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train_supervision.py
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import pytorch_lightning as pl
from pytorch_lightning.callbacks import ModelCheckpoint
from tools.cfg import py2cfg
import os
import torch
from torch import nn
import cv2
import numpy as np
import argparse
from pathlib import Path
from tools.metric import Evaluator
from pytorch_lightning.loggers import CSVLogger
import random
def seed_everything(seed):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = True
def get_args():
parser = argparse.ArgumentParser()
arg = parser.add_argument
arg("-c", "--config_path", type=Path, help="Path to the config.", required=True)
return parser.parse_args()
class Supervision_Train(pl.LightningModule):
def __init__(self, config):
super().__init__()
self.config = config
self.net = config.net
self.loss = config.loss
self.metrics_train = Evaluator(num_class=config.num_classes)
self.metrics_val = Evaluator(num_class=config.num_classes)
def forward(self, x):
# only net is used in the prediction/inference
seg_pre = self.net(x)
return seg_pre
def training_step(self, batch, batch_idx):
img, mask = batch['img'], batch['gt_semantic_seg']
prediction = self.net(img)
loss = self.loss(prediction, mask)
if self.config.use_aux_loss:
pre_mask = nn.Softmax(dim=1)(prediction[0])
else:
pre_mask = nn.Softmax(dim=1)(prediction)
pre_mask = pre_mask.argmax(dim=1)
for i in range(mask.shape[0]):
self.metrics_train.add_batch(mask[i].cpu().numpy(), pre_mask[i].cpu().numpy())
return {"loss": loss}
def on_train_epoch_end(self):
if 'vaihingen' in self.config.log_name:
mIoU = np.nanmean(self.metrics_train.Intersection_over_Union()[:-1])
F1 = np.nanmean(self.metrics_train.F1()[:-1])
elif 'potsdam' in self.config.log_name:
mIoU = np.nanmean(self.metrics_train.Intersection_over_Union()[:-1])
F1 = np.nanmean(self.metrics_train.F1()[:-1])
elif 'whubuilding' in self.config.log_name:
mIoU = np.nanmean(self.metrics_train.Intersection_over_Union()[:-1])
F1 = np.nanmean(self.metrics_train.F1()[:-1])
elif 'massbuilding' in self.config.log_name:
mIoU = np.nanmean(self.metrics_train.Intersection_over_Union()[:-1])
F1 = np.nanmean(self.metrics_train.F1()[:-1])
elif 'cropland' in self.config.log_name:
mIoU = np.nanmean(self.metrics_train.Intersection_over_Union()[:-1])
F1 = np.nanmean(self.metrics_train.F1()[:-1])
else:
mIoU = np.nanmean(self.metrics_train.Intersection_over_Union())
F1 = np.nanmean(self.metrics_train.F1())
OA = np.nanmean(self.metrics_train.OA())
iou_per_class = self.metrics_train.Intersection_over_Union()
eval_value = {'mIoU': mIoU,
'F1': F1,
'OA': OA}
print('train:', eval_value)
iou_value = {}
for class_name, iou in zip(self.config.classes, iou_per_class):
iou_value[class_name] = iou
print(iou_value)
self.metrics_train.reset()
log_dict = {'train_mIoU': mIoU, 'train_F1': F1, 'train_OA': OA}
self.log_dict(log_dict, prog_bar=True)
def validation_step(self, batch, batch_idx):
img, mask = batch['img'], batch['gt_semantic_seg']
prediction = self.forward(img)
pre_mask = nn.Softmax(dim=1)(prediction)
pre_mask = pre_mask.argmax(dim=1)
for i in range(mask.shape[0]):
self.metrics_val.add_batch(mask[i].cpu().numpy(), pre_mask[i].cpu().numpy())
loss_val = self.loss(prediction, mask)
return {"loss_val": loss_val}
def on_validation_epoch_end(self):
if 'vaihingen' in self.config.log_name:
mIoU = np.nanmean(self.metrics_val.Intersection_over_Union()[:-1])
F1 = np.nanmean(self.metrics_val.F1()[:-1])
elif 'potsdam' in self.config.log_name:
mIoU = np.nanmean(self.metrics_val.Intersection_over_Union()[:-1])
F1 = np.nanmean(self.metrics_val.F1()[:-1])
elif 'whubuilding' in self.config.log_name:
mIoU = np.nanmean(self.metrics_val.Intersection_over_Union()[:-1])
F1 = np.nanmean(self.metrics_val.F1()[:-1])
elif 'massbuilding' in self.config.log_name:
mIoU = np.nanmean(self.metrics_val.Intersection_over_Union()[:-1])
F1 = np.nanmean(self.metrics_val.F1()[:-1])
elif 'cropland' in self.config.log_name:
mIoU = np.nanmean(self.metrics_val.Intersection_over_Union()[:-1])
F1 = np.nanmean(self.metrics_val.F1()[:-1])
else:
mIoU = np.nanmean(self.metrics_val.Intersection_over_Union())
F1 = np.nanmean(self.metrics_val.F1())
OA = np.nanmean(self.metrics_val.OA())
iou_per_class = self.metrics_val.Intersection_over_Union()
eval_value = {'mIoU': mIoU,
'F1': F1,
'OA': OA}
print('val:', eval_value)
iou_value = {}
for class_name, iou in zip(self.config.classes, iou_per_class):
iou_value[class_name] = iou
print(iou_value)
self.metrics_val.reset()
log_dict = {'val_mIoU': mIoU, 'val_F1': F1, 'val_OA': OA}
self.log_dict(log_dict, prog_bar=True)
def configure_optimizers(self):
optimizer = self.config.optimizer
lr_scheduler = self.config.lr_scheduler
return [optimizer], [lr_scheduler]
def train_dataloader(self):
return self.config.train_loader
def val_dataloader(self):
return self.config.val_loader
# training
def main():
args = get_args()
config = py2cfg(args.config_path)
seed_everything(42)
checkpoint_callback = ModelCheckpoint(save_top_k=config.save_top_k, monitor=config.monitor,
save_last=config.save_last, mode=config.monitor_mode,
dirpath=config.weights_path,
filename=config.weights_name)
logger = CSVLogger('lightning_logs', name=config.log_name)
model = Supervision_Train(config)
if config.pretrained_ckpt_path:
model = Supervision_Train.load_from_checkpoint(config.pretrained_ckpt_path, config=config)
trainer = pl.Trainer(devices=config.gpus, max_epochs=config.max_epoch, accelerator='auto',
check_val_every_n_epoch=config.check_val_every_n_epoch,
callbacks=[checkpoint_callback], strategy='auto',
logger=logger)
trainer.fit(model=model, ckpt_path=config.resume_ckpt_path)
if __name__ == "__main__":
main()