gammalearn.tests package

Submodules

gammalearn.tests.test_criterions module

class gammalearn.tests.test_criterions.TestCriterions(methodName='runTest')[source]

Bases: TestCase

setUp() None[source]

Hook method for setting up the test fixture before exercising it.

test_loss_balancing_masked()[source]
test_loss_balancing_not_masked()[source]
test_onehot()[source]
test_uncertainty_loss_masked()[source]
test_uncertainty_loss_not_masked()[source]
class gammalearn.tests.test_criterions.TestModule[source]

Bases: LightningModule

configure_optimizers()[source]

Choose what optimizers and learning-rate schedulers to use in your optimization. Normally you’d need one. But in the case of GANs or similar you might have multiple.

Returns:

Any of these 6 options.

  • Single optimizer.

  • List or Tuple of optimizers.

  • Two lists - The first list has multiple optimizers, and the second has multiple LR schedulers (or multiple lr_scheduler_config).

  • Dictionary, with an "optimizer" key, and (optionally) a "lr_scheduler" key whose value is a single LR scheduler or lr_scheduler_config.

  • Tuple of dictionaries as described above, with an optional "frequency" key.

  • None - Fit will run without any optimizer.

The lr_scheduler_config is a dictionary which contains the scheduler and its associated configuration. The default configuration is shown below.

lr_scheduler_config = {
    # REQUIRED: The scheduler instance
    "scheduler": lr_scheduler,
    # The unit of the scheduler's step size, could also be 'step'.
    # 'epoch' updates the scheduler on epoch end whereas 'step'
    # updates it after a optimizer update.
    "interval": "epoch",
    # How many epochs/steps should pass between calls to
    # `scheduler.step()`. 1 corresponds to updating the learning
    # rate after every epoch/step.
    "frequency": 1,
    # Metric to to monitor for schedulers like `ReduceLROnPlateau`
    "monitor": "val_loss",
    # If set to `True`, will enforce that the value specified 'monitor'
    # is available when the scheduler is updated, thus stopping
    # training if not found. If set to `False`, it will only produce a warning
    "strict": True,
    # If using the `LearningRateMonitor` callback to monitor the
    # learning rate progress, this keyword can be used to specify
    # a custom logged name
    "name": None,
}

When there are schedulers in which the .step() method is conditioned on a value, such as the torch.optim.lr_scheduler.ReduceLROnPlateau scheduler, Lightning requires that the lr_scheduler_config contains the keyword "monitor" set to the metric name that the scheduler should be conditioned on.

Metrics can be made available to monitor by simply logging it using self.log('metric_to_track', metric_val) in your LightningModule.

Note

The frequency value specified in a dict along with the optimizer key is an int corresponding to the number of sequential batches optimized with the specific optimizer. It should be given to none or to all of the optimizers. There is a difference between passing multiple optimizers in a list, and passing multiple optimizers in dictionaries with a frequency of 1:

  • In the former case, all optimizers will operate on the given batch in each optimization step.

  • In the latter, only one optimizer will operate on the given batch at every step.

This is different from the frequency value specified in the lr_scheduler_config mentioned above.

def configure_optimizers(self):
    optimizer_one = torch.optim.SGD(self.model.parameters(), lr=0.01)
    optimizer_two = torch.optim.SGD(self.model.parameters(), lr=0.01)
    return [
        {"optimizer": optimizer_one, "frequency": 5},
        {"optimizer": optimizer_two, "frequency": 10},
    ]

In this example, the first optimizer will be used for the first 5 steps, the second optimizer for the next 10 steps and that cycle will continue. If an LR scheduler is specified for an optimizer using the lr_scheduler key in the above dict, the scheduler will only be updated when its optimizer is being used.

Examples:

# most cases. no learning rate scheduler
def configure_optimizers(self):
    return Adam(self.parameters(), lr=1e-3)

# multiple optimizer case (e.g.: GAN)
def configure_optimizers(self):
    gen_opt = Adam(self.model_gen.parameters(), lr=0.01)
    dis_opt = Adam(self.model_dis.parameters(), lr=0.02)
    return gen_opt, dis_opt

# example with learning rate schedulers
def configure_optimizers(self):
    gen_opt = Adam(self.model_gen.parameters(), lr=0.01)
    dis_opt = Adam(self.model_dis.parameters(), lr=0.02)
    dis_sch = CosineAnnealing(dis_opt, T_max=10)
    return [gen_opt, dis_opt], [dis_sch]

# example with step-based learning rate schedulers
# each optimizer has its own scheduler
def configure_optimizers(self):
    gen_opt = Adam(self.model_gen.parameters(), lr=0.01)
    dis_opt = Adam(self.model_dis.parameters(), lr=0.02)
    gen_sch = {
        'scheduler': ExponentialLR(gen_opt, 0.99),
        'interval': 'step'  # called after each training step
    }
    dis_sch = CosineAnnealing(dis_opt, T_max=10) # called every epoch
    return [gen_opt, dis_opt], [gen_sch, dis_sch]

# example with optimizer frequencies
# see training procedure in `Improved Training of Wasserstein GANs`, Algorithm 1
# https://arxiv.org/abs/1704.00028
def configure_optimizers(self):
    gen_opt = Adam(self.model_gen.parameters(), lr=0.01)
    dis_opt = Adam(self.model_dis.parameters(), lr=0.02)
    n_critic = 5
    return (
        {'optimizer': dis_opt, 'frequency': n_critic},
        {'optimizer': gen_opt, 'frequency': 1}
    )

Note

Some things to know:

  • Lightning calls .backward() and .step() on each optimizer as needed.

  • If learning rate scheduler is specified in configure_optimizers() with key "interval" (default “epoch”) in the scheduler configuration, Lightning will call the scheduler’s .step() method automatically in case of automatic optimization.

  • If you use 16-bit precision (precision=16), Lightning will automatically handle the optimizers.

  • If you use multiple optimizers, training_step() will have an additional optimizer_idx parameter.

  • If you use torch.optim.LBFGS, Lightning handles the closure function automatically for you.

  • If you use multiple optimizers, gradients will be calculated only for the parameters of current optimizer at each training step.

  • If you need to control how often those optimizers step or override the default .step() schedule, override the optimizer_step() hook.

train_dataloader()[source]

Implement one or more PyTorch DataLoaders for training.

Returns:

A collection of torch.utils.data.DataLoader specifying training samples. In the case of multiple dataloaders, please see this section.

The dataloader you return will not be reloaded unless you set :paramref:`~pytorch_lightning.trainer.Trainer.reload_dataloaders_every_n_epochs` to a positive integer.

For data processing use the following pattern:

  • download in prepare_data()

  • process and split in setup()

However, the above are only necessary for distributed processing.

Warning

do not assign state in prepare_data

  • fit()

  • prepare_data()

  • setup()

Note

Lightning adds the correct sampler for distributed and arbitrary hardware. There is no need to set it yourself.

Example:

# single dataloader
def train_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.batch_size,
        shuffle=True
    )
    return loader

# multiple dataloaders, return as list
def train_dataloader(self):
    mnist = MNIST(...)
    cifar = CIFAR(...)
    mnist_loader = torch.utils.data.DataLoader(
        dataset=mnist, batch_size=self.batch_size, shuffle=True
    )
    cifar_loader = torch.utils.data.DataLoader(
        dataset=cifar, batch_size=self.batch_size, shuffle=True
    )
    # each batch will be a list of tensors: [batch_mnist, batch_cifar]
    return [mnist_loader, cifar_loader]

# multiple dataloader, return as dict
def train_dataloader(self):
    mnist = MNIST(...)
    cifar = CIFAR(...)
    mnist_loader = torch.utils.data.DataLoader(
        dataset=mnist, batch_size=self.batch_size, shuffle=True
    )
    cifar_loader = torch.utils.data.DataLoader(
        dataset=cifar, batch_size=self.batch_size, shuffle=True
    )
    # each batch will be a dict of tensors: {'mnist': batch_mnist, 'cifar': batch_cifar}
    return {'mnist': mnist_loader, 'cifar': cifar_loader}
training_step()[source]

Here you compute and return the training loss and some additional metrics for e.g. the progress bar or logger.

Parameters:
Returns:

Any of.

  • Tensor - The loss tensor

  • dict - A dictionary. Can include any keys, but must include the key 'loss'

  • None - Training will skip to the next batch. This is only for automatic optimization.

    This is not supported for multi-GPU, TPU, IPU, or DeepSpeed.

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 model specific.

Example:

def training_step(self, batch, batch_idx):
    x, y, z = batch
    out = self.encoder(x)
    loss = self.loss(out, x)
    return loss

If you define multiple optimizers, this step will be called with an additional optimizer_idx parameter.

# Multiple optimizers (e.g.: GANs)
def training_step(self, batch, batch_idx, optimizer_idx):
    if optimizer_idx == 0:
        # do training_step with encoder
        ...
    if optimizer_idx == 1:
        # do training_step with decoder
        ...

If you add truncated back propagation through time you will also get an additional argument with the hidden states of the previous step.

# Truncated back-propagation through time
def training_step(self, batch, batch_idx, hiddens):
    # hiddens are the hidden states from the previous truncated backprop step
    out, hiddens = self.lstm(data, hiddens)
    loss = ...
    return {"loss": loss, "hiddens": hiddens}

Note

The loss value shown in the progress bar is smoothed (averaged) over the last values, so it differs from the actual loss returned in train/validation step.

Note

When accumulate_grad_batches > 1, the loss returned here will be automatically normalized by accumulate_grad_batches internally.

gammalearn.tests.test_datasets module

class gammalearn.tests.test_datasets.TestDL1Parameters(methodName='runTest')[source]

Bases: TestCase

setUp() None[source]

Hook method for setting up the test fixture before exercising it.

test_test_dl1_parameters()[source]
test_train_dl1_parameters()[source]
class gammalearn.tests.test_datasets.TestLSTDataset(methodName='runTest')[source]

Bases: TestCase

setUp() None[source]

Hook method for setting up the test fixture before exercising it.

test_energy_filter_file()[source]
test_energy_filter_memory()[source]
test_intensity_energy_filter_file()[source]
test_intensity_energy_filter_memory()[source]
test_intensity_filter_file()[source]
test_intensity_filter_memory()[source]
test_intensity_lstchain_filter_file()[source]
test_intensity_lstchain_filter_memory()[source]
test_mono_file()[source]
test_mono_memory()[source]
test_mono_memory_test_mode()[source]
test_subarray()[source]
class gammalearn.tests.test_datasets.TestLSTRealDataset(methodName='runTest')[source]

Bases: TestCase

setUp() None[source]

Hook method for setting up the test fixture before exercising it.

test_intensity_filter_file()[source]
test_intensity_filter_memory()[source]
test_intensity_lstchain_filter_file()[source]
test_intensity_lstchain_filter_memory()[source]
test_mono_file()[source]
test_mono_memory()[source]
test_mono_memory_test_mode()[source]

gammalearn.tests.test_nets module

class gammalearn.tests.test_nets.TestNets(methodName='runTest')[source]

Bases: TestCase

setUp() None[source]

Hook method for setting up the test fixture before exercising it.

test_efficient_net()[source]
test_mae()[source]
test_mobilenet_v2()[source]
test_mobilenet_v3()[source]
test_resnet18()[source]

gammalearn.tests.test_optimizers module

class gammalearn.tests.test_optimizers.TestOptimizers(methodName='runTest')[source]

Bases: TestCase

setUp() None[source]

Hook method for setting up the test fixture before exercising it.

test_prime_optimizer()[source]

gammalearn.tests.test_utils module

class gammalearn.tests.test_utils.MockLSTDataset[source]

Bases: object

class gammalearn.tests.test_utils.TestIndexMatrix(methodName='runTest')[source]

Bases: TestCase

setUp()[source]

Hook method for setting up the test fixture before exercising it.

test_compare_indexedconv_method()[source]

Test that the new method gives the same result as the previous one for the LSTCam geometry

test_get_index_matrix_from_geom_19()[source]

Test the converter with a simple 19 pixels geometry

Hexa: ```

0 1 2

3 4 5 6

7 8 9 10 11

12 13 14 15

16 17 18

` Square: `

0 1 2 -1 -1

3 4 5 6 -1

7 8 9 10 11

-1

12 13 14 15

-1
-1

16 17 18

```

test_get_index_matrix_from_geom_7()[source]

Test the converter with a simple 7 pixels geometry

Hexa: ```

0 1

2 3 4

5 6

` Square: `

0 1 -1 2 3 4

-1

5 6

```

class gammalearn.tests.test_utils.TestTransformerUtils(methodName='runTest')[source]

Bases: TestCase

test_2d_sincos_pos_embedding()[source]
test_patches_and_centroids_LSTCam()[source]
test_patches_and_centroids_lstchain_07_MC()[source]
test_patches_and_centroids_lstchain_07_real()[source]
class gammalearn.tests.test_utils.TestUtils(methodName='runTest')[source]

Bases: TestCase

setUp() None[source]

Hook method for setting up the test fixture before exercising it.

test_cleaning()[source]
test_emission_cone()[source]
test_energy()[source]
test_impact_distance()[source]
test_inject_geometry_into_parameters()[source]
test_leakage()[source]
test_multiplicity()[source]
test_multiplicity_strict()[source]
test_nets_definition_path()[source]
test_rotated_indices()[source]
class gammalearn.tests.test_utils.TestWrite(methodName='runTest')[source]

Bases: TestCase

test_write_dl2_dataframe()[source]

gammalearn.tests.test_version module

gammalearn.tests.test_version.test_version()[source]

Module contents