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main_avatar.py
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main_avatar.py
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import os
# os.environ['CUDA_LAUNCH_BLOCKING'] = '1'
# os.environ['TORCH_USE_CUDA_DSA'] = '1'
os.environ['OPENCV_IO_ENABLE_OPENEXR'] = '1'
import yaml
import shutil
import collections
import torch
import torch.utils.data
import torch.nn.functional as F
import numpy as np
import cv2 as cv
import glob
import datetime
import trimesh
from torch.utils.tensorboard import SummaryWriter
from tqdm import tqdm
import importlib
import config
from network.lpips import LPIPS
from dataset.dataset_pose import PoseDataset
import utils.net_util as net_util
import utils.visualize_util as visualize_util
from utils.renderer import Renderer
from utils.net_util import to_cuda
from utils.obj_io import save_mesh_as_ply
from gaussians.obj_io import save_gaussians_as_ply
def safe_exists(path):
if path is None:
return False
return os.path.exists(path)
class AvatarTrainer:
def __init__(self, opt):
self.opt = opt
self.patch_size = 512
self.iter_idx = 0
self.iter_num = 800000
self.lr_init = float(self.opt['train'].get('lr_init', 5e-4))
avatar_module = self.opt['model'].get('module', 'network.avatar')
print('Import AvatarNet from %s' % avatar_module)
AvatarNet = importlib.import_module(avatar_module).AvatarNet
self.avatar_net = AvatarNet(self.opt['model']).to(config.device)
self.optm = torch.optim.Adam(
self.avatar_net.parameters(), lr = self.lr_init
)
self.random_bg_color = self.opt['train'].get('random_bg_color', True)
self.bg_color = (1., 1., 1.)
self.bg_color_cuda = torch.from_numpy(np.asarray(self.bg_color)).to(torch.float32).to(config.device)
self.loss_weight = self.opt['train']['loss_weight']
self.finetune_color = self.opt['train']['finetune_color']
print('# Parameter number of AvatarNet is %d' % (sum([p.numel() for p in self.avatar_net.parameters()])))
def update_lr(self):
alpha = 0.05
progress = self.iter_idx / self.iter_num
learning_factor = (np.cos(np.pi * progress) + 1.0) * 0.5 * (1 - alpha) + alpha
lr = self.lr_init * learning_factor
for param_group in self.optm.param_groups:
param_group['lr'] = lr
return lr
@staticmethod
def requires_net_grad(net: torch.nn.Module, flag = True):
for p in net.parameters():
p.requires_grad = flag
def crop_image(self, gt_mask, patch_size, randomly, *args):
"""
:param gt_mask: (H, W)
:param patch_size: resize the cropped patch to the given patch_size
:param randomly: whether to randomly sample the patch
:param args: input images with shape of (C, H, W)
"""
mask_uv = torch.argwhere(gt_mask > 0.)
min_v, min_u = mask_uv.min(0)[0]
max_v, max_u = mask_uv.max(0)[0]
len_v = max_v - min_v
len_u = max_u - min_u
max_size = max(len_v, len_u)
cropped_images = []
if randomly and max_size > patch_size:
random_v = torch.randint(0, max_size - patch_size + 1, (1,)).to(max_size)
random_u = torch.randint(0, max_size - patch_size + 1, (1,)).to(max_size)
for image in args:
cropped_image = self.bg_color_cuda[:, None, None] * torch.ones((3, max_size, max_size), dtype = image.dtype, device = image.device)
if len_v > len_u:
start_u = (max_size - len_u) // 2
cropped_image[:, :, start_u: start_u + len_u] = image[:, min_v: max_v, min_u: max_u]
else:
start_v = (max_size - len_v) // 2
cropped_image[:, start_v: start_v + len_v, :] = image[:, min_v: max_v, min_u: max_u]
if randomly and max_size > patch_size:
cropped_image = cropped_image[:, random_v: random_v + patch_size, random_u: random_u + patch_size]
else:
cropped_image = F.interpolate(cropped_image[None], size = (patch_size, patch_size), mode = 'bilinear')[0]
cropped_images.append(cropped_image)
# cv.imshow('cropped_image', cropped_image.detach().cpu().numpy().transpose(1, 2, 0))
# cv.imshow('cropped_gt_image', cropped_gt_image.detach().cpu().numpy().transpose(1, 2, 0))
# cv.waitKey(0)
if len(cropped_images) > 1:
return cropped_images
else:
return cropped_images[0]
def compute_lpips_loss(self, image, gt_image):
assert image.shape[1] == image.shape[2] and gt_image.shape[1] == gt_image.shape[2]
lpips_loss = self.lpips.forward(
image[None, [2, 1, 0]],
gt_image[None, [2, 1, 0]],
normalize = True
).mean()
return lpips_loss
def forward_one_pass_pretrain(self, items):
total_loss = 0
batch_losses = {}
l1_loss = torch.nn.L1Loss()
items = net_util.delete_batch_idx(items)
pose_map = items['smpl_pos_map'][:3]
position_loss = l1_loss(self.avatar_net.get_positions(pose_map), self.avatar_net.cano_gaussian_model.get_xyz)
total_loss += position_loss
batch_losses.update({
'position': position_loss.item()
})
opacity, scales, rotations = self.avatar_net.get_others(pose_map)
opacity_loss = l1_loss(opacity, self.avatar_net.cano_gaussian_model.get_opacity)
total_loss += opacity_loss
batch_losses.update({
'opacity': opacity_loss.item()
})
scale_loss = l1_loss(scales, self.avatar_net.cano_gaussian_model.get_scaling)
total_loss += scale_loss
batch_losses.update({
'scale': scale_loss.item()
})
rotation_loss = l1_loss(rotations, self.avatar_net.cano_gaussian_model.get_rotation)
total_loss += rotation_loss
batch_losses.update({
'rotation': rotation_loss.item()
})
total_loss.backward()
self.optm.step()
self.optm.zero_grad()
return total_loss, batch_losses
def forward_one_pass(self, items):
# forward_start = torch.cuda.Event(enable_timing = True)
# forward_end = torch.cuda.Event(enable_timing = True)
# backward_start = torch.cuda.Event(enable_timing = True)
# backward_end = torch.cuda.Event(enable_timing = True)
# step_start = torch.cuda.Event(enable_timing = True)
# step_end = torch.cuda.Event(enable_timing = True)
if self.random_bg_color:
self.bg_color = np.random.rand(3)
self.bg_color_cuda = torch.from_numpy(np.asarray(self.bg_color)).to(torch.float32).to(config.device)
total_loss = 0
batch_losses = {}
items = net_util.delete_batch_idx(items)
""" Optimize generator """
if self.finetune_color:
self.requires_net_grad(self.avatar_net.color_net, True)
self.requires_net_grad(self.avatar_net.position_net, False)
self.requires_net_grad(self.avatar_net.other_net, True)
else:
self.requires_net_grad(self.avatar_net, True)
# forward_start.record()
render_output = self.avatar_net.render(items, self.bg_color)
image = render_output['rgb_map'].permute(2, 0, 1)
offset = render_output['offset']
# mask image & set bg color
items['color_img'][~items['mask_img']] = self.bg_color_cuda
gt_image = items['color_img'].permute(2, 0, 1)
mask_img = items['mask_img'].to(torch.float32)
boundary_mask_img = 1. - items['boundary_mask_img'].to(torch.float32)
image = image * boundary_mask_img[None] + (1. - boundary_mask_img[None]) * self.bg_color_cuda[:, None, None]
gt_image = gt_image * boundary_mask_img[None] + (1. - boundary_mask_img[None]) * self.bg_color_cuda[:, None, None]
# cv.imshow('image', image.detach().permute(1, 2, 0).cpu().numpy())
# cv.imshow('gt_image', gt_image.permute(1, 2, 0).cpu().numpy())
# cv.waitKey(0)
if self.loss_weight['l1'] > 0.:
l1_loss = torch.abs(image - gt_image).mean()
total_loss += self.loss_weight['l1'] * l1_loss
batch_losses.update({
'l1_loss': l1_loss.item()
})
if self.loss_weight.get('mask', 0.) and 'mask_map' in render_output:
rendered_mask = render_output['mask_map'].squeeze(-1) * boundary_mask_img
gt_mask = mask_img * boundary_mask_img
# cv.imshow('rendered_mask', rendered_mask.detach().cpu().numpy())
# cv.imshow('gt_mask', gt_mask.detach().cpu().numpy())
# cv.waitKey(0)
mask_loss = torch.abs(rendered_mask - gt_mask).mean()
# mask_loss = torch.nn.BCELoss()(rendered_mask, gt_mask)
total_loss += self.loss_weight.get('mask', 0.) * mask_loss
batch_losses.update({
'mask_loss': mask_loss.item()
})
if self.loss_weight['lpips'] > 0.:
# crop images
random_patch_flag = False if self.iter_idx < 300000 else True
image, gt_image = self.crop_image(mask_img, self.patch_size, random_patch_flag, image, gt_image)
# cv.imshow('image', image.detach().permute(1, 2, 0).cpu().numpy())
# cv.imshow('gt_image', gt_image.permute(1, 2, 0).cpu().numpy())
# cv.waitKey(0)
lpips_loss = self.compute_lpips_loss(image, gt_image)
total_loss += self.loss_weight['lpips'] * lpips_loss
batch_losses.update({
'lpips_loss': lpips_loss.item()
})
# if self.loss_weight['offset'] > 0.:
if True:
offset_loss = torch.linalg.norm(offset, dim = -1).mean()
total_loss += self.loss_weight['offset'] * offset_loss
batch_losses.update({
'offset_loss': offset_loss.item()
})
# forward_end.record()
# backward_start.record()
total_loss.backward()
# backward_end.record()
# step_start.record()
self.optm.step()
self.optm.zero_grad()
# step_end.record()
# torch.cuda.synchronize()
# print(f'Forward costs: {forward_start.elapsed_time(forward_end) / 1000.}, ',
# f'Backward costs: {backward_start.elapsed_time(backward_end) / 1000.}, ',
# f'Step costs: {step_start.elapsed_time(step_end) / 1000.}')
return total_loss, batch_losses
def pretrain(self):
dataset_module = self.opt['train'].get('dataset', 'MvRgbDatasetAvatarReX')
MvRgbDataset = importlib.import_module('dataset.dataset_mv_rgb').__getattribute__(dataset_module)
self.dataset = MvRgbDataset(**self.opt['train']['data'])
batch_size = self.opt['train']['batch_size']
num_workers = self.opt['train']['num_workers']
batch_num = len(self.dataset) // batch_size
dataloader = torch.utils.data.DataLoader(self.dataset,
batch_size = batch_size,
shuffle = True,
num_workers = num_workers,
drop_last = True)
# tb writer
log_dir = self.opt['train']['net_ckpt_dir'] + '/' + datetime.datetime.now().strftime('pretrain_%Y_%m_%d_%H_%M_%S')
writer = SummaryWriter(log_dir)
smooth_interval = 10
smooth_count = 0
smooth_losses = {}
for epoch_idx in range(0, 9999999):
self.epoch_idx = epoch_idx
for batch_idx, items in enumerate(dataloader):
self.iter_idx = batch_idx + epoch_idx * batch_num
items = to_cuda(items)
# one_step_start.record()
total_loss, batch_losses = self.forward_one_pass_pretrain(items)
# one_step_end.record()
# torch.cuda.synchronize()
# print('One step costs %f secs' % (one_step_start.elapsed_time(one_step_end) / 1000.))
# record batch loss
for key, loss in batch_losses.items():
if key in smooth_losses:
smooth_losses[key] += loss
else:
smooth_losses[key] = loss
smooth_count += 1
if self.iter_idx % smooth_interval == 0:
log_info = 'epoch %d, batch %d, iter %d, ' % (epoch_idx, batch_idx, self.iter_idx)
for key in smooth_losses.keys():
smooth_losses[key] /= smooth_count
writer.add_scalar('%s/Iter' % key, smooth_losses[key], self.iter_idx)
log_info = log_info + ('%s: %f, ' % (key, smooth_losses[key]))
smooth_losses[key] = 0.
smooth_count = 0
print(log_info)
with open(os.path.join(log_dir, 'loss.txt'), 'a') as fp:
fp.write(log_info + '\n')
if self.iter_idx % 200 == 0 and self.iter_idx != 0:
self.mini_test(pretraining = True)
if self.iter_idx == 5000:
model_folder = self.opt['train']['net_ckpt_dir'] + '/pretrained'
os.makedirs(model_folder, exist_ok = True)
self.save_ckpt(model_folder, save_optm = True)
self.iter_idx = 0
return
def train(self):
dataset_module = self.opt['train'].get('dataset', 'MvRgbDatasetAvatarReX')
MvRgbDataset = importlib.import_module('dataset.dataset_mv_rgb').__getattribute__(dataset_module)
self.dataset = MvRgbDataset(**self.opt['train']['data'])
batch_size = self.opt['train']['batch_size']
num_workers = self.opt['train']['num_workers']
batch_num = len(self.dataset) // batch_size
dataloader = torch.utils.data.DataLoader(self.dataset,
batch_size = batch_size,
shuffle = True,
num_workers = num_workers,
drop_last = True)
if 'lpips' in self.opt['train']['loss_weight']:
self.lpips = LPIPS(net = 'vgg').to(config.device)
for p in self.lpips.parameters():
p.requires_grad = False
if self.opt['train']['prev_ckpt'] is not None:
start_epoch, self.iter_idx = self.load_ckpt(self.opt['train']['prev_ckpt'], load_optm = True)
start_epoch += 1
self.iter_idx += 1
else:
prev_ckpt_path = self.opt['train']['net_ckpt_dir'] + '/epoch_latest'
if safe_exists(prev_ckpt_path):
start_epoch, self.iter_idx = self.load_ckpt(prev_ckpt_path, load_optm = True)
start_epoch += 1
self.iter_idx += 1
else:
if safe_exists(self.opt['train']['pretrained_dir']):
self.load_ckpt(self.opt['train']['pretrained_dir'], load_optm = False)
elif safe_exists(self.opt['train']['net_ckpt_dir'] + '/pretrained'):
self.load_ckpt(self.opt['train']['net_ckpt_dir'] + '/pretrained', load_optm = False)
else:
raise FileNotFoundError('Cannot find pretrained checkpoint!')
self.optm.state = collections.defaultdict(dict)
start_epoch = 0
self.iter_idx = 0
# one_step_start = torch.cuda.Event(enable_timing = True)
# one_step_end = torch.cuda.Event(enable_timing = True)
# tb writer
log_dir = self.opt['train']['net_ckpt_dir'] + '/' + datetime.datetime.now().strftime('%Y_%m_%d_%H_%M_%S')
writer = SummaryWriter(log_dir)
yaml.dump(self.opt, open(log_dir + '/config_bk.yaml', 'w'), sort_keys = False)
smooth_interval = 10
smooth_count = 0
smooth_losses = {}
for epoch_idx in range(start_epoch, 9999999):
self.epoch_idx = epoch_idx
for batch_idx, items in enumerate(dataloader):
lr = self.update_lr()
items = to_cuda(items)
# one_step_start.record()
total_loss, batch_losses = self.forward_one_pass(items)
# one_step_end.record()
# torch.cuda.synchronize()
# print('One step costs %f secs' % (one_step_start.elapsed_time(one_step_end) / 1000.))
# record batch loss
for key, loss in batch_losses.items():
if key in smooth_losses:
smooth_losses[key] += loss
else:
smooth_losses[key] = loss
smooth_count += 1
if self.iter_idx % smooth_interval == 0:
log_info = 'epoch %d, batch %d, iter %d, lr %e, ' % (epoch_idx, batch_idx, self.iter_idx, lr)
for key in smooth_losses.keys():
smooth_losses[key] /= smooth_count
writer.add_scalar('%s/Iter' % key, smooth_losses[key], self.iter_idx)
log_info = log_info + ('%s: %f, ' % (key, smooth_losses[key]))
smooth_losses[key] = 0.
smooth_count = 0
print(log_info)
with open(os.path.join(log_dir, 'loss.txt'), 'a') as fp:
fp.write(log_info + '\n')
torch.cuda.empty_cache()
if self.iter_idx % self.opt['train']['eval_interval'] == 0 and self.iter_idx != 0:
if self.iter_idx % (10 * self.opt['train']['eval_interval']) == 0:
eval_cano_pts = True
else:
eval_cano_pts = False
self.mini_test(eval_cano_pts = eval_cano_pts)
if self.iter_idx % self.opt['train']['ckpt_interval']['batch'] == 0 and self.iter_idx != 0:
for folder in glob.glob(self.opt['train']['net_ckpt_dir'] + '/batch_*'):
shutil.rmtree(folder)
model_folder = self.opt['train']['net_ckpt_dir'] + '/batch_%d' % self.iter_idx
os.makedirs(model_folder, exist_ok = True)
self.save_ckpt(model_folder, save_optm = True)
if self.iter_idx == self.iter_num:
print('# Training is done.')
return
self.iter_idx += 1
""" End of epoch """
if epoch_idx % self.opt['train']['ckpt_interval']['epoch'] == 0 and epoch_idx != 0:
model_folder = self.opt['train']['net_ckpt_dir'] + '/epoch_%d' % epoch_idx
os.makedirs(model_folder, exist_ok = True)
self.save_ckpt(model_folder)
if batch_num > 50:
latest_folder = self.opt['train']['net_ckpt_dir'] + '/epoch_latest'
os.makedirs(latest_folder, exist_ok = True)
self.save_ckpt(latest_folder)
@torch.no_grad()
def mini_test(self, pretraining = False, eval_cano_pts = False):
self.avatar_net.eval()
img_factor = self.opt['train'].get('eval_img_factor', 1.0)
# training data
pose_idx, view_idx = self.opt['train'].get('eval_training_ids', (310, 19))
intr = self.dataset.intr_mats[view_idx].copy()
intr[:2] *= img_factor
item = self.dataset.getitem(0,
pose_idx = pose_idx,
view_idx = view_idx,
training = False,
eval = True,
img_h = int(self.dataset.img_heights[view_idx] * img_factor),
img_w = int(self.dataset.img_widths[view_idx] * img_factor),
extr = self.dataset.extr_mats[view_idx],
intr = intr,
exact_hand_pose = True)
items = net_util.to_cuda(item, add_batch = False)
gs_render = self.avatar_net.render(items, self.bg_color)
# gs_render = self.avatar_net.render_debug(items)
rgb_map = gs_render['rgb_map']
rgb_map.clip_(0., 1.)
rgb_map = (rgb_map.cpu().numpy() * 255).astype(np.uint8)
# cv.imshow('rgb_map', rgb_map.cpu().numpy())
# cv.waitKey(0)
if not pretraining:
output_dir = self.opt['train']['net_ckpt_dir'] + '/eval/training'
else:
output_dir = self.opt['train']['net_ckpt_dir'] + '/eval_pretrain/training'
gt_image, _ = self.dataset.load_color_mask_images(pose_idx, view_idx)
if gt_image is not None:
gt_image = cv.resize(gt_image, (0, 0), fx = img_factor, fy = img_factor)
rgb_map = np.concatenate([rgb_map, gt_image], 1)
os.makedirs(output_dir, exist_ok = True)
cv.imwrite(output_dir + '/iter_%d.jpg' % self.iter_idx, rgb_map)
if eval_cano_pts:
os.makedirs(output_dir + '/cano_pts', exist_ok = True)
save_mesh_as_ply(output_dir + '/cano_pts/iter_%d.ply' % self.iter_idx, (self.avatar_net.init_points + gs_render['offset']).cpu().numpy())
# training data
pose_idx, view_idx = self.opt['train'].get('eval_testing_ids', (310, 19))
intr = self.dataset.intr_mats[view_idx].copy()
intr[:2] *= img_factor
item = self.dataset.getitem(0,
pose_idx = pose_idx,
view_idx = view_idx,
training = False,
eval = True,
img_h = int(self.dataset.img_heights[view_idx] * img_factor),
img_w = int(self.dataset.img_widths[view_idx] * img_factor),
extr = self.dataset.extr_mats[view_idx],
intr = intr,
exact_hand_pose = True)
items = net_util.to_cuda(item, add_batch = False)
gs_render = self.avatar_net.render(items, bg_color = self.bg_color)
# gs_render = self.avatar_net.render_debug(items)
rgb_map = gs_render['rgb_map']
rgb_map.clip_(0., 1.)
rgb_map = (rgb_map.cpu().numpy() * 255).astype(np.uint8)
# cv.imshow('rgb_map', rgb_map.cpu().numpy())
# cv.waitKey(0)
if not pretraining:
output_dir = self.opt['train']['net_ckpt_dir'] + '/eval/testing'
else:
output_dir = self.opt['train']['net_ckpt_dir'] + '/eval_pretrain/testing'
gt_image, _ = self.dataset.load_color_mask_images(pose_idx, view_idx)
if gt_image is not None:
gt_image = cv.resize(gt_image, (0, 0), fx = img_factor, fy = img_factor)
rgb_map = np.concatenate([rgb_map, gt_image], 1)
os.makedirs(output_dir, exist_ok = True)
cv.imwrite(output_dir + '/iter_%d.jpg' % self.iter_idx, rgb_map)
if eval_cano_pts:
os.makedirs(output_dir + '/cano_pts', exist_ok = True)
save_mesh_as_ply(output_dir + '/cano_pts/iter_%d.ply' % self.iter_idx, (self.avatar_net.init_points + gs_render['offset']).cpu().numpy())
self.avatar_net.train()
@torch.no_grad()
def test(self):
self.avatar_net.eval()
dataset_module = self.opt['train'].get('dataset', 'MvRgbDatasetAvatarReX')
MvRgbDataset = importlib.import_module('dataset.dataset_mv_rgb').__getattribute__(dataset_module)
training_dataset = MvRgbDataset(**self.opt['train']['data'], training = False)
if self.opt['test'].get('n_pca', -1) >= 1:
training_dataset.compute_pca(n_components = self.opt['test']['n_pca'])
if 'pose_data' in self.opt['test']:
testing_dataset = PoseDataset(**self.opt['test']['pose_data'], smpl_shape = training_dataset.smpl_data['betas'][0])
dataset_name = testing_dataset.dataset_name
seq_name = testing_dataset.seq_name
else:
testing_dataset = MvRgbDataset(**self.opt['test']['data'], training = False)
dataset_name = 'training'
seq_name = ''
self.opt['test']['n_pca'] = -1 # cancel PCA for training pose reconstruction
self.dataset = testing_dataset
iter_idx = self.load_ckpt(self.opt['test']['prev_ckpt'], False)[1]
output_dir = self.opt['test'].get('output_dir', None)
if output_dir is None:
view_setting = config.opt['test'].get('view_setting', 'free')
if view_setting == 'camera':
view_folder = 'cam_%03d' % config.opt['test']['render_view_idx']
else:
view_folder = view_setting + '_view'
exp_name = os.path.basename(os.path.dirname(self.opt['test']['prev_ckpt']))
output_dir = f'./test_results/{training_dataset.subject_name}/{exp_name}/{dataset_name}_{seq_name}_{view_folder}' + '/batch_%06d' % iter_idx
use_pca = self.opt['test'].get('n_pca', -1) >= 1
if use_pca:
output_dir += '/pca_%d_sigma_%.2f' % (self.opt['test'].get('n_pca', -1), float(self.opt['test'].get('sigma_pca', 1.)))
else:
output_dir += '/vanilla'
print('# Output dir: \033[1;31m%s\033[0m' % output_dir)
os.makedirs(output_dir + '/live_skeleton', exist_ok = True)
os.makedirs(output_dir + '/rgb_map', exist_ok = True)
os.makedirs(output_dir + '/mask_map', exist_ok = True)
geo_renderer = None
item_0 = self.dataset.getitem(0, training = False)
object_center = item_0['live_bounds'].mean(0)
global_orient = item_0['global_orient'].cpu().numpy() if isinstance(item_0['global_orient'], torch.Tensor) else item_0['global_orient']
global_orient = cv.Rodrigues(global_orient)[0]
# print('object_center: ', object_center.tolist())
# print('global_orient: ', global_orient.tolist())
# # exit(1)
time_start = torch.cuda.Event(enable_timing = True)
time_start_all = torch.cuda.Event(enable_timing = True)
time_end = torch.cuda.Event(enable_timing = True)
data_num = len(self.dataset)
if self.opt['test'].get('fix_hand', False):
self.avatar_net.generate_mean_hands()
log_time = False
for idx in tqdm(range(data_num), desc = 'Rendering avatars...'):
if log_time:
time_start.record()
time_start_all.record()
img_scale = self.opt['test'].get('img_scale', 1.0)
view_setting = config.opt['test'].get('view_setting', 'free')
if view_setting == 'camera':
# training view setting
cam_id = config.opt['test']['render_view_idx']
intr = self.dataset.intr_mats[cam_id].copy()
intr[:2] *= img_scale
extr = self.dataset.extr_mats[cam_id].copy()
img_h, img_w = int(self.dataset.img_heights[cam_id] * img_scale), int(self.dataset.img_widths[cam_id] * img_scale)
elif view_setting.startswith('free'):
# free view setting
# frame_num_per_circle = 360
frame_num_per_circle = 216
rot_Y = (idx % frame_num_per_circle) / float(frame_num_per_circle) * 2 * np.pi
extr = visualize_util.calc_free_mv(object_center,
tar_pos = np.array([0, 0, 2.5]),
rot_Y = rot_Y,
rot_X = 0.3 if view_setting.endswith('bird') else 0.,
global_orient = global_orient if self.opt['test'].get('global_orient', False) else None)
intr = np.array([[1100, 0, 512], [0, 1100, 512], [0, 0, 1]], np.float32)
intr[:2] *= img_scale
img_h = int(1024 * img_scale)
img_w = int(1024 * img_scale)
elif view_setting.startswith('front'):
# front view setting
extr = visualize_util.calc_free_mv(object_center,
tar_pos = np.array([0, 0, 2.5]),
rot_Y = 0.,
rot_X = 0.3 if view_setting.endswith('bird') else 0.,
global_orient = global_orient if self.opt['test'].get('global_orient', False) else None)
intr = np.array([[1100, 0, 512], [0, 1100, 512], [0, 0, 1]], np.float32)
intr[:2] *= img_scale
img_h = int(1024 * img_scale)
img_w = int(1024 * img_scale)
elif view_setting.startswith('back'):
# back view setting
extr = visualize_util.calc_free_mv(object_center,
tar_pos = np.array([0, 0, 2.5]),
rot_Y = np.pi,
rot_X = 0.5 * np.pi / 4. if view_setting.endswith('bird') else 0.,
global_orient = global_orient if self.opt['test'].get('global_orient', False) else None)
intr = np.array([[1100, 0, 512], [0, 1100, 512], [0, 0, 1]], np.float32)
intr[:2] *= img_scale
img_h = int(1024 * img_scale)
img_w = int(1024 * img_scale)
elif view_setting.startswith('moving'):
# moving camera setting
extr = visualize_util.calc_free_mv(object_center,
# tar_pos = np.array([0, 0, 3.0]),
# rot_Y = -0.3,
tar_pos = np.array([0, 0, 2.5]),
rot_Y = 0.,
rot_X = 0.3 if view_setting.endswith('bird') else 0.,
global_orient = global_orient if self.opt['test'].get('global_orient', False) else None)
intr = np.array([[1100, 0, 512], [0, 1100, 512], [0, 0, 1]], np.float32)
intr[:2] *= img_scale
img_h = int(1024 * img_scale)
img_w = int(1024 * img_scale)
elif view_setting.startswith('cano'):
cano_center = self.dataset.cano_bounds.mean(0)
extr = np.identity(4, np.float32)
extr[:3, 3] = -cano_center
rot_x = np.identity(4, np.float32)
rot_x[:3, :3] = cv.Rodrigues(np.array([np.pi, 0, 0], np.float32))[0]
extr = rot_x @ extr
f_len = 5000
extr[2, 3] += f_len / 512
intr = np.array([[f_len, 0, 512], [0, f_len, 512], [0, 0, 1]], np.float32)
# item = self.dataset.getitem(idx,
# training = False,
# extr = extr,
# intr = intr,
# img_w = 1024,
# img_h = 1024)
img_w, img_h = 1024, 1024
# item['live_smpl_v'] = item['cano_smpl_v']
# item['cano2live_jnt_mats'] = torch.eye(4, dtype = torch.float32)[None].expand(item['cano2live_jnt_mats'].shape[0], -1, -1)
# item['live_bounds'] = item['cano_bounds']
else:
raise ValueError('Invalid view setting for animation!')
getitem_func = self.dataset.getitem_fast if hasattr(self.dataset, 'getitem_fast') else self.dataset.getitem
item = getitem_func(
idx,
training = False,
extr = extr,
intr = intr,
img_w = img_w,
img_h = img_h
)
items = to_cuda(item, add_batch = False)
if view_setting.startswith('moving') or view_setting == 'free_moving':
current_center = items['live_bounds'].cpu().numpy().mean(0)
delta = current_center - object_center
object_center[0] += delta[0]
# object_center[1] += delta[1]
# object_center[2] += delta[2]
if log_time:
time_end.record()
torch.cuda.synchronize()
print('Loading data costs %.4f secs' % (time_start.elapsed_time(time_end) / 1000.))
time_start.record()
if self.opt['test'].get('render_skeleton', False):
from utils.visualize_skeletons import construct_skeletons
skel_vertices, skel_faces = construct_skeletons(item['joints'].cpu().numpy(), item['kin_parent'].cpu().numpy())
skel_mesh = trimesh.Trimesh(skel_vertices, skel_faces, process = False)
if geo_renderer is None:
geo_renderer = Renderer(item['img_w'], item['img_h'], shader_name = 'phong_geometry', bg_color = (1, 1, 1))
extr, intr = item['extr'], item['intr']
geo_renderer.set_camera(extr, intr)
geo_renderer.set_model(skel_vertices[skel_faces.reshape(-1)], skel_mesh.vertex_normals.astype(np.float32)[skel_faces.reshape(-1)])
skel_img = geo_renderer.render()[:, :, :3]
skel_img = (skel_img * 255).astype(np.uint8)
cv.imwrite(output_dir + '/live_skeleton/%08d.jpg' % item['data_idx'], skel_img)
if log_time:
time_end.record()
torch.cuda.synchronize()
print('Rendering skeletons costs %.4f secs' % (time_start.elapsed_time(time_end) / 1000.))
time_start.record()
if 'smpl_pos_map' not in items:
self.avatar_net.get_pose_map(items)
# pca
if use_pca:
mask = training_dataset.pos_map_mask
live_pos_map = items['smpl_pos_map'].permute(1, 2, 0).cpu().numpy()
front_live_pos_map, back_live_pos_map = np.split(live_pos_map, [3], 2)
pose_conds = front_live_pos_map[mask]
new_pose_conds = training_dataset.transform_pca(pose_conds, sigma_pca = float(self.opt['test'].get('sigma_pca', 2.)))
front_live_pos_map[mask] = new_pose_conds
live_pos_map = np.concatenate([front_live_pos_map, back_live_pos_map], 2)
items.update({
'smpl_pos_map_pca': torch.from_numpy(live_pos_map).to(config.device).permute(2, 0, 1)
})
if log_time:
time_end.record()
torch.cuda.synchronize()
print('Rendering pose conditions costs %.4f secs' % (time_start.elapsed_time(time_end) / 1000.))
time_start.record()
output = self.avatar_net.render(items, bg_color = self.bg_color, use_pca = use_pca)
if log_time:
time_end.record()
torch.cuda.synchronize()
print('Rendering avatar costs %.4f secs' % (time_start.elapsed_time(time_end) / 1000.))
time_start.record()
rgb_map = output['rgb_map']
rgb_map.clip_(0., 1.)
rgb_map = (rgb_map * 255).to(torch.uint8).cpu().numpy()
cv.imwrite(output_dir + '/rgb_map/%08d.jpg' % item['data_idx'], rgb_map)
if 'mask_map' in output:
os.makedirs(output_dir + '/mask_map', exist_ok = True)
mask_map = output['mask_map'][:, :, 0]
mask_map.clip_(0., 1.)
mask_map = (mask_map * 255).to(torch.uint8)
cv.imwrite(output_dir + '/mask_map/%08d.png' % item['data_idx'], mask_map.cpu().numpy())
if self.opt['test'].get('save_tex_map', False):
os.makedirs(output_dir + '/cano_tex_map', exist_ok = True)
cano_tex_map = output['cano_tex_map']
cano_tex_map.clip_(0., 1.)
cano_tex_map = (cano_tex_map * 255).to(torch.uint8)
cv.imwrite(output_dir + '/cano_tex_map/%08d.jpg' % item['data_idx'], cano_tex_map.cpu().numpy())
if self.opt['test'].get('save_ply', False):
save_gaussians_as_ply(output_dir + '/posed_gaussians/%08d.ply' % item['data_idx'], output['posed_gaussians'])
if log_time:
time_end.record()
torch.cuda.synchronize()
print('Saving images costs %.4f secs' % (time_start.elapsed_time(time_end) / 1000.))
print('Animating one frame costs %.4f secs' % (time_start_all.elapsed_time(time_end) / 1000.))
torch.cuda.empty_cache()
def save_ckpt(self, path, save_optm = True):
os.makedirs(path, exist_ok = True)
net_dict = {
'epoch_idx': self.epoch_idx,
'iter_idx': self.iter_idx,
'avatar_net': self.avatar_net.state_dict(),
}
print('Saving networks to ', path + '/net.pt')
torch.save(net_dict, path + '/net.pt')
if save_optm:
optm_dict = {
'avatar_net': self.optm.state_dict(),
}
print('Saving optimizers to ', path + '/optm.pt')
torch.save(optm_dict, path + '/optm.pt')
def load_ckpt(self, path, load_optm = True):
print('Loading networks from ', path + '/net.pt')
net_dict = torch.load(path + '/net.pt')
if 'avatar_net' in net_dict:
self.avatar_net.load_state_dict(net_dict['avatar_net'])
else:
print('[WARNING] Cannot find "avatar_net" from the network checkpoint!')
epoch_idx = net_dict['epoch_idx']
iter_idx = net_dict['iter_idx']
if load_optm and os.path.exists(path + '/optm.pt'):
print('Loading optimizers from ', path + '/optm.pt')
optm_dict = torch.load(path + '/optm.pt')
if 'avatar_net' in optm_dict:
self.optm.load_state_dict(optm_dict['avatar_net'])
else:
print('[WARNING] Cannot find "avatar_net" from the optimizer checkpoint!')
return epoch_idx, iter_idx
if __name__ == '__main__':
torch.manual_seed(31359)
np.random.seed(31359)
# torch.autograd.set_detect_anomaly(True)
from argparse import ArgumentParser
arg_parser = ArgumentParser()
arg_parser.add_argument('-c', '--config_path', type = str, help = 'Configuration file path.')
arg_parser.add_argument('-m', '--mode', type = str, help = 'Running mode.', default = 'train')
args = arg_parser.parse_args()
config.load_global_opt(args.config_path)
if args.mode is not None:
config.opt['mode'] = args.mode
trainer = AvatarTrainer(config.opt)
if config.opt['mode'] == 'train':
if not safe_exists(config.opt['train']['net_ckpt_dir'] + '/pretrained') \
and not safe_exists(config.opt['train']['pretrained_dir'])\
and not safe_exists(config.opt['train']['prev_ckpt']):
trainer.pretrain()
trainer.train()
elif config.opt['mode'] == 'test':
trainer.test()
else:
raise NotImplementedError('Invalid running mode!')