def test(opt): # logging logger = base_utils.get_logger('base') if opt['verbose']: logger.info('{} Configurations {}'.format('=' * 20, '=' * 20)) base_utils.print_options(opt, logger) # infer and evaluate performance for each model for load_path in opt['model']['generator']['load_path_lst']: # setup model index model_idx = osp.splitext(osp.split(load_path)[-1])[0] # log logger.info('=' * 40) logger.info('Testing model: {}'.format(model_idx)) logger.info('=' * 40) # create model opt['model']['generator']['load_path'] = load_path model = define_model(opt) # for each test dataset for dataset_idx in sorted(opt['dataset'].keys()): # use dataset with prefix `test` if not dataset_idx.startswith('test'): continue ds_name = opt['dataset'][dataset_idx]['name'] logger.info('Testing on {}: {}'.format(dataset_idx, ds_name)) # create data loader test_loader = create_dataloader(opt, dataset_idx=dataset_idx) # infer and store results for each sequence for i, data in enumerate(test_loader): # fetch data lr_data = data['lr'][0] seq_idx = data['seq_idx'][0] frm_idx = [frm_idx[0] for frm_idx in data['frm_idx']] # infer hr_seq = model.infer(lr_data) # thwc|rgb|uint8 # save results (optional) if opt['test']['save_res']: res_dir = osp.join(opt['test']['res_dir'], ds_name, model_idx) res_seq_dir = osp.join(res_dir, seq_idx) data_utils.save_sequence(res_seq_dir, hr_seq, frm_idx, to_bgr=True) logger.info('-' * 40) # logging logger.info('Finish testing') logger.info('=' * 40)
def train(opt): # logging logger = base_utils.get_logger('base') logger.info('{} Options {}'.format('='*20, '='*20)) base_utils.print_options(opt, logger) # create data loader train_loader = create_dataloader(opt, dataset_idx='train') # create downsampling kernels for BD degradation kernel = data_utils.create_kernel(opt) # create model model = define_model(opt) # training configs total_sample = len(train_loader.dataset) iter_per_epoch = len(train_loader) total_iter = opt['train']['total_iter'] total_epoch = int(math.ceil(total_iter / iter_per_epoch)) start_iter, iter = opt['train']['start_iter'], 0 test_freq = opt['test']['test_freq'] log_freq = opt['logger']['log_freq'] ckpt_freq = opt['logger']['ckpt_freq'] logger.info('Number of training samples: {}'.format(total_sample)) logger.info('Total epochs needed: {} for {} iterations'.format( total_epoch, total_iter)) # train for epoch in range(total_epoch): for data in train_loader: # update iter iter += 1 curr_iter = start_iter + iter if iter > total_iter: logger.info('Finish training') break # update learning rate model.update_learning_rate() # prepare data data = prepare_data(opt, data, kernel) # train for a mini-batch model.train(data) # update running log model.update_running_log() # log if log_freq > 0 and iter % log_freq == 0: # basic info msg = '[epoch: {} | iter: {}'.format(epoch, curr_iter) for lr_type, lr in model.get_current_learning_rate().items(): msg += ' | {}: {:.2e}'.format(lr_type, lr) msg += '] ' # loss info log_dict = model.get_running_log() msg += ', '.join([ '{}: {:.3e}'.format(k, v) for k, v in log_dict.items()]) logger.info(msg) # save model if ckpt_freq > 0 and iter % ckpt_freq == 0: model.save(curr_iter) # evaluate performance if test_freq > 0 and iter % test_freq == 0: # setup model index model_idx = 'G_iter{}'.format(curr_iter) # for each testset for dataset_idx in sorted(opt['dataset'].keys()): # use dataset with prefix `test` if not dataset_idx.startswith('test'): continue ds_name = opt['dataset'][dataset_idx]['name'] logger.info( 'Testing on {}: {}'.format(dataset_idx, ds_name)) # create data loader test_loader = create_dataloader(opt, dataset_idx=dataset_idx) # define metric calculator metric_calculator = MetricCalculator(opt) # infer and compute metrics for each sequence for data in test_loader: # fetch data lr_data = data['lr'][0] seq_idx = data['seq_idx'][0] frm_idx = [frm_idx[0] for frm_idx in data['frm_idx']] # infer hr_seq = model.infer(lr_data) # thwc|rgb|uint8 # save results (optional) if opt['test']['save_res']: res_dir = osp.join( opt['test']['res_dir'], ds_name, model_idx) res_seq_dir = osp.join(res_dir, seq_idx) data_utils.save_sequence( res_seq_dir, hr_seq, frm_idx, to_bgr=True) # compute metrics for the current sequence true_seq_dir = osp.join( opt['dataset'][dataset_idx]['gt_seq_dir'], seq_idx) metric_calculator.compute_sequence_metrics( seq_idx, true_seq_dir, '', pred_seq=hr_seq) # save/print metrics if opt['test'].get('save_json'): # save results to json file json_path = osp.join( opt['test']['json_dir'], '{}_avg.json'.format(ds_name)) metric_calculator.save_results( model_idx, json_path, override=True) else: # print directly metric_calculator.display_results()
def test(opt): # logging logger = base_utils.get_logger('base') if opt['verbose']: logger.info('{} Configurations {}'.format('=' * 20, '=' * 20)) base_utils.print_options(opt, logger) # infer and evaluate performance for each model for load_path in opt['model']['generator']['load_path_lst']: # setup model index model_idx = osp.splitext(osp.split(load_path)[-1])[0] # log logger.info('=' * 40) logger.info('Testing model: {}'.format(model_idx)) logger.info('=' * 40) # create model opt['model']['generator']['load_path'] = load_path model = define_model(opt) # for each test dataset for dataset_idx in sorted(opt['dataset'].keys()): # use dataset with prefix `test` if not dataset_idx.startswith('test'): continue ds_name = opt['dataset'][dataset_idx]['name'] logger.info('Testing on {}: {}'.format(dataset_idx, ds_name)) # define metric calculator try: metric_calculator = MetricCalculator(opt) except: print('No metirc need to compute!') # create data loader test_loader = create_dataloader(opt, dataset_idx=dataset_idx) # infer and store results for each sequence for i, data in enumerate(test_loader): # fetch data lr_data = data['lr'][0] seq_idx = data['seq_idx'][0] frm_idx = [frm_idx[0] for frm_idx in data['frm_idx']] # infer hr_seq = model.infer(lr_data) # thwc|rgb|uint8 # save results (optional) if opt['test']['save_res']: res_dir = osp.join(opt['test']['res_dir'], ds_name, model_idx) res_seq_dir = osp.join(res_dir, seq_idx) data_utils.save_sequence(res_seq_dir, hr_seq, frm_idx, to_bgr=True) # compute metrics for the current sequence true_seq_dir = osp.join( opt['dataset'][dataset_idx]['gt_seq_dir'], seq_idx) try: metric_calculator.compute_sequence_metrics(seq_idx, true_seq_dir, '', pred_seq=hr_seq) except: print('No metirc need to compute!') # save/print metrics try: if opt['test'].get('save_json'): # save results to json file json_path = osp.join(opt['test']['json_dir'], '{}_avg.json'.format(ds_name)) metric_calculator.save_results(model_idx, json_path, override=True) else: # print directly metric_calculator.display_results() except: print('No metirc need to save!') logger.info('-' * 40) # logging logger.info('Finish testing') logger.info('=' * 40)
def test(opt): # logging base_utils.print_options(opt) # infer and evaluate performance for each model for load_path in opt['model']['generator']['load_path_lst']: # set model index model_idx = osp.splitext(osp.split(load_path)[-1])[0] # log base_utils.log_info(f'{"=" * 40}') base_utils.log_info(f'Testing model: {model_idx}') base_utils.log_info(f'{"=" * 40}') # create model opt['model']['generator']['load_path'] = load_path model = define_model(opt) # for each test dataset for dataset_idx in sorted(opt['dataset'].keys()): # select testing dataset if 'test' not in dataset_idx: continue ds_name = opt['dataset'][dataset_idx]['name'] base_utils.log_info(f'Testing on {ds_name} dataset') # create data loader test_loader = create_dataloader(opt, phase='test', idx=dataset_idx) test_dataset = test_loader.dataset num_seq = len(test_dataset) # create metric calculator metric_calculator = create_metric_calculator(opt) # infer a sequence rank, world_size = dist_utils.get_dist_info() for idx in range(rank, num_seq, world_size): # fetch data data = test_dataset[idx] # prepare data model.prepare_inference_data(data) # infer hr_seq = model.infer() # save hr results if opt['test']['save_res']: res_dir = osp.join(opt['test']['res_dir'], ds_name, model_idx) res_seq_dir = osp.join(res_dir, data['seq_idx']) data_utils.save_sequence(res_seq_dir, hr_seq, data['frm_idx'], to_bgr=True) # compute metrics for the current sequence if metric_calculator is not None: gt_seq = data['gt'].numpy() metric_calculator.compute_sequence_metrics( data['seq_idx'], gt_seq, hr_seq) # save/print results if metric_calculator is not None: seq_idx_lst = [data['seq_idx'] for data in test_dataset] metric_calculator.gather(seq_idx_lst) if opt['test'].get('save_json'): # write results to a json file json_path = osp.join(opt['test']['json_dir'], f'{ds_name}_avg.json') metric_calculator.save(model_idx, json_path, override=True) else: # print directly metric_calculator.display() base_utils.log_info('-' * 40)
def train(opt): # print configurations base_utils.log_info(f'{20*"-"} Configurations {20*"-"}') base_utils.print_options(opt) # create data loader train_loader = create_dataloader(opt, phase='train', idx='train') # build model model = define_model(opt) # set training params total_sample, iter_per_epoch = len(train_loader.dataset), len(train_loader) total_iter = opt['train']['total_iter'] total_epoch = int(math.ceil(total_iter / iter_per_epoch)) start_iter, iter = opt['train']['start_iter'], 0 test_freq = opt['test']['test_freq'] log_freq = opt['logger']['log_freq'] ckpt_freq = opt['logger']['ckpt_freq'] base_utils.log_info(f'Number of the training samples: {total_sample}') base_utils.log_info( f'{total_epoch} epochs needed for {total_iter} iterations') # train for epoch in range(total_epoch): if opt['dist']: train_loader.sampler.set_epoch(epoch) for data in train_loader: # update iter iter += 1 curr_iter = start_iter + iter if iter > total_iter: break # prepare data model.prepare_training_data(data) # train a mini-batch model.train() # update running log model.update_running_log() # update learning rate model.update_learning_rate() # print messages if log_freq > 0 and curr_iter % log_freq == 0: msg = model.get_format_msg(epoch, curr_iter) base_utils.log_info(msg) # save model if ckpt_freq > 0 and curr_iter % ckpt_freq == 0: model.save(curr_iter) # evaluate model if test_freq > 0 and curr_iter % test_freq == 0: # set model index model_idx = f'G_iter{curr_iter}' # for each testset for dataset_idx in sorted(opt['dataset'].keys()): # select test dataset if 'test' not in dataset_idx: continue ds_name = opt['dataset'][dataset_idx]['name'] base_utils.log_info(f'Testing on {ds_name} dataset') # create data loader test_loader = create_dataloader(opt, phase='test', idx=dataset_idx) test_dataset = test_loader.dataset num_seq = len(test_dataset) # create metric calculator metric_calculator = create_metric_calculator(opt) # infer a sequence rank, world_size = dist_utils.get_dist_info() for idx in range(rank, num_seq, world_size): # fetch data data = test_dataset[idx] # prepare data model.prepare_inference_data(data) # infer hr_seq = model.infer() # save hr results if opt['test']['save_res']: res_dir = osp.join(opt['test']['res_dir'], ds_name, model_idx) res_seq_dir = osp.join(res_dir, data['seq_idx']) data_utils.save_sequence(res_seq_dir, hr_seq, data['frm_idx'], to_bgr=True) # compute metrics for the current sequence if metric_calculator is not None: gt_seq = data['gt'].numpy() metric_calculator.compute_sequence_metrics( data['seq_idx'], gt_seq, hr_seq) # save/print results if metric_calculator is not None: seq_idx_lst = [ data['seq_idx'] for data in test_dataset ] metric_calculator.gather(seq_idx_lst) if opt['test'].get('save_json'): # write results to a json file json_path = osp.join(opt['test']['json_dir'], f'{ds_name}_avg.json') metric_calculator.save(model_idx, json_path, override=True) else: # print directly metric_calculator.display()
def validate(opt, model, logger, dataset_idx, model_idx, compute_metrics=True): if opt['dataset'][dataset_idx].get('framewise', False): return validate_gen(opt, model, logger, dataset_idx, model_idx) ds_name = opt['dataset'][dataset_idx]['name'] folders = get_folders(opt, dataset_idx, model_idx) logger.info( 'Testing on {}: {}'.format(dataset_idx, ds_name)) # create data loader test_loader = create_dataloader(opt, dataset_idx=dataset_idx) if not len(test_loader.dataset): return # define metric calculator if compute_metrics: metric_calculator = MetricCalculator(opt) # infer and compute metrics for each sequence for data in tqdm(test_loader): input_data_type = opt['dataset']['degradation']['type'] input_seq, output_seq, seq_idx, frm_idx = data_processing(model, data, test_loader, input_data_type) out_c = output_seq.shape[-1] seq_to_save = np.dstack([output_seq, input_seq[:, :, :, :out_c]]) # t.h.2w.c|rgb|uint8 # save results (optional) if opt['test']['save_res']: res_dir = osp.join(*folders) res_seq_dir = osp.join(res_dir, seq_idx) data_utils.save_sequence( res_seq_dir, seq_to_save, frm_idx, to_bgr=True) # compute metrics for the current sequence if opt['dataset']['degradation']['type'] == 'Multimodal': true_seq_dir = osp.join( opt['dataset'][dataset_idx]['data_path'], opt['dataset'][dataset_idx]['domain'], seq_idx, opt['dataset'][dataset_idx]['modalities']['ground_truth']['name'] ) else: true_seq_dir = osp.join( opt['dataset'][dataset_idx]['gt_seq_dir'], seq_idx) if compute_metrics: metric_calculator.compute_sequence_metrics( seq_idx, true_seq_dir, '', pred_seq=output_seq) # save/print metrics if compute_metrics: if opt['test'].get('save_json'): # save results to json file json_path = osp.join( opt['test']['json_dir'], '{}_avg.json'.format(ds_name)) metric_calculator.save_results( model_idx, json_path, override=True) else: # print directly metric_calculator.display_results()