Classification¶

ClassificationNetworkBasePyTorch¶

class ClassificationNetworkBasePyTorch(in_channels: int, n_outputs: int, **kwargs)[source]¶

Bases: delira.models.abstract_network.AbstractPyTorchNetwork

Implements basic classification with ResNet18

References

https://arxiv.org/abs/1512.03385

See also

AbstractPyTorchNetwork

static _build_model(in_channels: int, n_outputs: int, **kwargs)[source]¶

builds actual model (resnet 18)

Parameters:	in_channels (int) – number of input channels n_outputs (int) – number of outputs (usually same as number of classes) *kwargs (dict*) – additional keyword arguments
Returns:	created model
Return type:	torch.nn.Module

_init_kwargs = {}¶

static closure(model: delira.models.abstract_network.AbstractPyTorchNetwork, data_dict: dict, optimizers: dict, criterions={}, metrics={}, fold=0, **kwargs)[source]¶

closure method to do a single backpropagation step

Parameters:

model (ClassificationNetworkBasePyTorch) – trainable model
data_dict (dict) – dictionary containing the data
optimizers (dict) – dictionary of optimizers to optimize model’s parameters
criterions (dict) – dict holding the criterions to calculate errors (gradients from different criterions will be accumulated)
metrics (dict) – dict holding the metrics to calculate
fold (int) – Current Fold in Crossvalidation (default: 0)
**kwargs – additional keyword arguments

Returns:

dict – Metric values (with same keys as input dict metrics)
dict – Loss values (with same keys as input dict criterions)
list – Arbitrary number of predictions as torch.Tensor

Raises:

AssertionError – if optimizers or criterions are empty or the optimizers are not specified

forward(input_batch: <sphinx.ext.autodoc.importer._MockObject object at 0x7fc9645e2b00>)[source]¶

Forward input_batch through network

Parameters:	input_batch (torch.Tensor) – batch to forward through network
Returns:	Classification Result
Return type:	torch.Tensor

init_kwargs¶

Returns all arguments registered as init kwargs

Returns:	init kwargs
Return type:	dict

static prepare_batch(batch: dict, input_device, output_device)[source]¶

Helper Function to prepare Network Inputs and Labels (convert them to correct type and shape and push them to correct devices)

Parameters:	batch (dict) – dictionary containing all the data input_device (torch.device) – device for network inputs output_device (torch.device) – device for network outputs
Returns:	dictionary containing data in correct type and shape and on correct device
Return type:	dict

VGG3DClassificationNetworkPyTorch¶

class VGG3DClassificationNetworkPyTorch(in_channels: int, n_outputs: int, **kwargs)[source]¶

Bases: delira.models.classification.classification_network.ClassificationNetworkBasePyTorch

Exemplaric VGG Network for 3D Classification

Notes

The original network has been adjusted to fit for 3D data

References

https://arxiv.org/abs/1409.1556

See also

ClassificationNetworkBasePyTorch

static _build_model(in_channels: int, n_outputs: int, **kwargs)[source]¶

Helper Function to build the actual model

Parameters:	in_channels (int) – number of input channels n_outputs (int) – number of outputs **kwargs – additional keyword arguments
Returns:	ensembeled model
Return type:	torch.nn.Module

_init_kwargs = {}¶

static closure(model: delira.models.abstract_network.AbstractPyTorchNetwork, data_dict: dict, optimizers: dict, criterions={}, metrics={}, fold=0, **kwargs)¶

closure method to do a single backpropagation step

Parameters:

model (ClassificationNetworkBasePyTorch) – trainable model
data_dict (dict) – dictionary containing the data
optimizers (dict) – dictionary of optimizers to optimize model’s parameters
criterions (dict) – dict holding the criterions to calculate errors (gradients from different criterions will be accumulated)
metrics (dict) – dict holding the metrics to calculate
fold (int) – Current Fold in Crossvalidation (default: 0)
**kwargs – additional keyword arguments

Returns:

dict – Metric values (with same keys as input dict metrics)
dict – Loss values (with same keys as input dict criterions)
list – Arbitrary number of predictions as torch.Tensor

Raises:

AssertionError – if optimizers or criterions are empty or the optimizers are not specified

forward(input_batch: <sphinx.ext.autodoc.importer._MockObject object at 0x7fc9645e2b00>)¶

Forward input_batch through network

Parameters:	input_batch (torch.Tensor) – batch to forward through network
Returns:	Classification Result
Return type:	torch.Tensor

init_kwargs¶

Returns all arguments registered as init kwargs

Returns:	init kwargs
Return type:	dict

static prepare_batch(batch: dict, input_device, output_device)¶

Helper Function to prepare Network Inputs and Labels (convert them to correct type and shape and push them to correct devices)

Parameters:	batch (dict) – dictionary containing all the data input_device (torch.device) – device for network inputs output_device (torch.device) – device for network outputs
Returns:	dictionary containing data in correct type and shape and on correct device
Return type:	dict

ClassificationNetworkBaseTf¶

class ClassificationNetworkBaseTf(in_channels: int, n_outputs: int, **kwargs)[source]¶

Bases: delira.models.abstract_network.AbstractTfNetwork

Implements basic classification with ResNet18

See also

AbstractTfNetwork

_add_losses(losses: dict)[source]¶

Adds losses to model that are to be used by optimizers or during evaluation

Parameters:	losses (dict) – dictionary containing all losses. Individual losses are averaged

_add_optims(optims: dict)[source]¶

Adds optims to model that are to be used by optimizers or during training

Parameters:	optim (dict) – dictionary containing all optimizers, optimizers should be of Type[tf.train.Optimizer]

static _build_model(n_outputs: int, **kwargs)[source]¶

builds actual model (resnet 18)

Parameters:	n_outputs (int) – number of outputs (usually same as number of classes) **kwargs – additional keyword arguments
Returns:	created model
Return type:	tf.keras.Model

_init_kwargs = {}¶

static closure(model: Type[delira.models.abstract_network.AbstractTfNetwork], data_dict: dict, metrics={}, fold=0, **kwargs)[source]¶

closure method to do a single prediction. This is followed by backpropagation or not based state of on model.train

Parameters:

model (AbstractTfNetwork) – AbstractTfNetwork or its child-clases
data_dict (dict) – dictionary containing the data
metrics (dict) – dict holding the metrics to calculate
fold (int) – Current Fold in Crossvalidation (default: 0)
**kwargs – additional keyword arguments

Returns:

dict – Metric values (with same keys as input dict metrics)
dict – Loss values (with same keys as those initially passed to model.init). Additionally, a total_loss key is added
list – Arbitrary number of predictions as np.array

init_kwargs¶

Returns all arguments registered as init kwargs

Returns:	init kwargs
Return type:	dict

static prepare_batch(batch: dict, input_device, output_device)¶

Converts a numpy batch of data and labels to suitable datatype and pushes them to correct devices

Parameters:	batch (dict) – dictionary containing the batch (must have keys ‘data’ and ‘label’ input_device – device for network inputs output_device – device for network outputs
Returns:	dictionary containing all necessary data in right format and type and on the correct device
Return type:	dict
Raises:	`NotImplementedError` – If not overwritten by subclass

run(*args)¶

Evaluates self.outputs_train or self.outputs_eval based on self.training

Parameters:	*args – arguments to feed as self.inputs. Must have same length as self.inputs
Returns:	based on len(self.outputs*), returns either list or np.ndarray
Return type:	np.ndarray or list