batch += 1 _, _, _, _, c = correct_preds(probs, labels.squeeze()) if disp: print(i, c) correct.append(c) PCE = np.mean(correct) return PCE if __name__ == "__main__": split = 1 seq_length = 64 n_cpu = 6 model = EventDetector( pretrain=True, width_mult=1.0, lstm_layers=1, lstm_hidden=256, bidirectional=True, dropout=False, ) save_dict = torch.load("models/swingnet_1800.pth.tar") model.load_state_dict(save_dict["model_state_dict"]) model.cuda() model.eval() PCE = eval(model, split, seq_length, n_cpu, True) print("Average PCE: {}".format(PCE))
n_cpu = 6 seq_length = args.seq_length bs = args.batch_size # batch size k = 10 # frozen layers use_no_element = args.use_no_element device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') print('Load Model') model = EventDetector(pretrain=True, width_mult=1., lstm_layers=1, lstm_hidden=256, device=device, bidirectional=True, dropout=False, use_no_element=use_no_element ) #print('model.py, class EventDetector()') freeze_layers(k, model) #print('utils.py, func freeze_laters()') model.train() model.to(device) print('Loading Data') # TODO: vid_dirのpathをかえる。stsqの動画を切り出したimage全部が含まれているdirにする if use_no_element == False:
version_name = 'original_' + str(split) + '_' + str(noise_level) print(version_name) if __name__ == '__main__': # training configuration iterations = 10000 it_save = 10000 # save model every 100 iterations n_cpu = 8 seq_length = 64 bs = 22 # batch size k = 10 # frozen layers model = EventDetector(pretrain=True, width_mult=1., lstm_layers=1, lstm_hidden=256, bidirectional=True, dropout=False) freeze_layers(k, model) model.train() model.cuda() dataset = GolfDB(data_file='data/train_split_{}.pkl'.format(split), vid_dir='data/videos_160/', seq_length=seq_length, transform=transforms.Compose([ ToTensor(), Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) ]), train=True,
_, _, _, _, c = correct_preds(probs, labels.squeeze()) if disp: print(i, c) correct.append(c) PCE = np.mean(correct) return PCE if __name__ == '__main__': seq_length = 64 n_cpu = 6 model = EventDetector(pretrain=True, width_mult=1., lstm_layers=1, lstm_hidden=256, bidirectional=True, dropout=False) save_dict = torch.load('models/' + version_name + '_10000.pth.tar', map_location=lambda storage, loc: storage) model.load_state_dict(save_dict['model_state_dict']) model.to(device) model.eval() PCE = eval(model, split, seq_length, n_cpu, True) print('Average PCE: {}'.format(PCE)) if not os.path.exists('results'): os.mkdir('results') if bool_classical_loss:
rgb_count += 1 else: preds[i] = optical_preds deltas = np.abs(events - preds) correct = (deltas <= tol).astype(np.uint8) return preds, deltas, correct if __name__ == '__main__': split = 1 seq_length = 64 n_cpu = 6 model = EventDetector(pretrain=True, width_mult=1, lstm_layers=1, lstm_hidden=256, bidirectional=True, dropout=False) rgb_save_dict = torch.load('swingnet_1600.pth.tar') model.load_state_dict(rgb_save_dict['model_state_dict']) model.cuda() model.eval() _, _, rgb_probs, rgb_tols, rgb_events = myeval(model, split, seq_length, n_cpu, False, 1) optical_save_dict = torch.load('swingnet_1200.pth.tar') model.load_state_dict(optical_save_dict['model_state_dict']) model.cuda() model.eval()
if __name__ == '__main__': # training configuration split = cfg.SPLIT iterations = cfg.ITERATIONS it_save = cfg.IT_SAVE # save model every 100 iterations n_cpu = cfg.CPU_NUM seq_length = cfg.SEQUENCE_LENGTH bs = cfg.BATCH_SIZE # batch size k = 10 # frozen layers model = EventDetector(pretrain=True, width_mult=1., lstm_layers=1, lstm_hidden=256, bidirectional=True, dropout=False) freeze_layers(k, model) model.train() model.cuda() # model = nn.DataParallel(model) # 用来训练非光流部分 # dataset = GolfDB(data_file='./data/train_split_{}.pkl'.format(split), # vid_dir='/home/zqr/data/videos_160/', # seq_length=seq_length, # transform=transforms.Compose([transforms.ToPILImage(), # transforms.RandomHorizontalFlip(0.5), # transforms.RandomAffine(5,shear=5), # transforms.ToTensor()]),
# training configuration split = 1 iterations = 2000 it_save = 100 # save model every 100 iterations n_cpu = 6 seq_length = 64 bs = 4 # batch size k = 10 # frozen layers device = 'cuda:0' model = EventDetector( # pretrain=False, pretrain=True, width_mult=1.0, lstm_layers=1, lstm_hidden=256, bidirectional=True, dropout=False, device=device, ) base_list = [-1] event_th = 30 dataset = PointImgGolfDB( data_file="data/train_split_{}.pkl".format(split), vid_dir="data/videos_160/", seq_length=seq_length, # transform=transforms.Compose( # [ToTensor(), Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])] # ), train=True,
list_loss = {'loss':[], 'soft':[], 'count':[]} if __name__ == '__main__': # training configuration (From McNally et al.) iterations = config['iterations'] it_save = 10000 # save model every 10000 iterations n_cpu = 6 seq_length = 64 bs = 22 # batch size k = 10 # frozen layers model = EventDetector(pretrain=True, width_mult=1., lstm_layers=1, lstm_hidden=256, bidirectional=True, dropout=False) freeze_layers(k, model) model.train() model.to(device) dataset = GolfDB(data_file='data/train_split_{}.pkl'.format(split), vid_dir='data/videos_160/', seq_length=seq_length, transform=transforms.Compose([ToTensor(), Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]), train=True, noise_level=noise_level) data_loader = DataLoader(dataset,
it_save = args.it_save # save model every 100 iterations n_cpu = 6 seq_length = args.seq_length bs = args.batch_size # batch size k = 10 # frozen layers use_no_element = args.use_no_element device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') print('Load Model') model = EventDetector(pretrain=True, width_mult=1., lstm_layers=1, lstm_hidden=256, device=device, bidirectional=True, dropout=False, use_no_element=use_no_element) #print('model.py, class EventDetector()') freeze_layers(k, model) #print('utils.py, func freeze_laters()') model.train() model.to(device) print('Loading Data') # TODO: vid_dirのpathをかえる。stsqの動画を切り出したimage全部が含まれているdirにする if use_no_element == False: dataset = StsqDB( data_file='data/no_ele/seq_length_{}/train_split_{}.pkl'.format(