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submission.py
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import argparse
import cv2
import math
from models import hsm
import numpy as np
import os
import pdb
import skimage.io
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
from torch.autograd import Variable
import time
from models.submodule import *
from utils.eval import mkdir_p, save_pfm
from utils.preprocess import get_transform
#cudnn.benchmark = True
cudnn.benchmark = False
parser = argparse.ArgumentParser(description='HSM')
parser.add_argument('--datapath', default='./data-mbtest/',
help='test data path')
parser.add_argument('--loadmodel', default=None,
help='model path')
parser.add_argument('--outdir', default='output',
help='output dir')
parser.add_argument('--clean', type=float, default=-1,
help='clean up output using entropy estimation')
parser.add_argument('--testres', type=float, default=0.5,
help='test time resolution ratio 0-x')
parser.add_argument('--max_disp', type=float, default=-1,
help='maximum disparity to search for')
parser.add_argument('--level', type=int, default=1,
help='output level of output, default is level 1 (stage 3),\
can also use level 2 (stage 2) or level 3 (stage 1)')
args = parser.parse_args()
# dataloader
from dataloader import listfiles as DA
test_left_img, test_right_img, _, _ = DA.dataloader(args.datapath)
# construct model
model = hsm(128,args.clean,level=args.level)
model = nn.DataParallel(model, device_ids=[0])
model.cuda()
if args.loadmodel is not None:
pretrained_dict = torch.load(args.loadmodel)
pretrained_dict['state_dict'] = {k:v for k,v in pretrained_dict['state_dict'].items() if 'disp' not in k}
model.load_state_dict(pretrained_dict['state_dict'],strict=False)
else:
print('run with random init')
print('Number of model parameters: {}'.format(sum([p.data.nelement() for p in model.parameters()])))
# dry run
multip = 48
imgL = np.zeros((1,3,24*multip,32*multip))
imgR = np.zeros((1,3,24*multip,32*multip))
imgL = Variable(torch.FloatTensor(imgL).cuda())
imgR = Variable(torch.FloatTensor(imgR).cuda())
with torch.no_grad():
model.eval()
pred_disp,entropy = model(imgL,imgR)
def main():
processed = get_transform()
model.eval()
for inx in range(len(test_left_img)):
print(test_left_img[inx])
imgL_o = (skimage.io.imread(test_left_img[inx]).astype('float32'))[:,:,:3]
imgR_o = (skimage.io.imread(test_right_img[inx]).astype('float32'))[:,:,:3]
imgsize = imgL_o.shape[:2]
if args.max_disp>0:
if args.max_disp % 16 != 0:
args.max_disp = 16 * math.floor(args.max_disp/16)
max_disp = int(args.max_disp)
else:
with open(test_left_img[inx].replace('im0.png','calib.txt')) as f:
lines = f.readlines()
max_disp = int(int(lines[6].split('=')[-1]))
## change max disp
tmpdisp = int(max_disp*args.testres//64*64)
if (max_disp*args.testres/64*64) > tmpdisp:
model.module.maxdisp = tmpdisp + 64
else:
model.module.maxdisp = tmpdisp
if model.module.maxdisp ==64: model.module.maxdisp=128
model.module.disp_reg8 = disparityregression(model.module.maxdisp,16).cuda()
model.module.disp_reg16 = disparityregression(model.module.maxdisp,16).cuda()
model.module.disp_reg32 = disparityregression(model.module.maxdisp,32).cuda()
model.module.disp_reg64 = disparityregression(model.module.maxdisp,64).cuda()
print(model.module.maxdisp)
# resize
imgL_o = cv2.resize(imgL_o,None,fx=args.testres,fy=args.testres,interpolation=cv2.INTER_CUBIC)
imgR_o = cv2.resize(imgR_o,None,fx=args.testres,fy=args.testres,interpolation=cv2.INTER_CUBIC)
imgL = processed(imgL_o).numpy()
imgR = processed(imgR_o).numpy()
imgL = np.reshape(imgL,[1,3,imgL.shape[1],imgL.shape[2]])
imgR = np.reshape(imgR,[1,3,imgR.shape[1],imgR.shape[2]])
##fast pad
max_h = int(imgL.shape[2] // 64 * 64)
max_w = int(imgL.shape[3] // 64 * 64)
if max_h < imgL.shape[2]: max_h += 64
if max_w < imgL.shape[3]: max_w += 64
top_pad = max_h-imgL.shape[2]
left_pad = max_w-imgL.shape[3]
imgL = np.lib.pad(imgL,((0,0),(0,0),(top_pad,0),(0,left_pad)),mode='constant',constant_values=0)
imgR = np.lib.pad(imgR,((0,0),(0,0),(top_pad,0),(0,left_pad)),mode='constant',constant_values=0)
# test
imgL = Variable(torch.FloatTensor(imgL).cuda())
imgR = Variable(torch.FloatTensor(imgR).cuda())
with torch.no_grad():
torch.cuda.synchronize()
start_time = time.time()
pred_disp,entropy = model(imgL,imgR)
torch.cuda.synchronize()
ttime = (time.time() - start_time); print('time = %.2f' % (ttime*1000) )
pred_disp = torch.squeeze(pred_disp).data.cpu().numpy()
top_pad = max_h-imgL_o.shape[0]
left_pad = max_w-imgL_o.shape[1]
entropy = entropy[top_pad:,:pred_disp.shape[1]-left_pad].cpu().numpy()
pred_disp = pred_disp[top_pad:,:pred_disp.shape[1]-left_pad]
# save predictions
idxname = test_left_img[inx].split('/')[-2]
if not os.path.exists('%s/%s'%(args.outdir,idxname)):
os.makedirs('%s/%s'%(args.outdir,idxname))
idxname = '%s/disp0HSM'%(idxname)
# resize to highres
pred_disp = cv2.resize(pred_disp/args.testres,(imgsize[1],imgsize[0]),interpolation=cv2.INTER_LINEAR)
# clip while keep inf
invalid = np.logical_or(pred_disp == np.inf,pred_disp!=pred_disp)
pred_disp[invalid] = np.inf
np.save('%s/%s-disp.npy'% (args.outdir, idxname.split('/')[0]),(pred_disp))
np.save('%s/%s-ent.npy'% (args.outdir, idxname.split('/')[0]),(entropy))
cv2.imwrite('%s/%s-disp.png'% (args.outdir, idxname.split('/')[0]),pred_disp/pred_disp[~invalid].max()*255)
cv2.imwrite('%s/%s-ent.png'% (args.outdir, idxname.split('/')[0]),entropy/entropy.max()*255)
with open('%s/%s.pfm'% (args.outdir, idxname),'w') as f:
save_pfm(f,pred_disp[::-1,:])
with open('%s/%s/timeHSM.txt'%(args.outdir,idxname.split('/')[0]),'w') as f:
f.write(str(ttime))
torch.cuda.empty_cache()
if __name__ == '__main__':
main()