| | 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 |
| |
|
| |
|
| |
|
