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import os
import numpy as np
import torch
import torch.nn.functional as F
from torchvision.transforms import v2
from torchvision.utils import make_grid, save_image
from torchmetrics.image.lpip import LearnedPerceptualImagePatchSimilarity
import pytorch_lightning as pl
from einops import rearrange, repeat
from src.utils.train_util import instantiate_from_config
# Regulrarization loss for FlexiCubes
def sdf_reg_loss_batch(sdf, all_edges):
sdf_f1x6x2 = sdf[:, all_edges.reshape(-1)].reshape(sdf.shape[0], -1, 2)
mask = torch.sign(sdf_f1x6x2[..., 0]) != torch.sign(sdf_f1x6x2[..., 1])
sdf_f1x6x2 = sdf_f1x6x2[mask]
sdf_diff = F.binary_cross_entropy_with_logits(
sdf_f1x6x2[..., 0], (sdf_f1x6x2[..., 1] > 0).float()) + \
F.binary_cross_entropy_with_logits(
sdf_f1x6x2[..., 1], (sdf_f1x6x2[..., 0] > 0).float())
return sdf_diff
class MVRecon(pl.LightningModule):
def __init__(
self,
lrm_generator_config,
input_size=256,
render_size=512,
init_ckpt=None,
):
super(MVRecon, self).__init__()
self.input_size = input_size
self.render_size = render_size
# init modules
self.lrm_generator = instantiate_from_config(lrm_generator_config)
self.lpips = LearnedPerceptualImagePatchSimilarity(net_type='vgg')
# Load weights from pretrained MVRecon model, and use the mlp
# weights to initialize the weights of sdf and rgb mlps.
if init_ckpt is not None:
sd = torch.load(init_ckpt, map_location='cpu')['state_dict']
sd = {k: v for k, v in sd.items() if k.startswith('lrm_generator')}
sd_fc = {}
for k, v in sd.items():
if k.startswith('lrm_generator.synthesizer.decoder.net.'):
if k.startswith('lrm_generator.synthesizer.decoder.net.6.'): # last layer
# Here we assume the density filed's isosurface threshold is t,
# we reverse the sign of density filed to initialize SDF field.
# -(w*x + b - t) = (-w)*x + (t - b)
if 'weight' in k:
sd_fc[k.replace('net.', 'net_sdf.')] = -v[0:1]
else:
sd_fc[k.replace('net.', 'net_sdf.')] = 3.0 - v[0:1]
sd_fc[k.replace('net.', 'net_rgb.')] = v[1:4]
else:
sd_fc[k.replace('net.', 'net_sdf.')] = v
sd_fc[k.replace('net.', 'net_rgb.')] = v
else:
sd_fc[k] = v
sd_fc = {k.replace('lrm_generator.', ''): v for k, v in sd_fc.items()}
# missing `net_deformation` and `net_weight` parameters
self.lrm_generator.load_state_dict(sd_fc, strict=False)
print(f'Loaded weights from {init_ckpt}')
self.validation_step_outputs = []
def on_fit_start(self):
device = torch.device(f'cuda:{self.global_rank}')
self.lrm_generator.init_flexicubes_geometry(device)
if self.global_rank == 0:
os.makedirs(os.path.join(self.logdir, 'images'), exist_ok=True)
os.makedirs(os.path.join(self.logdir, 'images_val'), exist_ok=True)
def prepare_batch_data(self, batch):
lrm_generator_input = {}
render_gt = {}
# input images
images = batch['input_images']
images = v2.functional.resize(
images, self.input_size, interpolation=3, antialias=True).clamp(0, 1)
lrm_generator_input['images'] = images.to(self.device)
# input cameras and render cameras
input_c2ws = batch['input_c2ws']
input_Ks = batch['input_Ks']
target_c2ws = batch['target_c2ws']
render_c2ws = torch.cat([input_c2ws, target_c2ws], dim=1)
render_w2cs = torch.linalg.inv(render_c2ws)
input_extrinsics = input_c2ws.flatten(-2)
input_extrinsics = input_extrinsics[:, :, :12]
input_intrinsics = input_Ks.flatten(-2)
input_intrinsics = torch.stack([
input_intrinsics[:, :, 0], input_intrinsics[:, :, 4],
input_intrinsics[:, :, 2], input_intrinsics[:, :, 5],
], dim=-1)
cameras = torch.cat([input_extrinsics, input_intrinsics], dim=-1)
# add noise to input_cameras
cameras = cameras + torch.rand_like(cameras) * 0.04 - 0.02
lrm_generator_input['cameras'] = cameras.to(self.device)
lrm_generator_input['render_cameras'] = render_w2cs.to(self.device)
# target images
target_images = torch.cat([batch['input_images'], batch['target_images']], dim=1)
target_depths = torch.cat([batch['input_depths'], batch['target_depths']], dim=1)
target_alphas = torch.cat([batch['input_alphas'], batch['target_alphas']], dim=1)
target_normals = torch.cat([batch['input_normals'], batch['target_normals']], dim=1)
render_size = self.render_size
target_images = v2.functional.resize(
target_images, render_size, interpolation=3, antialias=True).clamp(0, 1)
target_depths = v2.functional.resize(
target_depths, render_size, interpolation=0, antialias=True)
target_alphas = v2.functional.resize(
target_alphas, render_size, interpolation=0, antialias=True)
target_normals = v2.functional.resize(
target_normals, render_size, interpolation=3, antialias=True)
lrm_generator_input['render_size'] = render_size
render_gt['target_images'] = target_images.to(self.device)
render_gt['target_depths'] = target_depths.to(self.device)
render_gt['target_alphas'] = target_alphas.to(self.device)
render_gt['target_normals'] = target_normals.to(self.device)
return lrm_generator_input, render_gt
def prepare_validation_batch_data(self, batch):
lrm_generator_input = {}
# input images
images = batch['input_images']
images = v2.functional.resize(
images, self.input_size, interpolation=3, antialias=True).clamp(0, 1)
lrm_generator_input['images'] = images.to(self.device)
# input cameras
input_c2ws = batch['input_c2ws'].flatten(-2)
input_Ks = batch['input_Ks'].flatten(-2)
input_extrinsics = input_c2ws[:, :, :12]
input_intrinsics = torch.stack([
input_Ks[:, :, 0], input_Ks[:, :, 4],
input_Ks[:, :, 2], input_Ks[:, :, 5],
], dim=-1)
cameras = torch.cat([input_extrinsics, input_intrinsics], dim=-1)
lrm_generator_input['cameras'] = cameras.to(self.device)
# render cameras
render_c2ws = batch['render_c2ws']
render_w2cs = torch.linalg.inv(render_c2ws)
lrm_generator_input['render_cameras'] = render_w2cs.to(self.device)
lrm_generator_input['render_size'] = 384
return lrm_generator_input
def forward_lrm_generator(self, images, cameras, render_cameras, render_size=512):
planes = torch.utils.checkpoint.checkpoint(
self.lrm_generator.forward_planes,
images,
cameras,
use_reentrant=False,
)
out = self.lrm_generator.forward_geometry(
planes,
render_cameras,
render_size,
)
return out
def forward(self, lrm_generator_input):
images = lrm_generator_input['images']
cameras = lrm_generator_input['cameras']
render_cameras = lrm_generator_input['render_cameras']
render_size = lrm_generator_input['render_size']
out = self.forward_lrm_generator(
images, cameras, render_cameras, render_size=render_size)
return out
def training_step(self, batch, batch_idx):
lrm_generator_input, render_gt = self.prepare_batch_data(batch)
render_out = self.forward(lrm_generator_input)
loss, loss_dict = self.compute_loss(render_out, render_gt)
self.log_dict(loss_dict, prog_bar=True, logger=True, on_step=True, on_epoch=True)
if self.global_step % 1000 == 0 and self.global_rank == 0:
B, N, C, H, W = render_gt['target_images'].shape
N_in = lrm_generator_input['images'].shape[1]
target_images = rearrange(
render_gt['target_images'], 'b n c h w -> b c h (n w)')
render_images = rearrange(
render_out['img'], 'b n c h w -> b c h (n w)')
target_alphas = rearrange(
repeat(render_gt['target_alphas'], 'b n 1 h w -> b n 3 h w'), 'b n c h w -> b c h (n w)')
render_alphas = rearrange(
repeat(render_out['mask'], 'b n 1 h w -> b n 3 h w'), 'b n c h w -> b c h (n w)')
target_depths = rearrange(
repeat(render_gt['target_depths'], 'b n 1 h w -> b n 3 h w'), 'b n c h w -> b c h (n w)')
render_depths = rearrange(
repeat(render_out['depth'], 'b n 1 h w -> b n 3 h w'), 'b n c h w -> b c h (n w)')
target_normals = rearrange(
render_gt['target_normals'], 'b n c h w -> b c h (n w)')
render_normals = rearrange(
render_out['normal'], 'b n c h w -> b c h (n w)')
MAX_DEPTH = torch.max(target_depths)
target_depths = target_depths / MAX_DEPTH * target_alphas
render_depths = render_depths / MAX_DEPTH
grid = torch.cat([
target_images, render_images,
target_alphas, render_alphas,
target_depths, render_depths,
target_normals, render_normals,
], dim=-2)
grid = make_grid(grid, nrow=target_images.shape[0], normalize=True, value_range=(0, 1))
image_path = os.path.join(self.logdir, 'images', f'train_{self.global_step:07d}.png')
save_image(grid, image_path)
print(f"Saved image to {image_path}")
return loss
def compute_loss(self, render_out, render_gt):
# NOTE: the rgb value range of OpenLRM is [0, 1]
render_images = render_out['img']
target_images = render_gt['target_images'].to(render_images)
render_images = rearrange(render_images, 'b n ... -> (b n) ...') * 2.0 - 1.0
target_images = rearrange(target_images, 'b n ... -> (b n) ...') * 2.0 - 1.0
loss_mse = F.mse_loss(render_images, target_images)
loss_lpips = 2.0 * self.lpips(render_images, target_images)
render_alphas = render_out['mask']
target_alphas = render_gt['target_alphas']
loss_mask = F.mse_loss(render_alphas, target_alphas)
render_depths = render_out['depth']
target_depths = render_gt['target_depths']
loss_depth = 0.5 * F.l1_loss(render_depths[target_alphas>0], target_depths[target_alphas>0])
render_normals = render_out['normal'] * 2.0 - 1.0
target_normals = render_gt['target_normals'] * 2.0 - 1.0
similarity = (render_normals * target_normals).sum(dim=-3).abs()
normal_mask = target_alphas.squeeze(-3)
loss_normal = 1 - similarity[normal_mask>0].mean()
loss_normal = 0.2 * loss_normal
# flexicubes regularization loss
sdf = render_out['sdf']
sdf_reg_loss = render_out['sdf_reg_loss']
sdf_reg_loss_entropy = sdf_reg_loss_batch(sdf, self.lrm_generator.geometry.all_edges).mean() * 0.01
_, flexicubes_surface_reg, flexicubes_weights_reg = sdf_reg_loss
flexicubes_surface_reg = flexicubes_surface_reg.mean() * 0.5
flexicubes_weights_reg = flexicubes_weights_reg.mean() * 0.1
loss_reg = sdf_reg_loss_entropy + flexicubes_surface_reg + flexicubes_weights_reg
loss = loss_mse + loss_lpips + loss_mask + loss_normal + loss_reg
prefix = 'train'
loss_dict = {}
loss_dict.update({f'{prefix}/loss_mse': loss_mse})
loss_dict.update({f'{prefix}/loss_lpips': loss_lpips})
loss_dict.update({f'{prefix}/loss_mask': loss_mask})
loss_dict.update({f'{prefix}/loss_normal': loss_normal})
loss_dict.update({f'{prefix}/loss_depth': loss_depth})
loss_dict.update({f'{prefix}/loss_reg_sdf': sdf_reg_loss_entropy})
loss_dict.update({f'{prefix}/loss_reg_surface': flexicubes_surface_reg})
loss_dict.update({f'{prefix}/loss_reg_weights': flexicubes_weights_reg})
loss_dict.update({f'{prefix}/loss': loss})
return loss, loss_dict
@torch.no_grad()
def validation_step(self, batch, batch_idx):
lrm_generator_input = self.prepare_validation_batch_data(batch)
render_out = self.forward(lrm_generator_input)
render_images = render_out['img']
render_images = rearrange(render_images, 'b n c h w -> b c h (n w)')
self.validation_step_outputs.append(render_images)
def on_validation_epoch_end(self):
images = torch.cat(self.validation_step_outputs, dim=-1)
all_images = self.all_gather(images)
all_images = rearrange(all_images, 'r b c h w -> (r b) c h w')
if self.global_rank == 0:
image_path = os.path.join(self.logdir, 'images_val', f'val_{self.global_step:07d}.png')
grid = make_grid(all_images, nrow=1, normalize=True, value_range=(0, 1))
save_image(grid, image_path)
print(f"Saved image to {image_path}")
self.validation_step_outputs.clear()
def configure_optimizers(self):
lr = self.learning_rate
optimizer = torch.optim.AdamW(
self.lrm_generator.parameters(), lr=lr, betas=(0.90, 0.95), weight_decay=0.01)
scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, 100000, eta_min=0)
return {'optimizer': optimizer, 'lr_scheduler': scheduler}