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sig_main.py
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from __future__ import division
import os
import time
import random
import pprint
import numpy as np
import tensorflow as tf
import tensorflow.contrib.slim as slim
from sig_model import *
from test_depth import *
from test_pose import *
from test_flow import *
from data_loader import DataLoader
import matplotlib as mpl
mpl.use('Agg')
import matplotlib.pyplot as plt
flags = tf.app.flags
flags.DEFINE_string("mode", "", "(train_rigid, train_flow) or (test_depth, test_pose, test_flow)")
flags.DEFINE_string("dataset_dir", "", "Dataset directory")
flags.DEFINE_string("init_ckpt_file", None, "Specific checkpoint file to initialize from")
flags.DEFINE_integer("batch_size", 4, "The size of of a sample batch")
flags.DEFINE_integer("num_threads", 32, "Number of threads for data loading")
flags.DEFINE_integer("img_height", 128, "Image height")
flags.DEFINE_integer("img_width", 416, "Image width")
flags.DEFINE_integer("seq_length", 3, "Sequence length for each example")
#TODO set log level
tf.logging.set_verbosity(tf.logging.WARN)
#TODO multi-gpu settings
flags.DEFINE_integer("num_gpus", 1, "Number of GPUs")
#TODO S3 directory compatible setting
flags.DEFINE_string("filelist_dir", "", "File list directory")
#TODO low-level network flags to give more flexibility for training
flags.DEFINE_boolean("enable_batch_norm", True, "Shall we enable batch_norm configs")
flags.DEFINE_boolean("batch_norm_is_training", True, "Set the flag for slim.batch_norm is_training")
#TODO debug flags to help debugging
flags.DEFINE_boolean("train_lite", False, "Train on light weight dataset") # for quick debug
flags.DEFINE_integer("print_interval", 100, "Interval for printing things out") # for debugging
#TODO tensorboard visualization
flags.DEFINE_string("summary_dir", "", "Directory name to save the summaries")
flags.DEFINE_integer("save_summ_freq", 100, "Save the TF summary every save_summ_freq iterations")
flags.DEFINE_integer("max_outputs", 4, "How many images per mini-batch we want to save") # see https://www.tensorflow.org/api_docs/python/tf/summary/image
#TODO regularization
flags.DEFINE_boolean("use_regularization", False, "Whether or not to use regularization term")
#TODO semantic-related flags to help depth estimation
flags.DEFINE_boolean("sem_assist", False, "Add semantic into account")
flags.DEFINE_boolean("load_from_raw", True, "Load from raw binary format")
flags.DEFINE_integer("sem_num_class", 19, "Num of semantic classes")
flags.DEFINE_boolean("sem_as_loss", False, "Shall use semantic loss in loss function")
flags.DEFINE_boolean("sem_as_feat", False, "Shall add semantic as a feature in depth net")
flags.DEFINE_boolean("one_hot_sem_feat", True, "Shall we use one hot version of sem as input")
flags.DEFINE_float("sem_feat_weight", .0, "The weight of semantic feature in the network")
flags.DEFINE_boolean("fixed_posenet", False, "Shall we fixed the weight for posenet?")
flags.DEFINE_string("sem_test_kitti_dir", "", "Dir for semantic kitti testset")
flags.DEFINE_boolean("sem_nn_pyramid", False, "Use nearest neighbor to generate sem pyramid")
flags.DEFINE_boolean("sem_nn_warp", False, "Use nearest neighbor to execute flow warp")
#TODO explorations about the semantic loss: `sem_as_loss`==True
flags.DEFINE_boolean("sem_warp_explore", False, "Warp the semantic and compute the loss")
flags.DEFINE_string("sem_warp_function", "", "Loss function for semantic warping loss")
flags.DEFINE_float("sem_warp_weight", 0.0, "The weight for semantic warping loss")
flags.DEFINE_boolean("sem_mask_explore", False, "Extact the mask and guide the img warp loss")
flags.DEFINE_boolean("sem_mask_feature", False, "Extact the mask and use as sem features, sem_mask_explore=True")
flags.DEFINE_string("sem_mask_pattern", "", "Patterns to extract the mask")
flags.DEFINE_string("sem_mask_function", "", "Functions for the guidance to the img warp loss")
flags.DEFINE_float("sem_mask_weight", 0.0, "The weight for this img warp loss")
flags.DEFINE_boolean("sem_edge_explore", False, "Extact the edge and guide the smooth term")
flags.DEFINE_boolean("sem_edge_feature", False, "Extact the edge and use as sem features, sem_edge_explore=True")
flags.DEFINE_string("sem_edge_pattern", "", "Patterns to extract the edge")
flags.DEFINE_string("sem_edge_function", "", "Functions for the guidance to the smooth term")
flags.DEFINE_float("sem_edge_weight", 0.0, "The weight for this smooth term")
flags.DEFINE_boolean("data_aug_cast", True, "adapt to previous version, but try to avoid this in future version")
flags.DEFINE_float("lighting_factor", 1.0, "lighting factor")
flags.DEFINE_boolean("use_sem_weight_decay", False, "Adapt sem weight decay for sem features among training")
flags.DEFINE_string("sem_feat_struct", "channel", "Sem network type: channel or branch wise")
flags.DEFINE_float("weight_init_value", 1.0, "Initial value for sem features. May 1.0 for type-I and 0.5 for type-II")
flags.DEFINE_string("weight_decay_rule", "", "1) Finally comes to zero 2) at some drop speed defined")
#TODO explorations about semantic-guided depth prediction network
flags.DEFINE_boolean("add_segnet", False, "To enable the transfer network to learn multi scale semantic segmentation from depth")
flags.DEFINE_string("transfer_network_structure", "", "The structure for that transfer network")
flags.DEFINE_float("sem_seg_weight", 0.0, "The weight for semantic segmentation guidance loss weight")
flags.DEFINE_float("ins0_seg_weight", 0.0, "The weight for instance class segmentation guidance loss weight")
flags.DEFINE_float("ins1_edge_seg_weight", 0.0, "The weight for instance id edge segmentation guidance loss weight")
flags.DEFINE_boolean("transfer_learn_sem", True, "Use transfer network to learn semantic")
flags.DEFINE_boolean("transfer_learn_ins0", False, "Use transfer network to learn ins0")
flags.DEFINE_boolean("transfer_learn_ins1_edge", False, "Use transfer network to learn ins1 edges")
#TODO Try to observe the variation of architectures handling semantic inputs
flags.DEFINE_boolean("block_dispnet_sem", False, "Not to receive the semantic input for dispnet")
flags.DEFINE_boolean("block_posenet_sem", False, "Not to receive the semantic input for posenet")
flags.DEFINE_boolean("new_sem_dispnet", False, "To build a new dispnet for receiving semantic input")
flags.DEFINE_boolean("new_sem_posenet", False, "To build a new posenet for receiving semantic input")
#TODO instance-related flags to help depth estimation
flags.DEFINE_boolean("ins_assist", False, "Add instance into account")
flags.DEFINE_integer("ins_num_class", 81, "Maximum number of instances labels")
flags.DEFINE_boolean("ins_as_loss", False, "Shall use instance loss in loss function")
flags.DEFINE_float("ins_l2_norm_weight", .3, "The weight of ins l2-norm in loss function")
flags.DEFINE_boolean("ins_as_feat", False, "Shall add instance as a feature in depth net")
flags.DEFINE_boolean("ins0_dense_feature", False, "Shall use instance channel 0 dense info as features?")
flags.DEFINE_boolean("ins0_onehot_feature", False, "Shall we use one hot version of ins as input")
flags.DEFINE_boolean("ins0_edge_explore", False, "Shall explore instance channel 0 edge info?")
flags.DEFINE_boolean("ins0_edge_feature", False, "Shall use instance channel 0 edge info as features?")
flags.DEFINE_boolean("ins1_dense_feature", False, "Shall use instance channel 1 dense info as features?")
flags.DEFINE_boolean("ins1_onehot_feature", False, "Shall we use one hot version of ins as input")
flags.DEFINE_boolean("ins1_edge_explore", False, "Shall explore instance channel 1 edge info?")
flags.DEFINE_boolean("ins1_edge_feature", False, "Shall use instance channel 1 edge info as features?")
flags.DEFINE_string("ins_train_kitti_dir", "../../data/kitti_eigen_new_instance_labels_acc_only/", "kitt inst train path?")
flags.DEFINE_string("ins_test_kitti_dir", "../../data/test_files_eigen_semantic_acc_only/", "kitt inst testpath?")
flags.DEFINE_float("ins_feat_weight", .0, "The weight of instance feature in the network")
flags.DEFINE_boolean("ins_nn_pyramid", False, "Use nearest neighbor to generate ins pyramid")
flags.DEFINE_boolean("ins_nn_warp", False, "Use nearest neighbor to execute flow warp")
##### Training Configurations #####
flags.DEFINE_string("checkpoint_dir", "", "Directory name to save the checkpoints")
flags.DEFINE_float("learning_rate", 0.0002, "Learning rate for adam")
flags.DEFINE_integer("max_to_keep", 5000, "Maximum number of checkpoints to save")
flags.DEFINE_integer("max_steps", 300000, "Maximum number of training iterations")
flags.DEFINE_integer("save_ckpt_freq", 5000, "Save the checkpoint model every save_ckpt_freq iterations")
flags.DEFINE_float("alpha_recon_image", 0.85, "Alpha weight between SSIM and L1 in reconstruction loss")
##### Configurations about DepthNet & PoseNet of SigNet #####
flags.DEFINE_string("dispnet_encoder", "resnet50", "Type of encoder for dispnet, vgg or resnet50")
flags.DEFINE_boolean("scale_normalize", False, "Spatially normalize depth prediction")
flags.DEFINE_float("rigid_warp_weight", 1.0, "Weight for warping by rigid flow")
flags.DEFINE_float("disp_smooth_weight", 0.5, "Weight for disp smoothness")
##### Configurations about ResFlowNet of SigNet (or DirFlowNetS) #####
flags.DEFINE_string("flownet_type", "residual", "type of flownet, residual or direct")
flags.DEFINE_float("flow_warp_weight", 1.0, "Weight for warping by full flow")
flags.DEFINE_float("flow_smooth_weight", 0.2, "Weight for flow smoothness")
flags.DEFINE_float("flow_consistency_weight", 0.2, "Weight for bidirectional flow consistency")
flags.DEFINE_float("flow_consistency_alpha", 3.0, "Alpha for flow consistency check")
flags.DEFINE_float("flow_consistency_beta", 0.05, "Beta for flow consistency check")
##### Testing Configurations #####
flags.DEFINE_string("output_dir", None, "Test result output directory")
flags.DEFINE_string("depth_test_split", "eigen", "KITTI depth split, eigen or stereo")
flags.DEFINE_integer("pose_test_seq", 9, "KITTI Odometry Sequence ID to test")
##### adapt to tf-1.10 +py3 ref: https://github.com/tinghuiz/SfMLearner/pull/70/commits/ec3007d82a7d2205ec5e5ffb5fc99729d31faf88
flags.DEFINE_integer("num_source", 2, "Number of source images")
flags.DEFINE_integer("num_scales", 4, "Number of scaling points")
flags.DEFINE_boolean("add_flownet", False, "")
flags.DEFINE_boolean("add_dispnet", False, "")
flags.DEFINE_boolean("add_posenet", False, "")
opt = flags.FLAGS
def train():
seed = 8964
tf.set_random_seed(seed)
np.random.seed(seed)
random.seed(seed)
if not os.path.exists(opt.checkpoint_dir):
os.makedirs(opt.checkpoint_dir)
with tf.Graph().as_default():
global_step = tf.Variable(0,
name='global_step',
trainable=False)
incr_global_step = tf.assign(global_step,
global_step+1)
#optim = tf.train.AdamOptimizer(opt.learning_rate, 0.9)
lr = tf.placeholder(tf.float32, shape=[], name="lr")
optim = tf.train.AdamOptimizer(lr, 0.9)
loader = DataLoader(opt)
losses=[]
img_losses=[]
rigid_warp_losses=[]
disp_smooth_losses=[]
sem_losses=[]
sem_warp_losses=[]
sem_mask_losses=[]
sem_edge_losses=[]
sem_seg_losses=[]
ins0_seg_losses=[]
ins1_edge_seg_losses=[]
ins_losses=[]
with tf.variable_scope(tf.get_variable_scope()):
for i in range(opt.num_gpus):
with tf.device('/gpu:{:d}'.format(i)):
with tf.name_scope('gpu{:d}'.format(i)):
"""
Get images batch from data loader
'tgt_image' : target (middle frame)
'src_image_stack' : consists of 2 source images
'intrinsics' : camera intrinsic data
'tgt_sem_tuple' : semantic target data
'src_sem_stack_tuple' : semantic source data
'tgt_ins_tuple' : instance target data
'src_ins_stack_tuple' : instance mask source data
"""
tgt_image, src_image_stack, intrinsics, tgt_sem_tuple, src_sem_stack_tuple, tgt_ins_tuple, src_ins_stack_tuple = loader.load_train_batch()
# Build Model
model = SIGNetModel(opt, tgt_image, src_image_stack, intrinsics, tgt_sem_tuple, src_sem_stack_tuple, tgt_ins_tuple, src_ins_stack_tuple)
# Handle losses
losses.append(model.total_loss)
tf.get_variable_scope().reuse_variables()
img_losses.append(model.img_loss)
rigid_warp_losses.append(model.rigid_warp_loss)
disp_smooth_losses.append(model.disp_smooth_loss)
if opt.sem_as_loss:
sem_losses.append(model.sem_loss)
if opt.sem_warp_explore:
sem_warp_losses.append(model.sem_warp_loss)
if opt.sem_mask_explore:
sem_mask_losses.append(model.sem_mask_loss)
if opt.sem_edge_explore:
sem_edge_losses.append(model.sem_edge_loss)
if opt.ins_as_loss:
ins_losses.append(model.ins_loss)
if opt.sem_assist and opt.add_segnet:
sem_seg_losses.append(model.sem_seg_loss)
ins0_seg_losses.append(model.ins0_seg_loss)
ins1_edge_seg_losses.append(model.ins1_edge_seg_loss)
#TODO tensorboard
tf.summary.image('tgt_image_g%02d'%(i), tgt_image, max_outputs=opt.max_outputs)
tf.summary.image('src_image_prev_g%02d'%(i), src_image_stack[:, :, :, :3], max_outputs=opt.max_outputs)
tf.summary.image('src_image_next_g%02d'%(i), src_image_stack[:, :, :, 3:], max_outputs=opt.max_outputs)
tf.summary.scalar('loss_g%02d'%(i), model.total_loss)
tf.summary.scalar('img_loss_g%02d'%(i), model.img_loss)
tf.summary.scalar('rigid_warp_loss_g%02d'%(i),model.rigid_warp_loss)
tf.summary.scalar('disp_smooth_loss_g%02d'%(i),model.disp_smooth_loss)
if opt.sem_as_loss:
tf.summary.scalar('sem_loss_g%02d'%(i), model.sem_loss)
if opt.sem_warp_explore:
tf.summary.scalar('sem_warp_loss_g%02d'%(i), model.sem_warp_loss)
if opt.ins_as_loss:
tf.summary.scalar('ins_loss_g%02d'%(i), model.ins_loss)
if opt.sem_assist and opt.add_segnet:
tf.summary.scalar('sem_seg_loss_g%02d'%(i), model.sem_seg_loss)
tf.summary.scalar('ins0_seg_loss_g%02d'%(i), model.ins0_seg_loss)
tf.summary.scalar('ins1_edge_seg_loss_g%02d'%(i), model.ins1_edge_seg_loss)
#TODO Add bookkeeping ops
if i==0:
# Train Op
if opt.mode == 'train_flow' and opt.flownet_type == "residual":
train_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, "flow_net")
else:
#TODO try to enable a solution to fix posenet weight in first stage
if opt.mode == 'train_rigid' and opt.fixed_posenet:
if opt.new_sem_dispnet:
train_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, "depth_sem_net")
else:
train_vars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, "depth_net")
else:
train_vars = [var for var in tf.trainable_variables()]
loading_net=["depth_net", "pose_net"]
if opt.new_sem_dispnet:
loading_net.append("depth_sem_net")
if opt.new_sem_posenet:
loading_net.append("pose_sem_net")
vars_to_restore = slim.get_variables_to_restore(include=loading_net)
if opt.init_ckpt_file != None:
init_assign_op, init_feed_dict = slim.assign_from_checkpoint(
opt.init_ckpt_file, vars_to_restore)
#TODO Cal mean losses among gpus, and track the loss in TF Summary.
loss = tf.stack(axis=0, values=losses)
loss = tf.reduce_mean(loss, 0)
tf.summary.scalar('loss', loss)
rigid_warp_loss = tf.stack(axis=0, values=rigid_warp_losses)
rigid_warp_loss = tf.reduce_mean(rigid_warp_loss, 0)
tf.summary.scalar('rigid_warp_loss', rigid_warp_loss)
tf.summary.scalar('unit_rigid_warp_loss', rigid_warp_loss/ (opt.rigid_warp_weight+tf.convert_to_tensor(1e-8, dtype=tf.float32)
) )
disp_smooth_loss = tf.stack(axis=0, values=disp_smooth_losses)
disp_smooth_loss = tf.reduce_mean(disp_smooth_loss, 0)
tf.summary.scalar('disp_smooth_loss', disp_smooth_loss)
tf.summary.scalar('unit_disp_smooth_loss', disp_smooth_loss/ (opt.disp_smooth_weight+tf.convert_to_tensor(1e-8, dtype=tf.float32)
) )
img_loss = tf.stack(axis=0, values=img_losses)
img_loss = tf.reduce_mean(img_loss, 0)
tf.summary.scalar('img_loss', img_loss)
if opt.sem_as_loss:
sem_loss = tf.stack(axis=0, values=sem_losses)
sem_loss = tf.reduce_mean(sem_loss, 0)
tf.summary.scalar('sem_loss', sem_loss)
if opt.sem_warp_explore:
sem_warp_loss = tf.stack(axis=0, values=sem_warp_losses)
sem_warp_loss = tf.reduce_mean(sem_warp_loss, 0)
tf.summary.scalar('sem_warp_loss', model.sem_warp_loss)
tf.summary.scalar('unit_sem_warp_loss', model.sem_warp_loss/ (opt.sem_warp_weight+tf.convert_to_tensor(1e-8, dtype=tf.float32)
) )
if opt.sem_mask_explore:
sem_mask_loss = tf.stack(axis=0, values=sem_mask_losses)
sem_mask_loss = tf.reduce_mean(sem_mask_loss, 0)
tf.summary.scalar('sem_mask_loss', model.sem_mask_loss)
tf.summary.scalar('unit_sem_mask_loss', model.sem_mask_loss/ (opt.sem_mask_weight+tf.convert_to_tensor(1e-8, dtype=tf.float32)
) )
if opt.sem_edge_explore:
sem_edge_loss = tf.stack(axis=0, values=sem_edge_losses)
sem_edge_loss = tf.reduce_mean(sem_edge_loss, 0)
tf.summary.scalar('sem_edge_loss', model.sem_edge_loss)
tf.summary.scalar('unit_sem_edge_loss', model.sem_edge_loss/ (opt.sem_edge_weight+tf.convert_to_tensor(1e-8, dtype=tf.float32)
) )
if opt.sem_assist and opt.add_segnet:
sem_seg_loss = tf.stack(axis=0, values=sem_seg_losses)
sem_seg_loss = tf.reduce_mean(sem_seg_loss, 0)
tf.summary.scalar('sem_seg_loss', sem_seg_loss)
tf.summary.scalar('unit_sem_seg_loss', model.sem_seg_loss/ (opt.sem_seg_weight+tf.convert_to_tensor(1e-8, dtype=tf.float32)))
ins0_seg_loss = tf.stack(axis=0, values=ins0_seg_losses)
ins0_seg_loss = tf.reduce_mean(ins0_seg_loss, 0)
tf.summary.scalar('ins0_seg_loss', ins0_seg_loss)
tf.summary.scalar('unit_ins0_seg_loss', model.ins0_seg_loss/ (opt.ins0_seg_weight+tf.convert_to_tensor(1e-8, dtype=tf.float32)))
ins1_edge_seg_loss = tf.stack(axis=0, values=ins1_edge_seg_losses)
ins1_edge_seg_loss = tf.reduce_mean(ins1_edge_seg_loss, 0)
tf.summary.scalar('ins1_edge_seg_loss', ins1_edge_seg_loss)
tf.summary.scalar('unit_ins1_edge_seg_loss', model.ins1_edge_seg_loss/ (opt.ins1_edge_seg_weight+tf.convert_to_tensor(1e-8, dtype=tf.float32)))
if opt.ins_as_loss:
ins_loss = tf.stack(axis=0, values=ins_losses)
ins_loss = tf.reduce_mean(ins_loss, 0)
tf.summary.scalar('ins_loss', ins_loss)
train_op = slim.learning.create_train_op(loss, optim,
variables_to_train=train_vars,
colocate_gradients_with_ops=True)
# Saver
saver = tf.train.Saver([var for var in tf.model_variables()] + \
[global_step],
max_to_keep=opt.max_to_keep)
merged_summary = tf.summary.merge_all()
# Session
sv = tf.train.Supervisor(logdir=opt.checkpoint_dir,
save_summaries_secs=0,
saver=None)
config = tf.ConfigProto(allow_soft_placement=True)
config.gpu_options.allow_growth = True
with sv.managed_session(config=config) as sess:
train_writer = tf.summary.FileWriter(opt.summary_dir, sess.graph)
if opt.init_ckpt_file != None:
sess.run(init_assign_op, init_feed_dict)
start_time = time.time()
for step in range(1, opt.max_steps):
if step < 50000:
lrate = 0.0001
elif step < 100000:
lrate = 0.00005
fetches = {
"train": train_op,
"global_step": global_step,
"incr_global_step": incr_global_step
}
if step % opt.print_interval == 0:
fetches["loss"] = loss
fetches["img_loss"]=img_loss
if opt.sem_as_loss:
fetches["sem_loss"]=sem_loss
if opt.ins_as_loss:
fetches["ins_loss"]=ins_loss
if opt.add_segnet:
fetches["sem_seg_loss"] = sem_seg_loss
fetches["ins0_seg_loss"] = ins0_seg_loss
fetches["ins1_edge_seg_loss"] = ins1_edge_seg_loss
results = sess.run(fetches, feed_dict={lr: lrate})
#TODO Write TF Summary to file.
if step % opt.save_summ_freq == 0:
step_summary = sess.run(merged_summary)
train_writer.add_summary(step_summary, step)
if step % opt.print_interval == 0:
time_per_iter = (time.time() - start_time) / opt.print_interval
start_time = time.time()
if opt.sem_as_loss:
print('Iteration: [%7d] | Time: %4.4fs/iter | Loss: %.3f ImgLoss: %.3f SemLoss: %.3f' \
% (step, time_per_iter, results["loss"], results["img_loss"], results["sem_loss"]))
elif opt.ins_as_loss:
print('Iteration: [%7d] | Time: %4.4fs/iter | Loss: %.3f ImgLoss: %.3f InsLoss: %.3f' \
% (step, time_per_iter, results["loss"], results["img_loss"], results["ins_loss"]))
else:
print('Iteration: [%7d] | Time: %4.4fs/iter | ImgLoss: %.3f' \
% (step, time_per_iter, results["loss"]))
if step % opt.save_ckpt_freq == 0:
saver.save(sess, os.path.join(opt.checkpoint_dir, 'model'), global_step=step)
def main(_):
opt.num_source = opt.seq_length - 1
opt.num_scales = 4
opt.add_flownet = opt.mode in ['train_flow', 'test_flow']
opt.add_dispnet = opt.add_flownet and opt.flownet_type == 'residual' \
or opt.mode in ['train_rigid', 'test_depth']
opt.add_posenet = opt.add_flownet and opt.flownet_type == 'residual' \
or opt.mode in ['train_rigid', 'test_pose']
if opt.mode in ['train_rigid', 'train_flow']:
train()
elif opt.mode == 'test_depth':
test_depth(opt)
elif opt.mode == 'test_pose':
test_pose(opt)
elif opt.mode == 'test_flow':
test_flow(opt)
if __name__ == '__main__':
tf.app.run()