img_name = r'' gt_name = r'' if os.path.isdir(img_name): image = analysis_report = os.path.join(outpath, 'Analysis_' + img_name + '.txt') print('output analysis report path is {}'.format(analysis_report)) net = CrowdCounter() trained_model = os.path.join(model_path) # read model's parameter h5f = h5py.File(trained_model, mode='r') for k, v in net.state_dict().items(): param = torch.from_numpy(np.asarray(h5f[k])) v.copy_(param) net.cuda() net.eval() mae, mse = 0, 0 print(net)
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 save_name = os.path.join( output_dir, '{}_{}_{}.pth'.format(method, dataset_name, epoch)) torch.save(net.state_dict(), save_name) #if (epoch % 2 == 0): # 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)
def main(): # define output folder output_dir = './saved_models/' log_dir = './mae_mse/' checkpoint_dir = './checkpoint/' train_path = '/home/jake/Desktop/Projects/Python/dataset/SH_B/cooked/train/images' train_gt_path = '/home/jake/Desktop/Projects/Python/dataset/SH_B/cooked/train/ground_truth' val_path = '/home/jake/Desktop/Projects/Python/dataset/SH_B/cooked/val/images' val_gt_path = '/home/jake/Desktop/Projects/Python/dataset/SH_B/cooked/val/ground_truth' # last checkpoint checkpointfile = os.path.join(checkpoint_dir, 'checkpoint.94.pth.tar') # some description method = 'mcnn' dataset_name = 'SH_B' # log file f_train_loss = open(os.path.join(log_dir, "train_loss.csv"), "a+") f_val_loss = open(os.path.join(log_dir, "val_loss.csv"), "a+") # Training configuration start_epoch = 0 end_epoch = 97 lr = 0.00001 # momentum = 0.9 disp_interval = 1000 # log_interval = 250 # Flag CONTINUE_TRAIN = True # 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) # Define network 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 # exp = cc.create_experiment(exp_name) # else: # exp = cc.open_experiment(exp_name) # training param if CONTINUE_TRAIN: net, optimizer, start_epoch = utils.load_checkpoint( net, optimizer, filename=checkpointfile) train_loss = 0 step_cnt = 0 re_cnt = False t = Timer() t.tic() # Load data data_loader = ImageDataLoader( train_path, train_gt_path, shuffle=True, gt_downsample=True, pre_load=True) data_loader_val = ImageDataLoader( val_path, val_gt_path, shuffle=False, gt_downsample=True, pre_load=True) best_mae = sys.maxsize # Start training for this_epoch in range(start_epoch, end_epoch-1): step = -1 train_loss = 0 for blob in data_loader: step += 1 img_data = blob['data'] gt_data = blob['gt_density'] et_data = net(img_data, gt_data) loss = net.loss train_loss += loss.data step_cnt += 1 optimizer.zero_grad() loss.backward() optimizer.step() if step % disp_interval == 0: duration = t.toc(average=False) fps = step_cnt / duration gt_count = np.sum(gt_data) et_data = et_data.data.cpu().numpy() et_count = np.sum(et_data) utils.save_results(img_data, gt_data, et_data, output_dir, fname="{}.{}.png".format(this_epoch, step)) log_text = 'epoch: %4d, step %4d, Time: %.4fs, gt_cnt: %4.1f, et_cnt: %4.1f' % (this_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 # Save checkpoint state = {'epoch': this_epoch, 'state_dict': net.state_dict(), 'optimizer': optimizer.state_dict()} cp_filename = "checkpoint.{}.pth.tar".format(this_epoch) torch.save(state, os.path.join(checkpoint_dir, cp_filename)) # ========================== END 1 EPOCH================================================================================== train_mae, train_mse = evaluate_network(net, data_loader) f_train_loss.write("{},{}\n".format(train_mae, train_mse)) log_text = 'TRAINING - EPOCH: %d, MAE: %.1f, MSE: %0.1f' % ( this_epoch, train_mae, train_mse) log_print(log_text, color='green', attrs=['bold']) # =====================================================VALIDATION========================================================= # calculate error on the validation dataset val_mae, val_mse = evaluate_network(net, data_loader_val) f_val_loss.write("{},{}\n".format(val_mae, val_mse)) log_text = 'VALIDATION - EPOCH: %d, MAE: %.1f, MSE: %0.1f' % ( this_epoch, val_mae, val_mse) log_print(log_text, color='green', attrs=['bold']) # SAVE model is_save = False if val_mae <= best_mae: if val_mae < best_mae: is_save = True best_mae = val_mae best_mse = val_mse else: if val_mse < best_mse: is_save = True best_mse = val_mse if is_save: save_name = os.path.join(output_dir, '{}_{}_{}.h5'.format( method, dataset_name, this_epoch)) network.save_net(save_name, net) best_model = '{}_{}_{}.h5'.format(method, dataset_name, this_epoch) 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) f_train_loss.close() f_val_loss.close()