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426 lines
18 KiB
426 lines
18 KiB
from __future__ import print_function
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import argparse
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import os
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import sys
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import random
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import torch
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import torch.nn as nn
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import torch.nn.parallel
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import torch.backends.cudnn as cudnn
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cudnn.benchmark = True
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cudnn.fastest = True
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import torch.optim as optim
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import torchvision.utils as vutils
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from torch.autograd import Variable
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from misc import *
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import models.face_fed as net
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from myutils.vgg16 import Vgg16
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from myutils import utils
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import pdb
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import torch.nn.functional as F
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#from PIL import Image
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from torchvision import transforms
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import h5py
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from os import listdir
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from os.path import isfile, join
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parser = argparse.ArgumentParser()
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parser.add_argument('--dataset', required=False,
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default='pix2pix_class', help='')
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parser.add_argument('--dataroot', required=False,
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default='', help='path to trn dataset')
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parser.add_argument('--valDataroot', required=False,
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default='', help='path to val dataset')
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parser.add_argument('--mode', type=str, default='B2A', help='B2A: facade, A2B: edges2shoes')
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parser.add_argument('--batchSize', type=int, default=1, help='input batch size')
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parser.add_argument('--valBatchSize', type=int, default=120, help='input batch size')
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parser.add_argument('--originalSize', type=int,
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default=160, help='the height / width of the original input image')
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parser.add_argument('--imageSize', type=int,
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default=128, help='the height / width of the cropped input image to network')
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parser.add_argument('--inputChannelSize', type=int,
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default=3, help='size of the input channels')
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parser.add_argument('--outputChannelSize', type=int,
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default=3, help='size of the output channels')
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parser.add_argument('--ngf', type=int, default=64)
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parser.add_argument('--ndf', type=int, default=64)
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parser.add_argument('--niter', type=int, default=5000, help='number of epochs to train for')
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parser.add_argument('--lrD', type=float, default=0.0002, help='learning rate, default=0.0002')
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parser.add_argument('--lrG', type=float, default=0.0002, help='learning rate, default=0.0002')
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parser.add_argument('--annealStart', type=int, default=0, help='annealing learning rate start to')
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parser.add_argument('--annealEvery', type=int, default=1000, help='epoch to reaching at learning rate of 0')
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parser.add_argument('--lambdaGAN', type=float, default=0.01, help='lambdaGAN')
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parser.add_argument('--lambdaIMG', type=float, default=3.2, help='lambdaIMG')
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parser.add_argument('--poolSize', type=int, default=50, help='Buffer size for storing previously generated samples from G')
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parser.add_argument('--wd', type=float, default=0.0000, help='weight decay in D')
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parser.add_argument('--beta1', type=float, default=0.5, help='beta1 for adam')
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parser.add_argument('--netG', default='', help="path to netG (to continue training)")
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parser.add_argument('--netD', default='', help="path to netD (to continue training)")
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parser.add_argument('--workers', type=int, help='number of data loading workers', default=1)
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parser.add_argument('--exp', default='sample', help='folder to output images and model checkpoints')
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parser.add_argument('--display', type=int, default=5, help='interval for displaying train-logs')
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parser.add_argument('--evalIter', type=int, default=500, help='interval for evauating(generating) images from valDataroot')
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opt = parser.parse_args()
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print(opt)
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create_exp_dir(opt.exp)
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opt.manualSeed = random.randint(1, 10000)
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# opt.manualSeed = 101
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random.seed(opt.manualSeed)
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torch.manual_seed(opt.manualSeed)
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torch.cuda.manual_seed_all(opt.manualSeed)
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print("Random Seed: ", opt.manualSeed)
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# get dataloader
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opt.dataset='pix2pix_val'
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print (opt.dataroot)
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dataloader = getLoader(opt.dataset,
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opt.dataroot,
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opt.originalSize,
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opt.imageSize,
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opt.batchSize,
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opt.workers,
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mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
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split='train',
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shuffle=True,
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seed=opt.manualSeed)
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opt.dataset='pix2pix_val'
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valDataloader = getLoader(opt.dataset,
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opt.valDataroot,
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opt.originalSize,
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opt.imageSize,
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opt.valBatchSize,
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opt.workers,
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mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5),
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split='val',
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shuffle=False,
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seed=opt.manualSeed)
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# get logger
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trainLogger = open('%s/train.log' % opt.exp, 'w')
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def gradient(y):
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gradient_h=torch.abs(y[:, :, :, :-1] - y[:, :, :, 1:])
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gradient_y=torch.abs(y[:, :, :-1, :] - y[:, :, 1:, :])
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return gradient_h, gradient_y
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ngf = opt.ngf
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ndf = opt.ndf
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inputChannelSize = opt.inputChannelSize
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outputChannelSize= opt.outputChannelSize
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# get models
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# netG = net.G(inputChannelSize, outputChannelSize, ngf)
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netG=net.Segmentation()
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if opt.netG != '':
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netG.load_state_dict(torch.load(opt.netG))
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print(netG)
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#netG.load_state_dict(torch.load('./segs_faceblr/SMaps_60.pth'))
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from scipy import signal
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import h5py
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from scipy import signal
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import random
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k_filename ='./kernel.mat'
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kfp = h5py.File(k_filename)
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kernels = np.array(kfp['kernels'])
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kernels = kernels.transpose([0,2,1])
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netG.train()
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criterionCAE = nn.BCELoss()
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target= torch.FloatTensor(opt.batchSize, outputChannelSize, opt.imageSize, opt.imageSize)
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input = torch.FloatTensor(opt.batchSize, inputChannelSize, opt.imageSize, opt.imageSize)
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target_128= torch.FloatTensor(opt.batchSize, outputChannelSize, (opt.imageSize//4), (opt.imageSize//4))
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input_128 = torch.FloatTensor(opt.batchSize, inputChannelSize, (opt.imageSize//4), (opt.imageSize//4))
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target_256= torch.FloatTensor(opt.batchSize, outputChannelSize, (opt.imageSize//2), (opt.imageSize//2))
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input_256 = torch.FloatTensor(opt.batchSize, inputChannelSize, (opt.imageSize//2), (opt.imageSize//2))
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val_target= torch.FloatTensor(opt.valBatchSize, outputChannelSize, opt.imageSize, opt.imageSize)
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val_input = torch.FloatTensor(opt.valBatchSize, inputChannelSize, opt.imageSize, opt.imageSize)
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val_target_128= torch.FloatTensor(opt.batchSize, outputChannelSize, (opt.imageSize//4), (opt.imageSize//4))
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val_input_128 = torch.FloatTensor(opt.batchSize, inputChannelSize, (opt.imageSize//4), (opt.imageSize//4))
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val_target_256= torch.FloatTensor(opt.batchSize, outputChannelSize, (opt.imageSize//2), (opt.imageSize//2))
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val_input_256 = torch.FloatTensor(opt.batchSize, inputChannelSize, (opt.imageSize//2), (opt.imageSize//2))
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label_d = torch.FloatTensor(opt.batchSize)
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target = torch.FloatTensor(opt.batchSize, outputChannelSize, opt.imageSize, opt.imageSize)
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input = torch.FloatTensor(opt.batchSize, inputChannelSize, opt.imageSize, opt.imageSize)
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depth = torch.FloatTensor(opt.batchSize, inputChannelSize, opt.imageSize, opt.imageSize)
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ato = torch.FloatTensor(opt.batchSize, inputChannelSize, opt.imageSize, opt.imageSize)
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val_target = torch.FloatTensor(opt.valBatchSize, outputChannelSize, opt.imageSize, opt.imageSize)
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val_input = torch.FloatTensor(opt.valBatchSize, inputChannelSize, opt.imageSize, opt.imageSize)
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val_depth = torch.FloatTensor(opt.valBatchSize, inputChannelSize, opt.imageSize, opt.imageSize)
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val_ato = torch.FloatTensor(opt.valBatchSize, inputChannelSize, opt.imageSize, opt.imageSize)
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# NOTE: size of 2D output maps in the discriminator
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sizePatchGAN = 30
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real_label = 1
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fake_label = 0
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# image pool storing previously generated samples from G
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imagePool = ImagePool(opt.poolSize)
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# NOTE weight for L_cGAN and L_L1 (i.e. Eq.(4) in the paper)
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lambdaGAN = opt.lambdaGAN
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lambdaIMG = opt.lambdaIMG
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netG.cuda()
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criterionCAE.cuda()
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target, input, depth, ato = target.cuda(), input.cuda(), depth.cuda(), ato.cuda()
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val_target, val_input, val_depth, val_ato = val_target.cuda(), val_input.cuda(), val_depth.cuda(), val_ato.cuda()
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target = Variable(target)
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input = Variable(input)
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target_128, input_128 = target_128.cuda(), input_128.cuda()
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val_target_128, val_input_128 = val_target_128.cuda(), val_input_128.cuda()
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target_256, input_256 = target_256.cuda(), input_256.cuda()
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val_target_256, val_input_256 = val_target_256.cuda(), val_input_256.cuda()
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target_128 = Variable(target_128)
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input_128 = Variable(input_128)
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target_256 = Variable(target_256)
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input_256 = Variable(input_256)
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# input = Variable(input,requires_grad=False)
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# depth = Variable(depth)
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ato = Variable(ato)
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# Initialize VGG-16
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vgg = Vgg16()
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utils.init_vgg16('./models/')
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vgg.load_state_dict(torch.load(os.path.join('./models/', "vgg16.weight")))
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vgg.cuda()
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label_d = Variable(label_d.cuda())
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# get randomly sampled validation images and save it
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print(len(dataloader))
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val_iter = iter(valDataloader)
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data_val = val_iter.next()
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val_target_cpu, val_input_cpu = data_val
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val_target_cpu, val_input_cpu = val_target_cpu.float().cuda(), val_input_cpu.float().cuda()
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val_target.resize_as_(val_target_cpu).copy_(val_target_cpu)
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val_input.resize_as_(val_input_cpu).copy_(val_input_cpu)
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vutils.save_image(val_target, '%s/real_target.png' % opt.exp, normalize=True)
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vutils.save_image(val_input, '%s/real_input.png' % opt.exp, normalize=True)
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# pdb.set_trace()
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# get optimizer
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optimizerG = optim.Adam(netG.parameters(), lr = opt.lrG, betas = (opt.beta1, 0.999), weight_decay=0.00005)
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# NOTE training loop
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ganIterations = 0
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count = 1
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Best_Fs = 0
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Best_epoch = 0
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for epoch in range(1000):
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if epoch%60 == 0 and epoch>0:
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opt.lrG = opt.lrG/1.25
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for param_group in optimizerG.param_groups:
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param_group['lr'] = opt.lrG
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if epoch == 200:
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opt.lrG = 0.000001
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for param_group in optimizerG.param_groups:
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param_group['lr'] = opt.lrG
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if epoch >= opt.annealStart:
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adjust_learning_rate(optimizerG, opt.lrG, epoch, None, opt.annealEvery)
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for set_i in range(1,5):
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cl_filename ='./seg_mat_files/Images_m%d.mat'%set_i
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sm_filename ='./seg_mat_files/Labels_m%d.mat'%set_i
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fcl = h5py.File(cl_filename)
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fsm = h5py.File(sm_filename)
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clean_images = np.array(fcl['Images'])
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sm_maps = np.array(fsm['Labels'])
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#print(clean_images.shape)
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clean_images = clean_images.transpose([0,1,3,2])
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sm_maps = sm_maps.transpose([0,1,3,2])
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clean_images = clean_images/255.0
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clean_images = (clean_images-0.5)/0.5
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sm_maps = sm_maps/(1*255.0)
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sm_maps[sm_maps>0.49] = 1
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sm_maps[sm_maps<=0.49] = 0
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for i in range(50):
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sm_map = sm_maps[i*10:i*10+10,:,:,:]
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cl_image = clean_images[i*10:i*10+10,:,:,:]
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#print(np.amax(sm_map))
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#print(sm_map.shape)
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cl_image = np.reshape(cl_image,[10,3,160,160])
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sm_map = np.reshape(sm_map,[10,4,160,160])
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input_cpu = Variable(torch.from_numpy(cl_image))
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target_cpu = Variable(torch.from_numpy(sm_map))
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x1 = int((160-opt.imageSize)/2)
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y1 = int((160-opt.imageSize)/2)
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input_cpu = input_cpu.numpy()
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target_cpu = target_cpu.numpy()
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if (random.randint(0,100)<0) :
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for j in range(10):
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index = random.randint(0,24000)
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for k in range(4):
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target_cpu[j,k,:,:]= signal.convolve(target_cpu[j,k,:,:],kernels[index,:,:],mode='same')
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input_cpu[j,0,:,:]= signal.convolve(input_cpu[j,0,:,:],kernels[index,:,:],mode='same')
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input_cpu[j,1,:,:]= signal.convolve(input_cpu[j,1,:,:],kernels[index,:,:],mode='same')
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input_cpu[j,2,:,:]= signal.convolve(input_cpu[j,2,:,:],kernels[index,:,:],mode='same')
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#input_cpu = input_cpu + (1.0/255.0)* np.random.normal(0,4,input_cpu.shape)
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#input_cpu = input_cpu[:,:,x1:x1+opt.imageSize,y1:y1+opt.imageSize]
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#target_cpu = target_cpu[:,:,x1:x1+opt.imageSize,y1:y1+opt.imageSize]
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input_cpu = input_cpu[:,:,x1:x1+opt.imageSize,y1:y1+opt.imageSize]
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target_cpu = target_cpu[:,:,x1:x1+opt.imageSize,y1:y1+opt.imageSize]
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input_cpu = torch.from_numpy(input_cpu)
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target_cpu = torch.from_numpy(target_cpu)
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target_cpu, input_cpu = target_cpu.float().cuda(), input_cpu.float().cuda()
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target.data.resize_as_(target_cpu).copy_(target_cpu)
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input.data.resize_as_(input_cpu).copy_(input_cpu)
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input_256 = torch.nn.functional.interpolate(input,scale_factor=0.5)
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target_256 = torch.nn.functional.interpolate(target,scale_factor=0.5)
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x_hat, x_hat64 = netG(input,input_256)
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#print(x_hat.size())
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netG.zero_grad()
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L_img_ = criterionCAE(x_hat, target) + 0.5*criterionCAE(x_hat64, target_256)
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L_img = lambdaIMG * L_img_
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if lambdaIMG != 0:
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L_img.backward(retain_graph=True)
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#print("came")
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optimizerG.step()
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ganIterations += 1
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if ganIterations % (10*opt.display) == 0:
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print('[%d/%d][%d/%d] L_D: %f L_img: %f L_G: %f D(x): %f D(G(z)): %f / %f'
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% (epoch, opt.niter, 10*i, 2000,
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L_img.data[0], L_img.data[0], L_img.data[0], L_img.data[0], L_img.data[0], L_img.data[0]))
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if ganIterations % (40*opt.display) == 0:
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val_batch_output = torch.zeros([16,3,128,128], dtype=torch.float32)#torch.FloatTensor([10,3,128,128]).fill_(0)
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Fs = 0
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for idx in range(33):
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tcl_filename ='./seg_mat_files/Images_test.mat'
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tsm_filename ='./seg_mat_files/Labels_test.mat'
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tfcl = h5py.File(tcl_filename)
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tfsm = h5py.File(tsm_filename)
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tclean_images = np.array(tfcl['Images'])
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tsm_maps = np.array(tfsm['Labels'])
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#print(clean_images.shape)
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tclean_images = tclean_images.transpose([0,1,3,2])
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tsm_maps = tsm_maps.transpose([0,1,3,2])
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tclean_images = tclean_images/255.0
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tclean_images = (tclean_images-0.5)/0.5
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tsm_maps = tsm_maps/(1*255.0)
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tsm_maps[tsm_maps>0.49] = 1
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tsm_maps[tsm_maps<=0.49] = 0
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x1 = int((160-opt.imageSize)/2)
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y1 = int((160-opt.imageSize)/2)
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# print(tclean_images.shape,x1,y1,opt.imageSize)
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single_img = tclean_images[idx*10:idx*10+10,:,:,:]
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single_img = np.reshape(single_img,[10,3,160,160])#val_input[idx,:,:,:].unsqueeze(0)
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val_inputv = Variable(torch.from_numpy(single_img), volatile=True)
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yval = tsm_maps[idx*10:idx*10+10,:,:,:]
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yval = np.reshape(yval,[10,4,160,160])#val_input[idx,:,:,:].unsqueeze(0)
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# if epoch>=0:
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# index = idx+24500
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# val_inputv = val_inputv.cpu().numpy()
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# val_inputv[0,0,:,:]= signal.convolve(val_inputv[0,0,:,:],kernels[index,:,:],mode='same')
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# val_inputv[0,1,:,:]= signal.convolve(val_inputv[0,1,:,:],kernels[index,:,:],mode='same')
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# val_inputv[0,2,:,:]= signal.convolve(val_inputv[0,2,:,:],kernels[index,:,:],mode='same')
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# val_inputv = val_inputv[:,:,x1:x1+opt.imageSize,y1:y1+opt.imageSize]
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# val_inputv = val_inputv + (1.0/255.0)* np.random.normal(0,4,val_inputv.shape)
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# val_inputv = torch.from_numpy(val_inputv)
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# val_inputv = val_inputv.float().cuda()
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# else :
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val_inputv = val_inputv[:,:,x1:x1+opt.imageSize,y1:y1+opt.imageSize]
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yval = yval[:,:,x1:x1+opt.imageSize,y1:y1+opt.imageSize]
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val_inputv = val_inputv.float().cuda()
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val_inputv_256 = torch.nn.functional.interpolate(val_inputv,scale_factor=0.5)
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with torch.no_grad():
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x_hat_val,x_hat_val64 = netG(val_inputv,val_inputv_256)
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val_batch_output[idx%16,:,:,:].copy_(x_hat_val.data[0,idx%4,:,:])
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# print(val_inputv.size(),yval.min(),yval.max(),x_hat_val.min(),x_hat_val.max())
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x_hat_val = x_hat_val.cpu().numpy()
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x_hat_val[x_hat_val>0.5] = 1
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x_hat_val[x_hat_val<=0.5] = 0
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# print(val_inputv.size(),yval.min(),yval.max(),x_hat_val.min(),x_hat_val.max())
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for jdx in range(10):
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ttp = np.sum(yval[jdx,:,:,:]==1)
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tp = np.sum((x_hat_val[jdx,:,:,:]==1) & (yval[jdx,:,:,:]==1))
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fp = np.sum((x_hat_val[jdx,:,:,:] == 1) & (yval[jdx,:,:,:] == 0))
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fn = np.sum((x_hat_val[jdx,:,:,:] == 0) & (yval[jdx,:,:,:] == 1))
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tot = tp+fp
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tot2 = tp+fn
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if tot==0 or tot2 ==0 or tp==0:
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f1_s = 1
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else:
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precision = tp / (tp + fp);
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recall = tp / (tp + fn);
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f1_s = (2 * precision * recall) / (precision + recall);
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#print(f1_s)
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Fs = Fs + f1_s
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# print(f1_s)
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Fs = Fs/330
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if Best_Fs<Fs:
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Best_Fs = Fs
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Best_epoch = epoch
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print("Best epoch: %d"%(Best_epoch))
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print("Best Fs: %f\t Fs:%f"%(Best_Fs,Fs))
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if ganIterations % (40*opt.display) == 0:
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vutils.save_image(val_batch_output, '%s/generated_epoch_iter%08d.png' % \
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(opt.exp, ganIterations), normalize=True, scale_each=False)
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sys.stdout.flush()
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trainLogger.write('%d\t%f\t%f\t%f\t%f\t%f\t%f\n' % \
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(i, L_img.data[0], L_img.data[0], L_img.data[0], L_img.data[0], L_img.data[0], L_img.data[0]))
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trainLogger.flush()
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torch.save(netG.state_dict(), '%s/SMaps_%d.pth' % (opt.exp, epoch))
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trainLogger.close()
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