def build_fcrn_module(self, image):
batch, heigth, width, _ = image.get_shape().as_list()
with tf.variable_scope("FCRN") as scope:
# crop out boarder and flip color channels
fcrn_input = tf.image.resize_area(image[:, 4:-4, 6:-6, ::-1],
[228, 304])
net = fcrn.ResNet50UpProj({'data': fcrn_input}, batch, 1, False)
dpred = tf.stop_gradient(net.get_output())
dpred = tf.image.resize_bilinear(dpred, [heigth, width])
return dpred
def _build_fcrn_graph(self):
image = self.placeholders['keyframe']
batch, height, width, _ = image.get_shape().as_list()
with tf.variable_scope("FCRN") as scope:
# crop out boarder and flip color channels
fcrn_input = tf.image.resize_area(image[:, 4:-4, 6:-6, ::-1],
[228, 304])
net = fcrn.ResNet50UpProj({'data': fcrn_input}, batch, 1, False)
dpred = tf.stop_gradient(net.get_output())
dpred = tf.image.resize_bilinear(dpred, [height, width])
self.outputs['fcrn'] = dpred[0]
def _build_fcrn_graph(self):
""" Build single image initializion graph"""
images = self.images_placeholder
batch, ht, wd, _ = tf.unstack(tf.shape(images), num=4)
with tf.variable_scope("FCRN") as scope:
# crop out boarder and flip color channels
fcrn_input = tf.image.resize_area(images[:, 4:-4, 6:-6, ::-1],
[228, 304])
net = fcrn.ResNet50UpProj({'data': fcrn_input}, batch, 1, False)
fcrn_output = tf.stop_gradient(net.get_output())
fcrn_output = tf.image.resize_bilinear(fcrn_output, [ht, wd])
self.outputs['fcrn'] = tf.squeeze(fcrn_output, -1)