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https://github.com/wassname/pytorch-lightning.git
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Adrian WälchliandJirka Borovec
b7de42f70d
* added custom mnist without torchvision dep * move files so it does not conflict with mnist gitignore * mock torchvision for tests * fix line too long * fix line too long * fix "module level import not at top of file" warning * move mock imports to __init__.py * simplify MNIST a lot and download directly the .pt files * further simplify and clean up mnist * revert import overrides * make as before * drop PIL requirement * move mnist.py to datasets subfolder * use logging instead of print * choose same name as in torchvision * remove torchvision and pillow also from yml file * refactor if train Co-Authored-By: Jirka Borovec <Borda@users.noreply.github.com> * capitalized class attr * moved mnist to models * re-added datsets ignore * better name for file variable * Update mnist.py * move dataset classes to datasets.py * new line * update * update * fix automerge * move to base folder * adapt testingmnist to new mnist base class * remove temporal fix * fix datatype * remove old testingmnist * readable * fix import * fix whitespace * docstring Co-Authored-By: Jirka Borovec <Borda@users.noreply.github.com> * Update tests/base/datasets.py Co-Authored-By: Jirka Borovec <Borda@users.noreply.github.com> * changelog * added types * Update CHANGELOG.md Co-Authored-By: Jirka Borovec <Borda@users.noreply.github.com> * exist->isfile Co-Authored-By: Jirka Borovec <Borda@users.noreply.github.com> * index -> idx * temporary fix for trains error * better changelog message Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com>
52 lines
1.5 KiB
Python
52 lines
1.5 KiB
Python
import torch
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from torch.nn import functional as F
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from torch.utils.data import DataLoader
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import pytorch_lightning as pl
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from tests.base.datasets import MNIST
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# from test_models import assert_ok_test_acc, load_model, \
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# clear_save_dir, get_default_testtube_logger, get_default_hparams, init_save_dir, \
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# init_checkpoint_callback, reset_seed, set_random_master_port
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class CoolModel(pl.LightningModule):
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def __init(self):
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super().__init__()
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# not the best model...
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self.l1 = torch.nn.Linear(28 * 28, 10)
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def forward(self, x):
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return torch.relu(self.l1(x))
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def my_loss(self, y_hat, y):
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return F.cross_entropy(y_hat, y)
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def training_step(self, batch, batch_idx):
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x, y = batch
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y_hat = self(x)
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return {'training_loss': self.my_loss(y_hat, y)}
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def validation_step(self, batch, batch_idx):
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x, y = batch
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y_hat = self(x)
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return {'val_loss': self.my_loss(y_hat, y)}
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def validation_epoch_end(self, outputs):
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avg_loss = torch.stack([x for x in outputs['val_loss']]).mean()
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return avg_loss
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def configure_optimizers(self):
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return [torch.optim.Adam(self.parameters(), lr=0.02)]
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def train_dataloader(self):
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return DataLoader(MNIST('path/to/save', train=True), batch_size=32)
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def val_dataloader(self):
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return DataLoader(MNIST('path/to/save', train=False), batch_size=32)
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def test_dataloader(self):
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return DataLoader(MNIST('path/to/save', train=False), batch_size=32)
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