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303 lines
12 KiB
303 lines
12 KiB
import os
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import numpy as np
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import torch
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import torch.nn.functional as F
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from torchvision.transforms import v2
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from torchvision.utils import make_grid, save_image
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from torchmetrics.image.lpip import LearnedPerceptualImagePatchSimilarity
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import pytorch_lightning as pl
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from einops import rearrange, repeat
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from src.utils.train_util import instantiate_from_config
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class MVRecon(pl.LightningModule):
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def __init__(
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self,
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lrm_generator_config,
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lrm_path=None,
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input_size=256,
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render_size=192,
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):
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super(MVRecon, self).__init__()
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self.input_size = input_size
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self.render_size = render_size
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# init modules
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self.lrm_generator = instantiate_from_config(lrm_generator_config)
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if lrm_path is not None:
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lrm_ckpt = torch.load(lrm_path)
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self.lrm_generator.load_state_dict(lrm_ckpt['weights'], strict=False)
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self.lpips = LearnedPerceptualImagePatchSimilarity(net_type='vgg')
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self.validation_step_outputs = []
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def on_fit_start(self):
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if self.global_rank == 0:
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os.makedirs(os.path.join(self.logdir, 'images'), exist_ok=True)
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os.makedirs(os.path.join(self.logdir, 'images_val'), exist_ok=True)
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def prepare_batch_data(self, batch):
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lrm_generator_input = {}
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render_gt = {} # for supervision
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# input images
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images = batch['input_images']
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images = v2.functional.resize(
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images, self.input_size, interpolation=3, antialias=True).clamp(0, 1)
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lrm_generator_input['images'] = images.to(self.device)
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# input cameras and render cameras
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input_c2ws = batch['input_c2ws'].flatten(-2)
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input_Ks = batch['input_Ks'].flatten(-2)
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target_c2ws = batch['target_c2ws'].flatten(-2)
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target_Ks = batch['target_Ks'].flatten(-2)
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render_cameras_input = torch.cat([input_c2ws, input_Ks], dim=-1)
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render_cameras_target = torch.cat([target_c2ws, target_Ks], dim=-1)
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render_cameras = torch.cat([render_cameras_input, render_cameras_target], dim=1)
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input_extrinsics = input_c2ws[:, :, :12]
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input_intrinsics = torch.stack([
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input_Ks[:, :, 0], input_Ks[:, :, 4],
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input_Ks[:, :, 2], input_Ks[:, :, 5],
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], dim=-1)
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cameras = torch.cat([input_extrinsics, input_intrinsics], dim=-1)
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# add noise to input cameras
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cameras = cameras + torch.rand_like(cameras) * 0.04 - 0.02
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lrm_generator_input['cameras'] = cameras.to(self.device)
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lrm_generator_input['render_cameras'] = render_cameras.to(self.device)
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# target images
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target_images = torch.cat([batch['input_images'], batch['target_images']], dim=1)
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target_depths = torch.cat([batch['input_depths'], batch['target_depths']], dim=1)
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target_alphas = torch.cat([batch['input_alphas'], batch['target_alphas']], dim=1)
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# random crop
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render_size = np.random.randint(self.render_size, 513)
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target_images = v2.functional.resize(
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target_images, render_size, interpolation=3, antialias=True).clamp(0, 1)
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target_depths = v2.functional.resize(
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target_depths, render_size, interpolation=0, antialias=True)
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target_alphas = v2.functional.resize(
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target_alphas, render_size, interpolation=0, antialias=True)
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crop_params = v2.RandomCrop.get_params(
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target_images, output_size=(self.render_size, self.render_size))
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target_images = v2.functional.crop(target_images, *crop_params)
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target_depths = v2.functional.crop(target_depths, *crop_params)[:, :, 0:1]
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target_alphas = v2.functional.crop(target_alphas, *crop_params)[:, :, 0:1]
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lrm_generator_input['render_size'] = render_size
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lrm_generator_input['crop_params'] = crop_params
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render_gt['target_images'] = target_images.to(self.device)
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render_gt['target_depths'] = target_depths.to(self.device)
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render_gt['target_alphas'] = target_alphas.to(self.device)
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return lrm_generator_input, render_gt
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def prepare_validation_batch_data(self, batch):
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lrm_generator_input = {}
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# input images
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images = batch['input_images']
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images = v2.functional.resize(
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images, self.input_size, interpolation=3, antialias=True).clamp(0, 1)
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lrm_generator_input['images'] = images.to(self.device)
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input_c2ws = batch['input_c2ws'].flatten(-2)
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input_Ks = batch['input_Ks'].flatten(-2)
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input_extrinsics = input_c2ws[:, :, :12]
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input_intrinsics = torch.stack([
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input_Ks[:, :, 0], input_Ks[:, :, 4],
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input_Ks[:, :, 2], input_Ks[:, :, 5],
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], dim=-1)
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cameras = torch.cat([input_extrinsics, input_intrinsics], dim=-1)
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lrm_generator_input['cameras'] = cameras.to(self.device)
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render_c2ws = batch['render_c2ws'].flatten(-2)
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render_Ks = batch['render_Ks'].flatten(-2)
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render_cameras = torch.cat([render_c2ws, render_Ks], dim=-1)
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lrm_generator_input['render_cameras'] = render_cameras.to(self.device)
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lrm_generator_input['render_size'] = 384
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lrm_generator_input['crop_params'] = None
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return lrm_generator_input
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def forward_lrm_generator(
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self,
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images,
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cameras,
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render_cameras,
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render_size=192,
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crop_params=None,
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chunk_size=1,
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):
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planes = torch.utils.checkpoint.checkpoint(
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self.lrm_generator.forward_planes,
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images,
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cameras,
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use_reentrant=False,
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)
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frames = []
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for i in range(0, render_cameras.shape[1], chunk_size):
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frames.append(
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torch.utils.checkpoint.checkpoint(
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self.lrm_generator.synthesizer,
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planes,
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cameras=render_cameras[:, i:i+chunk_size],
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render_size=render_size,
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crop_params=crop_params,
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use_reentrant=False
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)
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)
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frames = {
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k: torch.cat([r[k] for r in frames], dim=1)
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for k in frames[0].keys()
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}
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return frames
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def forward(self, lrm_generator_input):
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images = lrm_generator_input['images']
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cameras = lrm_generator_input['cameras']
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render_cameras = lrm_generator_input['render_cameras']
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render_size = lrm_generator_input['render_size']
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crop_params = lrm_generator_input['crop_params']
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out = self.forward_lrm_generator(
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images,
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cameras,
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render_cameras,
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render_size=render_size,
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crop_params=crop_params,
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chunk_size=1,
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)
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render_images = torch.clamp(out['images_rgb'], 0.0, 1.0)
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render_depths = out['images_depth']
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render_alphas = torch.clamp(out['images_weight'], 0.0, 1.0)
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out = {
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'render_images': render_images,
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'render_depths': render_depths,
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'render_alphas': render_alphas,
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}
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return out
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def training_step(self, batch, batch_idx):
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lrm_generator_input, render_gt = self.prepare_batch_data(batch)
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render_out = self.forward(lrm_generator_input)
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loss, loss_dict = self.compute_loss(render_out, render_gt)
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self.log_dict(loss_dict, prog_bar=True, logger=True, on_step=True, on_epoch=True)
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if self.global_step % 1000 == 0 and self.global_rank == 0:
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B, N, C, H, W = render_gt['target_images'].shape
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N_in = lrm_generator_input['images'].shape[1]
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input_images = v2.functional.resize(
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lrm_generator_input['images'], (H, W), interpolation=3, antialias=True).clamp(0, 1)
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input_images = torch.cat(
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[input_images, torch.ones(B, N-N_in, C, H, W).to(input_images)], dim=1)
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input_images = rearrange(
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input_images, 'b n c h w -> b c h (n w)')
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target_images = rearrange(
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render_gt['target_images'], 'b n c h w -> b c h (n w)')
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render_images = rearrange(
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render_out['render_images'], 'b n c h w -> b c h (n w)')
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target_alphas = rearrange(
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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)')
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render_alphas = rearrange(
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repeat(render_out['render_alphas'], 'b n 1 h w -> b n 3 h w'), 'b n c h w -> b c h (n w)')
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target_depths = rearrange(
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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)')
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render_depths = rearrange(
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repeat(render_out['render_depths'], 'b n 1 h w -> b n 3 h w'), 'b n c h w -> b c h (n w)')
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MAX_DEPTH = torch.max(target_depths)
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target_depths = target_depths / MAX_DEPTH * target_alphas
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render_depths = render_depths / MAX_DEPTH
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grid = torch.cat([
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input_images,
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target_images, render_images,
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target_alphas, render_alphas,
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target_depths, render_depths,
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], dim=-2)
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grid = make_grid(grid, nrow=target_images.shape[0], normalize=True, value_range=(0, 1))
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save_image(grid, os.path.join(self.logdir, 'images', f'train_{self.global_step:07d}.png'))
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return loss
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def compute_loss(self, render_out, render_gt):
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# NOTE: the rgb value range of OpenLRM is [0, 1]
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render_images = render_out['render_images']
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target_images = render_gt['target_images'].to(render_images)
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render_images = rearrange(render_images, 'b n ... -> (b n) ...') * 2.0 - 1.0
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target_images = rearrange(target_images, 'b n ... -> (b n) ...') * 2.0 - 1.0
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loss_mse = F.mse_loss(render_images, target_images)
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loss_lpips = 2.0 * self.lpips(render_images, target_images)
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render_alphas = render_out['render_alphas']
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target_alphas = render_gt['target_alphas']
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loss_mask = F.mse_loss(render_alphas, target_alphas)
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loss = loss_mse + loss_lpips + loss_mask
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prefix = 'train'
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loss_dict = {}
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loss_dict.update({f'{prefix}/loss_mse': loss_mse})
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loss_dict.update({f'{prefix}/loss_lpips': loss_lpips})
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loss_dict.update({f'{prefix}/loss_mask': loss_mask})
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loss_dict.update({f'{prefix}/loss': loss})
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return loss, loss_dict
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@torch.no_grad()
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def validation_step(self, batch, batch_idx):
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lrm_generator_input = self.prepare_validation_batch_data(batch)
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render_out = self.forward(lrm_generator_input)
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render_images = render_out['render_images']
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render_images = rearrange(render_images, 'b n c h w -> b c h (n w)')
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self.validation_step_outputs.append(render_images)
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def on_validation_epoch_end(self):
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images = torch.cat(self.validation_step_outputs, dim=-1)
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all_images = self.all_gather(images)
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all_images = rearrange(all_images, 'r b c h w -> (r b) c h w')
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if self.global_rank == 0:
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image_path = os.path.join(self.logdir, 'images_val', f'val_{self.global_step:07d}.png')
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grid = make_grid(all_images, nrow=1, normalize=True, value_range=(0, 1))
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save_image(grid, image_path)
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print(f"Saved image to {image_path}")
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self.validation_step_outputs.clear()
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def configure_optimizers(self):
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lr = self.learning_rate
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params = []
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params.append({"params": self.lrm_generator.parameters(), "lr": lr, "weight_decay": 0.01 })
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optimizer = torch.optim.AdamW(params, lr=lr, betas=(0.90, 0.95))
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scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, 3000, eta_min=lr/10)
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return {'optimizer': optimizer, 'lr_scheduler': scheduler}
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