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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_cnn, 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
# ResNet-18 model architecture
class ResNet18(L.LightningModule):
def __init__(
self,
learning_rate: float=1e-3,
weight_decay: float=1e-2,
kernel_size: int=3,
stride: int=1,
padding: int=0,
use_weights: bool=True,
scheduler_factor: float=0.1,
scheduler_patience: int=10,
scheduler_threshold: float=1e-4
):
'''
Class to load the ResNet-50 architecture from PyTorch's torchvision library.
Parameters:
learning_rate (float): Learning rate. Defaults to 1e-3.
weight_decay (float): Weight decay for AdamW optimizer. Defaults to 1e-2.
kernel_size (int): Kernel size for convolutional filters. Defaults to 3.
stride (int): Stride for convolutional filters. Defaults to 1.
padding (int): Padding for convolutional filters. Defaults to 0.
use_weights (bool): Whether or not to use pretrained weights from ImageNet. Defaults to True.
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()
# Transforms flag to perform image augmentations
# self.use_transforms = use_transforms
# Save predictions for later
self.test_preds = []
self.test_labels = []
self.learning_rate = learning_rate
self.weight_decay = weight_decay
self.kernel_size = kernel_size
self.stride = stride
self.padding = padding
self.use_weights = use_weights
self.scheduler_factor = scheduler_factor
self.scheduler_patience = scheduler_patience
self.scheduler_threshold = scheduler_threshold
if use_weights==True:
self.model = models.resnet18(weights='DEFAULT')
else:
self.model = models.resnet18()
# Modify first conv. layer to accept grayscale/1 channel inputs
self.model.conv1 = nn.Conv2d(
in_channels=1,
out_channels=64,
kernel_size=kernel_size,
stride=stride,
padding=padding
)
# Modify classifier for regression output
num_features = self.model.fc.in_features
self.model.fc = nn.Linear(num_features, 1) # Single output for regression
# 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).squeeze(-1) # Remove extra dimension
# 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'}
# NOTE: pin_memory=True calls in the next three defs should be monitored for performance. Can easily set to false.
def train_dataloader(self):
return DataLoader(self.train_set, batch_size=self.batch_size, num_workers=self.num_workers, shuffle=True, pin_memory=True)
def val_dataloader(self):
return DataLoader(self.val_set, batch_size=self.batch_size, num_workers=self.num_workers, shuffle=False, pin_memory=True)
def test_dataloader(self):
return DataLoader(self.test_set, batch_size=self.batch_size, num_workers=self.num_workers, shuffle=False, pin_memory=True)
# ResNet-50 model architecture
class ResNet50(L.LightningModule):
def __init__(
self,
learning_rate: float=1e-3,
weight_decay: float=1e-2,
kernel_size: int=3,
stride: int=1,
padding: int=0,
use_weights: bool=True,
scheduler_factor: float=0.1,
scheduler_patience: int=10,
scheduler_threshold: float=1e-4
):
'''
Class to load the ResNet-50 architecture from PyTorch's torchvision library.
Parameters:
learning_rate (float): Learning rate. Defaults to 1e-3.
weight_decay (float): Weight decay for AdamW optimizer. Defaults to 1e-2.
kernel_size (int): Kernel size for convolutional filters. Defaults to 3.
stride (int): Stride for convolutional filters. Defaults to 1.
padding (int): Padding for convolutional filters. Defaults to 0.
use_weights (bool): Whether or not to use pretrained weights from ImageNet. Defaults to True.
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()
# Transforms flag to perform image augmentations
# self.use_transforms = use_transforms
# Save predictions for later
self.test_preds = []
self.test_labels = []
self.learning_rate = learning_rate
self.weight_decay = weight_decay
self.kernel_size = kernel_size
self.stride = stride
self.padding = padding
self.use_weights = use_weights
self.scheduler_factor = scheduler_factor
self.scheduler_patience = scheduler_patience
self.scheduler_threshold = scheduler_threshold
if use_weights==True:
self.model = models.resnet50(weights='DEFAULT')
else:
self.model = models.resnet50()
# Modify first conv. layer to accept grayscale/1 channel inputs
self.model.conv1 = nn.Conv2d(
in_channels=1,
out_channels=64,
kernel_size=kernel_size,
stride=stride,
padding=padding
)
# Modify classifier for regression output
num_features = self.model.fc.in_features
self.model.fc = nn.Linear(num_features, 1) # Single output for regression
# 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).squeeze(-1) # Remove extra dimension
# 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'}
# NOTE: pin_memory=True calls in the next three defs should be monitored for performance. Can easily set to false.
def train_dataloader(self):
return DataLoader(self.train_set, batch_size=self.batch_size, num_workers=self.num_workers, shuffle=True, pin_memory=True)
def val_dataloader(self):
return DataLoader(self.val_set, batch_size=self.batch_size, num_workers=self.num_workers, shuffle=False, pin_memory=True)
def test_dataloader(self):
return DataLoader(self.test_set, batch_size=self.batch_size, num_workers=self.num_workers, shuffle=False, pin_memory=True)
# ResNet-152 architecture
class ResNet152(L.LightningModule):
def __init__(
self,
learning_rate: float=1e-3,
weight_decay: float=1e-2,
kernel_size: int=3,
stride: int=1,
padding: int=0,
use_weights: bool=True,
scheduler_factor: float=0.1,
scheduler_patience: int=10,
scheduler_threshold: float=1e-4
):
'''
Class to load the EfficientNet B0 architecture from PyTorch's torchvision library.
Parameters:
learning_rate (float): Learning rate. Defaults to 1e-3.
weight_decay (float): Weight decay for AdamW optimizer. Defaults to 1e-2.
kernel_size (int): Kernel size for convolutional filters. Defaults to 3.
stride (int): Stride for convolutional filters. Defaults to 1.
padding (int): Padding for convolutional filters. Defaults to 0.
use_weights (bool): Whether or not to use pretrained weights from ImageNet. Defaults to True.
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()
# Transforms flag to perform image augmentations
# self.use_transforms = use_transforms
# Save predictions for later
self.test_preds = []
self.test_labels = []
self.learning_rate = learning_rate
self.weight_decay = weight_decay
self.kernel_size = kernel_size
self.stride = stride
self.padding = padding
self.use_weights = use_weights
self.scheduler_factor = scheduler_factor
self.scheduler_patience = scheduler_patience
self.scheduler_threshold = scheduler_threshold
if use_weights==True:
self.model = models.resnet152(weights='DEFAULT')
else:
self.model = models.resnet152()
# Modify first conv. layer to accept grayscale/1 channel inputs
self.model.conv1 = nn.Conv2d(
in_channels=1,
out_channels=64,
kernel_size=kernel_size,
stride=stride,
padding=padding
)
# Modify classifier for regression output
num_features = self.model.fc.in_features
self.model.fc = nn.Linear(num_features, 1) # Single output for regression
# 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).squeeze(-1) # Remove extra dimension
# 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'}
# NOTE: pin_memory=True calls in the next three defs should be monitored for performance. Can easily set to false.
def train_dataloader(self):
return DataLoader(self.train_set, batch_size=self.batch_size, num_workers=self.num_workers, shuffle=True, pin_memory=True)
def val_dataloader(self):
return DataLoader(self.val_set, batch_size=self.batch_size, num_workers=self.num_workers, shuffle=False, pin_memory=True)
def test_dataloader(self):
return DataLoader(self.test_set, batch_size=self.batch_size, num_workers=self.num_workers, shuffle=False, pin_memory=True)
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