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DytanVO.py
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DytanVO.py
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# Software License Agreement (BSD License)
#
# Copyright (c) 2020, Shihao Shen, CMU
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions
# are met:
#
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above
# copyright notice, this list of conditions and the following
# disclaimer in the documentation and/or other materials provided
# with the distribution.
# * Neither the name of CMU nor the names of its
# contributors may be used to endorse or promote products derived
# from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
# FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
# COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
# INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
# BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
# LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
# ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE.
import cv2
import torch
import torch.nn as nn
import numpy as np
import time
np.set_printoptions(precision=4, suppress=True, threshold=10000)
from torch.autograd import Variable
from Network.VONet import VONet
from Network.rigidmask.VCNplus import SegNet, WarpModule, flow_reg
from Datasets.utils import CropCenter, ResizeData
from Datasets.cowmask import cow_masks
from evaluator.transformation import se2SE
class DytanVO(object):
def __init__(self, vo_model_name, seg_model_name, image_height, image_width, is_kitti=False, flow_model_name=None, pose_model_name=None):
# import ipdb;ipdb.set_trace()
self.vonet = VONet()
# load VO model separately (flow + pose) or at once
if flow_model_name.endswith('.pkl') and pose_model_name.endswith('.pkl'):
modelname = 'models/' + flow_model_name
self.load_vo_model(self.vonet.flowNet, modelname)
modelname = 'models/' + pose_model_name
self.load_vo_model(self.vonet.flowPoseNet, modelname)
else:
modelname = 'models/' + vo_model_name
self.load_vo_model(self.vonet, modelname)
self.vonet.cuda()
self.test_count = 0
self.pose_norm = np.array([ 0.13, 0.13, 0.13, 0.013 , 0.013, 0.013], dtype=np.float32) # the output scale factor
self.flow_norm = 20 # scale factor for flow
# load the segmentation model
self.testres = 1.2
if is_kitti:
maxw, maxh = [int(self.testres * 1280), int(self.testres * 384)]
else:
maxw, maxh = [int(self.testres * 1024), int(self.testres * 448)]
max_h = int(maxh // 64 * 64)
max_w = int(maxw // 64 * 64)
if max_h < maxh: max_h += 64
if max_w < maxw: max_w += 64
maxh = max_h
maxw = max_w
self.segnet = SegNet([1, maxw, maxh], md=[4, 4, 4, 4, 4], fac=1, exp_unc=not ('kitti' in seg_model_name))
segmodelname = 'models/' + seg_model_name
self.segnet = self.load_seg_model(self.segnet, segmodelname)
self.segnet.cuda()
self.segnet_initialize = False
# To resize/crop segmentation mask
self.resizedata = ResizeData(size=(image_height,1226)) if is_kitti else None
self.cropdata = CropCenter((image_height, image_width))
# To transform coordinates from NED to Blender
Ry90 = np.array([[0,0,1,0], [0,1,0,0], [-1,0,0,0], [0,0,0,1]])
Rx90 = np.array([[1,0,0,0], [0,0,-1,0], [0,1,0,0], [0,0,0,1]])
self.camT = Rx90.dot(Ry90)
self.sigmoid = lambda x: 1/(1 + np.exp(-x))
def load_vo_model(self, model, modelname):
preTrainDict = torch.load(modelname)
model_dict = model.state_dict()
preTrainDictTemp = {k:v for k,v in preTrainDict.items() if k in model_dict}
if( 0 == len(preTrainDictTemp) ):
print("Does not find any module to load. Try DataParallel version.")
for k, v in preTrainDict.items():
kk = k[7:]
if ( kk in model_dict ):
preTrainDictTemp[kk] = v
if ( 0 == len(preTrainDictTemp) ):
raise Exception("Could not load model from %s." % (modelname), "load_model")
model_dict.update(preTrainDictTemp)
model.load_state_dict(model_dict)
print('VO Model %s loaded...' % modelname)
return model
def load_seg_model(self, model, modelname):
model = nn.DataParallel(model, device_ids=[0])
preTrainDict = torch.load(modelname, map_location='cpu')
self.mean_L = preTrainDict['mean_L']
self.mean_R = preTrainDict['mean_R']
preTrainDict['state_dict'] = {k:v for k,v in preTrainDict['state_dict'].items()}
model.load_state_dict(preTrainDict['state_dict'], strict=False)
print('Segmentation Model %s loaded...' % modelname)
return model
def test_batch(self, sample, intrinsics, seg_thresh, iter_num):
print("="*20)
self.test_count += 1
# import ipdb;ipdb.set_trace()
img0 = sample['img1'].cuda()
img1 = sample['img2'].cuda()
intrinsic = sample['intrinsic'].cuda() # intrinsic layer
img0_raw = sample['img1_raw'].detach().numpy().squeeze()
img1_raw = sample['img2_raw'].detach().numpy().squeeze()
if not self.segnet_initialize:
self.vonet.eval()
self.segnet.eval()
self.initialize_segnet_input(img0_raw, intrinsics)
self.segnet_initialize = True
with torch.no_grad():
imgL_noaug, imgLR = self.transform_segnet_input(img0_raw, img1_raw)
flowdc = self.segnet.module.forward_VCN(imgLR)
total_time = 0
start_time = time.time()
flow_output, _ = self.vonet([img0, img1], only_flow=True)
flownet_time = time.time() - start_time
total_time += flownet_time
print("Flownet time: %.2f" % flownet_time)
seg_thresholds = np.linspace(seg_thresh, 0.95, iter_num - 1)[::-1]
for iter in range(iter_num):
flow = flow_output.clone()
if iter == 0:
cow_sigma_range = (20, 60)
log_sigma_range = (np.log(cow_sigma_range[0]), np.log(cow_sigma_range[1]))
cow_prop_range = (0.3, 0.6)
segmask = cow_masks(flow.shape[-2:], log_sigma_range, cow_sigma_range[1], cow_prop_range).astype(np.float32)
segmask = segmask[None,None,...]
segmask = torch.from_numpy(np.concatenate((segmask,) * img0.shape[0], axis=0)).cuda()
start_time = time.time()
_, pose_output = self.vonet([img0, img1, intrinsic, flow, segmask], only_pose=True)
posenet_time = time.time() - start_time
total_time += posenet_time
print("Iter %d, Posenet time: %.2f; " % (iter, posenet_time), end='')
# Do not pass segnet in the last iteration
if iter == iter_num - 1:
break
seg_thresh = seg_thresholds[iter] if iter < iter_num-1 else seg_thresh
pose_input = pose_output.data.cpu().detach().numpy().squeeze()
pose_input = pose_input * self.pose_norm
pose_input = self.camT.T.dot(se2SE(pose_input)).dot(self.camT)
start_time = time.time()
disc_aux = [self.intr_list, imgL_noaug, pose_input[:3,:]]
fgmask = self.segnet(imgLR, disc_aux, flowdc)
segnet_time = time.time() - start_time
total_time += segnet_time
fgmask = cv2.resize(fgmask.cpu().numpy(), (self.input_size[1], self.input_size[0]), interpolation=cv2.INTER_LINEAR).astype(np.float32)
fg_probs = self.sigmoid(fgmask)
segmask = np.zeros(fgmask.shape[:2])
segmask[fg_probs > seg_thresh] = 1.0
# Resize/Crop segmask (Resize + Crop + Downscale 1/4)
dummysample = {'segmask': segmask}
if self.resizedata is not None:
dummysample = self.resizedata(dummysample)
dummysample = self.cropdata(dummysample)
segmask = dummysample['segmask']
segmask = cv2.resize(segmask, (0,0), fx=0.25, fy=0.25, interpolation=cv2.INTER_LINEAR)
segmask = segmask[None,None,...].astype(np.float32)
segmask = torch.from_numpy(np.concatenate((segmask,) * img0.shape[0], axis=0)).cuda()
print("Segnet time: %.2f" % segnet_time)
posenp = pose_output.data.cpu().detach().numpy().squeeze()
posenp = posenp * self.pose_norm # The output is normalized during training, now scale it back
flownp = flow.data.cpu().detach().numpy().squeeze()
flownp = flownp * self.flow_norm
# # calculate scale from GT posefile
# if 'motion' in sample:
# motions_gt = sample['motion']
# scale = np.linalg.norm(motions_gt[:,:3], axis=1)
# trans_est = posenp[:,:3]
# trans_est = trans_est/np.linalg.norm(trans_est,axis=1).reshape(-1,1)*scale.reshape(-1,1)
# posenp[:,:3] = trans_est
# else:
# print(' scale is not given, using 1 as the default scale value..')
print("\n{} Pose inference using {}s: \n{}\n".format(self.test_count, total_time, posenp))
return posenp, flownp
def initialize_segnet_input(self, imgL_o, intrinsics):
maxh = imgL_o.shape[0] * self.testres
maxw = imgL_o.shape[1] * self.testres
self.max_h = int(maxh // 64 * 64)
self.max_w = int(maxw // 64 * 64)
if self.max_h < maxh: self.max_h += 64
if self.max_w < maxw: self.max_w += 64
self.input_size = imgL_o.shape
# modify module according to inputs
for i in range(len(self.segnet.module.reg_modules)):
self.segnet.module.reg_modules[i] = flow_reg([1, self.max_w//(2**(6-i)), self.max_h//(2**(6-i))],
ent=getattr(self.segnet.module, 'flow_reg%d'%2**(6-i)).ent,\
maxdisp=getattr(self.segnet.module, 'flow_reg%d'%2**(6-i)).md,\
fac=getattr(self.segnet.module, 'flow_reg%d'%2**(6-i)).fac).cuda()
for i in range(len(self.segnet.module.warp_modules)):
self.segnet.module.warp_modules[i] = WarpModule([1, self.max_w//(2**(6-i)), self.max_h//(2**(6-i))]).cuda()
# foramt intrinsics input
fl, cx, cy, bl = intrinsics
fl_next = fl # assuming focal length remains the same across frames
self.intr_list = [torch.Tensor(inxx).cuda() for inxx in [[fl],[cx],[cy],[bl],[1],[0],[0],[1],[0],[0]]]
self.intr_list.append(torch.Tensor([self.input_size[1] / self.max_w]).cuda()) # delta fx
self.intr_list.append(torch.Tensor([self.input_size[0] / self.max_h]).cuda()) # delta fy
self.intr_list.append(torch.Tensor([fl_next]).cuda())
def transform_segnet_input(self, imgL_o, imgR_o):
imgL = cv2.resize(imgL_o, (self.max_w, self.max_h))
imgR = cv2.resize(imgR_o, (self.max_w, self.max_h))
imgL_noaug = torch.Tensor(imgL / 255.)[np.newaxis].float().cuda()
# flip channel, subtract mean
imgL = imgL[:,:,::-1].copy() / 255. - np.asarray(self.mean_L).mean(0)[np.newaxis,np.newaxis,:]
imgR = imgR[:,:,::-1].copy() / 255. - np.asarray(self.mean_R).mean(0)[np.newaxis,np.newaxis,:]
imgL = np.transpose(imgL, [2,0,1])[np.newaxis]
imgR = np.transpose(imgR, [2,0,1])[np.newaxis]
imgL = Variable(torch.FloatTensor(imgL).cuda())
imgR = Variable(torch.FloatTensor(imgR).cuda())
imgLR = torch.cat([imgL,imgR],0)
return imgL_noaug, imgLR