| import time |
| from options.test_options import TestOptions |
| from data.data_loader_test import CreateDataLoader |
| from models.networks import ResUnetGenerator, load_checkpoint |
| from models.afwm import AFWM |
| import torch.nn as nn |
| import os |
| import numpy as np |
| import torch |
| import cv2 |
| import torch.nn.functional as F |
|
|
| opt = TestOptions().parse() |
|
|
| start_epoch, epoch_iter = 1, 0 |
|
|
| |
| with open('demo.txt', 'w') as file: |
| lines = [f'3.png {cloth_img_fn}\n' for cloth_img_fn in os.listdir('dataset/test_clothes')] |
| file.writelines(lines) |
|
|
| data_loader = CreateDataLoader(opt) |
| dataset = data_loader.load_data() |
| dataset_size = len(data_loader) |
| print(dataset_size) |
|
|
| warp_model = AFWM(opt, 3) |
| print(warp_model) |
| warp_model.eval() |
| warp_model.cuda() |
| load_checkpoint(warp_model, opt.warp_checkpoint) |
|
|
| gen_model = ResUnetGenerator(7, 4, 5, ngf=64, norm_layer=nn.BatchNorm2d) |
| print(gen_model) |
| gen_model.eval() |
| gen_model.cuda() |
| load_checkpoint(gen_model, opt.gen_checkpoint) |
|
|
| total_steps = (start_epoch-1) * dataset_size + epoch_iter |
| step = 0 |
| step_per_batch = dataset_size / opt.batchSize |
|
|
| for epoch in range(1,2): |
|
|
| for i, data in enumerate(dataset, start=epoch_iter): |
| iter_start_time = time.time() |
| total_steps += opt.batchSize |
| epoch_iter += opt.batchSize |
|
|
| real_image = data['image'] |
| clothes = data['clothes'] |
| |
| edge = data['edge'] |
| edge = torch.FloatTensor((edge.detach().numpy() > 0.5).astype(np.int)) |
| clothes = clothes * edge |
|
|
| flow_out = warp_model(real_image.cuda(), clothes.cuda()) |
| warped_cloth, last_flow, = flow_out |
| warped_edge = F.grid_sample(edge.cuda(), last_flow.permute(0, 2, 3, 1), |
| mode='bilinear', padding_mode='zeros') |
|
|
| gen_inputs = torch.cat([real_image.cuda(), warped_cloth, warped_edge], 1) |
| gen_outputs = gen_model(gen_inputs) |
| p_rendered, m_composite = torch.split(gen_outputs, [3, 1], 1) |
| p_rendered = torch.tanh(p_rendered) |
| m_composite = torch.sigmoid(m_composite) |
| m_composite = m_composite * warped_edge |
| p_tryon = warped_cloth * m_composite + p_rendered * (1 - m_composite) |
|
|
| path = 'results/' + opt.name |
| os.makedirs(path, exist_ok=True) |
| sub_path = path + '/PFAFN' |
| os.makedirs(sub_path,exist_ok=True) |
|
|
| if step % 1 == 0: |
| a = real_image.float().cuda() |
| b= clothes.cuda() |
| c = p_tryon |
| combine = torch.cat([a[0],b[0],c[0]], 2).squeeze() |
| cv_img=(combine.permute(1,2,0).detach().cpu().numpy()+1)/2 |
| rgb=(cv_img*255).astype(np.uint8) |
| bgr=cv2.cvtColor(rgb,cv2.COLOR_RGB2BGR) |
| cv2.imwrite(sub_path+'/'+str(step)+'.jpg',bgr) |
|
|
| step += 1 |
| if epoch_iter >= dataset_size: |
| break |
|
|
|
|
|
|
