|
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 |
|
|
|
|
|
|
