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train_visib.py
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train_visib.py
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# Copyright (c) Microsoft Corporation.
# Copyright (c) University of Florida Research Foundation, Inc.
# Licensed under the MIT License.
#
# Permission is hereby granted, free of charge, to any person obtaining a copy of
# this software and associated documentation files (the "Software"), to deal in
# the Software without restriction, including without limitation the rights to
# use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies
# of the Software, and to permit persons to whom the Software is furnished to do
# so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
# IN THE SOFTWARE.
#
# train_visib.py
# Training script for VisibNet
# Author: Francesco Pittaluga
import os
import sys
import tensorflow as tf
import numpy as np
import ctrlc
import utils as ut
import load_data_tflo as ld
from models import VisibNet
#########################################################################
parser = ut.MyParser(description='Configure')
parser.add_argument("-log_file", default=False, action='store_true', help="%(type)s: Print stdout and stderr to log and err files")
parser.add_argument("--input_attr", type=str, default='depth_sift_rgb', choices=['depth','depth_sift','depth_rgb','depth_sift_rgb'],
help="%(type)s: Per-point attributes to inlcude in input tensor (default: %(default)s)")
parser.add_argument("--trn_anns", type=str, default='data/anns/demo_5k/train.txt',
help="%(type)s: Path to annotation file for training samples (default: %(default)s)")
parser.add_argument("--val_anns", type=str, default='data/anns/demo_5k/val.txt',
help="%(type)s: Path to annotation file for validation samples (default: %(default)s)")
parser.add_argument("--batch_size", type=int, default=4, help="%(type)s: Number of images in batch (default: %(default)s)")
parser.add_argument("--crop_size", type=int, default=256, help="%(type)s: Size to crop images to (default: %(default)s)")
parser.add_argument("--scale_size", type=lambda s: [int(i) for i in s.split(',')], default=[296,394,512],
help="int,int,int: Sizes to randomly scale images to before cropping them (default: 296,394,512)")
parser.add_argument("--pct_3D_points", type=lambda s: [float(i) for i in s.split(',')][:2], default=[5.,100.],
help="float,float: Min and max percent of 3D points to keep when performing random subsampling for data augmentation "+\
"(default: 5.,100.)")
parser.add_argument("--vis_thresh", type=float, default=.05, help="%(type)s: Threshold used to compute ground truth visibility mask."+\
"i.e., gt_visibibility_mask = ((inp_depth-gt_depth)/gt_depth) > VISIB_THRESH. (default: %(default)s)")
parser.add_argument("--max_iter", type=int, default=1e6, help="%(type)s: Stop training after MAX_ITER iterations (default: %(default)s)")
parser.add_argument("--log_freq", type=int, default=25, help="%(type)s: Log training stats every LOG_FREQ iterations (default: %(default)s)")
parser.add_argument("--chkpt_freq", type=int, default=1e4, help="%(type)s: Save model state every CHKPT_FREQ iterations. Previous model state "+\
"is deleted after each new save (default: %(default)s)")
parser.add_argument("--save_freq", type=int, default=5e4, help="%(type)s: Permanently save model state every SAVE_FREQ iterations "+\
"(default: %(default)s)")
parser.add_argument("--val_freq", type=int, default=5e3, help="%(type)s: Run validation loop every VAL_FREQ iterations (default: %(default)s)")
parser.add_argument("--val_iter", type=int, default=128, help="%(type)s: Number of validation samples per validation loop (default: %(default)s)")
parser.add_argument("--adam_eps", type=float, default=1e-8, help="%(type)s: Epsilon parameter for adam optimizer (default: %(default)s)")
parser.add_argument("--adam_mom", type=float, default=.9, help="%(type)s: Momentum parameter for adam optimizer (default: %(default)s)")
parser.add_argument("--adam_lr", type=float, default=1e-5, help="%(type)s: Learning rate parameter for adam optmizer (default: %(default)s)")
prm = parser.parse_args()
prm_str = 'Arguments:\n'+'\n'.join(['{} {}'.format(k.upper(),v) for k,v in vars(prm).items()])
print(prm_str+'\n')
#########################################################################
# Create exp dir if does not exist
exp_dir = 'wts/{}/visibnet'.format(prm.input_attr)
os.system('mkdir -p {}'.format(exp_dir))
# redirect stdout and stderr to log files
if prm.log_file:
sys.stdout = open(exp_dir+'/train.log', 'a')
sys.stderr = open(exp_dir+'/info.log', 'a')
# Check for saved weights & find iter
vsave = ut.ckpter(exp_dir+'/iter_*.vmodel.npz')
osave = ut.ckpter(exp_dir+'/iter_*.opt.npz')
vpath = lambda itr: '%s/iter_%07d.vmodel.npz'%(exp_dir,itr)
opath = lambda itr: '%s/iter_%07d.opt.npz'%(exp_dir,itr)
niter = vsave.iter
# Load annotations
ut.mprint("Loading annotations")
tbchr = ut.batcher(prm.trn_anns,prm.batch_size,niter)
vbchr = ut.batcher(prm.val_anns,prm.batch_size,niter)
ut.mprint("Done!")
#########################################################################
# Set up data fetch
camera_fps = [tf.placeholder(tf.string) for i in range(prm.batch_size)]
pts_xyz_fps = [tf.placeholder(tf.string) for i in range(prm.batch_size)]
pts_rgb_fps = [tf.placeholder(tf.string) for i in range(prm.batch_size)]
pts_sift_fps = [tf.placeholder(tf.string) for i in range(prm.batch_size)]
gt_depth_fps = [tf.placeholder(tf.string) for i in range(prm.batch_size)]
getfeed = lambda fps: \
dict([(ph,'data/'+fps[i,3]) for i,ph in enumerate(camera_fps)]+\
[(ph,'data/'+fps[i,0]) for i,ph in enumerate(pts_xyz_fps)]+\
[(ph,'data/'+fps[i,2]) for i,ph in enumerate(pts_sift_fps)]+\
[(ph,'data/'+fps[i,1]) for i,ph in enumerate(pts_rgb_fps)]+\
[(ph,'data/'+fps[i,5]) for i,ph in enumerate(gt_depth_fps)])
gt_depth = ld.load_img_bch(gt_depth_fps,prm.crop_size,prm.scale_size,isval=False,binary=True)
proj_depth,proj_sift,proj_rgb = ld.load_proj_bch(camera_fps,pts_xyz_fps,pts_sift_fps,pts_rgb_fps,
prm.crop_size,prm.scale_size,isval=False)
pd_b=[]; ps_b=[]; pr_b=[]; is_visible=[]; is_valid=[]
keep_prob = tf.random_uniform([prm.batch_size],minval=prm.pct_3D_points[0]/100.,
maxval=prm.pct_3D_points[1]/100.,dtype=tf.float32,seed=niter)
for i in range(prm.batch_size):
# Get visible points
proj_is_val = tf.to_float(tf.greater(proj_depth[i], 0.))
gt_is_val = tf.to_float(tf.greater(gt_depth[i], 0.))
is_val = proj_is_val*gt_is_val
pd = proj_depth[i]*is_val
ps = proj_sift[i]*is_val
pr = proj_rgb[i]*is_val
pct_diff = (pd-gt_depth[i])/(gt_depth[i]+1e-8)
is_vis = tf.to_float(tf.less(pct_diff,prm.vis_thresh))*is_val
# dropout (1-keep_prob)% of projected pts
pd = tf.nn.dropout(pd,keep_prob[i],noise_shape=[prm.crop_size,prm.crop_size,1],seed=niter)*keep_prob[i]
ps = tf.nn.dropout(ps,keep_prob[i],noise_shape=[prm.crop_size,prm.crop_size,1],seed=niter)*keep_prob[i]
pr = tf.nn.dropout(pr,keep_prob[i],noise_shape=[prm.crop_size,prm.crop_size,1],seed=niter)*keep_prob[i]
is_vis = tf.nn.dropout(is_vis,keep_prob[i],noise_shape=[prm.crop_size,prm.crop_size,1],seed=niter)*keep_prob[i]
is_val = tf.nn.dropout(is_val,keep_prob[i],noise_shape=[prm.crop_size,prm.crop_size,1],seed=niter)*keep_prob[i]
pd_b.append(tf.reshape(pd,[1,prm.crop_size,prm.crop_size,1]))
ps_b.append(tf.reshape(ps,[1,prm.crop_size,prm.crop_size,128]))
pr_b.append(tf.reshape(pr,[1,prm.crop_size,prm.crop_size,3]))
is_visible.append(tf.reshape(is_vis,[1,prm.crop_size,prm.crop_size,1]))
is_valid.append(tf.reshape(is_val,[1,prm.crop_size,prm.crop_size,1]))
proj_depth = tf.concat(pd_b,axis=0)
proj_sift = tf.concat(ps_b,axis=0) / 127.5 - 1.
proj_rgb = tf.concat(pr_b,axis=0) / 127.5 - 1.
is_visible = tf.concat(is_visible,axis=0)
is_valid = tf.concat(is_valid,axis=0)
#########################################################################
if prm.input_attr=='depth':
vinp = proj_depth
vinp_sz = [prm.batch_size,prm.crop_size,prm.crop_size,1]
elif prm.input_attr=='depth_sift':
vinp = tf.concat((proj_depth,proj_sift),axis=3)
vinp_sz = [prm.batch_size,prm.crop_size,prm.crop_size,129]
elif prm.input_attr=='depth_rgb':
vinp = tf.concat((proj_depth,proj_rgb),axis=3)
vinp_sz = [prm.batch_size,prm.crop_size,prm.crop_size,4]
elif prm.input_attr=='depth_sift_rgb':
vinp = tf.concat((proj_depth,proj_rgb,proj_sift),axis=3)
vinp_sz = [prm.batch_size,prm.crop_size,prm.crop_size,132]
# Set up pre-fetching
vinp_b0 = tf.Variable(tf.zeros(vinp_sz,dtype=tf.float32))
vinp_b1 = tf.Variable(tf.zeros(vinp_sz,dtype=tf.float32))
vgt = tf.concat([is_visible,is_valid],axis=3)
vgt_sz = [prm.batch_size,prm.crop_size,prm.crop_size,2]
vgt_b0 = tf.Variable(tf.zeros(vgt_sz,dtype=tf.float32))
vgt_b1 = tf.Variable(tf.zeros(vgt_sz,dtype=tf.float32))
tldr_fetchOp = [vinp_b0.assign(vinp).op, vgt_b0.assign(vgt).op]
vldr_fetchOp = [vinp_b1.assign(vinp).op, vgt_b1.assign(vgt).op]
tldr_swapOp = [vinp_b1.assign(vinp_b0).op, vgt_b1.assign(vgt_b0).op]
# Init coarse inverter
V = VisibNet(vinp_b1,bn='train',outp_act=False)
vpred = V.pred
#########################################################################
# Set optimizer
vvars = V.trainable_variables()
optV = tf.train.AdamOptimizer(prm.adam_lr,prm.adam_mom,epsilon=prm.adam_eps)
mask = tf.reshape(vgt_b1[:,:,:,1],[-1,1])
logs = tf.boolean_mask(tf.reshape(vpred,[-1,1]),mask)
lbls = tf.boolean_mask(tf.reshape(vgt_b1[:,:,:,0],[-1,1]),mask)
vloss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=lbls,logits=logs))
vacc = tf.reduce_mean(tf.to_float(tf.equal(lbls,tf.to_float(tf.greater(tf.sigmoid(logs),0.5)))))
vStep = optV.minimize(vloss,var_list=list(vvars.keys()))
#########################################################################
# Start TF session (respecting OMP_NUM_THREADS)
try: init_all_vars = tf.global_variables_initializer()
except: init_all_vars = tf.initialize_all_variables()
nthr = os.getenv('OMP_NUM_THREADS')
if nthr is None: sess=tf.Session()
else: sess=tf.Session(config=tf.ConfigProto(intra_op_parallelism_threads=int(nthr)))
sess.run(init_all_vars)
#########################################################################
# Load saved models & optimizers
# Load C wts
if vsave.latest != None:
ut.mprint("Restoring V from " + vsave.latest )
V.load(sess,vsave.latest)
ut.mprint("Done!")
# Load optimizers
optlist = [[optV,vvars]]
if osave.latest is not None:
ut.mprint("Restoring optimizers from " + osave.latest )
ut.loadopts(osave.latest,optlist,[],sess)
ut.mprint("Done!")
#########################################################################
# Main Training loop
sess.run(V.set_ifdo)
saviter = niter
tLossAcc=[]
vlog=''
fd=getfeed(tbchr.get_batch())
sess.run(tldr_fetchOp,feed_dict=fd)
ut.mprint("Starting from Iteration %d" % niter)
while not ctrlc.stop and niter < prm.max_iter:
# Val loop
if niter % prm.val_freq == 0:
ut.mprint("Validating networks")
sess.run(V.unset_ifdo)
vLossAcc=[];
for i in range(0,prm.val_iter):
try: # prevent occasional failure when no pts in projection
fps=vbchr.get_batch()
fd=getfeed(fps)
sess.run(vldr_fetchOp,feed_dict=fd)
vLossAcc.append(sess.run([vloss,vacc]))
except:
pass
sess.run(V.set_ifdo)
args = list(np.mean(vLossAcc,axis=0))
vlog=' val.loss {:.6f} val.acc {:.6f}'.format(*args)
# Swap data buffers
sess.run(tldr_swapOp)
# Set up nxt data fetch op
fps=tbchr.get_batch()
fd=getfeed(fps)
# Update vnet
try: # prevent occasional failure when no pts in projection
tLossAcc.append(sess.run([vloss,vacc,vStep]+tldr_fetchOp,feed_dict=fd)[:2])
except:
pass
# Print training loss & accuracy
niter+=1
if niter % prm.log_freq == 0:
args = [niter]+list(np.mean(tLossAcc,axis=0))
tlog = '[{:09d}] . trn.loss {:.6f} trn.acc {:.6f}'.format(*args)
ut.mprint(tlog+vlog)
tLossAcc=[]; vlog='';
# Save models
if niter % prm.chkpt_freq == 0:
#Save VisibNet
V.save(sess,vpath(niter))
vsave.clean(every=prm.save_freq,last=1)
ut.mprint("Saved weights to "+vpath(niter))
# Save Optimizers
ut.saveopts(opath(niter),optlist,{},sess)
osave.clean(last=1)
ut.mprint("Saved optimizers to "+opath(niter))
# Save models & optimizers
if niter > vsave.iter:
# Save VisibNet
V.save(sess,vpath(niter))
vsave.clean(every=prm.save_freq,last=1)
ut.mprint("Saved weights to "+vpath(niter))
# Save Optimizers
ut.saveopts(opath(niter),optlist,{},sess)
osave.clean(last=1)
ut.mprint("Saved optimizers to "+opath(niter))