-
Notifications
You must be signed in to change notification settings - Fork 63
/
main_kitti.py
192 lines (172 loc) · 9.96 KB
/
main_kitti.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
# from __future__ import print_function, division
import argparse
import os
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data
from torch.autograd import Variable
import torchvision.utils as vutils
import torch.nn.functional as F
import numpy as np
import time
from tensorboardX import SummaryWriter
from datasets import __datasets__
from models import __models__, model_loss_train, model_loss_test
from utils import *
from torch.utils.data import DataLoader
import gc
# from apex import amp
import cv2
cudnn.benchmark = True
os.environ['CUDA_VISIBLE_DEVICES'] = '0,1,2,3'
parser = argparse.ArgumentParser(description='Attention Concatenation Volume for Accurate and Efficient Stereo Matching (ACVNet)')
parser.add_argument('--model', default='acvnet', help='select a model structure', choices=__models__.keys())
parser.add_argument('--maxdisp', type=int, default=192, help='maximum disparity')
parser.add_argument('--dataset', default='kitti', help='dataset name', choices=__datasets__.keys())
parser.add_argument('--kitti15_datapath', default='/home/xgw/data/KITTI_2015/', help='data path')
parser.add_argument('--kitti12_datapath', default='/home/xgw/data/KITTI_2012/', help='data path')
parser.add_argument('--trainlist', default='./filenames/kitti12_15_all.txt', help='training list')
parser.add_argument('--testlist',default='./filenames/kitti15_val.txt', help='testing list')
parser.add_argument('--lr', type=float, default=0.001, help='base learning rate')
parser.add_argument('--batch_size', type=int, default=16, help='training batch size')
parser.add_argument('--test_batch_size', type=int, default=8, help='testing batch size')
parser.add_argument('--epochs', type=int, default=600, help='number of epochs to train')
parser.add_argument('--lrepochs',default="400:10", type=str, help='the epochs to decay lr: the downscale rate')
parser.add_argument('--logdir',default='./checkpoints/', help='the directory to save logs and checkpoints')
parser.add_argument('--loadckpt', default='./pretrained_model/sceneflow.ckpt',help='load the weights from a specific checkpoint')
parser.add_argument('--resume', action='store_true', help='continue training the model')
parser.add_argument('--seed', type=int, default=1, metavar='S', help='random seed (default: 1)')
parser.add_argument('--summary_freq', type=int, default=20, help='the frequency of saving summary')
parser.add_argument('--save_freq', type=int, default=1, help='the frequency of saving checkpoint')
# parse arguments, set seeds
args = parser.parse_args()
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
os.makedirs(args.logdir, exist_ok=True)
# create summary logger
print("creating new summary file")
logger = SummaryWriter(args.logdir)
# dataset, dataloader
StereoDataset = __datasets__[args.dataset]
train_dataset = StereoDataset(args.kitti15_datapath, args.kitti12_datapath, args.trainlist, True)
test_dataset = StereoDataset(args.kitti15_datapath, args.kitti12_datapath, args.testlist, False)
TrainImgLoader = DataLoader(train_dataset, args.batch_size, shuffle=True, num_workers=16, drop_last=True)
TestImgLoader = DataLoader(test_dataset, args.test_batch_size, shuffle=False, num_workers=16, drop_last=False)
# model, optimizer
model = __models__[args.model](args.maxdisp, False, False)
model = nn.DataParallel(model)
model.cuda()
optimizer = optim.Adam(model.parameters(), lr=args.lr, betas=(0.9, 0.999))
# load parameters
start_epoch = 0
if args.resume:
# find all checkpoints file and sort according to epoch id
all_saved_ckpts = [fn for fn in os.listdir(args.logdir) if fn.endswith(".ckpt")]
all_saved_ckpts = sorted(all_saved_ckpts, key=lambda x: int(x.split('_')[-1].split('.')[0]))
# use the latest checkpoint file
loadckpt = os.path.join(args.logdir, all_saved_ckpts[-1])
print("loading the lastest model in logdir: {}".format(loadckpt))
state_dict = torch.load(loadckpt)
model.load_state_dict(state_dict['model'])
optimizer.load_state_dict(state_dict['optimizer'])
start_epoch = state_dict['epoch'] + 1
elif args.loadckpt:
# load the checkpoint file specified by args.loadckpt
print("loading model {}".format(args.loadckpt))
state_dict = torch.load(args.loadckpt)
model.load_state_dict(state_dict['model'])
print("start at epoch {}".format(start_epoch))
def train():
for epoch_idx in range(start_epoch, args.epochs):
adjust_learning_rate(optimizer, epoch_idx, args.lr, args.lrepochs)
# training
for batch_idx, sample in enumerate(TrainImgLoader):
global_step = len(TrainImgLoader) * epoch_idx + batch_idx
start_time = time.time()
do_summary = global_step % args.summary_freq == 0
loss, scalar_outputs, image_outputs = train_sample(sample, compute_metrics=do_summary)
if do_summary:
save_scalars(logger, 'train', scalar_outputs, global_step)
save_images(logger, 'train', image_outputs, global_step)
del scalar_outputs, image_outputs
print('Epoch {}/{}, Iter {}/{}, train loss = {:.3f}, time = {:.3f}'.format(epoch_idx, args.epochs,
batch_idx,
len(TrainImgLoader), loss,
time.time() - start_time))
# saving checkpoints
if (epoch_idx + 1) % args.save_freq == 0:
checkpoint_data = {'epoch': epoch_idx, 'model': model.state_dict(), 'optimizer': optimizer.state_dict()}
#id_epoch = (epoch_idx + 1) % 100
torch.save(checkpoint_data, "{}/checkpoint_{:0>6}.ckpt".format(args.logdir, epoch_idx))
gc.collect()
if (epoch_idx) % 1 == 0:
# # testing
avg_test_scalars = AverageMeterDict()
for batch_idx, sample in enumerate(TestImgLoader):
global_step = len(TestImgLoader) * epoch_idx + batch_idx
start_time = time.time()
do_summary = global_step % args.summary_freq == 0
loss, scalar_outputs, image_outputs = test_sample(sample, compute_metrics=do_summary)
if do_summary:
save_scalars(logger, 'test', scalar_outputs, global_step)
save_images(logger, 'test', image_outputs, global_step)
avg_test_scalars.update(scalar_outputs)
del scalar_outputs, image_outputs
print('Epoch {}/{}, Iter {}/{}, test loss = {:.3f}, time = {:3f}'.format(epoch_idx, args.epochs,
batch_idx,
len(TestImgLoader), loss,
time.time() - start_time))
avg_test_scalars = avg_test_scalars.mean()
save_scalars(logger, 'fulltest', avg_test_scalars, len(TrainImgLoader) * (epoch_idx + 1))
print("avg_test_scalars", avg_test_scalars)
gc.collect()
# train one sample
def train_sample(sample, compute_metrics=False):
model.train()
imgL, imgR, disp_gt = sample['left'], sample['right'], sample['disparity']
imgL = imgL.cuda()
imgR = imgR.cuda()
disp_gt = disp_gt.cuda()
optimizer.zero_grad()
disp_ests = model(imgL, imgR)
mask = (disp_gt < args.maxdisp) & (disp_gt > 0)
loss = model_loss_train(disp_ests, disp_gt, mask)
scalar_outputs = {"loss": loss}
image_outputs = {"disp_est": disp_ests, "disp_gt": disp_gt, "imgL": imgL, "imgR": imgR}
if compute_metrics:
with torch.no_grad():
image_outputs["errormap"] = [disp_error_image_func.apply(disp_est, disp_gt) for disp_est in disp_ests]
scalar_outputs["EPE"] = [EPE_metric(disp_est, disp_gt, mask) for disp_est in disp_ests]
scalar_outputs["D1"] = [D1_metric(disp_est, disp_gt, mask) for disp_est in disp_ests]
scalar_outputs["Thres1"] = [Thres_metric(disp_est, disp_gt, mask, 1.0) for disp_est in disp_ests]
scalar_outputs["Thres2"] = [Thres_metric(disp_est, disp_gt, mask, 2.0) for disp_est in disp_ests]
scalar_outputs["Thres3"] = [Thres_metric(disp_est, disp_gt, mask, 3.0) for disp_est in disp_ests]
loss.backward()
optimizer.step()
return tensor2float(loss), tensor2float(scalar_outputs), image_outputs
# test one sample
@make_nograd_func
def test_sample(sample, compute_metrics=True):
model.eval()
imgL, imgR, disp_gt = sample['left'], sample['right'], sample['disparity']
imgL = imgL.cuda()
imgR = imgR.cuda()
disp_gt = disp_gt.cuda()
mask = (disp_gt < args.maxdisp) & (disp_gt > 0)
disp_ests = model(imgL, imgR)
disp_gts = [disp_gt, disp_gt, disp_gt, disp_gt, disp_gt, disp_gt]
loss = model_loss_test(disp_ests, disp_gt, mask)
scalar_outputs = {"loss": loss}
image_outputs = {"disp_est": disp_ests, "disp_gt": disp_gt, "imgL": imgL, "imgR": imgR}
image_outputs["errormap"] = [disp_error_image_func.apply(disp_est, disp_gt) for disp_est in disp_ests]
scalar_outputs["EPE"] = [EPE_metric(disp_est, disp_gt, mask) for disp_est in disp_ests]
scalar_outputs["D1"] = [D1_metric(disp_est, disp_gt, mask) for disp_est in disp_ests]
scalar_outputs["Thres1"] = [Thres_metric(disp_est, disp_gt, mask, 1.0) for disp_est in disp_ests]
scalar_outputs["Thres2"] = [Thres_metric(disp_est, disp_gt, mask, 2.0) for disp_est in disp_ests]
scalar_outputs["Thres3"] = [Thres_metric(disp_est, disp_gt, mask, 3.0) for disp_est in disp_ests]
return tensor2float(loss), tensor2float(scalar_outputs), image_outputs
if __name__ == '__main__':
train()