92 lines
2.4 KiB
ReStructuredText
92 lines
2.4 KiB
ReStructuredText
TorchMetrics
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============
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`TorchMetrics <https://torchmetrics.readthedocs.io>`_ is a collection of machine learning metrics for distributed,
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scalable PyTorch models and an easy-to-use API to create custom metrics. It has a collection of 60+ PyTorch metrics implementations and
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is rigorously tested for all edge cases.
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.. code-block:: bash
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pip install torchmetrics
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In TorchMetrics, we offer the following benefits:
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- A standardized interface to increase reproducibility
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- Reduced Boilerplate
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- Distributed-training compatible
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- Rigorously tested
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- Automatic accumulation over batches
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- Automatic synchronization across multiple devices
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-----------------
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Example 1: Functional Metrics
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-----------------------------
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Below is a simple example for calculating the accuracy using the functional interface:
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.. code-block:: python
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import torch
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import torchmetrics
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# simulate a classification problem
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preds = torch.randn(10, 5).softmax(dim=-1)
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target = torch.randint(5, (10,))
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acc = torchmetrics.functional.accuracy(preds, target)
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------------
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Example 2: Module Metrics
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-------------------------
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The example below shows how to use the class-based interface:
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.. code-block:: python
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import torch
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import torchmetrics
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# initialize metric
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metric = torchmetrics.Accuracy()
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n_batches = 10
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for i in range(n_batches):
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# simulate a classification problem
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preds = torch.randn(10, 5).softmax(dim=-1)
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target = torch.randint(5, (10,))
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# metric on current batch
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acc = metric(preds, target)
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print(f"Accuracy on batch {i}: {acc}")
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# metric on all batches using custom accumulation
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acc = metric.compute()
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print(f"Accuracy on all data: {acc}")
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# Reseting internal state such that metric ready for new data
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metric.reset()
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------------
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Example 3: TorchMetrics with Lightning
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--------------------------------------
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The example below shows how to use a metric in your :doc:`LightningModule <../common/lightning_module>`:
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.. code-block:: python
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class MyModel(LightningModule):
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def __init__(self):
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...
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self.accuracy = torchmetrics.Accuracy()
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def training_step(self, batch, batch_idx):
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x, y = batch
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preds = self(x)
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...
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# log step metric
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self.accuracy(preds, y)
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self.log("train_acc_step", self.accuracy, on_epoch=True)
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...
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