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:
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))