Spaces:
Running
on
Zero
Running
on
Zero
File size: 8,889 Bytes
cc0c59d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 |
from typing import *
import copy
import torch
from torch.utils.data import DataLoader
import numpy as np
from easydict import EasyDict as edict
import utils3d.torch
from ..basic import BasicTrainer
from ...representations import Strivec
from ...renderers import OctreeRenderer
from ...modules.sparse import SparseTensor
from ...utils.loss_utils import l1_loss, l2_loss, ssim, lpips
class SLatVaeRadianceFieldDecoderTrainer(BasicTrainer):
"""
Trainer for structured latent VAE Radiance Field Decoder.
Args:
models (dict[str, nn.Module]): Models to train.
dataset (torch.utils.data.Dataset): Dataset.
output_dir (str): Output directory.
load_dir (str): Load directory.
step (int): Step to load.
batch_size (int): Batch size.
batch_size_per_gpu (int): Batch size per GPU. If specified, batch_size will be ignored.
batch_split (int): Split batch with gradient accumulation.
max_steps (int): Max steps.
optimizer (dict): Optimizer config.
lr_scheduler (dict): Learning rate scheduler config.
elastic (dict): Elastic memory management config.
grad_clip (float or dict): Gradient clip config.
ema_rate (float or list): Exponential moving average rates.
fp16_mode (str): FP16 mode.
- None: No FP16.
- 'inflat_all': Hold a inflated fp32 master param for all params.
- 'amp': Automatic mixed precision.
fp16_scale_growth (float): Scale growth for FP16 gradient backpropagation.
finetune_ckpt (dict): Finetune checkpoint.
log_param_stats (bool): Log parameter stats.
i_print (int): Print interval.
i_log (int): Log interval.
i_sample (int): Sample interval.
i_save (int): Save interval.
i_ddpcheck (int): DDP check interval.
loss_type (str): Loss type. Can be 'l1', 'l2'
lambda_ssim (float): SSIM loss weight.
lambda_lpips (float): LPIPS loss weight.
"""
def __init__(
self,
*args,
loss_type: str = 'l1',
lambda_ssim: float = 0.2,
lambda_lpips: float = 0.2,
**kwargs
):
super().__init__(*args, **kwargs)
self.loss_type = loss_type
self.lambda_ssim = lambda_ssim
self.lambda_lpips = lambda_lpips
self._init_renderer()
def _init_renderer(self):
rendering_options = {"near" : 0.8,
"far" : 1.6,
"bg_color" : 'random'}
self.renderer = OctreeRenderer(rendering_options)
self.renderer.pipe.primitive = 'trivec'
def _render_batch(self, reps: List[Strivec], extrinsics: torch.Tensor, intrinsics: torch.Tensor) -> torch.Tensor:
"""
Render a batch of representations.
Args:
reps: The dictionary of lists of representations.
extrinsics: The [N x 4 x 4] tensor of extrinsics.
intrinsics: The [N x 3 x 3] tensor of intrinsics.
"""
ret = None
for i, representation in enumerate(reps):
render_pack = self.renderer.render(representation, extrinsics[i], intrinsics[i])
if ret is None:
ret = {k: [] for k in list(render_pack.keys()) + ['bg_color']}
for k, v in render_pack.items():
ret[k].append(v)
ret['bg_color'].append(self.renderer.bg_color)
for k, v in ret.items():
ret[k] = torch.stack(v, dim=0)
return ret
def training_losses(
self,
latents: SparseTensor,
image: torch.Tensor,
alpha: torch.Tensor,
extrinsics: torch.Tensor,
intrinsics: torch.Tensor,
return_aux: bool = False,
**kwargs
) -> Tuple[Dict, Dict]:
"""
Compute training losses.
Args:
latents: The [N x * x C] sparse latents
image: The [N x 3 x H x W] tensor of images.
alpha: The [N x H x W] tensor of alpha channels.
extrinsics: The [N x 4 x 4] tensor of extrinsics.
intrinsics: The [N x 3 x 3] tensor of intrinsics.
return_aux: Whether to return auxiliary information.
Returns:
a dict with the key "loss" containing a scalar tensor.
may also contain other keys for different terms.
"""
reps = self.training_models['decoder'](latents)
self.renderer.rendering_options.resolution = image.shape[-1]
render_results = self._render_batch(reps, extrinsics, intrinsics)
terms = edict(loss = 0.0, rec = 0.0)
rec_image = render_results['color']
gt_image = image * alpha[:, None] + (1 - alpha[:, None]) * render_results['bg_color'][..., None, None]
if self.loss_type == 'l1':
terms["l1"] = l1_loss(rec_image, gt_image)
terms["rec"] = terms["rec"] + terms["l1"]
elif self.loss_type == 'l2':
terms["l2"] = l2_loss(rec_image, gt_image)
terms["rec"] = terms["rec"] + terms["l2"]
else:
raise ValueError(f"Invalid loss type: {self.loss_type}")
if self.lambda_ssim > 0:
terms["ssim"] = 1 - ssim(rec_image, gt_image)
terms["rec"] = terms["rec"] + self.lambda_ssim * terms["ssim"]
if self.lambda_lpips > 0:
terms["lpips"] = lpips(rec_image, gt_image)
terms["rec"] = terms["rec"] + self.lambda_lpips * terms["lpips"]
terms["loss"] = terms["loss"] + terms["rec"]
if return_aux:
return terms, {}, {'rec_image': rec_image, 'gt_image': gt_image}
return terms, {}
@torch.no_grad()
def run_snapshot(
self,
num_samples: int,
batch_size: int,
verbose: bool = False,
) -> Dict:
dataloader = DataLoader(
copy.deepcopy(self.dataset),
batch_size=batch_size,
shuffle=True,
num_workers=0,
collate_fn=self.dataset.collate_fn if hasattr(self.dataset, 'collate_fn') else None,
)
# inference
ret_dict = {}
gt_images = []
exts = []
ints = []
reps = []
for i in range(0, num_samples, batch_size):
batch = min(batch_size, num_samples - i)
data = next(iter(dataloader))
args = {k: v[:batch].cuda() for k, v in data.items()}
gt_images.append(args['image'] * args['alpha'][:, None])
exts.append(args['extrinsics'])
ints.append(args['intrinsics'])
reps.extend(self.models['decoder'](args['latents']))
gt_images = torch.cat(gt_images, dim=0)
ret_dict.update({f'gt_image': {'value': gt_images, 'type': 'image'}})
# render single view
exts = torch.cat(exts, dim=0)
ints = torch.cat(ints, dim=0)
self.renderer.rendering_options.bg_color = (0, 0, 0)
self.renderer.rendering_options.resolution = gt_images.shape[-1]
render_results = self._render_batch(reps, exts, ints)
ret_dict.update({f'rec_image': {'value': render_results['color'], 'type': 'image'}})
# render multiview
self.renderer.rendering_options.resolution = 512
## Build camera
yaws = [0, np.pi / 2, np.pi, 3 * np.pi / 2]
yaws_offset = np.random.uniform(-np.pi / 4, np.pi / 4)
yaws = [y + yaws_offset for y in yaws]
pitch = [np.random.uniform(-np.pi / 4, np.pi / 4) for _ in range(4)]
## render each view
miltiview_images = []
for yaw, pitch in zip(yaws, pitch):
orig = torch.tensor([
np.sin(yaw) * np.cos(pitch),
np.cos(yaw) * np.cos(pitch),
np.sin(pitch),
]).float().cuda() * 2
fov = torch.deg2rad(torch.tensor(30)).cuda()
extrinsics = utils3d.torch.extrinsics_look_at(orig, torch.tensor([0, 0, 0]).float().cuda(), torch.tensor([0, 0, 1]).float().cuda())
intrinsics = utils3d.torch.intrinsics_from_fov_xy(fov, fov)
extrinsics = extrinsics.unsqueeze(0).expand(num_samples, -1, -1)
intrinsics = intrinsics.unsqueeze(0).expand(num_samples, -1, -1)
render_results = self._render_batch(reps, extrinsics, intrinsics)
miltiview_images.append(render_results['color'])
## Concatenate views
miltiview_images = torch.cat([
torch.cat(miltiview_images[:2], dim=-2),
torch.cat(miltiview_images[2:], dim=-2),
], dim=-1)
ret_dict.update({f'miltiview_image': {'value': miltiview_images, 'type': 'image'}})
self.renderer.rendering_options.bg_color = 'random'
return ret_dict
|