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unsuper_utils.py
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import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import pytorch_ssim
import time
import IPython, cv2
SSIM_WIN = 5
class WrappedModel(nn.Module):
def __init__(self, module):
super(WrappedModel, self).__init__()
self.module = module # that I actually define.
def forward(self, x):
return self.module(x)
def gradient_xy(img):
gx = img[:, :, :, :-1] - img[:, :, :, 1:]
gy = img[:, :, :-1, :] - img[:, :, 1:, :]
return gx, gy
def warp_disp(x, disp, args):
# result + flow(-disp) = x
# warp back to result
N, _, H, W = x.shape
x_ = torch.arange(W).view(1, -1).expand(H, -1)
y_ = torch.arange(H).view(-1, 1).expand(-1, W)
grid = torch.stack([x_, y_], dim=0).float()
if args.cuda:
grid = grid.cuda()
grid = grid.unsqueeze(0).expand(N, -1, -1, -1)
grid[:, 0, :, :] = 2 * grid[:, 0, :, :] / (W - 1) - 1
grid[:, 1, :, :] = 2 * grid[:, 1, :, :] / (H - 1) - 1
# disp = 30*torch.ones(N, H, W).cuda()
grid2 = grid.clone()
grid2[:, 0, :, :] = grid[:, 0, :, :] + 2*disp/W
grid2 = grid2.permute(0, 2, 3, 1)
return F.grid_sample(x, grid2, padding_mode='zeros')
# loss1
# appearance loss: the difference between reconstructed image and original image
def criterion1(imgC, imgR, imgL, outputR, outputL, maxdisp, args, down_factor=1):
if down_factor != 1:
imgC = F.interpolate(imgC, scale_factor=1.0/down_factor, mode='bicubic')
imgR = F.interpolate(imgR, scale_factor=1.0/down_factor, mode='bicubic')
imgL = F.interpolate(imgL, scale_factor=1.0/down_factor, mode='bicubic')
outputR = F.interpolate(outputR.unsqueeze(1), scale_factor=1.0/down_factor, mode='bicubic') / down_factor
outputL = F.interpolate(outputL.unsqueeze(1), scale_factor=1.0/down_factor, mode='bicubic') / down_factor
outputR = outputR.squeeze(1)
outputL = outputL.squeeze(1)
imgR2C = warp_disp(imgR, -outputR, args)
imgL2C = warp_disp(imgL, outputL, args)
imgR2C2 = warp_disp(imgR, -outputL, args)
imgL2C2 = warp_disp(imgL, outputR, args)
alpha2 = 0.85
crop_edge = 200
if imgC.shape[2] > SSIM_WIN:
ssim_loss = pytorch_ssim.SSIM(window_size = SSIM_WIN)
else:
ssim_loss = pytorch_ssim.SSIM(window_size = imgC.shape[2])
if crop_edge == 0:
diff_ssim = (1 - ssim_loss(imgC, imgR2C)) / 2.0 + \
(1 - ssim_loss(imgC, imgL2C)) / 2.0 + \
(1 - ssim_loss(imgC, imgR2C2)) / 2.0 + \
(1 - ssim_loss(imgC, imgL2C2)) / 2.0
diff_L1 = (F.smooth_l1_loss(imgC, imgR2C, reduction='mean')) + \
(F.smooth_l1_loss(imgC, imgL2C, reduction='mean')) + \
(F.smooth_l1_loss(imgC, imgR2C2, reduction='mean')) + \
(F.smooth_l1_loss(imgC, imgL2C2, reduction='mean'))
else:
diff_ssim = (1 - ssim_loss(imgC[:,:,:,crop_edge:], imgR2C[:,:,:,crop_edge:])) / 2.0 + \
(1 - ssim_loss(imgC[:,:,:,:-crop_edge], imgL2C[:,:,:,:-crop_edge])) / 2.0 + \
(1 - ssim_loss(imgC[:,:,:,crop_edge:], imgR2C2[:,:,:,crop_edge:])) / 2.0 + \
(1 - ssim_loss(imgC[:,:,:,:-crop_edge], imgL2C2[:,:,:,:-crop_edge])) / 2.0
diff_L1 = (F.smooth_l1_loss(imgC[:,:,:,crop_edge:], imgR2C[:,:,:,crop_edge:], reduction='mean')) + \
(F.smooth_l1_loss(imgC[:,:,:,:-crop_edge], imgL2C[:,:,:,:-crop_edge], reduction='mean')) + \
(F.smooth_l1_loss(imgC[:,:,:,crop_edge:], imgR2C2[:,:,:,crop_edge:], reduction='mean')) + \
(F.smooth_l1_loss(imgC[:,:,:,:-crop_edge], imgL2C2[:,:,:,:-crop_edge], reduction='mean'))
loss1 = 1.0/4 * (alpha2 * diff_ssim + (1-alpha2) * diff_L1)
return loss1, imgR2C, imgL2C, imgC, outputR
def criterion1_2frame(imgC, imgR, outputR, maxdisp, args, down_factor=1):
if down_factor != 1:
imgC = F.interpolate(imgC, scale_factor=1.0/down_factor, mode='bicubic')
imgR = F.interpolate(imgR, scale_factor=1.0/down_factor, mode='bicubic')
outputR = F.interpolate(outputR.unsqueeze(1), scale_factor=1.0/down_factor, mode='bicubic') / down_factor
outputR = outputR.squeeze(1)
imgR2C = warp_disp(imgR, -outputR, args)
alpha2 = 0.85
crop_edge = 0
if imgC.shape[2] > SSIM_WIN:
ssim_loss = pytorch_ssim.SSIM(window_size = SSIM_WIN)
else:
ssim_loss = pytorch_ssim.SSIM(window_size = imgC.shape[2])
if crop_edge == 0:
diff_ssim = (1 - ssim_loss(imgC, imgR2C)) / 2.0
diff_L1 = (F.smooth_l1_loss(imgC, imgR2C, reduction='mean'))
else:
diff_ssim = (1 - ssim_loss(imgC[:,:,:,crop_edge:], imgR2C[:,:,:,crop_edge:])) / 2.0
diff_L1 = (F.smooth_l1_loss(imgC[:,:,:,crop_edge:], imgR2C[:,:,:,crop_edge:], reduction='mean'))
loss1 = (alpha2 * diff_ssim + (1-alpha2) * diff_L1)
return loss1, imgR2C
# loss2
# consistency loss the difference between left output and right output
def criterion2(R, L):
alpha1 = 0
tau = 10 # truncation for occluded region
L1loss = F.smooth_l1_loss(R, L, reduction='none').clamp(min=0, max=tau).mean()
return L1loss
# R = R.unsqueeze(1)
# L = L.unsqueeze(1)
# R_gx, R_gy = gradient_xy(R)
# L_gx, L_gy = gradient_xy(L)
# gxloss = F.smooth_l1_loss(R_gx, L_gx, reduction='none').clamp(min=0, max=tau).mean()
# gyloss = F.smooth_l1_loss(R_gy, L_gy, reduction='none').clamp(min=0, max=tau).mean()
# g1loss = 0.5 * (gxloss + gyloss)
# R_gxx, R_gxy = gradient_xy(R_gx)
# R_gyx, R_gyy = gradient_xy(R_gy)
# L_gxx, L_gxy = gradient_xy(L_gx)
# L_gyx, L_gyy = gradient_xy(L_gy)
# gxxloss = F.smooth_l1_loss(R_gxx, L_gxx, reduction='none').clamp(min=0, max=tau).mean()
# gyyloss = F.smooth_l1_loss(R_gyy, L_gyy, reduction='none').clamp(min=0, max=tau).mean()
# g2loss = 0.5 * (gxxloss + gyyloss)
# return 0.5 * (L1loss + (g1loss*10 + g2loss*10)/2.0 )
# loss3
# smooth loss: force grident of intensity to be small
def criterion3(disp, img):
disp = disp.unsqueeze(1)
disp_gx, disp_gy = gradient_xy(disp)
intensity_gx, intensity_gy = gradient_xy(img)
weights_x = torch.exp(-10 * torch.abs(intensity_gx).mean(1).unsqueeze(1))
weights_y = torch.exp(-10 * torch.abs(intensity_gy).mean(1).unsqueeze(1))
disp_gx = torch.abs(disp_gx)
gx = disp_gx.clone()
gx[gx>0.5] = disp_gx[disp_gx>0.5] + 10
disp_gy = torch.abs(disp_gy)
gy = disp_gy.clone()
gy[gy>0.5] = disp_gy[disp_gy>0.5] + 10
smoothness_x = gx * weights_x
smoothness_y = gy * weights_y
return smoothness_x.mean() + smoothness_y.mean()
# loss4
# regularization term:
def criterion4(disp, maxdisp):
# r1 = disp.mean()
# r = torch.exp(-1 / 5.0 * disp) + torch.exp(1 / 5.0 * (disp - 90))
# r = torch.exp(-1 / 5.0 * disp)
r = (disp*2/maxdisp - 1).pow(2)
return r.mean()
def evaluate(model, imgL, imgC, imgR, gt, args, maxd):
use_cuda = args.cuda
# use_cuda = False
height = imgL.shape[1]
width = imgL.shape[2]
maxdisp = maxd
pad_h = (height // 32 + 1) * 32
pad_w = (width // 32 + 1) * 32
imgL = np.reshape(imgL, [1, imgL.shape[0], imgL.shape[1], imgL.shape[2]])
imgR = np.reshape(imgR, [1, imgR.shape[0], imgR.shape[1], imgR.shape[2]])
if imgC is not None:
imgC = np.reshape(imgC, [1, imgC.shape[0], imgC.shape[1], imgC.shape[2]])
# pad to (M x 32, N x 32)
top_pad = pad_h - imgL.shape[2]
left_pad = pad_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)
if imgC is not None:
imgC = np.lib.pad(imgC, ((0,0),(0,0),(top_pad,0),(0,left_pad)),mode='constant',constant_values=0)
imgL = torch.from_numpy(imgL)
imgR = torch.from_numpy(imgR)
if imgC is not None:
imgC = torch.from_numpy(imgC)
model.eval()
if imgC is not None:
# multiscopic mode
imgC_rot = imgC.flip(2).flip(3)
imgL_rot = imgL.flip(2).flip(3)
if use_cuda:
imgL, imgR, imgC, imgC_rot, imgL_rot = \
imgL.cuda(), imgR.cuda(), imgC.cuda(), imgC_rot.cuda(), imgL_rot.cuda()
if args.model == 'stackhourglass':
outputR, outputR_prob, _, _ = model(imgC, imgR, maxdisp)
if args.cuda and (not use_cuda):
outputR = outputR.cpu()
outputR_prob = outputR_prob.cpu()
outputL_rot, outputL_prob_rot, _, _ = model(imgC_rot, imgL_rot, maxdisp)
outputL = outputL_rot.flip(1).flip(2)
outputL_prob = outputL_prob_rot.flip(2).flip(3)
if args.cuda and (not use_cuda):
outputL = outputL.cpu()
outputL_prob = outputL_prob.cpu()
elif args.model == 'basic':
outputR = model(imgC, imgR, maxdisp)
outputL_rot = model(imgC_rot, imgL_rot)
outputL = outputL_rot.flip(1).flip(2)
mindisp = torch.min(torch.cat([outputR, outputL]), 0)[0]
diff = (outputR - outputL).squeeze()
outputR = outputR.squeeze()
outputL = outputL.squeeze()
outputR[diff>3] = mindisp[diff>3]
disp = outputL
disp = disp[top_pad:, :-left_pad]
else:
# stereo mode
if use_cuda:
imgL, imgR = imgL.cuda(), imgR.cuda()
if args.model == 'stackhourglass':
output, _, _, _ = model(imgL, imgR, maxdisp)
elif args.model == 'basic':
output = model(imgL, imgR, maxdisp)
if args.cuda and (not use_cuda):
output = output.cpu()
disp = output.squeeze()[top_pad:, :-left_pad]
gt = torch.from_numpy(gt).float()
if(use_cuda): gt = gt.cuda()
mask = (gt != 0)
diff = torch.abs(disp[mask] - gt[mask])
avgerr = torch.mean(diff)
rms = torch.sqrt( (diff**2).mean() )
bad05 = len(diff[diff>0.5])/float(len(diff))
bad1 = len(diff[diff>1])/float(len(diff))
bad2 = len(diff[diff>2])/float(len(diff))
bad3 = len(diff[diff>3])/float(len(diff))
return [avgerr.data.item(), rms.data.item(), bad05, bad1, bad2, bad3], disp.cpu().numpy()
def evaluate_kitti(model, imgL, imgR, gt_occ, gt_noc, args, maxd=160):
height = imgL.shape[1]
width = imgL.shape[2]
maxdisp = maxd
pad_h = (height / 32 + 1) * 32
pad_w = (width / 32 + 1) * 32
imgL = np.reshape(imgL, [1, imgL.shape[0], imgL.shape[1], imgL.shape[2]])
imgR = np.reshape(imgR, [1, imgR.shape[0], imgR.shape[1], imgR.shape[2]])
# pad to (M x 32, N x 32)
top_pad = pad_h - imgL.shape[2]
left_pad = pad_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)
imgL = torch.from_numpy(imgL)
imgR = torch.from_numpy(imgR)
model.eval()
if args.cuda:
imgL, imgR = imgL.cuda(), imgR.cuda()
if args.model == 'stackhourglass':
output, _, _, _ = model(imgL, imgR, maxdisp)
elif args.model == 'basic':
output = model(imgL, imgR, maxdisp)
disp = output.squeeze()[top_pad:, :-left_pad]
if gt_noc.any() == None:
return disp.cpu().numpy()
gt_occ = torch.from_numpy(gt_occ).float()
gt_noc = torch.from_numpy(gt_noc).float()
if args.cuda:
gt_noc = gt_noc.cuda()
gt_occ = gt_occ.cuda()
mask_occ = (gt_occ != 0)
mask_noc = (gt_noc != 0)
diff_occ = torch.abs(disp[mask_occ] - gt_occ[mask_occ])
diff_noc = torch.abs(disp[mask_noc] - gt_noc[mask_noc])
# bad3_occ = len(diff_occ[diff_occ>3])/float(len(diff_occ))
# bad3_noc = len(diff_noc[diff_noc>3])/float(len(diff_noc))
bad3_occ = torch.sum((diff_occ>3) & (diff_occ/gt_occ[mask_occ]>0.05)).float() / float(len(diff_occ))
bad3_noc = torch.sum((diff_noc>3) & (diff_noc/gt_noc[mask_noc]>0.05)).float() / float(len(diff_noc))
return [bad3_occ, bad3_noc], disp.cpu().numpy()
def predict(model, imgL, imgR, args, maxd):
height = imgL.shape[1]
width = imgL.shape[2]
pad_h = (height / 32 + 1) * 32
pad_w = (width / 32 + 1) * 32
imgL = np.reshape(imgL, [1, imgL.shape[0], imgL.shape[1], imgL.shape[2]])
imgR = np.reshape(imgR, [1, imgR.shape[0], imgR.shape[1], imgR.shape[2]])
# pad to (M x 32, N x 32)
top_pad = pad_h - imgL.shape[2]
left_pad = pad_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)
imgL = torch.from_numpy(imgL)
imgR = torch.from_numpy(imgR)
model.eval()
if args.cuda:
imgL, imgR = imgL.cuda(), imgR.cuda()
if args.model == 'stackhourglass':
output, _, _, _ = model(imgL, imgR, maxd)
elif args.model == 'basic':
output = model(imgL, imgR, maxd)
disp = output.squeeze()[top_pad:, :-left_pad]
return disp.cpu().numpy()