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import torch.optim as optim
import pytorch_lightning as pl
import timm
from torchmetrics import Accuracy, Precision, Recall, F1Score
import torch
class timm_backbones(pl.LightningModule):
"""
PyTorch Lightning model for image classification using a ResNet-18 architecture.
This model uses a pre-trained ResNet-18 model and fine-tunes it for a specific number of classes.
Args:
num_classes (int, optional): The number of classes in the dataset. Defaults to 2.
optimizer_cfg (DictConfig, optional): A Hydra configuration object for the optimizer.
Methods:
forward(x): Computes the forward pass of the model.
configure_optimizers(): Configures the optimizer for the model.
training_step(batch, batch_idx): Performs a training step on the model.
validation_step(batch, batch_idx): Performs a validation step on the model.
on_validation_epoch_end(): Called at the end of each validation epoch.
test_step(batch, batch_idx): Performs a test step on the model.
Example:
model = ResNet18(num_classes=2, optimizer_cfg=cfg.model.optimizer)
trainer.fit(model, train_dataloaders=train_dataloader, val_dataloaders=val_dataloader)
trainer.test(model, dataloaders=test_dataloader)
"""
def __init__(self, encoder='resnet18', num_classes=2, optimizer_cfg=None, l1_lambda=0.0):
super().__init__()
self.encoder = encoder
self.model = timm.create_model(encoder, pretrained=True)
if self.model.default_cfg["input_size"][1] == 3: # If model expects 3 channels
self.model.conv1 = torch.nn.Conv2d(
in_channels=1, # Change to single channel
out_channels=self.model.conv1.out_channels,
kernel_size=self.model.conv1.kernel_size,
stride=self.model.conv1.stride,
padding=self.model.conv1.padding,
bias=False
)
self.accuracy = Accuracy(task="multiclass", num_classes=num_classes)
self.precision = Precision(task="multiclass", num_classes=num_classes)
self.recall = Recall(task="multiclass", num_classes=num_classes)
self.f1 = F1Score(task="multiclass", num_classes=num_classes)
self.l1_lambda = l1_lambda
if hasattr(self.model, 'fc'): # For models with 'fc' as the classification layer
in_features = self.model.fc.in_features
self.model.fc = torch.nn.Linear(in_features, num_classes)
elif hasattr(self.model, 'classifier'): # For models with 'classifier'
in_features = self.model.classifier.in_features
self.model.classifier = torch.nn.Linear(in_features, num_classes)
elif hasattr(self.model, 'head'): # For models with 'head'
in_features = self.model.head.in_features
self.model.head = torch.nn.Linear(in_features, num_classes)
else:
raise ValueError(f"Unsupported model architecture for encoder: {encoder}")
if optimizer_cfg is not None:
optimizer_name = optimizer_cfg.name
optimizer_lr = optimizer_cfg.lr
optimizer_weight_decay = optimizer_cfg.weight_decay
if optimizer_name == 'Adam':
self.optimizer = optim.Adam(self.parameters(), lr=optimizer_lr, weight_decay=optimizer_weight_decay)
elif optimizer_name == 'SGD':
self.optimizer = optim.SGD(self.parameters(), lr=optimizer_lr, weight_decay=optimizer_weight_decay)
else:
raise ValueError(f"Unsupported optimizer: {optimizer_name}")
else:
self.optimizer = None
def forward(self, x):
return self.model(x)
def configure_optimizers(self):
optimizer = self.optimizer
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode="min", factor=0.2, patience=20, min_lr=5e-5)
return {"optimizer": optimizer, "lr_scheduler": scheduler, "monitor": "val_loss"}
def training_step(self, batch, batch_idx):
x, y = batch
y = y.long()
# Compute predictions and loss
logits = self(x)
loss = torch.nn.functional.cross_entropy(logits, y)
# Add L1 regularization
l1_norm = sum(param.abs().sum() for param in self.parameters())
loss += self.l1_lambda * l1_norm
self.log('train_loss', loss, prog_bar=True, on_epoch=True, on_step=False, logger=True)
return loss
def validation_step(self, batch, batch_idx):
x, y = batch
y = y.long()
logits = self(x)
loss = torch.nn.functional.cross_entropy(logits, y)
preds = torch.argmax(logits, dim=1)
accuracy = self.accuracy(y, preds)
precision = self.precision(y, preds)
recall = self.recall(y, preds)
f1 = self.f1(y, preds)
self.log('val_loss', loss, prog_bar=True, on_epoch=True, on_step=True)
self.log('val_acc', accuracy, prog_bar=True, on_epoch=True, on_step=True)
self.log('val_precision', precision, prog_bar=True, on_epoch=True, on_step=True)
self.log('val_recall', recall, prog_bar=True, on_epoch=True, on_step=True)
self.log('val_f1', f1, prog_bar=True, on_epoch=True, on_step=True)
return loss
def on_validation_epoch_end(self):
avg_loss = self.trainer.logged_metrics['val_loss_epoch']
accuracy = self.trainer.logged_metrics['val_acc_epoch']
self.log('val_loss', avg_loss, prog_bar=True, on_epoch=True)
self.log('val_acc', accuracy, prog_bar=True, on_epoch=True)
return {'Average Loss:': avg_loss, 'Accuracy:': accuracy}
def test_step(self, batch, batch_idx):
x, y = batch
y = y.long()
logits = self(x)
loss = torch.nn.functional.cross_entropy(logits, y)
preds = torch.argmax(logits, dim=1)
accuracy = self.accuracy(y, preds)
precision = self.precision(y, preds)
recall = self.recall(y, preds)
f1 = self.f1(y, preds)
# Log test metrics
self.log('test_loss', loss, prog_bar=True, logger=True)
self.log('test_acc', accuracy, prog_bar=True, logger=True)
self.log('test_precision', precision, prog_bar=True, logger=True)
self.log('test_recall', recall, prog_bar=True, logger=True)
self.log('test_f1', f1, prog_bar=True, logger=True)
return {'test_loss': loss, 'test_accuracy': accuracy, 'test_precision': precision, 'test_recall': recall, 'test_f1': f1}
class CTCEncoderPL(pl.LightningModule):
def __init__(self, ctc_encoder, num_classes, optimizer_cfg):
super(CTCEncoderPL, self).__init__()
self.ctc_encoder = ctc_encoder
self.ctc_loss = torch.nn.CTCLoss(blank=0, zero_infinity=True)
self.optimizer_cfg = optimizer_cfg
self.accuracy = Accuracy(task="multiclass", num_classes=num_classes)
self.precision = Precision(task="multiclass", num_classes=num_classes)
self.recall = Recall(task="multiclass", num_classes=num_classes)
self.f1 = F1Score(task="multiclass", num_classes=num_classes)
if optimizer_cfg is not None:
optimizer_name = optimizer_cfg.name
optimizer_lr = optimizer_cfg.lr
optimizer_weight_decay = optimizer_cfg.weight_decay
if optimizer_name == 'Adam':
self.optimizer = optim.Adam(self.parameters(), lr=optimizer_lr, weight_decay=optimizer_weight_decay)
elif optimizer_name == 'SGD':
self.optimizer = optim.SGD(self.parameters(), lr=optimizer_lr, weight_decay=optimizer_weight_decay)
else:
raise ValueError(f"Unsupported optimizer: {optimizer_name}")
else:
self.optimizer = None
def forward(self, x):
return self.ctc_encoder(x)
def training_step(self, batch, batch_idx):
x, y, input_lengths, target_lengths = batch
logits, input_lengths = self.ctc_encoder(x, input_lengths)
log_probs = torch.nn.functional.log_softmax(logits, dim=-1)
loss = self.ctc_loss(log_probs, y, input_lengths, target_lengths)
assert input_lengths.size(0) == x.size(0), f"input_lengths size ({input_lengths.size(0)}) must match batch size ({x.size(0)})"
preds = torch.argmax(log_probs, dim=-1)
self.log("train_loss", loss, on_epoch=True)
return loss
def validation_step(self, batch, batch_idx):
x, y, input_lengths, target_lengths = batch
# Compute logits and adjust input lengths
logits, input_lengths = self.ctc_encoder(x, input_lengths)
log_probs = torch.nn.functional.log_softmax(logits, dim=-1)
# Validate input_lengths size
assert input_lengths.size(0) == logits.size(0), "Mismatch between input_lengths and batch size"
# Compute CTC loss
loss = self.ctc_loss(log_probs, y, input_lengths, target_lengths)
# Compute metrics
preds = torch.argmax(log_probs, dim=-1)
accuracy = self.accuracy(y, preds)
precision = self.precision(y, preds)
recall = self.recall(y, preds)
f1 = self.f1(y, preds)
# Log metrics
self.log('val_loss', loss, prog_bar=True, on_epoch=True, on_step=True)
self.log('val_acc', accuracy, prog_bar=True, on_epoch=True, on_step=True)
self.log('val_precision', precision, prog_bar=True, on_epoch=True, on_step=True)
self.log('val_recall', recall, prog_bar=True, on_epoch=True, on_step=True)
self.log('val_f1', f1, prog_bar=True, on_epoch=True, on_step=True)
return loss
def on_validation_epoch_end(self):
avg_loss = self.trainer.logged_metrics['val_loss_epoch']
accuracy = self.trainer.logged_metrics['val_acc_epoch']
self.log('val_loss', avg_loss, prog_bar=True, on_epoch=True)
self.log('val_acc', accuracy, prog_bar=True, on_epoch=True)
return {'Average Loss:': avg_loss, 'Accuracy:': accuracy}
def test_step(self, batch, batch_idx):
x, y, input_lengths, target_lengths = batch
logits = self(x)
log_probs = torch.nn.functional.log_softmax(logits, dim=-1)
loss = self.ctc_loss(log_probs, y, input_lengths, target_lengths)
preds = torch.argmax(log_probs, dim=-1)
accuracy = self.accuracy(y, preds)
precision = self.precision(y, preds)
recall = self.recall(y, preds)
f1 = self.f1(y, preds)
self.log('test_loss', loss, prog_bar=True, logger=True)
self.log('test_acc', accuracy, prog_bar=True, logger=True)
self.log('test_precision', precision, prog_bar=True, logger=True)
self.log('test_recall', recall, prog_bar=True, logger=True)
self.log('test_f1', f1, prog_bar=True, logger=True)
return {'test_loss': loss, 'test_accuracy': accuracy, 'test_precision': precision, 'test_recall': recall, 'test_f1': f1}
def configure_optimizers(self):
optimizer = self.optimizer
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode="min", factor=0.2, patience=20, min_lr=5e-5)
return {"optimizer": optimizer, "lr_scheduler": scheduler, "monitor": "val_loss"}
def greedy_decode(self, log_probs):
"""
Perform greedy decoding to get predictions from log probabilities.
"""
preds = torch.argmax(log_probs, dim=-1)
return preds |