178 lines
6.0 KiB
Python
178 lines
6.0 KiB
Python
# Copyright The PyTorch Lightning team.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
import os
|
|
|
|
import torch
|
|
import torch.nn.functional as F
|
|
from sklearn.datasets import make_classification
|
|
from sklearn.model_selection import train_test_split
|
|
from torch import nn
|
|
from torch.utils.data import DataLoader, Dataset
|
|
from torchmetrics import Accuracy
|
|
|
|
import pytorch_lightning as pl
|
|
from pytorch_lightning import LightningDataModule, LightningModule, seed_everything
|
|
from pytorch_lightning.callbacks import EarlyStopping
|
|
|
|
PATH_LEGACY = os.path.dirname(__file__)
|
|
|
|
|
|
class SklearnDataset(Dataset):
|
|
def __init__(self, x, y, x_type, y_type):
|
|
self.x = x
|
|
self.y = y
|
|
self._x_type = x_type
|
|
self._y_type = y_type
|
|
|
|
def __getitem__(self, idx):
|
|
return torch.tensor(self.x[idx], dtype=self._x_type), torch.tensor(self.y[idx], dtype=self._y_type)
|
|
|
|
def __len__(self):
|
|
return len(self.y)
|
|
|
|
|
|
class SklearnDataModule(LightningDataModule):
|
|
def __init__(self, sklearn_dataset, x_type, y_type, batch_size: int = 128):
|
|
super().__init__()
|
|
self.batch_size = batch_size
|
|
self._x, self._y = sklearn_dataset
|
|
self._split_data()
|
|
self._x_type = x_type
|
|
self._y_type = y_type
|
|
|
|
def _split_data(self):
|
|
self.x_train, self.x_test, self.y_train, self.y_test = train_test_split(
|
|
self._x, self._y, test_size=0.20, random_state=42
|
|
)
|
|
self.x_train, self.x_predict, self.y_train, self.y_predict = train_test_split(
|
|
self._x, self._y, test_size=0.20, random_state=42
|
|
)
|
|
self.x_train, self.x_valid, self.y_train, self.y_valid = train_test_split(
|
|
self.x_train, self.y_train, test_size=0.40, random_state=42
|
|
)
|
|
|
|
def train_dataloader(self):
|
|
return DataLoader(
|
|
SklearnDataset(self.x_train, self.y_train, self._x_type, self._y_type),
|
|
shuffle=True,
|
|
batch_size=self.batch_size,
|
|
)
|
|
|
|
def val_dataloader(self):
|
|
return DataLoader(
|
|
SklearnDataset(self.x_valid, self.y_valid, self._x_type, self._y_type), batch_size=self.batch_size
|
|
)
|
|
|
|
def test_dataloader(self):
|
|
return DataLoader(
|
|
SklearnDataset(self.x_test, self.y_test, self._x_type, self._y_type), batch_size=self.batch_size
|
|
)
|
|
|
|
def predict_dataloader(self):
|
|
return DataLoader(
|
|
SklearnDataset(self.x_predict, self.y_predict, self._x_type, self._y_type), batch_size=self.batch_size
|
|
)
|
|
|
|
|
|
class ClassifDataModule(SklearnDataModule):
|
|
def __init__(self, num_features=24, length=6000, num_classes=3, batch_size=128):
|
|
data = make_classification(
|
|
n_samples=length,
|
|
n_features=num_features,
|
|
n_classes=num_classes,
|
|
n_clusters_per_class=2,
|
|
n_informative=int(num_features / num_classes),
|
|
random_state=42,
|
|
)
|
|
super().__init__(data, x_type=torch.float32, y_type=torch.long, batch_size=batch_size)
|
|
|
|
|
|
class ClassificationModel(LightningModule):
|
|
def __init__(self, num_features=24, num_classes=3, lr=0.01):
|
|
super().__init__()
|
|
self.save_hyperparameters()
|
|
|
|
self.lr = lr
|
|
for i in range(3):
|
|
setattr(self, f"layer_{i}", nn.Linear(num_features, num_features))
|
|
setattr(self, f"layer_{i}a", torch.nn.ReLU())
|
|
setattr(self, "layer_end", nn.Linear(num_features, num_classes))
|
|
|
|
self.train_acc = Accuracy()
|
|
self.valid_acc = Accuracy()
|
|
self.test_acc = Accuracy()
|
|
|
|
def forward(self, x):
|
|
x = self.layer_0(x)
|
|
x = self.layer_0a(x)
|
|
x = self.layer_1(x)
|
|
x = self.layer_1a(x)
|
|
x = self.layer_2(x)
|
|
x = self.layer_2a(x)
|
|
x = self.layer_end(x)
|
|
logits = F.softmax(x, dim=1)
|
|
return logits
|
|
|
|
def configure_optimizers(self):
|
|
optimizer = torch.optim.Adam(self.parameters(), lr=self.lr)
|
|
return [optimizer], []
|
|
|
|
def training_step(self, batch, batch_idx):
|
|
x, y = batch
|
|
logits = self.forward(x)
|
|
loss = F.cross_entropy(logits, y)
|
|
self.log("train_loss", loss, prog_bar=True)
|
|
self.log("train_acc", self.train_acc(logits, y), prog_bar=True)
|
|
return {"loss": loss}
|
|
|
|
def validation_step(self, batch, batch_idx):
|
|
x, y = batch
|
|
logits = self.forward(x)
|
|
self.log("val_loss", F.cross_entropy(logits, y), prog_bar=False)
|
|
self.log("val_acc", self.valid_acc(logits, y), prog_bar=True)
|
|
|
|
def test_step(self, batch, batch_idx):
|
|
x, y = batch
|
|
logits = self.forward(x)
|
|
self.log("test_loss", F.cross_entropy(logits, y), prog_bar=False)
|
|
self.log("test_acc", self.test_acc(logits, y), prog_bar=True)
|
|
|
|
|
|
def main_train(dir_path, max_epochs: int = 20):
|
|
seed_everything(42)
|
|
stopping = EarlyStopping(monitor="val_acc", mode="max", min_delta=0.005)
|
|
trainer = pl.Trainer(
|
|
default_root_dir=dir_path,
|
|
gpus=int(torch.cuda.is_available()),
|
|
precision=(16 if torch.cuda.is_available() else 32),
|
|
callbacks=[stopping],
|
|
min_epochs=3,
|
|
max_epochs=max_epochs,
|
|
accumulate_grad_batches=2,
|
|
deterministic=True,
|
|
)
|
|
|
|
dm = ClassifDataModule()
|
|
model = ClassificationModel()
|
|
trainer.fit(model, datamodule=dm)
|
|
res = trainer.test(model, datamodule=dm)
|
|
assert res[0]["test_loss"] <= 0.7
|
|
assert res[0]["test_acc"] >= 0.85
|
|
assert trainer.current_epoch < (max_epochs - 1)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
path_dir = os.path.join(PATH_LEGACY, "checkpoints", str(pl.__version__))
|
|
main_train(path_dir)
|