93 lines
2.7 KiB
ReStructuredText
93 lines
2.7 KiB
ReStructuredText
:orphan:
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Lightning Bolts
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===============
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`PyTorch Lightning Bolts <https://lightning-bolts.readthedocs.io/en/latest/>`_, is our official collection
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of prebuilt models across many research domains.
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.. code-block:: bash
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pip install lightning-bolts
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In bolts we have:
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- A collection of pretrained state-of-the-art models.
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- A collection of models designed to bootstrap your research.
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- A collection of callbacks, transforms, full datasets.
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- All models work on CPUs, TPUs, GPUs and 16-bit precision.
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-----------------
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Quality control
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---------------
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The Lightning community builds bolts and contributes them to Bolts.
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The lightning team guarantees that contributions are:
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- Rigorously Tested (CPUs, GPUs, TPUs).
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- Rigorously Documented.
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- Standardized via PyTorch Lightning.
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- Optimized for speed.
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- Checked for correctness.
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---------
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Example 1: Pretrained, prebuilt models
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--------------------------------------
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.. code-block:: python
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from pl_bolts.models import VAE, GPT2, ImageGPT, PixelCNN
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from pl_bolts.models.self_supervised import AMDIM, CPCV2, SimCLR, MocoV2
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from pl_bolts.models import LinearRegression, LogisticRegression
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from pl_bolts.models.gans import GAN
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from pl_bolts.callbacks import PrintTableMetricsCallback
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from pl_bolts.datamodules import FashionMNISTDataModule, CIFAR10DataModule, ImagenetDataModule
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------------
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Example 2: Extend for faster research
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-------------------------------------
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Bolts are contributed with benchmarks and continuous-integration tests. This means
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you can trust the implementations and use them to bootstrap your research much faster.
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.. code-block:: python
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from pl_bolts.models import ImageGPT
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from pl_bolts.self_supervised import SimCLR
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class VideoGPT(ImageGPT):
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def training_step(self, batch, batch_idx):
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x, y = batch
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x = _shape_input(x)
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logits = self.gpt(x)
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simclr_features = self.simclr(x)
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# -----------------
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# do something new with GPT logits + simclr_features
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# -----------------
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loss = self.criterion(logits.view(-1, logits.size(-1)), x.view(-1).long())
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self.log("loss", loss)
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return loss
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----------
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Example 3: Callbacks
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--------------------
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We also have a collection of callbacks.
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.. code-block:: python
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from pl_bolts.callbacks import PrintTableMetricsCallback
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import pytorch_lightning as pl
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trainer = pl.Trainer(callbacks=[PrintTableMetricsCallback()])
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# loss│train_loss│val_loss│epoch
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# ──────────────────────────────
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# 2.2541470527648926│2.2541470527648926│2.2158432006835938│0
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