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Simplify code #29

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Simplify code #29

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767e88a
Simplify the code.
Dec 11, 2019
831a80d
Simplify the code.
Dec 11, 2019
e359f48
Simplify the code.
Dec 15, 2019
ef197ae
Simplify the code.
Dec 15, 2019
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34 changes: 17 additions & 17 deletions bts.py
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Original file line number Diff line number Diff line change
Expand Up @@ -151,13 +151,13 @@ def reduction_1x1(self, net, num_filters, is_final=False):
net = self.conv(net, 1, 1, 1, activation_fn=tf.nn.sigmoid)
else:
net = self.conv(net, 3, 1, 1, activation_fn=None)
theta = tf.nn.sigmoid(net[:, :, :, 0]) * 3.1415926535 / 6
phi = tf.nn.sigmoid(net[:, :, :, 1]) * 3.1415926535 * 2
dist = tf.nn.sigmoid(net[:, :, :, 2]) * self.max_depth
n1 = tf.expand_dims(tf.multiply(tf.math.sin(theta), tf.math.cos(phi)), 3)
n2 = tf.expand_dims(tf.multiply(tf.math.sin(theta), tf.math.sin(phi)), 3)
n3 = tf.expand_dims(tf.math.cos(theta), 3)
n4 = tf.expand_dims(dist, 3)
theta = tf.nn.sigmoid(net[:, :, :, 0:1]) * np.pi / 6
phi = tf.nn.sigmoid(net[:, :, :, 1:2]) * np.pi * 2
dist = tf.nn.sigmoid(net[:, :, :, 2:3]) * self.max_depth
n1 = tf.sin(theta) * tf.cos(phi)
n2 = tf.sin(theta) * tf.sin(phi)
n3 = tf.cos(theta)
n4 = dist
net = tf.concat([n1, n2, n3, n4], axis=3)
break
else:
Expand All @@ -169,12 +169,12 @@ def reduction_1x1(self, net, num_filters, is_final=False):

def get_depth(self, x):
depth = self.max_depth * self.conv(x, 1, 3, 1, tf.nn.sigmoid, normalizer_fn=None)
#b = 1 if self.params.batch_size is None else self.params.batch_size
focal_expanded = tf.reshape(self.focal, [-1, 1, 1, 1])
if self.params.dataset == 'kitti':
focal_expanded = tf.expand_dims(self.focal, 1)
focal_expanded = tf.expand_dims(focal_expanded, 1)
focal_expanded = tf.expand_dims(focal_expanded, 1)
depth = depth * focal_expanded / 715.0873 # Average focal length in KITTI Eigen training set
return depth


def densenet(self, inputs, reduction=None, growth_rate=None, num_filters=None, num_layers=None, dropout_rate=None,
is_training=True, reuse=None, scope=None):
Expand Down Expand Up @@ -276,8 +276,8 @@ def bts(self, dense_features, skips, num_filters=256):

plane_eq_8x8 = self.reduction_1x1(daspp_feat, num_filters / 2)
plane_normal_8x8 = tf.nn.l2_normalize(plane_eq_8x8[:, :, :, 0:3], axis=3)
plane_dist_8x8 = plane_eq_8x8[:, :, :, 3]
plane_eq_8x8 = tf.concat([plane_normal_8x8, tf.expand_dims(plane_dist_8x8, 3)], 3)
plane_dist_8x8 = plane_eq_8x8[:, :, :, 3:4]
plane_eq_8x8 = tf.concat([plane_normal_8x8, plane_dist_8x8], 3)
depth_8x8 = lpg.local_planar_guidance(plane_eq_8x8, upratio=8, focal=self.focal)
depth_8x8_scaled = tf.expand_dims(depth_8x8, 3) / self.max_depth
depth_8x8_scaled_ds = self.downsample_nn(depth_8x8_scaled, 4)
Expand All @@ -291,8 +291,8 @@ def bts(self, dense_features, skips, num_filters=256):

plane_eq_4x4 = self.reduction_1x1(iconv3, num_filters / 2)
plane_normal_4x4 = tf.nn.l2_normalize(plane_eq_4x4[:, :, :, 0:3], axis=3)
plane_dist_4x4 = plane_eq_4x4[:, :, :, 3]
plane_eq_4x4 = tf.concat([plane_normal_4x4, tf.expand_dims(plane_dist_4x4, 3)], 3)
plane_dist_4x4 = plane_eq_4x4[:, :, :, 3:4]
plane_eq_4x4 = tf.concat([plane_normal_4x4, plane_dist_4x4], 3)
depth_4x4 = lpg.local_planar_guidance(plane_eq_4x4, upratio=4, focal=self.focal)
depth_4x4_scaled = tf.expand_dims(depth_4x4, 3) / self.max_depth
depth_4x4_scaled_ds = self.downsample_nn(depth_4x4_scaled, 2)
Expand All @@ -306,8 +306,8 @@ def bts(self, dense_features, skips, num_filters=256):

plane_eq_2x2 = self.reduction_1x1(iconv2, num_filters / 2)
plane_normal_2x2 = tf.nn.l2_normalize(plane_eq_2x2[:, :, :, 0:3], axis=3)
plane_dist_2x2 = plane_eq_2x2[:, :, :, 3]
plane_eq_2x2 = tf.concat([plane_normal_2x2, tf.expand_dims(plane_dist_2x2, 3)], 3)
plane_dist_2x2 = plane_eq_2x2[:, :, :, 3:4]
plane_eq_2x2 = tf.concat([plane_normal_2x2, plane_dist_2x2], 3)
depth_2x2 = lpg.local_planar_guidance(plane_eq_2x2, upratio=2, focal=self.focal)
depth_2x2_scaled = tf.expand_dims(depth_2x2, 3) / self.max_depth

Expand Down Expand Up @@ -457,4 +457,4 @@ def build_summaries(self):
tf.summary.image('lpg2x2', 1 / self.lpg2x2, max_outputs=4, collections=self.model_collection)
tf.summary.image('lpg4x4', 1 / self.lpg4x4, max_outputs=4, collections=self.model_collection)
tf.summary.image('lpg8x8', 1 / self.lpg8x8, max_outputs=4, collections=self.model_collection)
tf.summary.image('image', self.input_image, max_outputs=4, collections=self.model_collection)
tf.summary.image('image', self.input_image, max_outputs=4, collections=self.model_collection)