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 from misc import * import models.face_fed as net from myutils.vgg16 import Vgg16 from myutils import utils import pdb import torch.nn.functional as F #from PIL import Image from torchvision import transforms import h5py from os import listdir from os.path import isfile, join 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=160, help='the height / width of the original input image') parser.add_argument('--imageSize', type=int, default=128, 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=1000, 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=3.2, 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 opt.dataset='pix2pix_val' print (opt.dataroot) 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) opt.dataset='pix2pix_val' valDataloader = getLoader(opt.dataset, opt.valDataroot, opt.originalSize, opt.imageSize, opt.valBatchSize, opt.workers, mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5), split='val', shuffle=False, seed=opt.manualSeed) # get logger trainLogger = open('%s/train.log' % opt.exp, 'w') def gradient(y): gradient_h=torch.abs(y[:, :, :, :-1] - y[:, :, :, 1:]) gradient_y=torch.abs(y[:, :, :-1, :] - y[:, :, 1:, :]) return gradient_h, gradient_y ngf = opt.ngf ndf = opt.ndf inputChannelSize = opt.inputChannelSize outputChannelSize= opt.outputChannelSize # get models # netG = net.G(inputChannelSize, outputChannelSize, ngf) netG=net.Segmentation() if opt.netG != '': netG.load_state_dict(torch.load(opt.netG)) print(netG) #netG.load_state_dict(torch.load('./segs_faceblr/SMaps_60.pth')) from scipy import signal import h5py from scipy import signal import random k_filename ='./kernel.mat' kfp = h5py.File(k_filename) kernels = np.array(kfp['kernels']) kernels = kernels.transpose([0,2,1]) netG.train() criterionCAE = nn.BCELoss() target= torch.FloatTensor(opt.batchSize, outputChannelSize, opt.imageSize, opt.imageSize) input = torch.FloatTensor(opt.batchSize, inputChannelSize, opt.imageSize, opt.imageSize) target_128= torch.FloatTensor(opt.batchSize, outputChannelSize, (opt.imageSize//4), (opt.imageSize//4)) input_128 = torch.FloatTensor(opt.batchSize, inputChannelSize, (opt.imageSize//4), (opt.imageSize//4)) target_256= torch.FloatTensor(opt.batchSize, outputChannelSize, (opt.imageSize//2), (opt.imageSize//2)) input_256 = torch.FloatTensor(opt.batchSize, inputChannelSize, (opt.imageSize//2), (opt.imageSize//2)) val_target= torch.FloatTensor(opt.valBatchSize, outputChannelSize, opt.imageSize, opt.imageSize) val_input = torch.FloatTensor(opt.valBatchSize, inputChannelSize, opt.imageSize, opt.imageSize) val_target_128= torch.FloatTensor(opt.batchSize, outputChannelSize, (opt.imageSize//4), (opt.imageSize//4)) val_input_128 = torch.FloatTensor(opt.batchSize, inputChannelSize, (opt.imageSize//4), (opt.imageSize//4)) val_target_256= torch.FloatTensor(opt.batchSize, outputChannelSize, (opt.imageSize//2), (opt.imageSize//2)) val_input_256 = torch.FloatTensor(opt.batchSize, inputChannelSize, (opt.imageSize//2), (opt.imageSize//2)) 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) depth = torch.FloatTensor(opt.batchSize, inputChannelSize, opt.imageSize, opt.imageSize) ato = torch.FloatTensor(opt.batchSize, inputChannelSize, opt.imageSize, opt.imageSize) val_target = torch.FloatTensor(opt.valBatchSize, outputChannelSize, opt.imageSize, opt.imageSize) val_input = torch.FloatTensor(opt.valBatchSize, inputChannelSize, opt.imageSize, opt.imageSize) val_depth = torch.FloatTensor(opt.valBatchSize, inputChannelSize, opt.imageSize, opt.imageSize) val_ato = torch.FloatTensor(opt.valBatchSize, inputChannelSize, opt.imageSize, opt.imageSize) # NOTE: size of 2D output maps in the discriminator sizePatchGAN = 30 real_label = 1 fake_label = 0 # 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() criterionCAE.cuda() target, input, depth, ato = target.cuda(), input.cuda(), depth.cuda(), ato.cuda() val_target, val_input, val_depth, val_ato = val_target.cuda(), val_input.cuda(), val_depth.cuda(), val_ato.cuda() target = Variable(target) input = Variable(input) target_128, input_128 = target_128.cuda(), input_128.cuda() val_target_128, val_input_128 = val_target_128.cuda(), val_input_128.cuda() target_256, input_256 = target_256.cuda(), input_256.cuda() val_target_256, val_input_256 = val_target_256.cuda(), val_input_256.cuda() target_128 = Variable(target_128) input_128 = Variable(input_128) target_256 = Variable(target_256) input_256 = Variable(input_256) # input = Variable(input,requires_grad=False) # depth = Variable(depth) ato = Variable(ato) # Initialize VGG-16 vgg = Vgg16() utils.init_vgg16('./models/') vgg.load_state_dict(torch.load(os.path.join('./models/', "vgg16.weight"))) vgg.cuda() label_d = Variable(label_d.cuda()) # get randomly sampled validation images and save it print(len(dataloader)) val_iter = iter(valDataloader) data_val = val_iter.next() val_target_cpu, val_input_cpu = data_val val_target_cpu, val_input_cpu = val_target_cpu.float().cuda(), val_input_cpu.float().cuda() val_target.resize_as_(val_target_cpu).copy_(val_target_cpu) val_input.resize_as_(val_input_cpu).copy_(val_input_cpu) vutils.save_image(val_target, '%s/real_target.png' % opt.exp, normalize=True) vutils.save_image(val_input, '%s/real_input.png' % opt.exp, normalize=True) # pdb.set_trace() # get optimizer optimizerG = optim.Adam(netG.parameters(), lr = opt.lrG, betas = (opt.beta1, 0.999), weight_decay=0.00005) # NOTE training loop ganIterations = 0 count = 1 Best_Fs = 0 Best_epoch = 0 for epoch in range(1000): if epoch%60 == 0 and epoch>0: opt.lrG = opt.lrG/1.25 for param_group in optimizerG.param_groups: param_group['lr'] = opt.lrG if epoch == 200: opt.lrG = 0.000001 for param_group in optimizerG.param_groups: param_group['lr'] = opt.lrG if epoch >= opt.annealStart: adjust_learning_rate(optimizerG, opt.lrG, epoch, None, opt.annealEvery) for set_i in range(1,5): cl_filename ='./seg_mat_files/Images_m%d.mat'%set_i sm_filename ='./seg_mat_files/Labels_m%d.mat'%set_i fcl = h5py.File(cl_filename) fsm = h5py.File(sm_filename) clean_images = np.array(fcl['Images']) sm_maps = np.array(fsm['Labels']) #print(clean_images.shape) clean_images = clean_images.transpose([0,1,3,2]) sm_maps = sm_maps.transpose([0,1,3,2]) clean_images = clean_images/255.0 clean_images = (clean_images-0.5)/0.5 sm_maps = sm_maps/(1*255.0) sm_maps[sm_maps>0.49] = 1 sm_maps[sm_maps<=0.49] = 0 for i in range(50): sm_map = sm_maps[i*10:i*10+10,:,:,:] cl_image = clean_images[i*10:i*10+10,:,:,:] #print(np.amax(sm_map)) #print(sm_map.shape) cl_image = np.reshape(cl_image,[10,3,160,160]) sm_map = np.reshape(sm_map,[10,4,160,160]) input_cpu = Variable(torch.from_numpy(cl_image)) target_cpu = Variable(torch.from_numpy(sm_map)) x1 = int((160-opt.imageSize)/2) y1 = int((160-opt.imageSize)/2) input_cpu = input_cpu.numpy() target_cpu = target_cpu.numpy() if (random.randint(0,100)<0) : for j in range(10): index = random.randint(0,24000) for k in range(4): target_cpu[j,k,:,:]= signal.convolve(target_cpu[j,k,:,:],kernels[index,:,:],mode='same') input_cpu[j,0,:,:]= signal.convolve(input_cpu[j,0,:,:],kernels[index,:,:],mode='same') input_cpu[j,1,:,:]= signal.convolve(input_cpu[j,1,:,:],kernels[index,:,:],mode='same') input_cpu[j,2,:,:]= signal.convolve(input_cpu[j,2,:,:],kernels[index,:,:],mode='same') #input_cpu = input_cpu + (1.0/255.0)* np.random.normal(0,4,input_cpu.shape) #input_cpu = input_cpu[:,:,x1:x1+opt.imageSize,y1:y1+opt.imageSize] #target_cpu = target_cpu[:,:,x1:x1+opt.imageSize,y1:y1+opt.imageSize] input_cpu = input_cpu[:,:,x1:x1+opt.imageSize,y1:y1+opt.imageSize] target_cpu = target_cpu[:,:,x1:x1+opt.imageSize,y1:y1+opt.imageSize] input_cpu = torch.from_numpy(input_cpu) target_cpu = torch.from_numpy(target_cpu) target_cpu, input_cpu = target_cpu.float().cuda(), input_cpu.float().cuda() target.data.resize_as_(target_cpu).copy_(target_cpu) input.data.resize_as_(input_cpu).copy_(input_cpu) input_256 = torch.nn.functional.interpolate(input,scale_factor=0.5) target_256 = torch.nn.functional.interpolate(target,scale_factor=0.5) x_hat, x_hat64 = netG(input,input_256) #print(x_hat.size()) netG.zero_grad() L_img_ = criterionCAE(x_hat, target) + 0.5*criterionCAE(x_hat64, target_256) L_img = lambdaIMG * L_img_ if lambdaIMG != 0: L_img.backward(retain_graph=True) #print("came") optimizerG.step() ganIterations += 1 if ganIterations % (10*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, 10*i, 2000, L_img.data[0], L_img.data[0], L_img.data[0], L_img.data[0], L_img.data[0], L_img.data[0])) if ganIterations % (40*opt.display) == 0: val_batch_output = torch.zeros([16,3,128,128], dtype=torch.float32)#torch.FloatTensor([10,3,128,128]).fill_(0) Fs = 0 for idx in range(33): tcl_filename ='./seg_mat_files/Images_test.mat' tsm_filename ='./seg_mat_files/Labels_test.mat' tfcl = h5py.File(tcl_filename) tfsm = h5py.File(tsm_filename) tclean_images = np.array(tfcl['Images']) tsm_maps = np.array(tfsm['Labels']) #print(clean_images.shape) tclean_images = tclean_images.transpose([0,1,3,2]) tsm_maps = tsm_maps.transpose([0,1,3,2]) tclean_images = tclean_images/255.0 tclean_images = (tclean_images-0.5)/0.5 tsm_maps = tsm_maps/(1*255.0) tsm_maps[tsm_maps>0.49] = 1 tsm_maps[tsm_maps<=0.49] = 0 x1 = int((160-opt.imageSize)/2) y1 = int((160-opt.imageSize)/2) # print(tclean_images.shape,x1,y1,opt.imageSize) single_img = tclean_images[idx*10:idx*10+10,:,:,:] single_img = np.reshape(single_img,[10,3,160,160])#val_input[idx,:,:,:].unsqueeze(0) val_inputv = Variable(torch.from_numpy(single_img), volatile=True) yval = tsm_maps[idx*10:idx*10+10,:,:,:] yval = np.reshape(yval,[10,4,160,160])#val_input[idx,:,:,:].unsqueeze(0) # if epoch>=0: # index = idx+24500 # val_inputv = val_inputv.cpu().numpy() # val_inputv[0,0,:,:]= signal.convolve(val_inputv[0,0,:,:],kernels[index,:,:],mode='same') # val_inputv[0,1,:,:]= signal.convolve(val_inputv[0,1,:,:],kernels[index,:,:],mode='same') # val_inputv[0,2,:,:]= signal.convolve(val_inputv[0,2,:,:],kernels[index,:,:],mode='same') # val_inputv = val_inputv[:,:,x1:x1+opt.imageSize,y1:y1+opt.imageSize] # val_inputv = val_inputv + (1.0/255.0)* np.random.normal(0,4,val_inputv.shape) # val_inputv = torch.from_numpy(val_inputv) # val_inputv = val_inputv.float().cuda() # else : val_inputv = val_inputv[:,:,x1:x1+opt.imageSize,y1:y1+opt.imageSize] yval = yval[:,:,x1:x1+opt.imageSize,y1:y1+opt.imageSize] val_inputv = val_inputv.float().cuda() val_inputv_256 = torch.nn.functional.interpolate(val_inputv,scale_factor=0.5) with torch.no_grad(): x_hat_val,x_hat_val64 = netG(val_inputv,val_inputv_256) val_batch_output[idx%16,:,:,:].copy_(x_hat_val.data[0,idx%4,:,:]) # print(val_inputv.size(),yval.min(),yval.max(),x_hat_val.min(),x_hat_val.max()) x_hat_val = x_hat_val.cpu().numpy() x_hat_val[x_hat_val>0.5] = 1 x_hat_val[x_hat_val<=0.5] = 0 # print(val_inputv.size(),yval.min(),yval.max(),x_hat_val.min(),x_hat_val.max()) for jdx in range(10): ttp = np.sum(yval[jdx,:,:,:]==1) tp = np.sum((x_hat_val[jdx,:,:,:]==1) & (yval[jdx,:,:,:]==1)) fp = np.sum((x_hat_val[jdx,:,:,:] == 1) & (yval[jdx,:,:,:] == 0)) fn = np.sum((x_hat_val[jdx,:,:,:] == 0) & (yval[jdx,:,:,:] == 1)) tot = tp+fp tot2 = tp+fn if tot==0 or tot2 ==0 or tp==0: f1_s = 1 else: precision = tp / (tp + fp); recall = tp / (tp + fn); f1_s = (2 * precision * recall) / (precision + recall); #print(f1_s) Fs = Fs + f1_s # print(f1_s) Fs = Fs/330 if Best_Fs