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reconstruct.py
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reconstruct.py
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# -*- coding : utf-8 -*-
# @FileName : stylize_canonical.py
# @Author : Ruixiang JIANG (Songrise)
# @Time : Sep 25, 2022
# @Github : https://github.com/songrise
# @Description: Use neus to reconstruct the canonical avatar,
import os
import argparse
import imageio
import torch
import torch.nn.functional as F
from models import instant_nsr
from utils import render_utils, utils, debug_utils
from utils.SMPLDataset import SMPLDataset
from options import options
from utils.constant import WHITE_BKG, BLACK_BKG, NSR_BOUND
import einops
#! Sep 25: Overwrite for larger human
####DEBUG Variables######
CANONICAL_CAMERA_DIST_VAL = 1.6
all_gt = []
STYLE_CANONICAL = True
def main_reconstruct(opt):
utils.print_notification('reconstruct the canonical view of a human.')
utils.fix_randomness(42)
device = torch.device('cuda' if opt.use_cuda else 'cpu')
nerf = instant_nsr.NeRFNetwork()
nerf = nerf.to(device)
print('Model setup done.')
###prepare data####
H, W = opt.render_h, opt.render_w
optimizer = torch.optim.Adam(nerf.parameters(), lr=5e-4, betas=(0.9, 0.99), eps=1e-15)
#!HARDCODED Oct 02: decay to half
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=opt.epochs, eta_min=opt.lr//20)
if opt.data_format == "neuman":
raise NotImplementedError
elif opt.data_format == 'neus':
dataloader = SMPLDataset(opt.data_path)
gt_rgb = dataloader.images #n_cap x H x W x 3
poses = dataloader.poses #n_cap x 4 x 4
H, W = gt_rgb.shape[1], gt_rgb.shape[2]
gt_rgb_orig = gt_rgb.reshape(-1,3).to(device) #[n_cap x H x W , 3]
all_rays_o, all_rays_d = [], []
for i in range(poses.shape[0]):
rays_o, rays_d = dataloader.gen_rays_pose(poses[i,...])
all_rays_o.append(rays_o)
all_rays_d.append(rays_d)
all_rays_o_orig = torch.stack(all_rays_o).to(device).reshape(-1,3) #[n_cap x H x W , 3]
all_rays_d_orig = torch.stack(all_rays_d).to(device).reshape(-1,3) #[n_cap x H x W , 3]
batch_size = 1600
#!#######Main Train loop########
global_step = 0
print('Start training...')
for epoch in range(opt.epochs):
#permute rays
perm = torch.randperm(all_rays_o_orig.shape[0])
all_rays_o = all_rays_o_orig[perm]
all_rays_d = all_rays_d_orig[perm]
gt_rgb = gt_rgb_orig[perm]
for i in range(0,all_rays_o.shape[0],batch_size):
rays_o, rays_d = all_rays_o[i:i+batch_size], all_rays_d[i:i+batch_size]
with torch.enable_grad():
rgb_gt_patch = gt_rgb[i:i+batch_size,...]
loss = 0.0
if opt.implicit_model == 'instant_nsr':
rgb_pred_patch, eikonal_loss, _ = render_utils.render_instantnsr_naive(nerf, rays_o = rays_o, rays_d = rays_d,
render_can=STYLE_CANONICAL, requires_grad=True, bkg_key= WHITE_BKG if opt.white_bkg else BLACK_BKG,
return_torch = True, rays_per_batch = batch_size, perturb = 1.0,return_raw=True, bound = NSR_BOUND)
elif opt.implicit_model == 'neus':
raise NotImplementedError
rgb_pred_patch = rgb_pred_patch.squeeze(0)
optimizer.zero_grad()
loss = loss + F.smooth_l1_loss(rgb_pred_patch, rgb_gt_patch,reduction='mean')
loss = loss + eikonal_loss * 0.1
if i % 32000 == 0:
print ("loss: ", loss.item())
loss.backward()
optimizer.step()
#!####Logging and saving#####
if global_step == 0 or (global_step+1) % opt.i_val == 0:
# log rgb
#todo revise val_cap gen logic to avoid repeated calc
if opt.data_format == "neuman":
raise NotImplementedError
elif opt.data_format == "neus":
rays_o_val, rays_d_val = dataloader.gen_rays_at(62)
rays_o_val, rays_d_val = rays_o_val.reshape(-1,3), rays_d_val.reshape(-1,3) #[H*W, 3]
#! Dec 09: instant nsr val
if opt.implicit_model == 'instant_nsr':
with torch.no_grad():
rgb_val, _, __ = render_utils.render_instantnsr_naive(nerf, rays_o = rays_o_val, rays_d = rays_d_val,
render_can=STYLE_CANONICAL, requires_grad=False, bkg_key= WHITE_BKG if opt.white_bkg else BLACK_BKG,
return_torch = True, rays_per_batch = 4096, perturb = True, return_raw=True)
rgb_val = rgb_val.squeeze(0).cpu()
rgb_val = einops.rearrange(rgb_val, '(h w) c -> h w c', h=H, w=W)
elif opt.implicit_model == 'neus':
raise NotImplementedError
save_path = os.path.join('./style', 'canonical_360', opt.exp_name, f'{opt.exp_name}_{str(global_step+1).zfill(4)}.png')
if not os.path.isdir(os.path.dirname(save_path)):
os.makedirs(os.path.dirname(save_path))
imageio.imsave(save_path, rgb_val)
print(f'image saved: {save_path}')
# log weights
if (global_step+1) % opt.i_save == 0:
weights_path = os.path.join('./style', 'canonical_360', opt.exp_name, f'{opt.exp_name}_{str(global_step+1).zfill(4)}.pth.tar')
if not os.path.isdir(os.path.dirname(weights_path)):
os.makedirs(os.path.dirname(weights_path))
torch.save(nerf.state_dict(), weights_path)
print(f'weights saved: {weights_path}')
# log mesh
if (global_step+1) % opt.i_mesh == 0:
mesh_path = os.path.join('./style', 'canonical_360', opt.exp_name, f'{opt.exp_name}_{str(global_step+1).zfill(4)}.ply')
if opt.implicit_model == 'neus':
raise NotImplementedError
elif opt.implicit_model == 'instant_nsr':
#!HARDCODED Nov 15: https://github.com/Totoro97/NeuS/blob/main/models/dataset.py#L90
#TODO Dec 09: extract_geo change bound impl for instant-nsr, also change for others later.
vert, face = nerf.extract_geometry(NSR_BOUND, 512, device=device)
utils.save_mesh(vert, face, mesh_path)
global_step += 1
scheduler.step()
print("Current learning rate: {}".format(scheduler.get_last_lr()))
debug_utils.dump_tensor(all_gt, 'all_gt.pkl')
utils.print_notification(f'Finished training {opt.exp_name}.')
if __name__ == "__main__":
parser = argparse.ArgumentParser()
options.set_general_option(parser)
opt, _ = parser.parse_known_args()
options.set_nerf_option(parser)
options.set_pe_option(parser)
options.set_render_option(parser)
options.set_trajectory_option(parser)
parser.add_argument('--scene_dir', required=True, type=str, help='scene directory')
parser.add_argument('--image_dir', required=False, type=str, default=None, help='image directory')
parser.add_argument('--out_dir', default='./out', type=str, help='weights dir')
parser.add_argument('--offset_scale', default=1.0, type=float, help='scale the predicted offset')
parser.add_argument('--geo_threshold', default=-1, type=float, help='')
parser.add_argument('--normalize', default=True, type=options.str2bool, help='')
parser.add_argument('--bkg_range_scale', default=3, type=float, help='extend near/far range for background')
parser.add_argument('--human_range_scale', default=1.5, type=float, help='extend near/far range for human')
parser.add_argument('--mode', required=True, choices=['canonical_360', 'posed_360', 'style'], default = 'style',type=str, help='rendering mode')
parser.add_argument('--num_offset_nets', default=1, type=int, help='how many offset networks')
parser.add_argument('--offset_scale_type', default='linear', type=str, help='no/linear/tanh')
parser.add_argument('--data_format', default = "neuman", choices=["neuman","neus"], type=str, help='dataset type')
parser.add_argument('--data_path', default="data/da_09", type = str)
#########Stylization###########
#nerf related
parser.add_argument('--n_sample', default=64, type=int, help='number of pts to sample along a ray')
parser.add_argument('--model_config', default='config/instant_nsr.json', type=str, help='model config file')
parser.add_argument('--implicit_model', default='instant_nsr', type=str, choices = ["neus", "instant_nsr"], help='implicit model')
#! Oct 05: to set up hw, use --render_h and --render_w instead.
# parser.add_argument('--img_hw', default = [160, 160], type=list, help='image height and width')
#clip loss related
parser.add_argument('--canonical_path',required=True, default=None, type=str, help='path to canonical image')
#regularization related
parser.add_argument("--exp_name", type=str, default="recon", help="name of the experiment")
parser.add_argument('--epochs',default=2,type = int, help='number of epochs')
parser.add_argument('--lr',default=5e-4,type = float, help='learning rate')
parser.add_argument('--i_val',default=10,type = float, help='log image after i_val iterations')
parser.add_argument('--i_save',default=200,type = float, help='save model after i_save iterations')
parser.add_argument('--i_mesh',default=1000,type = int, help='save mesh after i_mesh iterations')
opt = parser.parse_args()
if opt.render_h is None:
opt.render_size = None
else:
opt.render_size = (opt.render_h, opt.render_w)
options.print_opt(opt)
#TODO Oct 01: only do style in this file
main_reconstruct(opt)