# -*- coding: utf-8 -*-
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# You can only use this computer program if you have closed
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# program from someone who is authorized to grant you that right.
# Any use of the computer program without a valid license is prohibited and
# liable to prosecution.
#
# Copyright©2019 Max-Planck-Gesellschaft zur Förderung
# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute
# for Intelligent Systems. All rights reserved.
#
# Contact: ps-license@tuebingen.mpg.de
import torch
import torch.nn as nn
import torch.nn.functional as F
from lib.net.BasePIFuNet import BasePIFuNet
from lib.net.FBNet import GANLoss, IDMRFLoss, VGGLoss, define_D, define_G
from lib.net.net_util import init_net
class NormalNet(BasePIFuNet):
"""
HG PIFu network uses Hourglass stacks as the image filter.
It does the following:
1. Compute image feature stacks and store it in self.im_feat_list
self.im_feat_list[-1] is the last stack (output stack)
2. Calculate calibration
3. If training, it index on every intermediate stacks,
If testing, it index on the last stack.
4. Classification.
5. During training, error is calculated on all stacks.
"""
def __init__(self, cfg):
super(NormalNet, self).__init__()
self.opt = cfg.net
self.F_losses = [item[0] for item in self.opt.front_losses]
self.B_losses = [item[0] for item in self.opt.back_losses]
self.F_losses_ratio = [item[1] for item in self.opt.front_losses]
self.B_losses_ratio = [item[1] for item in self.opt.back_losses]
self.ALL_losses = self.F_losses + self.B_losses
if self.training:
if 'vgg' in self.ALL_losses:
self.vgg_loss = VGGLoss()
if ('gan' in self.ALL_losses) or ('gan_feat' in self.ALL_losses):
self.gan_loss = GANLoss(use_lsgan=True)
if 'mrf' in self.ALL_losses:
self.mrf_loss = IDMRFLoss()
if 'l1' in self.ALL_losses:
self.l1_loss = nn.SmoothL1Loss()
self.in_nmlF = [
item[0] for item in self.opt.in_nml if "_F" in item[0] or item[0] == "image"
]
self.in_nmlB = [
item[0] for item in self.opt.in_nml if "_B" in item[0] or item[0] == "image"
]
self.in_nmlF_dim = sum([
item[1] for item in self.opt.in_nml if "_F" in item[0] or item[0] == "image"
])
self.in_nmlB_dim = sum([
item[1] for item in self.opt.in_nml if "_B" in item[0] or item[0] == "image"
])
self.netF = define_G(self.in_nmlF_dim, 3, 64, "global", 4, 9, 1, 3, "instance")
self.netB = define_G(self.in_nmlB_dim, 3, 64, "global", 4, 9, 1, 3, "instance")
if ('gan' in self.ALL_losses):
self.netD = define_D(3, 64, 3, 'instance', False, 2, 'gan_feat' in self.ALL_losses)
init_net(self)
def forward(self, in_tensor):
inF_list = []
inB_list = []
for name in self.in_nmlF:
inF_list.append(in_tensor[name])
for name in self.in_nmlB:
inB_list.append(in_tensor[name])
nmlF = self.netF(torch.cat(inF_list, dim=1))
nmlB = self.netB(torch.cat(inB_list, dim=1))
# ||normal|| == 1
nmlF_normalized = nmlF / torch.norm(nmlF, dim=1, keepdim=True)
nmlB_normalized = nmlB / torch.norm(nmlB, dim=1, keepdim=True)
# output: float_arr [-1,1] with [B, C, H, W]
mask = ((in_tensor["image"].abs().sum(dim=1, keepdim=True) != 0.0).detach().float())
return nmlF_normalized * mask, nmlB_normalized * mask
def get_norm_error(self, prd_F, prd_B, tgt):
"""calculate normal loss
Args:
pred (torch.tensor): [B, 6, 512, 512]
tagt (torch.tensor): [B, 6, 512, 512]
"""
tgt_F, tgt_B = tgt["normal_F"], tgt["normal_B"]
# netF, netB, netD
total_loss = {"netF": 0.0, "netB": 0.0}
if 'l1' in self.F_losses:
l1_F_loss = self.l1_loss(prd_F, tgt_F)
total_loss["netF"] += self.F_losses_ratio[self.F_losses.index('l1')] * l1_F_loss
total_loss["l1_F"] = self.F_losses_ratio[self.F_losses.index('l1')] * l1_F_loss
if 'l1' in self.B_losses:
l1_B_loss = self.l1_loss(prd_B, tgt_B)
total_loss["netB"] += self.B_losses_ratio[self.B_losses.index('l1')] * l1_B_loss
total_loss["l1_B"] = self.B_losses_ratio[self.B_losses.index('l1')] * l1_B_loss
if 'vgg' in self.F_losses:
vgg_F_loss = self.vgg_loss(prd_F, tgt_F)
total_loss["netF"] += self.F_losses_ratio[self.F_losses.index('vgg')] * vgg_F_loss
total_loss["vgg_F"] = self.F_losses_ratio[self.F_losses.index('vgg')] * vgg_F_loss
if 'vgg' in self.B_losses:
vgg_B_loss = self.vgg_loss(prd_B, tgt_B)
total_loss["netB"] += self.B_losses_ratio[self.B_losses.index('vgg')] * vgg_B_loss
total_loss["vgg_B"] = self.B_losses_ratio[self.B_losses.index('vgg')] * vgg_B_loss
scale_factor = 0.5
if 'mrf' in self.F_losses:
mrf_F_loss = self.mrf_loss(
F.interpolate(prd_F, scale_factor=scale_factor, mode='bicubic', align_corners=True),
F.interpolate(tgt_F, scale_factor=scale_factor, mode='bicubic', align_corners=True)
)
total_loss["netF"] += self.F_losses_ratio[self.F_losses.index('mrf')] * mrf_F_loss
total_loss["mrf_F"] = self.F_losses_ratio[self.F_losses.index('mrf')] * mrf_F_loss
if 'mrf' in self.B_losses:
mrf_B_loss = self.mrf_loss(
F.interpolate(prd_B, scale_factor=scale_factor, mode='bicubic', align_corners=True),
F.interpolate(tgt_B, scale_factor=scale_factor, mode='bicubic', align_corners=True)
)
total_loss["netB"] += self.B_losses_ratio[self.B_losses.index('mrf')] * mrf_B_loss
total_loss["mrf_B"] = self.B_losses_ratio[self.B_losses.index('mrf')] * mrf_B_loss
if 'gan' in self.ALL_losses:
total_loss["netD"] = 0.0
pred_fake = self.netD.forward(prd_B)
pred_real = self.netD.forward(tgt_B)
loss_D_fake = self.gan_loss(pred_fake, False)
loss_D_real = self.gan_loss(pred_real, True)
loss_G_fake = self.gan_loss(pred_fake, True)
total_loss["netD"] += 0.5 * (loss_D_fake + loss_D_real
) * self.B_losses_ratio[self.B_losses.index('gan')]
total_loss["D_fake"] = loss_D_fake * self.B_losses_ratio[self.B_losses.index('gan')]
total_loss["D_real"] = loss_D_real * self.B_losses_ratio[self.B_losses.index('gan')]
total_loss["netB"] += loss_G_fake * self.B_losses_ratio[self.B_losses.index('gan')]
total_loss["G_fake"] = loss_G_fake * self.B_losses_ratio[self.B_losses.index('gan')]
if 'gan_feat' in self.ALL_losses:
loss_G_GAN_Feat = 0
for i in range(2):
for j in range(len(pred_fake[i]) - 1):
loss_G_GAN_Feat += self.l1_loss(pred_fake[i][j], pred_real[i][j].detach())
total_loss["netB"] += loss_G_GAN_Feat * self.B_losses_ratio[
self.B_losses.index('gan_feat')]
total_loss["G_GAN_Feat"] = loss_G_GAN_Feat * self.B_losses_ratio[
self.B_losses.index('gan_feat')]
return total_loss