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lsim_model.py
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# Copyright UCL Business plc 2017. Patent Pending. All rights reserved.
#
# The MonoDepth Software is licensed under the terms of the UCLB ACP-A licence
# which allows for non-commercial use only, the full terms of which are made
# available in the LICENSE file.
#
# For any other use of the software not covered by the UCLB ACP-A Licence,
# please contact info@uclb.com
# This file is adopted from:
# Fully convolutional model for monocular depth estimation
# by Clement Godard, Oisin Mac Aodha and Gabriel J. Brostow
# http://visual.cs.ucl.ac.uk/pubs/monoDepth/
# Major changes have been made to the code in order to allow a siamese version
# We leave the above copy right notice as specified in the UCLB ACP-A Licence.
# Original file for reference and comparison:
# https://github.com/mrharicot/monodepth/blob/master/monodepth_model.py
from __future__ import absolute_import, division, print_function
from collections import namedtuple
import numpy as np
import tensorflow as tf
import tensorflow.contrib.slim as slim
from monodepth.bilinear_sampler import *
monodepth_parameters = namedtuple('parameters',
'encoder, '
'height, width, '
'batch_size, '
'num_threads, '
'num_epochs, '
'do_stereo, '
'wrap_mode, '
'use_deconv, '
'alpha_image_loss, '
'disp_gradient_loss_weight, '
'lr_loss_weight, '
'full_summary')
class LsimModel(object):
"""monodepth model"""
def __init__(self, params, mode, left, right, reuse_variables=None, model_index=0):
self.params = params
self.mode = mode
self.left = left
self.right = right
self.model_collection = ['model_' + str(model_index)]
self.disp1 = {}
self.disp2 = {}
self.disp3 = {}
self.disp4 = {}
self.reuse_variables = reuse_variables
self.build_model()
self.build_outputs()
if self.mode == 'test':
return
self.build_losses()
self.build_summaries()
def gradient_x(self, img):
gx = img[:,:,:-1,:] - img[:,:,1:,:]
return gx
def gradient_y(self, img):
gy = img[:,:-1,:,:] - img[:,1:,:,:]
return gy
def upsample_nn(self, x, ratio):
s = tf.shape(x)
h = s[1]
w = s[2]
return tf.image.resize_nearest_neighbor(x, [h * ratio, w * ratio])
def scale_pyramid(self, img, num_scales):
scaled_imgs = [img]
s = tf.shape(img)
h = s[1]
w = s[2]
for i in range(num_scales - 1):
ratio = 2 ** (i + 1)
nh = h // ratio
nw = w // ratio
scaled_imgs.append(tf.image.resize_area(img, [nh, nw]))
return scaled_imgs
def generate_image_left(self, img, disp):
return bilinear_sampler_1d_h(img, -disp)
def generate_image_right(self, img, disp):
return bilinear_sampler_1d_h(img, disp)
def SSIM(self, x, y):
ssim = tf.image.ssim(x, y, max_val=1.0)
return tf.clip_by_value((1 - ssim) / 2, 0, 1)
def get_total_var(self, disp, pyramid):
disp_gradients_x = [self.gradient_x(d) for d in disp]
disp_gradients_y = [self.gradient_y(d) for d in disp]
total_var = disp_gradients_x + disp_gradients_y
return total_var
def get_disp(self, x):
disp = 0.3 * self.conv(x, 1, 3, 1, tf.nn.sigmoid)
return disp
def conv(self, x, num_out_layers, kernel_size, stride, activation_fn=tf.nn.elu):
p = np.floor((kernel_size - 1) / 2).astype(np.int32)
p_x = tf.pad(x, [[0, 0], [p, p], [p, p], [0, 0]])
return slim.conv2d(p_x, num_out_layers, kernel_size, stride, 'VALID', activation_fn=activation_fn)
def conv_block(self, x, num_out_layers, kernel_size):
conv1 = self.conv(x, num_out_layers, kernel_size, 1)
conv2 = self.conv(conv1, num_out_layers, kernel_size, 2)
return conv2
def maxpool(self, x, kernel_size):
p = np.floor((kernel_size - 1) / 2).astype(np.int32)
p_x = tf.pad(x, [[0, 0], [p, p], [p, p], [0, 0]])
return slim.max_pool2d(p_x, kernel_size)
def resconv(self, x, num_layers, stride):
do_proj = tf.shape(x)[3] != num_layers or stride == 2
shortcut = []
conv1 = self.conv(x, num_layers, 1, 1)
conv2 = self.conv(conv1, num_layers, 3, stride)
conv3 = self.conv(conv2, 4 * num_layers, 1, 1, None)
if do_proj:
shortcut = self.conv(x, 4 * num_layers, 1, stride, None)
else:
shortcut = x
return tf.nn.elu(conv3 + shortcut)
def resblock(self, x, num_layers, num_blocks):
out = x
for i in range(num_blocks - 1):
out = self.resconv(out, num_layers, 1)
out = self.resconv(out, num_layers, 2)
return out
def upconv(self, x, num_out_layers, kernel_size, scale):
upsample = self.upsample_nn(x, scale)
conv = self.conv(upsample, num_out_layers, kernel_size, 1)
return conv
def deconv(self, x, num_out_layers, kernel_size, scale):
p_x = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]])
conv = slim.conv2d_transpose(p_x, num_out_layers, kernel_size, scale, 'SAME')
return conv[:,3:-1,3:-1,:]
def build_vgg(self, branch):
#set convenience functions
conv = self.conv
if self.params.use_deconv:
upconv = self.deconv
else:
upconv = self.upconv
if branch == 'left':
input = self.left
elif branch == 'right':
input = tf.reverse(self.right, [2])
else:
raise ValueError
with tf.variable_scope('encoder'):
conv1 = self.conv_block(input, 32, 7) # H/2
conv2 = self.conv_block(conv1, 64, 5) # H/4
conv3 = self.conv_block(conv2, 128, 3) # H/8
conv4 = self.conv_block(conv3, 256, 3) # H/16
conv5 = self.conv_block(conv4, 512, 3) # H/32
conv6 = self.conv_block(conv5, 512, 3) # H/64
conv7 = self.conv_block(conv6, 512, 3) # H/128
with tf.variable_scope('skips'):
skip1 = conv1
skip2 = conv2
skip3 = conv3
skip4 = conv4
skip5 = conv5
skip6 = conv6
with tf.variable_scope('decoder'):
upconv7 = upconv(conv7, 512, 3, 2) #H/64
concat7 = tf.concat([upconv7, skip6], 3)
iconv7 = conv(concat7, 512, 3, 1)
upconv6 = upconv(iconv7, 512, 3, 2) #H/32
concat6 = tf.concat([upconv6, skip5], 3)
iconv6 = conv(concat6, 512, 3, 1)
upconv5 = upconv(iconv6, 256, 3, 2) #H/16
concat5 = tf.concat([upconv5, skip4], 3)
iconv5 = conv(concat5, 256, 3, 1)
upconv4 = upconv(iconv5, 128, 3, 2) #H/8
concat4 = tf.concat([upconv4, skip3], 3)
iconv4 = conv(concat4, 128, 3, 1)
self.disp4[branch] = self.get_disp(iconv4)
udisp4 = self.upsample_nn(self.disp4[branch], 2)
upconv3 = upconv(iconv4, 64, 3, 2) #H/4
concat3 = tf.concat([upconv3, skip2, udisp4], 3)
iconv3 = conv(concat3, 64, 3, 1)
self.disp3[branch] = self.get_disp(iconv3)
udisp3 = self.upsample_nn(self.disp3[branch], 2)
upconv2 = upconv(iconv3, 32, 3, 2) #H/2
concat2 = tf.concat([upconv2, skip1, udisp3], 3)
iconv2 = conv(concat2, 32, 3, 1)
self.disp2[branch] = self.get_disp(iconv2)
udisp2 = self.upsample_nn(self.disp2[branch], 2)
upconv1 = upconv(iconv2, 16, 3, 2) #H
concat1 = tf.concat([upconv1, udisp2], 3)
iconv1 = conv(concat1, 16, 3, 1)
self.disp1[branch] = self.get_disp(iconv1)
if branch == 'right':
self.disp1[branch] = tf.reverse(self.disp1[branch], [2])
self.disp2[branch] = tf.reverse(self.disp2[branch], [2])
self.disp3[branch] = tf.reverse(self.disp3[branch], [2])
self.disp4[branch] = tf.reverse(self.disp4[branch], [2])
def build_resnet50(self, branch):
#set convenience functions
conv = self.conv
if self.params.use_deconv:
upconv = self.deconv
else:
upconv = self.upconv
if branch == 'left':
input = self.left
elif branch == 'right':
input = tf.reverse(self.right, [2])
else:
raise ValueError
with tf.variable_scope('encoder'):
conv1 = conv(self.model_input, 64, 7, 2) # H/2 - 64D
pool1 = self.maxpool(conv1, 3) # H/4 - 64D
conv2 = self.resblock(pool1, 64, 3) # H/8 - 256D
conv3 = self.resblock(conv2, 128, 4) # H/16 - 512D
conv4 = self.resblock(conv3, 256, 6) # H/32 - 1024D
conv5 = self.resblock(conv4, 512, 3) # H/64 - 2048D
with tf.variable_scope('skips'):
skip1 = conv1
skip2 = pool1
skip3 = conv2
skip4 = conv3
skip5 = conv4
# DECODING
with tf.variable_scope('decoder'):
upconv6 = upconv(conv5, 512, 3, 2) #H/32
concat6 = tf.concat([upconv6, skip5], 3)
iconv6 = conv(concat6, 512, 3, 1)
upconv5 = upconv(iconv6, 256, 3, 2) #H/16
concat5 = tf.concat([upconv5, skip4], 3)
iconv5 = conv(concat5, 256, 3, 1)
upconv4 = upconv(iconv5, 128, 3, 2) #H/8
concat4 = tf.concat([upconv4, skip3], 3)
iconv4 = conv(concat4, 128, 3, 1)
self.disp4[branch] = self.get_disp(iconv4)
udisp4 = self.upsample_nn(self.disp4[branch], 2)
upconv3 = upconv(iconv4, 64, 3, 2) #H/4
concat3 = tf.concat([upconv3, skip2, udisp4], 3)
iconv3 = conv(concat3, 64, 3, 1)
self.disp3[branch] = self.get_disp(iconv3)
udisp3 = self.upsample_nn(self.disp3[branch], 2)
upconv2 = upconv(iconv3, 32, 3, 2) #H/2
concat2 = tf.concat([upconv2, skip1, udisp3], 3)
iconv2 = conv(concat2, 32, 3, 1)
self.disp2[branch] = self.get_disp(iconv2)
udisp2 = self.upsample_nn(self.disp2[branch], 2)
upconv1 = upconv(iconv2, 16, 3, 2) #H
concat1 = tf.concat([upconv1, udisp2], 3)
iconv1 = conv(concat1, 16, 3, 1)
self.disp1[branch] = self.get_disp(iconv1)
if branch == 'right':
self.disp1[branch] = tf.reverse(self.disp1[branch], [2])
self.disp2[branch] = tf.reverse(self.disp2[branch], [2])
self.disp3[branch] = tf.reverse(self.disp3[branch], [2])
self.disp4[branch] = tf.reverse(self.disp4[branch], [2])
def build_model(self):
with slim.arg_scope([slim.conv2d, slim.conv2d_transpose], activation_fn=tf.nn.elu):
with tf.variable_scope('model', reuse=self.reuse_variables):
self.left_pyramid = self.scale_pyramid(self.left, 4)
if self.mode == 'train':
self.right_pyramid = self.scale_pyramid(self.right, 4)
if self.params.do_stereo:
self.model_input = tf.concat([self.left, self.right], 3)
else:
self.model_input = self.left
#build model
if self.params.encoder == 'vgg':
with tf.variable_scope('vgg') as scope:
self.build_vgg(branch='left')
scope.reuse_variables()
self.build_vgg(branch='right')
elif self.params.encoder == 'resnet50':
with tf.variable_scope('resnet50') as scope:
self.build_resnet50(branch='left')
scope.reuse_variables()
self.build_resnet50(branch='right')
else:
raise ValueError("Unknown encoder: {}!".format(self.params.encoder))
def build_outputs(self):
# STORE DISPARITIES
with tf.variable_scope('disparities'):
self.disp_est = [self.disp1, self.disp2, self.disp3, self.disp4]
self.disp_left_est = [d['left'] for d in self.disp_est]
self.disp_right_est = [d['right'] for d in self.disp_est]
if self.mode == 'test':
return
# GENERATE IMAGES
with tf.variable_scope('images'):
self.left_est = [self.generate_image_left(self.right_pyramid[i], self.disp_left_est[i]) for i in range(4)]
self.right_est = [self.generate_image_right(self.left_pyramid[i], self.disp_right_est[i]) for i in range(4)]
# LR CONSISTENCY
with tf.variable_scope('left-right'):
self.right_to_left_disp = [self.generate_image_left(self.disp_right_est[i], self.disp_left_est[i]) for i in range(4)]
self.left_to_right_disp = [self.generate_image_right(self.disp_left_est[i], self.disp_right_est[i]) for i in range(4)]
# DISPARITY SMOOTHNESS
with tf.variable_scope('smoothness'):
self.disp_left_total_var = self.get_total_var(self.disp_left_est, self.left_pyramid)
self.disp_right_total_var = self.get_total_var(self.disp_right_est, self.right_pyramid)
def build_losses(self):
with tf.variable_scope('losses', reuse=self.reuse_variables):
# IMAGE RECONSTRUCTION
# L1
self.l1_left = [tf.abs( self.left_est[i] - self.left_pyramid[i]) for i in range(4)]
self.l1_reconstruction_loss_left = [tf.reduce_mean(l) for l in self.l1_left]
self.l1_right = [tf.abs(self.right_est[i] - self.right_pyramid[i]) for i in range(4)]
self.l1_reconstruction_loss_right = [tf.reduce_mean(l) for l in self.l1_right]
# SSIM
self.ssim_left = [self.SSIM( self.left_est[i], self.left_pyramid[i]) for i in range(4)]
self.ssim_loss_left = [tf.reduce_mean(s) for s in self.ssim_left]
self.ssim_right = [self.SSIM(self.right_est[i], self.right_pyramid[i]) for i in range(4)]
self.ssim_loss_right = [tf.reduce_mean(s) for s in self.ssim_right]
# WEIGTHED SUM
self.image_loss_right = [self.params.alpha_image_loss * self.ssim_loss_right[i] + (1 - self.params.alpha_image_loss) * self.l1_reconstruction_loss_right[i] for i in range(4)]
self.image_loss_left = [self.params.alpha_image_loss * self.ssim_loss_left[i] + (1 - self.params.alpha_image_loss) * self.l1_reconstruction_loss_left[i] for i in range(4)]
self.image_loss = tf.add_n(self.image_loss_left + self.image_loss_right)
# DISPARITY SMOOTHNESS
self.disp_left_loss = [tf.reduce_mean(tf.abs(self.disp_left_total_var[i])) / 2 ** i for i in range(4)]
self.disp_right_loss = [tf.reduce_mean(tf.abs(self.disp_right_total_var[i])) / 2 ** i for i in range(4)]
self.disp_gradient_loss = tf.add_n(self.disp_left_loss + self.disp_right_loss)
# LR CONSISTENCY
self.lr_left_loss = [tf.reduce_mean(tf.abs(self.right_to_left_disp[i] - self.disp_left_est[i])) for i in range(4)]
self.lr_right_loss = [tf.reduce_mean(tf.abs(self.left_to_right_disp[i] - self.disp_right_est[i])) for i in range(4)]
self.lr_loss = tf.add_n(self.lr_left_loss + self.lr_right_loss)
# TOTAL LOSS
self.total_loss = self.image_loss + self.params.disp_gradient_loss_weight * self.disp_gradient_loss + self.params.lr_loss_weight * self.lr_loss
def build_summaries(self):
# SUMMARIES
for i in range(4):
tf.summary.scalar('ssim_loss_' + str(i), self.ssim_loss_left[i] + self.ssim_loss_right[i])
tf.summary.scalar('l1_loss_' + str(i), self.l1_reconstruction_loss_left[i] + self.l1_reconstruction_loss_right[i])
tf.summary.scalar('image_loss_' + str(i), self.image_loss_left[i] + self.image_loss_right[i])
tf.summary.scalar('disp_gradient_loss_' + str(i), self.disp_left_loss[i] + self.disp_right_loss[i])
tf.summary.scalar('lr_loss_' + str(i), self.lr_left_loss[i] + self.lr_right_loss[i])
tf.summary.image('disp_left_est_' + str(i), self.disp_left_est[i], max_outputs=4)
tf.summary.image('disp_right_est_' + str(i), self.disp_right_est[i], max_outputs=4)
tf.summary.image('right_to_left_disp' + str(i), self.right_to_left_disp[i], max_outputs=4)
tf.summary.image('left_to_right_disp' + str(i), self.left_to_right_disp[i], max_outputs=4)
if self.params.full_summary:
tf.summary.image('left_est_' + str(i), self.left_est[i], max_outputs=4)
tf.summary.image('right_est_' + str(i), self.right_est[i], max_outputs=4)
# tf.summary.image('ssim_left_' + str(i), self.ssim_left[i], max_outputs=4)
# tf.summary.image('ssim_right_' + str(i), self.ssim_right[i], max_outputs=4)
tf.summary.image('l1_left_' + str(i), self.l1_left[i], max_outputs=4)
tf.summary.image('l1_right_' + str(i), self.l1_right[i], max_outputs=4)
if self.params.full_summary:
tf.summary.image('left', self.left, max_outputs=4)
tf.summary.image('right', self.right, max_outputs=4)