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main.py
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main.py
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# -*- coding: utf-8 -*-
# Author: Hao Chun Chang <changhaochun84@gmail.comm>
#
# Entry-point for training, testing, and explaining models
import os
from os.path import join
from argparse import ArgumentParser
import torch
import numpy as np
import pytorch_lightning as pl
from datasets import ECGDataModule
from model import MyCNN
import utils
path = join(os.getcwd(), "data")
data_path = join(path, "data_raw_train.npz")
label_path = join(path, "meta_train.csv")
config_path = join(os.getcwd(), "config.json")
def train(args, dm, net):
"""
Train the model.
Args:
args (:obj:`pd.DataFrame`): Hyperparameters and configurations for model.
dm (:obj:`datasets.ECGData.ECGDataModule`): The module for loading data.
net: (:obj:`pytorch_lightning.LightningModule`): The network system instance.
"""
from pytorch_lightning.callbacks.early_stopping import EarlyStopping
from pytorch_lightning.callbacks import ModelCheckpoint
early_stop_callback = EarlyStopping(
monitor="val_accuracy",
patience=3,
verbose=True,
mode="max"
)
checkpoint_callback = ModelCheckpoint(
monitor="val_loss",
dirpath=args.ckpt_path,
filename="mycnn-{epoch:02d}-{val_loss:.2f}",
save_top_k=1,
mode="min"
)
callback_list = [checkpoint_callback, early_stop_callback]
trainer = pl.Trainer(
gpus=args.gpus,
callbacks=callback_list,
default_root_dir=args.ckpt_path,
terminate_on_nan=True,
deterministic=True
)
trainer.fit(net, datamodule=dm)
def test(args, dm, net):
from pytorch_lightning.metrics.functional.classification import confusion_matrix
model = net.load_from_checkpoint(
checkpoint_path=args.ckpt_path,
num_channel=dm.n_channels,
num_class=dm.n_classes
)
model.eval()
dm.config["batch_size"] = 1
dm.prepare_data()
dm.setup()
trainer = pl.Trainer()
trainer.test(model, datamodule=dm)
cmat = confusion_matrix(
torch.stack(model.predictions),
torch.stack(model.targets),
num_classes=dm.n_classes
)
utils.plot_confusion_matrix(
matrix=cmat.numpy(),
classes=dm.raw_Y.columns,
figure_name="./figures/cmat.jpg"
)
def explain(args, dm, net):
from interpret import Explainer
model = net.load_from_checkpoint(
checkpoint_path=args.ckpt_path,
num_channel=dm.n_channels,
num_class=dm.n_classes
)
dm.config["batch_size"] = 1
dm.prepare_data()
dm.setup()
print("Generating explanation using GradCam...")
data = next(iter(dm.train_dataloader()))
sample = utils.preprocess_signals(data["signal"])
label = data["label"].numpy().argmax()
GradCamExplainer = Explainer(
"GradCam",
model=model.network,
feature_module=model.network[:9],
target_layer_names=["8"]
)
cam_mask = GradCamExplainer.explain_instance(sample)
utils.show_cam_on_image(sample=sample, mask=cam_mask, figure_path="./figures/gradcam.jpg")
print("Generating explanation using LIME...")
LimeExplainer = Explainer("LIME", model=model)
explanation = LimeExplainer.explain_instance(data["signal"], labels=[label])
sample, mask = explanation.get_instance_and_mask(label)
masked_sample = (sample * mask)[0].transpose()
utils.plot_images(
sample=masked_sample,
figure_path="./figures/lime_label{}.jpg".format(label)
)
def main(args):
pl.seed_everything(args.seed)
ecg_dm = ECGDataModule(data_path, label_path, config_path)
net = MyCNN(
num_channel=ecg_dm.n_channels,
num_class=ecg_dm.n_classes,
chunk_size=ecg_dm.chunk_size
)
if args.mode == "train":
train(args, ecg_dm, net)
elif args.mode == "test":
test(args, ecg_dm, net)
elif args.mode == "explain":
explain(args, ecg_dm, net)
else:
raise ValueError(
"Unrecongnized mode. There are 3 modes: train, test and explain."
)
if __name__ == "__main__":
parser = ArgumentParser(description="Main entry point of this project.")
parser.add_argument(
"mode",
type=str,
help="Specifying modes: ['train', 'test', 'explain']"
)
parser.add_argument(
"--ckpt_path",
default="./model_checkpoints",
help="Checkpoint path for storing models"
)
parser.add_argument("--gpus", default=None, help="Numbers of gpus")
parser.add_argument("--seed", default=56, help="Random seed for all")
args = parser.parse_args()
main(args)