/
train_seg_cls.py
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/
train_seg_cls.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
#!pip install pytorch-lightning
#!pip install neptune-client
from argparse import ArgumentParser
# for dataset and dataloader
import os
from pytorch_lightning import Trainer
from pytorch_lightning.callbacks import ModelCheckpoint
from pytorch_lightning.callbacks import EarlyStopping
from pytorch_lightning.logging.neptune import NeptuneLogger
from pytorch_lightning import loggers
import glob
#from pytorch_lightning.logging import CometLogger
from utils import load_pytorch_model
from model import get_seg_model_from_name, get_cls_model_from_name
from systems_seg_cls import PLImageSegmentationClassificationSystem
def main(hparams):
neptune_logger = NeptuneLogger(
api_key="eyJhcGlfYWRkcmVzcyI6Imh0dHBzOi8vdWkubmVwdHVuZS5haSIsImFwaV91cmwiOiJodHRwczovL3VpLm5lcHR1bmUuYWkiLCJhcGlfa2V5IjoiN2I2ZWM0NmQtNjg0NS00ZjM5LTkzNTItN2I4Nzc0YTUzMmM0In0=",
project_name="hirune924/kaggle-PANDA",
close_after_fit=False,
upload_source_files=['*.py','*.ipynb'],
params=vars(hparams),
experiment_name=hparams.experiment_name, # Optional,
#tags=["pytorch-lightning", "mlp"] # Optional,
)
'''
comet_logger = CometLogger(
api_key="QCxbRVX2qhQj1t0ajIZl2nk2c",
workspace='hirune924', # Optional
save_dir='.', # Optional
project_name="kaggle-panda", # Optional
#rest_api_key=os.environ.get('COMET_REST_API_KEY'), # Optional
#experiment_name='default' # Optional
)'''
tb_logger = loggers.TensorBoardLogger(save_dir=hparams.log_dir, name='default', version=None)
logger_list = [tb_logger, neptune_logger] if hparams.distributed_backend!='ddp' else tb_logger
checkpoint_callback = ModelCheckpoint(
filepath=os.path.join(hparams.log_dir, '{epoch}-{avg_val_loss}-{val_qwk}'),
save_top_k=10,
verbose=True,
monitor='avg_val_loss',
mode='min',
save_weights_only = True,
period = 1
)
# default used by the Trainer
early_stop_callback = EarlyStopping(
monitor='avg_val_loss',
patience=20,
min_delta = 0.0,
strict=True,
verbose=True,
mode='min'
)
seg_model = get_seg_model_from_name(model_name=hparams.seg_model_name, in_channels=5, num_classes=2, pretrained=True)
seg_ckpt_pth = glob.glob(os.path.join(hparams.seg_ckpt_dir,'fold'+str(hparams.fold)+'*.ckpt'))
seg_model = load_pytorch_model(seg_ckpt_pth[0], seg_model)
if hparams.marge_type == 'cat':
in_channels = 7
elif hparams.marge_type == 'add':
in_channels = 3
cls_model = get_cls_model_from_name(model_name=hparams.cls_model_name, in_channels=in_channels, num_classes=1, pretrained=True)
pl_model = PLImageSegmentationClassificationSystem(seg_model, cls_model, hparams)
###
if hparams.auto_lr_find:
trainer = Trainer()
lr_finder = trainer.lr_find(pl_model)
print(lr_finder.results)
print(lr_finder.suggestion())
pl_model.learning_rate = lr_finder.suggestion()
###
trainer = Trainer(gpus=hparams.gpus, max_epochs=hparams.max_epochs,min_epochs=hparams.min_epochs,
max_steps=None,min_steps=None,
checkpoint_callback=checkpoint_callback,
early_stop_callback=early_stop_callback,
logger=logger_list,
accumulate_grad_batches=1,
precision=hparams.precision,
amp_level='O1',
auto_lr_find=False,
benchmark=True,
check_val_every_n_epoch=hparams.check_val_every_n_epoch,
distributed_backend=hparams.distributed_backend,
num_nodes=1,
fast_dev_run=False,
gradient_clip_val=0.0,
log_gpu_memory=None,
log_save_interval=100,
num_sanity_val_steps=5,
overfit_pct=0.0)
# fit model !
trainer.fit(pl_model)
#neptune_logger.experiment.log_artifact(hparams.log_dir)
if __name__ == '__main__':
parser = ArgumentParser()
parser.add_argument('-ng', '--gpus', help='num GPU all=-1',
type=int, required=False, default=-1)
parser.add_argument('-pr', '--precision', help='precision',
type=int, required=False, default=32)
parser.add_argument('-emax', '--max_epochs', help='max_epochs',
type=int, required=False, default=100)
parser.add_argument('-emin', '--min_epochs', help='min_epochs',
type=int, required=False, default=10)
parser.add_argument('-eval', '--check_val_every_n_epoch', help='check_val_every_n_epoch',
type=int, required=False, default=1)
parser.add_argument('-bs', '--batch_size', help='batch_size',
type=int, required=False, default=32)
parser.add_argument('-is', '--image_size', help='image_size',
type=int, required=False, default=256)
parser.add_argument('-nf', '--num_fold', help='fold num',
type=int, required=False, default=5)
parser.add_argument('-f', '--fold', help='target fold',
type=int, required=False, default=0)
parser.add_argument('-alf', '--auto_lr_find', help='auto lr find.',
action='store_true')
parser.add_argument('-lr', '--learning_rate', help='learning_rate',
type=float, required=False, default=1e-4)
parser.add_argument('-db', '--distributed_backend', help='distributed_backend',
type=str, required=False, default='dp')
parser.add_argument('-if', '--image_format', help='image_format',
type=str, required=False, default='tiff')
parser.add_argument('-smn', '--seg_model_name', help='seg_model_name',
type=str, required=False, default='resnet18_unet')
parser.add_argument('-scd', '--seg_ckpt_dir', help='seg_ckpt_dir',
type=str, required=False, default=None)
parser.add_argument('-cmn', '--cls_model_name', help='cls_model_name',
type=str, required=False, default='resnet18')
parser.add_argument('-mt', '--marge_type', help='marge_type',
type=str, required=False, default='cat')
parser.add_argument('-en', '--experiment_name', help='experiment_name',
type=str, required=False, default='default')
parser.add_argument('-ld', '--log_dir', help='path to log',
type=str, required=True)
parser.add_argument('-dd', '--data_dir', help='path to data dir',
type=str, required=True)
#args = parser.parse_args(['-ld', '../working/', '-dd','../input/prostate-cancer-grade-assessment/'])
args = parser.parse_args()
main(args)