148 lines
5.3 KiB
Python
148 lines
5.3 KiB
Python
import time
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import os
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import numpy as np
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import torch
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from torch.autograd import Variable
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from collections import OrderedDict
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from subprocess import call
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import fractions
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from options.train_options import TrainOptions
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from data.data_loader import CreateDataLoader
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from data.dataset_class import FaceDataSet
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from torch.utils.data import DataLoader
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from models.models import create_model
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import util.util as util
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from util.visualizer import Visualizer
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import cv2
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from torchvision import transforms
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def lcm(a,b): return abs(a * b)/fractions.gcd(a,b) if a and b else 0
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detransformer = transforms.Compose([
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transforms.Normalize([0, 0, 0], [1/0.229, 1/0.224, 1/0.225]),
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transforms.Normalize([-0.485, -0.456, -0.406], [1, 1, 1])
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])
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opt = TrainOptions().parse()
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iter_path = os.path.join(opt.checkpoints_dir, opt.name, 'iter.txt')
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if opt.continue_train:
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try:
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start_epoch, epoch_iter = np.loadtxt(iter_path , delimiter=',', dtype=int)
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except:
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start_epoch, epoch_iter = 1, 0
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print('Resuming from epoch %d at iteration %d' % (start_epoch, epoch_iter))
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else:
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start_epoch, epoch_iter = 1, 0
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opt.print_freq = lcm(opt.print_freq, opt.batchSize)
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if opt.debug:
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opt.display_freq = 1
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opt.print_freq = 1
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opt.niter = 1
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opt.niter_decay = 0
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opt.max_dataset_size = 10
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dataset = FaceDataSet('people_list.txt', opt.batchSize)
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data_loader = DataLoader(dataset, batch_size = opt.batchSize, shuffle=True)
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dataset_size = len(data_loader)
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device = torch.device("cuda:0")
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model = create_model(opt)
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visualizer = Visualizer(opt)
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optimizer_G, optimizer_D = model.module.optimizer_G, model.module.optimizer_D
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total_steps = (start_epoch-1) * 8608 + epoch_iter
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display_delta = total_steps % opt.display_freq
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print_delta = total_steps % opt.print_freq
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save_delta = total_steps % opt.save_latest_freq
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loss_avg = 0
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refresh_count = 0
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for epoch in range(start_epoch, opt.niter + opt.niter_decay + 1):
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epoch_start_time = time.time()
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if epoch != start_epoch:
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epoch_iter = epoch_iter % dataset_size
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for i, (img_id, img_att, latent_id, latent_att, data_type) in enumerate(data_loader):
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if total_steps % opt.print_freq == print_delta:
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iter_start_time = time.time()
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total_steps += opt.batchSize
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epoch_iter += opt.batchSize
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# convert numpy to tensor
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img_id = img_id.to(device)
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img_att = img_att.to(device)
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latent_id = latent_id.to(device)
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latent_att = latent_att.to(device)
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# whether to collect output images
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save_fake = total_steps % opt.display_freq == display_delta
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############## Forward Pass ######################
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losses, img_fake = model(img_id, img_att, latent_id, latent_att, for_G=True)
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# update Generator weights
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losses = [ torch.mean(x) if not isinstance(x, int) else x for x in losses ]
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loss_dict = dict(zip(model.module.loss_names, losses))
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loss_G = loss_dict['G_GAN'] + loss_dict.get('G_GAN_Feat', 0) + loss_dict['G_ID'] * opt.lambda_id
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if data_type[0] == 0:
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loss_G += loss_dict['G_Rec']
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optimizer_G.zero_grad()
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loss_G.backward(retain_graph=True)
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optimizer_G.step()
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loss_D = (loss_dict['D_fake'] + loss_dict['D_real']) * 0.5 + loss_dict['D_GP']
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optimizer_D.zero_grad()
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loss_D.backward()
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optimizer_D.step()
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############## Display results and errors ##########
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### print out errors
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if total_steps % opt.print_freq == print_delta:
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errors = {k: v.data.item() if not isinstance(v, int) else v for k, v in loss_dict.items()}
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t = (time.time() - iter_start_time) / opt.print_freq
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visualizer.print_current_errors(epoch, epoch_iter, errors, t)
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visualizer.plot_current_errors(errors, total_steps)
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### display output images
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if save_fake:
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'''visuals = OrderedDict([('input_label', util.tensor2label(data['label'][0], opt.label_nc)),
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('synthesized_image', util.tensor2im(generated.data[0])),
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('real_image', util.tensor2im(data['image'][0]))])'''
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for i in range(img_id.shape[0]):
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if i == 0:
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row1 = img_id[i]
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row2 = img_att[i]
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row3 = img_fake[i]
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else:
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row1 = torch.cat([row1, img_id[i]], dim=2)
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row2 = torch.cat([row2, img_att[i]], dim=2)
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row3 = torch.cat([row3, img_fake[i]], dim=2)
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full = torch.cat([row1, row2, row3], dim=1).detach()
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full = full.permute(1, 2, 0)
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output = full.to('cpu')
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output = np.array(output)*255
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output = output[..., ::-1]
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cv2.imwrite('samples/step_'+str(total_steps)+'.jpg', output)
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### save latest model
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if total_steps % opt.save_latest_freq == save_delta:
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print('saving the latest model (epoch %d, total_steps %d)' % (epoch, total_steps))
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model.module.save('latest')
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np.savetxt(iter_path, (epoch, epoch_iter), delimiter=',', fmt='%d')
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# end of epoch
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iter_end_time = time.time()
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print('End of epoch %d / %d \t Time Taken: %d sec' %
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(epoch, opt.niter + opt.niter_decay, time.time() - epoch_start_time)) |