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model.py
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model.py
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from warp import *
import tensorflow as tf
import tensorflow_addons as tfa
class LiteFlowNet():
def __init__(self):
self.dblBackward = [0.0, 0.0, 10.0, 5.0, 2.5, 1.25, 0.625]
def feature_extractor(self):
lrelu = lambda x: tf.nn.leaky_relu(x, 0.1)
# module one
m1 = tf.keras.Sequential()
m1.add(tf.keras.layers.Conv2D(filters=32, kernel_size=7, activation=lrelu, padding='SAME'))
# module two
m2 = tf.keras.Sequential()
m2.add(tf.keras.layers.ZeroPadding2D(padding=(1, 1)))
m2.add(tf.keras.layers.Conv2D(filters=32, kernel_size=3, strides=2, activation=lrelu, padding='valid'))
m2.add(tf.keras.layers.Conv2D(filters=32, kernel_size=3, activation=lrelu, padding='SAME'))
m2.add(tf.keras.layers.Conv2D(filters=32, kernel_size=3, activation=lrelu, padding='SAME'))
# module three
m3 = tf.keras.Sequential()
m3.add(tf.keras.layers.ZeroPadding2D(padding=(1, 1)))
m3.add(tf.keras.layers.Conv2D(filters=64, kernel_size=3, strides=2, activation=lrelu, padding='valid'))
m3.add(tf.keras.layers.Conv2D(filters=64, kernel_size=3, activation=lrelu, padding='SAME'))
# module four
m4 = tf.keras.Sequential()
m4.add(tf.keras.layers.ZeroPadding2D(padding=(1, 1)))
m4.add(tf.keras.layers.Conv2D(filters=96, kernel_size=3, strides=2, activation=lrelu, padding='valid'))
m4.add(tf.keras.layers.Conv2D(filters=96, kernel_size=3, activation=lrelu, padding='SAME'))
# module five
m5 = tf.keras.Sequential()
m5.add(tf.keras.layers.ZeroPadding2D(padding=(1, 1)))
m5.add(tf.keras.layers.Conv2D(filters=128, kernel_size=3, strides=2, activation=lrelu, padding='valid'))
# module six
m6 = tf.keras.Sequential()
m6.add(tf.keras.layers.ZeroPadding2D(padding=(1, 1)))
m6.add(tf.keras.layers.Conv2D(filters=192, kernel_size=3, strides=2, activation=lrelu, padding='valid'))
return [m1, m2, m3, m4, m5, m6]
def group_upconv(self, in1, groups, name):
# keras don't have an easy way of group conv so use old way
with tf.compat.v1.variable_scope('flownet'):
with tf.compat.v1.variable_scope(name):
filterc = tf.compat.v1.get_variable('filter_w', shape=[4, 4, 1, groups], dtype=tf.float32)
shp = tf.shape(in1)
output_shape = (shp[0], shp[1] * 2, shp[2] * 2, shp[3])
return tf.nn.conv2d_transpose(in1, filterc, output_shape, strides=[1, 2, 2, 1])
def matching(self, tensor_features1, tensor_features2, tensorFlow, int_level, name):
with tf.name_scope(name):
lrelu = lambda x: tf.nn.leaky_relu(x, 0.1)
def module_feat():
if int_level == 2:
return tf.keras.layers.Conv2D(filters=64, kernel_size=1, activation=lrelu, padding='valid')
else:
return tf.keras.Sequential()
def module_upcorr(x):
return self.group_upconv(x, 49, name + '/moduleUpcorr')
def module_upflow(x):
return self.group_upconv(x, 2, name + '/moduleUpflow')
def module_main(x):
kernel_size = [1, 1, 7, 5, 5, 3, 3][int_level]
with tf.name_scope("module_main"):
x = tf.keras.layers.Conv2D(filters=128, kernel_size=3, activation=lrelu, padding='SAME')(x)
x = tf.keras.layers.Conv2D(filters=64, kernel_size=3, activation=lrelu, padding='SAME')(x)
x = tf.keras.layers.Conv2D(filters=32, kernel_size=3, activation=lrelu, padding='SAME')(x)
x = tf.keras.layers.Conv2D(filters=2, kernel_size=kernel_size, activation=None, padding='SAME')(x)
return x
with tf.name_scope('module_feat'):
m_feat = module_feat()
tensor_features1 = m_feat(tensor_features1)
tensor_features2 = m_feat(tensor_features2)
if tensorFlow is not None:
tensorFlow = module_upflow(tensorFlow)
# warp features
tensor_features2 = tf_warp(tensor_features2, tensorFlow * self.dblBackward[int_level])
if int_level >= 4:
corr = tfa.layers.optical_flow.CorrelationCost(1, 3, 1, 1, 3, 'channels_last')([tensor_features1, tensor_features2])
corr = lrelu(corr)
else:
corr = tfa.layers.optical_flow.CorrelationCost(1, 6, 2, 2, 6, 'channels_last')([tensor_features1, tensor_features2])
corr = lrelu(module_upcorr(corr))
# hack cuz corr cost lost last dimension
corr.set_shape([None, None, None, 49])
return (tensorFlow if tensorFlow is not None else 0.0) + module_main(corr)
def subpixel(self, tensor_features1, tensor_features2, tensorFlow, int_level, name='subpixel'):
with tf.name_scope(name):
lrelu = lambda x: tf.nn.leaky_relu(x, 0.1)
def module_feat():
if int_level == 2:
return tf.keras.layers.Conv2D(filters=64, kernel_size=1, activation=lrelu, padding='valid')
else:
return tf.keras.Sequential()
def module_main(x):
kernel_size = [1, 1, 7, 5, 5, 3, 3][int_level]
with tf.name_scope("module_main"):
x = tf.keras.layers.Conv2D(filters=128, kernel_size=3, activation=lrelu, padding='SAME')(x)
x = tf.keras.layers.Conv2D(filters=64, kernel_size=3, activation=lrelu, padding='SAME')(x)
x = tf.keras.layers.Conv2D(filters=32, kernel_size=3, activation=lrelu, padding='SAME')(x)
x = tf.keras.layers.Conv2D(filters=2, kernel_size=kernel_size, activation=None, padding='SAME')(x)
return x
with tf.name_scope('module_feat'):
mfeat = module_feat()
tensor_features1 = mfeat(tensor_features1)
tensor_features2 = mfeat(tensor_features2)
tensorFlow1 = tensorFlow * self.dblBackward[int_level]
tensor_features2 = tf_warp(tensor_features2, tensorFlow1)
tens_flow = tf.concat([tensor_features1, tensor_features2, tensorFlow], -1)
return (tensorFlow if tensorFlow is not None else 0.0) + module_main(tens_flow)
def regularization(self, tensor1, tensor2, tensor_features1, tensor_features2, tensorFlow, int_level, name='module_regularization'):
with tf.name_scope(name):
lrelu = lambda x: tf.nn.leaky_relu(x, 0.1)
int_unfold = [1, 1, 7, 5, 5, 3, 3][int_level]
def module_feat(x):
with tf.name_scope('module_feat'):
if int_level < 5:
return tf.keras.layers.Conv2D(filters=128, kernel_size=1, activation=lrelu, padding='valid')(x)
else:
return x
moduleScaleY = lambda x: tf.keras.layers.Conv2D(filters=1, kernel_size=1, activation=None, padding='valid')(
x)
moduleScaleX = lambda x: tf.keras.layers.Conv2D(filters=1, kernel_size=1, activation=None, padding='valid')(
x)
def module_main(x):
with tf.name_scope('module_main'):
conv1 = tf.keras.layers.Conv2D(filters=128, kernel_size=3, activation=lrelu, padding='SAME')(x)
conv2 = tf.keras.layers.Conv2D(filters=128, kernel_size=3, activation=lrelu, padding='SAME')(conv1)
conv3 = tf.keras.layers.Conv2D(filters=64, kernel_size=3, activation=lrelu, padding='SAME')(conv2)
conv4 = tf.keras.layers.Conv2D(filters=64, kernel_size=3, activation=lrelu, padding='SAME')(conv3)
conv5 = tf.keras.layers.Conv2D(filters=32, kernel_size=3, activation=lrelu, padding='SAME')(conv4)
conv6 = tf.keras.layers.Conv2D(filters=32, kernel_size=3, activation=lrelu, padding='SAME')(conv5)
return conv6
def module_dist(x):
with tf.name_scope('module_dist'):
kernel_size = [1, 1, 7, 5, 5, 3, 3][int_level]
out_channels = [1, 1, 49, 25, 25, 9, 9][int_level]
if int_level >= 5:
return tf.keras.layers.Conv2D(filters=out_channels, kernel_size=kernel_size, padding='SAME', activation=None,)(x)
else:
x = tf.keras.layers.Conv2D(filters=out_channels, kernel_size=(kernel_size, 1), activation=None,
padding='same')(x)
x = tf.keras.layers.Conv2D(filters=out_channels, kernel_size=(1, kernel_size), activation=None,
padding='same')(x)
return x
tensor_diff = tf.sqrt(
tf.reduce_sum(tf.square(tensor1 - tf_warp(tensor2, tensorFlow * self.dblBackward[int_level])),
axis=3, keepdims=True))
feat = module_feat(tensor_features1)
tensor_dist = module_dist(module_main(tf.concat([tensor_diff,
tensorFlow - tf.reduce_mean(tensorFlow, keepdims=True,
axis=[1, 2]),
feat
], 3)))
tensor_dist = -tf.square(tensor_dist)
tensor_dist = tf.exp(tensor_dist - tf.reduce_max(tensor_dist, axis=3, keepdims=True))
tensor_div = 1. / tf.reduce_sum(tensor_dist, -1, keepdims=True)
with tf.name_scope('moduleScaleX'):
tensorScaleX = moduleScaleX(tensor_dist *
tf.image.extract_patches(tensorFlow[..., 0:1],
[1, int_unfold, int_unfold, 1],
[1, 1, 1, 1],
[1, 1, 1, 1],
"SAME"))
with tf.name_scope('moduleScaleY'):
tensorScaleY = moduleScaleY(tensor_dist *
tf.image.extract_patches(tensorFlow[..., 1:2],
[1, int_unfold, int_unfold, 1],
[1, 1, 1, 1],
[1, 1, 1, 1],
"SAME"))
return tf.concat([tensorScaleX * tensor_div, tensorScaleY * tensor_div], -1)
def correct_pan(self, x):
with tf.name_scope('correct_pan'):
lrelu = lambda x: tf.nn.leaky_relu(x, 0.1)
conv1 = tf.keras.layers.Conv2D(filters=64, kernel_size=3, activation=lrelu, padding='SAME')(x)
conv2 = tf.keras.layers.Conv2D(filters=32, kernel_size=3, activation=lrelu, padding='SAME')(conv1)
conv3 = tf.keras.layers.Conv2D(filters=1, kernel_size=1, activation=None, padding='valid')(conv2)
return tf.nn.tanh(conv3)
def module_chromas(self, x):
with tf.name_scope('module_chromas'):
lrelu = lambda x: tf.nn.leaky_relu(x, 0.1)
conv1 = tf.keras.layers.Conv2D(filters=32, kernel_size=3, activation=lrelu, padding='SAME')(x)
conv2 = tf.keras.layers.Conv2D(filters=2, kernel_size=1, activation=None, padding='valid')(conv1)
return conv2
def __call__(self, tensor1, tensor2, scope='flownet'):
tf.keras.backend.set_floatx('float32')
with tf.name_scope(scope):
tensor1_norm = tensor1 - [[[[0.411618, 0.434631, 0.454253]]]]
tensor2_norm = tensor2 - [[[[0.410782, 0.433645, 0.452793]]]]
m1, m2, m3, m4, m5, m6 = self.feature_extractor()
def shared_feat_modules(x):
with tf.name_scope('feature_extractor'):
t1 = m1(x)
t2 = m2(t1)
t3 = m3(t2)
t4 = m4(t3)
t5 = m5(t4)
t6 = m6(t5)
return [t1, t2, t3, t4, t5, t6]
tensor_feat1 = shared_feat_modules(tensor1_norm)
tensor_feat2 = shared_feat_modules(tensor2_norm)
self.tensor_features = tensor_feat1
tensor1 = [tensor1_norm]
tensor2 = [tensor2_norm]
for i in [1, 2, 3, 4, 5]:
tensor1.append(tf.image.resize(tensor1[-1], tf.shape(tensor_feat1[i])[1:3]))
tensor2.append(tf.image.resize(tensor2[-1], tf.shape(tensor_feat2[i])[1:3]))
flow = None
lvls = [2, 3, 4, 5, 6]
for i in [-1, -2, -3, -4, -5]:
flow = self.matching(tensor_feat1[i], tensor_feat2[i], flow, lvls[i], name='matching_%i' % abs(i))
flow = self.subpixel(tensor_feat1[i], tensor_feat2[i], flow, lvls[i], name='subpixel_%i' % abs(i))
flow = self.regularization(tensor1[i], tensor2[i], tensor_feat1[i], tensor_feat2[i], flow, lvls[i], name='regularization_%i' % abs(i))
flowr = tf.image.resize(flow, tf.shape(tensor1_norm)[1:3])
return flowr * 20.0