def test(): arguments_parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) arguments_parser.add_argument('--config', help='yml config path', type=str, required=True) arguments_parser.add_argument('--host', help='name of the host server', type=str, required=True) args = arguments_parser.parse_args() host = args.host with open(args.config, 'r') as yml_file: cfg = yaml.load(yml_file) train_params = dict([(key, cfg[key]) for key in cfg if not key in ['dataset', 'ini_net', 'logging', 'test']]) test_params = cfg['test'] dataset = dict([(key, test_params['dataset'][key][host]) for key in test_params['dataset']]) device = torch.device('cuda:' + str(test_params['cuda_device']) if torch.cuda.is_available() else "cpu") torch.manual_seed(train_params['seed']) if params['train']['cuda_device'] != -1: torch.cuda.manual_seed_all(train_params['seed']) cp = torch.load(test_params['file_model']) test_dataset_parser = AVDBParser(dataset_root=dataset['data_root'], file_list=dataset['test_file_list'], ungroup=train_params['preproc']['data_frame']['depth']==1) test_data_sampler = GroupRandomSampler(data=test_dataset_parser.get_data(), num_sample_per_classes=train_params['preproc']['data_frame']['depth'], samples_is_randomize=train_params['sampler']['samples_is_randomize'], step_size_for_samples=train_params['sampler']['step_size_for_samples'], is_shuffle=False) test_image_processor = TorchImageProcessor(image_size=[train_params['preproc']['data_frame']['width'], train_params['preproc']['data_frame']['height']], is_color=train_params['preproc']['is_color'], mean=train_params['preproc']['mean'], scale=train_params['preproc']['scale'], crop_size=train_params['preproc']['crop_size'], pad=train_params['preproc']['aug']['pad'], use_cutout=False, use_mirroring=False, use_random_crop=False, use_center_crop=train_params['preproc']['aug']['pad'] > 0 \ or train_params['preproc']['crop_size'] != train_params['preproc']['data_frame']['width'] \ or train_params['preproc']['crop_size'] != train_params['preproc']['data_frame']['height']) test_images_batcher = ImagesBatcher(queue_size=train_params['valid_batcher']['queue_size'], batch_size=train_params['valid_batcher']['batch'], data_sampler=test_data_sampler, image_processor=test_image_processor, single_epoch=True, cache_data=False, disk_reader_process_num=train_params['valid_batcher']['disk_reader_process_num']) batch_processor = BatchProcessor4D(depth=train_params['preproc']['data_frame']['depth'], use_pin_memory=train_params['batch_proc']['use_pin_memory'], cuda_id=test_params['cuda_device'], use_async=False) model = cp['model'] if params['train']['cuda_device'] != -1: model.to(device) model.eval() test_images_batcher.start() predict = np.zeros((0, 2), dtype=np.float32) while True: batch = test_images_batcher.next_batch() if batch is None: break data, _, _ = batch_processor.pre_processing(batch) logits = model(data) predict = np.concatenate((predict, logits.cpu().data.numpy()), axis=0) accuracy_fn = Accuracy(test_dataset_parser.get_data()) accuracy_fn.by_frames(predict) accuracy_fn.by_clips(predict)
def train(): arguments_parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) arguments_parser.add_argument('--config', help='yml config path', type=str, required=True) arguments_parser.add_argument('--host', help='name of the host server', type=str, required=True) args = arguments_parser.parse_args() # get host name for brevity host = args.host # read configuration with open(args.config, 'r') as yml_file: cfg = yaml.load(yml_file) params = dict([(key, cfg[key]) for key in cfg if not key in ['dataset', 'ini_net', 'logging', 'test']]) dataset = dict([(key, cfg['dataset'][key][host]) for key in cfg['dataset']]) log = dict([(key, cfg['logging'][key][host]) for key in cfg['logging']]) ini_net = cfg['ini_net'][host] # init logging experiment_name = params['train']['experiment_name'] + str(params['train']['cuda_device']) create_logger(log['log_dir'], experiment_name + '.log', console_level=logging.CRITICAL, file_level=logging.NOTSET) # log configuration pp = pprint.PrettyPrinter(indent=4) logging.info("Configuration is:\n%s" % pp.pformat(cfg)) experiment_path = os.path.join(log['log_dir'], params['train']['experiment_name']) if not os.path.exists(experiment_path): os.makedirs(experiment_path) copyfile(args.config, os.path.join(experiment_path, 'config.yml')) if params['train']['cuda_device'] != -1: device = torch.device('cuda:' + str(params['train']['cuda_device']) if torch.cuda.is_available() else "cpu") else: device = 'cpu' torch.manual_seed(params['seed']) if params['train']['cuda_device'] != -1: torch.cuda.manual_seed_all(params['seed']) train_dataset_parser = AVDBParser(dataset_root=dataset['train']['data_root'], file_list=dataset['train']['file_list'], max_num_clips=params['parser']['max_num_clips'], max_num_samples=params['parser']['max_num_samples'], ungroup=params['preproc']['data_frame']['depth']==1, normalize='AFEW-VA' in dataset['train']['data_root']) valid_dataset_parser = AVDBParser(dataset_root=dataset['valid']['data_root'], file_list=dataset['valid']['file_list'], max_num_clips=params['parser']['max_num_clips'], max_num_samples=params['parser']['max_num_samples'], ungroup=params['preproc']['data_frame']['depth']==1, normalize='AFEW-VA' in dataset['train']['data_root']) softmax_size = params['net']['softmax_size'] if params['net']['softmax_size'] > 0 else train_dataset_parser.get_class_num() print('\rsoftmax_size = %d' % softmax_size) # create train data sampler train_data_sampler = GroupRandomSampler(data=train_dataset_parser.get_data(), num_sample_per_classes=params['preproc']['data_frame']['depth'], samples_is_randomize=params['sampler']['samples_is_randomize'], step_size_for_samples=params['sampler']['step_size_for_samples']) valid_data_sampler = GroupRandomSampler(data=valid_dataset_parser.get_data(), num_sample_per_classes=params['preproc']['data_frame']['depth'], samples_is_randomize=params['sampler']['samples_is_randomize'], step_size_for_samples=params['sampler']['step_size_for_samples'], is_shuffle=False) # create train image processor train_image_processor = TorchImageProcessor(image_size=[params['preproc']['data_frame']['width'], params['preproc']['data_frame']['height']], is_color=params['preproc']['is_color'], mean=params['preproc']['mean'], scale=params['preproc']['scale'], crop_size=params['preproc']['crop_size'], pad=params['preproc']['aug']['pad'], use_cutout=params['preproc']['aug']['use_cutout'], use_mirroring=params['preproc']['aug']['use_mirroring'], use_random_crop=params['preproc']['aug']['use_random_crop'], use_center_crop=params['preproc']['aug']['use_center_crop']) valid_image_processor = TorchImageProcessor(image_size=[params['preproc']['data_frame']['width'], params['preproc']['data_frame']['height']], is_color=params['preproc']['is_color'], mean=params['preproc']['mean'], scale=params['preproc']['scale'], crop_size=params['preproc']['crop_size'], pad=params['preproc']['aug']['pad'], use_cutout=False, use_mirroring=False, use_random_crop=False, use_center_crop=params['preproc']['aug']['pad'] > 0 \ or params['preproc']['crop_size'] != params['preproc']['data_frame']['width'] \ or params['preproc']['crop_size'] != params['preproc']['data_frame']['height']) # create train image batcher train_images_batcher = ImagesBatcher(queue_size=params['train_batcher']['queue_size'], batch_size=params['train_batcher']['batch'], data_sampler=train_data_sampler, image_processor=train_image_processor, single_epoch=False, cache_data=False, disk_reader_process_num=params['train_batcher']['disk_reader_process_num']) valid_images_batcher = ImagesBatcher(queue_size=params['valid_batcher']['queue_size'], batch_size=params['valid_batcher']['batch'], data_sampler=valid_data_sampler, image_processor=valid_image_processor, single_epoch=True, cache_data=False, disk_reader_process_num=params['valid_batcher']['disk_reader_process_num']) # create batch processor batch_processor = BatchProcessor4D(depth=params['preproc']['data_frame']['depth'], use_pin_memory=params['batch_proc']['use_pin_memory'], cuda_id=params['train']['cuda_device'], use_async=params['batch_proc']['use_async']) net_type = params['net']['type'] if net_type == 'ResNet': #net = Alexnet(embedding_dim=softmax_size) #net = Resnet152(embedding_dim=softmax_size) net = CNNNet(softmax_size, depth=params['net']['depth'], data_size=params['preproc']['data_frame'], pretrain_weight='resnet-34-kinetics.pth') else: print('Type net is not supported!') exit(0) loss = TotalLoss(params['losses'], 1, params['train']['cuda_device']) #if params['train']['cuda_device'] != -1: net.to(device) loss.to(device) if params['net']['fine_tune']: net.load_state_dict(torch.load(ini_net)) lr = params['opt']['lr'] momentum = params['opt']['momentum'] weight_decay = params['opt']['weight_decay'] opt_type = params['opt']['type'] if opt_type == 'SGD': optimizer = optim.SGD([{'params': net.parameters()}, {'params': loss.parameters()}], lr=lr, momentum=momentum, weight_decay=weight_decay, nesterov=True)#, caffe_like=True) if opt_type == 'ASGD': optimizer = optim.ASGD([{'params': net.parameters()}, {'params': loss.parameters()}], lr=lr, weight_decay=weight_decay)#, caffe_like=True) if opt_type == 'Adagrad': optimizer = optim.Adagrad([{'params': net.parameters()}, {'params': loss.parameters()}], lr=lr, weight_decay=weight_decay) if opt_type == 'Adadelta': optimizer = optim.Adadelta([{'params': net.parameters()}, {'params': loss.parameters()}], lr=lr, weight_decay=weight_decay) if opt_type == 'Adam': optimizer = optim.Adam([{'params': net.parameters()}, {'params': loss.parameters()}], lr=lr, weight_decay=weight_decay) if opt_type == 'RMSprop': optimizer = optim.RMSprop([{'params': net.parameters()}, {'params': loss.parameters()}], lr=lr, weight_decay=weight_decay, momentum=momentum) if opt_type == 'LBFGS': optimizer = optim.LBFGS(net.parameters(), lr=lr, max_iter=5, max_eval=None, tolerance_grad=1e-05, tolerance_change=1e-09, history_size=1, line_search_fn=None) #if params['net']['init'] == 'fine_tune': # optimizer = cp['optimizer'] lr_scheduler_type = params['lr_scheduler']['type'] gamma = float(params['lr_scheduler']['gamma']) if lr_scheduler_type == 'ReduceLROnPlateau': scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, factor=gamma) if lr_scheduler_type == 'MultiStepLR': scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=[15000,30000,60000], gamma=gamma) if lr_scheduler_type == 'ExponentialLR': scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=gamma) if lr_scheduler_type == 'SGDR': scheduler = SGDR_scheduler(optimizer, lr_start=lr, lr_end=gamma*lr, lr_period=params['train']['epoch_size']//params['train']['step_size'], scale_lr=params['lr_scheduler']['scale_lr'], scale_lr_fc=params['lr_scheduler']['scale_lr_fc']) if lr_scheduler_type == 'LRFinder': scheduler = LRFinder_scheduler(optimizer, lr_start=lr, lr_end=gamma*lr, lr_period=params['train']['epoch_size']//params['train']['step_size'], use_linear_decay=params['lr_scheduler']['use_linear_decay'], scale_lr=params['lr_scheduler']['scale_lr'], scale_lr_fc=params['lr_scheduler']['scale_lr_fc']) if lr_scheduler_type == 'OneCyclePolicy': scheduler = OneCyclePolicy_scheduler(optimizer, lr_max=lr, lr_period=params['train']['epoch_size']//params['train']['step_size'], use_linear_decay=params['lr_scheduler']['use_linear_decay'], scale_lr=params['lr_scheduler']['scale_lr'], scale_lr_fc=params['lr_scheduler']['scale_lr_fc']) if lr_scheduler_type == 'MultiCyclePolicy': scheduler = MultiCyclePolicy_scheduler(optimizer, lr_max=lr, lr_period=params['train']['epoch_size']//params['train']['step_size'], use_linear_decay=params['lr_scheduler']['use_linear_decay'], scale_lr=params['lr_scheduler']['scale_lr'], scale_lr_fc=params['lr_scheduler']['scale_lr_fc']) net_trainer = NetTrainer(logs_dir=log['tb_log_dir'], cuda_id=params['train']['cuda_device'], experiment_name=experiment_name, snapshot_dir=log['snapshot_dir'], config_data=cfg) if params['preproc']['data_frame']['depth'] == 1: accuracy_fn = Accuracy(valid_dataset_parser.get_data()) else: accuracy_fn = Accuracy3D(valid_dataset_parser.get_data(), depth=params['preproc']['data_frame']['depth']) # lets try to train torch.set_num_threads(num_threads) net_trainer.train(model=net, optimizer=optimizer, scheduler=scheduler, train_data_batcher=train_images_batcher, val_data_batcher=valid_images_batcher, val_iter=params['train']['validate_iter'], batch_processor=batch_processor, loss_function=loss, max_iter=params['train']['max_iter'], step_size=params['train']['step_size'], snapshot_iter=params['train']['snapshot_iter'], step_print=params['train']['step_print'], accuracy_function=accuracy_fn) # finalize data loader queues train_images_batcher.finish() valid_images_batcher.finish()
max_num_samples=params['parser']['max_num_samples'], ungroup=False, normalize='AFEW-VA' in dataset['train']['data_root']) valid_dataset_parser = AVDBParser(dataset_root=dataset['valid']['data_root'], file_list=dataset['valid']['file_list'], max_num_clips=params['parser']['max_num_clips'], max_num_samples=params['parser']['max_num_samples'], ungroup=False, normalize='AFEW-VA' in dataset['train']['data_root']) softmax_size = params['net']['softmax_size'] if params['net']['softmax_size'] > 0 else train_dataset_parser.get_class_num() print('\rsoftmax_size = %d' % softmax_size) # create train data sampler train_data_sampler = GroupRandomSampler(data=train_dataset_parser.get_data(), num_sample_per_classes=1, samples_is_randomize=True, step_size_for_samples=1) valid_data_sampler = GroupRandomSampler(data=valid_dataset_parser.get_data(), num_sample_per_classes=1, samples_is_randomize=True, step_size_for_samples=1, is_shuffle=False) # create train audio processor audio_processor = LibrosaAudioProcessor(type=params['preproc']['audio']['type'], sampling_rate=params['preproc']['audio']['sampling_rate'], max_wave_value=params['preproc']['audio']['max_wave_value'], sfft=params['preproc']['audio']['sfft'], mel=params['preproc']['audio']['mel']) # create train image batcher