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visualize_inference.py
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# Copyright (c) 2020-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# NVIDIA CORPORATION, its affiliates and licensors retain all intellectual
# property and proprietary rights in and to this material, related
# documentation and any modifications thereto. Any use, reproduction,
# disclosure or distribution of this material and related documentation
# without an express license agreement from NVIDIA CORPORATION or
# its affiliates is strictly prohibited.
import os
import time
import json
import logging
import os
import random
import numpy as np
import torch
import nvdiffrast.torch as dr
import xatlas
import hydra
from omegaconf import OmegaConf, ListConfig, DictConfig
import wandb
import lpips
from tqdm import tqdm
# Import data readers / generators
# from dataset.dataset_mesh import DatasetMesh
# from dataset.dataset_nerf import DatasetNERF
# from dataset.dataset_llff import DatasetLLFF
# from dataset.dataset_zju_mocap import DatasetZJUMocap
# Import topology / geometry trainers
from geometry.dmtet_dyn import DMTetGeometryDyn
from geometry.dlmesh_dyn import DLMeshDyn
from geometry.vectoradam import VectorAdam
import render.renderutils as ru
from render import obj
from render import material
from render import util
from render import mesh
from render import texture
from render import mlptexture
from render import light
from render import render
from utils import training
from flask import Flask, Response
import cv2
RADIUS = 3.0
LOGGER = logging.getLogger(__name__)
print = LOGGER.info
app = Flask(__name__)
# Enable to debug back-prop anomalies
# torch.autograd.set_detect_anomaly(True)
###############################################################################
# Loss setup
###############################################################################
@torch.no_grad()
def createLoss(FLAGS):
if FLAGS.loss == "smape":
return lambda img, ref: ru.image_loss(img, ref, loss='smape', tonemapper='none')
elif FLAGS.loss == "mse":
return lambda img, ref: ru.image_loss(img, ref, loss='mse', tonemapper='none')
elif FLAGS.loss == "logl1":
return lambda img, ref: ru.image_loss(img, ref, loss='l1', tonemapper='log_srgb')
elif FLAGS.loss == "logl2":
return lambda img, ref: ru.image_loss(img, ref, loss='mse', tonemapper='log_srgb')
elif FLAGS.loss == "relmse":
return lambda img, ref: ru.image_loss(img, ref, loss='relmse', tonemapper='none')
else:
assert False
###############################################################################
# Mix background into a dataset image
###############################################################################
@torch.no_grad()
def prepare_batch(target, bg_type='black'):
batch_size = target['mv'].shape[0]
resolution = target['resolution']
background_resolution = (batch_size, *resolution, 3)
if bg_type == 'checker':
background = torch.tensor(util.checkerboard(resolution, 8), dtype=torch.float32, device='cuda')[None, ...]
elif bg_type == 'black':
background = torch.zeros(background_resolution, dtype=torch.float32, device='cuda')
elif bg_type == 'white':
background = torch.ones(background_resolution, dtype=torch.float32, device='cuda')
elif bg_type == 'grey':
background = torch.ones(background_resolution, dtype=torch.float32, device='cuda') * 0.5
elif bg_type == 'reference':
if target["img"] is None:
raise ValueError("Set `no_image_input` to False")
background = target['img'][..., 0:3]
elif bg_type == 'random':
background = torch.rand(background_resolution, dtype=torch.float32, device='cuda')
else:
assert False, "Unknown background type %s" % bg_type
target['mv'] = target['mv'].cuda()
target['mvp'] = target['mvp'].cuda()
target['campos'] = target['campos'].cuda()
target['background'] = background
if target['img'] is not None:
assert len(target['img'].shape) == 4, "Image shape should be [n, h, w, c]"
target['img'] = target['img'].cuda()
target['img'] = torch.cat((torch.lerp(background, target['img'][..., 0:3], target['img'][..., 3:4]), target['img'][..., 3:4]), dim=-1)
if 'params' in target:
target['params'] = target['params'].cuda()
return target
###############################################################################
# UV - map geometry & convert to a mesh
###############################################################################
@torch.no_grad()
def xatlas_uvmap(glctx, geometry, mat, FLAGS):
eval_mesh = geometry.getMesh(mat)
# Create uvs with xatlas
v_pos = eval_mesh.v_pos.detach().cpu().numpy()
t_pos_idx = eval_mesh.t_pos_idx.detach().cpu().numpy()
vmapping, indices, uvs = xatlas.parametrize(v_pos, t_pos_idx)
# Convert to tensors
indices_int64 = indices.astype(np.uint64, casting='same_kind').view(np.int64)
uvs = torch.tensor(uvs, dtype=torch.float32, device='cuda')
faces = torch.tensor(indices_int64, dtype=torch.int64, device='cuda')
new_mesh = mesh.Mesh(v_tex=uvs, t_tex_idx=faces, base=eval_mesh)
mask, kd, ks, normal = render.render_uv(glctx, new_mesh, FLAGS.texture_res, eval_mesh.material['kd_ks_normal'])
if FLAGS.layers > 1:
kd = torch.cat((kd, torch.rand_like(kd[...,0:1])), dim=-1)
kd_min, kd_max = torch.tensor(FLAGS.kd_min, dtype=torch.float32, device='cuda'), torch.tensor(FLAGS.kd_max, dtype=torch.float32, device='cuda')
ks_min, ks_max = torch.tensor(FLAGS.ks_min, dtype=torch.float32, device='cuda'), torch.tensor(FLAGS.ks_max, dtype=torch.float32, device='cuda')
nrm_min, nrm_max = torch.tensor(FLAGS.nrm_min, dtype=torch.float32, device='cuda'), torch.tensor(FLAGS.nrm_max, dtype=torch.float32, device='cuda')
new_mesh.material = material.Material({
'bsdf' : mat['bsdf'],
'kd' : texture.Texture2D(kd, min_max=[kd_min, kd_max]),
'ks' : texture.Texture2D(ks, min_max=[ks_min, ks_max]),
'normal' : texture.Texture2D(normal, min_max=[nrm_min, nrm_max])
})
return new_mesh
@torch.no_grad()
def mesh_uvmap(glctx, geometry, mat, FLAGS):
new_mesh = geometry.getMesh(mat)
mask, kd, ks, normal = render.render_uv(glctx, new_mesh, FLAGS.texture_res, new_mesh.material['kd_ks_normal'])
if FLAGS.layers > 1:
kd = torch.cat((kd, torch.rand_like(kd[...,0:1])), dim=-1)
kd_min, kd_max = torch.tensor(FLAGS.kd_min, dtype=torch.float32, device='cuda'), torch.tensor(FLAGS.kd_max, dtype=torch.float32, device='cuda')
ks_min, ks_max = torch.tensor(FLAGS.ks_min, dtype=torch.float32, device='cuda'), torch.tensor(FLAGS.ks_max, dtype=torch.float32, device='cuda')
nrm_min, nrm_max = torch.tensor(FLAGS.nrm_min, dtype=torch.float32, device='cuda'), torch.tensor(FLAGS.nrm_max, dtype=torch.float32, device='cuda')
new_mesh.material = material.Material({
'bsdf' : mat['bsdf'],
'kd' : texture.Texture2D(kd, min_max=[kd_min, kd_max]),
'ks' : texture.Texture2D(ks, min_max=[ks_min, ks_max]),
'normal' : texture.Texture2D(normal, min_max=[nrm_min, nrm_max])
})
return new_mesh
###############################################################################
# Utility functions for material
###############################################################################
def initial_guess_material(geometry, mlp, FLAGS, init_mat=None):
kd_min, kd_max = torch.tensor(FLAGS.kd_min, dtype=torch.float32, device='cuda'), torch.tensor(FLAGS.kd_max, dtype=torch.float32, device='cuda')
ks_min, ks_max = torch.tensor(FLAGS.ks_min, dtype=torch.float32, device='cuda'), torch.tensor(FLAGS.ks_max, dtype=torch.float32, device='cuda')
nrm_min, nrm_max = torch.tensor(FLAGS.nrm_min, dtype=torch.float32, device='cuda'), torch.tensor(FLAGS.nrm_max, dtype=torch.float32, device='cuda')
if mlp:
mlp_min = torch.cat((kd_min[0:3], ks_min, nrm_min), dim=0)
mlp_max = torch.cat((kd_max[0:3], ks_max, nrm_max), dim=0)
mlp_map_opt = mlptexture.MLPTexture3D(geometry.getAABB(), channels=9, min_max=[mlp_min, mlp_max], internal_dims=FLAGS.mlp_texture_internal_dims, FLAGS=FLAGS)
mat = material.Material({'kd_ks_normal' : mlp_map_opt})
else:
# Setup Kd (albedo) and Ks (x, roughness, metalness) textures
texture_res = list(FLAGS.texture_res)
if FLAGS.random_textures or init_mat is None:
num_channels = 4 if FLAGS.layers > 1 else 3
kd_init = torch.rand(size=texture_res + [num_channels], device='cuda') * (kd_max - kd_min)[None, None, 0:num_channels] + kd_min[None, None, 0:num_channels]
kd_map_opt = texture.create_trainable(kd_init , texture_res, not FLAGS.custom_mip, [kd_min, kd_max])
ksR = np.random.uniform(size=texture_res + [1], low=0.0, high=0.01)
ksG = np.random.uniform(size=texture_res + [1], low=ks_min[1].cpu(), high=ks_max[1].cpu())
ksB = np.random.uniform(size=texture_res + [1], low=ks_min[2].cpu(), high=ks_max[2].cpu())
ks_map_opt = texture.create_trainable(np.concatenate((ksR, ksG, ksB), axis=2), texture_res, not FLAGS.custom_mip, [ks_min, ks_max])
else:
kd_map_opt = texture.create_trainable(init_mat['kd'], texture_res, not FLAGS.custom_mip, [kd_min, kd_max])
ks_map_opt = texture.create_trainable(init_mat['ks'], texture_res, not FLAGS.custom_mip, [ks_min, ks_max])
# Setup normal map
if FLAGS.random_textures or init_mat is None or 'normal' not in init_mat:
normal_map_opt = texture.create_trainable(np.array([0, 0, 1]), texture_res, not FLAGS.custom_mip, [nrm_min, nrm_max])
else:
normal_map_opt = texture.create_trainable(init_mat['normal'], texture_res, not FLAGS.custom_mip, [nrm_min, nrm_max])
mat = material.Material({
'kd' : kd_map_opt,
'ks' : ks_map_opt,
'normal' : normal_map_opt
})
if init_mat is not None:
mat['bsdf'] = init_mat['bsdf']
else:
mat['bsdf'] = 'pbr'
mat["no_perturbed_nrm"] = FLAGS.no_perturbed_nrm
print(f"no_perturbed_nrm: {FLAGS.no_perturbed_nrm}")
return mat
###############################################################################
# Validation & testing
###############################################################################
def validate_itr(glctx, target, geometry: DMTetGeometryDyn, opt_material, lgt, FLAGS, update_base_mesh=False, build_mips=True):
result_dict = {}
extra_dict = {}
with torch.no_grad():
if build_mips:
lgt.build_mips()
if FLAGS.camera_space_light:
lgt.xfm(target['mv'])
if hasattr(geometry, 'update_base_mesh') and update_base_mesh:
LOGGER.debug("Updating base mesh in `validate_itr`")
geometry.update_base_mesh()
# LOGGER.debug("Updating base mesh in `validate_itr`")
# geometry.update_base_mesh() # [NOTE] unconment this line, the the iter per sec of outer loop rushes to 100+; match the FPS metrics
start = torch.cuda.Event(enable_timing=True)
end = torch.cuda.Event(enable_timing=True)
start.record()
render_start_time = time.time()
buffers = geometry.render(glctx, target, lgt, opt_material, return_kd_grad=False, update_base_mesh=False, update_weights=False)
# result_dict['ref'] = util.rgb_to_srgb(target['img'][...,0:3])[0]
# resolution = result_dict['ref'].shape[:2]
result_dict['opt'] = util.rgb_to_srgb(buffers['shaded'][...,0:3])
result_dict['opt'] = result_dict['opt'].flip(-1)
result_dict['opt'] = (torch.clamp(result_dict['opt'], 0.0, 1.0) * 255).byte()
# result_dict['opt_alpha'] = buffers['shaded'][...,3][0]
# result_dict['ref_alpha'] = target['img'][...,3][0]
# result_image = torch.cat([result_dict['opt'], result_dict['ref']], axis=1)
result_image = None
end.record()
# Waits for everything to finish running
torch.cuda.synchronize()
render_time = 1000 * (time.time() - render_start_time)
render_time = start.elapsed_time(end)
render_time_per_frame = render_time / result_dict['opt'].shape[0]
extra_dict["render_time_ms_per_frame"] = render_time_per_frame
extra_dict["fps"] = 1.0 / (render_time_per_frame / 1000.0)
# if FLAGS.display is not None:
# white_bg = torch.ones_like(target['background'])
# for layer in FLAGS.display:
# if 'latlong' in layer and layer['latlong']:
# if isinstance(lgt, light.EnvironmentLight):
# result_dict['light_image'] = util.cubemap_to_latlong(lgt.base, resolution)
# result_image = torch.cat([result_image, result_dict['light_image']], axis=1)
# elif 'relight' in layer:
# if not isinstance(layer['relight'], light.EnvironmentLight):
# layer['relight'] = light.load_env(layer['relight'])
# img = geometry.render(glctx, target, layer['relight'], opt_material, return_kd_grad=False, update_base_mesh=False, update_weights=False)
# result_dict['relight'] = util.rgb_to_srgb(img[..., 0:3])[0]
# result_image = torch.cat([result_image, result_dict['relight']], axis=1)
# elif 'bsdf' in layer:
# buffers = geometry.render(glctx, target, lgt, opt_material, bsdf=layer['bsdf'], return_kd_grad=False, update_base_mesh=False, update_weights=False)
# if layer['bsdf'] == 'kd':
# result_dict[layer['bsdf']] = util.rgb_to_srgb(buffers['shaded'][0, ..., 0:3])
# elif layer['bsdf'] == 'normal':
# result_dict[layer['bsdf']] = (buffers['shaded'][0, ..., 0:3] + 1) * 0.5
# else:
# result_dict[layer['bsdf']] = buffers['shaded'][0, ..., 0:3]
# result_image = torch.cat([result_image, result_dict[layer['bsdf']]], axis=1)
return result_image, result_dict, extra_dict
def validate_visualize(glctx, geometry, opt_material, lgt, dataset_validates, FLAGS):
# ==============================================================================================
# Validation loop
# ==============================================================================================
fps_values = []
visualize_interval = 1 / FLAGS.visualize_freq if FLAGS.visualize_freq is not None else None
for dataset_validate in dataset_validates:
dataloader_validate = torch.utils.data.DataLoader(dataset_validate, batch_size=1, collate_fn=dataset_validate.collate)
if hasattr(geometry, 'update_base_mesh'):
LOGGER.debug("Updating base mesh in `validate`")
geometry.update_base_mesh()
start_visualize = time.time()
for it, target in enumerate(tqdm(dataloader_validate)):
# Mix validation background
target = prepare_batch(target, FLAGS.background)
_, result_dict, extra_dict = validate_itr(glctx, target, geometry, opt_material, lgt, FLAGS, update_base_mesh=False, build_mips=(True if it == 0 else False))
fps_values.append(extra_dict.get('fps', np.nan))
for opt in result_dict['opt'].cpu().numpy():
if visualize_interval is not None:
time.sleep(visualize_interval)
opt_numpy = opt
_, frame = cv2.imencode('.jpg', opt_numpy)
yield (b'--frame\r\nContent-Type: image/jpeg\r\n\r\n' + frame.tobytes() + b'\r\n')
avg_visualize_fps = len(dataloader_validate.dataset) / (time.time() - start_visualize)
avg_render_fps = np.mean(np.array(fps_values))
print(f"Render FPS: {avg_render_fps}")
print(f"Visualize FPS: {avg_visualize_fps}")
return {
'rfps': avg_render_fps,
'vfps': avg_visualize_fps
}
def validate(glctx, geometry, opt_material, lgt, dataset_validate, out_dir, FLAGS):
# ==============================================================================================
# Validation loop
# ==============================================================================================
mse_values = []
psnr_values = []
ssim_values = []
fps_values = []
dataloader_validate = torch.utils.data.DataLoader(dataset_validate, batch_size=1, collate_fn=dataset_validate.collate)
if hasattr(geometry, 'update_base_mesh'):
LOGGER.debug("Updating base mesh in `validate`")
geometry.update_base_mesh()
os.makedirs(out_dir, exist_ok=True)
with open(os.path.join(out_dir, 'metrics.txt'), 'w') as fout:
fout.write('ID, MSE, PSNR, SSIM, FPS\n')
print(f"Running validation @ {out_dir}")
for it, target in enumerate(tqdm(dataloader_validate)):
# Mix validation background
target = prepare_batch(target, FLAGS.background)
result_image, result_dict, extra_dict = validate_itr(glctx, target, geometry, opt_material, lgt, FLAGS, update_base_mesh=False, build_mips=(True if it == 0 else False))
fps_values.append(extra_dict['fps'])
if FLAGS.compute_val_metrics is True:
# Compute metrics
opt = torch.clamp(result_dict['opt'], 0.0, 1.0)
ref = torch.clamp(result_dict['ref'], 0.0, 1.0)
mse = torch.nn.functional.mse_loss(opt, ref, size_average=None, reduce=None, reduction='mean').item()
psnr = util.mse_to_psnr(mse)
ssim = util.compute_ssim(opt, ref)
mse_values.append(float(mse))
psnr_values.append(float(psnr))
ssim_values.append(float(ssim))
line = "%d, %1.8f, %1.8f, %1.8f, %.2f\n" % (it, mse, psnr, ssim, fps_values[-1])
fout.write(str(line))
if FLAGS.save_val_images is True:
for k in result_dict.keys():
np_img = result_dict[k].detach().cpu().numpy()
sub_dir = os.path.join(out_dir, k)
if not os.path.exists(sub_dir):
os.makedirs(sub_dir, exist_ok=True)
util.save_image(sub_dir + '/' + ('val_%06d.png' % it), np_img)
avg_mse = np.mean(np.array(mse_values))
avg_psnr = np.mean(np.array(psnr_values))
avg_ssim = np.mean(np.array(ssim_values))
avg_fps = np.mean(np.array(fps_values))
line = "AVERAGES: %1.4f, %2.6f, %2.6f, %.6f\n" % (avg_mse, avg_psnr, avg_ssim, avg_fps)
fout.write(str(line))
print("MSE, PSNR, SSIM, FPS")
print("%1.8f, %2.6f, %2.6f, %.6f" % (avg_mse, avg_psnr, avg_ssim, avg_fps))
return {
'mse': avg_mse,
'psnr': avg_psnr,
'ssim': avg_ssim,
'fps': avg_fps
}
###############################################################################
# Main shape fitter function / optimization loop
###############################################################################
class Trainer(torch.nn.Module):
def __init__(self, glctx, geometry, lgt, mat, optimize_geometry, optimize_light, image_loss_fn, FLAGS, lpips_loss_fn):
super(Trainer, self).__init__()
self.glctx = glctx
self.geometry = geometry
self.light = lgt
self.material = mat
self.optimize_geometry = optimize_geometry
self.optimize_light = optimize_light
self.image_loss_fn = image_loss_fn
self.FLAGS = FLAGS
self.lpips_loss_fn = lpips_loss_fn
if not self.optimize_light:
with torch.no_grad():
self.light.build_mips()
self.update_params()
def update_params(self):
geometry = self.geometry
optimize_geometry = self.optimize_geometry,
optimize_light = self.optimize_light,
FLAGS = self.FLAGS
self.params = list(self.material.parameters())
self.params += list(self.light.parameters()) if optimize_light else []
self.params = [{"params": self.params, "initial_lr": FLAGS.learning_rate_material}]
self.geo_params = [{"params": params, "initial_lr": FLAGS.learning_rate_geometry} for name, params in geometry.named_parameters() if not name.startswith('skin_net')] if optimize_geometry else []
self.motion_params = [{"params": params, "initial_lr": FLAGS.learning_rate_motion} for params in geometry.skin_net.parameters()] if geometry.skin_net is not None else []
assert len(self.geo_params) + len(self.motion_params) == len(list(geometry.parameters())), f"Geometry parameters not fully optimized: {len(self.geo_params)} + {len(self.motion_params)} != {len(list(geometry.parameters()))}"
print(f"Material & light parameters: {len(self.params)}")
print(f"Geometry parameters: {len(self.geo_params)}")
print(f"Motion parameters: {len(self.motion_params)}")
def forward(self, target, it):
if self.optimize_light:
self.light.build_mips()
if self.FLAGS.camera_space_light:
self.light.xfm(target['mv'])
# Split non rigid net offsets
add_non_rigid_offsets = (it >= self.FLAGS.split_non_rigid_offset_net_optim_steps)
return self.geometry.tick(self.glctx, target, self.light, self.material, self.image_loss_fn, it, self.lpips_loss_fn, add_non_rigid_offsets)
def optimize_mesh(
glctx,
geometry,
opt_material,
lgt,
dataset_train,
dataset_validate,
FLAGS,
warmup_iter=0,
log_interval=10,
pass_idx=0,
pass_name="",
optimize_light=True,
optimize_geometry=True
):
# ==============================================================================================
# Setup torch optimizer
# ==============================================================================================
learning_rate_pos = FLAGS.learning_rate_geometry
learning_rate_mat = FLAGS.learning_rate_material
learning_rate_skn = FLAGS.learning_rate_motion
# if FLAGS.use_training_tricks is True:
def lr_schedule(iter, fraction=(1/(FLAGS.iter - warmup_iter)), learning_rate_final_mult=np.log10(1/FLAGS.learning_rate_final_mult), re_warmup_iters=FLAGS.subdivide_tetmesh_iters, re_warmup_interval=FLAGS.subdivide_learning_rate_warmup_interval, re_warmup_lr_mult=FLAGS.subdivide_learning_rate_mult):
if iter < warmup_iter:
adjust_mult = iter / warmup_iter
else:
# Exponential falloff from [1.0, learning_rate_final_mult] over (iter - warmup_iter) epochs.
adjust_mult = max(0.0, 10**(-(iter - warmup_iter)*fraction*learning_rate_final_mult))
for re_warmup_iter in re_warmup_iters:
if iter >= re_warmup_iter and iter < re_warmup_iter + re_warmup_interval:
adjust_mult *= max((iter - re_warmup_iter) / re_warmup_interval, re_warmup_lr_mult)
return adjust_mult
# print(f"use_training_tricks is True, enabling lr_schedule nd loss weight schedule")
print(f"Exponential falloff of lr from [lr * 1.0, lr * {FLAGS.learning_rate_final_mult}] from {warmup_iter} to {FLAGS.iter}")
# elif FLAGS.use_training_tricks is False:
# def lr_schedule(_):
# return 1.0
# print(f"use_training_tricks is False, disabling lr_schedule and loss weight schedule")
# else:
# raise ValueError(f"Invalid value for FLAGS.use_training_tricks: {FLAGS.use_training_tricks}")
# ==============================================================================================
# Image loss
# ==============================================================================================
image_loss_fn = createLoss(FLAGS)
if FLAGS.get("lpips_regularizer", 0) > 0:
print("Loading LPIPS model")
lpips_loss_fn = lpips.LPIPS(net='vgg').cuda()
else:
lpips_loss_fn = None
trainer_noddp = Trainer(glctx, geometry, lgt, opt_material, optimize_geometry, optimize_light, image_loss_fn, FLAGS, lpips_loss_fn)
# Relighting
if FLAGS.no_train is True and FLAGS.learn_light is False:
print(f"Do not load lights from checkpoint, using loaded lights {FLAGS.envmap}")
trainer_noddp.light = None
if FLAGS.external_ckpt_dir is not None:
# try to load weights if the sdf or deform weights are mis-matched
try:
training.resume_from_ckpt(FLAGS.external_ckpt_dir, trainer_noddp, optimizer=None, optimizer_mesh=None, optimizer_motion=None, step=FLAGS.external_ckpt_step)
except RuntimeWarning as e:
print("Fail to load weights due to shape mismatch. Copy the mismatched weights and retrying...")
training.resume_from_ckpt(FLAGS.external_ckpt_dir, trainer_noddp, optimizer=None, optimizer_mesh=None, optimizer_motion=None, step=FLAGS.external_ckpt_step)
# No training if validate_only is True
if FLAGS.no_train is True:
print(f"Rendering only, skipping training.")
return geometry, opt_material
# build optimizer
trainer, optimizer_mesh, optimizer_motion, optimizer = build_optims(trainer_noddp, FLAGS, optimize_geometry, learning_rate_pos, learning_rate_mat, learning_rate_skn)
if FLAGS.external_ckpt_dir is not None:
# [FIXME] last_step should be used when initializing
last_step = training.resume_from_ckpt(FLAGS.external_ckpt_dir, trainer_noddp, optimizer, optimizer_mesh, optimizer_motion, FLAGS.external_ckpt_step)
print(f"Loading external checkpoint from {FLAGS.external_ckpt_dir}, last step is {last_step}")
else:
last_step = -1
# ==============================================================================================
# Schedular
# ==============================================================================================
scheduler, scheduler_mesh, scheduler_motion = build_scheds(FLAGS, lr_schedule, optimizer_mesh, optimizer_motion, optimizer, last_step)
# ==============================================================================================
# Training loop
# ==============================================================================================
img_cnt = 0
img_loss_vec = []
reg_loss_vec = []
motion_loss_vec = []
iter_dur_vec = []
if FLAGS.multi_gpu:
# sampler = torch.utils.data.distributed.DistributedSampler(dataset_train, shuffle=True, num_replicas=FLAGS.world_size, rank=FLAGS.rank)
sampler = None
dataloader_train = torch.utils.data.DataLoader(dataset_train, batch_size=FLAGS.batch, collate_fn=dataset_train.collate, num_workers=FLAGS.num_workers, pin_memory=True, sampler=sampler)
else:
dataloader_train = torch.utils.data.DataLoader(dataset_train, batch_size=FLAGS.batch, collate_fn=dataset_train.collate, shuffle=True, num_workers=FLAGS.num_workers, pin_memory=True)
if FLAGS.val_batch != 1:
raise ValueError(f"val_batch must be 1 to properly write images, got {FLAGS.val_batch}.")
if not isinstance(dataset_validate, list):
dataset_validate = [dataset_validate]
dataloader_validate = [torch.utils.data.DataLoader(dataset_validate_, batch_size=FLAGS.val_batch, collate_fn=dataset_train.collate, num_workers=FLAGS.num_workers, pin_memory=True) for dataset_validate_ in dataset_validate]
def cycle(iterable):
iterator = iter(iterable)
while True:
try:
yield next(iterator)
except StopIteration:
iterator = iter(iterable)
v_it = [cycle(dataloader_validate_) for dataloader_validate_ in dataloader_validate]
print(f"Training {pass_name} pass with {len(dataloader_train)} batches per epoch. Each batch has {FLAGS.batch} images.")
save_dir = os.path.join(FLAGS.out_dir, "val_vis_in_train")
os.makedirs(save_dir, exist_ok=True)
ckpt_dir = os.path.join(FLAGS.out_dir, "ckpts")
os.makedirs(ckpt_dir, exist_ok=True)
mesh_dir = os.path.join(FLAGS.out_dir, "meshes")
os.makedirs(mesh_dir, exist_ok=True)
validate_split_names = [dataset_validate_.split for dataset_validate_ in dataset_validate]
for validate_split_name in validate_split_names:
os.makedirs(os.path.join(save_dir, validate_split_name), exist_ok=True)
for it, target in enumerate(dataloader_train):
if it <= last_step:
continue
if it in FLAGS.prune_tetmesh_iters:
num_repeat = FLAGS.prune_tetmesh_iters.index(it)
padding_percent = FLAGS.prune_tetmesh_padding_percent / (2 ** num_repeat)
print(f"Prune tetmesh at iteration {it}; padding_percent={padding_percent}")
trainer_noddp.geometry.prune_tetmesh(padding_percent=padding_percent, glctx=glctx)
if it in FLAGS.subdivide_tetmesh_iters:
print(f"Subdividing tetmesh at iteration {it}")
trainer_noddp.geometry.subdivide_tetmesh(permanent_subdivide=True)
if FLAGS.learn_sdf_with_mlp is False or (FLAGS.learn_tet_vert_deform_with_mlp is False and FLAGS.enable_tet_vert_deform is True):
print("Rebuild optimizers and schedulers")
trainer_noddp.update_params()
_, optimizer_mesh, _, _ = build_optims(trainer_noddp, FLAGS, optimize_geometry, learning_rate_pos, None, None)
_, scheduler_mesh, _ = build_scheds(FLAGS, lr_schedule, optimizer_mesh, None, None, it - 1)
# Mix randomized background into dataset image
target = prepare_batch(target, 'random')
# ==============================================================================================
# Display / save outputs. Do it before training so we get initial meshes
# ==============================================================================================
# Show/save image before training step (want to get correct rendering of input)
if FLAGS.local_rank == 0:
display_image = FLAGS.display_interval and (it % FLAGS.display_interval == 0)
save_image = FLAGS.save_interval and (it % FLAGS.save_interval == 0)
log_val = FLAGS.log_val_interval and (it % FLAGS.log_val_interval == 0)
if display_image or save_image or log_val:
for i_v_it, v_it_ in enumerate(v_it):
result_image, result_dict, extra_dict = validate_itr(glctx, prepare_batch(next(v_it_), FLAGS.background), geometry, opt_material, lgt, FLAGS, update_base_mesh=True)
if display_image:
np_result_image = result_image.detach().cpu().numpy()
util.display_image(np_result_image, title='%d / %d' % (it, FLAGS.iter))
if save_image:
np_result_image = result_image.detach().cpu().numpy()
util.save_image(os.path.join(save_dir, validate_split_names[i_v_it], ('img_%s_%06d.png' % (pass_name, img_cnt))), np_result_image)
if log_val:
# Compute metrics
val_img_loss = torch.nn.functional.mse_loss(result_dict['opt'], result_dict['ref'])
val_alpha_loss = torch.nn.functional.mse_loss(result_dict['opt_alpha'], result_dict['ref_alpha'])
wandb.log({f"val/{validate_split_names[i_v_it]}/val_img_loss": val_img_loss.item(), f"val/{validate_split_names[i_v_it]}/val_alpha_loss": val_alpha_loss}, step=it)
# Compute metrics
opt = torch.clamp(result_dict['opt'], 0.0, 1.0)
ref = torch.clamp(result_dict['ref'], 0.0, 1.0)
mse = torch.nn.functional.mse_loss(opt, ref, size_average=None, reduce=None, reduction='mean').item()
val_img_psnr = util.mse_to_psnr(mse)
opt_alpha = torch.clamp(result_dict['opt_alpha'], 0.0, 1.0)
ref_alpha = torch.clamp(result_dict['ref_alpha'], 0.0, 1.0)
alpha_mse = torch.nn.functional.mse_loss(opt_alpha, ref_alpha, size_average=None, reduce=None, reduction='mean').item()
val_alpha_psnr = util.mse_to_psnr(alpha_mse)
wandb.log({f"val/{validate_split_names[i_v_it]}/val_img_psnr": val_img_psnr, f"val/{validate_split_names[i_v_it]}/val_alpha_pnsr": val_alpha_psnr}, step=it)
wandb.log({f"time/val/{validate_split_names[i_v_it]}/render_time_ms_per_frame": extra_dict["render_time_ms_per_frame"], f"time/val/{validate_split_names[i_v_it]}/render_fps": extra_dict["fps"]}, step=it)
print(f"iter={it:5d}, split={validate_split_names[i_v_it]}, val_img_psnr={val_img_psnr:.3f}, val_alpha_psnr={val_alpha_psnr:.3f}, render_time_ms_per_frame={extra_dict['render_time_ms_per_frame']:.1f}, render_fps={extra_dict['fps']:.2f}")
if save_image:
img_cnt = img_cnt+1
iter_start_time = time.time()
# ==============================================================================================
# Zero gradients
# ==============================================================================================
optimizer.zero_grad()
if optimize_geometry:
optimizer_mesh.zero_grad()
if optimizer_motion is not None:
optimizer_motion.zero_grad()
# ==============================================================================================
# Training
# ==============================================================================================
img_loss, reg_losses, motion_losses, loss_weights = trainer(target, it)
reg_loss = sum(reg_losses.values())
motion_loss = sum(motion_losses.values())
if not isinstance(reg_loss, torch.Tensor):
reg_loss = torch.tensor(reg_loss)
if not isinstance(motion_loss, torch.Tensor):
motion_loss = torch.tensor(motion_loss)
# ==============================================================================================
# Final loss
# ==============================================================================================
total_loss = img_loss + reg_loss + motion_loss
if torch.is_anomaly_enabled():
assert torch.all(torch.isfinite(total_loss))
assert torch.all(torch.isfinite(img_loss))
for k, v in reg_losses.items():
assert torch.all(torch.isfinite(v)), f"{k} {v}"
for k, v in motion_losses.items():
assert torch.all(torch.isfinite(v)), f"{k} {v}"
img_loss_vec.append(img_loss.item())
reg_loss_vec.append(reg_loss.item())
motion_loss_vec.append(motion_loss.item())
# ==============================================================================================
# Backpropagate
# ==============================================================================================
total_loss.backward()
if FLAGS.use_training_tricks is True:
if hasattr(lgt, 'base') and lgt.base.grad is not None and optimize_light:
lgt.base.grad *= 64
if 'kd_ks_normal' in opt_material:
opt_material['kd_ks_normal'].encoder.params.grad /= 8.0
optimizer.step()
scheduler.step()
if optimize_geometry:
optimizer_mesh.step()
scheduler_mesh.step()
no_motion_update = any([(it >= i and it < i + FLAGS.subdivide_no_motion_update_interval) for i in FLAGS.subdivide_tetmesh_iters])
if optimizer_motion is not None:
if not no_motion_update:
optimizer_motion.step()
scheduler_motion.step()
# ==============================================================================================
# Clamp trainables to reasonable range
# ==============================================================================================
with torch.no_grad():
if 'kd' in opt_material:
opt_material['kd'].clamp_()
if 'ks' in opt_material:
opt_material['ks'].clamp_()
if 'normal' in opt_material:
opt_material['normal'].clamp_()
opt_material['normal'].normalize_()
if lgt is not None:
lgt.clamp_(min=0.0)
torch.cuda.current_stream().synchronize()
iter_dur_vec.append(time.time() - iter_start_time)
# ==============================================================================================
# Logging
# ==============================================================================================
if it % log_interval == 0 and FLAGS.local_rank == 0:
img_loss_avg = np.mean(np.asarray(img_loss_vec[-log_interval:]))
reg_loss_avg = np.mean(np.asarray(reg_loss_vec[-log_interval:]))
motion_loss_avg = np.mean(np.asarray(motion_loss_vec[-log_interval:]))
iter_dur_avg = np.mean(np.asarray(iter_dur_vec[-log_interval:]))
remaining_time = (FLAGS.iter-it)*iter_dur_avg
print("iter=%5d, img_loss=%.6f, reg_loss=%.6f, motion_loss=%.6f, texture_light_lr=%.4f, geometry_lr=%.4f, motion_lr=%.4f, time=%.1f ms, rem=%s" %
(it, img_loss_avg, reg_loss_avg, motion_loss_avg,optimizer.param_groups[0]['lr'], (optimizer_mesh.param_groups[0]['lr'] if optimizer_mesh is not None else -1), (optimizer_motion.param_groups[0]['lr'] if optimizer_motion is not None else -1), iter_dur_avg*1000, util.time_to_text(remaining_time)))
wandb.log({'train/img_loss': img_loss_avg, 'train/reg_loss': reg_loss_avg, 'train/motion_loss': motion_loss_avg}, step=it)
wandb.log({'train/learning_rate/texture_light_lr': optimizer.param_groups[0]['lr'], 'train/learning_rate/geometry_lr': (optimizer_mesh.param_groups[0]['lr'] if optimizer_mesh is not None else -1), 'train/learning_rate/motion_lr': (optimizer_motion.param_groups[0]['lr'] if optimizer_motion is not None and not no_motion_update else -1)}, step=it)
wandb.log({"time/train/ms_per_iter": iter_dur_avg*1000, "time/train/iter_per_sec": 1.0/iter_dur_avg}, step=it)
wandb.log({f"train/loss_weights/{k}": v for k, v in loss_weights.items()}, step=it)
# log reg_losses and motion_losses
wandb.log({f"train/reg_losses/{k}": v for k, v in reg_losses.items()})
wandb.log({f"train/motion_losses/{k}": v for k,v in motion_losses.items()})
# ==============================================================================================
# run validation on the fly
# ==============================================================================================
if it > 0 and it % FLAGS.validate_interval == 0 and FLAGS.local_rank == 0:
print(f"validate @ iter {it}")
for dataset_validate_ in dataset_validate:
item_name = f"validate_{dataset_validate_.split}"
metrics_value_dict = validate(glctx, geometry, opt_material, lgt, dataset_validate_, os.path.join(FLAGS.out_dir, "validate_otf", f"iter_{it}", item_name), FLAGS)
for k, v in metrics_value_dict.items():
wandb.log({f"metrics_otf/{k}/{item_name}": v})
# ==============================================================================================
# save checkpoints
# ==============================================================================================
if it > 0 and it % FLAGS.save_ckpt_interval == 0 and FLAGS.local_rank == 0:
training.save_ckpt(ckpt_dir, it, trainer_noddp, optimizer, optimizer_mesh, optimizer_motion)
training.clean_up_ckpt(ckpt_dir, FLAGS.num_kept_ckpts)
# ==============================================================================================
# save mesh
# ==============================================================================================
if it > 0 and it % FLAGS.save_mesh_interval == 0 and FLAGS.local_rank == 0:
bare_mesh = geometry.getMesh(None, None)
# write blend skinning mesh
if hasattr(geometry, "weights"):
bare_mesh.v_color = util.skin_weights2color(geometry.weights)
mesh_dir_it = os.path.join(mesh_dir, f"iter_{it}")
os.makedirs(mesh_dir_it, exist_ok=True)
obj.write_obj(mesh_dir_it, bare_mesh, save_material=False, save_uv=False, save_v_color=True)
print(f"save mesh to {mesh_dir_it}")
if FLAGS.multi_gpu:
torch.distributed.barrier()
return geometry, opt_material
def build_scheds(FLAGS, lr_schedule, optimizer_mesh, optimizer_motion, optimizer, last_step):
if optimizer is not None:
scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lambda x: lr_schedule(x), last_epoch=last_step)
print(f"Using Adam optimizer for material and light")
else:
scheduler = None
if optimizer_mesh is not None:
scheduler_mesh = torch.optim.lr_scheduler.LambdaLR(optimizer_mesh, lr_lambda=lambda x: lr_schedule(x), last_epoch=last_step)
print(f"Using {FLAGS.optimizer_geometry} optimizer for geometry")
else:
scheduler_mesh = None
if optimizer_motion is not None:
scheduler_motion = torch.optim.lr_scheduler.LambdaLR(optimizer_motion, lr_lambda=lambda x: lr_schedule(x), last_epoch=last_step)
print("Using Adam optimizer for motion")
else:
scheduler_motion = None
return scheduler,scheduler_mesh,scheduler_motion
def build_optims(trainer_noddp, FLAGS, optimize_geometry, learning_rate_pos=None, learning_rate_mat=None, learning_rate_skn=None):
if FLAGS.multi_gpu:
# Multi GPU training mode
import apex
from apex.parallel import DistributedDataParallel as DDP
trainer = DDP(trainer_noddp)
trainer.train()
if optimize_geometry and learning_rate_pos is not None:
if isinstance(trainer_noddp.geometry, DMTetGeometryDyn):
FLAGS.optimizer_geometry = 'adam'
print(f"DMTetGeometryDyn not supports vectoradam, using adam instead")
if FLAGS.optimizer_geometry == 'adam':
optimizer_mesh = apex.optimizers.FusedAdam(trainer_noddp.geo_params, lr=learning_rate_pos)
elif FLAGS.optimizer_geometry == 'vectoradam':
optimizer_mesh = VectorAdam(trainer_noddp.geo_params, lr=learning_rate_pos)
else:
optimizer_mesh = None
if len(trainer_noddp.motion_params) > 0 and learning_rate_skn is not None:
optimizer_motion = apex.optimizers.FusedAdam(trainer_noddp.motion_params, lr=learning_rate_skn)
else:
optimizer_motion = None
if learning_rate_mat is not None:
optimizer = apex.optimizers.FusedAdam(trainer_noddp.params, lr=learning_rate_mat)
else:
optimizer = None
set_seed(FLAGS.seed + int(FLAGS.rank), FLAGS.strict_reproducibility)
print(f"[rank {FLAGS.rank}] set seed to {FLAGS.seed + int(FLAGS.rank)}")
else:
# Single GPU training mode
trainer = trainer_noddp
if optimize_geometry and learning_rate_pos is not None:
if isinstance(trainer_noddp.geometry, DMTetGeometryDyn):
FLAGS.optimizer_geometry = 'adam'
print(f"DMTetGeometryDyn not supports vectoradam, using adam instead")
if FLAGS.optimizer_geometry == 'adam':
optimizer_mesh = torch.optim.Adam(trainer_noddp.geo_params, lr=learning_rate_pos)
elif FLAGS.optimizer_geometry == 'vectoradam':
optimizer_mesh = VectorAdam(trainer_noddp.geo_params, lr=learning_rate_pos)
else:
optimizer_mesh = None
if len(trainer_noddp.motion_params) > 0 and learning_rate_skn is not None:
optimizer_motion = torch.optim.Adam(trainer_noddp.motion_params, lr=learning_rate_skn)
else:
optimizer_motion = None
if learning_rate_mat is not None:
optimizer = torch.optim.Adam(trainer_noddp.params, lr=learning_rate_mat)
else:
optimizer = None
return trainer, optimizer_mesh, optimizer_motion, optimizer
#----------------------------------------------------------------------------
# Main function.
#----------------------------------------------------------------------------
global glctx, geometry, mat, lgt, dataset_validate, FLAGS
def main(FLAGS):
_check_cfg(FLAGS)
set_seed(FLAGS.seed, FLAGS.strict_reproducibility)
print(f"set seed to {FLAGS.seed}")
FLAGS.multi_gpu = "WORLD_SIZE" in os.environ and int(os.environ["WORLD_SIZE"]) > 1
if FLAGS.multi_gpu:
if "MASTER_ADDR" not in os.environ:
os.environ["MASTER_ADDR"] = 'localhost'
if "MASTER_PORT" not in os.environ:
os.environ["MASTER_PORT"] = str(_find_free_port())
print(f"MASTER_ADDR: {os.environ['MASTER_ADDR']}")
print(f"MASTER_PORT: {os.environ['MASTER_PORT']}")
FLAGS.world_size = int(os.environ["WORLD_SIZE"])
FLAGS.rank = int(os.environ["RANK"])
FLAGS.local_rank = int(os.environ["LOCAL_RANK"])
torch.cuda.set_device(FLAGS.local_rank)
torch.distributed.init_process_group(backend="nccl", init_method="env://")
print(f"actual interations: from {FLAGS.iter} to {FLAGS.iter * FLAGS.world_size}")
# FLAGS.iter = FLAGS.iter * FLAGS.world_size
else:
torch.cuda.set_device(FLAGS.local_rank)
if FLAGS.display_res is None:
FLAGS.display_res = FLAGS.train_res
# if FLAGS.local_rank == 0:
# os.makedirs(FLAGS.out_dir, exist_ok=True)
# print(f"output directory: {FLAGS.out_dir}")
# wandb.init(project=FLAGS.exp_name, name=FLAGS.out_dir,
# config=OmegaConf.to_container(FLAGS, resolve=True, throw_on_missing=True), dir=FLAGS.out_dir
# )
if FLAGS.rasterize_context == 'opengl':
print("Using OpenGL rasterizer")
glctx = dr.RasterizeGLContext()
elif FLAGS.rasterize_context == 'cuda':
print("Using CUDA rasterizer")
glctx = dr.RasterizeCudaContext()
else:
raise ValueError(f"Unknown rasterize_context: {FLAGS.rasterize_context}")
# ==============================================================================================
# Create data pipeline
# ==============================================================================================
if FLAGS.get("use_legacy_dataset", False):
LOGGER.warning("Use legacy dataset")
from dataset.legacy.dataset_mesh import DatasetMesh
from dataset.legacy.dataset_nerf import DatasetNERF
from dataset.legacy.dataset_llff import DatasetLLFF
from dataset.legacy.dataset_zju_mocap import DatasetZJUMocap
if os.path.splitext(FLAGS.ref_mesh)[1] == '.obj':
ref_mesh = mesh.load_mesh(FLAGS.ref_mesh, FLAGS.mtl_override)
dataset_train = DatasetMesh(ref_mesh, glctx, RADIUS, FLAGS, validate=False)
dataset_validate = DatasetMesh(ref_mesh, glctx, RADIUS, FLAGS, validate=True)
elif os.path.isdir(FLAGS.ref_mesh):
if os.path.isfile(os.path.join(FLAGS.ref_mesh, 'poses_bounds.npy')):
dataset_train = DatasetLLFF(FLAGS.ref_mesh, FLAGS, examples=(FLAGS.iter+1)*FLAGS.batch)
dataset_validate = DatasetLLFF(FLAGS.ref_mesh, FLAGS)
elif os.path.isfile(os.path.join(FLAGS.ref_mesh, 'transforms_train.json')):
dataset_train = DatasetNERF(os.path.join(FLAGS.ref_mesh, 'transforms_train.json'), FLAGS, examples=(FLAGS.iter+1)*FLAGS.batch)
dataset_validate = DatasetNERF(os.path.join(FLAGS.ref_mesh, 'transforms_test.json'), FLAGS)
elif os.path.isfile(os.path.join(FLAGS.ref_mesh, 'annots.npy')):
if not FLAGS.no_train:
dataset_train = DatasetZJUMocap(FLAGS, split="train" if not FLAGS.dev_run else "dev_run", examples=(FLAGS.iter+1)*FLAGS.batch)
else:
dataset_train = None
if FLAGS.dev_run and FLAGS.dev_run_same_train_val:
print(f"In dev_run mode.")
dataset_validate = [DatasetZJUMocap(FLAGS, split="train" if not FLAGS.dev_run else "dev_run", examples=FLAGS.num_val_examples)]
else:
print(f"validate_splits: {FLAGS.validate_splits}")
dataset_validate = [DatasetZJUMocap(FLAGS, split=split, examples=FLAGS.num_val_examples) for split in FLAGS.validate_splits + (["dev_run"] if FLAGS.dev_run else [])]
else:
if not FLAGS.no_train:
dataset_train = hydra.utils.instantiate(FLAGS.train_dataset)
else:
dataset_train = None
if isinstance(FLAGS.validate_dataset, DictConfig):
FLAGS.validate_dataset = ListConfig([FLAGS.validate_dataset])
dataset_validate = [hydra.utils.instantiate(cfg) for cfg in FLAGS.validate_dataset]
# ==============================================================================================
# Create env light with trainable parameters
# ==============================================================================================
if FLAGS.learn_light:
print("Learn light. Use random envmap.")
lgt = light.create_trainable_env_rnd(512, scale=0.0, bias=0.5)
else:
print(f"Not learn light. External envmap: {FLAGS.envmap} and scale: {FLAGS.env_scale}")
lgt = light.load_env(FLAGS.envmap, scale=FLAGS.env_scale)