update

2021-06-09 13:12:21 +08:00
parent 51db3ce668
commit 3ee304b0e1
39 changed files with 636 additions and 150 deletions
@@ -1,148 +0,0 @@
-import time
-import os
-import numpy as np
-import torch
-from torch.autograd import Variable
-from collections import OrderedDict
-from subprocess import call
-import fractions
-from options.train_options import TrainOptions
-from data.data_loader import CreateDataLoader
-from data.dataset_class import FaceDataSet
-from torch.utils.data import DataLoader
-from models.models import create_model
-import util.util as util
-from util.visualizer import Visualizer
-import cv2
-from torchvision import transforms
-
-def lcm(a,b): return abs(a * b)/fractions.gcd(a,b) if a and b else 0
-
-
-detransformer = transforms.Compose([
-        transforms.Normalize([0, 0, 0], [1/0.229, 1/0.224, 1/0.225]),
-        transforms.Normalize([-0.485, -0.456, -0.406], [1, 1, 1])
-    ])
-
-opt = TrainOptions().parse()
-iter_path = os.path.join(opt.checkpoints_dir, opt.name, 'iter.txt')
-
-if opt.continue_train:
-    try:
-        start_epoch, epoch_iter = np.loadtxt(iter_path , delimiter=',', dtype=int)
-    except:
-        start_epoch, epoch_iter = 1, 0
-    print('Resuming from epoch %d at iteration %d' % (start_epoch, epoch_iter))        
-else:    
-    start_epoch, epoch_iter = 1, 0
-
-opt.print_freq = lcm(opt.print_freq, opt.batchSize)    
-if opt.debug:
-    opt.display_freq = 1
-    opt.print_freq = 1
-    opt.niter = 1
-    opt.niter_decay = 0
-    opt.max_dataset_size = 10
-
-
-dataset = FaceDataSet('people_list.txt', opt.batchSize)
-data_loader = DataLoader(dataset, batch_size = opt.batchSize, shuffle=True)
-dataset_size = len(data_loader)
-
-device = torch.device("cuda:0")
-
-
-model = create_model(opt)
-visualizer = Visualizer(opt)
-
-optimizer_G, optimizer_D = model.module.optimizer_G, model.module.optimizer_D
-
-total_steps = (start_epoch-1) * 8608 + epoch_iter
-
-display_delta = total_steps % opt.display_freq
-print_delta = total_steps % opt.print_freq
-save_delta = total_steps % opt.save_latest_freq
-
-loss_avg = 0
-refresh_count = 0
-
-for epoch in range(start_epoch, opt.niter + opt.niter_decay + 1):
-    epoch_start_time = time.time()
-    if epoch != start_epoch:
-        epoch_iter = epoch_iter % dataset_size
-    for i, (img_id, img_att, latent_id, latent_att, data_type) in enumerate(data_loader):
-        if total_steps % opt.print_freq == print_delta:
-            iter_start_time = time.time()
-        total_steps += opt.batchSize
-        epoch_iter += opt.batchSize
-
-        # convert numpy to tensor
-        img_id = img_id.to(device)
-        img_att = img_att.to(device)
-        latent_id = latent_id.to(device)
-        latent_att = latent_att.to(device)
-
-
-        # whether to collect output images
-        save_fake = total_steps % opt.display_freq == display_delta
-
-        ############## Forward Pass ######################
-
-        losses, img_fake = model(img_id, img_att, latent_id, latent_att, for_G=True)
-
-        # update Generator weights
-        losses = [ torch.mean(x) if not isinstance(x, int) else x for x in losses ]
-        loss_dict = dict(zip(model.module.loss_names, losses))
-
-        loss_G = loss_dict['G_GAN'] + loss_dict.get('G_GAN_Feat', 0) + loss_dict['G_ID'] * opt.lambda_id
-        if data_type[0] == 0:
-            loss_G += loss_dict['G_Rec']
-
-        optimizer_G.zero_grad()
-        loss_G.backward(retain_graph=True)
-        optimizer_G.step()
-
-        loss_D = (loss_dict['D_fake'] + loss_dict['D_real']) * 0.5 + loss_dict['D_GP']
-        optimizer_D.zero_grad()
-        loss_D.backward()
-        optimizer_D.step()
-
-        ############## Display results and errors ##########
-        ### print out errors
-        if total_steps % opt.print_freq == print_delta:
-            errors = {k: v.data.item() if not isinstance(v, int) else v for k, v in loss_dict.items()}
-            t = (time.time() - iter_start_time) / opt.print_freq
-            visualizer.print_current_errors(epoch, epoch_iter, errors, t)
-            visualizer.plot_current_errors(errors, total_steps)
-
-        ### display output images
-        if save_fake:
-            '''visuals = OrderedDict([('input_label', util.tensor2label(data['label'][0], opt.label_nc)),
-                                   ('synthesized_image', util.tensor2im(generated.data[0])),
-                                   ('real_image', util.tensor2im(data['image'][0]))])'''
-            for i in range(img_id.shape[0]):
-                if i == 0:
-                    row1 = img_id[i]
-                    row2 = img_att[i]
-                    row3 = img_fake[i]
-                else:
-                    row1 = torch.cat([row1, img_id[i]], dim=2)
-                    row2 = torch.cat([row2, img_att[i]], dim=2)
-                    row3 = torch.cat([row3, img_fake[i]], dim=2)
-            full = torch.cat([row1, row2, row3], dim=1).detach()
-            full = full.permute(1, 2, 0)
-            output = full.to('cpu')
-            output = np.array(output)*255
-            output = output[..., ::-1]
-            cv2.imwrite('samples/step_'+str(total_steps)+'.jpg', output)
-
-        ### save latest model
-        if total_steps % opt.save_latest_freq == save_delta:
-            print('saving the latest model (epoch %d, total_steps %d)' % (epoch, total_steps))
-            model.module.save('latest')            
-            np.savetxt(iter_path, (epoch, epoch_iter), delimiter=',', fmt='%d')
-       
-    # end of epoch 
-    iter_end_time = time.time()
-    print('End of epoch %d / %d \t Time Taken: %d sec' %
-          (epoch, opt.niter + opt.niter_decay, time.time() - epoch_start_time))