File size: 8,641 Bytes
b976bf9 |
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 |
import torch, argparse, numpy as np
from torch.distributed.optim import ZeroRedundancyOptimizer
from nerf.network import NeRFNetwork
from nerf.renderer import NeRFRenderer
from nerf.provider import get_loaders
from nerf.utils import seed_everything, PSNRMeter
from diffusion.gaussian_diffusion import GaussianDiffusion, get_beta_schedule
from diffusion.unet import UNetModel
from diffusion.utils import Trainer
class DiffusionModel(torch.nn.Module):
def __init__(self, opt, criterion, fp16=False, device=None):
super().__init__()
self.opt = opt
self.criterion = criterion
self.device = device
self.betas = get_beta_schedule('linear', beta_start=0.0001, beta_end=self.opt.beta_end, num_diffusion_timesteps=1000)
self.diffusion_process = GaussianDiffusion(betas=self.betas)
attention_resolutions = (int(self.opt.coarse_volume_resolution / 4), int(self.opt.coarse_volume_resolution / 8))
channel_mult = [int(it) for it in self.opt.channel_mult.split(',')]
assert len(channel_mult) == 4
self.diffusion_network = UNetModel(
in_channels=self.opt.coarse_volume_channel,
model_channels=self.opt.model_channels,
out_channels=self.opt.coarse_volume_channel,
num_res_blocks=self.opt.num_res_blocks,
attention_resolutions=attention_resolutions,
dropout=0.0,
channel_mult=channel_mult,
dims=3,
use_checkpoint=True,
use_fp16=fp16,
num_head_channels=64,
use_scale_shift_norm=True,
resblock_updown=True,
encoder_channels=512,
)
self.diffusion_network.to(self.device)
def forward(self, x, t, cond):
if self.opt.low_freq_noise > 0:
alpha = self.opt.low_freq_noise
noise = np.sqrt(1 - alpha) * torch.randn_like(x) + np.sqrt(alpha) * torch.randn(x.shape[0], x.shape[1], 1, 1, 1, device=x.device, dtype=x.dtype)
else:
noise = torch.randn_like(x)
x_t = self.diffusion_process.q_sample(x, t, noise=noise)
x_pred = self.diffusion_network(x_t, t, cond)
loss = self.criterion(x, x_pred)
return loss, x_pred
def get_params(self, lr):
params = [
{'params': list(self.diffusion_network.parameters()), 'lr': lr},
]
return params
def load_encoder(opt, device):
volume_network = NeRFNetwork(opt=opt, device=device)
volume_renderer = NeRFRenderer(opt=opt, network=volume_network, device=device)
volume_renderer_checkpoint = torch.load(opt.encoder_ckpt, map_location='cpu')
volume_renderer_state_dict = {}
for k, v in volume_renderer_checkpoint['model'].items():
volume_renderer_state_dict[k.replace('module.', '')] = v
volume_renderer.load_state_dict(volume_renderer_state_dict)
volume_renderer.eval()
volume_encoder = volume_renderer.network.encoder
return volume_encoder, volume_renderer
def fn(i, opt):
world_size, global_rank, local_rank = opt.gpus * opt.nodes, i + opt.node * opt.gpus, i
if world_size > 1:
torch.distributed.init_process_group(backend='nccl', init_method=f'tcp://{opt.master}:{opt.port}', world_size=world_size, rank=global_rank)
if local_rank == 0:
print(opt)
print(f'initiate node{opt.node}, rank{global_rank}, gpu{local_rank}')
device = torch.device(f'cuda:{local_rank}' if torch.cuda.is_available() else 'cpu')
torch.cuda.set_device(local_rank)
seed_everything(opt.seed + global_rank)
train_ids = open(opt.path, 'r').read().strip().splitlines()
val_ids = train_ids[:opt.validate_objects]
test_ids = open(opt.test_list, 'r').read().splitlines()[:8]
vol_batch_size, opt.batch_size = opt.batch_size, 1
train_loader, val_loader, test_loader = get_loaders(opt, train_ids, val_ids, test_ids, batch_size=vol_batch_size)
volume_encoder, volume_renderer = load_encoder(opt, device)
criterion = torch.nn.MSELoss(reduction='none')
diffusion_model = DiffusionModel(opt, criterion, fp16=opt.fp16, device=device)
diffusion_model.to(device)
optimizer = ZeroRedundancyOptimizer(
diffusion_model.get_params(opt.lr),
optimizer_class=torch.optim.Adam,
betas=(0.9, 0.99),
eps=1e-6,
weight_decay=2e-3,
parameters_as_bucket_view=False,
overlap_with_ddp=False,
)
scheduler = torch.optim.lr_scheduler.LambdaLR(optimizer, lambda iter: 1)
trainer = Trainer(name='train',
opt=opt,
device=device,
metrics=[PSNRMeter()],
optimizer=optimizer,
scheduler=scheduler,
criterion=criterion,
model=diffusion_model,
encoder=volume_encoder,
renderer=volume_renderer,
clip_model="ViT-B/32",
ema_decay=opt.ema_decay,
eval_interval=opt.eval_interval,
workspace=opt.save_dir,
checkpoint_path=opt.ckpt,
local_rank=global_rank,
world_size=world_size,
)
trainer.train(train_loader, val_loader, test_loader, opt.epochs)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('path', type=str)
parser.add_argument('save_dir', type=str)
# data
parser.add_argument('--data_root', type=str, default='path/to/dataset')
parser.add_argument('--test_list', type=str, default='path/to/test_object_list')
parser.add_argument('--batch_size', type=int, default=4)
parser.add_argument('--validate_objects', type=int, default=8)
parser.add_argument('--downscale', type=int, default=1)
# training
parser.add_argument('--gpus', type=int, default=8)
parser.add_argument('--nodes', type=int, default=1)
parser.add_argument('--node', type=int, default=0)
parser.add_argument('--master', type=str, default='127.0.0.1')
parser.add_argument('--port', type=int, default=12345)
parser.add_argument('--seed', type=int, default=0)
parser.add_argument('--epochs', type=int, default=1000)
parser.add_argument('--lr', type=float, default=1e-5)
parser.add_argument('--ckpt', type=str, default='scratch')
parser.add_argument('--eval_interval', type=int, default=1)
parser.add_argument('--fp16', action='store_true')
parser.add_argument('--ema_decay', type=float, default=0.99)
parser.add_argument('--ema_freq', type=int, default=10)
parser.add_argument('--depth_loss', type=float, default=0)
parser.add_argument('--lpips_loss', type=float, default=0)
# encoder
parser.add_argument('--image_channel', type=int, default=3)
parser.add_argument('--extractor_channel', type=int, default=32)
parser.add_argument('--coarse_volume_resolution', type=int, default=32)
parser.add_argument('--coarse_volume_channel', type=int, default=4)
parser.add_argument('--fine_volume_channel', type=int, default=32)
parser.add_argument('--gaussian_lambda', type=float, default=1e4)
parser.add_argument('--n_source', type=int, default=32)
parser.add_argument('--mlp_layer', type=int, default=5)
parser.add_argument('--mlp_dim', type=int, default=256)
parser.add_argument('--costreg_ch_mult', type=str, default='2,4,8')
parser.add_argument('--encoder_clamp_range', type=float, default=100)
parser.add_argument('--encoder_ckpt', type=str, default='encoder.pth')
# diffusion
parser.add_argument('--beta_end', type=float, default=0.03)
parser.add_argument('--model_channels', type=int, default=128)
parser.add_argument('--num_res_blocks', type=int, default=2)
parser.add_argument('--channel_mult', type=str, default='1,2,3,5')
parser.add_argument('--timestep_range', type=str, default='0,1000')
parser.add_argument('--timestep_to_eval', type=str, default='-1')
parser.add_argument('--low_freq_noise', type=float, default=0.5)
parser.add_argument('--encoder_mean', type=float, default=-4.15856266)
parser.add_argument('--encoder_std', type=float, default=4.82153749)
parser.add_argument('--diffusion_clamp_range', type=float, default=3)
# render
parser.add_argument('--num_rays', type=int, default=24576)
parser.add_argument('--num_steps', type=int, default=256)
parser.add_argument('--bound', type=float, default=1)
opt = parser.parse_args()
torch.multiprocessing.spawn(fn, args=(opt,), nprocs=opt.gpus)
|