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trainInitEnv.py
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import torch
import numpy as np
from torch.autograd import Variable
import torch.optim as optim
import argparse
import random
import os
import models
import torchvision.utils as vutils
import utils
import dataLoader
import torch.nn as nn
from torch.utils.data import DataLoader
parser = argparse.ArgumentParser()
# The locationi of training set
parser.add_argument('--dataRoot', default='../Data/train/', help='path to images' )
parser.add_argument('--experiment', default=None, help='the path to store samples and models' )
parser.add_argument('--modelRootGlob', default='check_globalillumination', help='the path to store the network for global illumination prediction' )
parser.add_argument('--epochIdGlob', type=int, default=17, help='the training epoch for globalillum prediction' )
# The basic training setting
parser.add_argument('--nepoch', type=int, default=15, help='the number of epochs for training' )
parser.add_argument('--batchSize', type=int, default=16, help='input batch size' )
parser.add_argument('--imageSize', type=int, default=256, help='the height / width of the input image to network' )
parser.add_argument('--cuda', action='store_true', help='enables cuda' )
parser.add_argument('--deviceIds', type=int, nargs='+', default=[0, 1], help='the gpus used for training network' )
# The training weight
parser.add_argument('--albedoWeight', type=float, default=1.0, help='the weight for the diffuse component' )
parser.add_argument('--normalWeight', type=float, default=1.0, help='the weight for the diffuse component' )
parser.add_argument('--roughWeight', type=float, default=0.5, help='the weight for the roughness component' )
parser.add_argument('--depthWeight', type=float, default=0.5, help='the weight for the depth component' )
parser.add_argument('--globalIllu1', type=float, default=1.0, help='the weight of bounce 1' )
parser.add_argument('--globalIllu2', type=float, default=0.01, help='the weight of bounce 2' )
parser.add_argument('--globalIllu3', type=float, default=0.01, help='the weight of bounce 3' )
parser.add_argument('--envWeight', type=float, default=0.01, help = 'the weight of training network for environmap prediction' )
parser.add_argument('--imgEnvWeight', type=float, default=0.01, help='the reconstruction weight')
# The detail network setting
parser.add_argument('--cascadeLevel', type=int, default=0, help='cascade level' )
parser.add_argument('--inputMode', type=int, default=0, help='whether to include back ground' )
opt = parser.parse_args()
print(opt)
assert(opt.cascadeLevel == 0)
opt.gpuId = opt.deviceIds[0]
if opt.experiment is None:
opt.experiment = 'check_initEnvGlob'
opt.experiment += '_cascade0'
if opt.inputMode == 1:
opt.experiment = opt.experiment + '_nobg'
os.system('mkdir {0}'.format(opt.experiment) )
os.system('cp *.py %s' % opt.experiment )
albeW, normW = opt.albedoWeight, opt.normalWeight
rougW, deptW = opt.roughWeight, opt.depthWeight
g1W, g2W, g3W = opt.globalIllu1, opt.globalIllu2, opt.globalIllu3
eW = opt.envWeight
imEW = opt.imgEnvWeight
opt.seed = 0
print("Random Seed: ", opt.seed)
random.seed(opt.seed)
torch.manual_seed(opt.seed)
if torch.cuda.is_available() and not opt.cuda:
print("WARNING: You have a CUDA device, so you should probably run with --cuda")
####################################
# initalize tensors
albedoBatch = Variable(torch.FloatTensor(opt.batchSize, 3, opt.imageSize, opt.imageSize) )
normalBatch = Variable(torch.FloatTensor(opt.batchSize, 3, opt.imageSize, opt.imageSize) )
roughBatch = Variable(torch.FloatTensor(opt.batchSize, 1, opt.imageSize, opt.imageSize) )
segBatch = Variable(torch.FloatTensor(opt.batchSize, 3, opt.imageSize, opt.imageSize) )
depthBatch = Variable(torch.FloatTensor(opt.batchSize, 1, opt.imageSize, opt.imageSize) )
imBatch = Variable(torch.FloatTensor(opt.batchSize, 3, opt.imageSize, opt.imageSize) )
imBgBatch = Variable(torch.FloatTensor(opt.batchSize, 3, opt.imageSize, opt.imageSize) )
SHBatch = Variable(torch.FloatTensor(opt.batchSize, 3, 9) )
imP1Batch = Variable(torch.FloatTensor(opt.batchSize, 3, opt.imageSize, opt.imageSize) )
imP2Batch = Variable(torch.FloatTensor(opt.batchSize, 3, opt.imageSize, opt.imageSize) )
imP3Batch = Variable(torch.FloatTensor(opt.batchSize, 3, opt.imageSize, opt.imageSize) )
imEBatch = Variable(torch.FloatTensor(opt.batchSize, 3, opt.imageSize, opt.imageSize) )
# Initial Network
encoderInit = nn.DataParallel(models.encoderInitial(), device_ids = opt.deviceIds)
albedoInit = nn.DataParallel(models.decoderInitial(mode=0), device_ids = opt.deviceIds)
normalInit = nn.DataParallel(models.decoderInitial(mode=1), device_ids = opt.deviceIds)
roughInit = nn.DataParallel(models.decoderInitial(mode=2), device_ids = opt.deviceIds)
depthInit = nn.DataParallel(models.decoderInitial(mode=3), device_ids = opt.deviceIds)
envInit = nn.DataParallel(models.envmapInitial(), device_ids = opt.deviceIds)
renderLayer = models.renderingLayer(gpuId = opt.gpuId, isCuda = opt.cuda)
# Global illumination
globIllu1to2 = models.globalIllumination()
globIllu2to3 = models.globalIllumination()
#########################################
#########################################
# Load weight of network
globIllu1to2.load_state_dict(torch.load('{0}/globIllu1to2_{1}.pth'.format(opt.modelRootGlob, opt.epochIdGlob) ) )
globIllu2to3.load_state_dict(torch.load('{0}/globIllu2to3_{1}.pth'.format(opt.modelRootGlob, opt.epochIdGlob) ) )
globIllu1to2 = globIllu1to2.eval()
globIllu2to3 = globIllu2to3.eval()
for param in globIllu1to2.parameters():
param.requires_grad = False
for param in globIllu2to3.parameters():
param.requires_grad = False
#########################################
############## ######################
# Send things into GPU
if opt.cuda:
albedoBatch = albedoBatch.cuda(opt.gpuId)
normalBatch = normalBatch.cuda(opt.gpuId)
roughBatch = roughBatch.cuda(opt.gpuId)
depthBatch = depthBatch.cuda(opt.gpuId)
segBatch = segBatch.cuda(opt.gpuId)
imBatch = imBatch.cuda(opt.gpuId)
imBgBatch = imBgBatch.cuda(opt.gpuId)
SHBatch = SHBatch.cuda(opt.gpuId)
imP1Batch = imP1Batch.cuda(opt.gpuId)
imP2Batch = imP2Batch.cuda(opt.gpuId)
imP3Batch = imP3Batch.cuda(opt.gpuId)
imEBatch = imEBatch.cuda(opt.gpuId)
encoderInit = encoderInit.cuda(opt.gpuId)
albedoInit = albedoInit.cuda(opt.gpuId)
normalInit = normalInit.cuda(opt.gpuId)
roughInit = roughInit.cuda(opt.gpuId)
depthInit = depthInit.cuda(opt.gpuId)
envInit = envInit.cuda(opt.gpuId)
globIllu1to2 = globIllu1to2.cuda(opt.gpuId)
globIllu2to3 = globIllu2to3.cuda(opt.gpuId)
####################################
####################################
# Initial Optimizer
opEncoderInit = optim.Adam(encoderInit.parameters(), lr=1e-4, betas=(0.5, 0.999) )
opAlbedoInit = optim.Adam(albedoInit.parameters(), lr=4e-4, betas=(0.5, 0.999) )
opNormalInit = optim.Adam(normalInit.parameters(), lr=4e-4, betas=(0.5, 0.999) )
opRoughInit = optim.Adam(roughInit.parameters(), lr=4e-4, betas=(0.5, 0.999) )
opDepthInit = optim.Adam(depthInit.parameters(), lr=4e-4, betas=(0.5, 0.999) )
opEnvInit = optim.Adam(envInit.parameters(), lr=4e-4, betas=(0.5, 0.999) )
#####################################
####################################
brdfDataset = dataLoader.BatchLoader(opt.dataRoot, imSize = opt.imageSize)
brdfLoader = DataLoader(brdfDataset, batch_size = opt.batchSize, num_workers = 8, shuffle = True)
j = 0
albedoErrsNpList = np.ones( [1, 1], dtype = np.float32 )
normalErrsNpList = np.ones( [1, 1], dtype = np.float32 )
roughErrsNpList= np.ones( [1, 1], dtype = np.float32 )
depthErrsNpList = np.ones( [1, 1], dtype = np.float32 )
globalIllu1ErrsNpList= np.ones( [1, 1], dtype = np.float32 )
globalIllu2ErrsNpList= np.ones( [1, 1], dtype = np.float32 )
globalIllu3ErrsNpList= np.ones( [1, 1], dtype = np.float32 )
imgEnvErrsNpList = np.ones( [1, 1], dtype=np.float32 )
envErrsNpList = np.ones([1, 1+opt.cascadeLevel], dtype = np.float32)
for epoch in list(range(0, opt.nepoch)):
trainingLog = open('{0}/trainingLog_{1}.txt'.format(opt.experiment, epoch), 'w')
for i, dataBatch in enumerate(brdfLoader):
j += 1
# Load data from cpu to gpu
albedo_cpu = dataBatch['albedo']
albedoBatch.data.resize_(albedo_cpu.size() )
albedoBatch.data.copy_(albedo_cpu )
normal_cpu = dataBatch['normal']
normalBatch.data.resize_(normal_cpu.size() )
normalBatch.data.copy_(normal_cpu )
rough_cpu = dataBatch['rough']
roughBatch.data.resize_(rough_cpu.size() )
roughBatch.data.copy_(rough_cpu )
seg_cpu = dataBatch['seg']
segBatch.data.resize_(seg_cpu.size() )
segBatch.data.copy_(seg_cpu )
depth_cpu = dataBatch['depth']
depthBatch.data.resize_(depth_cpu.size() )
depthBatch.data.copy_(depth_cpu )
# Load the image from cpu to gpu
im_cpu = (dataBatch['imP'] + dataBatch['imE'] + 1) * seg_cpu.expand_as(normal_cpu)
imBatch.data.resize_(im_cpu.shape )
imBatch.data.copy_(im_cpu )
imBg_cpu = 0.5*(dataBatch['imP'] + 1) * seg_cpu.expand_as(normal_cpu ) \
+ 0.5*(dataBatch['imEbg'] + 1)
imBg_cpu = 2*imBg_cpu - 1
if opt.inputMode == 1:
imBg_cpu = 0 * imBg_cpu
imBgBatch.data.resize_(imBg_cpu.size() )
imBgBatch.data.copy_(imBg_cpu )
# Load the spherical harmonics
SH_cpu = dataBatch['SH']
SHBatch.data.resize_(SH_cpu.size() )
SHBatch.data.copy_(SH_cpu )
nameBatch = dataBatch['name']
# Load the image with multiple bounce
imP1_cpu = dataBatch['imP1']
imP1Batch.data.resize_(imP1_cpu.size() )
imP1Batch.data.copy_(imP1_cpu )
imP2_cpu = dataBatch['imP2']
imP2Batch.data.resize_(imP2_cpu.size() )
imP2Batch.data.copy_(imP2_cpu )
imP3_cpu = dataBatch['imP3']
imP3Batch.data.resize_(imP3_cpu.size() )
imP3Batch.data.copy_(imP3_cpu)
imE_cpu = dataBatch['imE']
imEBatch.data.resize_(imE_cpu.size() )
imEBatch.data.copy_(imE_cpu )
# Clear the gradient in optimizer
opEncoderInit.zero_grad()
opAlbedoInit.zero_grad()
opNormalInit.zero_grad()
opRoughInit.zero_grad()
opDepthInit.zero_grad()
opEnvInit.zero_grad()
########################################################
# Build the cascade network architecture #
albedoPreds = []
normalPreds = []
roughPreds = []
depthPreds = []
SHPreds = []
globalIllu1s = []
globalIllu2s = []
globalIllu3s = []
renderedEnvs = []
# Initial Prediction
inputInit = torch.cat([imBatch, imBgBatch, segBatch], dim=1)
x1, x2, x3, x4, x5, x = encoderInit(inputInit)
albedoPred = albedoInit(x1, x2, x3, x4, x5, x) * segBatch.expand_as(albedoBatch )
normalPred = normalInit(x1, x2, x3, x4, x5, x) * segBatch.expand_as(normalBatch )
roughPred = roughInit(x1, x2, x3, x4, x5, x) * segBatch.expand_as(roughBatch )
depthPred = depthInit(x1, x2, x3, x4, x5, x) * segBatch.expand_as(depthBatch )
SHPred = envInit(x)
globalIllu1 = renderLayer.forward(albedoPred, normalPred,
roughPred, depthPred, segBatch)
globalIllu2 = globIllu1to2(torch.cat([ (2*globalIllu1 -1), \
albedoPred, normalPred, roughPred, depthPred, segBatch], dim=1) )
globalIllu3 =globIllu2to3(torch.cat([globalIllu2, \
albedoPred, normalPred, roughPred, depthPred, segBatch], dim=1) )
renderedEnv = renderLayer.forwardEnv(albedoPred, normalPred, roughPred, SHPred, segBatch)
albedoPreds.append(albedoPred)
normalPreds.append(normalPred)
roughPreds.append(roughPred)
depthPreds.append(depthPred)
SHPreds.append(SHPred)
globalIllu1s.append(globalIllu1)
globalIllu2s.append(0.5 *(globalIllu2+1) )
globalIllu3s.append(0.5 *(globalIllu3+1) )
renderedEnvs.append(renderedEnv )
########################################################
# Compute the error
albedoErrs = []
normalErrs = []
roughErrs = []
depthErrs = []
globalIllu1Errs = []
globalIllu2Errs = []
globalIllu3Errs = []
imgEnvErrs = []
envErrs = []
pixelNum = (torch.sum(segBatch ).cpu().data).item()
for m in range(0, len(albedoPreds) ):
albedoErrs.append( torch.sum( (albedoPreds[m] - albedoBatch)
* (albedoPreds[m] - albedoBatch) * segBatch.expand_as(albedoBatch) ) / pixelNum / 3.0 )
for m in range(0, len(normalPreds) ):
normalErrs.append( torch.sum( (normalPreds[m] - normalBatch)
* (normalPreds[m] - normalBatch) * segBatch.expand_as(normalBatch) ) / pixelNum / 3.0 )
for m in range(0, len(roughPreds) ):
roughErrs.append( torch.sum( (roughPreds[m] - roughBatch)
* (roughPreds[m] - roughBatch) * segBatch ) / pixelNum )
for m in range(0, len(depthPreds) ):
depthErrs.append( torch.sum( (depthPreds[m] - depthBatch)
* (depthPreds[m] - depthBatch) * segBatch ) / pixelNum )
for m in range(0, len(globalIllu1s) ):
globalIllu1Errs.append( torch.sum( (globalIllu1s[m] - 0.5*(imP1Batch+1) )
* (globalIllu1s[m] - 0.5*(imP1Batch+1) ) * segBatch.expand_as(imBatch) ) / pixelNum / 3.0 )
for m in range(0, len(globalIllu2s) ):
globalIllu2Errs.append( torch.sum( (globalIllu2s[m] - 0.5*(imP2Batch+1) )
* (globalIllu2s[m] - 0.5*(imP2Batch+1) ) * segBatch.expand_as(imBatch) ) / pixelNum / 3.0 )
for m in range(0, len(globalIllu3s) ):
globalIllu3Errs.append( torch.sum( (globalIllu3s[m] - 0.5*(imP3Batch+1) )
* (globalIllu3s[m] - 0.5*(imP3Batch+1) ) * segBatch.expand_as(imBatch) ) / pixelNum / 3.0 )
for m in range(0, len(renderedEnvs) ):
imgEnvErrs.append( torch.sum( (renderedEnvs[m] - 0.5*(imEBatch+1) )
* (renderedEnvs[m] - 0.5*(imEBatch+1) ) * segBatch.expand_as(imBatch) ) / pixelNum / 3.0 )
for m in range(0, len(SHPreds) ):
envErrs.append( torch.mean( (SHPreds[m] - SHBatch)*(SHPreds[m] - SHBatch) ) )
# Back propagate the gradients
totalErr = albeW * albedoErrs[-1] + normW * normalErrs[-1] + rougW *roughErrs[-1] \
+ deptW * depthErrs[-1] + g1W * globalIllu1Errs[-1] + eW * envErrs[-1] \
+ g2W * globalIllu2Errs[-1] + g3W * globalIllu3Errs[-1] + imEW * imgEnvErrs[-1]
totalErr.backward()
# Update the network parameter
opEncoderInit.step()
opAlbedoInit.step()
opNormalInit.step()
opRoughInit.step()
opDepthInit.step()
opEnvInit.step()
# Output training error
utils.writeErrToScreen('albedo', albedoErrs, epoch, j)
utils.writeErrToScreen('normal', normalErrs, epoch, j)
utils.writeErrToScreen('rough', roughErrs, epoch, j)
utils.writeErrToScreen('depth', depthErrs, epoch, j)
utils.writeErrToScreen('globalIllu1', globalIllu1Errs, epoch, j)
utils.writeErrToScreen('globalIllu2', globalIllu2Errs, epoch, j)
utils.writeErrToScreen('globalIllu3', globalIllu3Errs, epoch, j)
utils.writeErrToScreen('imgEnv', imgEnvErrs, epoch, j)
utils.writeErrToScreen('env', envErrs, epoch, j)
utils.writeErrToFile('albedo', albedoErrs, trainingLog, epoch, j)
utils.writeErrToFile('normal', normalErrs, trainingLog, epoch, j)
utils.writeErrToFile('rough', roughErrs, trainingLog, epoch, j)
utils.writeErrToFile('depth', depthErrs, trainingLog, epoch, j)
utils.writeErrToFile('globalIllu1', globalIllu1Errs, trainingLog, epoch, j)
utils.writeErrToFile('globalIllu2', globalIllu2Errs, trainingLog, epoch, j)
utils.writeErrToFile('globalIllu3', globalIllu3Errs, trainingLog, epoch, j)
utils.writeErrToFile('imgEnv', imgEnvErrs, trainingLog, epoch, j)
utils.writeErrToFile('env', envErrs, trainingLog, epoch, j)
albedoErrsNpList = np.concatenate( [albedoErrsNpList, utils.turnErrorIntoNumpy(albedoErrs)], axis=0)
normalErrsNpList = np.concatenate( [normalErrsNpList, utils.turnErrorIntoNumpy(normalErrs)], axis=0)
roughErrsNpList = np.concatenate( [roughErrsNpList, utils.turnErrorIntoNumpy(roughErrs)], axis=0)
depthErrsNpList = np.concatenate( [depthErrsNpList, utils.turnErrorIntoNumpy(depthErrs)], axis=0)
globalIllu1ErrsNpList = np.concatenate( [globalIllu1ErrsNpList, utils.turnErrorIntoNumpy(globalIllu1Errs)], axis=0)
globalIllu2ErrsNpList = np.concatenate( [globalIllu2ErrsNpList, utils.turnErrorIntoNumpy(globalIllu2Errs)], axis=0)
globalIllu3ErrsNpList = np.concatenate( [globalIllu3ErrsNpList, utils.turnErrorIntoNumpy(globalIllu3Errs)], axis=0)
imgEnvErrsNpList = np.concatenate( [imgEnvErrsNpList, utils.turnErrorIntoNumpy(imgEnvErrs)], axis=0)
envErrsNpList = np.concatenate( [envErrsNpList, utils.turnErrorIntoNumpy(envErrs)], axis=0)
if j < 1000:
utils.writeNpErrToScreen('albedoAccu', np.mean(albedoErrsNpList[1:j+1, :], axis=0), epoch, j)
utils.writeNpErrToScreen('normalAccu', np.mean(normalErrsNpList[1:j+1, :], axis=0), epoch, j)
utils.writeNpErrToScreen('roughAccu', np.mean(roughErrsNpList[1:j+1, :], axis=0), epoch, j)
utils.writeNpErrToScreen('depthAccu', np.mean(depthErrsNpList[1:j+1, :], axis=0), epoch, j)
utils.writeNpErrToScreen('globalIllu1Accu', np.mean(globalIllu1ErrsNpList[1:j+1, :], axis=0), epoch, j)
utils.writeNpErrToScreen('globalIllu2Accu', np.mean(globalIllu2ErrsNpList[1:j+1, :], axis=0), epoch, j)
utils.writeNpErrToScreen('globalIllu3Accu', np.mean(globalIllu3ErrsNpList[1:j+1, :], axis=0), epoch, j)
utils.writeNpErrToScreen('imgEnvAccu', np.mean(imgEnvErrsNpList[1:j+1, :], axis=0), epoch, j)
utils.writeNpErrToScreen('envAccu:', np.mean(envErrsNpList[1:j+1, :], axis=0), epoch, j)
utils.writeNpErrToFile('albedoAccu', np.mean(albedoErrsNpList[1:j+1, :], axis=0), trainingLog, epoch, j)
utils.writeNpErrToFile('normalAccu', np.mean(normalErrsNpList[1:j+1, :], axis=0), trainingLog, epoch, j)
utils.writeNpErrToFile('roughAccu', np.mean(roughErrsNpList[1:j+1, :], axis=0), trainingLog, epoch, j)
utils.writeNpErrToFile('depthAccu', np.mean(depthErrsNpList[1:j+1, :], axis=0), trainingLog, epoch, j)
utils.writeNpErrToFile('globalIllu1Accu', np.mean(globalIllu1ErrsNpList[1:j+1, :], axis=0), trainingLog, epoch, j)
utils.writeNpErrToFile('globalIllu2Accu', np.mean(globalIllu2ErrsNpList[1:j+1, :], axis=0), trainingLog, epoch, j)
utils.writeNpErrToFile('globalIllu3Accu', np.mean(globalIllu3ErrsNpList[1:j+1, :], axis=0), trainingLog, epoch, j)
utils.writeNpErrToFile('imgEnvAccu', np.mean(imgEnvErrsNpList[1:j+1, :], axis=0), trainingLog, epoch, j)
utils.writeNpErrToFile('envAccu:', np.mean(envErrsNpList[1:j+1, :], axis=0), trainingLog, epoch, j)
else:
utils.writeNpErrToScreen('albedoAccu', np.mean(albedoErrsNpList[j-999:j+1, :], axis=0), epoch, j)
utils.writeNpErrToScreen('normalAccu', np.mean(normalErrsNpList[j-999:j+1, :], axis=0), epoch, j)
utils.writeNpErrToScreen('roughAccu', np.mean(roughErrsNpList[j-999:j+1, :], axis=0), epoch, j)
utils.writeNpErrToScreen('depthAccu', np.mean(depthErrsNpList[j-999:j+1, :], axis=0), epoch, j)
utils.writeNpErrToScreen('globalIllu1Accu', np.mean(globalIllu1ErrsNpList[j-999:j+1, :], axis=0), epoch, j)
utils.writeNpErrToScreen('globalIllu2Accu', np.mean(globalIllu2ErrsNpList[j-999:j+1, :], axis=0), epoch, j)
utils.writeNpErrToScreen('globalIllu3Accu', np.mean(globalIllu3ErrsNpList[j-999:j+1, :], axis=0), epoch, j)
utils.writeNpErrToScreen('imgEnvAccu', np.mean(imgEnvErrsNpList[j-999:j+1, :], axis=0), epoch, j)
utils.writeNpErrToScreen('envAccu', np.mean(envErrsNpList[j-999:j+1, :], axis=0), epoch, j)
utils.writeNpErrToFile('albedoAccu', np.mean(albedoErrsNpList[j-999:j+1, :], axis=0), trainingLog, epoch, j)
utils.writeNpErrToFile('normalAccu', np.mean(normalErrsNpList[j-999:j+1, :], axis=0), trainingLog, epoch, j)
utils.writeNpErrToFile('roughAccu', np.mean(roughErrsNpList[j-999:j+1, :], axis=0), trainingLog, epoch, j)
utils.writeNpErrToFile('depthAccu', np.mean(depthErrsNpList[j-999:j+1, :], axis=0), trainingLog, epoch, j)
utils.writeNpErrToFile('globalIllu1Accu', np.mean(globalIllu1ErrsNpList[j-999:j+1, :], axis=0), trainingLog, epoch, j)
utils.writeNpErrToFile('globalIllu2Accu', np.mean(globalIllu2ErrsNpList[j-999:j+1, :], axis=0), trainingLog, epoch, j)
utils.writeNpErrToFile('globalIllu3Accu', np.mean(globalIllu3ErrsNpList[j-999:j+1, :], axis=0), trainingLog, epoch, j)
utils.writeNpErrToFile('imgEnvAccu', np.mean(imgEnvErrsNpList[j-999:j+1, :], axis=0), trainingLog, epoch, j)
utils.writeNpErrToFile('envAccu:', np.mean(envErrsNpList[j-999:j+1, :], axis=0), trainingLog, epoch, j)
if j == 1 or j == 1000 or j% 5000 == 0:
# Save the ground truth and the input
vutils.save_image( (0.5*(albedoBatch + 1)*segBatch.expand_as(albedoBatch) ).data,
'{0}/{1}_albedoGt.png'.format(opt.experiment, j) )
vutils.save_image( (0.5*(normalBatch + 1)*segBatch.expand_as(normalBatch) ).data,
'{0}/{1}_normalGt.png'.format(opt.experiment, j) )
vutils.save_image( (0.5*(roughBatch + 1)*segBatch.expand_as(roughBatch) ).data,
'{0}/{1}_roughGt.png'.format(opt.experiment, j) )
depthOut = 1 / torch.clamp(depthBatch, 1e-6, 10) * segBatch.expand_as(depthBatch)
depthOut = (depthOut - 0.25) /0.8
vutils.save_image( ( depthOut*segBatch.expand_as(depthBatch) ).data,
'{0}/{1}_depthGt.png'.format(opt.experiment, j) )
vutils.save_image( ( (0.5*(imBatch + 1)*segBatch.expand_as(imBatch))**(1.0/2.2) ).data,
'{0}/{1}_im.png'.format(opt.experiment, j) )
vutils.save_image( ( (0.5*(imBgBatch + 1) )**(1.0/2.2) ).data,
'{0}/{1}_imBg.png'.format(opt.experiment, j) )
vutils.save_image( ( (0.5*(imEBatch + 1) )**(1.0/2.2) * segBatch.expand_as(imEBatch) ).data,
'{0}/{1}_imE.png'.format(opt.experiment, j) )
vutils.save_image( ( (0.5*(imP1Batch + 1) )**(1.0/2.2) * segBatch.expand_as(imP1Batch) ).data,
'{0}/{1}_imP1.png'.format(opt.experiment, j) )
vutils.save_image( ( (0.5*(imP2Batch + 1) )**(1.0/2.2) * segBatch.expand_as(imP2Batch) ).data,
'{0}/{1}_imP2.png'.format(opt.experiment, j) )
vutils.save_image( ( (0.5*(imP3Batch + 1) )**(1.0/2.2) * segBatch.expand_as(imP3Batch) ).data,
'{0}/{1}_imP3.png'.format(opt.experiment, j) )
utils.visualizeSH('{0}/{1}_gtSH.png'.format(opt.experiment, j),
SHBatch, nameBatch, 128, 256, 2, 8)
# Save the predicted results
for n in range(0, len(albedoPreds) ):
vutils.save_image( ( 0.5*(albedoPreds[n] + 1)*segBatch.expand_as(albedoPreds[n]) ).data,
'{0}/{1}_albedoPred_{2}.png'.format(opt.experiment, j, n) )
for n in range(0, len(normalPreds) ):
vutils.save_image( ( 0.5*(normalPreds[n] + 1)*segBatch.expand_as(normalPreds[n]) ).data,
'{0}/{1}_normalPred_{2}.png'.format(opt.experiment, j, n) )
for n in range(0, len(roughPreds) ):
vutils.save_image( ( 0.5*(roughPreds[n] + 1)*segBatch.expand_as(roughPreds[n]) ).data,
'{0}/{1}_roughPred_{2}.png'.format(opt.experiment, j, n) )
for n in range(0, len(depthPreds) ):
depthOut = 1 / torch.clamp(depthPreds[n], 1e-6, 10) * segBatch.expand_as(depthPreds[n])
depthOut = (depthOut - 0.25) /0.8
vutils.save_image( ( depthOut * segBatch.expand_as(depthPreds[n]) ).data,
'{0}/{1}_depthPred_{2}.png'.format(opt.experiment, j, n) )
for n in range(0, len(globalIllu1s) ):
vutils.save_image( ( ( globalIllu1s[n] * segBatch.expand_as(imBatch) )**(1.0/2.2) ).data,
'{0}/{1}_imP1Pred_{2}.png'.format(opt.experiment, j, n) )
for n in range(0, len(globalIllu2s) ):
vutils.save_image( ( ( globalIllu2s[n] * segBatch.expand_as(imBatch) )**(1.0/2.2) ).data,
'{0}/{1}_imP2Pred_{2}.png'.format(opt.experiment, j, n) )
for n in range(0, len(globalIllu3s) ):
vutils.save_image( ( ( globalIllu3s[n] * segBatch.expand_as(imBatch) )**(1.0/2.2) ).data,
'{0}/{1}_imP3Pred_{2}.png'.format(opt.experiment, j, n) )
for n in range(0, len(renderedEnvs) ):
vutils.save_image( ( ( renderedEnvs[n] * segBatch.expand_as(imBatch) )**(1.0/2.2) ).data,
'{0}/{1}_imEPred_{2}.png'.format(opt.experiment, j, n) )
for n in range(0, len(SHPreds) ):
utils.visualizeSH('{0}/{1}_predSH_{2}.png'.format(opt.experiment, j, n),
SHPreds[n], nameBatch, 128, 256, 2, 8)
trainingLog.close()
# Update the training rate
if (epoch + 1) % 2 == 0:
for param_group in opEncoderInit.param_groups:
param_group['lr'] /= 2
for param_group in opAlbedoInit.param_groups:
param_group['lr'] /= 2
for param_group in opNormalInit.param_groups:
param_group['lr'] /= 2
for param_group in opRoughInit.param_groups:
param_group['lr'] /= 2
for param_group in opDepthInit.param_groups:
param_group['lr'] /= 2
for param_group in opEnvInit.param_groups:
param_group['lr'] /= 2
# Save the error record
np.save('{0}/albedoError_{1}.npy'.format(opt.experiment, epoch), albedoErrsNpList )
np.save('{0}/normalError_{1}.npy'.format(opt.experiment, epoch), normalErrsNpList )
np.save('{0}/roughError_{1}.npy'.format(opt.experiment, epoch), roughErrsNpList )
np.save('{0}/depthError_{1}.npy'.format(opt.experiment, epoch), depthErrsNpList )
np.save('{0}/globalIllu1_{1}.npy'.format(opt.experiment, epoch), globalIllu1ErrsNpList )
np.save('{0}/globalIllu2_{1}.npy'.format(opt.experiment, epoch), globalIllu2ErrsNpList )
np.save('{0}/globalIllu3_{1}.npy'.format(opt.experiment, epoch), globalIllu3ErrsNpList )
np.save('{0}/imgEnv_{1}.npy'.format(opt.experiment, epoch), imgEnvErrsNpList )
np.save('{0}/envError_{1}.npy'.format(opt.experiment, epoch), envErrsNpList )
# save the models
torch.save(encoderInit.module.state_dict(), '{0}/encoderInit_{1}.pth'.format(opt.experiment, epoch) )
torch.save(albedoInit.module.state_dict(), '{0}/albedoInit_{1}.pth'.format(opt.experiment, epoch) )
torch.save(normalInit.module.state_dict(), '{0}/normalInit_{1}.pth'.format(opt.experiment, epoch) )
torch.save(roughInit.module.state_dict(), '{0}/roughInit_{1}.pth'.format(opt.experiment, epoch) )
torch.save(depthInit.module.state_dict(), '{0}/depthInit_{1}.pth'.format(opt.experiment, epoch) )
torch.save(envInit.module.state_dict(), '{0}/envInit_{1}.pth'.format(opt.experiment, epoch) )