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trainer.py
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trainer.py
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import numpy as np
import torch
import wandb
from torchmetrics.classification import MulticlassF1Score
class Trainer:
def __init__(
self,
model: torch.nn.Module,
device: torch.device,
criterion: torch.nn.Module,
optimizer: torch.optim.Optimizer,
training_DataLoader: torch.utils.data.Dataset,
validation_DataLoader: torch.utils.data.Dataset = None,
lr_scheduler: torch.optim.lr_scheduler = None,
epochs: int = 100,
epoch: int = 0,
notebook: bool = False,
):
self.model = model
self.criterion = criterion
self.optimizer = optimizer
self.lr_scheduler = lr_scheduler
self.training_DataLoader = training_DataLoader
self.validation_DataLoader = validation_DataLoader
self.device = device
self.epochs = epochs
self.epoch = epoch
self.notebook = notebook
self.training_loss = []
self.validation_loss = []
self.learning_rate = []
def run_trainer(self):
if self.notebook:
from tqdm.notebook import tqdm, trange
else:
from tqdm import tqdm, trange
progressbar = trange(self.epochs, desc="Progress")
for i in progressbar:
"""Epoch counter"""
self.epoch += 1 # epoch counter
"""Training block"""
self._train()
"""Validation block"""
if self.validation_DataLoader is not None:
self._validate()
"""Learning rate scheduler block"""
if self.lr_scheduler is not None:
if (
self.validation_DataLoader is not None
and self.lr_scheduler.__class__.__name__ == "ReduceLROnPlateau"
):
self.lr_scheduler.batch(
self.validation_loss[i]
) # learning rate scheduler step with validation loss
else:
self.lr_scheduler.batch() # learning rate scheduler step
# wandb.log({"lr": self.learning_rate[i], "train_loss": self.training_loss[i]})
# wandb.log({"val_loss": self.validation_loss[i]})
return self.training_loss, self.validation_loss, self.learning_rate
# %%
def _train(self):
if self.notebook:
from tqdm.notebook import tqdm, trange
else:
from tqdm import tqdm, trange
self.model.train() # train mode
train_losses = [] # accumulate the losses here
batch_iter = tqdm(
enumerate(self.training_DataLoader),
"Training",
total=len(self.training_DataLoader),
leave=False,
)
f1 = [0, 0, 0]
self.optimizer.zero_grad() # zerograd the parameters
for i, data_dict in batch_iter:
x = data_dict["img"]
y = data_dict["seg"]
input, target = x.to(self.device), y.to(
self.device
) # send to device (GPU or CPU)
out = self.model(input) # one forward pass
loss = self.criterion(out, target) # calculate loss
loss_value = loss.item()
train_losses.append(loss_value)
loss.backward() # one backward pass
self.optimizer.step() # update the parameters
self.optimizer.zero_grad() # zerograd the parameters
target = target.squeeze(1).long() # because multiclass receives size (N, ...)
mcf1s = MulticlassF1Score(num_classes=3, average=None).to(self.device)
f1 = f1 + mcf1s(out, target).cpu().numpy()
batch_iter.set_description(
f"Training: (loss {loss_value:.4f})"
) # update progressbar
self.training_loss.append(np.mean(train_losses))
print("Train loss: ", np.mean(train_losses))
# wandb.log({"train_loss": np.mean(train_losses)})
self.learning_rate.append(self.optimizer.param_groups[0]["lr"])
f1 = f1 / len(batch_iter)
print(f1)
# wandb.log({"f1_background": f1[0]})
# wandb.log({"f1_liver": f1[1]})
# wandb.log({"f1_tumor": f1[2]})
batch_iter.close()
def _validate(self):
if self.notebook:
from tqdm.notebook import tqdm, trange
else:
from tqdm import tqdm, trange
self.model.eval() # evaluation mode
valid_losses = [] # accumulate the losses here
batch_iter = tqdm(
enumerate(self.validation_DataLoader),
"Validation",
total=len(self.validation_DataLoader),
leave=False,
)
f1 = [0, 0, 0]
for i, dict in batch_iter:
x = dict["img"]
y = dict["seg"]
input, target = x.to(self.device), y.to(
self.device
) # send to device (GPU or CPU)
with torch.inference_mode():
input = input.float()
out = self.model(input)
loss = self.criterion(out, target)
loss_value = loss.item()
valid_losses.append(loss_value)
target = target.squeeze(0).long()
mcf1s = MulticlassF1Score(num_classes=3, average=None).to(self.device)
f1 = f1 + mcf1s(out, target).cpu().numpy()
batch_iter.set_description(f"Validation: (loss {loss_value:.4f})")
self.validation_loss.append(np.mean(valid_losses))
f1 = f1 / len(batch_iter)
print("Validation_f1:", f1)
# wandb.log({"val_loss": np.mean(valid_losses)})
batch_iter.close()