def train():
opt = Options().parse()
opt.baseline = True
print("Model Config: ", opt)
model = Model(opt)
model.train()
def eval_baseline(weight):
opt = Options().parse()
opt.baseline = True
opt.sequence_length = 20
print("Model Config: ", opt)
model = Model(opt)
model.load_weight(weight)
return model.evaluate(0, keep_frame=False)
def eval_baseline(weight, behavior):
opt = Options().parse()
opt.baseline = True
opt.sequence_length = 20
opt.behavior_layer = 0
opt.data_dir = '../data/' + behavior
print("Model Config: ", opt)
model = Model(opt)
model.load_weight(weight)
return model.evaluate(0, keep_frame=True)
def predict_baseline(weight, filelist):
opt = Options().parse()
opt.baseline = True
opt.sequence_length = 20
print("Model Config: ", opt)
model = Model(opt)
model.load_weight(weight)
resultlist, _ = model.predict(filelist)
resultlist = [[(frame.squeeze().permute([1,2,0]).cpu().detach().numpy()*255).astype(np.uint8) for frame in result]
for result in resultlist]
return resultlist