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demo.py
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"""
stage2,3,4, is corrsponding with the 'layer1,2,3, in the paper.
"""
import torch
from torch.autograd import Variable
from torch.utils.data import DataLoader
import torch.nn.functional as F
import torch.optim as optim
from dataset_loader import MyData, MyTestData
from functions import imsave
import argparse
from train import Trainer
from utils.evaluateFM import get_FM
from model_depth import DepthNet
from model_baseline import BaselineNet
from model_ladder import LadderNet
import time
import torchvision
import os
configurations = {
1: dict(
max_iteration=1000000,
lr=1.0e-10,
momentum=0.99,
weight_decay=0.0005,
spshot=20000,
nclass=2,
sshow=10,
),
'stage2_cfg': dict(
NUM_BRANCHES = 2,
NUM_CHANNELS = [32, 64],
NUM_BLOCKS = [4, 4],
),
'stage3_cfg': dict(
NUM_BRANCHES = 3,
NUM_CHANNELS=[32, 64, 128],
NUM_BLOCKS=[4, 4, 4],
),
'stage4_cfg': dict(
NUM_MODULES = 1,
NUM_BRANCHES = 4,
NUM_BLOCKS = [4, 4, 4, 4],
NUM_CHANNELS = [32, 64, 128, 256],
)
}
parser=argparse.ArgumentParser()
parser.add_argument('--phase', type=str, default='test', help='train or test')
parser.add_argument('--param', type=str, default=True, help='path to pre-trained parameters')
parser.add_argument('--train_dataroot', type=str, default='/media/lewis/Win 10 Pro x64/datasets/version7.23_ablation/'
'RGB-D saliency dataset/train_data-augment', help=
'path to train data')
parser.add_argument('--test_dataroot', type=str, default='/media/lewis/Win 10 Pro x64/datasets/version7.23_ablation/'
'RGB-D saliency dataset/test_data/DUT-RGBD/test_data', help=
'path to test data')
parser.add_argument('--snapshot_root', type=str, default='./snapshot', help='path to snapshot')
parser.add_argument('--salmap_root', type=str, default='./sal_map/DUT-RGBD/', help='path to saliency map')
parser.add_argument('-c', '--config', type=int, default=1, choices=configurations.keys())
args = parser.parse_args()
cfg = configurations
cuda = torch.cuda.is_available
"""""""""""dataset loader"""""""""
train_dataRoot = args.train_dataroot
test_dataRoot = args.test_dataroot
if not os.path.exists(args.snapshot_root):
os.mkdir(args.snapshot_root)
if not os.path.exists(args.salmap_root):
os.mkdir(args.salmap_root)
if args.phase == 'train':
SnapRoot = args.snapshot_root # checkpoint
train_loader = torch.utils.data.DataLoader(MyData(train_dataRoot, transform=True),
batch_size=2, shuffle=True, num_workers=4, pin_memory=True)
else:
MapRoot = args.salmap_root
test_loader = torch.utils.data.DataLoader(MyTestData(test_dataRoot, transform=True),
batch_size=1, shuffle=True, num_workers=4, pin_memory=True)
print ('data already')
"""""""""""train_data/test_data through nets"""""""""
start_epoch = 0
start_iteration = 0
model_depth = DepthNet()
model_baseline = BaselineNet()
model_ladder = LadderNet(cfg)
# print(model_rgb)
# Batch test code: When training, remove the outer ‘for’ loop.
f = open("DUT-RGBDresult.txt", mode='a+') # When training, remove this line.
for ckpt in range(90,91): # When training, remove this line.
if args.param is True:
ckpt = str(ckpt)
model_depth.load_state_dict(torch.load(os.path.join(args.snapshot_root, 'depth_snapshot_iter_' + ckpt + '0000.pth')))
model_baseline.load_state_dict(torch.load(os.path.join(args.snapshot_root, 'baseline_snapshot_iter_'+ckpt+'0000.pth')))
model_ladder.load_state_dict(torch.load(os.path.join(args.snapshot_root, 'ladder_snapshot_iter_'+ckpt+'0000.pth')))
else:
model_depth.init_weights()
vgg19_bn = torchvision.models.vgg19_bn(pretrained=True)
model_baseline.copy_params_from_vgg19_bn(vgg19_bn)
model_ladder.init_weights()
if cuda:
model_depth = model_depth.cuda()
model_baseline = model_baseline.cuda()
model_ladder = model_ladder.cuda()
if args.phase == 'train':
optimizer_depth = optim.SGD(model_depth.parameters(), lr=cfg[1]['lr'], momentum=cfg[1]['momentum'], weight_decay=cfg[1]['weight_decay'])
optimizer_baseline = optim.SGD(model_baseline.parameters(), lr=cfg[1]['lr'], momentum=cfg[1]['momentum'], weight_decay=cfg[1]['weight_decay'])
optimizer_ladder = optim.SGD(model_ladder.parameters(), lr=cfg[1]['lr'], momentum=cfg[1]['momentum'], weight_decay=cfg[1]['weight_decay'])
training = Trainer(
cuda=cuda,
model_depth=model_depth,
model_baseline=model_baseline,
model_ladder=model_ladder,
optimizer_depth=optimizer_depth,
optimizer_baseline=optimizer_baseline,
optimizer_ladder=optimizer_ladder,
train_loader=train_loader,
max_iter=cfg[1]['max_iteration'],
snapshot=cfg[1]['spshot'],
outpath=args.snapshot_root,
sshow=cfg[1]['sshow']
)
training.epoch = start_epoch
training.iteration = start_iteration
training.train()
else:
res = []
for id, (data, depth, img_name, img_size) in enumerate(test_loader):
# print('testing bach %d' % id)
inputs = Variable(data).cuda()
depth = Variable(depth).cuda()
n, c, h, w = inputs.size()
# depth = torch.unsqueeze(depth, 1)
depth = depth.view(n, 1, h, w).repeat(1, c, 1, 1)
torch.cuda.synchronize()
start = time.time()
h2, h3, h4, h5 = model_baseline(inputs)
d2, d3, d4 = model_depth(depth)
predict_stage2_mask, predict_stage3_mask, predict_stage4_mask = model_ladder(h2, h3, h4, h5, d2, d3, d4)
# predict_mask = model_ladder(h2, h3, h4, h5, d2, d3, d4)
torch.cuda.synchronize()
end = time.time()
res.append(end - start)
outputs_all = F.softmax(predict_stage4_mask, dim=1)
outputs = outputs_all[0][1]
outputs = outputs.cpu().data.resize_(h, w)
imsave(os.path.join(MapRoot,img_name[0] + '.png'), outputs, img_size)
# imsave(os.path.join(MapRoot,img_name[0][-1] + '.png'), outputs, img_size)
time_sum = 0
for i in res:
time_sum += i
print("FPS: %f" % (1.0 / (time_sum / len(res))))
# -------------------------- validation --------------------------- #
torch.cuda.empty_cache()
print('the testing process has finished!')
# print("\nevaluating mae....")
F_measure, mae = get_FM(salpath=MapRoot+'/', gtpath=test_dataRoot+'/test_masks/')
print('F_measure:', F_measure)
print('MAE:', mae)
F_measure = str(F_measure)
mae = str(mae)
f.write(ckpt + '0000.pth : \t' + F_measure + '\t' + mae + '\n') # 去掉
f.close() # When training, remove this line.