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# Copyright (c) Owkin, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# Copyright (c) Owkin, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import pickle
from pathlib import Path
from typing import Callable, Dict, List, Optional, Tuple, Union
import numpy as np
import torch
from torch import nn
from torch.optim import Adam
from torch.utils.data import DataLoader, Subset
def slide_level_train_step(
model: torch.nn.Module,
train_dataloader: torch.utils.data.DataLoader,
criterion: torch.nn.Module,
optimizer: torch.optim.Optimizer,
device: str = "cpu",
) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
"""Training step for slide-level experiments. This will serve as the
``train_step`` in ``TorchTrainer``printclass.
Parameters
----------
model: nn.Module
The PyTorch model to be trained.
train_dataloader: torch.utils.data.DataLoader
Training data loader.
criterion: nn.Module
The loss criterion used for training.
optimizer: Callable = Adam
The optimizer class to use.
device : str = "cpu"
The device to use for training and evaluation.
"""
model.train()
_epoch_loss, _epoch_logits, _epoch_labels = [], [], []
for batch in train_dataloader:
# Get data.
features, mask, labels = batch
# Put on device.
features = features.to(device)
mask = mask.to(device)
labels = labels.to(device)
# Compute logits and loss.
logits = model(features, mask)
loss = criterion(logits, labels)
loss.backward()
optimizer.step()
optimizer.zero_grad()
# Stack logits & labels to compute epoch metrics.
_epoch_loss.append(loss.detach().cpu().numpy())
_epoch_logits.append(logits.detach())
_epoch_labels.append(labels.detach())
_epoch_loss = np.mean(_epoch_loss)
_epoch_logits = torch.cat(_epoch_logits, dim=0).cpu().numpy()
_epoch_labels = torch.cat(_epoch_labels, dim=0).cpu().numpy()
return _epoch_loss, _epoch_logits, _epoch_labels
def slide_level_val_step(
model: torch.nn.Module,
val_dataloader: torch.utils.data.DataLoader,
criterion: torch.nn.Module,
device: str,
) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
"""Inference step for slide-level experiments. This will serve as the
``val_step`` in ``TorchTrainer``class.
Parameters
----------
model: nn.Module
The PyTorch model to be trained.
val_dataloader: torch.utils.data.DataLoader
Inference data loader.
criterion: nn.Module
The loss criterion used for training.
device : str = "cpu"
The device to use for training and evaluation.
"""
model.eval()
with torch.no_grad():
_epoch_loss, _epoch_logits, _epoch_labels = [], [], []
for batch in val_dataloader:
# Get data.
features, mask, labels = batch
# Put on device.
features = features.to(device)
mask = mask.to(device)
labels = labels.to(device)
# Compute logits and loss.
logits = model(features, mask)
loss = criterion(logits, labels)
# Stack logits & labels to compute epoch metrics.
_epoch_loss.append(loss.detach().cpu().numpy())
_epoch_logits.append(logits.detach())
_epoch_labels.append(labels.detach())
_epoch_loss = np.mean(_epoch_loss)
_epoch_logits = torch.cat(_epoch_logits, dim=0).cpu().numpy()
_epoch_labels = torch.cat(_epoch_labels, dim=0).cpu().numpy()
return _epoch_loss, _epoch_logits, _epoch_labels
class TorchTrainer:
"""Trainer class for training and evaluating PyTorch models.
Parameters
----------
model: nn.Module
The PyTorch model to be trained.
criterion: nn.Module
The loss criterion used for training.
metrics: Dict[str, Callable]
Dictionary of metrics functions to evaluate the model's performance.
batch_size: int = 16
The batch size for training and evaluation
num_epochs : int = 10
The number of training epochs.
learning_rate: float = 1.0e-3
The learning rate for the optimizer.
weight_decay: float = 0.0
The weight decay for the optimizer.
device : str = "cpu"
The device to use for training and evaluation.
num_workers: int = 8
Number of workers.
optimizer: Callable = Adam
The optimizer class to use.
train_step: Callable = slide_level_train_step
The function for training step.
val_step: Callable = slide_level_val_step
The function for validation step.
collator: Optional[Callable] = None
The collator function for data preprocessing.
"""
def __init__(
self,
model: nn.Module,
criterion: nn.Module,
metrics: Dict[str, Callable],
batch_size: int = 16,
num_epochs: int = 10,
learning_rate: float = 1.0e-3,
weight_decay: float = 0.0,
device: str = "cpu",
num_workers: int = 8,
optimizer: Callable = Adam,
train_step: Callable = slide_level_train_step,
val_step: Callable = slide_level_val_step,
collator: Optional[Callable] = None,
):
super().__init__()
self.model = model
self.criterion = criterion
self.optimizer = optimizer
self.metrics = metrics
self.train_step = train_step
self.val_step = val_step
self.num_epochs = num_epochs
self.batch_size = batch_size
self.learning_rate = learning_rate
self.weight_decay = weight_decay
self.collator = collator
self.device = device
self.num_workers = num_workers
self.train_losses: List[float]
self.val_losses: List[float]
self.train_metrics: Dict[str, List[float]]
self.val_metrics: Dict[str, List[float]]
def train(
self,
train_set: Subset,
val_set: Subset,
) -> Tuple[Dict[str, List[float]], Dict[str, List[float]]]:
"""
Train the model using the provided training and validation datasets.
Parameters
----------
train_set: Subset
The training dataset.
val_set: Subset
The validation dataset.
Returns
-------
Tuple[Dict[str, List[float]], Dict[str, List[float]]]
2 dictionaries containing the training and validation metrics for each epoch.
"""
# Dataloaders.
train_dataloader = DataLoader(
dataset=train_set,
shuffle=True,
batch_size=self.batch_size,
pin_memory=True,
collate_fn=self.collator,
drop_last=True,
num_workers=self.num_workers,
)
val_dataloader = DataLoader(
dataset=val_set,
shuffle=False,
batch_size=self.batch_size,
pin_memory=True,
collate_fn=self.collator,
drop_last=False,
num_workers=self.num_workers,
)
# Prepare modules.
model = self.model.to(self.device)
criterion = self.criterion.to(self.device)
optimizer = self.optimizer(
params=model.parameters(),
lr=self.learning_rate,
weight_decay=self.weight_decay,
)
# Training.
train_losses, val_losses = [], []
train_metrics: Dict[str, List[float]] = {
k: [] for k in self.metrics.keys()
}
val_metrics: Dict[str, List[float]] = {
k: [] for k in self.metrics.keys()
}
for ep in range(self.num_epochs):
# Train step.
(
train_epoch_loss,
train_epoch_logits,
train_epoch_labels,
) = self.train_step(
model=model,
train_dataloader=train_dataloader,
criterion=criterion,
optimizer=optimizer,
device=self.device,
)
# Inference step.
val_epoch_loss, val_epoch_logits, val_epoch_labels = self.val_step(
model=model,
val_dataloader=val_dataloader,
criterion=criterion,
device=self.device,
)
# Compute metrics.
for k, m in self.metrics.items():
train_metric = m(train_epoch_labels, train_epoch_logits)
val_metric = m(val_epoch_labels, val_epoch_logits)
train_metrics[k].append(train_metric)
val_metrics[k].append(val_metric)
print(
f"Epoch {ep+1}: train_loss={train_epoch_loss:.5f}, train_{k}={train_metric:.4f}, val_loss={val_epoch_loss:.5f}, val_{k}={val_metric:.4f}"
)
train_losses.append(train_epoch_loss)
val_losses.append(val_epoch_loss)
self.train_losses = train_losses
self.val_losses = val_losses
self.train_metrics = train_metrics
self.val_metrics = val_metrics
return train_metrics, val_metrics
def evaluate(
self,
test_set: Subset,
) -> Dict[str, float]:
"""Evaluate the model using the provided test dataset.
Parameters
----------
test_set: Subset
The test dataset.
Returns
-------
Dict[str, float]
A dictionary containing the test metrics.
"""
# Dataloader.
test_dataloader = DataLoader(
dataset=test_set,
shuffle=False,
batch_size=self.batch_size,
pin_memory=True,
collate_fn=self.collator,
drop_last=False,
num_workers=self.num_workers,
)
# Prepare modules.
model = self.model.to(self.device)
criterion = self.criterion.to(self.device)
# Inference step.
_, test_epoch_logits, test_epoch_labels = self.val_step(
model=model,
val_dataloader=test_dataloader,
criterion=criterion,
device=self.device,
)
# Compute metrics.
test_metrics = {
k: m(test_epoch_labels, test_epoch_logits)
for k, m in self.metrics.items()
}
return test_metrics
def predict(
self,
test_set: Subset,
) -> Tuple[np.array, np.array]:
"""Make predictions using the provided test dataset.
Parameters
----------
test_set: Subset
The test dataset.
Returns
--------
Tuple[np.array, np.array]
A tuple containing the test labels and logits.
"""
# Dataloader
test_dataloader = DataLoader(
dataset=test_set,
shuffle=False,
batch_size=self.batch_size,
pin_memory=True,
collate_fn=self.collator,
drop_last=False,
num_workers=self.num_workers,
)
# Prepare modules
model = self.model.to(self.device)
criterion = self.criterion.to(self.device)
# Val step
_, test_epoch_logits, test_epoch_labels = self.val_step(
model=model,
val_dataloader=test_dataloader,
criterion=criterion,
device=self.device,
)
return test_epoch_labels, test_epoch_logits |