File size: 3,677 Bytes
5471715 | 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 | import sys
import os
parent_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
sys.path.insert(0, parent_dir)
from ddpm import Unet3D, Trainer, GaussianDiffusion_Nolatent
import hydra
from omegaconf import DictConfig
from Dataset.TS_Dataset import get_TS_dataloader
from Dataset.MMWHS_Dataset import get_MMWHS_dataloader
import torch
from ddpm.unet import UNet
import torch.nn as nn
import sys
import os
from datetime import datetime
import atexit
import random
import numpy as np
from omegaconf import OmegaConf
def set_seed(seed):
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.deterministic = True
@hydra.main(config_path='config', config_name='base_cfg', version_base=None)
def run(cfg: DictConfig):
set_seed(1)
print(OmegaConf.to_container(cfg, resolve=True))
# torch.cuda.set_device(cfg.model.gpus)
# set_seed(cfg.model.seed)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
if cfg.model.denoising_fn == 'Unet3D':
model = Unet3D(
dim=cfg.model.diffusion_img_size,
dim_mults=cfg.model.dim_mults,
channels=cfg.model.diffusion_num_channels,
cond_dim=16,
)
elif cfg.model.denoising_fn == 'UNet':
model = UNet(
in_ch=cfg.model.diffusion_num_channels,
out_ch=cfg.model.diffusion_num_channels,
spatial_dims=3
)
else:
raise ValueError(f"Model {cfg.model.denoising_fn} doesn't exist")
model = nn.DataParallel(model)
diffusion = GaussianDiffusion_Nolatent(
model,
image_size=cfg.model.diffusion_img_size,
num_frames=cfg.model.diffusion_depth_size,
channels=cfg.model.diffusion_num_channels,
timesteps=cfg.model.timesteps,
loss_type=cfg.model.loss_type,
device=device
).to(device)
if cfg.dataset.name == 'MMWHS':
train_dataset = get_MMWHS_dataloader(root_dir=cfg.dataset.root_dir, mode=cfg.dataset.mode, data_type=cfg.dataset.data_type)
elif cfg.dataset.name == 'TS' :
train_dataset = get_TS_dataloader(root_dir=cfg.dataset.root_dir, mode=cfg.dataset.mode)
else :
raise ValueError ("No Such Dataset")
trainer = Trainer(
diffusion,
cfg=cfg,
dataset=train_dataset,
train_batch_size=cfg.model.batch_size,
save_and_sample_every=cfg.model.save_and_sample_every,
train_lr=cfg.model.train_lr,
train_num_steps=cfg.model.train_num_steps,
gradient_accumulate_every=cfg.model.gradient_accumulate_every,
ema_decay=cfg.model.ema_decay,
amp=cfg.model.amp,
results_folder=cfg.model.results_folder,
num_workers=cfg.model.num_workers,
device=device,
)
if cfg.model.load_milestone:
trainer.load(cfg.model.load_milestone)
trainer.train()
class Tee:
def __init__(self, *files):
self.files = files
def write(self, obj):
for f in self.files:
if not f.closed:
f.write(obj)
f.flush()
def flush(self):
for f in self.files:
if not f.closed:
f.flush()
if __name__ == '__main__':
log_dir = "./log_train"
os.makedirs(log_dir, exist_ok=True)
filename = os.path.join(
log_dir,
datetime.now().strftime("%Y%m%d_%H%M%S") + ".log"
)
log_file = open(filename, 'w', encoding='utf-8')
sys.stdout = Tee(sys.stdout, log_file)
atexit.register(lambda: log_file.close())
run()
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