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from email.mime import audio
from pathlib import Path
from datetime import datetime
import torch
import torch.nn as nn
from pytorch_lightning.trainer import Trainer
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
import numpy as np
import torchio as tio
from medical_diffusion.data.datamodules import SimpleDataModule
from medical_diffusion.data.datasets import AIROGSDataset, MSIvsMSS_2_Dataset, CheXpert_2_Dataset
from medical_diffusion.models.pipelines import DiffusionPipeline
from medical_diffusion.models.estimators import UNet
from medical_diffusion.external.stable_diffusion.unet_openai import UNetModel
from medical_diffusion.models.noise_schedulers import GaussianNoiseScheduler
from medical_diffusion.models.embedders import LabelEmbedder, TimeEmbbeding
from medical_diffusion.models.embedders.latent_embedders import VAE, VAEGAN, VQVAE, VQGAN
import torch.multiprocessing
torch.multiprocessing.set_sharing_strategy('file_system')
if __name__ == "__main__":
# ------------ Load Data ----------------
# ds = AIROGSDataset(
# crawler_ext='jpg',
# augment_horizontal_flip = False,
# augment_vertical_flip = False,
# # path_root='/home/gustav/Documents/datasets/AIROGS/data_256x256/',
# path_root='/mnt/hdd/datasets/eye/AIROGS/data_256x256',
# )
# ds = MSIvsMSS_2_Dataset(
# crawler_ext='jpg',
# image_resize=None,
# image_crop=None,
# augment_horizontal_flip=False,
# augment_vertical_flip=False,
# # path_root='/home/gustav/Documents/datasets/Kather_2/train',
# path_root='/mnt/hdd/datasets/pathology/kather_msi_mss_2/train/',
# )
ds = CheXpert_2_Dataset( # 256x256
augment_horizontal_flip=False,
augment_vertical_flip=False,
path_root = '/mnt/hdd/datasets/chest/CheXpert/ChecXpert-v10/preprocessed_tianyu'
)
dm = SimpleDataModule(
ds_train = ds,
batch_size=32,
# num_workers=0,
pin_memory=True,
# weights=ds.get_weights()
)
current_time = datetime.now().strftime("%Y_%m_%d_%H%M%S")
path_run_dir = Path.cwd() / 'runs' / str(current_time)
path_run_dir.mkdir(parents=True, exist_ok=True)
accelerator = 'gpu' if torch.cuda.is_available() else 'cpu'
# ------------ Initialize Model ------------
# cond_embedder = None
cond_embedder = LabelEmbedder
cond_embedder_kwargs = {
'emb_dim': 1024,
'num_classes': 2
}
time_embedder = TimeEmbbeding
time_embedder_kwargs ={
'emb_dim': 1024 # stable diffusion uses 4*model_channels (model_channels is about 256)
}
noise_estimator = UNet
noise_estimator_kwargs = {
'in_ch':8,
'out_ch':8,
'spatial_dims':2,
'hid_chs': [ 256, 256, 512, 1024],
'kernel_sizes':[3, 3, 3, 3],
'strides': [1, 2, 2, 2],
'time_embedder':time_embedder,
'time_embedder_kwargs': time_embedder_kwargs,
'cond_embedder':cond_embedder,
'cond_embedder_kwargs': cond_embedder_kwargs,
'deep_supervision': False,
'use_res_block':True,
'use_attention':'none',
}
# ------------ Initialize Noise ------------
noise_scheduler = GaussianNoiseScheduler
noise_scheduler_kwargs = {
'timesteps': 1000,
'beta_start': 0.002, # 0.0001, 0.0015
'beta_end': 0.02, # 0.01, 0.0195
'schedule_strategy': 'scaled_linear'
}
# ------------ Initialize Latent Space ------------
# latent_embedder = None
# latent_embedder = VQVAE
latent_embedder = VAE
latent_embedder_checkpoint = 'runs/2022_12_12_133315_chest_vaegan/last_vae.ckpt'
# ------------ Initialize Pipeline ------------
pipeline = DiffusionPipeline(
noise_estimator=noise_estimator,
noise_estimator_kwargs=noise_estimator_kwargs,
noise_scheduler=noise_scheduler,
noise_scheduler_kwargs = noise_scheduler_kwargs,
latent_embedder=latent_embedder,
latent_embedder_checkpoint = latent_embedder_checkpoint,
estimator_objective='x_T',
estimate_variance=False,
use_self_conditioning=False,
use_ema=False,
classifier_free_guidance_dropout=0.5, # Disable during training by setting to 0
do_input_centering=False,
clip_x0=False,
sample_every_n_steps=1000
)
# pipeline_old = pipeline.load_from_checkpoint('runs/2022_11_27_085654_chest_diffusion/last.ckpt')
# pipeline.noise_estimator.load_state_dict(pipeline_old.noise_estimator.state_dict(), strict=True)
# -------------- Training Initialization ---------------
to_monitor = "train/loss" # "pl/val_loss"
min_max = "min"
save_and_sample_every = 100
early_stopping = EarlyStopping(
monitor=to_monitor,
min_delta=0.0, # minimum change in the monitored quantity to qualify as an improvement
patience=30, # number of checks with no improvement
mode=min_max
)
checkpointing = ModelCheckpoint(
dirpath=str(path_run_dir), # dirpath
monitor=to_monitor,
every_n_train_steps=save_and_sample_every,
save_last=True,
save_top_k=2,
mode=min_max,
)
trainer = Trainer(
accelerator=accelerator,
# devices=[0],
# precision=16,
# amp_backend='apex',
# amp_level='O2',
# gradient_clip_val=0.5,
default_root_dir=str(path_run_dir),
callbacks=[checkpointing],
# callbacks=[checkpointing, early_stopping],
enable_checkpointing=True,
check_val_every_n_epoch=1,
log_every_n_steps=save_and_sample_every,
auto_lr_find=False,
# limit_train_batches=1000,
limit_val_batches=0, # 0 = disable validation - Note: Early Stopping no longer available
min_epochs=100,
max_epochs=1001,
num_sanity_val_steps=2,
)
# ---------------- Execute Training ----------------
trainer.fit(pipeline, datamodule=dm)
# ------------- Save path to best model -------------
pipeline.save_best_checkpoint(trainer.logger.log_dir, checkpointing.best_model_path)
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