74 lines
2.3 KiB
Python
74 lines
2.3 KiB
Python
# Copyright The Lightning AI team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import torch
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from jsonargparse import lazy_instance
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from lightning_habana import HPUPrecisionPlugin
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from torch.nn import functional as F
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from lightning.pytorch import LightningModule
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from lightning.pytorch.cli import LightningCLI
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from lightning.pytorch.demos.mnist_datamodule import MNISTDataModule
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class LitClassifier(LightningModule):
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def __init__(self):
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super().__init__()
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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.view(x.size(0), -1)))
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def training_step(self, batch, batch_idx):
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x, y = batch
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return F.cross_entropy(self(x), y)
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def validation_step(self, batch, batch_idx):
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x, y = batch
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probs = self(x)
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acc = self.accuracy(probs, y)
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self.log("val_acc", acc)
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def test_step(self, batch, batch_idx):
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x, y = batch
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logits = self(x)
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acc = self.accuracy(logits, y)
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self.log("test_acc", acc)
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@staticmethod
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def accuracy(logits, y):
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return torch.sum(torch.eq(torch.argmax(logits, -1), y).to(torch.float32)) / len(y)
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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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if __name__ == "__main__":
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cli = LightningCLI(
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LitClassifier,
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MNISTDataModule,
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trainer_defaults={
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"accelerator": "hpu",
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"devices": 1,
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"max_epochs": 1,
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"plugins": lazy_instance(HPUPrecisionPlugin, precision="16-mixed"),
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},
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run=False,
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save_config_kwargs={"overwrite": True},
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)
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# Run the model ⚡
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cli.trainer.fit(cli.model, datamodule=cli.datamodule)
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cli.trainer.validate(cli.model, datamodule=cli.datamodule)
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cli.trainer.test(cli.model, datamodule=cli.datamodule)
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