Progress Bar Variables from Validation Step

[Spawnfile]

Greetings,

I can only show metrics of variables calculated on training step but can't show validation step metrics on the progress bar. How can show a metric in the validation step ? self.log(...., prog_bar=True) does not work.

[awaelchli]

Hi

It works fine for me. Have a look at this running example (latest PL version 1.2.6:

import os

import torch
from torch.utils.data import Dataset

from pytorch_lightning import LightningModule, Trainer


class RandomDataset(Dataset):

    def __init__(self, size, length):
        self.len = length
        self.data = torch.randn(length, size)

    def __getitem__(self, index):
        return self.data[index]

    def __len__(self):
        return self.len


class BoringModel(LightningModule):

    def __init__(self):
        super().__init__()
        self.layer = torch.nn.Linear(32, 2)

    def forward(self, x):
        return self.layer(x)

    def loss(self, batch, prediction):
        # An arbitrary loss to have a loss that updates the model weights during `Trainer.fit` calls
        return torch.nn.functional.mse_loss(prediction, torch.ones_like(prediction))

    def step(self, x):
        x = self.layer(x)
        out = torch.nn.functional.mse_loss(x, torch.ones_like(x))
        return out

    def training_step(self, batch, batch_idx):
        output = self.layer(batch)
        loss = self.loss(batch, output)
        return {"loss": loss}

    def training_step_end(self, training_step_outputs):
        return training_step_outputs

    def training_epoch_end(self, outputs) -> None:
        torch.stack([x["loss"] for x in outputs]).mean()

    def validation_step(self, batch, batch_idx):
        output = self.layer(batch)
        loss = self.loss(batch, output)
        self.log("VALIDATION_STEP", loss, prog_bar=True)
        return {"x": loss}

    def validation_epoch_end(self, outputs) -> None:
        torch.stack([x['x'] for x in outputs]).mean()

    def test_step(self, batch, batch_idx):
        output = self.layer(batch)
        loss = self.loss(batch, output)
        return {"y": loss}

    def test_epoch_end(self, outputs) -> None:
        torch.stack([x["y"] for x in outputs]).mean()

    def configure_optimizers(self):
        optimizer = torch.optim.SGD(self.layer.parameters(), lr=0.1)
        lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=1)
        return [optimizer], [lr_scheduler]


def test_run():

    train_data = torch.utils.data.DataLoader(RandomDataset(32, 64))
    val_data = torch.utils.data.DataLoader(RandomDataset(32, 64))

    # model
    model = BoringModel()
    trainer = Trainer(
        default_root_dir=os.getcwd(),
        limit_train_batches=1,
        limit_val_batches=1,
        max_epochs=3,
        weights_summary=None,
    )
    trainer.fit(model, train_data, val_data)


if __name__ == '__main__':
    test_run()

[kingmbc]

Hi. It seems that you run the program in pycharm.

I would also encounter that problem as below.

Epoch 0:  50%|███████▌       | 1/2 [00:00<00:00, 793.10it/s, loss=1.28, v_num=0]
Validating: 0it [00:00, ?it/s]
Epoch 0: 100%|█| 2/2 [00:00<00:00, 547.39it/s, loss=1.28, v_num=0, VALIDATION_ST
Epoch 1:  50%|▌| 1/2 [00:00<00:00, 1027.76it/s, loss=4.07, v_num=0, VALIDATION_S
Validating: 0it [00:00, ?it/s]
Epoch 1: 100%|█| 2/2 [00:00<00:00, 621.22it/s, loss=4.07, v_num=0, VALIDATION_ST
Epoch 2:  50%|▌| 1/2 [00:00<00:00, 1037.94it/s, loss=3.73, v_num=0, VALIDATION_S
Validating: 0it [00:00, ?it/s]
Epoch 2: 100%|█| 2/2 [00:00<00:00, 634.89it/s, loss=3.73, v_num=0, VALIDATION_ST
Epoch 2: 100%|█| 2/2 [00:00<00:00, 475.49it/s, loss=3.73, v_num=0, VALIDATION_ST

Process finished with exit code 0

I found this solution, but didn't try yet.

• https://stackoverflow.com/questions/59455268/how-to-disable-progress-bar-in-pytorch-lightning/66731318#66731318