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import os
import imageio
import torch
import numpy as np
import tqdm
import comfy.utils
from .flexicubes_renderer import FlexiCubesRenderer
from .flexicubes import FlexiCubes
from .util import SimpleMesh
from .loss import sdf_reg_loss
from shared_utils.camera_utils import OrbitCamera
from mesh_processer.mesh import Mesh
def lr_schedule(iter):
return max(0.0, 10**(-(iter)*0.0002)) # Exponential falloff from [1.0, 0.1] over 5k epochs.
class FlexiCubesTrainer:
def __init__(
self,
training_iterations,
batch_size,
learning_rate,
voxel_grids_resolution,
depth_near=0.5,
depth_far=5.5,
mask_loss_weight=1.0,
depth_loss_weight=100.0,
normal_loss_weight=1.0,
sdf_regularizer_weight=0.2,
remove_floaters_weight=0.5,
cube_stabilizer_weight=0.1,
force_cuda_rast=False,
device='cuda'
):
self.device = torch.device(device)
self.renderer = FlexiCubesRenderer(force_cuda_rast)
# Create and initialize FlexiCubes
self.voxel_grid_res = voxel_grids_resolution
self.fc = FlexiCubes(self.device)
self.x_nx3, self.cube_fx8 = self.fc.construct_voxel_grid(self.voxel_grid_res)
self.x_nx3 *= 2 # scale up the grid so that it's larger than the target object
self.sdf = torch.rand_like(self.x_nx3[:,0]) - 0.1 # randomly init SDF
self.sdf = torch.nn.Parameter(self.sdf.clone().detach(), requires_grad=True)
# set per-cube learnable weights to zeros
self.weight = torch.zeros((self.cube_fx8.shape[0], 21), dtype=torch.float, device=self.device)
self.weight = torch.nn.Parameter(self.weight.clone().detach(), requires_grad=True)
self.deform = torch.nn.Parameter(torch.zeros_like(self.x_nx3), requires_grad=True)
# Retrieve all the edges of the voxel grid; these edges will be utilized to
# compute the regularization loss in subsequent steps of the process.
all_edges = self.cube_fx8[:, self.fc.cube_edges].reshape(-1, 2)
self.grid_edges = torch.unique(all_edges, dim=0)
# Setup optimizer
self.optimizer = torch.optim.Adam([self.sdf, self.weight, self.deform], lr=learning_rate)
self.scheduler = torch.optim.lr_scheduler.LambdaLR(self.optimizer, lr_lambda=lambda x: lr_schedule(x))
self.training_iterations = training_iterations
self.batch_size = batch_size
self.depth_near = -depth_near
self.depth_far = -depth_far
self.mask_loss_weight = mask_loss_weight
self.depth_loss_weight = depth_loss_weight
self.normal_loss_weight = normal_loss_weight
self.sdf_regularizer_weight = sdf_regularizer_weight
self.remove_floaters_weight = remove_floaters_weight
self.cube_stabilizer_weight = cube_stabilizer_weight
def prepare_training(self, reference_depth_images, reference_masks, reference_orbit_camera_poses, reference_orbit_camera_fovy, reference_normals=None):
self.ref_imgs_num = len(reference_depth_images)
self.all_ref_cam_poses = reference_orbit_camera_poses
self.ref_cam_fovy = reference_orbit_camera_fovy
self.ref_size_H = reference_depth_images[0].shape[0]
self.ref_size_W = reference_depth_images[0].shape[1]
self.cam = OrbitCamera(self.ref_size_W, self.ref_size_H, fovy=reference_orbit_camera_fovy)
# prepare reference images and masks
self.ref_depth_imgs_torch = reference_depth_images[:, :, :, 0].unsqueeze(3).to(self.device) # (N, H, W, C) -> (N, H, W, 1)
self.ref_masks_torch = reference_masks.unsqueeze(3).to(self.device)
if reference_normals is not None:
self.ref_normal_imgs_torch = (reference_normals * 2 - 1).to(self.device) # change value from [0, 1] -> [-1, 1]
else:
self.ref_normal_imgs_torch = None
# prepare reference camera projection matrix for all camera poses
mv_all = []
mvp_all = []
for pose in self.all_ref_cam_poses:
mv, mvp = self.renderer.get_orbit_camera(pose[2], pose[1], cam_radius=pose[0], device=self.device)
mv_all.append(mv)
mvp_all.append(mvp)
self.mv_all = torch.stack(mv_all).to(self.device)
self.mvp_all = torch.stack(mvp_all).to(self.device)
self.camposes_len = len(self.all_ref_cam_poses)
def training(self):
starter = torch.cuda.Event(enable_timing=True)
ender = torch.cuda.Event(enable_timing=True)
starter.record()
comfy_pbar = comfy.utils.ProgressBar(self.training_iterations)
if self.ref_normal_imgs_torch is not None:
return_types = ["mask", "depth", "normal"]
else:
return_types = ["mask", "depth"]
for step in tqdm.trange(self.training_iterations):
# sample random render & camera pose from multi-views
batch_index = np.random.randint(0, self.camposes_len, size=self.batch_size)
mv = self.mv_all[batch_index, :, :]
mvp = self.mvp_all[batch_index, :, :]
target_mask = self.ref_masks_torch[batch_index, :, :, :]
target_depth = self.ref_depth_imgs_torch[batch_index, :, :, :]
# extract and render FlexiCubes mesh
grid_verts = self.x_nx3 + (2-1e-8) / (self.voxel_grid_res * 2) * torch.tanh(self.deform)
vertices, faces, L_dev = self.fc(grid_verts, self.sdf, self.cube_fx8, self.voxel_grid_res, beta_fx12=self.weight[:,:12], alpha_fx8=self.weight[:,12:20],
gamma_f=self.weight[:,20], training=True)
flexicubes_mesh = SimpleMesh(vertices, faces)
if self.ref_normal_imgs_torch is not None:
flexicubes_mesh.auto_normals()
buffers = self.renderer.render_mesh(flexicubes_mesh, mv, mvp, (self.ref_size_H, self.ref_size_W), self.depth_far, self.depth_near, return_types)
t_iter = step / self.training_iterations
# evaluate reconstruction loss
# mask & depth shape: (N, H, W, 1)
mask_loss = (buffers['mask'] - target_mask).abs().mean() * self.mask_loss_weight
depth_loss = (((((buffers['depth'] - target_depth)* target_mask)**2).sum(-1)+1e-8)).sqrt().mean() * self.depth_loss_weight
total_loss = mask_loss + depth_loss
if self.ref_normal_imgs_torch is not None:
target_normal = self.ref_normal_imgs_torch[batch_index, :, :, :]
normal_loss = (((((buffers['normal'] - (target_normal))* target_mask)**2).sum(-1)+1e-8)).sqrt().mean() * self.normal_loss_weight * t_iter
total_loss += normal_loss
sdf_weight = self.sdf_regularizer_weight - (self.sdf_regularizer_weight - self.sdf_regularizer_weight/20)*min(1.0, 4.0 * t_iter)
reg_loss = sdf_reg_loss(self.sdf, self.grid_edges).mean() * sdf_weight # Loss to eliminate internal floaters that are not visible
reg_loss += L_dev.mean() * self.remove_floaters_weight
reg_loss += (self.weight[:,:20]).abs().mean() * self.cube_stabilizer_weight
total_loss += reg_loss
# optimize step
total_loss.backward()
self.optimizer.step()
self.scheduler.step()
self.optimizer.zero_grad()
comfy_pbar.update_absolute(step + 1)
#self.test_save(step, mv, mvp, grid_verts, total_loss, out_dir="C:\\Users\\reall\\Softwares\\ComfyUI_windows_portable\\ComfyUI\\output\\FlexiCubes_Output\\Test_Normals")
ender.record()
def get_mesh(self):
grid_verts = self.x_nx3 + (2-1e-8) / (self.voxel_grid_res * 2) * torch.tanh(self.deform)
vertices, faces, L_dev = self.fc(grid_verts, self.sdf, self.cube_fx8, self.voxel_grid_res, beta_fx12=self.weight[:,:12], alpha_fx8=self.weight[:,12:20],
gamma_f=self.weight[:,20], training=False)
v = vertices.detach().contiguous().float().to(self.device)
f = faces.detach().contiguous().float().to(self.device)
mesh = Mesh(v=v, f=f, device=self.device)
mesh.auto_normal()
mesh.auto_uv()
# Trimesh seems output better mesh
#import trimesh
#import os
#mesh_np = trimesh.Trimesh(vertices = vertices.detach().cpu().numpy(), faces=faces.detach().cpu().numpy(), process=False)
#mesh_np.export(os.path.join("C:\\Users\\reall\\Softwares\\ComfyUI_windows_portable\\ComfyUI\\output\\FlexiCubes_Output", 'output_trimesh.obj'))
return mesh
def test_save(self, step, mv, mvp, grid_verts, total_loss, out_dir, save_interval=20, display_res=[1024, 1024]):
if (step % save_interval == 0 or step == (self.training_iterations-1)): # save normal image for visualization
with torch.no_grad():
# extract mesh with training=False
vertices, faces, L_dev = self.fc(grid_verts, self.sdf, self.cube_fx8, self.voxel_grid_res, beta_fx12=self.weight[:,:12], alpha_fx8=self.weight[:,12:20],
gamma_f=self.weight[:,20], training=False)
flexicubes_mesh = SimpleMesh(vertices, faces)
flexicubes_mesh.auto_normals() # compute face normals for visualization
mv, mvp = self.renderer.get_rotate_camera(step//save_interval, iter_res=display_res, device=self.device)
mv = mv.unsqueeze(0)
mvp = mvp.unsqueeze(0)
val_buffers = self.renderer.render_mesh(flexicubes_mesh, mv, mvp, display_res, return_types=["normal"], white_bg=True)
val_image = ((val_buffers["normal"][0].detach().cpu().numpy()+1)/2*255).astype(np.uint8)
#val_image = (val_buffers["depth"][0].detach().cpu().numpy()*255).astype(np.uint8)
#val_repeat = np.repeat(val_image, 4, axis=2)
#val_repeat[:, :, 3] = 255
#gt_image = (gt_image[0].detach().cpu().numpy()*255).astype(np.uint8)
#gt_repeat = np.repeat(gt_image, 4, axis=2)
#gt_repeat[:, :, 3] = 255
imageio.imwrite(os.path.join(out_dir, '{:04d}.png'.format(step)), val_image)
#imageio.imwrite(os.path.join(out_dir, '{:04d}.png'.format(step)), np.concatenate([val_repeat, gt_repeat], 1))
print(f"Optimization Step [{step}/{self.training_iterations}], Loss: {total_loss.item():.4f}") |