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Train_Stage2g_K.py
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Train_Stage2g_K.py
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# Copyright (C) 2021 Juan Luis Gonzalez Bello (juanluisgb@kaist.ac.kr)
# This software is not for commercial use
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 2 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License along
# with this program; if not, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
# Select your GPU ID, if you have multiple GPU.
gpu = '0'
import argparse
import datetime
import time
import numpy as np
from imageio import imsave
import matplotlib.pyplot as plt
import Datasets
import models
dataset_names = sorted(name for name in Datasets.__all__)
model_names = sorted(name for name in models.__all__)
parser = argparse.ArgumentParser(description='FAL_net in pytorch',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('-d', '--data', metavar='DIR', default='E:\\Datasets\\', help='path to dataset')
parser.add_argument('-n0', '--dataName0', metavar='Data Set Name 0', default='Kitti', choices=dataset_names)
parser.add_argument('-train_split', '--train_split', default='eigen_train_split')
parser.add_argument('-vdn', '--vdataName', metavar='Val data set Name', default='Kitti2015', choices=dataset_names)
parser.add_argument('-relbase_test', '--rel_baset', default=1, help='Relative baseline of testing dataset')
parser.add_argument('-maxd', '--max_disp', default=300)
parser.add_argument('-mind', '--min_disp', default=2)
parser.add_argument('-maxup', '--maxup', default=1.5)
parser.add_argument('-maxdwn', '--maxdwn', default=0.75)
# ---------------------------------------------------------------------------------------------------------------------
parser.add_argument('-mm', '--m_model', metavar='Mono Model', default='FAL_net2B_gep', choices=model_names)
parser.add_argument('-no_levels', '--no_levels', default=49, help='Number of quantization levels in MED')
parser.add_argument('-perc', '--a_p', default=0.01, help='Perceptual loss weight')
parser.add_argument('-smooth', '--a_sm', default=0.4 * 2 / 512, help='Smoothness loss weight')
parser.add_argument('-mirror_loss', '--a_mr', default=1.0, help='Mirror loss weight')
parser.add_argument('-dcl_loss', '--a_dcl', default=0.01, help='Deep correlation loss weight')
parser.add_argument('-dcl_layer', '--dcl_layer', default=2, help='Deep correlation loss layer')
parser.add_argument('-dcml_loss', '--a_dcml', default=0.00, help='Deep correlated matting loss weight')
parser.add_argument('-dcml_feat', '--dcml_feat', default=0, help='Deep correlated matting loss weight')
parser.add_argument('-ddm_loss', '--a_ddm', default=0.25, help='Distilled depth matting loss')
parser.add_argument('-ddm_thres', '--ddm_thres', default=0.0, help='Distilled depth matting loss')
parser.add_argument('-use_wmean_fac', '--use_wmean_fac', default=True, help='Distilled depth matting loss')
parser.add_argument('-ksize', '--ksize', default=5, help='Distilled depth matting loss')
parser.add_argument('-lrc_loss', '--a_lrc', default=1.0, help='LR const loss')
# ---------------------------------------------------------------------------------------------------------------------
parser.add_argument('-w', '--workers', metavar='Workers', default=4)
parser.add_argument('-b', '--batch_size', metavar='Batch Size', default=4)
parser.add_argument('-ch', '--crop_height', metavar='Batch crop H Size', default=192)
parser.add_argument('-cw', '--crop_width', metavar='Batch crop W Size', default=640)
parser.add_argument('-tbs', '--tbatch_size', metavar='Val Batch Size', default=1)
parser.add_argument('-op', '--optimizer', metavar='Optimizer', default='adam')
parser.add_argument('--lr', metavar='learning Rate', default=0.00005)
parser.add_argument('--beta', metavar='BETA', type=float, help='Beta parameter for adam', default=0.999)
parser.add_argument('--momentum', default=0.5, type=float, metavar='Momentum', help='Momentum for Optimizer')
parser.add_argument('--milestones', default=[5, 10], metavar='N', nargs='*',
help='epochs at which learning rate is divided by 2')
parser.add_argument('--weight-decay', '--wd', default=0.0, type=float, metavar='W', help='weight decay')
parser.add_argument('--bias-decay', default=0.0, type=float, metavar='B', help='bias decay')
parser.add_argument('--epochs', default=20, type=int, metavar='N', help='number of total epochs to run')
parser.add_argument('--epoch_size', default=7800, type=int, metavar='N',
help='manual epoch size (will match dataset size if set to 0)')
parser.add_argument('--sparse', default=True, action='store_true',
help='Depth GT is sparse, automatically seleted when choosing a KITTIdataset')
parser.add_argument('--print-freq', '-p', default=100, type=int, metavar='N', help='print frequency')
parser.add_argument('--start-epoch', default=0, type=int, metavar='N',
help='manual epoch number (useful on restarts)')
parser.add_argument('--fix_model', dest='fix_model',
default='Kitti_stage1g/11-08-14_54/FAL_net2B_gep,e52es,b4,lr0.0001/checkpoint.pth.tar')
parser.add_argument('--pretrained', dest='pretrained',
default='Kitti_stage1g/11-08-14_54/FAL_net2B_gep,e52es,b4,lr0.0001/checkpoint.pth.tar',
help='directory of run')
def display_config(save_path):
settings = ''
settings = settings + '############################################################\n'
settings = settings + '# FAL_net stage 2 g - Pytorch implementation #\n'
settings = settings + '# by Juan Luis Gonzalez juanluisgb@kaist.ac.kr #\n'
settings = settings + '############################################################\n'
settings = settings + '-------YOUR TRAINING SETTINGS---------\n'
for arg in vars(args):
settings = settings + "%15s: %s\n" % (str(arg), str(getattr(args, arg)))
print(settings)
# Save config in txt file
with open(os.path.join(save_path, 'settings.txt'), 'w+') as f:
f.write(settings)
def main():
print('-------Training on gpu ' + gpu + '-------')
best_rmse = -1
save_path = '{},e{}es{},b{},lr{}'.format(
args.m_model,
args.epochs,
str(args.epoch_size) if args.epoch_size > 0 else '',
args.batch_size,
args.lr,
)
timestamp = datetime.datetime.now().strftime("%m-%d-%H_%M")
save_path = os.path.join(timestamp, save_path)
save_path = os.path.join(args.dataName0 + "_stage2g", save_path)
if not os.path.exists(save_path):
os.makedirs(save_path)
display_config(save_path)
print('=> will save everything to {}'.format(save_path))
# Set output writters for showing up progress on tensorboardX
train_writer = SummaryWriter(os.path.join(save_path, 'train'))
test_writer = SummaryWriter(os.path.join(save_path, 'test'))
output_writers = []
for i in range(3):
output_writers.append(SummaryWriter(os.path.join(save_path, 'test', str(i))))
# Set up data augmentations
co_transform = data_gtransforms.Compose([
data_gtransforms.RandomResizeCrop((args.crop_height, args.crop_width), down=args.maxdwn, up=args.maxup),
data_gtransforms.RandomHorizontalFlipG(),
data_gtransforms.RandomGamma(min=0.8, max=1.2),
data_gtransforms.RandomBrightness(min=0.5, max=2.0),
data_gtransforms.RandomCBrightness2(min=0.8, max=1.0),
])
input_transform = transforms.Compose([
data_gtransforms.ArrayToTensor(),
transforms.Normalize(mean=[0, 0, 0], std=[255, 255, 255]), # (input - mean) / std
transforms.Normalize(mean=[0.411, 0.432, 0.45], std=[1, 1, 1])
])
target_transform = transforms.Compose([
data_gtransforms.ArrayToTensor(),
transforms.Normalize(mean=[0], std=[1]),
])
# Torch Data Set List
input_path = os.path.join(args.data, args.dataName0)
[train_dataset0, _] = Datasets.__dict__[args.dataName0](split=1, # all for training
root=input_path,
transform=input_transform,
target_transform=target_transform,
co_transform=co_transform,
max_pix=args.max_disp,
train_split=args.train_split,
fix=True,
use_grid=True,
read_matted_depth=args.a_dcml > 0 or args.a_ddm > 0)
input_path = os.path.join(args.data, args.vdataName)
[_, test_dataset] = Datasets.__dict__[args.vdataName](split=0, # all to be tested
root=input_path,
disp=True,
of=False,
shuffle_test=False,
transform=input_transform,
target_transform=target_transform,
co_transform=co_transform)
# Torch Data Loaders
train_loader0 = torch.utils.data.DataLoader(train_dataset0, batch_size=args.batch_size,
num_workers=args.workers,
pin_memory=False, shuffle=True)
val_loader = torch.utils.data.DataLoader(test_dataset, batch_size=args.tbatch_size, num_workers=args.workers,
pin_memory=False, shuffle=False)
# create model
if args.pretrained:
network_data = torch.load(args.pretrained)
args.m_model = network_data['m_model']
print("=> using pre-trained model '{}'".format(args.m_model))
else:
network_data = None
print("=> creating m model '{}'".format(args.m_model))
m_model = models.__dict__[args.m_model](network_data, no_levels=args.no_levels).cuda()
m_model = torch.nn.DataParallel(m_model, device_ids=[0]).cuda()
print("=> Number of parameters m-model '{}'".format(utils.get_n_params(m_model)))
# create fix model
network_data = torch.load(args.fix_model)
fix_model_name = network_data['m_model']
print("=> using pre-trained model '{}'".format(fix_model_name))
fix_model = models.__dict__[fix_model_name](network_data, no_levels=args.no_levels).cuda()
fix_model = torch.nn.DataParallel(fix_model, device_ids=[0]).cuda()
print("=> Number of parameters m-model '{}'".format(utils.get_n_params(fix_model)))
fix_model.eval()
# Folder for debugging
deb_path = os.path.join(save_path, 'Debug')
if not os.path.exists(deb_path):
os.makedirs(deb_path)
# Optimizer Settings
print('Setting {} Optimizer'.format(args.optimizer))
param_groups = [{'params': m_model.module.bias_parameters(), 'weight_decay': args.bias_decay},
{'params': m_model.module.weight_parameters(), 'weight_decay': args.weight_decay}]
if args.optimizer == 'adam':
g_optimizer = torch.optim.Adam(params=param_groups, lr=args.lr, betas=(args.momentum, args.beta))
g_scheduler = torch.optim.lr_scheduler.MultiStepLR(g_optimizer, milestones=args.milestones, gamma=0.5)
for epoch in range(args.start_epoch):
g_scheduler.step()
for epoch in range(args.start_epoch, args.epochs):
print('Learning rate {}'.format(g_scheduler.get_last_lr()))
# train for one epoch
train_loss = train(train_loader0, m_model, fix_model, g_optimizer, epoch, deb_path)
train_writer.add_scalar('train_loss', train_loss, epoch)
# evaluate on validation set, RMSE is from stereoscopic view synthesis task
rmse = validate(val_loader, m_model, epoch, output_writers)
test_writer.add_scalar('mean RMSE', rmse, epoch)
# Apply LR schedule (after optimizer.step() has been called for recent pyTorch versions)
g_scheduler.step()
if best_rmse < 0:
best_rmse = rmse
is_best = rmse < best_rmse
best_rmse = min(rmse, best_rmse)
if epoch == (args.epochs - 1):
utils.save_checkpoint({
'epoch': epoch + 1,
'm_model': args.m_model,
'state_dict': m_model.module.state_dict(),
'best_rmse': best_rmse,
}, is_best, save_path, filename='checkpoint_l.pth.tar')
else:
utils.save_checkpoint({
'epoch': epoch + 1,
'm_model': args.m_model,
'state_dict': m_model.module.state_dict(),
'best_rmse': best_rmse,
}, is_best, save_path)
def train(train_loader, m_model, fix_model, g_optimizer, epoch, deb_path):
global args
epoch_size = len(train_loader) if args.epoch_size == 0 else min(len(train_loader), args.epoch_size)
batch_time = utils.AverageMeter()
data_time = utils.AverageMeter()
rec_losses = utils.AverageMeter()
losses = utils.AverageMeter()
# switch to train mode
m_model.train()
end = time.time()
for i, input_data0 in enumerate(train_loader):
# Read training data from dataset0
left_view = input_data0[0][0].cuda()
right_view = input_data0[0][1].cuda()
in_grid = input_data0[1][0].cuda()
max_disp = input_data0[2].unsqueeze(1).unsqueeze(1).type(left_view.type())
if args.a_dcml > 0 or args.a_ddm > 0:
left_dm = input_data0[3][0].cuda()
right_dm = input_data0[3][1].cuda()
min_disp = max_disp * args.min_disp / args.max_disp
B, C, H, W = left_view.shape
# measure data loading time
data_time.update(time.time() - end)
# Reset gradients
g_optimizer.zero_grad()
# Flip Grid (differentiable)
i_tetha = torch.autograd.Variable(torch.zeros(B, 2, 3)).cuda()
i_tetha[:, 0, 0] = 1
i_tetha[:, 1, 1] = 1
i_grid = F.affine_grid(i_tetha, [B, C, H, W], align_corners=True)
flip_grid = i_grid.clone()
flip_grid[:, :, :, 0] = -flip_grid[:, :, :, 0]
# Grid needs to be flipped!?
in_grid_fliped = F.grid_sample(in_grid, flip_grid, align_corners=True)
in_grid_fliped[:, 0, :, :] = -in_grid_fliped[:, 0, :, :]
# Get mirrored disparity from fixed falnet model
if args.a_mr > 0:
with torch.no_grad():
disp = fix_model(torch.cat(
(F.grid_sample(left_view, flip_grid, align_corners=True), right_view), 0),
torch.cat((in_grid_fliped, in_grid), 0),
torch.cat((min_disp, min_disp), 0),
torch.cat((max_disp, max_disp), 0),
ret_disp=True, ret_pan=False, ret_subocc=False)
mldisp = F.grid_sample(disp[0:B, :, :, :], flip_grid, align_corners=True).detach()
mrdisp = disp[B::, :, :, :].detach()
### Run forward pass ###
min_disp = max_disp * args.min_disp / args.max_disp
pan, disp, mask0, mask1 = m_model(torch.cat((left_view,
F.grid_sample(right_view, flip_grid, align_corners=True)), 0),
torch.cat((in_grid, in_grid_fliped), 0),
torch.cat((min_disp, min_disp), 0),
torch.cat((max_disp, max_disp), 0),
ret_disp=True, ret_pan=True, ret_subocc=True)
# Separate left and right
rpan = pan[0:B, :, :, :]
lpan = pan[B::, :, :, :]
ldisp = disp[0:B, :, :, :]
# ldispr = dispr[0:B, :, :, :]
rdisp = disp[B::, :, :, :]
# rdispl = dispr[B::, :, :, :]
lmask = mask0[0:B, :, :, :]
rmask = mask0[B::, :, :, :]
rlmask = mask1[0:B, :, :, :]
lrmask = mask1[B::, :, :, :]
# Unflip right view stuff
lpan = F.grid_sample(lpan, flip_grid, align_corners=True)
rdisp = F.grid_sample(rdisp, flip_grid, align_corners=True)
rmask = F.grid_sample(rmask, flip_grid, align_corners=True)
lrmask = F.grid_sample(lrmask, flip_grid, align_corners=True)
# rdispl = F.grid_sample(rdispl, flip_grid, align_corners=True)
# Compute rec loss
if args.a_p > 0:
vgg_right = vgg(right_view)
vgg_left = vgg(left_view)
else:
vgg_right = None
vgg_left = None
# Obtain final occlusion masks
O_L = lmask * lrmask
margin_l = 0.05
margin_r = 0.95
O_L[:, :, :, 0:int(margin_l * W)] = 1
O_R = rmask * rlmask
O_R[:, :, :, int(margin_r * W)::] = 1
if args.a_mr == 0: # no mirror loss, then it is just more training
O_L = 1
O_R = 1
# Over 2 as measured twice for left and right
rec_loss = (rec_loss_fnc(O_R, rpan, right_view, vgg_right, args.a_p) + \
rec_loss_fnc(O_L, lpan, left_view, vgg_left, args.a_p)) / 2
rec_losses.update(rec_loss.detach().cpu(), args.batch_size)
# Compute smooth loss
sm_loss = 0
if args.a_sm > 0:
# Here we ignore the 20% left dis-occluded region, as there is no suppervision for it due to parralax
sm_loss = (smoothness(left_view[:, :, :, int(margin_l * W)::], ldisp[:, :, :, int(margin_l * W)::], gamma=2) +
smoothness(right_view[:, :, :, 0:int(margin_r * W)], rdisp[:, :, :, 0:int(margin_r * W)], gamma=2)) / 2
# Compute mirror loss
mirror_loss = 0
nmaxl = 1 / F.max_pool2d(mldisp, kernel_size=(H, W))
nmaxr = 1 / F.max_pool2d(mrdisp, kernel_size=(H, W))
if args.a_mr > 0:
# Normalize error ~ between 0-1, by diving over the max disparity value
mirror_loss = (torch.mean(nmaxl * (1 - O_L)[:, :, :, int(margin_l * W)::] *
torch.abs(ldisp - mldisp)[:, :, :, int(margin_l * W)::]) +
torch.mean(nmaxr * (1 - O_R)[:, :, :, 0:int(margin_r * W)] *
torch.abs(rdisp - mrdisp)[:, :, :, 0:int(margin_r * W)])) / 2
# Commpute ps loss
deep_corr_loss = 0
if args.a_dcl > 0:
# Remove non-necesary vgg parts
vgg_left = vgg(left_view[:, :, :, int(margin_l * W)::])
vgg_right = vgg(right_view[:, :, :, 0:int(margin_r * W)])
y_dl = ldisp / (max_disp.unsqueeze(1)) - 0.5
y_dr = rdisp / (max_disp.unsqueeze(1)) - 0.5
y_dl = torch.cat((y_dl, y_dl, y_dl), 1)
y_dr = torch.cat((y_dr, y_dr, y_dr), 1)
deep_corr_loss = (corrL1Loss(vgg(y_dl[:, :, :, int(margin_l * W)::]), vgg_left, layer=args.dcl_layer) +
corrL1Loss(vgg(y_dr[:, :, :, 0:int(margin_r * W)]), vgg_right, layer=args.dcl_layer)) / 2
# Compute lr loss
lrc_loss = 0
if args.a_lrc > 0:
warp_gridl = i_grid.clone()
warp_gridl[:, :, :, 0] = warp_gridl[:, :, :, 0] - ldisp.squeeze(1) * 2 / W
warp_gridr = i_grid.clone()
warp_gridr[:, :, :, 0] = warp_gridr[:, :, :, 0] + rdisp.squeeze(1) * 2 / W
lrc_loss = torch.sum(nmaxl * O_L * torch.abs(ldisp - F.grid_sample(rdisp, warp_gridl, align_corners=True))) \
+ torch.sum(nmaxr * O_R * torch.abs(rdisp - F.grid_sample(ldisp, warp_gridr, align_corners=True)))
# lrc_loss = (torch.mean(nmaxl * O_L[:, :, :, int(margin_l * W)::] *
# torch.abs(ldisp.detach() - rdispl)[:, :, :, int(margin_l * W)::]) +
# torch.mean(nmaxr * O_R[:, :, :, 0:int(margin_r * W)] *
# torch.abs(rdisp.detach() - ldispr)[:, :, :, 0:int(margin_r * W)])) / 2
# Commpute ps loss
deep_corr_matting_loss = 0
if args.a_dcml > 0:
y_dl = ldisp * nmaxl - 0.5
y_dr = rdisp * nmaxr - 0.5
y_dl = torch.cat((y_dl, y_dl, y_dl), 1)
y_dr = torch.cat((y_dr, y_dr, y_dr), 1)
y_dml = left_dm / F.max_pool2d(left_dm, kernel_size=(H, W)) - 0.5
y_dmr = right_dm / F.max_pool2d(right_dm, kernel_size=(H, W)) - 0.5
y_dml = torch.cat((y_dml, y_dml, y_dml), 1)
y_dmr = torch.cat((y_dmr, y_dmr, y_dmr), 1)
deep_corr_matting_loss = (corrL1Loss(vgg(y_dl[:, :, :, int(margin_l * W)::]),
vgg(y_dml[:, :, :, int(margin_l * W)::]), layer=args.dcml_feat) +
corrL1Loss(vgg(y_dr[:, :, :, 0:int(margin_r * W)]),
vgg(y_dmr[:, :, :, 0:int(margin_r * W)]), layer=args.dcml_feat)) / 2
l1_matting = 0
if args.a_ddm > 0:
with torch.no_grad():
# Locally or globally mean-re-scale matted disparity maps
if args.use_wmean_fac:
ksize = args.ksize
llmean = F.avg_pool2d(ldisp, kernel_size=ksize, padding=(ksize - 1) // 2, stride=ksize)
dmllmean = F.avg_pool2d(left_dm, kernel_size=ksize, padding=(ksize - 1) // 2, stride=ksize)
lmfac = llmean / (dmllmean + 0.0000001)
lmfac = F.interpolate(lmfac, size=(H, W), mode='nearest')
rlmean = F.avg_pool2d(rdisp, kernel_size=ksize, padding=(ksize - 1) // 2, stride=ksize)
dmrlmean = F.avg_pool2d(right_dm, kernel_size=ksize, padding=(ksize - 1) // 2, stride=ksize)
rmfac = rlmean / (dmrlmean + 0.0000001)
rmfac = F.interpolate(rmfac, size=(H, W), mode='nearest')
else:
lmfac = torch.median(ldisp.detach().view([B, H * W]), dim=1)[0].unsqueeze(1).unsqueeze(2) \
.unsqueeze(3) / \
torch.median(left_dm.view([B, H * W]), dim=1)[0].unsqueeze(1).unsqueeze(2).unsqueeze(3)
rmfac = torch.median(rdisp.detach().view([B, H * W]), dim=1)[0].unsqueeze(1).unsqueeze(2) \
.unsqueeze(3) / \
torch.median(right_dm.view([B, H * W]), dim=1)[0].unsqueeze(1).unsqueeze(2).unsqueeze(3)
left_dm0 = left_dm.clone()
left_dm = left_dm * lmfac
right_dm = right_dm * rmfac
thres = args.ddm_thres
warp_grid0 = i_grid.clone()
warp_grid0[:, :, :, 0] = warp_grid0[:, :, :, 0] - left_dm.squeeze(1) * 2 / W
warp_grid1 = i_grid.clone()
warp_grid1[:, :, :, 0] = warp_grid1[:, :, :, 0] - ldisp.detach().squeeze(1) * 2 / W
ddm_lmask0 = torch.sum(torch.abs(left_view - F.grid_sample(right_view, warp_grid0, align_corners=True)),
dim=1, keepdim=True) < (1 - thres) * torch.sum(torch.abs(left_view -
F.grid_sample(right_view, warp_grid1, align_corners=True)), dim=1, keepdim=True)
ddm_lmask1 = torch.sum(torch.abs(left_dm - F.grid_sample(right_dm, warp_grid0, align_corners=True)),
dim=1, keepdim=True) < (1 - thres) * torch.sum(torch.abs(ldisp.detach() -
F.grid_sample(rdisp.detach(), warp_grid1, align_corners=True)), dim=1, keepdim=True)
ddm_lmask = ddm_lmask0.type(left_view.type()) * ddm_lmask1.type(left_view.type()) # * O_L
warp_grid0 = i_grid.clone()
warp_grid0[:, :, :, 0] = warp_grid0[:, :, :, 0] + right_dm.squeeze(1) * 2 / W
warp_grid1 = i_grid.clone()
warp_grid1[:, :, :, 0] = warp_grid1[:, :, :, 0] + rdisp.detach().squeeze(1) * 2 / W
ddm_rmask0 = torch.sum(torch.abs(right_view - F.grid_sample(left_view, warp_grid0, align_corners=True)),
dim=1, keepdim=True) < (1 - thres) * torch.sum(torch.abs(right_view -
F.grid_sample(left_view, warp_grid1, align_corners=True)), dim=1, keepdim=True)
ddm_rmask1 = torch.sum(torch.abs(right_dm - F.grid_sample(left_dm, warp_grid0, align_corners=True)),
dim=1, keepdim=True) < (1 - thres) * torch.sum(torch.abs(rdisp.detach() -
F.grid_sample(ldisp.detach(), warp_grid1, align_corners=True)), dim=1, keepdim=True)
ddm_rmask = ddm_rmask0.type(right_view.type()) * ddm_rmask1.type(right_view.type()) # * O_R
l1_matting = (torch.mean(nmaxl * ddm_lmask * torch.abs(ldisp - left_dm))
+ torch.mean(nmaxr * ddm_rmask * torch.abs(rdisp - right_dm))) / 2
# compute gradient and do optimization step
loss = rec_loss + args.a_sm * sm_loss + args.a_mr * mirror_loss + args.a_dcl * deep_corr_loss + \
args.a_dcml * deep_corr_matting_loss + args.a_lrc * lrc_loss + args.a_ddm * l1_matting
losses.update(loss.detach().cpu(), args.batch_size)
loss.backward()
g_optimizer.step()
g_optimizer.zero_grad()
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
# Debug stuff
with torch.no_grad():
if i % 100 == 0:
index = 0
denormalize = np.array([0.411, 0.432, 0.45])
denormalize = denormalize[:, np.newaxis, np.newaxis]
# Save samples
p_im = left_view[index].detach().squeeze().cpu().numpy() + denormalize
imsave(os.path.join(deb_path, '_1_left_e{}.png'.format(epoch)),
np.rint(255 * p_im.transpose(1, 2, 0)).astype(np.uint8))
disp = mldisp[index].detach().squeeze().cpu().numpy()
disp = 255 * np.clip(disp / (np.percentile(disp, 98) + 1e-6), 0, 1)
imsave(os.path.join(deb_path, '_0_fdisp_e{}.png'.format(epoch)), np.rint(disp).astype(np.uint8))
disp = ldisp[index].detach().squeeze().cpu().numpy()
disp = 255 * np.clip(disp / (np.percentile(disp, 98) + 1e-6), 0, 1)
imsave(os.path.join(deb_path, '_1_disp_e{}.png'.format(epoch)), np.rint(disp).astype(np.uint8))
if args.a_dcml > 0 or args.a_ddm > 0:
disp = left_dm[index].detach().squeeze().cpu().numpy()
disp = 255 * np.clip(disp / (np.percentile(disp, 98) + 1e-6), 0, 1)
imsave(os.path.join(deb_path, '_2_dm_e{}.png'.format(epoch)), np.rint(disp).astype(np.uint8))
disp = left_dm0[index].detach().squeeze().cpu().numpy()
disp = 255 * np.clip(disp / (np.percentile(disp, 98) + 1e-6), 0, 1)
imsave(os.path.join(deb_path, '_2_dm0_e{}.png'.format(epoch)), np.rint(disp).astype(np.uint8))
disp = 255 * O_L[index].detach().squeeze().cpu().numpy()
imsave(os.path.join(deb_path, '_3_occ_e{}.png'.format(epoch)), np.rint(disp).astype(np.uint8))
if args.a_ddm > 0:
disp = (ddm_lmask * left_dm)[index].detach().squeeze().cpu().numpy()
disp = 255 * np.clip(disp / (np.percentile(disp, 98) + 1e-6), 0, 1)
plt.imsave(os.path.join(deb_path, '_2_dmmsk_e{}.png'.format(epoch)), np.rint(disp).astype(np.int32),
cmap='plasma', vmin=0, vmax=255)
disp = 255 * ddm_lmask[index].detach().squeeze().cpu().numpy()
imsave(os.path.join(deb_path, '_4_ddm_e{}.png'.format(epoch)), np.rint(disp).astype(np.uint8))
if i % args.print_freq == 0:
print('Epoch: [{0}][{1}/{2}] Time {3} Data {4} Loss {5} RecLoss {6}'
.format(epoch, i, epoch_size, batch_time, data_time, losses, rec_losses))
# End training epoch earlier if args.epoch_size != 0
if i >= epoch_size:
break
return losses.avg
def validate(val_loader, m_model, epoch, output_writers):
global args
test_time = utils.AverageMeter()
RMSES = utils.AverageMeter()
EPEs = utils.AverageMeter()
kitti_erros = utils.multiAverageMeter(utils.kitti_error_names)
# switch to evaluate mode
m_model.eval()
# Disable gradients to save memory
with torch.no_grad():
for i, input_data in enumerate(val_loader):
# Prepare input data
input_left = input_data[0][0].cuda()
input_right = input_data[0][1].cuda()
target = input_data[1][0].cuda()
max_disp = torch.Tensor([args.max_disp * args.rel_baset]).unsqueeze(1).unsqueeze(1).type(input_left.type())
B, _, H, W = input_left.shape
min_disp = max_disp * args.min_disp / args.max_disp
# Prepare identity grid
i_tetha = torch.zeros(B, 2, 3).cuda()
i_tetha[:, 0, 0] = 1
i_tetha[:, 1, 1] = 1
a_grid = F.affine_grid(i_tetha, [B, 3, H, W], align_corners=True)
in_grid = torch.zeros(B, 2, H, W).type(a_grid.type())
in_grid[:, 0, :, :] = a_grid[:, :, :, 0]
in_grid[:, 1, :, :] = a_grid[:, :, :, 1]
#### Measure inference time (start) ###
end = time.time()
p_im, disp, maskL, maskRL = m_model(input_left, in_grid, min_disp, max_disp,
ret_disp=True, ret_pan=True, ret_subocc=True)
### Measure inference time (end) ###
test_time.update(time.time() - end)
# Measure RMSE
rmse = utils.get_rmse(p_im, input_right)
RMSES.update(rmse)
# record EPE
flow2_EPE = realEPE(disp, target, sparse=args.sparse)
EPEs.update(flow2_EPE.detach(), target.size(0))
# Record kitti metrics
target_depth, pred_depth = utils.disps_to_depths_kitti2015(target.detach().squeeze(1).cpu().numpy(),
disp.detach().squeeze(1).cpu().numpy())
kitti_erros.update(utils.compute_kitti_errors(target_depth[0], pred_depth[0]), target.size(0))
denormalize = np.array([0.411, 0.432, 0.45])
denormalize = denormalize[:, np.newaxis, np.newaxis]
if i < len(output_writers): # log first output of first batches
if epoch == 0:
output_writers[i].add_image('Input left', input_left[0].cpu().numpy() + denormalize, 0)
# Plot disp
output_writers[i].add_image('Left disparity', utils.disp2rgb(disp[0].cpu().numpy(), None), epoch)
# output_writers[i].add_image('Right-L disparity', utils.disp2rgb(dispr[0].cpu().numpy(), None), epoch)
# Plot left subocclsion mask (even if it is not used during training)
output_writers[i].add_image('Left sub-occ', utils.disp2rgb(maskL[0].cpu().numpy(), None), epoch)
# Plot right-from-left subocclsion mask (even if it is not used during training)
output_writers[i].add_image('RightL sub-occ', utils.disp2rgb(maskRL[0].cpu().numpy(), None), epoch)
# Plot synthetic right (or panned) view output
p_im = p_im[0].detach().cpu().numpy() + denormalize
p_im[p_im > 1] = 1
p_im[p_im < 0] = 0
output_writers[i].add_image('Output Pan', p_im, epoch)
if i % args.print_freq == 0:
print('Test: [{0}/{1}]\t Time {2}\t RMSE {3}'.format(i, len(val_loader), test_time, RMSES))
print('* RMSE {0}'.format(RMSES.avg))
print(' * EPE {:.3f}'.format(EPEs.avg))
print(kitti_erros)
return RMSES.avg
if __name__ == '__main__':
import os
os.environ['CUDA_VISIBLE_DEVICES'] = gpu
import torch
import torch.utils.data
import torchvision.transforms as transforms
from tensorboardX import SummaryWriter
import torch.nn.functional as F
import myUtils as utils
import data_gtransforms
from loss_functions import rec_loss_fnc, realEPE, smoothness, vgg, corrL1Loss, get_corr, perceptual_loss
args = parser.parse_args()
main()