Spaces:
Build error
Build error
File size: 7,839 Bytes
e206fc8 |
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 |
import tyro
import time
import random
import torch
from core.options import AllConfigs
from core.models import LGM
from accelerate import Accelerator, DistributedDataParallelKwargs
from safetensors.torch import load_file
import kiui
def main():
opt = tyro.cli(AllConfigs)
# ddp_kwargs = DistributedDataParallelKwargs(find_unused_parameters=True)
accelerator = Accelerator(
mixed_precision=opt.mixed_precision,
gradient_accumulation_steps=opt.gradient_accumulation_steps,
# kwargs_handlers=[ddp_kwargs],
)
# model
model = LGM(opt)
# resume
if opt.resume is not None:
if opt.resume.endswith('safetensors'):
ckpt = load_file(opt.resume, device='cpu')
else:
ckpt = torch.load(opt.resume, map_location='cpu')
# tolerant load (only load matching shapes)
# model.load_state_dict(ckpt, strict=False)
state_dict = model.state_dict()
for k, v in ckpt.items():
if k in state_dict:
if state_dict[k].shape == v.shape:
state_dict[k].copy_(v)
else:
accelerator.print(f'[WARN] mismatching shape for param {k}: ckpt {v.shape} != model {state_dict[k].shape}, ignored.')
else:
accelerator.print(f'[WARN] unexpected param {k}: {v.shape}')
# data
if opt.data_mode == 's3':
from core.provider_objaverse import ObjaverseDataset as Dataset
else:
raise NotImplementedError
train_dataset = Dataset(opt, training=True)
train_dataloader = torch.utils.data.DataLoader(
train_dataset,
batch_size=opt.batch_size,
shuffle=True,
num_workers=opt.num_workers,
pin_memory=True,
drop_last=True,
)
test_dataset = Dataset(opt, training=False)
test_dataloader = torch.utils.data.DataLoader(
test_dataset,
batch_size=opt.batch_size,
shuffle=False,
num_workers=0,
pin_memory=True,
drop_last=False,
)
# optimizer
optimizer = torch.optim.AdamW(model.parameters(), lr=opt.lr, weight_decay=0.05, betas=(0.9, 0.95))
# scheduler (per-iteration)
# scheduler = torch.optim.lr_scheduler.CosineAnnealingWarmRestarts(optimizer, T_0=3000, eta_min=1e-6)
total_steps = opt.num_epochs * len(train_dataloader)
pct_start = 3000 / total_steps
scheduler = torch.optim.lr_scheduler.OneCycleLR(optimizer, max_lr=opt.lr, total_steps=total_steps, pct_start=pct_start)
# accelerate
model, optimizer, train_dataloader, test_dataloader, scheduler = accelerator.prepare(
model, optimizer, train_dataloader, test_dataloader, scheduler
)
# loop
for epoch in range(opt.num_epochs):
# train
model.train()
total_loss = 0
total_psnr = 0
for i, data in enumerate(train_dataloader):
with accelerator.accumulate(model):
optimizer.zero_grad()
step_ratio = (epoch + i / len(train_dataloader)) / opt.num_epochs
out = model(data, step_ratio)
loss = out['loss']
psnr = out['psnr']
accelerator.backward(loss)
# gradient clipping
if accelerator.sync_gradients:
accelerator.clip_grad_norm_(model.parameters(), opt.gradient_clip)
optimizer.step()
scheduler.step()
total_loss += loss.detach()
total_psnr += psnr.detach()
if accelerator.is_main_process:
# logging
if i % 100 == 0:
mem_free, mem_total = torch.cuda.mem_get_info()
print(f"[INFO] {i}/{len(train_dataloader)} mem: {(mem_total-mem_free)/1024**3:.2f}/{mem_total/1024**3:.2f}G lr: {scheduler.get_last_lr()[0]:.7f} step_ratio: {step_ratio:.4f} loss: {loss.item():.6f}")
# save log images
if i % 500 == 0:
gt_images = data['images_output'].detach().cpu().numpy() # [B, V, 3, output_size, output_size]
gt_images = gt_images.transpose(0, 3, 1, 4, 2).reshape(-1, gt_images.shape[1] * gt_images.shape[3], 3) # [B*output_size, V*output_size, 3]
kiui.write_image(f'{opt.workspace}/train_gt_images_{epoch}_{i}.jpg', gt_images)
# gt_alphas = data['masks_output'].detach().cpu().numpy() # [B, V, 1, output_size, output_size]
# gt_alphas = gt_alphas.transpose(0, 3, 1, 4, 2).reshape(-1, gt_alphas.shape[1] * gt_alphas.shape[3], 1)
# kiui.write_image(f'{opt.workspace}/train_gt_alphas_{epoch}_{i}.jpg', gt_alphas)
pred_images = out['images_pred'].detach().cpu().numpy() # [B, V, 3, output_size, output_size]
pred_images = pred_images.transpose(0, 3, 1, 4, 2).reshape(-1, pred_images.shape[1] * pred_images.shape[3], 3)
kiui.write_image(f'{opt.workspace}/train_pred_images_{epoch}_{i}.jpg', pred_images)
# pred_alphas = out['alphas_pred'].detach().cpu().numpy() # [B, V, 1, output_size, output_size]
# pred_alphas = pred_alphas.transpose(0, 3, 1, 4, 2).reshape(-1, pred_alphas.shape[1] * pred_alphas.shape[3], 1)
# kiui.write_image(f'{opt.workspace}/train_pred_alphas_{epoch}_{i}.jpg', pred_alphas)
total_loss = accelerator.gather_for_metrics(total_loss).mean()
total_psnr = accelerator.gather_for_metrics(total_psnr).mean()
if accelerator.is_main_process:
total_loss /= len(train_dataloader)
total_psnr /= len(train_dataloader)
accelerator.print(f"[train] epoch: {epoch} loss: {total_loss.item():.6f} psnr: {total_psnr.item():.4f}")
# checkpoint
# if epoch % 10 == 0 or epoch == opt.num_epochs - 1:
accelerator.wait_for_everyone()
accelerator.save_model(model, opt.workspace)
# eval
with torch.no_grad():
model.eval()
total_psnr = 0
for i, data in enumerate(test_dataloader):
out = model(data)
psnr = out['psnr']
total_psnr += psnr.detach()
# save some images
if accelerator.is_main_process:
gt_images = data['images_output'].detach().cpu().numpy() # [B, V, 3, output_size, output_size]
gt_images = gt_images.transpose(0, 3, 1, 4, 2).reshape(-1, gt_images.shape[1] * gt_images.shape[3], 3) # [B*output_size, V*output_size, 3]
kiui.write_image(f'{opt.workspace}/eval_gt_images_{epoch}_{i}.jpg', gt_images)
pred_images = out['images_pred'].detach().cpu().numpy() # [B, V, 3, output_size, output_size]
pred_images = pred_images.transpose(0, 3, 1, 4, 2).reshape(-1, pred_images.shape[1] * pred_images.shape[3], 3)
kiui.write_image(f'{opt.workspace}/eval_pred_images_{epoch}_{i}.jpg', pred_images)
# pred_alphas = out['alphas_pred'].detach().cpu().numpy() # [B, V, 1, output_size, output_size]
# pred_alphas = pred_alphas.transpose(0, 3, 1, 4, 2).reshape(-1, pred_alphas.shape[1] * pred_alphas.shape[3], 1)
# kiui.write_image(f'{opt.workspace}/eval_pred_alphas_{epoch}_{i}.jpg', pred_alphas)
torch.cuda.empty_cache()
total_psnr = accelerator.gather_for_metrics(total_psnr).mean()
if accelerator.is_main_process:
total_psnr /= len(test_dataloader)
accelerator.print(f"[eval] epoch: {epoch} psnr: {psnr:.4f}")
if __name__ == "__main__":
main()
|