def init_model(self):
self.model = KSession(self.name, self.model_path, model_kwargs=dict())
self.model.load_model()
placeholder = np.zeros(
(self.batchsize, self.input_size, self.input_size, 3),
dtype="float32")
self.model.predict(placeholder)
def init_model(self):
self.model = KSession(self.name, self.model_path, model_kwargs=dict())
self.model.load_model()
self.model.append_softmax_activation(layer_index=-1)
placeholder = np.zeros((self.batchsize, self.input_size, self.input_size, 3),
dtype="float32")
self.model.predict(placeholder)
def init_model(self):
""" Initialize VGG Face 2 Model. """
model_kwargs = dict(custom_objects={'L2_normalize': L2_normalize})
self.model = KSession(self.name,
self.model_path,
model_kwargs=model_kwargs,
allow_growth=self.config["allow_growth"],
exclude_gpus=self._exclude_gpus)
self.model.load_model()
def init_model(self):
self.model = KSession(self.name,
self.model_path,
model_kwargs=dict(),
allow_growth=self.config["allow_growth"],
exclude_gpus=self._exclude_gpus)
self.model.load_model()
placeholder = np.zeros((self.batchsize, self.input_size, self.input_size, 3),
dtype="float32")
self.model.predict(placeholder)
def init_model(self):
""" Initialize FAN model """
self.model = KSession(self.name,
self.model_path,
allow_growth=self.config["allow_growth"],
exclude_gpus=self._exclude_gpus)
self.model.load_model()
# Feed a placeholder so Aligner is primed for Manual tool
placeholder_shape = (self.batchsize, self.input_size, self.input_size, 3)
placeholder = np.zeros(placeholder_shape, dtype="float32")
self.model.predict(placeholder)
def init_model(self):
""" Initialize FAN model """
model_kwargs = dict(custom_objects={'TorchBatchNorm2D': TorchBatchNorm2D})
self.model = KSession(self.name,
self.model_path,
model_kwargs=model_kwargs,
allow_growth=self.config["allow_growth"])
self.model.load_model()
# Feed a placeholder so Aligner is primed for Manual tool
placeholder_shape = (self.batchsize, 3, self.input_size, self.input_size)
placeholder = np.zeros(placeholder_shape, dtype="float32")
self.model.predict(placeholder)
