# -*- coding: utf-8 -*-
from __future__ import division, print_function, absolute_import
import tensorflow as tf
import numpy as np
import os
import sys
import time
import cv2
from six.moves import xrange
from scipy import misc, io
from tensorflow.contrib import slim
import matplotlib.pyplot as plt
from network import pyramid_processing, pyramid_processing_bidirection, get_shape
from datasets import BasicDataset
from utils import average_gradients, lrelu, occlusion, rgb_bgr
from data_augmentation import flow_resize
from flowlib import flow_to_color, write_flo
from warp import tf_warp
class DDFlowModel(object):
def __init__(self, batch_size=8, iter_steps=1000000, initial_learning_rate=1e-4, decay_steps=2e5,
decay_rate=0.5, is_scale=True, num_input_threads=4, buffer_size=5000,
beta1=0.9, num_gpus=1, save_checkpoint_interval=5000, write_summary_interval=200,
display_log_interval=50, allow_soft_placement=True, log_device_placement=False,
regularizer_scale=1e-4, cpu_device='/cpu:0', save_dir='KITTI', checkpoint_dir='checkpoints',
model_name='model', sample_dir='sample', summary_dir='summary', training_mode="no_distillation",
is_restore_model=False, restore_model='./models/KITTI/no_census_no_occlusion',
dataset_config={}, distillation_config={}):
self.batch_size = batch_size
self.iter_steps = iter_steps
self.initial_learning_rate = initial_learning_rate
self.decay_steps = decay_steps
self.decay_rate = decay_rate
self.is_scale = is_scale
self.num_input_threads = num_input_threads
self.buffer_size = buffer_size
self.beta1 = beta1
self.num_gpus = num_gpus
self.save_checkpoint_interval = save_checkpoint_interval
self.write_summary_interval = write_summary_interval
self.display_log_interval = display_log_interval
self.allow_soft_placement = allow_soft_placement
self.log_device_placement = log_device_placement
self.regularizer_scale = regularizer_scale
self.training_mode = training_mode
self.is_restore_model = is_restore_model
self.restore_model = restore_model
self.dataset_config = dataset_config
self.distillation_config = distillation_config
self.shared_device = '/gpu:0' if self.num_gpus == 1 else cpu_device
assert(np.mod(batch_size, num_gpus) == 0)
self.batch_size_per_gpu = int(batch_size / np.maximum(num_gpus, 1))
self.save_dir = save_dir
if not os.path.exists(self.save_dir):
os.makedirs(self.save_dir)
self.checkpoint_dir = '/'.join([self.save_dir, checkpoint_dir])
if not os.path.exists(self.checkpoint_dir):
os.makedirs(self.checkpoint_dir)
self.model_name = model_name
if not os.path.exists('/'.join([self.checkpoint_dir, model_name])):
os.makedirs(('/'.join([self.checkpoint_dir, self.model_name])))
self.sample_dir = '/'.join([self.save_dir, sample_dir])
if not os.path.exists(self.sample_dir):
os.makedirs(self.sample_dir)
if not os.path.exists('/'.join([self.sample_dir, self.model_name])):
os.makedirs(('/'.join([self.sample_dir, self.model_name])))
self.summary_dir = '/'.join([self.save_dir, summary_dir])
if not os.path.exists(self.summary_dir):
os.makedirs(self.summary_dir)
if not os.path.exists('/'.join([self.summary_dir, 'train'])):
os.makedirs(('/'.join([self.summary_dir, 'train'])))
if not os.path.exists('/'.join([self.summary_dir, 'test'])):
os.makedirs(('/'.join([self.summary_dir, 'test'])))
def create_dataset_and_iterator(self, training_mode='no_distillation'):
if training_mode=='no_distillation':
dataset = BasicDataset(crop_h=self.dataset_config['crop_h'],
crop_w=self.dataset_config['crop_w'],
batch_size=self.batch_size_per_gpu,
data_list_file=self.dataset_config['data_list_file'],
img_dir=self.dataset_config['img_dir'])
iterator = dataset.create_batch_iterator(data_list=dataset.data_list, batch_size=dataset.batch_size,
shuffle=True, buffer_size=self.buffer_size, num_parallel_calls=self.num_input_threads)
elif training_mode == 'distillation':
dataset = BasicDataset(crop_h=self.dataset_config['crop_h'],
crop_w=self.dataset_config['crop_w'],
batch_size=self.batch_size_per_gpu,
data_list_file=self.dataset_config['data_list_file'],
img_dir=self.dataset_config['img_dir'],
fake_flow_occ_dir=self.distillation_config['fake_flow_occ_dir'])
iterator = dataset.create_batch_distillation_iterator(data_list=dataset.data_list, batch_size=dataset.batch_size,
shuffle=True, buffer_size=self.buffer_size, num_parallel_calls=self.num_input_threads)
else:
raise ValueError('Invalid training_mode. Training_mode should be one of {no_distillation, distillation}')
return dataset, iterator
def epe_loss(self, diff, mask):
diff_norm = tf.norm(diff, axis=-1, keepdims=True)
diff_norm = tf.multiply(diff_norm, mask)
diff_norm_sum = tf.reduce_sum(diff_norm)
loss_mean = diff_norm_sum / (tf.reduce_sum(mask) + 1e-6)
return loss_mean
def abs_robust_loss(self, diff, mask, q=0.4):
diff = tf.pow((tf.abs(diff)+0.01), q)
diff = tf.multiply(diff, mask)
diff_sum = tf.reduce_sum(diff)
loss_mean = diff_sum / (tf.reduce_sum(mask) * 2 + 1e-6)
return loss_mean
def create_mask(self, tensor, paddings):
with tf.variable_scope('create_mask'):
shape = tf.shape(tensor)
inner_width = shape[1] - (paddings[0][0] + paddings[0][1])
inner_height = shape[2] - (paddings[1][0] + paddings[1][1])
inner = tf.ones([inner_width, inner_height])
mask2d = tf.pad(inner, paddings)
mask3d = tf.tile(tf.expand_dims(mask2d, 0), [shape[0], 1, 1])
mask4d = tf.expand_dims(mask3d, 3)
return tf.stop_gradient(mask4d)
def census_loss(self, img1, img2_warped, mask, max_distance=3):
patch_size = 2 * max_distance + 1
with tf.variable_scope('census_loss'):
def _ternary_transform(image):
intensities = tf.image.rgb_to_grayscale(image) * 255
#patches = tf.extract_image_patches( # fix rows_in is None
# intensities,
# ksizes=[1, patch_size, patch_size, 1],
# strides=[1, 1, 1, 1],
# rates=[1, 1, 1, 1],
# padding='SAME')
out_channels = patch_size * patch_size
w = np.eye(out_channels).reshape((patch_size, patch_size, 1, out_channels))
weights = tf.constant(w, dtype=tf.float32)
patches = tf.nn.conv2d(intensities, weights, strides=[1, 1, 1, 1], padding='SAME')
transf = patches - intensities
transf_norm = transf / tf.sqrt(0.81 + tf.square(transf))
return transf_norm
def _hamming_distance(t1, t2):
dist = tf.square(t1 - t2)
dist_norm = dist / (0.1 + dist)
dist_sum = tf.reduce_sum(dist_norm, 3, keepdims=True)
return dist_sum
t1 = _ternary_transform(img1)
t2 = _ternary_transform(img2_warped)
dist = _hamming_distance(t1, t2)
transform_mask = self.create_mask(mask, [[max_distance, max_distance],
[max_distance, max_distance]])
return self.abs_robust_loss(dist, mask * transform_mask)
def compute_losses(self, batch_img1, batch_img2, flow_fw, flow_bw, mask_fw, mask_bw, train=True, is_scale=True):
img_size = get_shape(batch_img1, train=train)
img1_warp = tf_warp(batch_img1, flow_bw['full_res'], img_size[1], img_size[2])
img2_warp = tf_warp(batch_img2, flow_fw['full_res'], img_size[1], img_size[2])
losses = {}
abs_robust_mean = {}
abs_robust_mean['no_occlusion'] = self.abs_robust_loss(batch_img1-img2_warp, tf.ones_like(mask_fw)) + self.abs_robust_loss(batch_img2-img1_warp, tf.ones_like(mask_bw))
abs_robust_mean['occlusion'] = self.abs_robust_loss(batch_img1-img2_warp, mask_fw) + self.abs_robust_loss(batch_img2-img1_warp, mask_bw)
losses['abs_robust_mean'] = abs_robust_mean
census_loss = {}
census_loss['no_occlusion'] = self.census_loss(batch_img1, img2_warp, tf.ones_like(mask_fw), max_distance=3) + \
self.census_loss(batch_img2, img1_warp, tf.ones_like(mask_bw), max_distance=3)
census_loss['occlusion'] = self.census_loss(batch_img1, img2_warp, mask_fw, max_distance=3) + \
self.census_loss(batch_img2, img1_warp, mask_bw, max_distance=3)
losses['census'] = census_loss
return losses
def add_loss_summary(self, losses, keys=['abs_robust_mean'], prefix=None):
for key in keys:
for loss_key, loss_value in losses[key].items():
if prefix:
loss_name = '%s/%s/%s' % (prefix, key, loss_key)
else:
loss_name = '%s/%s' % (key, loss_key)
tf.summary.scalar(loss_name, loss_value)
def build_no_data_distillation(self, iterator, regularizer_scale=1e-4, train=True, trainable=True, is_scale=True):
batch_img1, batch_img2 = iterator.get_next()
regularizer = slim.l2_regularizer(scale=regularizer_scale)
flow_fw, flow_bw = pyramid_processing_bidirection(batch_img1, batch_img2,
train=train, trainable=trainable, reuse=None, regularizer=regularizer, is_scale=is_scale)
occ_fw, occ_bw = occlusion(flow_fw['full_res'], flow_bw['full_res'])
mask_fw = 1. - occ_fw
mask_bw = 1. - occ_bw
losses = self.compute_losses(batch_img1, batch_img2, flow_fw, flow_bw, mask_fw, mask_bw, train=train, is_scale=is_scale)
l2_regularizer = tf.losses.get_regularization_losses()
regularizer_loss = tf.add_n(l2_regularizer)
return losses, regularizer_loss
def build_data_distillation(self, iterator, regularizer_scale=1e-4, train=True, trainable=True, is_scale=True):
batch_img1, batch_img2, flow_fw, flow_bw, occ_fw, occ_bw = iterator.get_next()
regularizer = slim.l2_regularizer(scale=regularizer_scale)
h = self.dataset_config['crop_h']
w = self.dataset_config['crop_w']
target_h = self.distillation_config['target_h']
target_w = self.distillation_config['target_w']
offect_h = tf.random_uniform([], minval=0, maxval=h-target_h, dtype=tf.int32)
offect_w = tf.random_uniform([], minval=0, maxval=w-target_w, dtype=tf.int32)
batch_img1_cropped_patch = tf.image.crop_to_bounding_box(batch_img1, offect_h, offect_w, target_h, target_w)
batch_img2_cropped_patch = tf.image.crop_to_bounding_box(batch_img2, offect_h, offect_w, target_h, target_w)
flow_fw_cropped_patch = tf.image.crop_to_bounding_box(flow_fw, offect_h, offect_w, target_h, target_w)
flow_bw_cropped_patch = tf.image.crop_to_bounding_box(flow_bw, offect_h, offect_w, target_h, target_w)
occ_fw_cropped_patch = tf.image.crop_to_bounding_box(occ_fw, offect_h, offect_w, target_h, target_w)
occ_bw_cropped_patch = tf.image.crop_to_bounding_box(occ_bw, offect_h, offect_w, target_h, target_w)
flow_fw_patch, flow_bw_patch = pyramid_processing_bidirection(batch_img1_cropped_patch, batch_img2_cropped_patch,
train=train, trainable=trainable, reuse=None, regularizer=regularizer, is_scale=is_scale)
occ_fw_patch, occ_bw_patch = occlusion(flow_fw_patch['full_res'], flow_bw_patch['full_res'])
mask_fw_patch = 1. - occ_fw_patch
mask_bw_patch = 1. - occ_bw_patch
losses = self.compute_losses(batch_img1_cropped_patch, batch_img2_cropped_patch, flow_fw_patch, flow_bw_patch, mask_fw_patch, mask_bw_patch, train=train, is_scale=is_scale)
valid_mask_fw = tf.clip_by_value(occ_fw_patch - occ_fw_cropped_patch, 0., 1.)
valid_mask_bw = tf.clip_by_value(occ_bw_patch - occ_bw_cropped_patch, 0., 1.)
data_distillation_loss = {}
data_distillation_loss['distillation'] = (self.abs_robust_loss(flow_fw_cropped_patch-flow_fw_patch['full_res'], valid_mask_fw) + \
self.abs_robust_loss(flow_bw_cropped_patch-flow_bw_patch['full_res'], valid_mask_bw)) / 2
losses['data_distillation'] = data_distillation_loss
l2_regularizer = tf.losses.get_regularization_losses()
regularizer_loss = tf.add_n(l2_regularizer)
return losses, regularizer_loss
def build(self, iterator, regularizer_scale=1e-4, train=True, trainable=True, is_scale=True, training_mode='no_distillation'):
if training_mode == 'no_distillation':
losses, regularizer_loss = self.build_no_data_distillation(iterator=iterator, regularizer_scale=regularizer_scale, train=train, trainable=trainable, is_scale=is_scale)
elif training_mode == 'distillation':
losses, regularizer_loss = self.build_data_distillation(iterator=iterator, regularizer_scale=regularizer_scale, train=train, trainable=trainable, is_scale=is_scale)
else:
raise ValueError('Invalid training_mode. Training_mode should be one of {no_distillation, distillation}')
return losses, regularizer_loss
def create_train_op(self, optim, iterator, global_step, regularizer_scale=1e-4, train=True, trainable=True, is_scale=True, training_mode='no_distillation'):
if self.num_gpus == 1:
losses, regularizer_loss = self.build(iterator, regularizer_scale=regularizer_scale, train=train, trainable=trainable, is_scale=is_scale, training_mode=training_mode)
optim_loss = losses['abs_robust_mean']['no_occlusion']
train_op = optim.minimize(optim_loss, var_list=tf.trainable_variables(), global_step=global_step)
else:
tower_grads = []
tower_losses = []
tower_regularizer_losses = []
with tf.variable_scope(tf.get_variable_scope()):
for i in range(self.num_gpus):
with tf.device('/gpu:%d' % i):
with tf.name_scope('tower_{}'.format(i)) as scope:
losses_, regularizer_loss_ = self.build(iterator, regularizer_scale=regularizer_scale, train=train, trainable=trainable, is_scale=is_scale, training_mode=training_mode)
optim_loss = losses_['abs_robust_mean']['no_occlusion']
# Reuse variables for the next tower.
tf.get_variable_scope().reuse_variables()
grads = self.optim.compute_gradients(optim_loss, var_list=tf.trainable_variables())
tower_grads.append(grads)
tower_losses.append(losses_)
tower_regularizer_losses.append(regularizer_loss_)
#self.add_loss_summary(losses_, keys=['abs_robust_mean', 'census'], prefix='tower_%d' % i)
grads = average_gradients(tower_grads)
train_op = optim.apply_gradients(grads, global_step=global_step)
losses = tower_losses[0].copy()
for key in losses.keys():
for loss_key, loss_value in losses[key].items():
for i in range(1, self.num_gpus):
losses[key][loss_key] += tower_losses[i][key][loss_key]
losses[key][loss_key] /= self.num_gpus
regularizer_loss = 0.
for i in range(self.num_gpus):
regularizer_loss += tower_regularizer_losses[i]
regularizer_loss /= self.num_gpus
self.add_loss_summary(losses, keys=losses.keys())
tf.summary.scalar('regularizer_loss', regularizer_loss)
return train_op, losses, regularizer_loss
def train(self):
with tf.Graph().as_default(), tf.device(self.shared_device):
self.global_step = tf.Variable(0, trainable=False)
self.dataset, self.iterator = self.create_dataset_and_iterator(training_mode=self.training_mode)
self.lr_decay = tf.train.exponential_decay(self.initial_learning_rate, self.global_step, decay_steps=self.decay_steps, decay_rate=self.decay_rate, staircase=True)
tf.summary.scalar('learning_rate', self.lr_decay)
self.optim = tf.train.AdamOptimizer(self.lr_decay, self.beta1)
self.train_op, self.losses, self.regularizer_loss = self.create_train_op(optim=self.optim, iterator=self.iterator,
global_step=self.global_step, regularizer_scale=self.regularizer_scale, train=True, trainable=True, is_scale=self.is_scale, training_mode=self.training_mode)
merge_summary = tf.summary.merge_all()
summary_writer = tf.summary.FileWriter(logdir='/'.join([self.summary_dir, 'train', self.model_name]))
self.trainable_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)
self.saver = tf.train.Saver(var_list=self.trainable_vars + [self.global_step], max_to_keep=500)
self.sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=self.allow_soft_placement, log_device_placement=self.log_device_placement))
self.sess.run(tf.global_variables_initializer())
self.sess.run(tf.local_variables_initializer())
if self.is_restore_model:
self.saver.restore(self.sess, self.restore_model)
self.sess.run(tf.assign(self.global_step, 0))
start_step = self.sess.run(self.global_step)
self.sess.run(self.iterator.initializer)
start_time = time.time()
for step in range(start_step+1, self.iter_steps+1):
_, abs_robust_mean_no_occlusion, census_occlusion = self.sess.run([self.train_op,
self.losses['abs_robust_mean']['no_occlusion'], self.losses['census']['occlusion']])
if np.mod(step, self.display_log_interval) == 0:
print('step: %d time: %.6fs, abs_robust_mean_no_occlusion: %.6f, census_occlusion: %.6f' %
(step, time.time() - start_time, abs_robust_mean_no_occlusion, census_occlusion))
if np.mod(step, self.write_summary_interval) == 0:
summary_str = self.sess.run(merge_summary)
summary_writer.add_summary(summary_str, global_step=step)
if np.mod(step, self.save_checkpoint_interval) == 0:
self.saver.save(self.sess, '/'.join([self.checkpoint_dir, self.model_name, 'model']), global_step=step,
write_meta_graph=False, write_state=False)
def test(self, restore_model, save_dir):
dataset = BasicDataset(data_list_file=self.dataset_config['data_list_file'], img_dir=self.dataset_config['img_dir'])
save_name_list = dataset.data_list[:, 2]
iterator = dataset.create_one_shot_iterator(dataset.data_list, num_parallel_calls=self.num_input_threads)
batch_img1, batch_img2 = iterator.get_next()
flow_est = pyramid_processing(batch_img1, batch_img2, train=False, trainable=False, regularizer=None, is_scale=True)
flow_est_color = flow_to_color(flow_est['full_res'], mask=None, max_flow=256)
restore_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
saver = tf.train.Saver(var_list=restore_vars)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
sess.run(iterator.initializer)
saver.restore(sess, restore_model)
if not os.path.exists(save_dir):
os.makedirs(save_dir)
for i in range(dataset.data_num):
np_flow_est, np_flow_est_color = sess.run([flow_est['full_res'], flow_est_color])
misc.imsave('%s/flow_est_color_%s.png' % (save_dir, save_name_list[i]), np_flow_est_color[0])
write_flo('%s/flow_est_%s.flo' % (save_dir, save_name_list[i]), np_flow_est[0])
print('Finish %d/%d' % (i, dataset.data_num))
def generate_fake_flow_occlusion(self, restore_model, save_dir):
dataset = BasicDataset(data_list_file=self.dataset_config['data_list_file'], img_dir=self.dataset_config['img_dir'])
save_name_list = dataset.data_list[:, 2]
iterator = dataset.create_one_shot_iterator(dataset.data_list, num_parallel_calls=self.num_input_threads)
batch_img1, batch_img2 = iterator.get_next()
flow_fw, flow_bw = pyramid_processing_bidirection(batch_img1, batch_img2,
train=False, trainable=False, reuse=None, regularizer=None, is_scale=True)
occ_fw, occ_bw = occlusion(flow_fw['full_res'], flow_bw['full_res'])
flow_fw_full_res = flow_fw['full_res'] * 64. + 32768
flow_occ_fw = tf.concat([flow_fw_full_res, occ_fw], -1)
flow_occ_fw = tf.cast(flow_occ_fw, tf.uint16)
flow_bw_full_res = flow_bw['full_res'] * 64. + 32768
flow_occ_bw = tf.concat([flow_bw_full_res, occ_bw], -1)
flow_occ_bw = tf.cast(flow_occ_bw, tf.uint16)
restore_vars = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
saver = tf.train.Saver(var_list=restore_vars)
sess = tf.Session()
sess.run(tf.global_variables_initializer())
sess.run(iterator.initializer)
saver.restore(sess, restore_model)
#save_dir = '/'.join([self.save_dir, 'sample', self.model_name])
if not os.path.exists(save_dir):
os.makedirs(save_dir)
for i in range(dataset.data_num):
np_flow_occ_fw, np_flow_occ_bw, np_occ_fw = sess.run([flow_occ_fw, flow_occ_bw, occ_fw])
# opencv read and save image as bgr format, here we change rgb to bgr
np_flow_occ_fw = rgb_bgr(np_flow_occ_fw[0])
np_flow_occ_bw = rgb_bgr(np_flow_occ_bw[0])
np_flow_occ_fw = np_flow_occ_fw.astype(np.uint16)
np_flow_occ_bw = np_flow_occ_bw.astype(np.uint16)
cv2.imwrite('%s/flow_occ_fw_%s.png' % (save_dir, save_name_list[i]), np_flow_occ_fw)
cv2.imwrite('%s/flow_occ_bw_%s.png' % (save_dir, save_name_list[i]), np_flow_occ_bw)
print('Finish %d/%d' % (i, dataset.data_num))
