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flyingChairsWrapFlow.py
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flyingChairsWrapFlow.py
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import tensorflow as tf
import tensorflow.contrib.slim as slim
import numpy as np
def flowNet(inputs, outputs, loss_weight):
"""Creates a warp flow model based on flowNet simple.
Args:
inputs: 4D image tensor corresponding to prev frames
outputs: 4D image tensor corresponding to next frames
Returns:
predicted next frames
"""
# Mean subtraction (BGR) for flying chairs
mean = tf.constant([97.533268117955444, 99.238235788550085, 97.055973199626948], dtype=tf.float32, name="img_global_mean")
# tf.tile(mean, [4,192,256,1])
inputs = inputs - mean
outputs = outputs - mean
# Scaling to 0 ~ 1 or -0.4 ~ 0.6?
inputs = tf.truediv(inputs, 255.0)
outputs = tf.truediv(outputs, 255.0)
# Add local response normalization (ACROSS_CHANNELS) for computing photometric loss
inputs_norm = tf.nn.local_response_normalization(inputs, depth_radius=4, beta=0.7)
outputs_norm = tf.nn.local_response_normalization(outputs, depth_radius=4, beta=0.7)
with slim.arg_scope([slim.conv2d, slim.conv2d_transpose],
activation_fn=tf.nn.elu): # original use leaky ReLU, now we use elu
# Contracting part
conv1 = slim.conv2d(tf.concat(3, [inputs, outputs]), 64, [7, 7], stride=2, scope='conv1')
conv2 = slim.conv2d(conv1, 128, [5, 5], stride=2, scope='conv2')
conv3_1 = slim.conv2d(conv2, 256, [5, 5], stride=2, scope='conv3_1')
conv3_2 = slim.conv2d(conv3_1, 256, [3, 3], scope='conv3_2')
conv4_1 = slim.conv2d(conv3_2, 512, [3, 3], stride=2, scope='conv4_1')
conv4_2 = slim.conv2d(conv4_1, 512, [3, 3], scope='conv4_2')
conv5_1 = slim.conv2d(conv4_2, 512, [3, 3], stride=2, scope='conv5_1')
conv5_2 = slim.conv2d(conv5_1, 512, [3, 3], scope='conv5_2')
conv6_1 = slim.conv2d(conv5_2, 1024, [3, 3], stride=2, scope='conv6_1')
conv6_2 = slim.conv2d(conv6_1, 1024, [3, 3], scope='conv6_2')
# Hyper-params for computing unsupervised loss
epsilon = 0.0001
alpha_c = 0.25
alpha_s = 0.37
lambda_smooth = 1.0
deltaWeights = {}
deltaWeights["FlowDeltaWeights"] = tf.constant([0,0,0,0,1,-1,0,0,0,0,0,0,0,1,0,0,-1,0], dtype=tf.float32, shape=[3,3,2,2], name="FlowDeltaWeights")
deltaWeights["ImgDeltaWeights"] = tf.constant([-1,0,1,-2,0,2,-1,0,1,-1,-2,-1,0,0,0,1,2,1], dtype=tf.float32, shape=[3,3,1,2], name="ImgDeltaWeights")
#deltaWeights["xDeltaFlow"] = tf.constant([0,0,0,0,1,0,0,-1,0], dtype=tf.float32, shape=[3,3,2,2], name="xDeltaFlow")
#deltaWeights["yDeltaFlow"] = tf.constant([0,0,0,0,1,-1,0,0,0], dtype=tf.float32, shape=[3,3,2,2], name="yDeltaFlow")
#deltaWeights["xDeltaImg"] = tf.constant([-1,-2,-1,0,0,0,1,2,1], dtype=tf.float32, shape=[3,3,1,1], name="xDeltaImg")
#deltaWeights["yDeltaImg"] = tf.constant([-1,0,1,-2,0,2,-1,0,1], dtype=tf.float32, shape=[3,3,1,1], name="yDeltaImg")
deltaWeights["mean"] = mean
scale = 2 # for deconvolution
# Expanding part
pr6 = slim.conv2d(conv6_2, 2, [3, 3], activation_fn=None, scope='pr6')
h6 = pr6.get_shape()[1].value
w6 = pr6.get_shape()[2].value
pr6_input = tf.image.resize_bilinear(inputs_norm, [h6, w6])
pr6_output = tf.image.resize_bilinear(outputs_norm, [h6, w6])
flow_scale_6 = 0.3125 # (*20/64)
loss6, _ = loss_interp(pr6, pr6_input, pr6_output, epsilon, alpha_c, alpha_s, lambda_smooth, flow_scale_6, deltaWeights)
upconv5 = slim.conv2d_transpose(conv6_2, 512, [2*scale, 2*scale], stride=scale, scope='upconv5')
pr6to5 = slim.conv2d_transpose(pr6, 2, [2*scale, 2*scale], stride=scale, activation_fn=None, scope='up_pr6to5')
concat5 = tf.concat(3, [conv5_2, upconv5, pr6to5])
pr5 = slim.conv2d(concat5, 2, [3, 3], activation_fn=None, scope='pr5')
h5 = pr5.get_shape()[1].value
w5 = pr5.get_shape()[2].value
pr5_input = tf.image.resize_bilinear(inputs_norm, [h5, w5])
pr5_output = tf.image.resize_bilinear(outputs_norm, [h5, w5])
flow_scale_5 = 0.625 # (*20/32)
loss5, _ = loss_interp(pr5, pr5_input, pr5_output, epsilon, alpha_c, alpha_s, lambda_smooth, flow_scale_5, deltaWeights)
upconv4 = slim.conv2d_transpose(concat5, 256, [2*scale, 2*scale], stride=scale, scope='upconv4')
pr5to4 = slim.conv2d_transpose(pr5, 2, [2*scale, 2*scale], stride=scale, activation_fn=None, scope='up_pr5to4')
concat4 = tf.concat(3, [conv4_2, upconv4, pr5to4])
pr4 = slim.conv2d(concat4, 2, [3, 3], activation_fn=None, scope='pr4')
h4 = pr4.get_shape()[1].value
w4 = pr4.get_shape()[2].value
pr4_input = tf.image.resize_bilinear(inputs_norm, [h4, w4])
pr4_output = tf.image.resize_bilinear(outputs_norm, [h4, w4])
flow_scale_4 = 1.25 # (*20/16)
loss4, _ = loss_interp(pr4, pr4_input, pr4_output, epsilon, alpha_c, alpha_s, lambda_smooth, flow_scale_4, deltaWeights)
upconv3 = slim.conv2d_transpose(concat4, 128, [2*scale, 2*scale], stride=scale, scope='upconv3')
pr4to3 = slim.conv2d_transpose(pr4, 2, [2*scale, 2*scale], stride=scale, activation_fn=None, scope='up_pr4to3')
concat3 = tf.concat(3, [conv3_2, upconv3, pr4to3])
pr3 = slim.conv2d(concat3, 2, [3, 3], activation_fn=None, scope='pr3')
h3 = pr3.get_shape()[1].value
w3 = pr3.get_shape()[2].value
pr3_input = tf.image.resize_bilinear(inputs_norm, [h3, w3])
pr3_output = tf.image.resize_bilinear(outputs_norm, [h3, w3])
flow_scale_3 = 2.5 # (*20/8)
loss3, _ = loss_interp(pr3, pr3_input, pr3_output, epsilon, alpha_c, alpha_s, lambda_smooth, flow_scale_3, deltaWeights)
upconv2 = slim.conv2d_transpose(concat3, 64, [2*scale, 2*scale], stride=scale, scope='upconv2')
pr3to2 = slim.conv2d_transpose(pr3, 2, [2*scale, 2*scale], stride=scale, activation_fn=None, scope='up_pr3to2')
concat2 = tf.concat(3, [conv2, upconv2, pr3to2])
pr2 = slim.conv2d(concat2, 2, [3, 3], activation_fn=None, scope='pr2')
h2 = pr2.get_shape()[1].value
w2 = pr2.get_shape()[2].value
pr2_input = tf.image.resize_bilinear(inputs_norm, [h2, w2])
pr2_output = tf.image.resize_bilinear(outputs_norm, [h2, w2])
flow_scale_2 = 5.0 # (*20/4)
loss2, _ = loss_interp(pr2, pr2_input, pr2_output, epsilon, alpha_c, alpha_s, lambda_smooth, flow_scale_2, deltaWeights)
upconv1 = slim.conv2d_transpose(concat2, 32, [2*scale, 2*scale], stride=scale, scope='upconv1')
pr2to1 = slim.conv2d_transpose(pr2, 2, [2*scale, 2*scale], stride=scale, activation_fn=None, scope='up_pr2to1')
concat1 = tf.concat(3, [conv1, upconv1, pr2to1])
pr1 = slim.conv2d(concat1, 2, [3, 3], activation_fn=None, scope='pr1')
h1 = pr1.get_shape()[1].value
w1 = pr1.get_shape()[2].value
pr1_input = tf.image.resize_bilinear(inputs_norm, [h1, w1])
pr1_output = tf.image.resize_bilinear(outputs_norm, [h1, w1])
flow_scale_1 = 10.0 # (*20/2)
loss1, prev1 = loss_interp(pr1, pr1_input, pr1_output, epsilon, alpha_c, alpha_s, lambda_smooth, flow_scale_1, deltaWeights)
# Adding intermediate losses
all_loss = loss_weight[0]*loss1["total"] + loss_weight[1]*loss2["total"] + loss_weight[2]*loss3["total"] + \
loss_weight[3]*loss4["total"] + loss_weight[4]*loss5["total"] + loss_weight[5]*loss6["total"]
slim.losses.add_loss(all_loss)
losses = [loss1, loss2, loss3, loss4, loss5, loss6]
flows_all = [pr1*flow_scale_1, pr2*flow_scale_2, pr3*flow_scale_3, pr4*flow_scale_4, pr5*flow_scale_5, pr6*flow_scale_6]
# flows_all = [pr1, pr2, pr3, pr4, pr5, pr6]
return losses, flows_all, prev1
def inception_v3_base(inputs,
min_depth=16,
depth_multiplier=1.0,
scope=None):
end_points = {}
if depth_multiplier <= 0:
raise ValueError('depth_multiplier is not greater than zero.')
depth = lambda d: max(int(d * depth_multiplier), min_depth)
with tf.variable_scope(scope, 'InceptionV3', [inputs]):
with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
stride=1, padding='SAME'):
# 299 x 299 x 3
end_point = 'Conv2d_1a_3x3'
net = slim.conv2d(inputs, depth(32), [3, 3], stride=2, scope=end_point)
#print end_point, net.get_shape()
end_points[end_point] = net
# 149 x 149 x 32
end_point = 'Conv2d_2a_3x3'
net = slim.conv2d(net, depth(32), [3, 3], scope=end_point)
#print end_point, net.get_shape()
# end_points[end_point] = net
# 147 x 147 x 32
end_point = 'Conv2d_2b_3x3'
net = slim.conv2d(net, depth(64), [3, 3], padding='SAME', scope=end_point)
#print end_point, net.get_shape()
# end_points[end_point] = net
# 147 x 147 x 64
end_point = 'MaxPool_3a_3x3'
net = slim.max_pool2d(net, [3, 3], stride=2, scope=end_point)
#print end_point, net.get_shape()
end_points[end_point] = net
# 73 x 73 x 64
end_point = 'Conv2d_3b_1x1'
net = slim.conv2d(net, depth(80), [1, 1], scope=end_point)
#print end_point, net.get_shape()
# end_points[end_point] = net
# 73 x 73 x 80.
end_point = 'Conv2d_4a_3x3'
net = slim.conv2d(net, depth(192), [3, 3], scope=end_point)
#print end_point, net.get_shape()
# end_points[end_point] = net
# 71 x 71 x 192.
end_point = 'MaxPool_5a_3x3'
net = slim.max_pool2d(net, [3, 3], stride=2, scope=end_point)
#print end_point, net.get_shape()
end_points[end_point] = net
# 35 x 35 x 192.
# Inception blocks
with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
stride=1, padding='SAME'):
# mixed: 35 x 35 x 256.
end_point = 'Mixed_5b'
with tf.variable_scope(end_point):
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1')
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(net, depth(48), [1, 1], scope='Conv2d_0a_1x1')
branch_1 = slim.conv2d(branch_1, depth(64), [5, 5],
scope='Conv2d_0b_5x5')
with tf.variable_scope('Branch_2'):
branch_2 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1')
branch_2 = slim.conv2d(branch_2, depth(96), [3, 3],
scope='Conv2d_0b_3x3')
branch_2 = slim.conv2d(branch_2, depth(96), [3, 3],
scope='Conv2d_0c_3x3')
with tf.variable_scope('Branch_3'):
branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
branch_3 = slim.conv2d(branch_3, depth(32), [1, 1],
scope='Conv2d_0b_1x1')
net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
#print end_point, net.get_shape()
# end_points[end_point] = net
# mixed_1: 35 x 35 x 288.
end_point = 'Mixed_5c'
with tf.variable_scope(end_point):
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1')
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(net, depth(48), [1, 1], scope='Conv2d_0b_1x1')
branch_1 = slim.conv2d(branch_1, depth(64), [5, 5],
scope='Conv_1_0c_5x5')
with tf.variable_scope('Branch_2'):
branch_2 = slim.conv2d(net, depth(64), [1, 1],
scope='Conv2d_0a_1x1')
branch_2 = slim.conv2d(branch_2, depth(96), [3, 3],
scope='Conv2d_0b_3x3')
branch_2 = slim.conv2d(branch_2, depth(96), [3, 3],
scope='Conv2d_0c_3x3')
with tf.variable_scope('Branch_3'):
branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
branch_3 = slim.conv2d(branch_3, depth(64), [1, 1],
scope='Conv2d_0b_1x1')
net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
#print end_point, net.get_shape()
# end_points[end_point] = net
# mixed_2: 35 x 35 x 288.
end_point = 'Mixed_5d'
with tf.variable_scope(end_point):
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1')
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(net, depth(48), [1, 1], scope='Conv2d_0a_1x1')
branch_1 = slim.conv2d(branch_1, depth(64), [5, 5],
scope='Conv2d_0b_5x5')
with tf.variable_scope('Branch_2'):
branch_2 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1')
branch_2 = slim.conv2d(branch_2, depth(96), [3, 3],
scope='Conv2d_0b_3x3')
branch_2 = slim.conv2d(branch_2, depth(96), [3, 3],
scope='Conv2d_0c_3x3')
with tf.variable_scope('Branch_3'):
branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
branch_3 = slim.conv2d(branch_3, depth(64), [1, 1],
scope='Conv2d_0b_1x1')
net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
#print end_point, net.get_shape()
end_points[end_point] = net
# mixed_3: 17 x 17 x 768.
end_point = 'Mixed_6a'
with tf.variable_scope(end_point):
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(net, depth(384), [3, 3], stride=2,
padding='SAME', scope='Conv2d_1a_1x1') # VALID
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1')
branch_1 = slim.conv2d(branch_1, depth(96), [3, 3],
scope='Conv2d_0b_3x3')
branch_1 = slim.conv2d(branch_1, depth(96), [3, 3], stride=2,
padding='SAME', scope='Conv2d_1a_1x1')
with tf.variable_scope('Branch_2'):
branch_2 = slim.max_pool2d(net, [3, 3], stride=2, padding='SAME',
scope='MaxPool_1a_3x3')
net = tf.concat(3, [branch_0, branch_1, branch_2])
#print end_point, net.get_shape()
# end_points[end_point] = net
# mixed4: 17 x 17 x 768.
end_point = 'Mixed_6b'
with tf.variable_scope(end_point):
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1')
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(net, depth(128), [1, 1], scope='Conv2d_0a_1x1')
branch_1 = slim.conv2d(branch_1, depth(128), [1, 7],
scope='Conv2d_0b_1x7')
branch_1 = slim.conv2d(branch_1, depth(192), [7, 1],
scope='Conv2d_0c_7x1')
with tf.variable_scope('Branch_2'):
branch_2 = slim.conv2d(net, depth(128), [1, 1], scope='Conv2d_0a_1x1')
branch_2 = slim.conv2d(branch_2, depth(128), [7, 1],
scope='Conv2d_0b_7x1')
branch_2 = slim.conv2d(branch_2, depth(128), [1, 7],
scope='Conv2d_0c_1x7')
branch_2 = slim.conv2d(branch_2, depth(128), [7, 1],
scope='Conv2d_0d_7x1')
branch_2 = slim.conv2d(branch_2, depth(192), [1, 7],
scope='Conv2d_0e_1x7')
with tf.variable_scope('Branch_3'):
branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
branch_3 = slim.conv2d(branch_3, depth(192), [1, 1],
scope='Conv2d_0b_1x1')
net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
#print end_point, net.get_shape()
# end_points[end_point] = net
# mixed_5: 17 x 17 x 768.
end_point = 'Mixed_6c'
with tf.variable_scope(end_point):
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1')
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(net, depth(160), [1, 1], scope='Conv2d_0a_1x1')
branch_1 = slim.conv2d(branch_1, depth(160), [1, 7],
scope='Conv2d_0b_1x7')
branch_1 = slim.conv2d(branch_1, depth(192), [7, 1],
scope='Conv2d_0c_7x1')
with tf.variable_scope('Branch_2'):
branch_2 = slim.conv2d(net, depth(160), [1, 1], scope='Conv2d_0a_1x1')
branch_2 = slim.conv2d(branch_2, depth(160), [7, 1],
scope='Conv2d_0b_7x1')
branch_2 = slim.conv2d(branch_2, depth(160), [1, 7],
scope='Conv2d_0c_1x7')
branch_2 = slim.conv2d(branch_2, depth(160), [7, 1],
scope='Conv2d_0d_7x1')
branch_2 = slim.conv2d(branch_2, depth(192), [1, 7],
scope='Conv2d_0e_1x7')
with tf.variable_scope('Branch_3'):
branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
branch_3 = slim.conv2d(branch_3, depth(192), [1, 1],
scope='Conv2d_0b_1x1')
net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
#print end_point, net.get_shape()
# end_points[end_point] = net
# mixed_6: 17 x 17 x 768.
end_point = 'Mixed_6d'
with tf.variable_scope(end_point):
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1')
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(net, depth(160), [1, 1], scope='Conv2d_0a_1x1')
branch_1 = slim.conv2d(branch_1, depth(160), [1, 7],
scope='Conv2d_0b_1x7')
branch_1 = slim.conv2d(branch_1, depth(192), [7, 1],
scope='Conv2d_0c_7x1')
with tf.variable_scope('Branch_2'):
branch_2 = slim.conv2d(net, depth(160), [1, 1], scope='Conv2d_0a_1x1')
branch_2 = slim.conv2d(branch_2, depth(160), [7, 1],
scope='Conv2d_0b_7x1')
branch_2 = slim.conv2d(branch_2, depth(160), [1, 7],
scope='Conv2d_0c_1x7')
branch_2 = slim.conv2d(branch_2, depth(160), [7, 1],
scope='Conv2d_0d_7x1')
branch_2 = slim.conv2d(branch_2, depth(192), [1, 7],
scope='Conv2d_0e_1x7')
with tf.variable_scope('Branch_3'):
branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
branch_3 = slim.conv2d(branch_3, depth(192), [1, 1],
scope='Conv2d_0b_1x1')
net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
#print end_point, net.get_shape()
# end_points[end_point] = net
# mixed_7: 17 x 17 x 768.
end_point = 'Mixed_6e'
with tf.variable_scope(end_point):
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1')
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1')
branch_1 = slim.conv2d(branch_1, depth(192), [1, 7],
scope='Conv2d_0b_1x7')
branch_1 = slim.conv2d(branch_1, depth(192), [7, 1],
scope='Conv2d_0c_7x1')
with tf.variable_scope('Branch_2'):
branch_2 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1')
branch_2 = slim.conv2d(branch_2, depth(192), [7, 1],
scope='Conv2d_0b_7x1')
branch_2 = slim.conv2d(branch_2, depth(192), [1, 7],
scope='Conv2d_0c_1x7')
branch_2 = slim.conv2d(branch_2, depth(192), [7, 1],
scope='Conv2d_0d_7x1')
branch_2 = slim.conv2d(branch_2, depth(192), [1, 7],
scope='Conv2d_0e_1x7')
with tf.variable_scope('Branch_3'):
branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
branch_3 = slim.conv2d(branch_3, depth(192), [1, 1],
scope='Conv2d_0b_1x1')
net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
#print end_point, net.get_shape()
end_points[end_point] = net
# mixed_8: 8 x 8 x 1280.
end_point = 'Mixed_7a' # VALID
with tf.variable_scope(end_point):
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1')
branch_0 = slim.conv2d(branch_0, depth(320), [3, 3], stride=2,
padding='SAME', scope='Conv2d_1a_3x3')
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1')
branch_1 = slim.conv2d(branch_1, depth(192), [1, 7],
scope='Conv2d_0b_1x7')
branch_1 = slim.conv2d(branch_1, depth(192), [7, 1],
scope='Conv2d_0c_7x1')
branch_1 = slim.conv2d(branch_1, depth(192), [3, 3], stride=2,
padding='SAME', scope='Conv2d_1a_3x3')
with tf.variable_scope('Branch_2'):
branch_2 = slim.max_pool2d(net, [3, 3], stride=2, padding='SAME',
scope='MaxPool_1a_3x3')
net = tf.concat(3, [branch_0, branch_1, branch_2])
#print end_point, net.get_shape()
# end_points[end_point] = net
# mixed_9: 8 x 8 x 2048.
end_point = 'Mixed_7b'
with tf.variable_scope(end_point):
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(net, depth(320), [1, 1], scope='Conv2d_0a_1x1')
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(net, depth(384), [1, 1], scope='Conv2d_0a_1x1')
branch_1 = tf.concat(3, [
slim.conv2d(branch_1, depth(384), [1, 3], scope='Conv2d_0b_1x3'),
slim.conv2d(branch_1, depth(384), [3, 1], scope='Conv2d_0b_3x1')])
with tf.variable_scope('Branch_2'):
branch_2 = slim.conv2d(net, depth(448), [1, 1], scope='Conv2d_0a_1x1')
branch_2 = slim.conv2d(
branch_2, depth(384), [3, 3], scope='Conv2d_0b_3x3')
branch_2 = tf.concat(3, [
slim.conv2d(branch_2, depth(384), [1, 3], scope='Conv2d_0c_1x3'),
slim.conv2d(branch_2, depth(384), [3, 1], scope='Conv2d_0d_3x1')])
with tf.variable_scope('Branch_3'):
branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
branch_3 = slim.conv2d(
branch_3, depth(192), [1, 1], scope='Conv2d_0b_1x1')
net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
#print end_point, net.get_shape()
# end_points[end_point] = net
# mixed_10: 8 x 8 x 2048.
end_point = 'Mixed_7c'
with tf.variable_scope(end_point):
with tf.variable_scope('Branch_0'):
branch_0 = slim.conv2d(net, depth(320), [1, 1], scope='Conv2d_0a_1x1')
with tf.variable_scope('Branch_1'):
branch_1 = slim.conv2d(net, depth(384), [1, 1], scope='Conv2d_0a_1x1')
branch_1 = tf.concat(3, [
slim.conv2d(branch_1, depth(384), [1, 3], scope='Conv2d_0b_1x3'),
slim.conv2d(branch_1, depth(384), [3, 1], scope='Conv2d_0c_3x1')])
with tf.variable_scope('Branch_2'):
branch_2 = slim.conv2d(net, depth(448), [1, 1], scope='Conv2d_0a_1x1')
branch_2 = slim.conv2d(
branch_2, depth(384), [3, 3], scope='Conv2d_0b_3x3')
branch_2 = tf.concat(3, [
slim.conv2d(branch_2, depth(384), [1, 3], scope='Conv2d_0c_1x3'),
slim.conv2d(branch_2, depth(384), [3, 1], scope='Conv2d_0d_3x1')])
with tf.variable_scope('Branch_3'):
branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
branch_3 = slim.conv2d(
branch_3, depth(192), [1, 1], scope='Conv2d_0b_1x1')
net = tf.concat(3, [branch_0, branch_1, branch_2, branch_3])
#print end_point, net.get_shape()
end_points[end_point] = net
return net, end_points
def inception_v3(inputs,
outputs,
loss_weight,
num_classes=1000,
is_training=True,
dropout_keep_prob=0.8,
min_depth=16,
depth_multiplier=1.0,
prediction_fn=slim.softmax,
spatial_squeeze=True,
reuse=None,
scope='InceptionV3'):
mean = tf.constant([97.533268117955444, 99.238235788550085, 97.055973199626948], dtype=tf.float32, name="img_global_mean")
# tf.tile(mean, [4,192,256,1])
inputs = inputs - mean
outputs = outputs - mean
# Scaling to 0 ~ 1 or -0.4 ~ 0.6?
inputs = tf.truediv(inputs, 255.0)
outputs = tf.truediv(outputs, 255.0)
network_in = tf.concat(3, [inputs, outputs])
# Add local response normalization (ACROSS_CHANNELS) for computing photometric loss
inputs_norm = tf.nn.local_response_normalization(inputs, depth_radius=4, beta=0.7)
outputs_norm = tf.nn.local_response_normalization(outputs, depth_radius=4, beta=0.7)
# end_points will collect relevant activations for external use, for example summaries or losses.
if depth_multiplier <= 0:
raise ValueError('depth_multiplier is not greater than zero.')
depth = lambda d: max(int(d * depth_multiplier), min_depth)
with tf.variable_scope(scope, 'InceptionV3', [network_in, num_classes],
reuse=reuse) as scope:
with slim.arg_scope([slim.batch_norm, slim.dropout],
is_training=is_training):
_, end_points = inception_v3_base(network_in, scope=scope, min_depth=min_depth,depth_multiplier=depth_multiplier)
# Hyper-params for computing unsupervised loss
epsilon = 0.0001
alpha_c = 0.25
alpha_s = 0.37
lambda_smooth = 1.0
deltaWeights = {}
deltaWeights["FlowDeltaWeights"] = tf.constant([0,0,0,0,1,-1,0,0,0,0,0,0,0,1,0,0,-1,0], dtype=tf.float32, shape=[3,3,2,2], name="FlowDeltaWeights")
#deltaWeights["ImgDeltaWeights"] = tf.constant([-1,0,1,-2,0,2,-1,0,1,-1,-2,-1,0,0,0,1,2,1], dtype=tf.float32, shape=[3,3,1,2], name="ImgDeltaWeights")
#deltaWeights["mean"] = mean
scale = 2 # for deconvolution
# Expanding part
with slim.arg_scope([slim.conv2d, slim.conv2d_transpose],
activation_fn=tf.nn.elu):
pr6 = slim.conv2d(end_points["Mixed_7c"], 2, [3, 3], activation_fn=None, scope='pr6')
h6 = pr6.get_shape()[1].value
w6 = pr6.get_shape()[2].value
pr6_input = tf.image.resize_bilinear(inputs_norm, [h6, w6])
pr6_output = tf.image.resize_bilinear(outputs_norm, [h6, w6])
flow_scale_6 = 0.625 # (*20/64)
loss6, _ = loss_interp(pr6, pr6_input, pr6_output, epsilon, alpha_c, alpha_s, lambda_smooth, flow_scale_6, deltaWeights)
upconv5 = slim.conv2d_transpose(end_points["Mixed_7c"], 512, [2*scale, 2*scale], stride=scale, scope='upconv5')
pr6to5 = slim.conv2d_transpose(pr6, 2, [2*scale, 2*scale], stride=scale, activation_fn=None, scope='up_pr6to5')
concat5 = tf.concat(3, [end_points["Mixed_6e"], upconv5, pr6to5])
pr5 = slim.conv2d(concat5, 2, [3, 3], activation_fn=None, scope='pr5')
h5 = pr5.get_shape()[1].value
w5 = pr5.get_shape()[2].value
pr5_input = tf.image.resize_bilinear(inputs_norm, [h5, w5])
pr5_output = tf.image.resize_bilinear(outputs_norm, [h5, w5])
flow_scale_5 = 1.25 # (*20/32)
loss5, _ = loss_interp(pr5, pr5_input, pr5_output, epsilon, alpha_c, alpha_s, lambda_smooth, flow_scale_5, deltaWeights)
upconv4 = slim.conv2d_transpose(concat5, 256, [2*scale, 2*scale], stride=scale, scope='upconv4')
pr5to4 = slim.conv2d_transpose(pr5, 2, [2*scale, 2*scale], stride=scale, activation_fn=None, scope='up_pr5to4')
concat4 = tf.concat(3, [end_points["Mixed_5d"], upconv4, pr5to4])
pr4 = slim.conv2d(concat4, 2, [3, 3], activation_fn=None, scope='pr4')
h4 = pr4.get_shape()[1].value
w4 = pr4.get_shape()[2].value
pr4_input = tf.image.resize_bilinear(inputs_norm, [h4, w4])
pr4_output = tf.image.resize_bilinear(outputs_norm, [h4, w4])
flow_scale_4 = 2.5 # (*20/16)
loss4, _ = loss_interp(pr4, pr4_input, pr4_output, epsilon, alpha_c, alpha_s, lambda_smooth, flow_scale_4, deltaWeights)
scale = 1
upconv3 = slim.conv2d_transpose(concat4, 128, [2*scale, 2*scale], stride=scale, scope='upconv3')
pr4to3 = slim.conv2d_transpose(pr4, 2, [2*scale, 2*scale], stride=scale, activation_fn=None, scope='up_pr4to3')
concat3 = tf.concat(3, [end_points["MaxPool_5a_3x3"], upconv3, pr4to3])
scale = 2
pr3 = slim.conv2d(concat3, 2, [3, 3], activation_fn=None, scope='pr3')
h3 = pr3.get_shape()[1].value
w3 = pr3.get_shape()[2].value
pr3_input = tf.image.resize_bilinear(inputs_norm, [h3, w3])
pr3_output = tf.image.resize_bilinear(outputs_norm, [h3, w3])
flow_scale_3 = 2.5 # (*20/8)
loss3, _ = loss_interp(pr3, pr3_input, pr3_output, epsilon, alpha_c, alpha_s, lambda_smooth, flow_scale_3, deltaWeights)
upconv2 = slim.conv2d_transpose(concat3, 64, [2*scale, 2*scale], stride=scale, scope='upconv2')
pr3to2 = slim.conv2d_transpose(pr3, 2, [2*scale, 2*scale], stride=scale, activation_fn=None, scope='up_pr3to2')
concat2 = tf.concat(3, [end_points["MaxPool_3a_3x3"], upconv2, pr3to2])
pr2 = slim.conv2d(concat2, 2, [3, 3], activation_fn=None, scope='pr2')
h2 = pr2.get_shape()[1].value
w2 = pr2.get_shape()[2].value
pr2_input = tf.image.resize_bilinear(inputs_norm, [h2, w2])
pr2_output = tf.image.resize_bilinear(outputs_norm, [h2, w2])
flow_scale_2 = 5.0 # (*20/4)
loss2, _ = loss_interp(pr2, pr2_input, pr2_output, epsilon, alpha_c, alpha_s, lambda_smooth, flow_scale_2, deltaWeights)
upconv1 = slim.conv2d_transpose(concat2, 32, [2*scale, 2*scale], stride=scale, scope='upconv1')
pr2to1 = slim.conv2d_transpose(pr2, 2, [2*scale, 2*scale], stride=scale, activation_fn=None, scope='up_pr2to1')
concat1 = tf.concat(3, [end_points["Conv2d_1a_3x3"], upconv1, pr2to1])
pr1 = slim.conv2d(concat1, 2, [3, 3], activation_fn=None, scope='pr1')
h1 = pr1.get_shape()[1].value
w1 = pr1.get_shape()[2].value
pr1_input = tf.image.resize_bilinear(inputs_norm, [h1, w1])
pr1_output = tf.image.resize_bilinear(outputs_norm, [h1, w1])
flow_scale_1 = 10.0 # (*20/2)
loss1, prev1 = loss_interp(pr1, pr1_input, pr1_output, epsilon, alpha_c, alpha_s, lambda_smooth, flow_scale_1, deltaWeights)
# Adding intermediate losses
all_loss = loss_weight[0]*loss1["total"] + loss_weight[1]*loss2["total"] + loss_weight[2]*loss3["total"] + \
loss_weight[3]*loss4["total"] + loss_weight[4]*loss5["total"] + loss_weight[5]*loss6["total"]
slim.losses.add_loss(all_loss)
losses = [loss1, loss2, loss3, loss4, loss5, loss6]
flows_all = [pr1*flow_scale_1, pr2*flow_scale_2, pr3*flow_scale_3, pr4*flow_scale_4, pr5*flow_scale_5, pr6*flow_scale_6]
# flows_all = [pr1, pr2, pr3, pr4, pr5, pr6]
return losses, flows_all, prev1
def inception_v3_arg_scope(weight_decay=0.00004,
batch_norm_var_collection='moving_vars'):
"""Defines the default InceptionV3 arg scope.
Args:
weight_decay: The weight decay to use for regularizing the model.
batch_norm_var_collection: The name of the collection for the batch norm
variables.
Returns:
An `arg_scope` to use for the inception v3 model.
"""
batch_norm_params = {
# Decay for the moving averages.
'decay': 0.9997,
# epsilon to prevent 0s in variance.
'epsilon': 0.001,
# collection containing update_ops.
'updates_collections': tf.GraphKeys.UPDATE_OPS,
# collection containing the moving mean and moving variance.
'variables_collections': {
'beta': None,
'gamma': None,
'moving_mean': [batch_norm_var_collection],
'moving_variance': [batch_norm_var_collection],
}
}
# Set weight_decay for weights in Conv and FC layers.
with slim.arg_scope([slim.conv2d, slim.fully_connected],
activation_fn=tf.nn.elu,
weights_initializer=initializers.xavier_initializer(),
weights_regularizer=slim.l2_regularizer(weight_decay)):
with slim.arg_scope(
[slim.conv2d],
normalizer_fn=slim.batch_norm,
normalizer_params=batch_norm_params) as sc:
return sc
def VGG16(inputs, outputs, loss_weight):
# Mean subtraction (BGR) for flying chairs
mean = tf.constant([97.533268117955444, 99.238235788550085, 97.055973199626948], dtype=tf.float32, name="img_global_mean")
# tf.tile(mean, [4,192,256,1])
inputs = inputs - mean
outputs = outputs - mean
# Scaling to 0 ~ 1 or -0.4 ~ 0.6?
inputs = tf.truediv(inputs, 255.0)
outputs = tf.truediv(outputs, 255.0)
# Add local response normalization (ACROSS_CHANNELS) for computing photometric loss
inputs_norm = tf.nn.local_response_normalization(inputs, depth_radius=4, beta=0.7)
outputs_norm = tf.nn.local_response_normalization(outputs, depth_radius=4, beta=0.7)
with slim.arg_scope([slim.conv2d, slim.fully_connected, slim.conv2d_transpose],
activation_fn=tf.nn.elu): # original use leaky ReLU, now we use elu
conv1_1 = slim.conv2d(tf.concat(3, [inputs, outputs]), 64, [3, 3], scope='conv1_1')
# conv1_1 = slim.conv2d(inputs, 64, [3, 3], scope='conv1_1')
conv1_2 = slim.conv2d(conv1_1, 64, [3, 3], scope='conv1_2')
pool1 = slim.max_pool2d(conv1_2, [2, 2], scope='pool1')
conv2_1 = slim.conv2d(pool1, 128, [3, 3], scope='conv2_1')
conv2_2 = slim.conv2d(conv2_1, 128, [3, 3], scope='conv2_2')
pool2 = slim.max_pool2d(conv2_2, [2, 2], scope='pool2')
conv3_1 = slim.conv2d(pool2, 256, [3, 3], scope='conv3_1')
conv3_2 = slim.conv2d(conv3_1, 256, [3, 3], scope='conv3_2')
conv3_3 = slim.conv2d(conv3_2, 256, [3, 3], scope='conv3_3')
pool3 = slim.max_pool2d(conv3_3, [2, 2], scope='pool3')
conv4_1 = slim.conv2d(pool3, 512, [3, 3], scope='conv4_1')
conv4_2 = slim.conv2d(conv4_1, 512, [3, 3], scope='conv4_2')
conv4_3 = slim.conv2d(conv4_2, 512, [3, 3], scope='conv4_3')
pool4 = slim.max_pool2d(conv4_3, [2, 2], scope='pool4')
conv5_1 = slim.conv2d(pool4, 512, [3, 3], scope='conv5_1')
conv5_2 = slim.conv2d(conv5_1, 512, [3, 3], scope='conv5_2')
conv5_3 = slim.conv2d(conv5_2, 512, [3, 3], scope='conv5_3')
pool5 = slim.max_pool2d(conv5_3, [2, 2], scope='pool5')
# Hyper-params for computing unsupervised loss
epsilon = 0.0001
alpha_c = 0.25
alpha_s = 0.37
lambda_smooth = 1.0
deltaWeights = {}
deltaWeights["FlowDeltaWeights"] = tf.constant([0,0,0,0,1,-1,0,0,0,0,0,0,0,1,0,0,-1,0], dtype=tf.float32, shape=[3,3,2,2], name="FlowDeltaWeights")
# deltaWeights["ImgDeltaWeights"] = tf.constant([-1,0,1,-2,0,2,-1,0,1,-1,-2,-1,0,0,0,1,2,1], dtype=tf.float32, shape=[3,3,1,2], name="ImgDeltaWeights")
# deltaWeights["mean"] = mean
scale = 2 # for deconvolution
# Expanding part
pr5 = slim.conv2d(pool5, 2, [3, 3], activation_fn=None, scope='pr5')
h5 = pr5.get_shape()[1].value
w5 = pr5.get_shape()[2].value
pr5_input = tf.image.resize_bilinear(inputs_norm, [h5, w5])
pr5_output = tf.image.resize_bilinear(outputs_norm, [h5, w5])
flow_scale_5 = 0.625 # (*20/32)
loss5, _ = loss_interp(pr5, pr5_input, pr5_output, epsilon, alpha_c, alpha_s, lambda_smooth, flow_scale_5, deltaWeights)
upconv4 = slim.conv2d_transpose(pool5, 256, [2*scale, 2*scale], stride=scale, scope='upconv4')
pr5to4 = slim.conv2d_transpose(pr5, 2, [2*scale, 2*scale], stride=scale, activation_fn=None, scope='up_pr5to4')
concat4 = tf.concat(3, [pool4, upconv4, pr5to4])
pr4 = slim.conv2d(concat4, 2, [3, 3], activation_fn=None, scope='pr4')
h4 = pr4.get_shape()[1].value
w4 = pr4.get_shape()[2].value
pr4_input = tf.image.resize_bilinear(inputs_norm, [h4, w4])
pr4_output = tf.image.resize_bilinear(outputs_norm, [h4, w4])
flow_scale_4 = 1.25 # (*20/16)
loss4, _ = loss_interp(pr4, pr4_input, pr4_output, epsilon, alpha_c, alpha_s, lambda_smooth, flow_scale_4, deltaWeights)
upconv3 = slim.conv2d_transpose(concat4, 128, [2*scale, 2*scale], stride=scale, scope='upconv3')
pr4to3 = slim.conv2d_transpose(pr4, 2, [2*scale, 2*scale], stride=scale, activation_fn=None, scope='up_pr4to3')
concat3 = tf.concat(3, [pool3, upconv3, pr4to3])
pr3 = slim.conv2d(concat3, 2, [3, 3], activation_fn=None, scope='pr3')
h3 = pr3.get_shape()[1].value
w3 = pr3.get_shape()[2].value
pr3_input = tf.image.resize_bilinear(inputs_norm, [h3, w3])
pr3_output = tf.image.resize_bilinear(outputs_norm, [h3, w3])
flow_scale_3 = 2.5 # (*20/8)
loss3, _ = loss_interp(pr3, pr3_input, pr3_output, epsilon, alpha_c, alpha_s, lambda_smooth, flow_scale_3, deltaWeights)
upconv2 = slim.conv2d_transpose(concat3, 64, [2*scale, 2*scale], stride=scale, scope='upconv2')
pr3to2 = slim.conv2d_transpose(pr3, 2, [2*scale, 2*scale], stride=scale, activation_fn=None, scope='up_pr3to2')
concat2 = tf.concat(3, [pool2, upconv2, pr3to2])
pr2 = slim.conv2d(concat2, 2, [3, 3], activation_fn=None, scope='pr2')
h2 = pr2.get_shape()[1].value
w2 = pr2.get_shape()[2].value
pr2_input = tf.image.resize_bilinear(inputs_norm, [h2, w2])
pr2_output = tf.image.resize_bilinear(outputs_norm, [h2, w2])
flow_scale_2 = 5.0 # (*20/4)
loss2, _ = loss_interp(pr2, pr2_input, pr2_output, epsilon, alpha_c, alpha_s, lambda_smooth, flow_scale_2, deltaWeights)
upconv1 = slim.conv2d_transpose(concat2, 32, [2*scale, 2*scale], stride=scale, scope='upconv1')
pr2to1 = slim.conv2d_transpose(pr2, 2, [2*scale, 2*scale], stride=scale, activation_fn=None, scope='up_pr2to1')
concat1 = tf.concat(3, [pool1, upconv1, pr2to1])
pr1 = slim.conv2d(concat1, 2, [3, 3], activation_fn=None, scope='pr1')
h1 = pr1.get_shape()[1].value
w1 = pr1.get_shape()[2].value
pr1_input = tf.image.resize_bilinear(inputs_norm, [h1, w1])
pr1_output = tf.image.resize_bilinear(outputs_norm, [h1, w1])
flow_scale_1 = 10.0 # (*20/2)
loss1, prev1 = loss_interp(pr1, pr1_input, pr1_output, epsilon, alpha_c, alpha_s, lambda_smooth, flow_scale_1, deltaWeights)
# Adding intermediate losses
all_loss = loss_weight[0]*loss1["total"] + loss_weight[1]*loss2["total"] + loss_weight[2]*loss3["total"] + \
loss_weight[3]*loss4["total"] + loss_weight[4]*loss5["total"]
slim.losses.add_loss(all_loss)
losses = [loss1, loss2, loss3, loss4, loss5, loss5]
flows_all = [pr1*flow_scale_1, pr2*flow_scale_2, pr3*flow_scale_3, pr4*flow_scale_4, pr5*flow_scale_5, pr5*flow_scale_5]
return losses, flows_all, prev1
def loss_interp(flows, inputs, outputs, epsilon, alpha_c, alpha_s, lambda_smooth, flow_scale, deltaWeights):
# def loss_interp(flows, outputs, inputs, epsilon, alpha_c, alpha_s, lambda_smooth, flow_scale, deltaWeights):
shape = inputs.get_shape()
shape = [int(dim) for dim in shape]
num_batch = shape[0]
height = shape[1]
width = shape[2]
channels = shape[3]
needMask = True
# Create border mask for image
border_ratio = 0.1
shortestDim = height
borderWidth = int(np.ceil(shortestDim * border_ratio))
smallerMask = tf.ones([height-2*borderWidth, width-2*borderWidth])
borderMask = tf.pad(smallerMask, [[borderWidth,borderWidth], [borderWidth,borderWidth]], "CONSTANT")
borderMask = tf.tile(tf.expand_dims(borderMask, 0), [num_batch, 1, 1])
borderMaskImg = tf.tile(tf.expand_dims(borderMask, 3), [1, 1, 1, channels])
borderMaskFlow = tf.tile(tf.expand_dims(borderMask, 3), [1, 1, 1, 2])
# Create smoothness border mask for optical flow
smallerSmoothMaskx = tf.ones([height-1, width])
smallerSmoothMasky = tf.ones([height, width-1])
smoothnessMaskx = tf.pad(smallerSmoothMaskx, [[0,1], [0,0]], "CONSTANT") # vertical
smoothnessMasky = tf.pad(smallerSmoothMasky, [[0,0], [0,1]], "CONSTANT") # horizontal
smoothnessMask = tf.pack([smoothnessMasky, smoothnessMaskx], axis=2)
smoothnessMask = tf.tile(tf.expand_dims(smoothnessMask, 0), [num_batch, 1, 1, 1])
inputs_flat = tf.reshape(inputs, [num_batch, -1, channels])
outputs_flat = tf.reshape(outputs, [num_batch, -1, channels])
borderMask_flat = tf.reshape(borderMaskImg, [num_batch, -1, channels])
flows = tf.mul(flows, flow_scale)
flows_flat = tf.reshape(flows, [num_batch, -1, 2])
floor_flows = tf.to_int32(tf.floor(flows_flat))
weights_flows = flows_flat - tf.floor(flows_flat)
# Construct the grids
pos_x = tf.range(height)
pos_x = tf.tile(tf.expand_dims(pos_x, 1), [1, width])
pos_x = tf.reshape(pos_x, [-1])
pos_y = tf.range(width)
pos_y = tf.tile(tf.expand_dims(pos_y, 0), [height, 1])
pos_y = tf.reshape(pos_y, [-1])
zero = tf.zeros([], dtype='int32')
# Warp two images based on optical flow
batch = []
for b in range(num_batch):
channel = []
x = floor_flows[b, :, 0] # U, horizontal displacement
y = floor_flows[b, :, 1] # V, vertical displacement
xw = weights_flows[b, :, 0]
yw = weights_flows[b, :, 1]
for c in range(channels):
x0 = pos_y + x
x1 = x0 + 1
y0 = pos_x + y
y1 = y0 + 1
x0 = tf.clip_by_value(x0, zero, width-1)
x1 = tf.clip_by_value(x1, zero, width-1)
y0 = tf.clip_by_value(y0, zero, height-1)
y1 = tf.clip_by_value(y1, zero, height-1)
idx_a = y0 * width + x0
idx_b = y1 * width + x0
idx_c = y0 * width + x1
idx_d = y1 * width + x1
Ia = tf.gather(outputs_flat[b, :, c], idx_a)
Ib = tf.gather(outputs_flat[b, :, c], idx_b)
Ic = tf.gather(outputs_flat[b, :, c], idx_c)
Id = tf.gather(outputs_flat[b, :, c], idx_d)
wa = (1-xw) * (1-yw)
wb = (1-xw) * yw
wc = xw * (1-yw)
wd = xw * yw
img = tf.mul(Ia, wa) + tf.mul(Ib, wb) + tf.mul(Ic, wc) + tf.mul(Id, wd)
channel.append(img)
batch.append(tf.pack(channel, axis=1))
reconstructs = tf.pack(batch)
# Recostruction loss
diff_reconstruct = tf.scalar_mul(255.0, tf.sub(reconstructs, inputs_flat))
eleWiseLoss = tf.pow(tf.square(diff_reconstruct) + tf.square(epsilon), alpha_c)
Charbonnier_reconstruct = 0.0
numValidPixels = 0.0
if needMask:
eleWiseLoss = tf.mul(borderMask_flat, eleWiseLoss)
validPixels = tf.equal(borderMask_flat, tf.ones_like(borderMask_flat))
numValidPixels = tf.to_float(tf.reduce_sum(tf.to_int32(validPixels)))
Charbonnier_reconstruct = tf.reduce_sum(eleWiseLoss) / numValidPixels
else:
Charbonnier_reconstruct = tf.reduce_mean(eleWiseLoss)
# Smoothness loss
flow_delta = tf.nn.conv2d(flows, deltaWeights["FlowDeltaWeights"], [1,1,1,1], padding="SAME")
U_loss = 0.0
V_loss = 0.0
if needMask:
flow_delta_clean = flow_delta * smoothnessMask
eleWiseULoss = tf.pow(tf.square(flow_delta_clean[:,:,:,0]) + tf.square(epsilon), alpha_s)
U_loss = tf.reduce_sum(eleWiseULoss) / numValidPixels
eleWiseVLoss = tf.pow(tf.square(flow_delta_clean[:,:,:,1]) + tf.square(epsilon), alpha_s)
V_loss = tf.reduce_sum(eleWiseVLoss) / numValidPixels
else:
U_loss = tf.reduce_mean(tf.pow(tf.square(flow_delta[:,:,:,0]) + tf.square(epsilon), alpha_s))
V_loss = tf.reduce_mean(tf.pow(tf.square(flow_delta[:,:,:,1]) + tf.square(epsilon), alpha_s))
loss_smooth = U_loss + V_loss
total_loss = Charbonnier_reconstruct + lambda_smooth * loss_smooth
# Define a loss structure
lossDict = {}
lossDict["total"] = total_loss
lossDict["Charbonnier_reconstruct"] = Charbonnier_reconstruct
lossDict["U_loss"] = U_loss
lossDict["V_loss"] = V_loss
return lossDict, tf.reshape(reconstructs, [num_batch, height, width, 3])
def loss_interp_bk(flows, inputs, outputs, epsilon, alpha_c, alpha_s, lambda_smooth, flow_scale, deltaWeights):
shape = inputs.get_shape()
shape = [int(dim) for dim in shape]
num_batch = shape[0]
height = shape[1]
width = shape[2]
channels = shape[3]
needMask = True
# Create border mask for image
border_ratio = 0.1
shortestDim = height
borderWidth = int(np.ceil(shortestDim * border_ratio))
smallerMask = tf.ones([height-2*borderWidth, width-2*borderWidth])
borderMask = tf.pad(smallerMask, [[borderWidth,borderWidth], [borderWidth,borderWidth]], "CONSTANT")
borderMask = tf.tile(tf.expand_dims(borderMask, 0), [num_batch, 1, 1])
borderMaskImg = tf.tile(tf.expand_dims(borderMask, 3), [1, 1, 1, channels])
borderMaskFlow = tf.tile(tf.expand_dims(borderMask, 3), [1, 1, 1, 2])
# Create smoothness border mask for optical flow
smallerSmoothMaskx = tf.ones([height-1, width])
smallerSmoothMasky = tf.ones([height, width-1])
smoothnessMaskx = tf.pad(smallerSmoothMaskx, [[0,1], [0,0]], "CONSTANT") # vertical
smoothnessMasky = tf.pad(smallerSmoothMasky, [[0,0], [0,1]], "CONSTANT") # horizontal
smoothnessMask = tf.pack([smoothnessMasky, smoothnessMaskx], axis=2)
smoothnessMask = tf.tile(tf.expand_dims(smoothnessMask, 0), [num_batch, 1, 1, 1])
inputs_flat = tf.reshape(inputs, [num_batch, -1, channels])
outputs_flat = tf.reshape(outputs, [num_batch, -1, channels])
borderMask_flat = tf.reshape(borderMaskImg, [num_batch, -1, channels])
flows = tf.mul(flows, flow_scale)
flows_flat = tf.reshape(flows, [num_batch, -1, 2])
floor_flows = tf.to_int32(tf.floor(flows_flat))
weights_flows = flows_flat - tf.floor(flows_flat)
# Construct the grids
pos_x = tf.range(height)
pos_x = tf.tile(tf.expand_dims(pos_x, 1), [1, width])
pos_x = tf.reshape(pos_x, [-1])
pos_y = tf.range(width)
pos_y = tf.tile(tf.expand_dims(pos_y, 0), [height, 1])
pos_y = tf.reshape(pos_y, [-1])
zero = tf.zeros([], dtype='int32')
# Warp two images based on optical flow
batch = []
for b in range(num_batch):
channel = []
x = floor_flows[b, :, 0] # U, horizontal displacement
y = floor_flows[b, :, 1] # V, vertical displacement
xw = weights_flows[b, :, 0]
yw = weights_flows[b, :, 1]
for c in range(channels):
x0 = pos_y + x
x1 = x0 + 1
y0 = pos_x + y
y1 = y0 + 1
x0 = tf.clip_by_value(x0, zero, width-1)
x1 = tf.clip_by_value(x1, zero, width-1)
y0 = tf.clip_by_value(y0, zero, height-1)
y1 = tf.clip_by_value(y1, zero, height-1)
idx_a = y0 * width + x0
idx_b = y1 * width + x0
idx_c = y0 * width + x1
idx_d = y1 * width + x1
Ia = tf.gather(outputs_flat[b, :, c], idx_a)
Ib = tf.gather(outputs_flat[b, :, c], idx_b)
Ic = tf.gather(outputs_flat[b, :, c], idx_c)
Id = tf.gather(outputs_flat[b, :, c], idx_d)
wa = (1-xw) * (1-yw)
wb = (1-xw) * yw
wc = xw * (1-yw)
wd = xw * yw
img = tf.mul(Ia, wa) + tf.mul(Ib, wb) + tf.mul(Ic, wc) + tf.mul(Id, wd)
channel.append(img)
batch.append(tf.pack(channel, axis=1))
reconstructs = tf.pack(batch)
# Recostruction loss
diff_reconstruct = tf.scalar_mul(255.0, tf.sub(reconstructs, inputs_flat))
eleWiseLoss = tf.pow(tf.square(diff_reconstruct) + tf.square(epsilon), alpha_c)
Charbonnier_reconstruct = 0.0
numValidPixels = 0.0
if needMask:
eleWiseLoss = tf.mul(borderMask_flat, eleWiseLoss)
validPixels = tf.equal(borderMask_flat, tf.ones_like(borderMask_flat))
numValidPixels = tf.to_float(tf.reduce_sum(tf.to_int32(validPixels)))
Charbonnier_reconstruct = tf.reduce_sum(eleWiseLoss) / numValidPixels
else:
Charbonnier_reconstruct = tf.reduce_mean(eleWiseLoss)
# Smoothness loss
flow_delta = tf.nn.conv2d(flows, deltaWeights["FlowDeltaWeights"], [1,1,1,1], padding="SAME") # vertical
#U_delta = flow_delta[:,:,:,0:2]
#V_delta = flow_delta[:,:,:,2:4]
#flow_delta_x = tf.nn.conv2d(flows, deltaWeights["xDeltaFlow"], [1,1,1,1], padding="SAME") # vertical
#flow_delta_y = tf.nn.conv2d(flows, deltaWeights["yDeltaFlow"], [1,1,1,1], padding="SAME") # horizontal
# Add image gradients, edge-aware smoothness
img_delta = tf.scalar_mul(255.0, inputs) + deltaWeights["mean"]
img_delta_clip = tf.clip_by_value(tf.to_int32(img_delta), zero, 255)
inputs_gray = tf.to_float(tf.image.rgb_to_grayscale(img_delta_clip))
img_delta = tf.nn.conv2d(inputs_gray, deltaWeights["ImgDeltaWeights"], [1,1,1,1], padding="SAME")
#img_delta_x = tf.nn.conv2d(inputs_gray, deltaWeights["xDeltaImg"], [1,1,1,1], padding="SAME") # vertical
#img_delta_y = tf.nn.conv2d(inputs_gray, deltaWeights["yDeltaImg"], [1,1,1,1], padding="SAME") # horizontal
#U_delta = tf.pack([flow_delta_x[:,:,:,0], flow_delta_y[:,:,:,0]], axis=3)
#V_delta = tf.pack([flow_delta_x[:,:,:,1], flow_delta_y[:,:,:,1]], axis=3)
U_loss = 0.0
V_loss = 0.0
# not the same number of valid pixels for images and optical flow, 2/3 relation, channel is different
if needMask:
flow_delta_clean = flow_delta * smoothnessMask