ShapeNetwork

class ShapeNetwork(layer_cls, layer_kwargs, in_channels=1, norm_type='instance', img_size=224, feature_extractor=None, **kwargs)

Bases: shapenet.networks.abstract_network.AbstractShapeNetwork

Network to Predict a single shape

static closure(model, data_dict: dict, optimizers: dict, criterions={}, metrics={}, fold=0, **kwargs)

closure method to do a single backpropagation step

Parameters:

• model (ShapeNetwork) – 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_images)

Forward input batch through network and shape layer

Parameters:input_images (torch.Tensor) – input batch Returns:predicted shapes Return type:torch.Tensor

model

static norm_type_to_class(norm_type)

helper function to map a string to an actual normalization class

Parameters:norm_type (str) – string specifying the normalization class Returns:Normalization Class (subclass of torch.nn.Module) Return type:type