action='store_true', default=True, help='save log') # Working directory - model_path. Currently disabled # parser.add_argument('--work-dir', type=str, default=model_path, metavar='WD', # help='path to save') # TO ADD: save_result args = parser.parse_args() device = 'cuda:0' # pr = processor1.Processor(args, None, device) # data, labels, data_train, labels_train, data_test, labels_test = \ # loader.load_data(data_path) a = loader.TrainTestLoader(False) d, l = a.__getitem__(0) print(type(d)) print(type(l)) print(l.shape) print(d.shape) # print(np.amax(d)) img__ = d.numpy() # print(np.amax(l)) img_ = np.zeros((d.shape[1], d.shape[2], 3)) img_[:, :, 0] = d[0, :, :] img_[:, :, 1] = d[1, :, :] img_[:, :, 2] = d[2, :, :] img__ = img_.astype('float32') io.imshow(img__) io.show()
parser.add_argument('--work-dir', type=str, default=model_path, metavar='WD', help='path to save') # TO ADD: save_result args = parser.parse_args() device = 'cuda:0' data, labels, data_train, labels_train, data_test, labels_test =\ loader.load_data(data_path, ftype_real, ftype_synth, coords, joints, cycles=cycles) num_classes = np.unique(labels_train).shape[0] data_loader = { 'train': torch.utils.data.DataLoader(dataset=loader.TrainTestLoader( data_train, labels_train, joints, coords, num_classes), batch_size=args.batch_size, shuffle=True, drop_last=True), 'test': torch.utils.data.DataLoader(dataset=loader.TrainTestLoader( data_test, labels_test, joints, coords, num_classes), batch_size=args.batch_size, shuffle=True, drop_last=True) } graph_dict = {'strategy': 'spatial'} print('Train set size: {:d}'.format(len(data_train))) print('Test set size: {:d}'.format(len(data_test))) print('Number of classes: {:d}'.format(num_classes)) pr = processor.Processor(args,
metrics_file_full_path = 'metrics.txt' if not os.path.exists(metrics_file_full_path): for init_idx in range(num_inits): for fold_idx, (data_train, labels_train, data_test, labels_test) in enumerate(zip(data_train_all_folds, labels_train_all_folds, data_test_all_folds, labels_test_all_folds)): print('Running init {:02d}, fold {:02d}'.format(init_idx, fold_idx)) # saving trained models for each init and split in separate folders model_path = os.path.join(base_path, 'model_classifier_combined_lstm_init_{:02d}_fold_{:02d}/features'.format(init_idx, fold_idx) + ftype) args.work_dir = model_path os.makedirs(model_path, exist_ok=True) aff_features = len(data_train[0][0]) num_classes = np.unique(labels_train).shape[0] data_loader = list() data_loader.append(torch.utils.data.DataLoader( dataset=loader.TrainTestLoader(data_train, labels_train, joints, coords), batch_size=args.batch_size, shuffle=True, num_workers=args.num_worker * ngpu(device), drop_last=True)) data_loader.append(torch.utils.data.DataLoader( dataset=loader.TrainTestLoader(data_test, labels_test, joints, coords), batch_size=args.batch_size, shuffle=True, num_workers=args.num_worker * ngpu(device), drop_last=True)) data_loader = dict(train=data_loader[0], test=data_loader[1]) graph_dict = {'strategy': 'spatial'} pr = processor.Processor(args, data_loader, coords*joints, aff_features, num_classes, graph_dict, device=device) if args.train: pr.train()
# Working directory - model_path. Currently disabled # parser.add_argument('--work-dir', type=str, default=model_path, metavar='WD', # help='path to save') # TO ADD: save_result args = parser.parse_args() device = 'cuda:0' #%% TBD: Load the dataset if DEBUG == False: # data, labels, data_train, labels_train, data_test, labels_test = \ # loader.load_data(data_path, ftype, coords, joints, cycles=cycles) # num_classes = np.unique(labels_train).shape[0] data_loader_train_test = list() data_loader_train_test.append( torch.utils.data.DataLoader(dataset=loader.TrainTestLoader(train=True), batch_size=args.batch_size, shuffle=True, num_workers=args.num_worker * torchlight.ngpu(device), drop_last=True)) data_loader_train_test.append( torch.utils.data.DataLoader( dataset=loader.TrainTestLoader(train=False), batch_size=args.batch_size, shuffle=True, num_workers=args.num_worker * torchlight.ngpu(device), drop_last=True)) data_loader_train_test = dict(train=data_loader_train_test[0], test=data_loader_train_test[1]) else:
affs_dim = affective_features_train.shape[-1] + deep_dim affective_features = np.concatenate( (affective_features_train, affective_features_test), axis=0) affective_features, affs_max, affs_min = loader.scale_data(affective_features) affective_features_train, _, _ = loader.scale_data(affective_features_train, affs_max, affs_min) affective_features_test, _, _ = loader.scale_data(affective_features_test, affs_max, affs_min) num_frames_max = rotations_train.shape[1] num_frames_out = num_frames_max - 1 num_frames_train_norm = num_frames_train / num_frames_max num_frames_test_norm = num_frames_test / num_frames_max data_loader = list() data_loader.append( torch.utils.data.DataLoader(dataset=loader.TrainTestLoader( data_train, poses_train, rotations_train, translations_train, affective_features_train, num_frames_train_norm, labels_train, coords, num_labels), batch_size=args.batch_size, shuffle=True, num_workers=args.num_worker * ngpu(device), drop_last=True)) data_loader.append( torch.utils.data.DataLoader(dataset=loader.TrainTestLoader( data_test, poses_test, rotations_test, translations_test, affective_features_test, num_frames_test_norm, labels_test, coords, num_labels), batch_size=args.batch_size, shuffle=True, num_workers=args.num_worker * ngpu(device), drop_last=True)) data_loader = dict(train=data_loader[0], test=data_loader[1])