mirror of
https://github.com/clearml/clearml
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116 lines
3.8 KiB
Python
116 lines
3.8 KiB
Python
# Copyright The PyTorch Lightning 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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# Notice that this file has been modified to examplify the use of
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# ClearML when used with PyTorch Lightning
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import sys
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import torch
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import torchvision.transforms as T
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from torch.nn import functional as F
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import torch.nn as nn
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from torchmetrics import Accuracy
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from torchvision.datasets.mnist import MNIST
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from pytorch_lightning import LightningModule
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from clearml import Task
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try:
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from pytorch_lightning.cli import LightningCLI
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except ImportError:
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try:
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from pytorch_lightning.utilities.cli import LightningCLI
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except ImportError:
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print("Looks like you are using pytorch_lightning>=2.0. This example only works with older versions")
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sys.exit(0)
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class Net(nn.Module):
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def __init__(self):
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super().__init__()
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self.conv1 = nn.Conv2d(1, 32, 3, 1)
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self.conv2 = nn.Conv2d(32, 64, 3, 1)
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self.dropout1 = nn.Dropout(0.25)
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self.dropout2 = nn.Dropout(0.5)
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self.fc1 = nn.Linear(9216, 128)
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self.fc2 = nn.Linear(128, 10)
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def forward(self, x):
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x = self.conv1(x)
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x = F.relu(x)
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x = self.conv2(x)
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x = F.relu(x)
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x = F.max_pool2d(x, 2)
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x = self.dropout1(x)
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x = torch.flatten(x, 1)
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x = self.fc1(x)
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x = F.relu(x)
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x = self.dropout2(x)
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x = self.fc2(x)
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output = F.log_softmax(x, dim=1)
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return output
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class ImageClassifier(LightningModule):
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def __init__(self, model=None, lr=1.0, gamma=0.7, batch_size=32):
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super().__init__()
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self.save_hyperparameters(ignore="model")
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self.model = model or Net()
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try:
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self.test_acc = Accuracy()
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except TypeError:
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self.test_acc = Accuracy("binary")
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def forward(self, x):
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return self.model(x)
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def training_step(self, batch, batch_idx):
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x, y = batch
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logits = self.forward(x)
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loss = F.nll_loss(logits, y.long())
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self.log("train_loss", loss)
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return loss
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def test_step(self, batch, batch_idx):
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x, y = batch
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logits = self.forward(x)
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loss = F.nll_loss(logits, y.long())
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self.test_acc(logits, y)
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self.log("test_acc", self.test_acc)
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self.log("test_loss", loss)
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def configure_optimizers(self):
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optimizer = torch.optim.Adadelta(self.model.parameters(), lr=self.hparams.lr)
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return [optimizer], [torch.optim.lr_scheduler.StepLR(optimizer, step_size=1, gamma=self.hparams.gamma)]
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@property
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def transform(self):
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return T.Compose([T.ToTensor(), T.Normalize((0.1307,), (0.3081,))])
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def prepare_data(self) -> None:
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MNIST("./data", download=True)
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def train_dataloader(self):
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train_dataset = MNIST("./data", train=True, download=False, transform=self.transform)
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return torch.utils.data.DataLoader(train_dataset, batch_size=self.hparams.batch_size)
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def test_dataloader(self):
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test_dataset = MNIST("./data", train=False, download=False, transform=self.transform)
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return torch.utils.data.DataLoader(test_dataset, batch_size=self.hparams.batch_size)
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if __name__ == "__main__":
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Task.add_requirements("requirements.txt")
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Task.init(project_name="example", task_name="pytorch_lightning_jsonargparse")
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LightningCLI(ImageClassifier, seed_everything_default=42, run=True)
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