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c2d9714 | 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 | import os
import json
import torch
import numpy as np
import click
import lightning.pytorch as pl
from lightning.pytorch.loggers import TensorBoardLogger
from lightning.pytorch.callbacks import ModelCheckpoint, LearningRateMonitor
from pytorchvideo.transforms import Normalize, Permute, RandAugment
from torch.utils.data import DataLoader
from torchvision.transforms import transforms as T
from torchvision.transforms._transforms_video import ToTensorVideo
from torchvision.transforms import InterpolationMode
from rnn_dataset import SyntaxDataset
from rnn_model import SyntaxLightningModule
torch.set_float32_matmul_precision("medium")
"""
Обучение RNN-head поверх предобученного backbone для SYNTAX score.
Этапы:
1) pretrain — обучается только head (backbone заморожен);
2) full — fine-tuning всей модели (backbone + head).
"""
def get_transforms(video_size, imagenet_mean, imagenet_std, train=True):
"""Трансформации для видео (train с аугментациями, test без)."""
interpolation_choices = [InterpolationMode.BILINEAR, InterpolationMode.BICUBIC]
if train:
return T.Compose([
ToTensorVideo(),
Permute(dims=[1, 0, 2, 3]), # C,T,H,W → T,C,H,W
RandAugment(magnitude=10, num_layers=2),
T.RandomHorizontalFlip(),
Permute(dims=[1, 0, 2, 3]), # T,C,H,W → C,T,H,W
T.RandomChoice([
T.Resize(size=video_size, interpolation=interp, antialias=True)
for interp in interpolation_choices
]),
Normalize(mean=imagenet_mean, std=imagenet_std),
])
return T.Compose([
ToTensorVideo(),
T.Resize(size=video_size, interpolation=InterpolationMode.BICUBIC, antialias=True),
Normalize(mean=imagenet_mean, std=imagenet_std),
])
def make_dataloader(dataset, batch_size, num_workers):
"""DataLoader с shuffle (sampler закомментирован)."""
# dataset.get_sample_weights() # можно включить WeightedRandomSampler
return DataLoader(
dataset,
batch_size=batch_size,
num_workers=num_workers,
shuffle=True if not dataset.inference else False,
drop_last=True,
pin_memory=True,
)
def make_model(num_classes, video_shape, lr, variant, weight_decay, max_epochs,
weight_path=None, pl_weight_path=None, pt_weights_format=False):
"""Создание SyntaxLightningModule."""
return SyntaxLightningModule(
num_classes=num_classes,
lr=lr,
variant=variant,
weight_decay=weight_decay,
max_epochs=max_epochs,
weight_path=weight_path,
pl_weight_path=pl_weight_path,
pt_weights_format=pt_weights_format,
)
def make_callbacks(artery: str, fold: int, phase: str):
"""Callbacks: LR monitor + checkpoint по val_mae."""
lr_monitor = LearningRateMonitor(logging_interval="epoch")
if phase == "pre":
checkpoint = ModelCheckpoint(
monitor="val_mae",
save_top_k=1,
mode="min",
filename="model-{epoch:02d}-{val_rmse:.3f}",
save_last=True,
)
elif phase == "full":
checkpoint = ModelCheckpoint(
monitor="val_mae",
save_top_k=3,
mode="min",
filename="model-{epoch:02d}-{val_rmse:.3f}",
save_last=True,
)
else:
raise ValueError(f"phase must be 'pre' or 'full'")
return [lr_monitor, checkpoint]
def make_trainer(max_epochs, logger_name, callbacks):
"""Lightning Trainer с TensorBoard."""
logger = TensorBoardLogger(save_dir="rnn_logs", name=logger_name)
trainer = pl.Trainer(
max_epochs=max_epochs,
accelerator="gpu",
devices=1, # легко поменять
strategy="ddp_find_unused_parameters_true",
precision="bf16-mixed",
callbacks=callbacks,
log_every_n_steps=10,
logger=logger,
)
return trainer
@click.command()
@click.option(
"-r", "--dataset-root", type=click.Path(exists=True), required=True,
help="Корень датасета (где лежат folds/*.json и DICOM).",
)
@click.option("--fold", type=int, default=0, help="Номер фолда (0-4).")
@click.option("-a", "--artery", type=str, default="right", help="'left' или 'right'.")
@click.option("--variant", type=str, default="lstm_mean", help="Тип head (lstm_mean и др.).")
@click.option("-nc", "--num-classes", type=int, default=2)
@click.option("-b", "--batch-size", type=int, default=8)
@click.option("-f", "--frames-per-clip", type=int, default=32)
@click.option("-v", "--video-size", type=click.Tuple([int, int]), default=(256, 256))
@click.option("--max-epochs", type=int, default=10)
@click.option("--num-workers", type=int, default=8)
@click.option("--fast-dev-run", is_flag=True)
@click.option("--seed", type=int, default=42)
@click.option("--backbone-ckpt", type=str, help="Путь к backbone-чекпоинту для pretrain.")
def main(
dataset_root, fold, artery, variant, num_classes, batch_size, frames_per_clip,
video_size, max_epochs, num_workers, fast_dev_run, seed, backbone_ckpt,
):
pl.seed_everything(seed)
artery = artery.lower()
artery_bin = {"left": 0, "right": 1}[artery]
print(f"Training {variant} head for {artery} artery, fold {fold}")
imagenet_mean = [0.485, 0.456, 0.406]
imagenet_std = [0.229, 0.224, 0.225]
# Datasets с относительными путями
train_meta = os.path.join("rnn_folds", f"step2_rnn_fold{fold:02d}_train.json")
val_meta = os.path.join("rnn_folds", f"step2_rnn_fold{fold:02d}_eval.json")
train_set = SyntaxDataset(
root=dataset_root,
meta=train_meta,
train=True,
length=frames_per_clip,
label=f"syntax_{artery}",
artery=artery,
transform=get_transforms(video_size, imagenet_mean, imagenet_std, train=True),
)
val_set = SyntaxDataset(
root=dataset_root,
meta=val_meta,
train=False,
length=frames_per_clip,
label=f"syntax_{artery}",
artery=artery,
validation=True,
transform=get_transforms(video_size, imagenet_mean, imagenet_std, train=False),
)
# DataLoaders
train_loader_pre = make_dataloader(train_set, batch_size * 2, num_workers)
train_loader_post = make_dataloader(train_set, batch_size, num_workers)
val_loader = make_dataloader(val_set, 1, num_workers)
# Форма видео
x, *_ = next(iter(train_loader_pre))
video_shape = x.shape[1:]
# Pretrain head
callbacks_pre = make_callbacks(artery, fold, "pre")
model_pre = make_model(
num_classes, video_shape, lr=1e-4, variant=variant,
weight_decay=0.01, max_epochs=max_epochs, weight_path=backbone_ckpt,
)
trainer_pre = make_trainer(max_epochs, f"{artery}_{variant}_pre_fold{fold:02d}", callbacks_pre)
trainer_pre.fit(model_pre, train_loader_pre, val_loader)
# Full fine-tune
callbacks_full = make_callbacks(artery, fold, "full")
model_full = make_model(
num_classes, video_shape, lr=2e-5, variant=variant,
weight_decay=0.01, max_epochs=max_epochs,
pl_weight_path=trainer_pre.checkpoint_callback.best_model_path,
)
trainer_full = make_trainer(max_epochs, f"{artery}_{variant}_full_fold{fold:02d}", callbacks_full)
trainer_full.fit(model_full, train_loader_post, val_loader)
if __name__ == "__main__":
main()
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