def execute(self, model_simi: BaseModel):
model = ModelFactory.get_model(self.model_type)
if os.path.isfile(self.model_path):
model.load_model(self.model_path)
return model
sal_data = None
for i, path in enumerate(self.img_path):
im_data = self.parse_data(self.img_data[i],
self.img_data_level0[i], model_simi)
for j, rlist in enumerate(im_data.rlists):
data = Region2Csv.generate_seg_csv(rlist,
im_data.feature93s[j],
self.seg_path[i])
if data is None:
continue
if sal_data is None:
sal_data = data
else:
sal_data = np.vstack((sal_data, data))
y_train, x_train = self.prepare_data(sal_data)
model.train(x_train, y_train)
model.save_model(self.model_path)
return model
def execute(self):
model = ModelFactory.get_model(model_name=self.model_type)
if os.path.isfile(self.model_path):
model.load_model(self.model_path)
return model
simi_data = None
for i, path in enumerate(self.img_path):
im_data = self.check_exist(
img_path=self.img_path[i], img_path_level0=self.img_data_level0[i]
)
data = Region2Csv.generate_similar_csv(
im_data.rlist, im_data.comb_features, self.seg_path[i]
)
if simi_data is None:
simi_data = data
else:
simi_data = np.vstack((simi_data, data))
logging.info("Finished simi {}".format(i))
y_train, x_train = self.prepare_data(simi_data)
model.train(x_train, y_train)
model.save_model(self.model_path)
return model
def execute(self, model_sal):
model = ModelFactory.get_model(self.model_type)
if os.path.isfile(self.model_path):
model.load_model(self.model_path)
return model
ground_truths = None
salience_maps = None
for i, path in enumerate(self.img_data):
im_data = pickle.load(open(path, "rb+"))
seg_num = len(im_data.rlists)
if seg_num < len(self.C_LIST) + 1:
continue
height = im_data.rmat.shape[0]
width = im_data.rmat.shape[1]
salience_map = np.zeros([seg_num, height, width])
for j, rlist in enumerate(im_data.rlists):
Y = model_sal.predict(im_data.feature93s[j])[:, 1]
for k, r in enumerate(rlist):
salience_map[j][r] = Y[k]
ground_truth = cv2.imread(self.seg_path[i])[:, :, 0]
ground_truth[ground_truth == 255] = 1
if salience_maps is None:
salience_maps = salience_map.reshape([-1, height * width]).T
else:
salience_maps = np.append(salience_maps,
salience_map.reshape(
[-1, height * width]).T,
axis=0)
if ground_truths is None:
ground_truths = ground_truth.reshape(-1)
else:
ground_truths = np.append(ground_truths,
ground_truth.reshape(-1),
axis=0)
x_train = salience_maps
y_train = ground_truths
model.train(x_train, y_train)
model.save_model(self.model_path)
def get_model(self):
model = ModelFactory.get_model(self.model_type)
model.load_model(self.model_path)
return model
def build_model(problem_name, observation_shape, num_actions, model_params):
# Model
model_manager = ModelFactory(problem_name, observation_shape, num_actions, model_params)
model = model_manager.get_model()
return model