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test_warping.py
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from options.train_options import TrainOptions
from models.networks import load_checkpoint_parallel
from models.afwm import AFWM_Vitonhd_lrarms, AFWM_Dressescode_lrarms
import torch.nn.functional as F
import os
import numpy as np
import torch
from torch.utils.data import DataLoader
import cv2
import tqdm
opt = TrainOptions().parse()
os.makedirs('sample/'+opt.name, exist_ok=True)
def CreateDataset(opt):
if opt.dataset == 'vitonhd':
from data.aligned_dataset_vitonhd import AlignedDataset
dataset = AlignedDataset()
dataset.initialize(opt, mode='test')
elif opt.dataset == 'dresscode':
from data.aligned_dataset_dresscode import AlignedDataset
dataset = AlignedDataset()
dataset.initialize(opt, mode='test', stage='warp')
return dataset
torch.cuda.set_device(opt.gpu_ids[0])
device = torch.device('cuda')
train_data = CreateDataset(opt)
train_loader = DataLoader(train_data, batch_size=opt.batchSize, shuffle=False,
num_workers=2, pin_memory=True)
if opt.dataset == 'vitonhd':
warp_model = AFWM_Vitonhd_lrarms(opt, 51)
elif opt.dataset == 'dresscode':
warp_model = AFWM_Dressescode_lrarms(opt, 51)
warp_model.train()
warp_model.cuda()
load_checkpoint_parallel(warp_model, opt.PBAFN_warp_checkpoint)
softmax = torch.nn.Softmax(dim=1)
model = warp_model
for ii, data in enumerate(tqdm.tqdm(train_loader)):
with torch.no_grad():
pre_clothes_edge = data['edge'].to(device)
clothes = data['color'].to(device)
pose = data['pose'].to(device)
size = data['color'].size()
oneHot_size1 = (size[0], 25, size[2], size[3])
densepose = torch.cuda.FloatTensor(torch.Size(oneHot_size1)).zero_()
densepose = densepose.scatter_(1,data['densepose'].data.long().cuda(),1.0)
densepose = densepose * 2.0 - 1.0
densepose_fore = data['densepose']/24.0
left_cloth_sleeve_mask = data['flat_clothes_left_mask'].cuda()
cloth_torso_mask = data['flat_clothes_middle_mask'].cuda()
right_cloth_sleeve_mask = data['flat_clothes_right_mask'].cuda()
clothes_left = clothes * left_cloth_sleeve_mask
clothes_torso = clothes * cloth_torso_mask
clothes_right = clothes * right_cloth_sleeve_mask
cloth_parse_for_d = data['flat_clothes_label'].cuda()
pose = pose.cuda()
clothes = clothes.cuda()
clothes_left = clothes_left.cuda()
clothes_torso = clothes_torso.cuda()
clothes_right = clothes_right.cuda()
pre_clothes_edge = pre_clothes_edge.cuda()
left_cloth_sleeve_mask = left_cloth_sleeve_mask.cuda()
cloth_torso_mask = cloth_torso_mask.cuda()
right_cloth_sleeve_mask = right_cloth_sleeve_mask.cuda()
preserve_mask3 = data['preserve_mask3'].cuda()
if opt.resolution == 512:
concat = torch.cat([densepose, pose, preserve_mask3], 1)
if opt.dataset == 'vitonhd':
flow_out = model(concat, clothes, pre_clothes_edge, cloth_parse_for_d, \
clothes_left, clothes_torso, clothes_right, \
left_cloth_sleeve_mask, cloth_torso_mask, right_cloth_sleeve_mask, \
preserve_mask3)
elif opt.dataset == 'dresscode':
cloth_type = data['flat_clothes_type'].cuda()
flow_out = model(concat, clothes, pre_clothes_edge, cloth_parse_for_d, \
clothes_left, clothes_torso, clothes_right, \
left_cloth_sleeve_mask, cloth_torso_mask, right_cloth_sleeve_mask, \
preserve_mask3, cloth_type)
last_flow, last_flow_all, delta_list, x_all, x_edge_all, delta_x_all, delta_y_all, \
x_full_all, x_edge_full_all, attention_all, seg_list = flow_out
else:
densepose_ds = F.interpolate(densepose, scale_factor=0.5, mode='nearest')
pose_ds = F.interpolate(pose, scale_factor=0.5, mode='nearest')
preserve_mask3_ds = F.interpolate(preserve_mask3, scale_factor=0.5, mode='nearest')
concat = torch.cat([densepose_ds, pose_ds, preserve_mask3_ds], 1)
clothes_ds = F.interpolate(clothes, scale_factor=0.5, mode='bilinear')
pre_clothes_edge_ds = F.interpolate(pre_clothes_edge, scale_factor=0.5, mode='nearest')
cloth_parse_for_d_ds = F.interpolate(cloth_parse_for_d, scale_factor=0.5, mode='nearest')
clothes_left_ds = F.interpolate(clothes_left, scale_factor=0.5, mode='bilinear')
clothes_torso_ds = F.interpolate(clothes_torso, scale_factor=0.5, mode='bilinear')
clothes_right_ds = F.interpolate(clothes_right, scale_factor=0.5, mode='bilinear')
left_cloth_sleeve_mask_ds = F.interpolate(left_cloth_sleeve_mask, scale_factor=0.5, mode='nearest')
cloth_torso_mask_ds = F.interpolate(cloth_torso_mask, scale_factor=0.5, mode='nearest')
right_cloth_sleeve_mask_ds = F.interpolate(right_cloth_sleeve_mask, scale_factor=0.5, mode='nearest')
if opt.dataset == 'vitonhd':
flow_out = model(concat, clothes_ds, pre_clothes_edge_ds, cloth_parse_for_d_ds, \
clothes_left_ds, clothes_torso_ds, clothes_right_ds, \
left_cloth_sleeve_mask_ds, cloth_torso_mask_ds, right_cloth_sleeve_mask_ds, \
preserve_mask3_ds)
elif opt.dataset == 'dresscode':
cloth_type = data['flat_clothes_type'].cuda()
cloth_type_ds = F.interpolate(cloth_type, scale_factor=0.5, mode='bilinear')
flow_out = model(concat, clothes_ds, pre_clothes_edge_ds, cloth_parse_for_d_ds, \
clothes_left_ds, clothes_torso_ds, clothes_right_ds, \
left_cloth_sleeve_mask_ds, cloth_torso_mask_ds, right_cloth_sleeve_mask_ds, \
preserve_mask3_ds, cloth_type_ds)
last_flow, last_flow_all, delta_list, x_all, x_edge_all, delta_x_all, delta_y_all, \
x_full_all, x_edge_full_all, attention_all, seg_list = flow_out
last_flow = F.interpolate(last_flow, scale_factor=2, mode='bilinear')
bz = pose.size(0)
left_last_flow = last_flow[0:bz]
torso_last_flow = last_flow[bz:2*bz]
right_last_flow = last_flow[2*bz:]
left_warped_full_cloth = F.grid_sample(clothes_left.cuda(), left_last_flow.permute(0, 2, 3, 1),mode='bilinear', padding_mode='zeros')
torso_warped_full_cloth = F.grid_sample(clothes_torso.cuda(), torso_last_flow.permute(0, 2, 3, 1),mode='bilinear', padding_mode='zeros')
right_warped_full_cloth = F.grid_sample(clothes_right.cuda(), right_last_flow.permute(0, 2, 3, 1),mode='bilinear', padding_mode='zeros')
left_warped_cloth_edge = F.grid_sample(left_cloth_sleeve_mask.cuda(), left_last_flow.permute(0, 2, 3, 1),mode='nearest', padding_mode='zeros')
torso_warped_cloth_edge = F.grid_sample(cloth_torso_mask.cuda(), torso_last_flow.permute(0, 2, 3, 1),mode='nearest', padding_mode='zeros')
right_warped_cloth_edge = F.grid_sample(right_cloth_sleeve_mask.cuda(), right_last_flow.permute(0, 2, 3, 1),mode='nearest', padding_mode='zeros')
for bb in range(bz):
seg_preds = torch.argmax(softmax(seg_list[-1]),dim=1)[:,None,...].float()
if opt.resolution == 1024:
seg_preds = F.interpolate(seg_preds, scale_factor=2, mode='nearest')
c_type = data['c_type'][bb]
if opt.dataset == 'vitonhd':
left_mask = (seg_preds[bb]==1).float()
torso_mask = (seg_preds[bb]==2).float()
right_mask = (seg_preds[bb]==3).float()
left_arm_mask = (seg_preds[bb]==4).float()
right_arm_mask = (seg_preds[bb]==5).float()
neck_mask = (seg_preds[bb]==6).float()
warped_cloth_fusion = left_warped_full_cloth[bb] * left_mask + \
torso_warped_full_cloth[bb] * torso_mask + \
right_warped_full_cloth[bb] * right_mask
warped_edge_fusion = left_warped_cloth_edge[bb] * left_mask * 1 + \
torso_warped_cloth_edge[bb] * torso_mask * 2 + \
right_warped_cloth_edge[bb] * right_mask * 3
warped_cloth_fusion = warped_cloth_fusion * (1-preserve_mask3[bb])
warped_edge_fusion = warped_edge_fusion * (1-preserve_mask3[bb])
warped_edge_fusion = warped_edge_fusion + \
left_arm_mask * 4 + \
right_arm_mask * 5 + \
neck_mask * 6
elif opt.dataset == 'dresscode':
if c_type == 'upper' or c_type == 'dresses':
left_mask = (seg_preds[bb]==1).float()
torso_mask = (seg_preds[bb]==2).float()
right_mask = (seg_preds[bb]==3).float()
else:
left_mask = (seg_preds[bb]==4).float()
torso_mask = (seg_preds[bb]==5).float()
right_mask = (seg_preds[bb]==6).float()
left_arms_mask = (seg_preds[bb]==7).float()
right_arms_mask = (seg_preds[bb]==8).float()
neck_mask = (seg_preds[bb]==9).float()
warped_cloth_fusion = left_warped_full_cloth[bb] * left_mask + \
torso_warped_full_cloth[bb] * torso_mask + \
right_warped_full_cloth[bb] * right_mask
if c_type == 'upper' or c_type == 'dresses':
warped_edge_fusion = left_warped_cloth_edge[bb] * left_mask * 1 + \
torso_warped_cloth_edge[bb] * torso_mask * 2 + \
right_warped_cloth_edge[bb] * right_mask * 3
else:
warped_edge_fusion = left_warped_cloth_edge[bb] * left_mask * 4 + \
torso_warped_cloth_edge[bb] * torso_mask * 5 + \
right_warped_cloth_edge[bb] * right_mask * 6
warped_cloth_fusion = warped_cloth_fusion * (1-preserve_mask3[bb])
warped_edge_fusion = warped_edge_fusion * (1-preserve_mask3[bb])
warped_edge_fusion = warped_edge_fusion + \
left_arms_mask * 7 + \
right_arms_mask * 8 + \
neck_mask * 9
eee = warped_cloth_fusion
eee_edge = torch.cat([warped_edge_fusion,warped_edge_fusion,warped_edge_fusion],0)
eee_edge = eee_edge.permute(1,2,0).detach().cpu().numpy().astype(np.uint8)
cv_img = (eee.permute(1, 2, 0).detach().cpu().numpy()+1)/2
rgb = (cv_img*255).astype(np.uint8)
bgr = cv2.cvtColor(rgb, cv2.COLOR_RGB2BGR)
bgr = np.concatenate([bgr,eee_edge],1)
cloth_id = data['color_path'][bb].split('/')[-1]
person_id = data['img_path'][bb].split('/')[-1]
save_path = 'sample/'+opt.name+'/'+c_type+'___'+person_id+'___'+cloth_id[:-4]+'.png'
cv2.imwrite(save_path, bgr)