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test.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
import argparse
import os
import time
from tensorboardX import SummaryWriter
from datasets import CPDataset, CPDataLoader
from models.gmm import GMM
from models.unet import UnetGenerator
from utilities import load_checkpoint
from visualization import board_add_images, save_images
def get_opt():
parser = argparse.ArgumentParser()
parser.add_argument("--name", default = "GMM")
parser.add_argument("--gpu_ids", default = "")
parser.add_argument('-j', '--workers', type=int, default=1)
parser.add_argument('-b', '--batch-size', type=int, default=4)
parser.add_argument("--dataroot", default = "data")
parser.add_argument("--datamode", default = "train")
parser.add_argument("--stage", default = "GMM")
parser.add_argument("--data_list", default = "train_pairs.txt")
parser.add_argument("--fine_width", type=int, default = 192)
parser.add_argument("--fine_height", type=int, default = 256)
parser.add_argument("--radius", type=int, default = 5)
parser.add_argument("--grid_size", type=int, default = 5)
parser.add_argument('--tensorboard_dir', type=str, default='tensorboard', help='save tensorboard infos')
parser.add_argument('--result_dir', type=str, default='result', help='save result infos')
parser.add_argument('--checkpoint', type=str, default='', help='model checkpoint for test')
parser.add_argument("--display_count", type=int, default = 1)
parser.add_argument("--shuffle", action='store_true', help='shuffle input data')
parser.add_argument("--use_cuda", action=argparse.BooleanOptionalAction, default = False)
opt = parser.parse_args()
return opt
def test_gmm(opt, test_loader, model, board):
if opt.use_cuda:
model.cuda()
model.eval()
base_name = os.path.basename(opt.checkpoint)
save_dir = os.path.join(opt.result_dir, base_name, opt.datamode)
if not os.path.exists(save_dir):
os.makedirs(save_dir)
warp_cloth_dir = os.path.join(save_dir, 'warp-cloth')
if not os.path.exists(warp_cloth_dir):
os.makedirs(warp_cloth_dir)
warp_mask_dir = os.path.join(save_dir, 'warp-mask')
if not os.path.exists(warp_mask_dir):
os.makedirs(warp_mask_dir)
for step, inputs in enumerate(test_loader.data_loader):
iter_start_time = time.time()
c_names = inputs['c_name']
if opt.use_cuda:
im = inputs['image'].cuda()
im_pose = inputs['pose_image'].cuda()
im_h = inputs['head'].cuda()
shape = inputs['shape'].cuda()
agnostic = inputs['agnostic'].cuda()
c = inputs['cloth'].cuda()
cm = inputs['cloth_mask'].cuda()
im_c = inputs['parse_cloth'].cuda()
im_g = inputs['grid_image'].cuda()
else:
im = inputs['image']
im_pose = inputs['pose_image']
im_h = inputs['head']
shape = inputs['shape']
agnostic = inputs['agnostic']
c = inputs['cloth']
cm = inputs['cloth_mask']
im_c = inputs['parse_cloth']
im_g = inputs['grid_image']
# grid, theta = model(agnostic, c)
grid, _ = model(agnostic, c)
warped_cloth = F.grid_sample(c, grid, padding_mode='border', align_corners=False)
warped_mask = F.grid_sample(cm, grid, padding_mode='zeros', align_corners=False)
warped_grid = F.grid_sample(im_g, grid, padding_mode='zeros', align_corners=False)
# print(f"grid: {grid.shape}")
# print(f"cm: {cm.shape}")
# print(f"im_g: {im_g.shape}")
# print(f"warped_cloth: {warped_cloth.shape}")
# print(f"warped_mask: {warped_mask.shape}")
# print(f"warped_grid: {warped_grid.shape}")
visuals = [
[im_h, shape, im_pose],
[c, warped_cloth, im_c],
[warped_grid, (warped_cloth + im) * 0.5, im]
]
# print(f" im_h size: {im_h.shape}") # [4, 3, 256, 192]
# print(f" shape size: {shape.shape}") # [4, 1, 256, 192]
# print(f" im_pose size: {im_pose.shape}") # [4, 1, 256, 192]
# print(f" c size: {c.shape}") # [4, 3, 256, 192]
# print(f" warped_cloth size: {warped_cloth.shape}") # [4, 3, 256, 192]
# print(f" im_c size: {im_c.shape}") # [4, 3, 256, 192]
# print(f" warped_grid size: {warped_grid.shape}") # [4, 3, 256, 192]
save_images(warped_cloth, c_names, warp_cloth_dir)
save_images(warped_mask * 2 - 1, c_names, warp_mask_dir)
if (step + 1) % opt.display_count == 0:
board_add_images(board, 'combine', visuals, step + 1)
t = time.time() - iter_start_time
print('step: %8d, time: %.3f' % (step + 1, t), flush=True)
def test_tom(opt, test_loader, model, board):
if opt.use_cuda:
model.cuda()
model.eval()
base_name = os.path.basename(opt.checkpoint)
save_dir = os.path.join(opt.result_dir, base_name, opt.datamode)
if not os.path.exists(save_dir):
os.makedirs(save_dir)
try_on_dir = os.path.join(save_dir, 'try-on')
if not os.path.exists(try_on_dir):
os.makedirs(try_on_dir)
print('Dataset size: %05d!' % (len(test_loader.dataset)), flush=True)
for step, inputs in enumerate(test_loader.data_loader):
iter_start_time = time.time()
im_names = inputs['im_name']
im_pose = inputs['pose_image']
im_h = inputs['head']
shape = inputs['shape']
if opt.use_cuda:
im = inputs['image'].cuda()
agnostic = inputs['agnostic'].cuda()
c = inputs['cloth'].cuda()
cm = inputs['cloth_mask'].cuda()
else:
im = inputs['image']
agnostic = inputs['agnostic']
c = inputs['cloth']
cm = inputs['cloth_mask']
outputs = model(torch.cat([agnostic, c], 1))
p_rendered, m_composite = torch.split(outputs, 3, 1)
p_rendered = F.tanh(p_rendered)
m_composite = F.sigmoid(m_composite)
p_tryon = c * m_composite + p_rendered * (1 - m_composite)
visuals = [
[im_h, shape, im_pose],
[c, 2 * cm -1, m_composite],
[p_rendered, p_tryon, im]
]
# print(f" im_h size: {im_h.shape}") # [4, 3, 256, 192]
# print(f" shape size: {shape.shape}") # [4, 1, 256, 192]
# print(f" im_pose size: {im_pose.shape}") # [4, 1, 256, 192]
# print(f" c size: {c.shape}") # [4, 3, 256, 192]
# print(f" cm size: {cm.shape}") # [4, 1, 256, 192]
# print(f" m_composite size: {m_composite.shape}") # [4, 1, 256, 192]
# print(f" p_rendered size: {p_rendered.shape}") # [4, 3, 256, 192]
# print(f" p_tryon size: {p_tryon.shape}") # [4, 3, 256, 192]
# print(f" im size: {im.shape}") # [4, 3, 256, 192]
save_images(p_tryon, im_names, try_on_dir)
if (step + 1) % opt.display_count == 0:
board_add_images(board, 'combine', visuals, step + 1)
t = time.time() - iter_start_time
print('step: %8d, time: %.3f' % (step + 1, t), flush=True)
def main():
opt = get_opt()
print(opt)
print(f'Start to test stage: {opt.stage}, named: {opt.name}!')
train_dataset = CPDataset(opt)
train_loader = CPDataLoader(opt, train_dataset)
if not os.path.exists(opt.tensorboard_dir):
os.makedirs(opt.tensorboard_dir)
board = SummaryWriter(log_dir = os.path.join(opt.tensorboard_dir, opt.name))
# create model and train
if opt.stage == 'GMM':
model = GMM(opt)
load_checkpoint(model, opt.checkpoint, opt.use_cuda)
with torch.no_grad():
test_gmm(opt, train_loader, model, board)
elif opt.stage == 'TOM':
model = UnetGenerator(25, 4, 6, ngf=64, norm_layer=nn.InstanceNorm2d)
load_checkpoint(model, opt.checkpoint, opt.use_cuda)
with torch.no_grad():
test_tom(opt, train_loader, model, board)
else:
raise NotImplementedError(f'Model [{opt.stage}] is not implemented')
print(f'Finished test {opt.stage}, named: {opt.name}!')
if __name__ == '__main__':
main()