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train_rain_class.py
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train_rain_class.py
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from __future__ import print_function
import argparse
import os
import sys
import random
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
cudnn.benchmark = True
cudnn.fastest = True
import torch.optim as optim
import torchvision.utils as vutils
from torch.autograd import Variable
# import models.UNet as net
from misc import *
import models.derain_residual as net2
import models.derain_dense as net1
from myutils.vgg16 import Vgg16
from myutils import utils
import pdb
import torch.nn.functional as F
import torchvision.models as models
parser = argparse.ArgumentParser()
parser.add_argument('--dataset', required=False,
default='pix2pix_class', help='')
parser.add_argument('--dataroot', required=False,
default='', help='path to trn dataset')
parser.add_argument('--valDataroot', required=False,
default='', help='path to val dataset')
parser.add_argument('--mode', type=str, default='B2A', help='B2A: facade, A2B: edges2shoes')
parser.add_argument('--batchSize', type=int, default=1, help='input batch size')
parser.add_argument('--valBatchSize', type=int, default=120, help='input batch size')
parser.add_argument('--originalSize', type=int,
default=586, help='the height / width of the original input image')
parser.add_argument('--imageSize', type=int,
default=512, help='the height / width of the cropped input image to network')
parser.add_argument('--inputChannelSize', type=int,
default=3, help='size of the input channels')
parser.add_argument('--outputChannelSize', type=int,
default=3, help='size of the output channels')
parser.add_argument('--ngf', type=int, default=64)
parser.add_argument('--ndf', type=int, default=64)
parser.add_argument('--niter', type=int, default=5000, help='number of epochs to train for')
parser.add_argument('--lrD', type=float, default=0.0002, help='learning rate, default=0.0002')
parser.add_argument('--lrG', type=float, default=0.0002, help='learning rate, default=0.0002')
parser.add_argument('--annealStart', type=int, default=0, help='annealing learning rate start to')
parser.add_argument('--annealEvery', type=int, default=400, help='epoch to reaching at learning rate of 0')
parser.add_argument('--lambdaGAN', type=float, default=0.01, help='lambdaGAN')
parser.add_argument('--lambdaIMG', type=float, default=1, help='lambdaIMG')
parser.add_argument('--poolSize', type=int, default=50, help='Buffer size for storing previously generated samples from G')
parser.add_argument('--wd', type=float, default=0.0000, help='weight decay in D')
parser.add_argument('--beta1', type=float, default=0.5, help='beta1 for adam')
parser.add_argument('--netG', default='', help="path to netG (to continue training)")
parser.add_argument('--netD', default='', help="path to netD (to continue training)")
parser.add_argument('--workers', type=int, help='number of data loading workers', default=1)
parser.add_argument('--exp', default='sample', help='folder to output images and model checkpoints')
parser.add_argument('--display', type=int, default=5, help='interval for displaying train-logs')
parser.add_argument('--evalIter', type=int, default=500, help='interval for evauating(generating) images from valDataroot')
opt = parser.parse_args()
print(opt)
create_exp_dir(opt.exp)
opt.manualSeed = random.randint(1, 10000)
# opt.manualSeed = 101
random.seed(opt.manualSeed)
torch.manual_seed(opt.manualSeed)
torch.cuda.manual_seed_all(opt.manualSeed)
print("Random Seed: ", opt.manualSeed)
# get dataloader
dataloader = getLoader(opt.dataset,
opt.dataroot,
opt.originalSize,
opt.imageSize,
opt.batchSize,
opt.workers,
mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
split='train',
shuffle=True,
seed=opt.manualSeed)
# get logger
trainLogger = open('%s/train.log' % opt.exp, 'w')
ngf = opt.ngf
ndf = opt.ndf
inputChannelSize = opt.inputChannelSize
outputChannelSize= opt.outputChannelSize
netG=net1.vgg19ca()
netG.load_state_dict(torch.load('./classification/netG_epoch_9.pth'))
print(netG)
netG.train()
criterion_class = nn.CrossEntropyLoss().cuda()
target= torch.FloatTensor(opt.batchSize, outputChannelSize, opt.imageSize, opt.imageSize)
input = torch.FloatTensor(opt.batchSize, inputChannelSize, opt.imageSize, opt.imageSize)
label_d = torch.FloatTensor(opt.batchSize)
target = torch.FloatTensor(opt.batchSize, outputChannelSize, opt.imageSize, opt.imageSize)
input = torch.FloatTensor(opt.batchSize, inputChannelSize, opt.imageSize, opt.imageSize)
# image pool storing previously generated samples from G
imagePool = ImagePool(opt.poolSize)
# NOTE weight for L_cGAN and L_L1 (i.e. Eq.(4) in the paper)
lambdaGAN = opt.lambdaGAN
lambdaIMG = opt.lambdaIMG
netG.cuda()
target, input = target.cuda(), input.cuda()
target = Variable(target)
input = Variable(input)
residue_net=net2.Dense_rain_residual()
residue_net.load_state_dict(torch.load('./residual_heavy/netG_epoch_6.pth'))
residue_net=residue_net.cuda()
label_d = Variable(label_d.cuda())
# get optimizer
optimizerG = optim.Adam(netG.parameters(), lr = opt.lrG, betas = (opt.beta1, 0.999), weight_decay=0.00005)
# NOTE training loop
ganIterations = 0
for epoch in range(opt.niter):
if epoch > opt.annealStart:
adjust_learning_rate(optimizerD, opt.lrD, epoch, None, opt.annealEvery)
adjust_learning_rate(optimizerG, opt.lrG, epoch, None, opt.annealEvery)
for i, data in enumerate(dataloader, 0):
### Get the rainy image and coreesponding ground truth label (0: Heavy, 1:Medium, 2: Light)##
input_cpu, target_cpu, label_cpu = data
target_label=label_cpu
target_label=target_label.long().cuda()
target_label=Variable(target_label)
batch_size = target_cpu.size(0)
target_cpu, input_cpu = target_cpu.float().cuda(), input_cpu.float().cuda()
### Using the Heavy rain Label (0) to estimate the residual first ##
z = 0
label_cpu = torch.FloatTensor(opt.batchSize).fill_(z)
label_cpu=label_cpu.long().cuda()
label_cpu=Variable(label_cpu)
# get paired data
target.data.resize_as_(target_cpu).copy_(target_cpu)
input.data.resize_as_(input_cpu).copy_(input_cpu)
netG.zero_grad() # start to update G
residue_net.zero_grad() # start to update G
### Using the Heavy rain Label (0) to get the residual ##
output=residue_net(input, label_cpu)
residue=input-output
### Using the estimated resiudal to predict the label ##
label = netG(residue)
#label_final=label.max(1)[1]
#zz1=label_final.data.cpu().numpy()
#zz2=target_label.data.cpu().numpy()
#print(zz1)
#print(zz2)
netG.zero_grad() # start to update G
class_loss = criterion_class(label, target_label)
class_loss.backward()
L_img = class_loss
optimizerG.step()
ganIterations += 1
if ganIterations % opt.display == 0:
print('[%d/%d][%d/%d] L_D: %f L_img: %f L_G: %f D(x): %f D(G(z)): %f / %f'
% (epoch, opt.niter, i, len(dataloader),
class_loss.data[0], L_img.data[0], L_img.data[0], L_img.data[0], L_img.data[0], L_img.data[0]))
if epoch % 1 == 0:
torch.save(netG.state_dict(), '%s/netG_epoch_%d.pth' % (opt.exp, epoch))
trainLogger.close()