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train.py
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train.py
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""" Training module
"""
from pathlib import Path
from typing import Dict, List, Union
import pandas as pd
import pytorch_lightning as pl
import torch
import torch.nn.init as init
from pytorch_lightning import Trainer
from pytorch_lightning.callbacks import ModelCheckpoint
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from torch import Tensor
from torch.nn import (Conv2d, CrossEntropyLoss, Linear, MaxPool2d, ReLU,
Sequential)
from torch.optim import Adam
from torch.optim.optimizer import Optimizer
from torch.utils.data import DataLoader
from dataset import MaskDataset
class MaskDetector(pl.LightningModule):
""" MaskDetector PyTorch Lightning class
"""
def __init__(self, maskDFPath: Path=None):
super(MaskDetector, self).__init__()
self.maskDFPath = maskDFPath
self.maskDF = None
self.trainDF = None
self.validateDF = None
self.crossEntropyLoss = None
self.learningRate = 0.00001
self.convLayer1 = convLayer1 = Sequential(
Conv2d(3, 32, kernel_size=(3, 3), padding=(1, 1)),
ReLU(),
MaxPool2d(kernel_size=(2, 2))
)
self.convLayer2 = convLayer2 = Sequential(
Conv2d(32, 64, kernel_size=(3, 3), padding=(1, 1)),
ReLU(),
MaxPool2d(kernel_size=(2, 2))
)
self.convLayer3 = convLayer3 = Sequential(
Conv2d(64, 128, kernel_size=(3, 3), padding=(1, 1), stride=(3,3)),
ReLU(),
MaxPool2d(kernel_size=(2, 2))
)
self.linearLayers = linearLayers = Sequential(
Linear(in_features=2048, out_features=1024),
ReLU(),
Linear(in_features=1024, out_features=2),
)
# Initialize layers' weights
for sequential in [convLayer1, convLayer2, convLayer3, linearLayers]:
for layer in sequential.children():
if isinstance(layer, (Linear, Conv2d)):
init.xavier_uniform_(layer.weight)
def forward(self, x: Tensor): # pylint: disable=arguments-differ
""" forward pass
"""
out = self.convLayer1(x)
out = self.convLayer2(out)
out = self.convLayer3(out)
out = out.view(-1, 2048)
out = self.linearLayers(out)
return out
def prepare_data(self) -> None:
self.maskDF = maskDF = pd.read_pickle(self.maskDFPath)
train, validate = train_test_split(maskDF, test_size=0.3, random_state=0,
stratify=maskDF['mask'])
self.trainDF = MaskDataset(train)
self.validateDF = MaskDataset(validate)
# Create weight vector for CrossEntropyLoss
maskNum = maskDF[maskDF['mask']==1].shape[0]
nonMaskNum = maskDF[maskDF['mask']==0].shape[0]
nSamples = [nonMaskNum, maskNum]
normedWeights = [1 - (x / sum(nSamples)) for x in nSamples]
self.crossEntropyLoss = CrossEntropyLoss(weight=torch.tensor(normedWeights))
def train_dataloader(self) -> DataLoader:
return DataLoader(self.trainDF, batch_size=32, shuffle=True, num_workers=4)
def val_dataloader(self) -> DataLoader:
return DataLoader(self.validateDF, batch_size=32, num_workers=4)
def configure_optimizers(self) -> Optimizer:
return Adam(self.parameters(), lr=self.learningRate)
def training_step(self, batch: dict, _batch_idx: int) -> Dict[str, Tensor]: # pylint: disable=arguments-differ
inputs, labels = batch['image'], batch['mask']
labels = labels.flatten()
outputs = self.forward(inputs)
loss = self.crossEntropyLoss(outputs, labels)
tensorboardLogs = {'train_loss': loss}
return {'loss': loss, 'log': tensorboardLogs}
def validation_step(self, batch: dict, _batch_idx: int) -> Dict[str, Tensor]: # pylint: disable=arguments-differ
inputs, labels = batch['image'], batch['mask']
labels = labels.flatten()
outputs = self.forward(inputs)
loss = self.crossEntropyLoss(outputs, labels)
_, outputs = torch.max(outputs, dim=1)
valAcc = accuracy_score(outputs.cpu(), labels.cpu())
valAcc = torch.tensor(valAcc)
print ("valAcc",valAcc,"val_loss",val_loss)
return {'val_loss': loss, 'val_acc':valAcc}
def validation_epoch_end(self, outputs: List[Dict[str, Tensor]]) \
-> Dict[str, Union[Tensor, Dict[str, Tensor]]]:
avgLoss = torch.stack([x['val_loss'] for x in outputs]).mean()
avgAcc = torch.stack([x['val_acc'] for x in outputs]).mean()
tensorboardLogs = {'val_loss': avgLoss, 'val_acc':avgAcc}
return {'val_loss': avgLoss, 'log': tensorboardLogs}
if __name__ == '__main__':
model = MaskDetector(Path('covid-mask-detector/data/mask_df.pickle'))
checkpoint_callback = ModelCheckpoint(
filepath='covid-mask-detector/checkpoints/weights.ckpt',
save_weights_only=True,
verbose=True,
monitor='val_acc',
mode='max'
)
trainer = Trainer(gpus=1 if torch.cuda.is_available() else 0,
max_epochs=10,
checkpoint_callback=checkpoint_callback,
profiler=True)
trainer.fit(model)