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import torch
from torch import nn
import torch.nn.functional as F
import numpy as np
from mono.utils.inverse_warp import inverse_warp2
#device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
class SSIM(nn.Module):
"""Layer to compute the SSIM loss between a pair of images
"""
def __init__(self):
super(SSIM, self).__init__()
k = 7
self.mu_x_pool = nn.AvgPool2d(k, 1)
self.mu_y_pool = nn.AvgPool2d(k, 1)
self.sig_x_pool = nn.AvgPool2d(k, 1)
self.sig_y_pool = nn.AvgPool2d(k, 1)
self.sig_xy_pool = nn.AvgPool2d(k, 1)
self.refl = nn.ReflectionPad2d(k//2)
self.C1 = 0.01 ** 2
self.C2 = 0.03 ** 2
def forward(self, x, y):
x = self.refl(x)
y = self.refl(y)
mu_x = self.mu_x_pool(x)
mu_y = self.mu_y_pool(y)
sigma_x = self.sig_x_pool(x ** 2) - mu_x ** 2
sigma_y = self.sig_y_pool(y ** 2) - mu_y ** 2
sigma_xy = self.sig_xy_pool(x * y) - mu_x * mu_y
SSIM_n = (2 * mu_x * mu_y + self.C1) * (2 * sigma_xy + self.C2)
SSIM_d = (mu_x ** 2 + mu_y ** 2 + self.C1) * (sigma_x + sigma_y + self.C2)
return torch.clamp((1 - SSIM_n / SSIM_d) / 2, 0, 1)
class PhotometricGeometricLoss(nn.Module):
"""The photometric and geometric loss between target and reference frames."""
def __init__(self, loss_weight=1.0, data_type=['sfm', 'stereo', 'lidar'], **kwargs):
super(PhotometricGeometricLoss, self).__init__()
self.no_min_optimize = False
self.no_auto_mask = False
self.return_dynamic_mask = True
self.ssim_loss = SSIM()
self.no_ssim = False
self.no_dynamic_mask = False
self.loss_weight_photo = 1.0
self.loss_weight_geometry = 0.5
self.total_loss_weight = loss_weight
self.data_type = data_type
def photo_and_geometry_loss(self, tgt_img, ref_imgs, tgt_depth, ref_depths, intrinsics, poses, poses_inv):
diff_img_list = []
diff_color_list = []
diff_depth_list = []
valid_mask_list = []
auto_mask_list = []
for ref_img, ref_depth, pose, pose_inv in zip(ref_imgs, ref_depths, poses, poses_inv):
(
diff_img_tmp1,
diff_color_tmp1,
diff_depth_tmp1,
valid_mask_tmp1,
auto_mask_tmp1
) = self.compute_pairwise_loss(
tgt_img,
ref_img,
tgt_depth,
ref_depth,
pose,
intrinsics,
)
(
diff_img_tmp2,
diff_color_tmp2,
diff_depth_tmp2,
valid_mask_tmp2,
auto_mask_tmp2
) = self.compute_pairwise_loss(
ref_img,
tgt_img,
ref_depth,
tgt_depth,
pose_inv,
intrinsics,
)
diff_img_list += [diff_img_tmp1, diff_img_tmp2]
diff_color_list += [diff_color_tmp1, diff_color_tmp2]
diff_depth_list += [diff_depth_tmp1, diff_depth_tmp2]
valid_mask_list += [valid_mask_tmp1, valid_mask_tmp2]
auto_mask_list += [auto_mask_tmp1, auto_mask_tmp2]
diff_img = torch.cat(diff_img_list, dim=1)
diff_color = torch.cat(diff_color_list, dim=1)
diff_depth = torch.cat(diff_depth_list, dim=1)
valid_mask = torch.cat(valid_mask_list, dim=1)
auto_mask = torch.cat(auto_mask_list, dim=1)
# using photo loss to select best match in multiple views
if not self.no_min_optimize:
indices = torch.argmin(diff_color, dim=1, keepdim=True)
diff_img = torch.gather(diff_img, 1, indices)
diff_depth = torch.gather(diff_depth, 1, indices)
valid_mask = torch.gather(valid_mask, 1, indices)
auto_mask = torch.gather(auto_mask, 1, indices)
if not self.no_auto_mask:
photo_loss = self.mean_on_mask(diff_img, valid_mask * auto_mask)
geometry_loss = self.mean_on_mask(diff_depth, valid_mask * auto_mask)
else:
photo_loss = self.mean_on_mask(diff_img, valid_mask)
geometry_loss = self.mean_on_mask(diff_depth, valid_mask)
dynamic_mask = None
if self.return_dynamic_mask:
# get dynamic mask for tgt image
dynamic_mask_list = []
for i in range(0, len(diff_depth_list), 2):
tmp = diff_depth_list[i]
tmp[valid_mask_list[1]<1] = 0
dynamic_mask_list += [1-tmp]
dynamic_mask = torch.cat(dynamic_mask_list, dim=1).mean(dim=1, keepdim=True)
return photo_loss, geometry_loss, dynamic_mask
def compute_pairwise_loss(self, tgt_img, ref_img, tgt_depth, ref_depth, pose, intrinsic):
ref_img_warped, projected_depth, computed_depth = inverse_warp2(ref_img, tgt_depth, ref_depth, pose, intrinsic, padding_mode='zeros')
diff_depth = (computed_depth-projected_depth).abs()/(computed_depth+projected_depth)
# masking zero values
valid_mask_ref = (ref_img_warped.abs().mean(dim=1, keepdim=True) > 1e-3).float()
valid_mask_tgt = (tgt_img.abs().mean(dim=1, keepdim=True) > 1e-3).float()
valid_mask = valid_mask_tgt * valid_mask_ref
diff_color = (tgt_img-ref_img_warped).abs().mean(dim=1, keepdim=True)
identity_warp_err = (tgt_img-ref_img).abs().mean(dim=1, keepdim=True)
auto_mask = (diff_color<identity_warp_err).float()
diff_img = (tgt_img-ref_img_warped).abs().clamp(0,1)
if not self.no_ssim:
ssim_map = self.ssim_loss(tgt_img, ref_img_warped)
diff_img = (0.15 * diff_img + 0.85 * ssim_map)
diff_img = torch.mean(diff_img, dim=1, keepdim=True)
# reduce photometric loss weight for dynamic regions
if not self.no_dynamic_mask:
weight_mask = (1-diff_depth)
diff_img = diff_img * weight_mask
return diff_img, diff_color, diff_depth, valid_mask, auto_mask
# compute mean value on a binary mask
def mean_on_mask(self, diff, valid_mask):
mask = valid_mask.expand_as(diff)
# if mask.sum() > 100:
# mean_value = (diff * mask).sum() / mask.sum()
# else:
# mean_value = torch.tensor(0).float().to(device)
mean_value = (diff * mask).sum() / (mask.sum() + 1e-6)
return mean_value
def forward(self, input, ref_input, prediction, ref_prediction, intrinsic, **kwargs):
photo_loss, geometry_loss, dynamic_mask = self.photo_and_geometry_loss(
tgt_img=input,
ref_imgs=ref_input,
tgt_depth=prediction,
ref_depths=ref_prediction,
intrinsics=intrinsic,
poses=kwargs['pose'],
poses_inv=kwargs['inv_pose'])
loss = self.loss_weight_geometry * geometry_loss + self.loss_weight_photo * photo_loss
if torch.isnan(loss).item() | torch.isinf(loss).item():
raise RuntimeError(f'VNL error, {loss}')
return loss * self.total_loss_weight
# def compute_smooth_loss(tgt_depth, tgt_img):
# def get_smooth_loss(disp, img):
# """
# Computes the smoothness loss for a disparity image
# The color image is used for edge-aware smoothness
# """
# # normalize
# mean_disp = disp.mean(2, True).mean(3, True)
# norm_disp = disp / (mean_disp + 1e-7)
# disp = norm_disp
# grad_disp_x = torch.abs(disp[:, :, :, :-1] - disp[:, :, :, 1:])
# grad_disp_y = torch.abs(disp[:, :, :-1, :] - disp[:, :, 1:, :])
# grad_img_x = torch.mean(torch.abs(img[:, :, :, :-1] - img[:, :, :, 1:]), 1, keepdim=True)
# grad_img_y = torch.mean(torch.abs(img[:, :, :-1, :] - img[:, :, 1:, :]), 1, keepdim=True)
# grad_disp_x *= torch.exp(-grad_img_x)
# grad_disp_y *= torch.exp(-grad_img_y)
# return grad_disp_x.mean() + grad_disp_y.mean()
# loss = get_smooth_loss(tgt_depth, tgt_img)
# return loss
# @torch.no_grad()
# def compute_errors(gt, pred, dataset):
# # pred : b c h w
# # gt: b h w
# abs_diff = abs_rel = sq_rel = log10 = rmse = rmse_log = a1 = a2 = a3 = 0.0
# batch_size, h, w = gt.size()
# if pred.nelement() != gt.nelement():
# pred = F.interpolate(pred, [h,w], mode='bilinear', align_corners=False)
# # pred = F.interpolate(pred, [h,w], mode='nearest')
# pred = pred.view(batch_size, h, w)
# if dataset == 'kitti':
# crop_mask = gt[0] != gt[0]
# y1, y2 = int(0.40810811 * gt.size(1)), int(0.99189189 * gt.size(1))
# x1, x2 = int(0.03594771 * gt.size(2)), int(0.96405229 * gt.size(2))
# crop_mask[y1:y2, x1:x2] = 1
# max_depth = 80
# if dataset == 'cs':
# crop_mask = gt[0] != gt[0]
# crop_mask[256:, 192:1856] = 1
# max_depth = 80
# if dataset == 'nyu':
# crop_mask = gt[0] != gt[0]
# crop = np.array([45, 471, 41, 601]).astype(np.int32)
# crop_mask[crop[0]:crop[1], crop[2]:crop[3]] = 1
# max_depth = 10
# if dataset == 'bonn':
# crop_mask = gt[0] != gt[0]
# crop_mask[:,:] = 1
# max_depth = 10
# if dataset == 'ddad':
# crop_mask = gt[0] != gt[0]
# crop_mask[:,:] = 1
# max_depth = 200
# min_depth = 1e-3
# for current_gt, current_pred in zip(gt, pred):
# valid = (current_gt > min_depth) & (current_gt < max_depth)
# valid = valid & crop_mask
# valid_gt = current_gt[valid]
# valid_pred = current_pred[valid]
# # align scale
# valid_pred = valid_pred * torch.median(valid_gt)/torch.median(valid_pred)
# valid_pred = valid_pred.clamp(min_depth, max_depth)
# thresh = torch.max((valid_gt / valid_pred), (valid_pred / valid_gt))
# a1 += (thresh < 1.25).float().mean()
# a2 += (thresh < 1.25 ** 2).float().mean()
# a3 += (thresh < 1.25 ** 3).float().mean()
# diff_i = valid_gt - valid_pred
# abs_diff += torch.mean(torch.abs(diff_i))
# abs_rel += torch.mean(torch.abs(diff_i) / valid_gt)
# sq_rel += torch.mean(((diff_i)**2) / valid_gt)
# rmse += torch.sqrt(torch.mean(diff_i ** 2))
# rmse_log += torch.sqrt(torch.mean((torch.log(valid_gt) - torch.log(valid_pred)) ** 2))
# log10 += torch.mean(torch.abs((torch.log10(valid_gt) - torch.log10(valid_pred))))
# return [metric.item() / batch_size for metric in [abs_diff, abs_rel, sq_rel, log10, rmse, rmse_log, a1, a2, a3]]
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