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exp_runner.py
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import os
import logging
import argparse
import random
import numpy as np
import cv2 as cv
import trimesh
import torch
import torch.nn.functional as F
from torch.utils.tensorboard import SummaryWriter
from shutil import copyfile
from tqdm import tqdm
from pyhocon import ConfigFactory
from models.dataset import Dataset, MiniDataset
from models.fields import RenderingNetwork, SDFNetwork, SingleVarianceNetwork, DeformNetwork, AppearanceNetwork, TopoNetwork, GazeDeformNetwork
from models.renderer import NeuSRenderer, DeformNeuSRenderer
class Runner:
def __init__(self, conf_path, mode='train', case='CASE_NAME', is_continue=False):
self.device = torch.device('cuda')
self.gpu = torch.cuda.current_device()
self.dtype = torch.get_default_dtype()
# Configuration
self.conf_path = conf_path
f = open(self.conf_path)
conf_text = f.read()
conf_text = conf_text.replace('CASE_NAME', case)
f.close()
self.conf = ConfigFactory.parse_string(conf_text)
self.conf['dataset.data_dir'] = self.conf['dataset.data_dir'].replace('CASE_NAME', case)
self.base_exp_dir = self.conf['general.base_exp_dir']
os.makedirs(self.base_exp_dir, exist_ok=True)
# Backup codes and configs
if mode[:5] == 'train':
self.file_backup()
if mode == 'train' or mode == 'image':
self.dataset = Dataset(self.conf['dataset'])
else:
self.dataset = MiniDataset(self.conf['dataset'])
self.iter_step = 0
# set seed for exp
self.random_seed = self.conf.get_int('train.random_seed', default=-1)
if self.random_seed != -1:
torch.manual_seed(self.random_seed)
torch.cuda.manual_seed(self.random_seed)
np.random.seed(self.random_seed)
random.seed(self.random_seed)
os.environ['PYTHONHASHSEED'] = str(self.random_seed)
print('[Seed] set %d' % self.random_seed)
self.use_normal = self.conf.get_bool('dataset.use_normal', default=False)
self.use_gazeDA = self.conf.get_bool('dataset.use_gazeDA', default=False)
self.use_exclude = self.conf.get_bool('dataset.use_exclude', default=False)
self.use_disentangle = self.conf.get_bool('dataset.use_disentangle', default=False)
print('[use_normal]:', self.use_normal)
print('[use_gazeDA]:', self.use_gazeDA)
print('[use_exclude]:', self.use_exclude)
print('[use_disentangle]:', self.use_disentangle)
# Gaze and Eyeball
self.use_gaze = self.conf.get_bool('dataset.use_gaze', default=False)
self.use_split = self.conf.get_bool('dataset.use_split', default=False)
self.use_eyeball = self.conf.get_bool('dataset.use_eyeball', default=False)
self.use_closing = self.conf.get_bool('train.use_closing', default=False)
print('[use_gaze]:', self.use_gaze)
print('[use_split]:', self.use_split)
print('[use_eyeball]:', self.use_eyeball)
print('[use_closing]:', self.use_closing)
# Deform
self.use_deform = self.conf.get_bool('train.use_deform')
if self.use_deform:
self.deform_dim = self.conf.get_int('model.deform_network.d_feature')
if self.use_gaze:
self.variance_dim = self.conf.get_int('model.gazedeform_network.d_variance', default=0)
self.gazedeform_dim = self.conf.get_int('model.gazedeform_network.d_out') - self.variance_dim
if self.variance_dim != 0:
print('[use_variance_codes]:', self.variance_dim)
self.variance_codes = torch.randn(self.dataset.n_images, self.variance_dim, requires_grad=True).to(self.device)
else:
self.deform_codes = torch.randn(self.dataset.n_images, self.deform_dim, requires_grad=True).to(self.device)
if self.use_closing:
self.closing_dim = self.gazedeform_dim
self.closing_codes = torch.cat([torch.zeros(1, self.closing_dim).to(self.device),
torch.ones(1, self.closing_dim).to(self.device) * 0.5], dim=0)
else:
self.closing_dim = 0
self.appearance_dim = self.conf.get_int('model.appearance_rendering_network.d_global_feature')
self.appearance_codes = torch.randn(self.dataset.n_images, self.appearance_dim, requires_grad=True).to(self.device)
# Training parameters
self.end_iter = self.conf.get_int('train.end_iter')
self.important_begin_iter = self.conf.get_int('model.neus_renderer.important_begin_iter')
# Anneal
self.max_pe_iter = self.conf.get_int('train.max_pe_iter')
self.save_freq = self.conf.get_int('train.save_freq')
self.report_freq = self.conf.get_int('train.report_freq')
self.val_freq = self.conf.get_int('train.val_freq')
self.val_mesh_freq = self.conf.get_int('train.val_mesh_freq')
self.validate_idx = self.conf.get_int('train.validate_idx', default=-1)
self.batch_size = self.conf.get_int('train.batch_size')
self.validate_resolution_level = self.conf.get_int('train.validate_resolution_level')
self.learning_rate = self.conf.get_float('train.learning_rate')
self.learning_rate_alpha = self.conf.get_float('train.learning_rate_alpha')
self.warm_up_end = self.conf.get_float('train.warm_up_end', default=0.0)
self.anneal_end = self.conf.get_float('train.anneal_end', default=0.0)
self.test_batch_size = self.conf.get_int('test.test_batch_size')
# Weights
self.igr_weight = self.conf.get_float('train.igr_weight', default=0.1)
self.mask_weight = self.conf.get_float('train.mask_weight', default=0.1)
self.esp_weight = self.conf.get_float('train.esp_weight', default=0.1)
self.disentangle_weight = self.conf.get_float('train.disentangle_weight', default=0.1)
self.normal_penalty_weight = self.conf.get_float('train.normal_penalty_weight', default=3e-5)
self.normal_penalty = self.conf.get_bool('train.normal_penalty', default=True)
print(f"[INFO] Training setting -- normal penalty: {self.normal_penalty} -- weight: {self.normal_penalty_weight}")
self.is_continue = is_continue
self.mode = mode
self.model_list = []
self.writer = None
# Deform
if self.use_deform:
if self.use_gaze:
if self.use_split:
self.gazedeform_network_os = GazeDeformNetwork(**self.conf['model.gazedeform_network']).to(self.device)
self.gazedeform_network_od = GazeDeformNetwork(**self.conf['model.gazedeform_network']).to(self.device)
else:
self.gazedeform_network = GazeDeformNetwork(**self.conf['model.gazedeform_network']).to(self.device)
self.deform_network = DeformNetwork(**self.conf['model.deform_network']).to(self.device)
self.topo_network = TopoNetwork(**self.conf['model.topo_network']).to(self.device)
if self.use_gazeDA:
if self.use_split:
self.sdf_network = SDFNetwork(**self.conf['model.sdf_network'], eye_bbox=(self.dataset.os_bbox, self.dataset.od_bbox)).to(self.device)
else:
self.sdf_network = SDFNetwork(**self.conf['model.sdf_network'], eye_bbox=self.dataset.eye_bbox).to(self.device)
else:
self.sdf_network = SDFNetwork(**self.conf['model.sdf_network']).to(self.device)
self.deviation_network = SingleVarianceNetwork(**self.conf['model.variance_network']).to(self.device)
# Deform
if self.use_deform:
self.color_network = AppearanceNetwork(**self.conf['model.appearance_rendering_network']).to(self.device)
else:
self.color_network = RenderingNetwork(**self.conf['model.rendering_network']).to(self.device)
# Deform
if self.use_deform:
self.renderer = DeformNeuSRenderer(self.report_freq,
self.deform_network,
self.topo_network,
self.sdf_network,
self.deviation_network,
self.color_network,
gazedeform_dim=self.gazedeform_dim + self.closing_dim,
**self.conf['model.neus_renderer'])
else:
self.renderer = NeuSRenderer(self.sdf_network,
self.deviation_network,
self.color_network,
**self.conf['model.neus_renderer'])
# Load Optimizer
params_to_train = []
if self.use_deform:
params_to_train += [{'name':'deform_network', 'params':self.deform_network.parameters(), 'lr':self.learning_rate}]
params_to_train += [{'name':'topo_network', 'params':self.topo_network.parameters(), 'lr':self.learning_rate}]
if self.use_gaze:
if self.use_split:
params_to_train += [{'name':'gazedeform_network_os', 'params':self.gazedeform_network_os.parameters(), 'lr':self.learning_rate}]
params_to_train += [{'name':'gazedeform_network_od', 'params':self.gazedeform_network_od.parameters(), 'lr':self.learning_rate}]
else:
params_to_train += [{'name':'gazedeform_network', 'params':self.gazedeform_network.parameters(), 'lr':self.learning_rate}]
if self.variance_dim != 0:
params_to_train += [{'name':'variance_codes', 'params':self.variance_codes, 'lr':self.learning_rate}]
else:
params_to_train += [{'name':'deform_codes', 'params':self.deform_codes, 'lr':self.learning_rate}]
if self.use_closing:
params_to_train += [{'name':'closing_codes', 'params':self.closing_codes, 'lr':self.learning_rate}]
params_to_train += [{'name':'appearance_codes', 'params':self.appearance_codes, 'lr':self.learning_rate}]
params_to_train += [{'name':'sdf_network', 'params':self.sdf_network.parameters(), 'lr':self.learning_rate}]
params_to_train += [{'name':'deviation_network', 'params':self.deviation_network.parameters(), 'lr':self.learning_rate}]
params_to_train += [{'name':'color_network', 'params':self.color_network.parameters(), 'lr':self.learning_rate}]
# Camera
if self.dataset.camera_trainable:
params_to_train += [{'name':'intrinsics_paras', 'params':self.dataset.intrinsics_paras, 'lr':self.learning_rate}]
params_to_train += [{'name':'poses_paras', 'params':self.dataset.poses_paras, 'lr':self.learning_rate}]
self.optimizer = torch.optim.Adam(params_to_train)
# Load checkpoint
latest_model_name = None
if is_continue:
if self.mode == 'validate_pretrained':
latest_model_name = 'pretrained.pth'
else:
model_list_raw = os.listdir(os.path.join(self.base_exp_dir, 'checkpoints'))
model_list = []
for model_name in model_list_raw:
if model_name[-3:] == 'pth' and int(model_name[5:-4]) <= self.end_iter:
model_list.append(model_name)
model_list.sort()
latest_model_name = model_list[-1]
if latest_model_name is not None:
logging.info('Find checkpoint: {}'.format(latest_model_name))
self.load_checkpoint(latest_model_name)
def get_gaze(self, image_idx):
if self.use_gaze:
return self.dataset.get_gaze(image_idx)[None, ...]
else:
return None
def get_gazedeform(self, gaze, alpha_ratio):
if self.use_split:
deform_code_os = self.gazedeform_network_os(gaze[:, 0:2], alpha_ratio)
deform_code_od = self.gazedeform_network_od(gaze[:, 2:4], alpha_ratio)
deform_code = torch.cat([deform_code_os, deform_code_od], dim=-1)
else:
deform_code = self.gazedeform_network(gaze, alpha_ratio)
return deform_code
def get_deformcode(self, image_idx, alpha_ratio):
if self.use_gaze:
deform_code = self.get_gazedeform(self.get_gaze(image_idx), alpha_ratio)
if self.use_closing:
closing_idx = 1 if image_idx in self.dataset.skip_frames else 0
if self.use_split:
deform_code = torch.cat([deform_code[:, :self.gazedeform_dim], self.closing_codes[closing_idx][None, ...],
deform_code[:, self.gazedeform_dim:], self.closing_codes[closing_idx][None, ...]], dim=-1)
else:
deform_code = torch.cat([deform_code, self.closing_codes[closing_idx][None, ...]], dim=-1)
if self.variance_dim != 0:
deform_code = torch.cat([deform_code, self.variance_codes[image_idx][None, ...]], dim=-1)
else:
deform_code = self.deform_codes[image_idx][None, ...]
return deform_code
def get_deformcode_interp(self, gaze, alpha_ratio, is_closed=False, variance_idx=0):
assert self.use_gaze
deform_code = self.get_gazedeform(gaze, alpha_ratio)
if self.use_closing:
closing_idx = 1 if is_closed else 0
if self.use_split:
deform_code = torch.cat([deform_code[:, :self.gazedeform_dim], self.closing_codes[closing_idx][None, ...],
deform_code[:, self.gazedeform_dim:], self.closing_codes[closing_idx][None, ...]], dim=-1)
else:
deform_code = torch.cat([deform_code, self.closing_codes[closing_idx][None, ...]], dim=-1)
if self.variance_dim != 0:
deform_code = torch.cat([deform_code, self.variance_codes[variance_idx][None, ...]], dim=-1)
return deform_code
def get_pseudocode(self, image_idx, deform_code):
if self.use_disentangle:
pseudo_code = torch.randn_like(deform_code).to(deform_code)
# closing_code
if self.use_closing:
if self.use_split:
os_st = self.gazedeform_dim # 16
os_ed = os_st + self.closing_dim # 32
od_st = self.gazedeform_dim + self.closing_dim + self.gazedeform_dim # 48
od_ed = od_st + self.closing_dim # 64
assert os_st == 16 and os_ed == 32 and od_st == 48 and od_ed == 64
if self.iter_step >= 20000:
if image_idx in self.dataset.skip_frames:
# [closing frame] change closing_code, keep gazedeform_code
pseudo_code.data[:, 0:os_st] = deform_code.data[:, 0:os_st]
pseudo_code.data[:, os_ed:od_st] = deform_code.data[:, os_ed:od_st]
else:
if self.iter_step % 2 == 0:
# [open frame] change gazedeform_code, keep closing_code
pseudo_code.data[:, os_st:os_ed] = deform_code.data[:, os_st:os_ed]
pseudo_code.data[:, od_st:od_ed] = deform_code.data[:, od_st:od_ed]
#else
# [open frame] change both gazedeform_code and closing_code
else:
pseudo_code.data[:, os_st:os_ed] = self.closing_codes.data[0] + self.closing_codes.data[1] - deform_code.data[:, os_st:os_ed]
pseudo_code.data[:, od_st:od_ed] = self.closing_codes.data[0] + self.closing_codes.data[1] - deform_code.data[:, od_st:od_ed]
else:
pseudo_code = None
assert pseudo_code.requires_grad == False, pseudo_code.requires_grad
return pseudo_code
def get_alpha_ratio(self):
return max(min(self.iter_step/self.max_pe_iter, 1.), 0.)
def get_decay_weight(self, w0, ratio, alpha):
# ratio should be [0, 1]
# decay from w0 to ratio * w0
return w0 - (1 - ratio) * w0 * alpha
def get_disentangle_weight(self, image_idx):
if self.use_closing:
if self.use_split:
if self.iter_step < 20000:
scale_1 = 0.5
scale_23 = 10.0
elif self.iter_step < self.max_pe_iter:
scale_1 = 10.0
scale_23 = 100.0 if image_idx not in self.dataset.skip_frames and self.iter_step % 2 == 0 else 10.0
else:
scale_1 = 0.5
scale_23 = 10.0
else:
scale_1 = 0.5
scale_23 = 10.0
else:
scale_1 = 0.5
scale_23 = 1.0
return scale_1, scale_23
def get_more_sample(self):
if self.use_closing:
return self.iter_step < self.max_pe_iter
return False
def train(self):
self.writer = SummaryWriter(log_dir=os.path.join(self.base_exp_dir, 'logs'))
self.update_learning_rate()
res_step = self.end_iter - self.iter_step
image_perm = self.get_image_perm()
print(len(image_perm))
for iter_i in tqdm(range(res_step)):
# Deform
if self.use_deform:
image_idx = image_perm[self.iter_step % len(image_perm)]
# Deform
appearance_code = self.appearance_codes[image_idx][None, ...]
# Anneal
alpha_ratio = self.get_alpha_ratio()
# Change closing_codes.requires_grad
if self.use_closing and self.closing_codes.requires_grad == False:
if self.use_split:
if self.iter_step >= 20000:
self.closing_codes.requires_grad_()
else:
self.closing_codes.requires_grad_()
deform_code = self.get_deformcode(image_idx, alpha_ratio)
pseudo_code = self.get_pseudocode(image_idx, deform_code)
if iter_i == 0:
print('The files will be saved in:', self.base_exp_dir)
print('Used GPU:', self.gpu)
self.validate_observation_mesh(self.validate_idx)
data = self.dataset.gen_random_rays_at(image_idx, self.batch_size, sample_more_eye=self.get_more_sample())
if self.use_normal:
rays_o, rays_d, true_rgb, mask, eyemask, true_normal = data[:, :3], data[:, 3:6], data[:, 6:9], data[:, 9:10], data[:, 10:11], data[:, 11:14]
else:
rays_o, rays_d, true_rgb, mask, eyemask = data[:, :3], data[:, 3:6], data[:, 6:9], data[:, 9:10], data[:, 10:11]
near, far = self.dataset.near_far_from_sphere(rays_o, rays_d)
if self.mask_weight > 0.0:
mask = (mask > 0.5).to(self.dtype)
else:
mask = torch.ones_like(mask)
mask_sum = mask.sum() + 1e-5
if self.use_split:
input_eye_bbox = (self.dataset.os_bbox, self.dataset.od_bbox)
else:
input_eye_bbox = self.dataset.eye_bbox
if self.use_eyeball:
xyz_eyeball, nor_eyeball = self.dataset.get_esp_xyz_nor(image_idx)
if self.use_exclude:
render_out = self.renderer.render(deform_code, appearance_code, rays_o, rays_d, near, far,
cos_anneal_ratio=self.get_cos_anneal_ratio(),
alpha_ratio=alpha_ratio, iter_step=self.iter_step,
extra_samples=xyz_eyeball,
eye_bbox=input_eye_bbox,
deform_code2=pseudo_code,
shoot_eye=eyemask, inside_info=self.dataset.inside_info, gaze=self.get_gaze(image_idx))
else:
render_out = self.renderer.render(deform_code, appearance_code, rays_o, rays_d, near, far,
cos_anneal_ratio=self.get_cos_anneal_ratio(),
alpha_ratio=alpha_ratio, iter_step=self.iter_step,
extra_samples=xyz_eyeball,
eye_bbox=input_eye_bbox,
deform_code2=pseudo_code,
gaze=self.get_gaze(image_idx))
else:
render_out = self.renderer.render(deform_code, appearance_code, rays_o, rays_d, near, far,
cos_anneal_ratio=self.get_cos_anneal_ratio(),
alpha_ratio=alpha_ratio, iter_step=self.iter_step,
eye_bbox=input_eye_bbox,
deform_code2=pseudo_code,
gaze=self.get_gaze(image_idx))
color_fine = render_out['color_fine']
s_val = render_out['s_val']
cdf_fine = render_out['cdf_fine']
gradient_o_error = render_out['gradient_o_error']
weight_max = render_out['weight_max']
weight_sum = render_out['weight_sum']
# use normal penalty
pred_normal = render_out['pred_normal']
grad_normal = render_out['grad_normal']
weights = render_out['weights'].reshape(-1, 1).detach()
# Loss
color_error = (color_fine - true_rgb) * mask
color_fine_loss = F.l1_loss(color_error, torch.zeros_like(color_error), reduction='sum') / mask_sum
psnr = 20.0 * torch.log10(1.0 / (((color_fine - true_rgb)**2 * mask).sum() / (mask_sum * 3.0)).sqrt())
eikonal_loss = gradient_o_error
mask_loss = F.binary_cross_entropy(weight_sum.clip(1e-5, 1.0 - 1e-5), mask)
if self.use_disentangle:
self.mask_weight = self.conf.get_float('train.mask_weight', default=0.1) + 0.1 * alpha_ratio
if self.iter_step < self.max_pe_iter:
regular_scale = 10.0
else:
regular_scale = 1.0
loss = color_fine_loss +\
(eikonal_loss * self.igr_weight + mask_loss * self.mask_weight) * regular_scale
# normal_penalty
if self.normal_penalty:
normal_error = (pred_normal - grad_normal) * weights
normal_penalty = F.l1_loss(normal_error, torch.zeros_like(normal_error), reduction='sum')
loss += normal_penalty * self.normal_penalty_weight
if self.use_eyeball:
# eyeball sample points loss (sdf == 0)
sdf_eyeball = render_out['extra_sdf']
grad_eyeball = render_out['extra_grad']
grad_nor_eyeball = grad_eyeball / torch.linalg.norm(grad_eyeball, dim=-1, keepdim=True)
assert nor_eyeball.shape == grad_nor_eyeball.shape, (nor_eyeball.shape, grad_nor_eyeball.shape)
esp_xyz_loss = F.l1_loss(sdf_eyeball, torch.zeros_like(sdf_eyeball), reduction='sum') / sdf_eyeball.shape[0]
nor_dot = torch.sum(grad_nor_eyeball * nor_eyeball, dim=-1)
esp_nor_loss = F.l1_loss(nor_dot, torch.ones_like(nor_dot) * -1, reduction='sum') / grad_nor_eyeball.shape[0]
loss += esp_xyz_loss * self.esp_weight
loss += esp_nor_loss * self.esp_weight
if self.use_disentangle:
coord_err1 = render_out['coord_err1']
coord_err2 = render_out['coord_err2']
coord_loss1 = F.l1_loss(coord_err1, torch.zeros_like(coord_err1), reduction='sum') #/ coord_err1.shape[0]
coord_loss2 = F.l1_loss(coord_err2, torch.zeros_like(coord_err2), reduction='sum') #/ coord_err2.shape[0]
scale_1, scale_23 = self.get_disentangle_weight(image_idx)
weight_1 = self.disentangle_weight * scale_1
weight_23 = self.disentangle_weight * scale_23
if self.use_split:
coord_err3 = render_out['coord_err3']
coord_loss3 = F.l1_loss(coord_err3, torch.zeros_like(coord_err3), reduction='sum') #/ coord_err3.shape[0]
# balance weight_2 and weight_3
scale_2 = 1.5 if coord_loss2 > coord_loss3 else 0.5
scale_3 = 2.0 - scale_2
loss += (coord_loss1 * weight_1 + coord_loss2 * weight_23 * scale_2 + coord_loss3 * weight_23 * scale_3)
else:
loss += (coord_loss1 * weight_1 + coord_loss2 * weight_23)
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
self.iter_step += 1
self.writer.add_scalar('Loss/loss', loss, self.iter_step)
self.writer.add_scalar('Loss/color_loss', color_fine_loss, self.iter_step)
del color_fine_loss
self.writer.add_scalar('Loss/eikonal_loss', eikonal_loss, self.iter_step)
self.writer.add_scalar('Loss/mask_loss', mask_loss, self.iter_step)
del eikonal_loss
del mask_loss
if self.use_disentangle:
self.writer.add_scalar('Loss/coord_loss1', coord_loss1, self.iter_step)
self.writer.add_scalar('Loss/coord_loss2', coord_loss2, self.iter_step)
del coord_loss1
del coord_loss2
if self.use_split:
self.writer.add_scalar('Loss/coord_loss3', coord_loss3, self.iter_step)
del coord_loss3
self.writer.add_scalar('Statistics/s_val', s_val.mean(), self.iter_step)
self.writer.add_scalar('Statistics/cdf', (cdf_fine[:, :1] * mask).sum() / mask_sum, self.iter_step)
self.writer.add_scalar('Statistics/weight_max', (weight_max * mask).sum() / mask_sum, self.iter_step)
self.writer.add_scalar('Statistics/psnr', psnr, self.iter_step)
if self.iter_step % self.report_freq == 0:
print('The files have been saved in:', self.base_exp_dir)
print('Used GPU:', self.gpu)
print('iter:{:8>d} loss={} idx={} alpha_ratio={} lr={}'.format(self.iter_step, loss, image_idx,
alpha_ratio, self.optimizer.param_groups[0]['lr']))
if self.iter_step % self.save_freq == 0:
self.save_checkpoint()
if self.iter_step % self.val_freq == 0:
self.validate_image(self.validate_idx)
# if self.iter_step % self.val_mesh_freq == 0:
# self.validate_observation_mesh(self.validate_idx, resolution=512)
self.update_learning_rate()
if self.iter_step % len(image_perm) == 0:
image_perm = self.get_image_perm()
else:
if self.iter_step == 0:
self.validate_mesh()
data = self.dataset.gen_random_rays_at(image_perm[self.iter_step % len(image_perm)], self.batch_size)
rays_o, rays_d, true_rgb, mask = data[:, :3], data[:, 3: 6], data[:, 6: 9], data[:, 9: 10]
near, far = self.dataset.near_far_from_sphere(rays_o, rays_d)
if self.mask_weight > 0.0:
mask = (mask > 0.5).to(self.dtype)
else:
mask = torch.ones_like(mask)
mask_sum = mask.sum() + 1e-5
render_out = self.renderer.render(rays_o, rays_d, near, far,
cos_anneal_ratio=self.get_cos_anneal_ratio())
color_fine = render_out['color_fine']
s_val = render_out['s_val']
cdf_fine = render_out['cdf_fine']
gradient_error = render_out['gradient_error']
weight_max = render_out['weight_max']
weight_sum = render_out['weight_sum']
# use normal penalty
pred_normal = render_out['pred_normal']
grad_normal = render_out['grad_normal']
weights = render_out['weights'].reshape(-1, 1).detach()
# Loss
color_error = (color_fine - true_rgb) * mask
color_fine_loss = F.l1_loss(color_error, torch.zeros_like(color_error), reduction='sum') / mask_sum
psnr = 20.0 * torch.log10(1.0 / (((color_fine - true_rgb)**2 * mask).sum() / (mask_sum * 3.0)).sqrt())
eikonal_loss = gradient_error
mask_loss = F.binary_cross_entropy(weight_sum.clip(1e-3, 1.0 - 1e-3), mask)
loss = color_fine_loss +\
eikonal_loss * self.igr_weight +\
mask_loss * self.mask_weight
# normal_penalty
if self.normal_penalty:
normal_error = (pred_normal - grad_normal) * weights
normal_penalty = F.l1_loss(normal_error, torch.zeros_like(normal_error), reduction='sum')
loss += normal_penalty * self.normal_penalty_weight
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
self.iter_step += 1
self.writer.add_scalar('Loss/loss', loss, self.iter_step)
self.writer.add_scalar('Loss/color_loss', color_fine_loss, self.iter_step)
self.writer.add_scalar('Loss/eikonal_loss', eikonal_loss, self.iter_step)
del color_fine_loss
del eikonal_loss
if self.mask_weight > 0.0:
self.writer.add_scalar('Loss/mask_loss', mask_loss, self.iter_step)
del mask_loss
self.writer.add_scalar('Statistics/s_val', s_val.mean(), self.iter_step)
self.writer.add_scalar('Statistics/cdf', (cdf_fine[:, :1] * mask).sum() / mask_sum, self.iter_step)
self.writer.add_scalar('Statistics/weight_max', (weight_max * mask).sum() / mask_sum, self.iter_step)
self.writer.add_scalar('Statistics/psnr', psnr, self.iter_step)
if self.iter_step % self.report_freq == 0:
print('The file have been saved in:', self.base_exp_dir)
print('Used GPU:', self.gpu)
print('iter:{:8>d} loss = {} lr={}'.format(self.iter_step, loss, self.optimizer.param_groups[0]['lr']))
if self.iter_step % self.save_freq == 0:
self.save_checkpoint()
if self.iter_step % self.val_freq == 0:
self.validate_image()
if self.iter_step % self.val_mesh_freq == 0:
self.validate_mesh()
self.update_learning_rate()
if self.iter_step % len(image_perm) == 0:
image_perm = self.get_image_perm()
def get_image_perm(self):
if self.use_closing and self.use_split and self.use_disentangle:
if self.iter_step > self.warm_up_end and self.iter_step < self.max_pe_iter:
base_list = np.arange(self.dataset.n_images).tolist()
n_repeat = max(len(base_list) // len(self.dataset.skip_frames), 1) - 1
merge_list = base_list + n_repeat * self.dataset.skip_frames # make the raito of non-closed and closed samples approximate to 1:1
np.random.shuffle(merge_list)
return merge_list
else:
return torch.randperm(self.dataset.n_images)
else:
return torch.randperm(self.dataset.n_images)
def get_cos_anneal_ratio(self):
if self.anneal_end == 0.0:
return 1.0
else:
return np.min([1.0, self.iter_step / self.anneal_end])
def update_learning_rate(self, scale_factor=1):
if self.iter_step < self.warm_up_end:
learning_factor = self.iter_step / self.warm_up_end
else:
alpha = self.learning_rate_alpha
progress = (self.iter_step - self.warm_up_end) / (self.end_iter - self.warm_up_end)
learning_factor = (np.cos(np.pi * progress) + 1.0) * 0.5 * (1 - alpha) + alpha
learning_factor *= scale_factor
current_learning_rate = self.learning_rate * learning_factor
for g in self.optimizer.param_groups:
if g['name'] in ['intrinsics_paras', 'poses_paras', 'depth_intrinsics_paras']:
g['lr'] = current_learning_rate * 1e-1
elif self.iter_step >= self.max_pe_iter and g['name'] == 'deviation_network':
g['lr'] = current_learning_rate * 1.5
elif self.use_closing and self.iter_step < 20000 and g['name'] in ['gazedeform_network_os', 'gazedeform_network_od']:
g['lr'] = 0.0
else:
g['lr'] = current_learning_rate
def file_backup(self):
dir_lis = self.conf['general.recording']
if os.path.exists(os.path.join(self.base_exp_dir, 'recording')):
print('recording exists! please delete first!')
exit(0)
os.makedirs(os.path.join(self.base_exp_dir, 'recording'), exist_ok=True)
for dir_name in dir_lis:
cur_dir = os.path.join(self.base_exp_dir, 'recording', dir_name)
os.makedirs(cur_dir, exist_ok=True)
files = os.listdir(dir_name)
for f_name in files:
if f_name[-3:] == '.py':
copyfile(os.path.join(dir_name, f_name), os.path.join(cur_dir, f_name))
copyfile(self.conf_path, os.path.join(self.base_exp_dir, 'recording', 'config.conf'))
logging.info('File Saved')
def load_checkpoint(self, checkpoint_name):
checkpoint = torch.load(os.path.join(self.base_exp_dir, 'checkpoints', checkpoint_name), map_location=self.device)
self.sdf_network.load_state_dict(checkpoint['sdf_network_fine'])
self.deviation_network.load_state_dict(checkpoint['variance_network_fine'])
self.color_network.load_state_dict(checkpoint['color_network_fine'])
# Deform
if self.use_deform:
if self.use_gaze:
if self.use_split:
self.gazedeform_network_os.load_state_dict(checkpoint['gazedeform_network_os'])
self.gazedeform_network_od.load_state_dict(checkpoint['gazedeform_network_od'])
else:
self.gazedeform_network.load_state_dict(checkpoint['gazedeform_network'])
if self.variance_dim != 0:
self.variance_codes.data = torch.from_numpy(checkpoint['variance_codes']).to(self.device).data
else:
self.deform_codes.data = torch.from_numpy(checkpoint['deform_codes']).to(self.device).data
if self.use_closing:
self.closing_codes.data = torch.from_numpy(checkpoint['closing_codes']).to(self.device).data
self.appearance_codes.data = torch.from_numpy(checkpoint['appearance_codes']).to(self.device).data
self.deform_network.load_state_dict(checkpoint['deform_network'])
self.topo_network.load_state_dict(checkpoint['topo_network'])
logging.info('Use_deform True')
self.dataset.intrinsics_paras.data = torch.from_numpy(checkpoint['intrinsics_paras']).to(self.device).data
self.dataset.poses_paras.data = torch.from_numpy(checkpoint['poses_paras']).to(self.device).data
# Camera
if self.dataset.camera_trainable:
self.dataset.intrinsics_paras.requires_grad_()
self.dataset.poses_paras.requires_grad_()
else:
self.dataset.static_paras_to_mat()
self.optimizer.load_state_dict(checkpoint['optimizer'])
self.iter_step = checkpoint['iter_step']
logging.info('End')
def save_checkpoint(self):
# Deform
if self.use_deform:
if self.use_gaze:
if self.use_split:
checkpoint = {
'gazedeform_network_os': self.gazedeform_network_os.state_dict(),
'gazedeform_network_od': self.gazedeform_network_od.state_dict(),
'deform_network': self.deform_network.state_dict(),
'topo_network': self.topo_network.state_dict(),
'sdf_network_fine': self.sdf_network.state_dict(),
'variance_network_fine': self.deviation_network.state_dict(),
'color_network_fine': self.color_network.state_dict(),
'optimizer': self.optimizer.state_dict(),
'iter_step': self.iter_step,
'closing_codes': self.closing_codes.data.cpu().numpy() if self.use_closing else None,
'variance_codes': self.variance_codes.data.cpu().numpy() if self.variance_dim != 0 else None,
'appearance_codes': self.appearance_codes.data.cpu().numpy(),
'intrinsics_paras': self.dataset.intrinsics_paras.data.cpu().numpy(),
'poses_paras': self.dataset.poses_paras.data.cpu().numpy(),
}
else:
checkpoint = {
'gazedeform_network': self.gazedeform_network.state_dict(),
'deform_network': self.deform_network.state_dict(),
'topo_network': self.topo_network.state_dict(),
'sdf_network_fine': self.sdf_network.state_dict(),
'variance_network_fine': self.deviation_network.state_dict(),
'color_network_fine': self.color_network.state_dict(),
'optimizer': self.optimizer.state_dict(),
'iter_step': self.iter_step,
'closing_codes': self.closing_codes.data.cpu().numpy() if self.use_closing else None,
'variance_codes': self.variance_codes.data.cpu().numpy() if self.variance_dim != 0 else None,
'appearance_codes': self.appearance_codes.data.cpu().numpy(),
'intrinsics_paras': self.dataset.intrinsics_paras.data.cpu().numpy(),
'poses_paras': self.dataset.poses_paras.data.cpu().numpy(),
}
else:
checkpoint = {
'deform_network': self.deform_network.state_dict(),
'topo_network': self.topo_network.state_dict(),
'sdf_network_fine': self.sdf_network.state_dict(),
'variance_network_fine': self.deviation_network.state_dict(),
'color_network_fine': self.color_network.state_dict(),
'optimizer': self.optimizer.state_dict(),
'iter_step': self.iter_step,
'deform_codes': self.deform_codes.data.cpu().numpy(),
'appearance_codes': self.appearance_codes.data.cpu().numpy(),
'intrinsics_paras': self.dataset.intrinsics_paras.data.cpu().numpy(),
'poses_paras': self.dataset.poses_paras.data.cpu().numpy(),
}
else:
checkpoint = {
'sdf_network_fine': self.sdf_network.state_dict(),
'variance_network_fine': self.deviation_network.state_dict(),
'color_network_fine': self.color_network.state_dict(),
'optimizer': self.optimizer.state_dict(),
'iter_step': self.iter_step,
'intrinsics_paras': self.dataset.intrinsics_paras.data.cpu().numpy(),
'poses_paras': self.dataset.poses_paras.data.cpu().numpy(),
}
os.makedirs(os.path.join(self.base_exp_dir, 'checkpoints'), exist_ok=True)
torch.save(checkpoint, os.path.join(self.base_exp_dir, 'checkpoints', 'ckpt_{:0>7d}.pth'.format(self.iter_step)))
def validate_image(self, idx=-1, resolution_level=-1, mode='train', normal_filename='normals', rgb_filename='rgbs', depth_filename='depths'):
if idx < 0:
idx = np.random.randint(self.dataset.n_images)
# Deform
if self.use_deform:
deform_code = self.get_deformcode(idx, self.get_alpha_ratio())
appearance_code = self.appearance_codes[idx][None, ...]
print('Validate: iter: {}, camera: {}'.format(self.iter_step, idx))
if mode == 'train':
batch_size = self.batch_size
else:
batch_size = self.test_batch_size
if resolution_level < 0:
resolution_level = self.validate_resolution_level
rays_o, rays_d, eyemask = self.dataset.gen_rays_at(idx, resolution_level=resolution_level)
H, W, _ = rays_o.shape
rays_o = rays_o.reshape(-1, 3).split(batch_size)
rays_d = rays_d.reshape(-1, 3).split(batch_size)
eyemask = eyemask.reshape(-1, 1).split(batch_size)
out_rgb_fine = []
out_normal_fine = []
out_depth_fine = []
for rays_o_batch, rays_d_batch, eyemask_batch in zip(rays_o, rays_d, eyemask):
near, far = self.dataset.near_far_from_sphere(rays_o_batch, rays_d_batch)
if self.use_deform:
render_out = self.renderer.render(deform_code,
appearance_code,
rays_o_batch,
rays_d_batch,
near,
far,
cos_anneal_ratio=self.get_cos_anneal_ratio(),
alpha_ratio=self.get_alpha_ratio(),
iter_step=self.iter_step, gaze=self.get_gaze(idx))
# shoot_eye=eyemask_batch, inside_info=self.dataset.inside_info)
render_out['gradients'] = render_out['gradients_o']
else:
render_out = self.renderer.render(rays_o_batch,
rays_d_batch,
near,
far,
cos_anneal_ratio=self.get_cos_anneal_ratio())
def feasible(key): return (key in render_out) and (render_out[key] is not None)
if feasible('color_fine'):
out_rgb_fine.append(render_out['color_fine'].detach().cpu().numpy())
if feasible('gradients') and feasible('weights'):
if self.iter_step >= self.important_begin_iter:
n_samples = self.renderer.n_samples + self.renderer.n_importance
else:
n_samples = self.renderer.n_samples
normals = render_out['gradients'] * render_out['weights'][:, :n_samples, None]
if feasible('inside_sphere'):
normals = normals * render_out['inside_sphere'][..., None]
normals = normals.sum(dim=1).detach().cpu().numpy()
out_normal_fine.append(normals)
del render_out['depth_map'] # Annotate it if you want to visualize estimated depth map!
if feasible('depth_map'):
out_depth_fine.append(render_out['depth_map'].detach().cpu().numpy())
del render_out
img_fine = None
if len(out_rgb_fine) > 0:
img_fine = (np.concatenate(out_rgb_fine, axis=0).reshape([H, W, 3, -1]) * 256).clip(0, 255)
normal_img = None
if len(out_normal_fine) > 0:
normal_img = np.concatenate(out_normal_fine, axis=0)
# Camera
if self.dataset.camera_trainable:
_, pose = self.dataset.dynamic_paras_to_mat(idx)
else:
pose = self.dataset.poses_all[idx]
rot = np.linalg.inv(pose[:3, :3].detach().cpu().numpy())
normal_img = (np.matmul(rot[None, :, :], normal_img[:, :, None])
.reshape([H, W, 3, -1]) * 128 + 128).clip(0, 255)
depth_img = None
if len(out_depth_fine) > 0:
depth_img = np.concatenate(out_depth_fine, axis=0)
depth_img = depth_img.reshape([H, W, 1, -1])
depth_img = 255. - np.clip(depth_img/depth_img.max(), a_max=1, a_min=0) * 255.
depth_img = np.uint8(depth_img)
os.makedirs(os.path.join(self.base_exp_dir, rgb_filename), exist_ok=True)
os.makedirs(os.path.join(self.base_exp_dir, normal_filename), exist_ok=True)
os.makedirs(os.path.join(self.base_exp_dir, depth_filename), exist_ok=True)
for i in range(img_fine.shape[-1]):
if len(out_rgb_fine) > 0:
cv.imwrite(os.path.join(self.base_exp_dir,
rgb_filename,
'{:0>8d}_{}.png'.format(self.iter_step, idx)),
np.concatenate([img_fine[..., i],
self.dataset.image_at(idx, resolution_level=resolution_level)]))
if len(out_normal_fine) > 0:
cv.imwrite(os.path.join(self.base_exp_dir,
normal_filename,
'{:0>8d}_{}.png'.format(self.iter_step, idx)),
normal_img[..., i])
if len(out_depth_fine) > 0:
cv.imwrite(os.path.join(self.base_exp_dir, depth_filename,
'{:0>8d}_{}.png'.format(self.iter_step, idx)),
cv.applyColorMap(depth_img[..., i], cv.COLORMAP_JET))
def validate_all_image(self, resolution_level=-1):
idx_list = list(range(290, self.dataset.n_images))
for image_idx in idx_list:
self.validate_image(image_idx, resolution_level, 'test', 'validations_normals', 'validations_rgbs', 'validations_depths')
print('Used GPU:', self.gpu)
def validate_mesh(self, world_space=False, resolution=64, threshold=0.0):
bound_min = torch.tensor(self.dataset.object_bbox_min, dtype=self.dtype)
bound_max = torch.tensor(self.dataset.object_bbox_max, dtype=self.dtype)
vertices, triangles =\
self.renderer.extract_geometry(bound_min, bound_max, resolution=resolution, threshold=threshold)
os.makedirs(os.path.join(self.base_exp_dir, 'meshes'), exist_ok=True)
if world_space:
vertices = vertices * self.dataset.scale_mats_np[0][0, 0] + self.dataset.scale_mats_np[0][:3, 3][None]
mesh = trimesh.Trimesh(vertices, triangles)
mesh.export(os.path.join(self.base_exp_dir, 'meshes', '{:0>8d}.ply'.format(self.iter_step)))
logging.info('End')
# Deform
def validate_canonical_mesh(self, world_space=False, resolution=64, threshold=0.0, filename='meshes_canonical'):
bound_min = torch.tensor(self.dataset.object_bbox_min, dtype=self.dtype)
bound_max = torch.tensor(self.dataset.object_bbox_max, dtype=self.dtype)
vertices, triangles =\
self.renderer.extract_canonical_geometry(bound_min, bound_max, resolution=resolution, threshold=threshold,
alpha_ratio=self.get_alpha_ratio())
os.makedirs(os.path.join(self.base_exp_dir, filename), exist_ok=True)
if world_space:
vertices = vertices * self.dataset.scale_mats_np[0][0, 0] + self.dataset.scale_mats_np[0][:3, 3][None]
mesh = trimesh.Trimesh(vertices, triangles)
mesh.export(os.path.join(self.base_exp_dir, filename, '{:0>8d}_canonical.ply'.format(self.iter_step)))
logging.info('End')
# Deform
def validate_observation_mesh(self, idx=-1, world_space=False, resolution=64, threshold=0.0, filename='meshes', use_eye_bbox=False):
if idx < 0:
idx = np.random.randint(self.dataset.n_images)
# Deform
deform_code = self.get_deformcode(idx, self.get_alpha_ratio())
if not use_eye_bbox:
bound_min = torch.tensor(self.dataset.object_bbox_min, dtype=self.dtype)
bound_max = torch.tensor(self.dataset.object_bbox_max, dtype=self.dtype)
else:
bound_min = torch.tensor(self.dataset.eye_bbox_min, dtype=self.dtype)
bound_max = torch.tensor(self.dataset.eye_bbox_max, dtype=self.dtype)
# bound_min = torch.tensor(self.dataset.os_bbox_min, dtype=self.dtype)
# bound_max = torch.tensor(self.dataset.os_bbox_max, dtype=self.dtype)
# bound_min = torch.tensor(self.dataset.od_bbox_min, dtype=self.dtype)
# bound_max = torch.tensor(self.dataset.od_bbox_max, dtype=self.dtype)
vertices, triangles =\
self.renderer.extract_observation_geometry(deform_code, bound_min, bound_max, resolution=resolution, threshold=threshold,
alpha_ratio=self.get_alpha_ratio(), gaze=self.get_gaze(idx))
os.makedirs(os.path.join(self.base_exp_dir, filename), exist_ok=True)
if world_space:
vertices = vertices * self.dataset.scale_mats_np[0][0, 0] + self.dataset.scale_mats_np[0][:3, 3][None]
mesh = trimesh.Trimesh(vertices, triangles)
mesh.export(os.path.join(self.base_exp_dir, filename, '{:0>8d}_{}.ply'.format(self.iter_step, idx)))
logging.info('End')
# Deform
def validate_all_mesh(self, world_space=False, resolution=64, threshold=0.0):
idx_list = [0] + list(range(290, self.dataset.n_images))
# idx_list = list(range(self.dataset.n_images))
for image_idx in idx_list:
self.validate_observation_mesh(image_idx, world_space, resolution, threshold, 'validations_meshes')
print('Used GPU:', self.gpu)
def bilinear_interpolate(self, code_0, code_1, offset):
return code_0 * (1. - offset) + code_1 * offset
def insert_closing(self, code_list, code_closing, idx, length=4):
"""
Insert a closing frame into an animation sequence.
:param code_list: deform_code sequence
:param code_closing: deform_code of closing frame
:param idx: index of the closing frame (insertion position)
:param length: frame number of closing animation
"""
n_total = len(code_list)
code_list[idx] = code_closing
start_idx = idx - length
end_idx = idx + length
assert start_idx >= 0 and end_idx < n_total
# start closing
for i in range(start_idx+1, idx):
offset = (i - start_idx) / length
new_code = self.bilinear_interpolate(code_list[start_idx], code_closing, offset)
code_list[i] = new_code
# back to open
for i in range(idx+1, end_idx):
offset = (i - idx) / length
new_code = self.bilinear_interpolate(code_closing, code_list[end_idx], offset)