=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 exp = cc.create_experiment(exp_name) else: exp = cc.open_experiment(exp_name) # training train_loss = 0 step_cnt = 0 re_cnt = False t = Timer() t.tic() data_loader = ExrImageDataLoader(train_path, train_gt_path, mask_path=train_mask_path, shuffle=True, gt_downsample=True, pre_load=args.preload) data_loader_val = ExrImageDataLoader(val_path, val_gt_path, mask_path=val_mask_path, shuffle=False, gt_downsample=True, pre_load=False) best_mae = 10000000 for epoch in range(start_step, end_step+1): step = -1 train_loss = 0 for blob in data_loader: step = step + 1 im_data = blob['data'] gt_data = blob['gt_density'] mask = blob['mask'] density_map = net(im_data, gt_data, mask=mask) loss = net.loss train_loss += loss.item()#.data[0] step_cnt += 1 optimizer.zero_grad() loss.backward() optimizer.step() if step % disp_interval == 0: print("current loss: {}".format(loss.item())) duration = t.toc(average=False) fps = step_cnt / duration gt_count = np.sum(gt_data) density_map = density_map.data.cpu().numpy() et_count = np.sum(density_map) utils.save_results(im_data,gt_data,density_map, output_dir) log_text = 'epoch: %4d, step %4d, Time: %.4fs, gt_cnt: %4.1f, et_cnt: %4.1f' % (epoch, step, 1./fps, gt_count,et_count) log_print(log_text, color='green', attrs=['bold']) re_cnt = True if re_cnt: t.tic() re_cnt = False if (epoch % 2 == 0): save_name = os.path.join(output_dir, '{}_{}_{}.h5'.format(method,dataset_name,epoch)) network.save_net(save_name, net) #calculate error on the validation dataset mae,mse = evaluate_model(save_name, data_loader_val) if mae < best_mae: best_mae = mae best_mse = mse best_model = '{}_{}_{}.h5'.format(method,dataset_name,epoch) log_text = 'EPOCH: %d, MAE: %.1f, MSE: %0.1f' % (epoch,mae,mse) log_print(log_text, color='green', attrs=['bold']) log_text = 'BEST MAE: %0.1f, BEST MSE: %0.1f, BEST MODEL: %s' % (best_mae,best_mse, best_model) log_print(log_text, color='green', attrs=['bold']) if use_tensorboard: exp.add_scalar_value('MAE', mae, step=epoch) exp.add_scalar_value('MSE', mse, step=epoch) exp.add_scalar_value('train_loss', train_loss/data_loader.get_num_samples(), step=epoch)