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from typing import Optional, List, Dict, Tuple, Any
import numpy as np
import pandas as pd
import piexif
from sklearn.preprocessing import StandardScaler
from PIL import Image
import torch
from torch.optim.lr_scheduler import ReduceLROnPlateau
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset, TensorDataset, DataLoader, random_split
from torchvision.io import decode_image, read_file, image
from torchvision.transforms import v2
import torchvision.models as models
import lightning as L
from lightning.pytorch.callbacks.early_stopping import EarlyStopping
import torchmetrics
from lightning.pytorch.loggers import WandbLogger
from lightning.pytorch import Trainer, seed_everything
import wandb
import lmdb
import msgpack
import sys
import torchvision
# Dynamically construct the path based on the user's home directory
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__name__), '..')))
from utils import export_mlp, test_report
torch.set_float32_matmul_precision('highest') # Options are medium, high, highest
tensor_dtype = torch.bfloat16 # Can change to torch.bfloat16
# For training on AMD GPUs, as bfloat16 isn't supported
# tensor_dtype = torch.float16
class MLP(L.LightningModule):
def __init__(
self,
input_size: int,
hidden_size: int=128,
num_layers: int=2,
learning_rate: float=1e-3,
weight_decay: float=1e-2,
scheduler_factor: float=0.1,
scheduler_patience: int=10,
scheduler_threshold: float=1e-4
):
'''
Class to create a simple multi-layered perceptron (MLP) model for tabular data.
Parameters:
input_size (int): Size of the input data.
hidden_size (int): Number of neurons for the hidden layers. Defaults to 100.
num_layers (int): Number of hidden layers. Defaults to 2.
learning_rate (float): Learning rate. Defaults to 1e-3
weight_decay (float): Weight decay for AdamW optimizer. Defaults to 1e-5
scheduler_factor (float): Factor for ReduceLROnPlateau. Defaults to 0.1.
scheduler_patience (int): Patience for ReduceLROnPlateau. Defaults to 10.
scheduler_threshold (float): Threshold for ReduceLROnPlateau. Defaults to 1e-4.
Methods:
All of the methods within this class are subclassed from PyTorch Lightning's LightningModule. Their documentation for each of these methods can be found here: https://lightning.ai/docs/pytorch/stable/common/lightning_module.html
'''
super().__init__()
self.save_hyperparameters() # Save model hyperparameters for checkpointing
self.input_size = input_size
self.hidden_size = hidden_size
self.num_layers = num_layers
self.learning_rate = learning_rate
self.weight_decay = weight_decay
self.input_size = input_size
self.scheduler_factor = scheduler_factor
self.scheduler_patience = scheduler_patience
self.scheduler_threshold = scheduler_threshold
# Save predictions for later
self.test_preds = []
self.test_labels = []
# Define model layers
layers = []
layers.append(nn.Linear(self.input_size, hidden_size))
layers.append(nn.ReLU())
# Set number of hidden layers dynamically via class definition
for _ in range(num_layers - 1):
layers.append(nn.Linear(hidden_size, hidden_size))
layers.append(nn.BatchNorm1d(num_features=hidden_size))
layers.append(nn.ReLU())
layers.append(nn.Linear(hidden_size, 1)) # Output layer for regression and permeability
self.model = nn.Sequential(*layers) # Build model
# Create dictionary of metrics to track for training, validation, and testing
self.train_metrics = torchmetrics.MetricCollection(
{
'MAE': torchmetrics.regression.MeanAbsoluteError(num_outputs=1),
'RMSE': torchmetrics.regression.MeanSquaredError(squared=False, num_outputs=1),
'MSE': torchmetrics.regression.MeanSquaredError(squared=True, num_outputs=1),
'r_squared': torchmetrics.regression.R2Score()
},
prefix='train_'
)
self.valid_metrics = self.train_metrics.clone(prefix='valid_')
self.test_metrics = self.train_metrics.clone(prefix='test_')
def forward(self, x):
return self.model(x)
# Reset metrics
def on_train_epoch_start(self):
self.train_metrics.reset()
def training_step(self, batch, batch_idx):
x, y = batch # Get inputs and labels
y_pred = self(x) # Forward pass (call model's forward method)
loss = F.mse_loss(y_pred, y)
self.log('train_loss', loss, on_step=True, on_epoch=True, logger=True, sync_dist=True) # Log MSE loss for monitoring
# Log metrics
batch_values = self.train_metrics(y_pred, y)
self.log_dict(batch_values, on_step=True, on_epoch=True, sync_dist=True)
return loss
# Reset metrics
def on_validation_epoch_start(self):
self.valid_metrics.reset()
def validation_step(self, batch, batch_idx):
x, y = batch # Get features and labels
y_pred = self(x)
loss = F.mse_loss(y_pred, y)
self.log('val_loss', loss, on_step=True, on_epoch=True, logger=True, sync_dist=True)
# Update validation metrics
batch_values = self.valid_metrics(y_pred, y)
self.log_dict(batch_values, on_step=True, on_epoch=True, sync_dist=True)
return loss
# Reset metrics
def on_test_epoch_start(self):
self.test_metrics.reset()
def test_step(self, batch, batch_idx):
x, y = batch # Get features and labels
y_pred = self(x)
loss = F.mse_loss(y_pred, y)
self.log('test_loss', loss, on_step=True, on_epoch=True, logger=True, sync_dist=True)
# Update validation metrics
batch_values = self.test_metrics(y_pred, y)
self.log_dict(batch_values, on_step=True, on_epoch=True, sync_dist=True)
# Store predictions and labels
self.test_preds.append(y_pred.cpu())
self.test_labels.append(y.cpu())
return loss
def on_test_epoch_end(self):
self.test_preds = torch.cat([pred.float() for pred in self.test_preds], dim=0).numpy()
self.test_labels = torch.cat([label.float() for label in self.test_labels], dim=0).numpy()
# Log transform test predictions and labels
self.test_preds = np.exp(self.test_preds)
self.test_labels = np.exp(self.test_labels)
# Store results for access after trainer.test()
self.test_results = {'preds': self.test_preds, 'labels': self.test_labels}
def configure_optimizers(self):
optimizer = torch.optim.AdamW(self.parameters(), lr=self.learning_rate, weight_decay=self.weight_decay)
scheduler = ReduceLROnPlateau(optimizer=optimizer, mode='min', factor=self.scheduler_factor, patience=self.scheduler_patience, threshold=self.scheduler_threshold)
return {'optimizer': optimizer,
'lr_scheduler': scheduler,
'monitor': 'val_loss'} |