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test_THU.py
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test_THU.py
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import argparse
import os
import random
import math
import tqdm
import shutil
import imageio
import numpy as np
import trimesh
import yaml
from multiprocessing import Pool
# import torch related
import torch
import torch.nn as nn
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data
import torchvision
from torchvision.transforms.functional import to_pil_image
import torchvision.utils as vutils
import torch.nn.functional as F
from torch.autograd import Variable
from torch.utils.tensorboard import SummaryWriter
from torch.optim.swa_utils import AveragedModel, SWALR, update_bn
from networks import MS_Discriminator, Discriminator, DiffRender, Landmark_Consistency, AttributeEncoder, weights_init, deep_copy
# import kaolin related
import kaolin as kal
from kaolin.metrics.render import mask_iou
from kaolin.render.camera import generate_perspective_projection
from kaolin.render.mesh import dibr_rasterization, texture_mapping, \
spherical_harmonic_lighting, prepare_vertices
from PIL import Image
from pytorch_msssim import ssim
#from skimage.metrics import structural_similarity as ssim
# draw
import matplotlib
matplotlib.use('agg')
import matplotlib.pyplot as plt
# import from folder
from fid_score import calculate_fid_given_paths
from datasets.bird import CUBDataset
from datasets.market import MarketDataset
from datasets.atr import ATRDataset
from datasets.atr2 import ATR2Dataset
from datasets.thuman2 import THUMan2Dataset
from smr_utils import fliplr, mask, camera_position_from_spherical_angles, generate_transformation_matrix, compute_gradient_penalty, compute_gradient_penalty_list, Timer
from network.model_res import VGG19, CameraEncoder, ShapeEncoder, LightEncoder, TextureEncoder
def save_img(output_name):
output, name = output_name
output.save(name, 'JPEG', quality=100)
return
#torch.autograd.set_detect_anomaly(True)
parser = argparse.ArgumentParser()
parser.add_argument('--name', default='baseline-MKT', help='folder to output images and model checkpoints')
parser.add_argument('--configs_yml', default='configs/image.yml', help='folder to output images and model checkpoints')
parser.add_argument('--dataroot', default='../ICON/data/thuman2_36views/', help='path to dataset root dir')
parser.add_argument('--ratio', type=int, default=2, help='height/width')
parser.add_argument('--gan_type', default='wgan', help='wgan or lsgan')
parser.add_argument('--template_path', default='./template/sphere.obj', help='template mesh path')
parser.add_argument('--ellipsoid', type=float, default = 2, help='init sphere to ellipsoid' )
parser.add_argument('--category', type=str, default='bird', help='list of object classes to use')
parser.add_argument('--pretrains', type=str, default='hr18sv2', help='pretrain shape encoder. default is hr18sv2 or hr18 or none or hr18sv1')
parser.add_argument('--pretrainc', type=str, default='hr18sv1', help='pretrain camera encoder. default is hr18sv2 or hr18 or none or hr18sv1')
parser.add_argument('--pretraint', type=str, default='res34', help='pretrain texture encoder. default is hr18sv2 or hr18 or none or hr18sv1')
parser.add_argument('--norm', type=str, default='bn', help='norm function')
parser.add_argument('--workers', type=int, help='number of data loading workers', default=4)
parser.add_argument('--batchSize', type=int, default=64, help='input batch size')
parser.add_argument('--imageSize', type=int, default=128, help='the height / width of the input image to network')
parser.add_argument('--nk', type=int, default=5, help='size of kerner')
parser.add_argument('--nf', type=int, default=32, help='dim of unit channel')
parser.add_argument('--niter', type=int, default=500, help='number of epochs to train for')
parser.add_argument('--lr', type=float, default=0.0001, help='leaning rate, default=0.0001')
parser.add_argument('--clip', type=float, default=0.05, help='the clip for template update.')
parser.add_argument('--beta1', type=float, default=0.5, help='beta1 for adam. default=0.5')
parser.add_argument('--droprate', type=float, default=0.2, help='dropout in encoders. default=0.2')
parser.add_argument('--cuda', default=1, type=int, help='enables cuda')
parser.add_argument('--manualSeed', type=int, default=0, help='manual seed')
parser.add_argument('--start_epoch', type=int, default=0, help='start epoch')
parser.add_argument('--warm_epoch', type=int, default=20, help='warm epoch')
parser.add_argument('--multigpus', action='store_true', default=False, help='whether use multiple gpus mode')
parser.add_argument('--resume', action='store_true', default=False, help='whether resume ckpt')
parser.add_argument('--chamfer', action='store_true', default=False, help='use chamfer loss for vertices')
parser.add_argument('--bg', action='store_true', default=False, help='use background')
parser.add_argument('--nolpl', action='store_true', default=False, help='ablation study for no template in camera and shape encoder')
parser.add_argument('--white', action='store_true', default=False, help='use normalized template')
parser.add_argument('--makeup', type=int, default=0, help='whether makeup texture 0:nomakeup 1:in 2:bn 3:ln 4.none')
parser.add_argument('--beta', type=float, default=0, help='using beta distribution instead of uniform.')
parser.add_argument('--hard', action='store_true', default=False, help='using Xer90 instead of Xer.')
parser.add_argument('--L1', action='store_true', default=False, help='using L1 for ic loss.')
parser.add_argument('--flipL1', action='store_true', default=False, help='using flipL1 for flipz loss.')
parser.add_argument('--coordconv', action='store_false', default=True, help='using coordconv for texture mapping.')
parser.add_argument('--unmask', action='store_true', default=False, help='using L1 for ic loss.')
parser.add_argument('--romp', action='store_true', default=False, help='using romp.')
parser.add_argument('--swa', action='store_true', default=False, help='using swa.')
parser.add_argument('--em', type=float, default=0.0, help='update template')
parser.add_argument('--swa_start', type=int, default=400, help='switch to swa at epoch swa_start')
parser.add_argument('--update_shape', type=int, default=1, help='train shape every XX iteration')
parser.add_argument('--swa_lr', type=float, default=0.0003, help='swa learning rate')
parser.add_argument('--lambda_gan', type=float, default=0.0001, help='parameter')
parser.add_argument('--ganw', type=float, default=1, help='parameter for Xir. Since it is hard.')
parser.add_argument('--lambda_reg', type=float, default=0.1, help='parameter')
parser.add_argument('--lambda_edge', type=float, default=0.001, help='parameter')
parser.add_argument('--lambda_deform', type=float, default=0.1, help='parameter')
parser.add_argument('--lambda_flipz', type=float, default=0.1, help='parameter')
parser.add_argument('--lambda_data', type=float, default=1.0, help='parameter')
parser.add_argument('--lambda_ic', type=float, default=1, help='parameter')
parser.add_argument('--dis1', type=float, default=0, help='parameter')
parser.add_argument('--dis2', type=float, default=0, help='parameter')
parser.add_argument('--lambda_lc', type=float, default=0, help='parameter')
parser.add_argument('--image_weight', type=float, default=1, help='parameter')
parser.add_argument('--reg', type=float, default=0.0, help='parameter')
parser.add_argument('--em_step', type=float, default=0.1, help='parameter')
parser.add_argument('--hmr', type=float, default=0.0, help='parameter')
parser.add_argument('--threshold', type=str, default='0.09, 0.64', help='parameter')
parser.add_argument('--bias_range', type=float, default=0.5, help='parameter bias range')
parser.add_argument('--azi_scope', type=float, default=360, help='parameter')
parser.add_argument('--elev_range', type=str, default="-25~25", help='~ elevantion')
parser.add_argument('--hard_range', type=int, default=0, help='~ range from x to 180-x. x<90')
parser.add_argument('--dist_range', type=str, default="2~6", help='~ separated list of classes for the lsun data set')
opt = parser.parse_args()
opt.outf = './log/'+ opt.name
if not os.path.isdir(opt.outf):
os.mkdir(opt.outf)
if opt.manualSeed is None:
opt.manualSeed = random.randint(1, 10000)
print("Random Seed: ", opt.manualSeed)
random.seed(opt.manualSeed)
torch.manual_seed(opt.manualSeed)
### load option
with open('log/%s/opts.yaml'%opt.name,'r') as fp:
config = yaml.load(fp, Loader=yaml.FullLoader)
opt.template_path = config['template_path']
opt.name = config['name']
opt.dataroot = config['dataroot']
opt.gan_type = config['gan_type']
opt.category = config['category']
opt.workers = config['workers']
opt.batchSize = config['batchSize']
opt.imageSize = config['imageSize']
opt.nk = config['nk']
opt.nf = config['nf']
opt.niter = config['niter']
opt.makeup = config['makeup']
opt.azi_scope = config['azi_scope']
opt.bias_range = config['bias_range']
opt.elev_range= config['elev_range']
opt.dist_range = config['dist_range']
opt.bg = config['bg']
opt.coordconv = config['coordconv']
opt.pretrains = config['pretrains']
opt.pretrainc = config['pretrainc']
opt.pretraint = config['pretraint']
opt.norm = config['norm']
opt.threshold = config['threshold']
opt.droprate = config['droprate']
opt.ratio = config['ratio']
opt.ellipsoid = config['ellipsoid']
opt.nolpl = config['nolpl']
print(opt)
if torch.cuda.is_available():
cudnn.benchmark = True
train_dataset = THUMan2Dataset('../ICON/data/thuman2_36views/', opt.imageSize, train=True, threshold=opt.threshold, bg = opt.bg, hmr = opt.hmr)
test_dataset = THUMan2Dataset('../ICON/data/thuman2_36views/', opt.imageSize, train=False, threshold=opt.threshold, bg = opt.bg, hmr = opt.hmr)
print('THuman2.0')
ratio = 2
torch.set_num_threads(int(opt.workers)*2)
train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=opt.batchSize,
shuffle=True, drop_last=True, pin_memory=True, num_workers=int(opt.workers),
prefetch_factor=2, persistent_workers=True) # for pytorch>1.6.0
test_dataloader = torch.utils.data.DataLoader(test_dataset, batch_size=opt.batchSize,
shuffle=False, pin_memory=True,
num_workers=int(opt.workers), prefetch_factor=2)
if __name__ == '__main__':
# differentiable renderer
template_file = kal.io.obj.import_mesh(opt.template_path, with_materials=True)
# load updated template
resume_path = os.path.join(opt.outf, 'ckpts/best_ckpt.pth')
#resume_path = os.path.join(opt.outf, 'ckpts/latest_ckpt.pth')
if os.path.exists(resume_path):
checkpoint = torch.load(resume_path)
epoch = checkpoint['epoch']
diffRender = DiffRender(mesh_name=opt.template_path, image_size=opt.imageSize, ratio = opt.ratio, init_ellipsoid = opt.ellipsoid, image_weight=opt.image_weight)
#latest_template_file = kal.io.obj.import_mesh(opt.outf + '/epoch_{:03d}_template.obj'.format(epoch), with_materials=True)
latest_template_file = kal.io.obj.import_mesh(opt.outf + '/ckpts/best_mesh.obj', with_materials=True)
#latest_template_file = kal.io.obj.import_mesh(opt.outf + '/ckpts/best_mesh.obj', with_materials=True)
#print('Loading template as epoch_{:03d}_template.obj'.format(epoch))
diffRender.vertices_init = latest_template_file.vertices
print('Vertices Number:', template_file.vertices.shape[0]) #642
print('Faces Number:', template_file.faces.shape[0]) #1280
# netE: 3D attribute encoder: Camera, Light, Shape, and Texture
netE = AttributeEncoder(num_vertices=diffRender.num_vertices, vertices_init=diffRender.vertices_init,
azi_scope=opt.azi_scope, elev_range=opt.elev_range, dist_range=opt.dist_range,
nc=4, nk=opt.nk, nf=opt.nf, ratio=opt.ratio, makeup=opt.makeup, bg = opt.bg,
pretraint = opt.pretraint, pretrainc = opt.pretrainc, pretrains = opt.pretrains,
droprate = opt.droprate, romp = opt.romp,
coordconv = opt.coordconv, nolpl = opt.nolpl,
norm = opt.norm, lpl = diffRender.vertices_laplacian_matrix) # height = 2 * width
if opt.multigpus:
netE = torch.nn.DataParallel(netE)
netE = netE.cuda()
# restore from latest_ckpt.path
resume_path = os.path.join(opt.outf, 'ckpts/best_ckpt.pth')
if os.path.exists(resume_path):
# Map model to be loaded to specified single gpu.
# checkpoint has been loaded
# start_epoch = checkpoint['epoch']
# start_iter = 0
#netD.load_state_dict(checkpoint['netD'])
if opt.swa:
print('using swa')
netE = AveragedModel(netE)
netE.load_state_dict(checkpoint['swa_modelE'], strict=True)
else:
netE.load_state_dict(checkpoint['netE'], strict=True)
print("=> loaded checkpoint '{}' (epoch {})"
.format(resume_path, checkpoint['epoch']))
netE = netE.eval()
opt.outf = 'outputs/THU/'
ori_dir = os.path.join(opt.outf, 'fid/ori')
ori_mask_dir = os.path.join(opt.outf, 'fid/ori_mask')
rec_dir = os.path.join(opt.outf, 'fid/rec_tmp') # open one new
rec_mask_dir = os.path.join(opt.outf, 'fid/rec_mask')
inter_dir = os.path.join(opt.outf, 'fid/inter')
inter90_dir = os.path.join(opt.outf, 'fid/inter90')
norm_dir = os.path.join(opt.outf, 'fid/norm')
rec_norm_dir = os.path.join(opt.outf, 'fid/rec_norm')
# ckpt_dir = os.path.join(opt.outf, 'ckpts')
os.makedirs(ori_dir, exist_ok=True)
os.makedirs(ori_mask_dir, exist_ok=True)
os.makedirs(rec_dir, exist_ok=True)
os.makedirs(rec_mask_dir, exist_ok=True)
os.makedirs(inter_dir, exist_ok=True)
os.makedirs(inter90_dir, exist_ok=True)
os.makedirs(norm_dir, exist_ok=True)
os.makedirs(rec_norm_dir, exist_ok=True)
# os.makedirs(ckpt_dir, exist_ok=True)
os.system('rm -r %s'%ori_dir)
os.system('rm -r %s'%ori_mask_dir)
os.system('rm -r %s'%rec_dir)
os.system('rm -r %s'%rec_mask_dir)
os.system('rm -r %s'%inter_dir)
os.system('rm -r %s'%inter90_dir) # rm list too long.
os.system('rm -r %s'%norm_dir)
os.system('rm -r %s'%rec_norm_dir)
# clean again
os.makedirs(ori_dir, exist_ok=True)
os.makedirs(ori_mask_dir, exist_ok=True)
os.makedirs(rec_dir, exist_ok=True)
os.makedirs(rec_mask_dir, exist_ok=True)
os.makedirs(inter_dir, exist_ok=True)
os.makedirs(inter90_dir, exist_ok=True)
os.makedirs(norm_dir, exist_ok=True)
os.makedirs(rec_norm_dir, exist_ok=True)
# summary_writer = SummaryWriter(os.path.join(opt.outf + "/logs"))
output_txt = './log/%s/result.txt'%opt.name
dists = torch.tensor([]).cuda()
azimuths = torch.tensor([]).cuda()
biases = torch.tensor([]).cuda()
elevations = torch.tensor([]).cuda()
xyz_min = torch.tensor([]).cuda()
xyz_mean = torch.tensor([]).cuda()
xyz_max = torch.tensor([]).cuda()
filename = []
X_all = []
path_all = []
norm_error = []
for i, data in tqdm.tqdm(enumerate(test_dataloader)):
#for i, data in tqdm.tqdm(enumerate(train_dataloader)):
Xa = Variable(data['data']['images']).cuda()
paths = data['data']['path']
norms = data['data']['norm']
# Xa = fliplr(Xa)
with torch.no_grad():
Ae = netE(Xa)
Xer, Ae = diffRender.render(**Ae)
#print('max: {}\nmin: {}\navg: {}'.format(torch.max(Ae['distances']), torch.min(Ae['distances']), torch.mean(Ae['distances'])))
# clamp
#Ae['vertices'] = torch.clamp(Ae['vertices'], min = torch.FloatTensor([-1, -2, -1]).cuda())
#Ae['vertices'] = torch.clamp(Ae['vertices'], max = torch.FloatTensor([1, 2, 1]).cuda())
azimuths = torch.cat((azimuths, Ae['azimuths']))
biases = torch.cat((biases, Ae['biases']))
dists = torch.cat((dists, Ae['distances']))
elevations = torch.cat((elevations, Ae['elevations']))
xyz_min = torch.cat((xyz_min, torch.min(Ae['delta_vertices'], dim=1)[0]))
xyz_max = torch.cat((xyz_max, torch.max(Ae['delta_vertices'], dim=1)[0]))
xyz_mean = torch.cat((xyz_mean, torch.mean(torch.abs(Ae['delta_vertices']), dim=1)))
Ai = deep_copy(Ae)
Ai2 = deep_copy(Ae)
Ae90 = deep_copy(Ae)
Ae270 = deep_copy(Ae)
Ai['azimuths'] = - torch.empty((Xa.shape[0]), dtype=torch.float32).uniform_(-opt.azi_scope/2, opt.azi_scope/2).cuda()
Ai2['azimuths'] = Ai['azimuths'] + 90.0 # -90, 270
Ai2['azimuths'][Ai2['azimuths']>180] -= 360.0 # -180, 180
Ae90['azimuths'] += 90.0
Ae270['azimuths'] -= 90.0
Xir, Ai = diffRender.render(**Ai)
Xir2, Ai2 = diffRender.render(**Ai2)
Xer90, Ae90 = diffRender.render(**Ae90)
Xer270, Ae270 = diffRender.render(**Ae270)
###
#Ae90_recon = netE(Xer90)
#print(Ae90_recon['azimuths'])
#break
Maska = Xa[:,3,:,:].unsqueeze(1)
Xa = mask(Xa) # remove bg
### Face Normals
Xer_norm = Ae['imnormal'].permute(0, 3, 1, 2)
Xer_norm = (Xer_norm+1)/2
for i in range(len(paths)):
path = paths[i]
filename.append(path)
image_name = os.path.basename(path) + 'A%.2f'%Ae['azimuths'][i] + '.jpg'
rec_path = os.path.join(rec_dir, image_name)
output_Xer = to_pil_image(Xer[i, :3].detach().cpu())
#output_Xer.save(rec_path, 'JPEG', quality=100)
inter_path = os.path.join(inter_dir, image_name)
output_Xir = to_pil_image(Xir[i, :3].detach().cpu())
#output_Xir.save(inter_path, 'JPEG', quality=100)
inter_path2 = os.path.join(inter_dir, '2+'+image_name)
output_Xir2 = to_pil_image(Xir2[i, :3].detach().cpu())
#output_Xir2.save(inter_path2, 'JPEG', quality=100)
inter90_path = os.path.join(inter90_dir, image_name)
output_Xer90 = to_pil_image(Xer90[i, :3].detach().cpu())
#output_Xer90.save(inter90_path, 'JPEG', quality=100)
inter270_path = os.path.join(inter90_dir, '2+'+image_name)
output_Xer270 = to_pil_image(Xer270[i, :3].detach().cpu())
ori_path = os.path.join(ori_dir, image_name)
output_Xa = to_pil_image(Xa[i, :3].detach().cpu())
#output_Xa.save(ori_path, 'JPEG', quality=100)
ori_mask_path = os.path.join(ori_mask_dir, image_name)
output_ori_mask = to_pil_image(Maska[i].detach().cpu())
rec_mask_path = os.path.join(rec_mask_dir, image_name)
output_rec_mask = to_pil_image(Xer[i, 3].detach().cpu())
ori_norm_path = os.path.join(norm_dir, image_name)
output_ori_norm = to_pil_image(norms[i].detach().cpu())
Xer_norm[i] *= Xer[i,3,:,:] # remove background
rec_norm_path = os.path.join(rec_norm_dir, image_name)
output_rec_norm = to_pil_image(Xer_norm[i].detach().cpu())
X_all.extend([output_Xer, output_Xir, output_Xir2, output_Xer90, output_Xer270, output_Xa, output_ori_mask, output_rec_mask, output_ori_norm, output_rec_norm])
path_all.extend([rec_path, inter_path, inter_path2, inter90_path, inter270_path, ori_path, ori_mask_path, rec_mask_path, ori_norm_path, rec_norm_path])
norm_error.append(torch.mean((norms[i].detach().cpu()-Xer_norm[i].detach().cpu())**2))
print(np.mean(norm_error))
with Pool(4) as p:
p.map(save_img, zip(X_all, path_all) )