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add_model_specific_args
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<div class="md-content">
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<article class="md-content__inner md-typeset">
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<a href="https://github.com/williamFalcon/pytorch-lightning/edit/master/docs/LightningModule/RequiredTrainerInterface.md" title="Edit this page" class="md-icon md-content__icon"></a>
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<h1 id="lightning-module-interface">Lightning Module interface</h1>
|
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<p>[<a href="https://github.com/williamFalcon/pytorch-lightning/blob/master/pytorch_lightning/root_module/root_module.py">Github Code</a>]</p>
|
|
<p>A lightning module is a strict superclass of nn.Module, it provides a standard interface for the trainer to interact with the model.</p>
|
|
<p>The easiest thing to do is copy <a href="https://williamfalcon.github.io/pytorch-lightning/LightningModule/RequiredTrainerInterface/#minimal-example">this minimal example</a> and modify accordingly. </p>
|
|
<p>Otherwise, to Define a Lightning Module, implement the following methods:</p>
|
|
<p><strong>Required</strong>: </p>
|
|
<ul>
|
|
<li><a href="./#training_step">training_step</a> </li>
|
|
<li><a href="./#validation_step">validation_step</a></li>
|
|
<li>
|
|
<p><a href="./#validation_end">validation_end</a></p>
|
|
</li>
|
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<li>
|
|
<p><a href="./#configure_optimizers">configure_optimizers</a></p>
|
|
</li>
|
|
<li>
|
|
<p><a href="./#tng_dataloader">tng_dataloader</a></p>
|
|
</li>
|
|
<li><a href="./#tng_dataloader">tng_dataloader</a></li>
|
|
<li><a href="./#test_dataloader">test_dataloader</a></li>
|
|
</ul>
|
|
<p><strong>Optional</strong>: </p>
|
|
<ul>
|
|
<li><a href="./#on_save_checkpoint">on_save_checkpoint</a></li>
|
|
<li><a href="./#on_load_checkpoint">on_load_checkpoint</a></li>
|
|
<li><a href="./#update_tng_log_metrics">update_tng_log_metrics</a></li>
|
|
<li><a href="./#add_model_specific_args">add_model_specific_args</a></li>
|
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</ul>
|
|
<hr />
|
|
<p><strong>Minimal example</strong></p>
|
|
<pre><code class="python">import os
|
|
import torch
|
|
from torch.nn import functional as F
|
|
from torch.utils.data import DataLoader
|
|
from torchvision.datasets import MNIST
|
|
import torchvision.transforms as transforms
|
|
|
|
import pytorch_lightning as ptl
|
|
|
|
class CoolModel(ptl.LightningModule):
|
|
|
|
def __init__(self):
|
|
super(CoolModel, self).__init__()
|
|
# not the best model...
|
|
self.l1 = torch.nn.Linear(28 * 28, 10)
|
|
|
|
def forward(self, x):
|
|
return torch.relu(self.l1(x.view(x.size(0), -1)))
|
|
|
|
def my_loss(self, y_hat, y):
|
|
return F.cross_entropy(y_hat, y)
|
|
|
|
def training_step(self, batch, batch_nb):
|
|
x, y = batch
|
|
y_hat = self.forward(x)
|
|
return {'loss': self.my_loss(y_hat, y)}
|
|
|
|
def validation_step(self, batch, batch_nb):
|
|
x, y = batch
|
|
y_hat = self.forward(x)
|
|
return {'val_loss': self.my_loss(y_hat, y)}
|
|
|
|
def validation_end(self, outputs):
|
|
avg_loss = torch.stack([x['val_loss'] for x in outputs]).mean()
|
|
return {'avg_val_loss': avg_loss}
|
|
|
|
def configure_optimizers(self):
|
|
return [torch.optim.Adam(self.parameters(), lr=0.02)]
|
|
|
|
@ptl.data_loader
|
|
def tng_dataloader(self):
|
|
return DataLoader(MNIST(os.getcwd(), train=True, download=True, transform=transforms.ToTensor()), batch_size=32)
|
|
|
|
@ptl.data_loader
|
|
def val_dataloader(self):
|
|
return DataLoader(MNIST(os.getcwd(), train=True, download=True, transform=transforms.ToTensor()), batch_size=32)
|
|
|
|
@ptl.data_loader
|
|
def test_dataloader(self):
|
|
return DataLoader(MNIST(os.getcwd(), train=True, download=True, transform=transforms.ToTensor()), batch_size=32)
|
|
</code></pre>
|
|
|
|
<hr />
|
|
<h3 id="training_step">training_step</h3>
|
|
<pre><code class="python">def training_step(self, data_batch, batch_nb)
|
|
</code></pre>
|
|
|
|
<p>In this step you'd normally do the forward pass and calculate the loss for a batch. You can also do fancier things like multiple forward passes or something specific to your model.</p>
|
|
<p><strong>Params</strong> </p>
|
|
<table>
|
|
<thead>
|
|
<tr>
|
|
<th>Param</th>
|
|
<th>description</th>
|
|
</tr>
|
|
</thead>
|
|
<tbody>
|
|
<tr>
|
|
<td>data_batch</td>
|
|
<td>The output of your dataloader. A tensor, tuple or list</td>
|
|
</tr>
|
|
<tr>
|
|
<td>batch_nb</td>
|
|
<td>Integer displaying which batch this is</td>
|
|
</tr>
|
|
</tbody>
|
|
</table>
|
|
<p><strong>Return</strong> </p>
|
|
<p>Dictionary or OrderedDict </p>
|
|
<table>
|
|
<thead>
|
|
<tr>
|
|
<th>key</th>
|
|
<th>value</th>
|
|
<th>is required</th>
|
|
</tr>
|
|
</thead>
|
|
<tbody>
|
|
<tr>
|
|
<td>loss</td>
|
|
<td>tensor scalar</td>
|
|
<td>Y</td>
|
|
</tr>
|
|
<tr>
|
|
<td>prog</td>
|
|
<td>Dict for progress bar display. Must have only tensors</td>
|
|
<td>N</td>
|
|
</tr>
|
|
</tbody>
|
|
</table>
|
|
<p><strong>Example</strong></p>
|
|
<pre><code class="python">def training_step(self, data_batch, batch_nb):
|
|
x, y, z = data_batch
|
|
|
|
# implement your own
|
|
out = self.forward(x)
|
|
loss = self.loss(out, x)
|
|
|
|
output = {
|
|
'loss': loss, # required
|
|
'prog': {'tng_loss': loss, 'batch_nb': batch_nb} # optional
|
|
}
|
|
|
|
# return a dict
|
|
return output
|
|
</code></pre>
|
|
|
|
<hr />
|
|
<h3 id="validation_step">validation_step</h3>
|
|
<pre><code class="python">def validation_step(self, data_batch, batch_nb)
|
|
</code></pre>
|
|
|
|
<p>In this step you'd normally do the forward pass and calculate the loss for a batch. You can also do fancier things like multiple forward passes or something specific to your model.
|
|
This is most likely the same as your training_step. But unlike training step, the outputs from here will go to validation_end for collation.</p>
|
|
<p><strong>Params</strong> </p>
|
|
<table>
|
|
<thead>
|
|
<tr>
|
|
<th>Param</th>
|
|
<th>description</th>
|
|
</tr>
|
|
</thead>
|
|
<tbody>
|
|
<tr>
|
|
<td>data_batch</td>
|
|
<td>The output of your dataloader. A tensor, tuple or list</td>
|
|
</tr>
|
|
<tr>
|
|
<td>batch_nb</td>
|
|
<td>Integer displaying which batch this is</td>
|
|
</tr>
|
|
</tbody>
|
|
</table>
|
|
<p><strong>Return</strong> </p>
|
|
<table>
|
|
<thead>
|
|
<tr>
|
|
<th>Return</th>
|
|
<th>description</th>
|
|
<th>optional</th>
|
|
</tr>
|
|
</thead>
|
|
<tbody>
|
|
<tr>
|
|
<td>dict</td>
|
|
<td>Dict of OrderedDict with metrics to display in progress bar. All keys must be tensors.</td>
|
|
<td>Y</td>
|
|
</tr>
|
|
</tbody>
|
|
</table>
|
|
<p><strong>Example</strong></p>
|
|
<pre><code class="python">def validation_step(self, data_batch, batch_nb):
|
|
x, y, z = data_batch
|
|
|
|
# implement your own
|
|
out = self.forward(x)
|
|
loss = self.loss(out, x)
|
|
|
|
# calculate acc
|
|
labels_hat = torch.argmax(out, dim=1)
|
|
val_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0)
|
|
|
|
# all optional...
|
|
# return whatever you need for the collation function validation_end
|
|
output = OrderedDict({
|
|
'val_loss': loss_val,
|
|
'val_acc': torch.tensor(val_acc), # everything must be a tensor
|
|
})
|
|
|
|
# return an optional dict
|
|
return output
|
|
</code></pre>
|
|
|
|
<hr />
|
|
<h3 id="validation_end">validation_end</h3>
|
|
<pre><code class="python">def validation_end(self, outputs)
|
|
</code></pre>
|
|
|
|
<p>Called at the end of the validation loop with the output of each validation_step.</p>
|
|
<p><strong>Params</strong> </p>
|
|
<table>
|
|
<thead>
|
|
<tr>
|
|
<th>Param</th>
|
|
<th>description</th>
|
|
</tr>
|
|
</thead>
|
|
<tbody>
|
|
<tr>
|
|
<td>outputs</td>
|
|
<td>List of outputs you defined in validation_step</td>
|
|
</tr>
|
|
</tbody>
|
|
</table>
|
|
<p><strong>Return</strong> </p>
|
|
<table>
|
|
<thead>
|
|
<tr>
|
|
<th>Return</th>
|
|
<th>description</th>
|
|
<th>optional</th>
|
|
</tr>
|
|
</thead>
|
|
<tbody>
|
|
<tr>
|
|
<td>dict</td>
|
|
<td>Dict of OrderedDict with metrics to display in progress bar</td>
|
|
<td>Y</td>
|
|
</tr>
|
|
</tbody>
|
|
</table>
|
|
<p><strong>Example</strong></p>
|
|
<pre><code class="python">def validation_end(self, outputs):
|
|
"""
|
|
Called at the end of validation to aggregate outputs
|
|
:param outputs: list of individual outputs of each validation step
|
|
:return:
|
|
"""
|
|
val_loss_mean = 0
|
|
val_acc_mean = 0
|
|
for output in outputs:
|
|
val_loss_mean += output['val_loss']
|
|
val_acc_mean += output['val_acc']
|
|
|
|
val_loss_mean /= len(outputs)
|
|
val_acc_mean /= len(outputs)
|
|
tqdm_dic = {'val_loss': val_loss_mean.item(), 'val_acc': val_acc_mean.item()}
|
|
return tqdm_dic
|
|
</code></pre>
|
|
|
|
<hr />
|
|
<h3 id="configure_optimizers">configure_optimizers</h3>
|
|
<pre><code class="python">def configure_optimizers(self)
|
|
</code></pre>
|
|
|
|
<p>Set up as many optimizers as you need. Normally you'd need one. But in the case of GANs or something more esoteric you might have multiple.
|
|
Lightning will call .backward() and .step() on each one. If you use 16 bit precision it will also handle that.</p>
|
|
<h5 id="return">Return</h5>
|
|
<p>List - List of optimizers</p>
|
|
<p><strong>Example</strong></p>
|
|
<pre><code class="python"># most cases
|
|
def configure_optimizers(self):
|
|
opt = Adam(lr=0.01)
|
|
return [opt]
|
|
|
|
# gan example
|
|
def configure_optimizers(self):
|
|
generator_opt = Adam(lr=0.01)
|
|
disriminator_opt = Adam(lr=0.02)
|
|
return [generator_opt, disriminator_opt]
|
|
</code></pre>
|
|
|
|
<hr />
|
|
<h3 id="on_save_checkpoint">on_save_checkpoint</h3>
|
|
<pre><code class="python">def on_save_checkpoint(self, checkpoint)
|
|
</code></pre>
|
|
|
|
<p>Called by lightning to checkpoint your model. Lightning saves the training state (current epoch, global_step, etc)
|
|
and also saves the model state_dict. If you want to save anything else, use this method to add your own
|
|
key-value pair.</p>
|
|
<h5 id="return_1">Return</h5>
|
|
<p>Nothing</p>
|
|
<p><strong>Example</strong></p>
|
|
<pre><code class="python">def on_save_checkpoint(self, checkpoint):
|
|
# 99% of use cases you don't need to implement this method
|
|
checkpoint['something_cool_i_want_to_save'] = my_cool_pickable_object
|
|
</code></pre>
|
|
|
|
<hr />
|
|
<h3 id="on_load_checkpoint">on_load_checkpoint</h3>
|
|
<pre><code class="python">def on_load_checkpoint(self, checkpoint)
|
|
</code></pre>
|
|
|
|
<p>Called by lightning to restore your model. Lighting auto-restores global step, epoch, etc...
|
|
It also restores the model state_dict.
|
|
If you saved something with <strong>on_save_checkpoint</strong> this is your chance to restore this.</p>
|
|
<h5 id="return_2">Return</h5>
|
|
<p>Nothing </p>
|
|
<p><strong>Example</strong></p>
|
|
<pre><code class="python">def on_load_checkpoint(self, checkpoint):
|
|
# 99% of the time you don't need to implement this method
|
|
self.something_cool_i_want_to_save = checkpoint['something_cool_i_want_to_save']
|
|
</code></pre>
|
|
|
|
<hr />
|
|
<h3 id="tng_dataloader">tng_dataloader</h3>
|
|
<pre><code class="python">@ptl.data_loader
|
|
def tng_dataloader(self)
|
|
</code></pre>
|
|
|
|
<p>Called by lightning during training loop. Make sure to use the @ptl.data_loader decorator, this ensures not calling this function until the data are needed.</p>
|
|
<h5 id="return_3">Return</h5>
|
|
<p>Pytorch DataLoader</p>
|
|
<p><strong>Example</strong></p>
|
|
<pre><code class="python">@ptl.data_loader
|
|
def tng_dataloader(self):
|
|
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (1.0,))])
|
|
dataset = MNIST(root='/path/to/mnist/', train=True, transform=transform, download=True)
|
|
loader = torch.utils.data.DataLoader(
|
|
dataset=dataset,
|
|
batch_size=self.hparams.batch_size,
|
|
shuffle=True
|
|
)
|
|
return loader
|
|
</code></pre>
|
|
|
|
<hr />
|
|
<h3 id="val_dataloader">val_dataloader</h3>
|
|
<pre><code class="python">@ptl.data_loader
|
|
def tng_dataloader(self)
|
|
</code></pre>
|
|
|
|
<p>Called by lightning during validation loop. Make sure to use the @ptl.data_loader decorator, this ensures not calling this function until the data are needed.</p>
|
|
<h5 id="return_4">Return</h5>
|
|
<p>Pytorch DataLoader</p>
|
|
<p><strong>Example</strong></p>
|
|
<pre><code class="python">@ptl.data_loader
|
|
def val_dataloader(self):
|
|
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (1.0,))])
|
|
dataset = MNIST(root='/path/to/mnist/', train=False, transform=transform, download=True)
|
|
loader = torch.utils.data.DataLoader(
|
|
dataset=dataset,
|
|
batch_size=self.hparams.batch_size,
|
|
shuffle=True
|
|
)
|
|
|
|
return loader
|
|
</code></pre>
|
|
|
|
<hr />
|
|
<h3 id="test_dataloader">test_dataloader</h3>
|
|
<pre><code class="python">@ptl.data_loader
|
|
def test_dataloader(self)
|
|
</code></pre>
|
|
|
|
<p>Called by lightning during test loop. Make sure to use the @ptl.data_loader decorator, this ensures not calling this function until the data are needed.</p>
|
|
<h5 id="return_5">Return</h5>
|
|
<p>Pytorch DataLoader</p>
|
|
<p><strong>Example</strong></p>
|
|
<pre><code class="python">@ptl.data_loader
|
|
def test_dataloader(self):
|
|
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (1.0,))])
|
|
dataset = MNIST(root='/path/to/mnist/', train=False, transform=transform, download=True)
|
|
loader = torch.utils.data.DataLoader(
|
|
dataset=dataset,
|
|
batch_size=self.hparams.batch_size,
|
|
shuffle=True
|
|
)
|
|
|
|
return loader
|
|
</code></pre>
|
|
|
|
<hr />
|
|
<h3 id="update_tng_log_metrics">update_tng_log_metrics</h3>
|
|
<pre><code class="python">def update_tng_log_metrics(self, logs)
|
|
</code></pre>
|
|
|
|
<p>Called by lightning right before it logs metrics for this batch.
|
|
This is a chance to ammend or add to the metrics about to be logged.</p>
|
|
<h5 id="return_6">Return</h5>
|
|
<p>Dict </p>
|
|
<p><strong>Example</strong></p>
|
|
<pre><code class="python">def update_tng_log_metrics(self, logs):
|
|
# modify or add to logs
|
|
return logs
|
|
</code></pre>
|
|
|
|
<hr />
|
|
<h3 id="add_model_specific_args">add_model_specific_args</h3>
|
|
<pre><code class="python">@staticmethod
|
|
def add_model_specific_args(parent_parser, root_dir)
|
|
</code></pre>
|
|
|
|
<p>Lightning has a list of default argparse commands.
|
|
This method is your chance to add or modify commands specific to your model.
|
|
The <a href="https://williamfalcon.github.io/test-tube/hyperparameter_optimization/HyperOptArgumentParser/">hyperparameter argument parser</a> is available anywhere in your model by calling self.hparams.</p>
|
|
<h5 id="return_7">Return</h5>
|
|
<p>An argument parser</p>
|
|
<p><strong>Example</strong></p>
|
|
<pre><code class="python">@staticmethod
|
|
def add_model_specific_args(parent_parser, root_dir):
|
|
parser = HyperOptArgumentParser(strategy=parent_parser.strategy, parents=[parent_parser])
|
|
|
|
# param overwrites
|
|
# parser.set_defaults(gradient_clip=5.0)
|
|
|
|
# network params
|
|
parser.opt_list('--drop_prob', default=0.2, options=[0.2, 0.5], type=float, tunable=False)
|
|
parser.add_argument('--in_features', default=28*28)
|
|
parser.add_argument('--out_features', default=10)
|
|
parser.add_argument('--hidden_dim', default=50000) # use 500 for CPU, 50000 for GPU to see speed difference
|
|
|
|
# data
|
|
parser.add_argument('--data_root', default=os.path.join(root_dir, 'mnist'), type=str)
|
|
|
|
# training params (opt)
|
|
parser.opt_list('--learning_rate', default=0.001, type=float, options=[0.0001, 0.0005, 0.001, 0.005],
|
|
tunable=False)
|
|
parser.opt_list('--batch_size', default=256, type=int, options=[32, 64, 128, 256], tunable=False)
|
|
parser.opt_list('--optimizer_name', default='adam', type=str, options=['adam'], tunable=False)
|
|
return parser
|
|
</code></pre>
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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