Initial commit

d5a0332 over 1 year ago

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import os
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'}

	import os
	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'}