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Copy pathloss.py
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254 lines (194 loc) · 8.72 KB
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from __future__ import absolute_import, division, print_function
import sys
import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from warper import *
def gradient_x(img):
gx = img[:, :, :, :-1] - img[:, :, :, 1:]
return gx
def gradient_y(img):
gy = img[:, :, :-1, :] - img[:, :, 1:, :]
return gy
def mean_on_mask(diff, valid_mask):
mask = valid_mask.expand_as(diff)
mean_value = (diff * mask).sum() / mask.sum()
return mean_value
class Loss(nn.Module):
def __init__(self, opt, warper, dataset=None):
super(Loss, self).__init__()
self.opt = opt
global device
device = torch.device(opt.cuda)
self.scales = opt.scales
self.intervals = opt.intervals
self.ssim_weight = opt.ssim_weight
self.ssim = SSIM(window_size=3).to(device)
self.warper = warper
self.width = opt.width
self.height = opt.height
self.weights = {
'photo':opt.photometric_loss,
'flow': opt.flow_loss,
'geo': opt.geometry_loss
}
def preprocess_minibatch_weights(self, items):
opt = self.opt
self.bs = items['imgs'].size(0)
self.interval_weights = {}
self.adaptive_weights = {}
# compute the weights for different source view
alpha_sum = 0
self.alpha = {}
self.beta = {}
for i in self.intervals:
if i >= self.bs: continue
self.alpha[i] = np.power(opt.adaptive_alpha, i)
self.beta[i] = np.power(opt.adaptive_beta, i)
alpha_sum = sum([self.alpha[k] for k in self.alpha.keys()])
beta_sum = sum([self.beta[k] for k in self.beta.keys()])
for k in self.alpha.keys():
self.alpha[k] /= alpha_sum
self.beta[k] /= beta_sum
def compute_loss_terms(self, items):
# compute the loss of a given snippet with multiple frame intervals
bs = items['imgs'].size(0)
loss_items = {}
for key in self.weights.keys():
loss_items[key] = 0
poses = items['poses']
poses_inv = items['poses_inv']
for i in self.intervals:
if i >= bs: continue
pair_item = {'img1': items['imgs'][:-i],
'img2': items['imgs'][i:],
'depth1': [depth[:-i] for depth in items['depths']],
'depth2': [depth[i:] for depth in items['depths']],
'pose21': poses_inv[:-i] @ poses[i:],
'pose12': poses_inv[i:] @ poses[:-i],
'flow12': items[('flow_fwd', i)],
'flow21': items[('flow_bwd', i)]}
pair_item['alpha'] = self.alpha[i]
pair_item['beta'] = self.beta[i]
pair_loss, err_mask = self.compute_pairwise_loss(pair_item)
for name in loss_items.keys():
if name not in pair_loss.keys(): continue
loss_items[name] += pair_loss[name]
try:
m = err_mask.size(0)
n = items['err_mask'].size(0)
items['err_mask'][:m-n] += err_mask
except Exception as e:
items['err_mask'] = err_mask
return loss_items
def compute_pairwise_loss(self, item):
# compute the loss a given snippet with a frame interval
img1, img2 = item['img1'], item['img2']
pose12, pose21 = item['pose12'], item['pose21']
input_flow12 = item['flow12'].permute(0, 3, 1, 2)
input_flow21 = item['flow21'].permute(0, 3, 1, 2)
bs = img1.size(0)
loss_items = {}
for key in self.weights.keys():
loss_items[key] = 0
for scale in self.scales:
depth1_scaled = item['depth1'][scale]
depth2_scaled = item['depth2'][scale]
ret1 = self.warper.inverse_warp(img2, depth1_scaled, depth2_scaled, pose12)
ret2 = self.warper.inverse_warp(img1, depth2_scaled, depth1_scaled, pose21)
rec1, mask1, projected_depth1, computed_depth1, warp_sample1, pt1, pt12 = ret1
rec2, mask2, projected_depth2, computed_depth2, warp_sample2, pt2, pt21 = ret2
# geometry loss
diff_depth1 = ((computed_depth1 - projected_depth1).abs() /
(computed_depth1 + projected_depth1).abs()).clamp(0, 1)
diff_depth2 = ((computed_depth2 - projected_depth2).abs() /
(computed_depth2 + projected_depth2).abs()).clamp(0, 1)
diff_depth1 *= item['alpha']
diff_depth2 *= item['alpha']
loss_items['geo'] += mean_on_mask(diff_depth1, mask1)
loss_items['geo'] += mean_on_mask(diff_depth2, mask2)
weight_mask1 = (1 - diff_depth1) * mask1
weight_mask2 = (1 - diff_depth2) * mask2
# photometric loss
diff_img1 = (img1 - rec1).abs()
diff_img2 = (img2 - rec2).abs()
if self.ssim_weight > 0:
ssim_map1 = self.ssim(img1, rec1)
ssim_map2 = self.ssim(img2, rec2)
diff_img1 = (1-self.ssim_weight)*diff_img1 + self.ssim_weight*ssim_map1
diff_img2 = (1-self.ssim_weight)*diff_img2 + self.ssim_weight*ssim_map2
loss_items['photo'] += mean_on_mask(diff_img1 * item['alpha'], mask1)
loss_items['photo'] += mean_on_mask(diff_img2 * item['alpha'], mask2)
warp_flow1 = warp_sample1.permute(0, 3, 1, 2)
warp_flow2 = warp_sample2.permute(0, 3, 1, 2)
# flow
diff_flow1 = (warp_flow1 - input_flow12).abs().sum(1, keepdim=True)
diff_flow2 = (warp_flow2 - input_flow21).abs().sum(1, keepdim=True)
diff_flow1 *= item['beta']
diff_flow2 *= item['beta']
loss_items['flow'] += mean_on_mask(diff_flow1, mask1)
loss_items['flow'] += mean_on_mask(diff_flow2, mask2)
# return error mask for post-processing
err_mask = torch.abs(diff_img1.mean(1, keepdim=True)) * mask1
return loss_items, err_mask
def forward(self, items):
bs = items['imgs'].size(0)
loss_items = self.compute_loss_terms(items)
loss_items['full'] = 0
for key in self.weights.keys():
loss_items['full'] += self.weights[key] * loss_items[key]
return loss_items
class SSIM(nn.Module):
"""Layer to compute the SSIM loss between a pair of images
"""
def __init__(self, window_size=3, alpha=1, beta=1, gamma=1):
super(SSIM, self).__init__()
self.mu_x_pool = nn.AvgPool2d(window_size, 1)
self.mu_y_pool = nn.AvgPool2d(window_size, 1)
self.sig_x_pool = nn.AvgPool2d(window_size, 1)
self.sig_y_pool = nn.AvgPool2d(window_size, 1)
self.sig_xy_pool = nn.AvgPool2d(window_size, 1)
self.refl = nn.ReflectionPad2d(window_size//2)
self.C1 = 0.01 ** 2
self.C2 = 0.03 ** 2
self.C3 = self.C2 / 2
self.alpha = alpha
self.beta = beta
self.gamma = gamma
if alpha == 1 and beta == 1 and gamma == 1:
self.run_compute = self.compute_simplified
else:
self.run_compute = self.compute
def compute(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
l = (2 * mu_x * mu_y + self.C1) / \
(mu_x * mu_x + mu_y * mu_y + self.C1)
c = (2 * sigma_x * sigma_y + self.C2) / \
(sigma_x + sigma_y + self.C2)
s = (sigma_xy + self.C3) / \
(torch.sqrt(sigma_x * sigma_y) + self.C3)
ssim_xy = torch.pow(l, self.alpha) * \
torch.pow(c, self.beta) * \
torch.pow(s, self.gamma)
return torch.clamp((1 - ssim_xy) / 2, 0, 1)
def compute_simplified(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)
def forward(self, x, y):
return self.run_compute(x, y)