dasf.ml.dl.models.devconvnet

Classes

`MyAccuracy`	Base class for all metrics present in the Metrics API.
`NNModule`	Base class for all neural network modules.
`TorchPatchDeConvNet`	Base class for all neural network modules.
`TorchPatchDeConvNetSkip`	Base class for all neural network modules.
`TorchSectionDeConvNet`	Base class for all neural network modules.
`TorchSectionDeConvNetSkip`	Base class for all neural network modules.

Module Contents

class dasf.ml.dl.models.devconvnet.MyAccuracy(dist_sync_on_step=False)[source]

Bases: torchmetrics.Metric

Base class for all metrics present in the Metrics API.

This class is inherited by all metrics and implements the following functionality: 1. Handles the transfer of metric states to correct device 2. Handles the synchronization of metric states across processes

The three core methods of the base class are * add_state() * forward() * reset()

which should almost never be overwritten by child classes. Instead, the following methods should be overwritten * update() * compute()

Args:

kwargs: additional keyword arguments, see Metric kwargs for more info.

compute_on_cpu: If metric state should be stored on CPU during computations. Only works for list states.

dist_sync_on_step: If metric state should synchronize on forward(). Default is False

process_group: The process group on which the synchronization is called. Default is the world.

dist_sync_fn: Function that performs the allgather option on the metric state. Default is an custom implementation that calls torch.distributed.all_gather internally.

distributed_available_fn: Function that checks if the distributed backend is available. Defaults to a check of torch.distributed.is_available() and torch.distributed.is_initialized().

sync_on_compute: If metric state should synchronize when compute is called. Default is True

compute_with_cache: If results from compute should be cached. Default is False

Initializes internal Module state, shared by both nn.Module and ScriptModule.

idx = 0

set_idx(idx)[source]

update(preds, target)[source]

Parameters:

preds (torch.Tensor)
target (torch.Tensor)

__str__()[source]

compute()[source]

class dasf.ml.dl.models.devconvnet.NNModule(n_classes=4, learned_billinear=False, clip=0.1, class_weights=None)[source]

Bases: pytorch_lightning.LightningModule

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes:

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will have their parameters converted too when you call to(), etc.

Note

As per the example above, an __init__() call to the parent class must be made before assignment on the child.

Variables:: training (bool) – Boolean represents whether this module is in training or evaluation mode.

Initializes internal Module state, shared by both nn.Module and ScriptModule.

learned_billinear

n_classes

clip

class_names

cross_entropy_loss(input, target, weight=None, ignore_index=255)[source]: Use 255 to fill empty values when padding or doing any augmentation operations like rotation.

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.

Return:

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 and learning rate scheduler as needed.
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.

training_step(batch, batch_idx)[source]

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

Args:

batch (Tensor | (Tensor, …) | [Tensor, …]):: The output of your DataLoader. A tensor, tuple or list.

batch_idx (int): Integer displaying index of this batch optimizer_idx (int): When using multiple optimizers, this argument will also be present. hiddens (Any): Passed in if

:paramref:`~pytorch_lightning.core.module.LightningModule.truncated_bptt_steps` > 0.

Return:

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.

test_step(test_batch, batch_idx)[source]

Operates on a single batch of data from the test set. In this step you’d normally generate examples or calculate anything of interest such as accuracy.

# the pseudocode for these calls
test_outs = []
for test_batch in test_data:
    out = test_step(test_batch)
    test_outs.append(out)
test_epoch_end(test_outs)

Args:

batch: The output of your DataLoader. batch_idx: The index of this batch. dataloader_id: The index of the dataloader that produced this batch.

(only if multiple test dataloaders used).

Return:

Any of.

Any object or value

None - Testing will skip to the next batch

# if you have one test dataloader:
def test_step(self, batch, batch_idx):
    ...


# if you have multiple test dataloaders:
def test_step(self, batch, batch_idx, dataloader_idx=0):
    ...

Examples:

# CASE 1: A single test dataset
def test_step(self, batch, batch_idx):
    x, y = batch

    # implement your own
    out = self(x)
    loss = self.loss(out, y)

    # log 6 example images
    # or generated text... or whatever
    sample_imgs = x[:6]
    grid = torchvision.utils.make_grid(sample_imgs)
    self.logger.experiment.add_image('example_images', grid, 0)

    # calculate acc
    labels_hat = torch.argmax(out, dim=1)
    test_acc = torch.sum(y == labels_hat).item() / (len(y) * 1.0)

    # log the outputs!
    self.log_dict({'test_loss': loss, 'test_acc': test_acc})

If you pass in multiple test dataloaders, test_step() will have an additional argument. We recommend setting the default value of 0 so that you can quickly switch between single and multiple dataloaders.

# CASE 2: multiple test dataloaders
def test_step(self, batch, batch_idx, dataloader_idx=0):
    # dataloader_idx tells you which dataset this is.
    ...

Note:: If you don’t need to test you don’t need to implement this method.
Note:: When the test_step() is called, the model has been put in eval mode and PyTorch gradients have been disabled. At the end of the test epoch, the model goes back to training mode and gradients are enabled.

class dasf.ml.dl.models.devconvnet.TorchPatchDeConvNet(n_classes=4, learned_billinear=False, clip=0.1, class_weights=None)[source]

Bases: NNModule

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes:

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will have their parameters converted too when you call to(), etc.

Note

As per the example above, an __init__() call to the parent class must be made before assignment on the child.

Variables:: training (bool) – Boolean represents whether this module is in training or evaluation mode.

Initializes internal Module state, shared by both nn.Module and ScriptModule.

unpool

conv_block1

conv_block2

conv_block3

conv_block4

conv_block5

conv_block6

conv_block7

deconv_block8

unpool_block9

deconv_block10

unpool_block11

deconv_block12

unpool_block13

deconv_block14

unpool_block15

deconv_block16

unpool_block17

deconv_block18

seg_score19

forward(x)[source]

Same as torch.nn.Module.forward().

Args:: *args: Whatever you decide to pass into the forward method. **kwargs: Keyword arguments are also possible.
Return:: Your model’s output

init_vgg16_params(vgg16, copy_fc8=True)[source]

load()[source]: This is just a no-op load method.

class dasf.ml.dl.models.devconvnet.TorchPatchDeConvNetSkip(n_classes=4, learned_billinear=False, clip=0.1, class_weights=None)[source]

Bases: NNModule

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes:

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will have their parameters converted too when you call to(), etc.

Note

As per the example above, an __init__() call to the parent class must be made before assignment on the child.

Variables:: training (bool) – Boolean represents whether this module is in training or evaluation mode.

Initializes internal Module state, shared by both nn.Module and ScriptModule.

unpool

conv_block1

conv_block2

conv_block3

conv_block4

conv_block5

conv_block6

conv_block7

deconv_block8

unpool_block9

deconv_block10

unpool_block11

deconv_block12

unpool_block13

deconv_block14

unpool_block15

deconv_block16

unpool_block17

deconv_block18

seg_score19

forward(x)[source]

Same as torch.nn.Module.forward().

Args:: *args: Whatever you decide to pass into the forward method. **kwargs: Keyword arguments are also possible.
Return:: Your model’s output

init_vgg16_params(vgg16, copy_fc8=True)[source]

load()[source]: This is just a no-op load method.

class dasf.ml.dl.models.devconvnet.TorchSectionDeConvNet(n_classes=4, learned_billinear=False, clip=0.1, class_weights=False)[source]

Bases: NNModule

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes:

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will have their parameters converted too when you call to(), etc.

Note

As per the example above, an __init__() call to the parent class must be made before assignment on the child.

Variables:: training (bool) – Boolean represents whether this module is in training or evaluation mode.

Initializes internal Module state, shared by both nn.Module and ScriptModule.

unpool

conv_block1

conv_block2

conv_block3

conv_block4

conv_block5

conv_block6

conv_block7

deconv_block8

unpool_block9

deconv_block10

unpool_block11

deconv_block12

unpool_block13

deconv_block14

unpool_block15

deconv_block16

unpool_block17

deconv_block18

seg_score19

forward(x)[source]

Same as torch.nn.Module.forward().

Args:: *args: Whatever you decide to pass into the forward method. **kwargs: Keyword arguments are also possible.
Return:: Your model’s output

init_vgg16_params(vgg16, copy_fc8=True)[source]

load()[source]: This is just a no-op load method.

class dasf.ml.dl.models.devconvnet.TorchSectionDeConvNetSkip(n_classes=4, learned_billinear=False, clip=0.1, class_weights=None)[source]

Bases: NNModule

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes:

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will have their parameters converted too when you call to(), etc.

Note

As per the example above, an __init__() call to the parent class must be made before assignment on the child.

Variables:: training (bool) – Boolean represents whether this module is in training or evaluation mode.

Initializes internal Module state, shared by both nn.Module and ScriptModule.

unpool

conv_block1

conv_block2

conv_block3

conv_block4

conv_block5

conv_block6

conv_block7

deconv_block8

unpool_block9

deconv_block10

unpool_block11

deconv_block12

unpool_block13

deconv_block14

unpool_block15

deconv_block16

unpool_block17

deconv_block18

seg_score19

forward(x)[source]

Same as torch.nn.Module.forward().

Args:: *args: Whatever you decide to pass into the forward method. **kwargs: Keyword arguments are also possible.
Return:: Your model’s output

init_vgg16_params(vgg16, copy_fc8=True)[source]

load()[source]: This is just a no-op load method.