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meta_test.py
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# from livelossplot import PlotLosses
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import os
os.environ['KMP_DUPLICATE_LIB_OK']='True'
from tqdm.notebook import tqdm as tqdm
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
import numpy as np
from collections import OrderedDict
from torchmeta.modules import (MetaModule, MetaSequential, MetaConv2d,
MetaBatchNorm2d, MetaLinear)
from torchmeta.utils.gradient_based import gradient_update_parameters
from torch.utils.data import DataLoader
import random
from torchvision import datasets
import torchvision
import torchvision.transforms as transforms
import torchvision.utils as vutils
import torch.utils.data as data
from os import listdir
from PIL import Image
from models.renderer import *
from models.networks import VGGLoss,Net_Des19,FeatureLoss
import argparse
from torch.utils.tensorboard import SummaryWriter
from util.util import EPSILON, open_url
from util.descriptor import TDLoss_2
from skimage.transform import resize
from collections import OrderedDict
from torchvision.transforms import Normalize
from meta_model import *
from meta_utils import *
from pandas import DataFrame
import lpips
loss_fn_alex = lpips.LPIPS(net='alex').cuda() # best forward scores
def normalize_vgg19(input, isGram):
if isGram:
transform = Normalize(
mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.255]
)
else:
transform = Normalize(
mean=[0.48501961, 0.45795686, 0.40760392],
std=[1./255, 1./255, 1./255]
)
return transform(input.cpu()).cuda()
## assign the grad of source to target
def write_params(model, param_dict):
for name, param in model.named_parameters():
param.data.copy_(param_dict[name])
def N_light_sample(Number, r_max, mydevice):
# u_1= torch.rand((Number,1),device=mydevice)*r_max #+ r_max # rmax: 0.95 (default)
# u_2= torch.rand((Number,1),device=mydevice)
u_1= torch.rand((Number,1),device=mydevice)*0.0 + r_max # gif
u_2= 1.0/Number*torch.arange(1,Number+1,dtype=torch.float32).unsqueeze(-1).to(mydevice) # gif
# print('u_2:', u_2)
# print('u_22:', u_22)
r = torch.sqrt(u_1)
theta = 2*PI*u_2
x = r*torch.cos(theta)
y = r*torch.sin(theta)
z = torch.sqrt(1-r*r)
temp_out = torch.cat([x,y,z],1)
return temp_out
def N_Light_demo(Near_Number,r_max, mydevice):
rand_light = N_light_sample(Near_Number, r_max, device)
# dist = torch.exp(torch.normal(torch.tensor([1.0]), torch.tensor([0.2]))).cuda()
Light_po = rand_light *4.0
return Light_po
def test(opt, model, coords, val_data, device, FeaExtractor=None):
val_steps = opt.val_step #@param {type:"integer"}
first_order = opt.first_order #@param {type:"boolean"}
L1 = torch.nn.L1Loss()
mse = torch.nn.MSELoss().to(device)
params = OrderedDict(model.meta_named_parameters())
for key in params:
print(key)
criterionVGG = VGGLoss(opt)
weights = [0.1, 0.1, 0.1, 0.1]
keys = [1, 2, 3,4]
if opt.netloss=='Des19Net':
criterionNet = MyLoss(opt, keys, weights, device, val=True)
log_step = []
val_log_ren = OrderedDict()
val_log_fea = OrderedDict()
step = 1
flag=False
# LightPos = torch.tensor([[0,0,2.14]], device=device)
LightPos = torch.tensor([[0,0,4]], device=device)
Position_map=PositionMap(opt.res,opt.res,3).to(device)
if opt.netloss=='Des19Net':
criterionNet.set_gradient(False)
# Plot_iter=[1,3,5,7,10,12,15,17,20,25,30,35,45,50,60,70,80,90,100,120,140,150]
Plot_iter=[t for t in range(0,val_steps+1)]
print(Plot_iter)
# Plot_iter=[]
val_rens = OrderedDict()
val_feas = OrderedDict()
val_feas_loss = OrderedDict()
val_total_loss = OrderedDict()
val_ren_loss = OrderedDict()
if opt.excel:
Excel_dict = {}
Fea_Excel_dict = {}
Ren_Excel_dict = {}
model.eval()
g_test = None
out_val = None
valfea_loss = 0
valren_loss = 0
load_model = 'pretrain' if opt.load_pretrain else opt.model
# create directory
save_valfea_path = f'test/{opt.name}/fea'
save_in_path = f'test/{opt.name}/in'
save_valren_path = f'test/{opt.name}/ren'
save_pro_path = f'test/{opt.name}/pro'
if not os.path.exists(save_in_path):
os.makedirs(save_in_path)
if not os.path.exists(save_valfea_path):
os.makedirs(save_valfea_path)
if not os.path.exists(save_valren_path):
os.makedirs(save_valren_path)
if not os.path.exists(save_pro_path):
os.makedirs(save_pro_path)
Num = 40
def delogTensor(tensor):
temp = torch.exp(tensor*(np.log(1.01)-np.log(0.01)) + np.log(0.01))-0.01
return temp**(1/2.2)
if opt.test_img=='SynPlot':
# rand_light_pos_loss = NLight_VaryH(Num, 0.03, device)
# np.save('40_light.npy',rand_light_pos_loss.cpu().numpy())
rand_light_pos_loss =torch.from_numpy(np.load('40_light_3.npy')).to(device)
print(rand_light_pos_loss.shape)
if opt.loss_after1=='TD':
criterionTD = TDLoss_2(device, 0)
for img_id, val_example in enumerate(val_data):
val_example, name=val_example
print(name)
val_img = val_example.to(device)
image_gt = val_img[0].permute(1,2,0) # [256, 256, C]
# load model
load_network(model, "final", 'ckpt/', name='model')
if opt.netloss=='Des19Net':
load_network(criterionNet.lossnet, "final", 'ckpt/', name='des19net')
params_test = OrderedDict(model.meta_named_parameters())
inner_lr = opt.inner_lr #@param
flag_lr = True
inner_loss = 0.0
TD_loss = 0.0
TD16Loss = 0.0
if not opt.no_use_adam:
optimAdam = torch.optim.Adam(model.parameters(), lr=opt.after_innerlr, betas=[0.5, 0.5])
if image_gt.shape[-1]>3:
image_te = SingleRender_NumberPointLight_FixedCamera(2*image_gt[:,:,0:3]-1, image_gt[:,:,3:6],image_gt[:,:,6:7], image_gt[:,:,9:12], LightPos, Position_map, device, True, lightinten=opt.light_inten) #[B,N, W,H,C]
image_te = (image_te.clamp(0,1)+EPSILON)**(1/2.2)
else:
image_te = image_gt.unsqueeze(0)
for in_step in range(val_steps+1):
if opt.model=='UNet':
myinput = 2*image_te.permute(0,3,1,2)-1 # [-1,1] # [B,3,H,W]
elif opt.model=='InConSiren':
gt_ren_te = image_te
myinput = 2*image_te.permute(0,3,1,2)-1 # [B,3,H,W] [-1,1]
myinput = [coords[0],myinput] # coords:[H,W,2] myinput: [B,3,H,W]
elif opt.model=='OutConSiren':
print(image_te.shape)
myfea_in = FeaExtractor(2*image_te.permute(0,3,1,2)-1).squeeze(0).permute(1,2,0) #[B,3,H,W] --> [H,W,32]
myinput = torch.cat([coords[0], myfea_in],dim=-1)
g_te = model(myinput, params=params_test)
# print('g_te ',g_te.shape)
if opt.fea=='all_N1' or opt.fea=='D+R' or opt.fea=='all_N2' or opt.fea=='all_N3':
g_te = paramize_out(opt,g_te)
elif opt.fea=='N' or opt.fea=='N2' or opt.fea=='N3':
g_te = ProcessNormal(opt, g_te)
if in_step==0:
val_feas[str(in_step)] = g_te
elif in_step==1:
val_feas[str(in_step)] = g_te
elif in_step==2:
val_feas[str(in_step)] = g_te
elif in_step==5:
val_feas[str(in_step)] = g_te
elif in_step==opt.val_step:
val_feas[str(in_step)] = g_te
out_ren_te = SingleRender_NumberPointLight_FixedCamera(2*g_te[:,:,0:3]-1, g_te[:,:,3:6], g_te[:,:,6:7], g_te[:,:,9:12], LightPos, Position_map, device, True, lightinten=opt.light_inten) #[B,N, W,H,C]
out_ren_te = (out_ren_te.clamp(0,1)+EPSILON)**(1/2.2)
ren_loss = mse(gt_ren_te, out_ren_te)*opt.Winner_ren
if in_step==0:
if opt.netloss=='Des19Net':
# inner_loss, des19_svbrdf = criterionNet(2*out_ren_tr.permute(0,3,1,2)-1, 2*gt_ren_tr.permute(0,3,1,2)-1)
# inner_loss, des19_svbrdf_te = criterionNet(g_te, 2*gt_ren_te.permute(0,3,1,2)-1)
inner_loss, des19_svbrdf_te = criterionNet(g_te, 2*gt_ren_te.permute(0,3,1,2)-1)
inner_loss += ren_loss
elif opt.netloss=='GT':
# print(g_tr.shape)
# print(image_tr.shape)
inner_loss = mse(g_te, image_gt)
inner_loss += ren_loss
else:
inner_loss = ren_loss
if opt.Wvgg_inner>0:
out_ren_te_vg = VGGpreprocess(out_ren_te.permute(0,3,1,2))
gt_ren_te_vg = VGGpreprocess(gt_ren_te.permute(0,3,1,2))
inner_ren_vgg_loss = criterionVGG(out_ren_te_vg, gt_ren_te_vg)*opt.Wvgg_inner
inner_loss += inner_ren_vgg_loss
if opt.Wtd_inner>0:
out_ren_te_td = out_ren_te.permute(0,3,1,2)
gt_ren_te_td = gt_ren_te.permute(0,3,1,2)
inner_ren_td = criterionTD(out_ren_te_td, gt_ren_te_td)*opt.Wtd_inner
inner_loss += inner_ren_td
if opt.loss_after1 =='mse':
temp_loss = ren_loss
elif opt.loss_after1=='vgg':
out_ren_tr_vg = VGGpreprocess(out_ren_te.permute(0,3,1,2))
gt_ren_tr_vg = VGGpreprocess(gt_ren_te.permute(0,3,1,2))
temp_loss = criterionVGG(out_ren_tr_vg, gt_ren_tr_vg) * 0.005 + ren_loss
elif opt.loss_after1=='TD':
out_ren = out_ren_te[0:1,...].permute(0,3,1,2)
gt_ren = gt_ren_te[0:1,...].permute(0,3,1,2)
out_16 = nn.functional.interpolate(out_ren, size = opt.dres, mode='bilinear', align_corners=True)
gt_16 = nn.functional.interpolate(gt_ren, size = opt.dres, mode='bilinear', align_corners=True)
dren_loss = mse(out_16, gt_16)*opt.Wdren_outer
TDren_loss = criterionTD(out_ren, gt_ren)*opt.WTDren_outer
temp_loss = dren_loss + TDren_loss
if in_step>0 or opt.no_inner_step:
inner_loss = temp_loss
vis_loss = temp_loss
else:
vis_loss = temp_loss
# after 1 step
if flag_lr and in_step>0:
inner_lr = opt.after_innerlr
if 'MG' in opt.loss_after1 and in_step>0:
# print(in_step)
flag_lr = False
if in_step%opt.decay_interval==0:
inner_lr = inner_lr*0.5
if not opt.no_use_adam:
for g in optimAdam.param_groups:
g['lr'] = inner_lr
if in_step==0:
val_rens[str(in_step)] = out_ren_te
elif in_step==1:
val_rens[str(in_step)] = out_ren_te
elif in_step==2:
val_rens[str(in_step)] = out_ren_te
elif in_step==5:
val_rens[str(in_step)] = out_ren_te
elif in_step==opt.val_step:
val_rens[str(in_step)] = out_ren_te
if in_step%10==0:
print(f'step: {in_step:d}, inner lr: {inner_lr:.8f} vis loss: {vis_loss:.5f} inner loss: {inner_loss:.5f} TD_loss: {TD_loss:.5f} TD16Loss: {TD16Loss:.5f}')
if (in_step==0 or opt.no_use_adam) and not opt.no_inner_step:
print('1st step')
params_test = gradient_update_parameters(model, inner_loss, params=params_test,
step_size=inner_lr, first_order=True)
write_params(model, params_test)
else:
# print('use_adam')
if in_step==0:
write_params(model, params_test)
optimAdam.zero_grad()
inner_loss.backward()
optimAdam.step()
params_test = OrderedDict(model.named_parameters())
# save loss
if in_step in Plot_iter and opt.test_img=='SynPlot':
g_te_d = g_te.detach()
# print(image_gt.shape, g_te_d.shape)
outer_fea_loss = 0.25*(mse(image_gt[:,:,0:3], g_te_d[:,:,0:3])*opt.wN_outer
+ mse(image_gt[:,:,3:6], g_te_d[:,:,3:6]) *opt.wD_outer
+ mse(image_gt[:,:,6:9], g_te_d[:,:,6:9])*opt.wR_outer
+ mse(image_gt[:,:,9:12], g_te_d[:,:,9:12]))*opt.wS_outer
# add VGG loss to features
if opt.Wfea_vgg>0:
D_vg_pred = VGGpreprocess(g_te_d[:,:,3:6].unsqueeze(0).permute(0,3,1,2))
D_vg_gt = VGGpreprocess(image_gt[:,:,3:6].unsqueeze(0).permute(0,3,1,2))
D_vg = criterionVGG(D_vg_pred, D_vg_gt)*opt.Wfea_vgg
S_vg_pred = VGGpreprocess(g_te_d[:,:,9:12].unsqueeze(0).permute(0,3,1,2))
S_vg_gt = VGGpreprocess(image_gt[:,:,9:12].unsqueeze(0).permute(0,3,1,2))
S_vg = criterionVGG(S_vg_pred, S_vg_gt)*opt.Wfea_vgg
outer_fea_loss += (D_vg + S_vg)*0.5
# compute loss of 20 renderings
outer_ren_loss=0
for i in range(20):
gt_ren_sampled = SingleRender_NumberPointLight_FixedCamera(2*image_gt[:,:,0:3]-1, image_gt[:,:,3:6],image_gt[:,:,6:7], image_gt[:,:,9:12], rand_light_pos_loss[i:i+1,...], Position_map, device, CamLi_co=True) #[B,N, W,H,C]
gt_ren_sampled = (gt_ren_sampled.clamp(0,1)+opt.EPSILON)**(1/2.2)
pred_ren_sampled = SingleRender_NumberPointLight_FixedCamera(2*g_te_d[:,:,0:3]-1, g_te_d[:,:,3:6], g_te_d[:,:,6:7], g_te_d[:,:,9:12], rand_light_pos_loss[i:i+1,...], Position_map, device, CamLi_co=True) #[B,N, W,H,C]
pred_ren_sampled = (pred_ren_sampled.clamp(0,1)+opt.EPSILON)**(1/2.2)
if opt.loss_after1=='TD':
out_ren = pred_ren_sampled[0:1,...].permute(0,3,1,2)
gt_ren = gt_ren_sampled[0:1,...].permute(0,3,1,2)
out_16 = nn.functional.interpolate(out_ren, size = opt.dres, mode='bilinear', align_corners=True)
gt_16 = nn.functional.interpolate(gt_ren, size = opt.dres, mode='bilinear', align_corners=True)
outer_ren_loss += mse(out_16, gt_16)*opt.Wdren_outer
outer_ren_loss += criterionTD(out_ren, gt_ren)*opt.WTDren_outer
else:
outer_ren_loss += mse(pred_ren_sampled, gt_ren_sampled)
out_ren_te_vg = VGGpreprocess(pred_ren_sampled[0:1,...].permute(0,3,1,2))
gt_ren_te_vg = VGGpreprocess(gt_ren_sampled[0:1,...].permute(0,3,1,2))
outer_ren_vgg_loss = criterionVGG(out_ren_te_vg, gt_ren_te_vg)*opt.Wvgg_outer
outer_ren_loss += outer_ren_vgg_loss
total_outer = outer_fea_loss + outer_ren_loss/20.0
excel_total = total_outer
excel_fea = outer_fea_loss
excel_ren = outer_ren_loss/20.0
val_ren_loss[str(in_step)] = outer_ren_loss/20.0 if str(in_step) not in val_ren_loss else val_ren_loss[str(in_step)] + outer_ren_loss/20.0
val_total_loss[str(in_step)] = total_outer if str(in_step) not in val_total_loss else val_total_loss[str(in_step)] + total_outer
val_feas_loss[str(in_step)] = outer_fea_loss if str(in_step) not in val_feas_loss else val_feas_loss[str(in_step)] + outer_fea_loss
if opt.excel:
if name[0] in Excel_dict:
Excel_dict[name[0]].append(excel_total.cpu().detach().numpy())
else:
Excel_dict[name[0]] = [excel_total.cpu().detach().numpy()]
if name[0] in Fea_Excel_dict:
Fea_Excel_dict[name[0]].append(excel_fea.cpu().detach().numpy())
else:
Fea_Excel_dict[name[0]] = [excel_fea.cpu().detach().numpy()]
if name[0] in Ren_Excel_dict:
Ren_Excel_dict[name[0]].append(excel_ren.cpu().detach().numpy())
else:
Ren_Excel_dict[name[0]] = [excel_ren.cpu().detach().numpy()]
################# save imgs ################33
final_vis_tr = OrderedDict()
final_vis_te = OrderedDict()
if 'all' in opt.fea:
for key_fea, fea in val_feas.items():
final_vis_te[key_fea] = torch.cat((fea[:,:,0:3],(fea[:,:,3:6]+EPSILON)**(1/2.2),fea[:,:,6:9],(fea[:,:,9:12]+EPSILON)**(1/2.2)),dim=1)
if image_gt.shape[-1]>3:
final_vis_te['gt'] = torch.cat((image_gt[:,:,0:3],image_gt[:,:,3:6]**(1/2.2),image_gt[:,:,6:9],image_gt[:,:,9:12]**(1/2.2)),dim=1)
else:
final_vis_te['gt'] = torch.cat((image_gt,image_gt,image_gt,image_gt),dim=1)
svbrdf_vis = final_vis_te[f'{val_steps}'].detach()
if opt.netloss=='Des19Net':
final_vis_te['des19'] = torch.cat((des19_svbrdf_te[:,:,0:3],des19_svbrdf_te[:,:,3:6]**(1/2.2),des19_svbrdf_te[:,:,6:9],des19_svbrdf_te[:,:,9:12]**(1/2.2),des19_svbrdf_te[:,:,3:6]**(1/2.2)),dim=1)
for key_ren, val_ren in val_rens.items():
for temp in range(val_ren.shape[0]):
final_vis_te[key_ren] = torch.cat((final_vis_te[key_ren], val_ren[temp,...]),dim=1)
for temp in range(gt_ren_te.shape[0]):
final_vis_te['gt'] = torch.cat((final_vis_te['gt'], gt_ren_te[temp,...]),dim=1)
# save progressive images
fig = plt.figure(figsize=(40,20))
for i, key in enumerate(final_vis_te):
print(i, key)
plt.subplot(len(final_vis_te), 1, int(i)+1)
plt.imshow((torch.clamp(final_vis_te[key], 0, 1).cpu().detach().numpy()*255.0).astype(np.uint8))
fig.savefig(os.path.join(save_pro_path,'progressive_%s.png'%(name[0])))
plt.close()
save_image(svbrdf_vis, os.path.join(save_valfea_path,'%s.png'%(name[0])))
print(image_te.shape)
save_image(image_te[0,...], os.path.join(save_in_path,'%s.png'%(name[0])))
# save illustration images
if opt.demo:
print(final_vis_te['0'].shape)
save_image(final_vis_te['0'][:,0:256,:], os.path.join(save_valfea_path,'%s_init_N.png'%(name[0])))
save_image(final_vis_te['0'][:,256:512,:], os.path.join(save_valfea_path,'%s_init_D.png'%(name[0])))
save_image(final_vis_te['0'][:,512:768,:], os.path.join(save_valfea_path,'%s_init_R.png'%(name[0])))
save_image(final_vis_te['0'][:,768:1024,:], os.path.join(save_valfea_path,'%s_init_S.png'%(name[0])))
save_image(final_vis_te['1'][:,0:256,:], os.path.join(save_valfea_path,'%s_1_N.png'%(name[0])))
save_image(final_vis_te['1'][:,256:512,:], os.path.join(save_valfea_path,'%s_1_D.png'%(name[0])))
save_image(final_vis_te['1'][:,512:768,:], os.path.join(save_valfea_path,'%s_1_R.png'%(name[0])))
save_image(final_vis_te['1'][:,768:1024,:], os.path.join(save_valfea_path,'%s_1_S.png'%(name[0])))
save_image(final_vis_te[f'{val_steps}'][:,0:256,:], os.path.join(save_valfea_path,'%s_final_N.png'%(name[0])))
save_image(final_vis_te[f'{val_steps}'][:,256:512,:], os.path.join(save_valfea_path,'%s_final_D.png'%(name[0])))
save_image(final_vis_te[f'{val_steps}'][:,512:768,:], os.path.join(save_valfea_path,'%s_final_R.png'%(name[0])))
save_image(final_vis_te[f'{val_steps}'][:,768:1024,:], os.path.join(save_valfea_path,'%s_final_S.png'%(name[0])))
save_image(final_vis_te['gt'][:,0:256,:], os.path.join(save_valfea_path,'%s_gt_N.png'%(name[0])))
save_image(final_vis_te['gt'][:,256:512,:], os.path.join(save_valfea_path,'%s_gt_D.png'%(name[0])))
save_image(final_vis_te['gt'][:,512:768,:], os.path.join(save_valfea_path,'%s_gt_R.png'%(name[0])))
save_image(final_vis_te['gt'][:,768:1024,:], os.path.join(save_valfea_path,'%s_gt_S.png'%(name[0])))
save_image(final_vis_te['des19'][:,0:256,:], os.path.join(save_valfea_path,'%s_pseudo_N.png'%(name[0])))
save_image(final_vis_te['des19'][:,256:512,:], os.path.join(save_valfea_path,'%s_pseudo_D.png'%(name[0])))
save_image(final_vis_te['des19'][:,512:768,:], os.path.join(save_valfea_path,'%s_pseudo_R.png'%(name[0])))
save_image(final_vis_te['des19'][:,768:1024,:], os.path.join(save_valfea_path,'%s_pseudo_S.png'%(name[0])))
g_te_d = g_te.detach()
N = g_te_d[...,0:3]
D = g_te_d[...,3:6]
R = g_te_d[...,6:7]
S = g_te_d[...,9:12]
if opt.excel:
df = DataFrame(Excel_dict)
df.to_excel(f'{load_model}/{opt.resume_name}/test/{opt.name}/test.xlsx', sheet_name='sheet1', index=True)
df = DataFrame(Fea_Excel_dict)
df.to_excel(f'{load_model}/{opt.resume_name}/test/{opt.name}/fea_test.xlsx', sheet_name='sheet1', index=True)
df = DataFrame(Ren_Excel_dict)
df.to_excel(f'{load_model}/{opt.resume_name}/test/{opt.name}/ren_test.xlsx', sheet_name='sheet1', index=True)
# plot loss
if opt.test_img=='SynPlot':
fealoss_log = []
for p in val_feas_loss:
if p=='0':
continue
val_feas_loss[p] = val_feas_loss[p].detach().cpu()/len(val_data)
fealoss_log.append(val_feas_loss[p])
totalloss_log = []
for p in val_total_loss:
if p=='0':
continue
val_total_loss[p] = val_total_loss[p].detach().cpu()/len(val_data)
totalloss_log.append(val_total_loss[p])
renloss_log = []
for p in val_ren_loss:
if p=='0':
continue
val_ren_loss[p] = val_ren_loss[p].detach().cpu()/len(val_data)
renloss_log.append(val_ren_loss[p])
save_loss(totalloss_log, save_valfea_path, Plot_iter, save_name='total')
save_loss(fealoss_log, save_valfea_path, Plot_iter, save_name='fea')
save_loss(renloss_log, save_valfea_path, Plot_iter, save_name='ren')
def output_ff(opt, model, coords, val_data, device, FeaExtractor=None):
val_steps = opt.val_step #@param {type:"integer"}
first_order = opt.first_order #@param {type:"boolean"}
L1 = torch.nn.L1Loss()
mse = torch.nn.MSELoss().to(device)
params = OrderedDict(model.meta_named_parameters())
for key in params:
print(key)
criterionVGG = VGGLoss(opt)
weights = [0.1, 0.1, 0.1, 0.1]
keys = [1, 2, 3,4]
if opt.netloss=='Des19Net':
criterionNet = MyLoss(opt, keys, weights, device, val=True)
log_step = []
val_log_ren = OrderedDict()
val_log_fea = OrderedDict()
step = 1
flag=False
# LightPos = torch.tensor([[0,0,2.14]], device=device)
LightPos = torch.tensor([[0,0,4]], device=device)
Position_map=PositionMap(opt.res,opt.res,3).to(device)
# resize = transforms.Resize((opt.res,opt.res))
# resize = transforms.Compose([transforms.ToPILImage(), transforms.Resize((opt.res,opt.res)), transforms.ToTensor()])
# FeaExtractNet.set_gradient(False)
if opt.netloss=='Des19Net':
criterionNet.set_gradient(False)
# Plot_iter=[1,3,5,7,10,12,15,17,20,25,30,35,45,50,60,70,80,90,100,120,140,150]
Plot_iter=[t for t in range(0,val_steps+1)]
print(Plot_iter)
# Plot_iter=[]
val_rens = OrderedDict()
val_feas = OrderedDict()
val_feas_loss = OrderedDict()
val_total_loss = OrderedDict()
val_ren_loss = OrderedDict()
model.eval()
g_test = None
out_val = None
valfea_loss = 0
valren_loss = 0
load_model = 'pretrain' if opt.load_pretrain else opt.model
# create directory
save_valfea_path = f'{load_model}/{opt.resume_name}/test/{opt.name}/fea'
save_in_path = f'{load_model}/{opt.resume_name}/test/{opt.name}/in'
save_valren_path = f'{load_model}/{opt.resume_name}/test/{opt.name}/ren'
save_pro_path = f'{load_model}/{opt.resume_name}/test/{opt.name}/pro'
if not os.path.exists(save_in_path):
os.makedirs(save_in_path)
if not os.path.exists(save_valfea_path):
os.makedirs(save_valfea_path)
if not os.path.exists(save_valren_path):
os.makedirs(save_valren_path)
if not os.path.exists(save_pro_path):
os.makedirs(save_pro_path)
Num = 40
def delogTensor(tensor):
temp = torch.exp(tensor*(np.log(1.01)-np.log(0.01)) + np.log(0.01))-0.01
return temp**(1/2.2)
# return (tf.log(tf.add(tensor,0.01)) - tf.log(0.01)) / (tf.log(1.01)-tf.log(0.01))
if opt.loss_after1=='TD':
criterionTD = TDLoss_2(device, 0)
for img_id, val_example in enumerate(val_data):
# if opt.demo:
# LightPos = NLight_VaryH(1, 0.03, device)
# if img_id > 20:
# if img_id > 40 or img_id<=20:
# if img_id > 60 or img_id<=40:
# if img_id <= 60:
# continue
val_example, name=val_example
# if name[0] != '0000027;leather_tilesXbrick_uneven_stones;defaultX2':
# continue
val_img = val_example.to(device)
image_gt = val_img[0].permute(1,2,0) # [256, 256, C]
# image_gt = delogTensor(image_gt)
if opt.resume_name!='':
if opt.load_pretrain:
load_network(model, opt.load_iter,'pretrain/%s/models'%(opt.resume_name), name='model')
else:
load_network(model,opt.load_iter,'%s/%s/models'%(opt.model, opt.resume_name), name='model')
if opt.netloss=='Des19Net':
load_network(criterionNet.lossnet, opt.load_iter,'%s/%s/models'%(opt.model, opt.resume_name), name='des19net')
if FeaExtractor is not None:
load_network(FeaExtractor, opt.load_iter,'%s/%s/models'%(opt.model, opt.resume_name), name='FeaExtractor')
params_test = OrderedDict(model.meta_named_parameters())
inner_lr = opt.inner_lr #@param
flag_lr = True
inner_loss = 0.0
TD_loss = 0.0
TD16Loss = 0.0
if not opt.no_use_adam:
optimAdam = torch.optim.Adam(model.parameters(), lr=opt.after_innerlr, betas=[0.5, 0.5])
if image_gt.shape[-1]>3:
image_te = SingleRender_NumberPointLight_FixedCamera(2*image_gt[:,:,0:3]-1, image_gt[:,:,3:6],image_gt[:,:,6:7], image_gt[:,:,9:12], LightPos, Position_map, device, True, lightinten=opt.light_inten) #[B,N, W,H,C]
image_te = (image_te.clamp(0,1)+EPSILON)**(1/2.2)
else:
image_te = image_gt.unsqueeze(0)
for in_step in range(val_steps+1):
if opt.model=='UNet':
myinput = 2*image_te.permute(0,3,1,2)-1 # [-1,1] # [B,3,H,W]
elif opt.model=='InConSiren':
gt_ren_te = image_te
myinput = 2*image_te.permute(0,3,1,2)-1 # [B,3,H,W] [-1,1]
myinput = [coords[0],myinput] # coords:[H,W,2] myinput: [B,3,H,W]
elif opt.model=='OutConSiren':
print(image_te.shape)
myfea_in = FeaExtractor(2*image_te.permute(0,3,1,2)-1).squeeze(0).permute(1,2,0) #[B,3,H,W] --> [H,W,32]
myinput = torch.cat([coords[0], myfea_in],dim=-1)
g_te = model(myinput, params=params_test)
# print('g_te ',g_te.shape)
if opt.fea=='all_N1' or opt.fea=='D+R' or opt.fea=='all_N2' or opt.fea=='all_N3':
g_te = paramize_out(opt,g_te)
elif opt.fea=='N' or opt.fea=='N2' or opt.fea=='N3':
g_te = ProcessNormal(opt, g_te)
if in_step==0:
val_feas[str(in_step)] = g_te
elif in_step==1:
val_feas[str(in_step)] = g_te
elif in_step==2:
val_feas[str(in_step)] = g_te
elif in_step==5:
val_feas[str(in_step)] = g_te
elif in_step==opt.val_step:
val_feas[str(in_step)] = g_te
out_ren_te = SingleRender_NumberPointLight_FixedCamera(2*g_te[:,:,0:3]-1, g_te[:,:,3:6], g_te[:,:,6:7], g_te[:,:,9:12], LightPos, Position_map, device, CamLi_co=True, lightinten=opt.light_inten) #[B,N, W,H,C]
out_ren_te = (out_ren_te.clamp(0,1)+EPSILON)**(1/2.2)
ren_loss = mse(gt_ren_te, out_ren_te)*opt.Winner_ren
if in_step==0:
if opt.netloss=='Des19Net':
# inner_loss, des19_svbrdf = criterionNet(2*out_ren_tr.permute(0,3,1,2)-1, 2*gt_ren_tr.permute(0,3,1,2)-1)
# inner_loss, des19_svbrdf_te = criterionNet(g_te, 2*gt_ren_te.permute(0,3,1,2)-1)
inner_loss, des19_svbrdf_te = criterionNet(g_te, 2*gt_ren_te.permute(0,3,1,2)-1)
inner_loss += ren_loss
elif opt.netloss=='GT':
# print(g_tr.shape)
# print(image_tr.shape)
inner_loss = mse(g_te, image_gt)
inner_loss += ren_loss
else:
inner_loss = ren_loss
if opt.Wvgg_inner>0:
out_ren_te_vg = VGGpreprocess(out_ren_te.permute(0,3,1,2))
gt_ren_te_vg = VGGpreprocess(gt_ren_te.permute(0,3,1,2))
inner_ren_vgg_loss = criterionVGG(out_ren_te_vg, gt_ren_te_vg)*opt.Wvgg_inner
inner_loss += inner_ren_vgg_loss
if opt.Wtd_inner>0:
out_ren_te_td = out_ren_te.permute(0,3,1,2)
gt_ren_te_td = gt_ren_te.permute(0,3,1,2)
inner_ren_td = criterionTD(out_ren_te_td, gt_ren_te_td)*opt.Wtd_inner
inner_loss += inner_ren_td
if opt.loss_after1 =='mse':
temp_loss = ren_loss
elif opt.loss_after1=='vgg':
out_ren_tr_vg = VGGpreprocess(out_ren_te.permute(0,3,1,2))
gt_ren_tr_vg = VGGpreprocess(gt_ren_te.permute(0,3,1,2))
temp_loss = criterionVGG(out_ren_tr_vg, gt_ren_tr_vg) * 0.005 + ren_loss
elif opt.loss_after1=='TD':
out_ren = out_ren_te[0:1,...].permute(0,3,1,2)
gt_ren = gt_ren_te[0:1,...].permute(0,3,1,2)
out_16 = nn.functional.interpolate(out_ren, size = opt.dres, mode='bilinear', align_corners=True)
gt_16 = nn.functional.interpolate(gt_ren, size = opt.dres, mode='bilinear', align_corners=True)
dren_loss = mse(out_16, gt_16)*opt.Wdren_outer
TDren_loss = criterionTD(out_ren, gt_ren)*opt.WTDren_outer
temp_loss = dren_loss + TDren_loss
if in_step>0 or opt.no_inner_step:
inner_loss = temp_loss
vis_loss = temp_loss
else:
vis_loss = temp_loss
# after 1 step
if flag_lr and in_step>0:
inner_lr = opt.after_innerlr
if 'MG' in opt.loss_after1 and in_step>0:
# print(in_step)
flag_lr = False
if in_step%opt.decay_interval==0:
inner_lr = inner_lr*0.5
if not opt.no_use_adam:
for g in optimAdam.param_groups:
g['lr'] = inner_lr
if in_step==0:
val_rens[str(in_step)] = out_ren_te
elif in_step==1:
val_rens[str(in_step)] = out_ren_te
elif in_step==2:
val_rens[str(in_step)] = out_ren_te
elif in_step==5:
val_rens[str(in_step)] = out_ren_te
elif in_step==opt.val_step:
val_rens[str(in_step)] = out_ren_te
if in_step%10==0:
print(f'step: {in_step:d}, inner lr: {inner_lr:.8f} vis loss: {vis_loss:.5f} inner loss: {inner_loss:.5f} TD_loss: {TD_loss:.5f} TD16Loss: {TD16Loss:.5f}')
if (in_step==0 or opt.no_use_adam) and not opt.no_inner_step:
print('1st step')
params_test = gradient_update_parameters(model, inner_loss, params=params_test,
step_size=inner_lr, first_order=True)
write_params(model, params_test)
else:
# print('use_adam')
if in_step==0:
write_params(model, params_test)
optimAdam.zero_grad()
inner_loss.backward()
optimAdam.step()
params_test = OrderedDict(model.named_parameters())
# save loss
if in_step in Plot_iter and opt.test_img=='SynPlot':
g_te_d = g_te.detach()
# print(image_gt.shape, g_te_d.shape)
outer_fea_loss = 0.25*(mse(image_gt[:,:,0:3], g_te_d[:,:,0:3])*opt.wN_outer
+ mse(image_gt[:,:,3:6], g_te_d[:,:,3:6]) *opt.wD_outer
+ mse(image_gt[:,:,6:9], g_te_d[:,:,6:9])*opt.wR_outer
+ mse(image_gt[:,:,9:12], g_te_d[:,:,9:12]))*opt.wS_outer
# add VGG loss to features
if opt.Wfea_vgg>0:
D_vg_pred = VGGpreprocess(g_te_d[:,:,3:6].unsqueeze(0).permute(0,3,1,2))
D_vg_gt = VGGpreprocess(image_gt[:,:,3:6].unsqueeze(0).permute(0,3,1,2))
D_vg = criterionVGG(D_vg_pred, D_vg_gt)*opt.Wfea_vgg
S_vg_pred = VGGpreprocess(g_te_d[:,:,9:12].unsqueeze(0).permute(0,3,1,2))
S_vg_gt = VGGpreprocess(image_gt[:,:,9:12].unsqueeze(0).permute(0,3,1,2))
S_vg = criterionVGG(S_vg_pred, S_vg_gt)*opt.Wfea_vgg
outer_fea_loss += (D_vg + S_vg)*0.5
# compute loss of 20 renderings
outer_ren_loss=0
for i in range(20):
gt_ren_sampled = SingleRender_NumberPointLight_FixedCamera(2*image_gt[:,:,0:3]-1, image_gt[:,:,3:6],image_gt[:,:,6:7], image_gt[:,:,9:12], rand_light_pos_loss[i:i+1,...], Position_map, device, CamLi_co=True) #[B,N, W,H,C]
gt_ren_sampled = (gt_ren_sampled.clamp(0,1)+opt.EPSILON)**(1/2.2)
pred_ren_sampled = SingleRender_NumberPointLight_FixedCamera(2*g_te_d[:,:,0:3]-1, g_te_d[:,:,3:6], g_te_d[:,:,6:7], g_te_d[:,:,9:12], rand_light_pos_loss[i:i+1,...], Position_map, device, CamLi_co=True) #[B,N, W,H,C]
pred_ren_sampled = (pred_ren_sampled.clamp(0,1)+opt.EPSILON)**(1/2.2)
if opt.loss_after1=='TD':
out_ren = pred_ren_sampled[0:1,...].permute(0,3,1,2)
gt_ren = gt_ren_sampled[0:1,...].permute(0,3,1,2)
out_16 = nn.functional.interpolate(out_ren, size = opt.dres, mode='bilinear', align_corners=True)
gt_16 = nn.functional.interpolate(gt_ren, size = opt.dres, mode='bilinear', align_corners=True)
outer_ren_loss += mse(out_16, gt_16)*opt.Wdren_outer
outer_ren_loss += criterionTD(out_ren, gt_ren)*opt.WTDren_outer
else:
outer_ren_loss += mse(pred_ren_sampled, gt_ren_sampled)
out_ren_te_vg = VGGpreprocess(pred_ren_sampled[0:1,...].permute(0,3,1,2))
gt_ren_te_vg = VGGpreprocess(gt_ren_sampled[0:1,...].permute(0,3,1,2))
outer_ren_vgg_loss = criterionVGG(out_ren_te_vg, gt_ren_te_vg)*opt.Wvgg_outer
outer_ren_loss += outer_ren_vgg_loss
total_outer = outer_fea_loss + outer_ren_loss/20.0
excel_total = total_outer
excel_fea = outer_fea_loss
excel_ren = outer_ren_loss/20.0
val_ren_loss[str(in_step)] = outer_ren_loss/20.0 if str(in_step) not in val_ren_loss else val_ren_loss[str(in_step)] + outer_ren_loss/20.0
val_total_loss[str(in_step)] = total_outer if str(in_step) not in val_total_loss else val_total_loss[str(in_step)] + total_outer
val_feas_loss[str(in_step)] = outer_fea_loss if str(in_step) not in val_feas_loss else val_feas_loss[str(in_step)] + outer_fea_loss
################# save imgs ################33
final_vis_tr = OrderedDict()
final_vis_te = OrderedDict()
if 'all' in opt.fea:
for key_fea, fea in val_feas.items():
final_vis_te[key_fea] = torch.cat((fea[:,:,0:3],(fea[:,:,3:6]+EPSILON)**(1/2.2),fea[:,:,6:9],(fea[:,:,9:12]+EPSILON)**(1/2.2)),dim=1)
if image_gt.shape[-1]>3:
final_vis_te['gt'] = torch.cat((image_gt[:,:,0:3],image_gt[:,:,3:6]**(1/2.2),image_gt[:,:,6:9],image_gt[:,:,9:12]**(1/2.2)),dim=1)
else:
final_vis_te['gt'] = torch.cat((image_gt,image_gt,image_gt,image_gt),dim=1)
svbrdf_vis = final_vis_te[f'{val_steps}'].detach()
if opt.netloss=="Des19Net":
top = torch.cat((des19_svbrdf_te[:,:,3:6]**(1/2.2),des19_svbrdf_te[:,:,0:3]), dim=1)
bot = torch.cat((des19_svbrdf_te[:,:,6:9],des19_svbrdf_te[:,:,9:12]**(1/2.2)), dim=1)
final_vis_te['des19'] = torch.cat((top, bot), dim=0)
save_image(final_vis_te['des19'], os.path.join(save_valfea_path,'%s_pseudo_gt.png'%(name[0])))
h = 256
# save gt
N = final_vis_te['gt'][:,0:h,:]
D = final_vis_te['gt'][:,h:2*h,:]
R = final_vis_te['gt'][:,2*h:3*h,:]
S = final_vis_te['gt'][:,3*h:4*h,:]
top = torch.cat((D,N), dim=1)
bot = torch.cat((R,S), dim=1)
maps = torch.cat((top, bot), dim=0)
save_image(maps, os.path.join(save_valfea_path,'%s_gt.png'%(name[0])))
save_image(out_ren_te[0,...], os.path.join(save_valren_path,'%s_ren.png'%(name[0])))
# save fea
N = svbrdf_vis[:,0:h,:]
D = svbrdf_vis[:,h:2*h,:]
R = svbrdf_vis[:,2*h:3*h,:]
S = svbrdf_vis[:,3*h:4*h,:]
top = torch.cat((D,N), dim=1)
bot = torch.cat((R,S), dim=1)
maps = torch.cat((top, bot), dim=0)
save_image(maps, os.path.join(save_valfea_path,'%s.png'%(name[0])))
# save init
N = final_vis_te['0'][:,0:h,:]
D = final_vis_te['0'][:,h:2*h,:]
R = final_vis_te['0'][:,2*h:3*h,:]
S = final_vis_te['0'][:,3*h:4*h,:]
top = torch.cat((D,N), dim=1)
bot = torch.cat((R,S), dim=1)
maps = torch.cat((top, bot), dim=0)
save_image(maps, os.path.join(save_pro_path,'%s_init.png'%(name[0])))
# save input
save_image(image_te[0,...], os.path.join(save_in_path,'%s.png'%(name[0])))
# save_image(final_vis_te['gt'], os.path.join(save_valfea_path,'%s_gt.png'%(name[0])))
# save_image(final_vis_te['0'], os.path.join(save_pro_path,'%s_init.png'%(name[0])))
# save_image(svbrdf_vis, os.path.join(save_valfea_path,'%s.png'%(name[0])))
# save_image(image_te[0,...], os.path.join(save_in_path,'%s.png'%(name[0])))
def test_real(opt, model, coords, device, FeaExtractor=None):
if opt.test_img=='OurReal2':
realdata_path = "./dataset/OurReal/OurRealData_scaled"
elif opt.test_img=='MGReal2':
realdata_path = "./dataset/MGReal/MGRealData_scaled"
load_model='pretrain' if opt.load_pretrain else opt.model
scenes = os.listdir(realdata_path)
txt_dir = f'test/{opt.name}'
if not os.path.exists(txt_dir):
os.makedirs(txt_dir)
RenRMSE = open(os.path.join(txt_dir,'RenRMSE.txt'),'w')
RenLPIPS = open(os.path.join(txt_dir,'RenLPIPS.txt'),'w')
scene_N = 0
TotalRMSE_ren = 0
TotalLPIPS_ren = 0
if opt.loss_after1=='TD':
criterionTD = TDLoss_2(device, 0)
for scene in scenes:
scene_N += 1
print('....................now training scene %s .................'% scene )
# if scene != 'nima_wood_1':
# continue
img_dir = f'test/{opt.name}/{scene}'
if not os.path.exists(img_dir):
os.makedirs(img_dir)
val_steps = opt.val_step #@param {type:"integer"}
first_order = opt.first_order #@param {type:"boolean"}
L1 = torch.nn.L1Loss()
mse = torch.nn.MSELoss().to(device)
criterionVGG = VGGLoss(opt)
weights = [0.1, 0.1, 0.1, 0.1]
keys = [1, 2, 3,4]
if opt.netloss=='Des19Net':
criterionNet = MyLoss(opt, keys, weights, device, val=True)
# load model
load_network(model, "final", 'ckpt/', name='model')
if opt.netloss=='Des19Net':
load_network(criterionNet.lossnet, "final", 'ckpt/', name='des19net')
if opt.no_load:
model = SirenNet(2, 256, out_nc, opt.num_layers, w0_initial=200., w0=200.,
final_activation=lambda x: x + .5).to(device)
val_log_ren = OrderedDict()
val_log_fea = OrderedDict()
flag=False
if opt.netloss=='Des19Net':
criterionNet.set_gradient(False)
val_rens = OrderedDict()
val_feas = OrderedDict()