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test.py
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import argparse
import os
import torch
from torch import nn
from torch.nn import functional as F
import torchgeometry as tgm
from datasets import VITONDataset,VITONDATALoader
from network import SegGenerator,GMM,ALIASGenerator
from utils import gen_noise,load_checkpoint,save_images
def get_opt() :
parser = argparse.ArgumentParser()
parser.add_argument('--name',type=str,required=True)
parser.add_argument('-b','--batch_size',type = int,default=1)
parser.add_argument('-j', '--workers', type=int, default=1)
parser.add_argument('--load_height', type=int, default=1024)
parser.add_argument('--load_width', type=int, default=768)
parser.add_argument('--shuffle', action='store_true')
parser.add_argument('--dataset_dir', type=str, default='./datasets/')
parser.add_argument('--dataset_mode', type=str, default='test')
parser.add_argument('--dataset_list', type=str, default='test_pairs.txt')
parser.add_argument('--checkpoint_dir', type=str, default='./checkpoints/')
parser.add_argument('--save_dir', type=str, default='./results/')
parser.add_argument('--display_freq', type=int, default=1)
parser.add_argument('--seg_checkpoint', type=str, default='seg_final.pth')
parser.add_argument('--gmm_checkpoint', type=str, default='gmm_final.pth')
parser.add_argument('--alias_checkpoint', type=str, default='alias_final.pth')
# common
parser.add_argument('--semantic_nc', type=int, default=13, help='# of human-parsing map classes')
parser.add_argument('--init_type', choices=['normal', 'xavier', 'xavier_uniform', 'kaiming', 'orthogonal', 'none'], default='xavier')
parser.add_argument('--init_variance', type=float, default=0.02, help='variance of the initialization distribution')
# for GMM
parser.add_argument('--grid_size',type=int,default=5)
# for ALIASGenerator
parser.add_argument('--norm_G', type=str, default='spectralaliasinstance')
parser.add_argument('--ngf', type=int, default=64, help='# of generator filters in the first conv layer')
parser.add_argument('--num_upsampling_layers', choices=['normal', 'more', 'most'], default='most',
help='If \'more\', add upsampling layer between the two middle resnet blocks. '
'If \'most\', also add one more (upsampling + resnet) layer at the end of the generator.')
opt = parser.parse_args()
return opt
def test(opt,seg,gmm,alias) :
up = nn.Upsample(size=(opt.load_height, opt.load_width), mode='bilinear')
gauss = tgm.image.GaussianBlur((15,15),(3,3))
gauss.cuda()
test_dataset = VITONDataset(opt)
test_loader = VITONDATALoader(opt,test_dataset)
with torch.no_grad() :
for i,inputs in enumerate(test_loader.data_loader):
img_names = inputs['img_name']
c_names = inputs['c_name']['unpaired']
img_agnostic = inputs['img_agnostic'].cuda()
parse_agnostic = inputs['parse_agnostic'].cuda()
pose = inputs['pose'].cuda()
c = inputs['cloth']['unpaired'].cuda()
cm = inputs['cloth_mask']['unpaired'].cuda()
#Part 1 Segmentation generation
parse_agnostic_down = F.interpolate(parse_agnostic,size=(256,192),mode="bilinear")
pose_down = F.interpolate(pose,size=(256,192),mode = "bilinear")
c_masked_down = F.interpolate(c * cm,size = (256,192),mode="bilinear")
cm_down = F.interpolate(cm,size=(256,192),mode="bilinear")
seg_input = torch.cat((cm_down,c_masked_down,parse_agnostic_down,pose_down,gen_noise(cm_down.size()).cuda()),dim=1)
parse_pred_down = seg(seg_input)
parse_pred = gauss(up(parse_pred_down))
parse_pred = parse_pred.argmax(dim=1)[:,None]
parse_old = torch.zeros(parse_pred.size(0),13,opt.load_height,opt.load_width,dtype=torch.float).cuda()
parse_old.scatter_(1,parse_pred,1.0)
labels = {
0 : ['background',[0]],
1 : ['paste', [2,4,7,8,9,10,11]],
2 : ['upper', [3]],
3 : ['hair', [1]],
4 : ['left_arm',[5]],
5 : ['right_arm',[6]],
6 : ['noise',[12]],
}
parse = torch.zeroes(parse_pred.size(0),7,opt.load_height,opt.load_width,dtype=torch.float).cuda()
for j in range(len(labels)):
for label in labels[j][1]:
parse[:,j] += parse_old[:,label]
#Part 2. Clothes Deformation
agnostic_gmm = F.interpolate(img_agnostic,size=(256,192),mode="nearest")
parse_cloth_gmm = F.interpolate(parse[:,2:3],size=(256,192),mode = "nearest")
pose_gmm = F.interpolate(pose,size=(256,192),mode='nearest')
c_gmm = F.interpolate(c,size=(256,192),mode="nearest")
gmm_input = torch.cat((parse_cloth_gmm,pose_gmm,agnostic_gmm),dim=1)
_,warped_grid = gmm(gmm_input,c_gmm)
warped_c = F.grid_sample(c,warped_grid,padding_mode='border')
warped_cm = F.grid_sample(cm,warped_grid,padding_mode ="border")
#Part 3 Try on synthesis
misalign_mask = parse[:,2:3] - warped_cm
misalign_mask[misalign_mask < 0.0] = 0.0
parse_div = torch.cat((parse,misalign_mask),dim=1)
parse_div[:,2:3] -= misalign_mask
output = alias(torch.cat((img_agnostic,pose,warped_c),dim=1),parse,parse_div,misalign_mask)
unpaired_names = []
for img_name,c_name in zip(img_name,c_names) :
unpaired_names.append('{}_{}'.format(img_name.split('_')[0],c_name))
save_images(output,unpaired_names,os.path.join(opt.save_dir,opt.name))
if(i+1) % opt.display_freq == 0 :
print("step : {}".format(i+1))
def main() :
opt = get_opt()
print(opt)
if not os.path.exists(os.path.join(opt.save_dir,opt.name)):
os.makedirs(os.path.join(opt.save_dir,opt.name))
seg = SegGenerator(opt,input_nc = opt.semantic_nc + 8,output_nc=opt.semantic_nc)
gmm = GMM(opt,inputA_nc=7,inputB_nc=3)
opt.semantic_nc = 7
alias = ALIASGenerator(opt,input_nc =9)
opt.semantic_nc = 13
load_checkpoint(seg,os.path.join(opt.checkpoint_dir,opt.seg_checkpoint))
load_checkpoint(seg,os.path.join(opt.checkpoint_dir,opt.seg_checkpoint))
load_checkpoint(seg,os.path.join(opt.checkpoint_dir,opt.seg_checkpoint))
seg.cuda().eval()
gmm.cuda().eval()
alias.cuda().eval()
test(opt,seg,gmm,alias)
if __name__ == '__main__':
main()