storm_kit.geom.nn_model.network_macros module¶
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MLP
(channels, dropout_ratio=0.0, batch_norm=False, act_fn=<class 'torch.nn.modules.activation.ReLU'>, layer_norm=False, nerf=True)[source]¶ Automatic generation of mlp given some
- Parameters
channels (int) – number of channels in input
dropout_ratio (float, optional) – dropout used after every layer. Defaults to 0.0.
batch_norm (bool, optional) – batch norm after every layer. Defaults to False.
act_fn ([type], optional) – activation function after every layer. Defaults to ReLU.
layer_norm (bool, optional) – layer norm after every layer. Defaults to False.
nerf (bool, optional) – use positional encoding (x->[sin(x),cos(x)]). Defaults to True.
- Returns
nn sequential layers
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class
MLPRegression
(input_dims, output_dims, mlp_layers=[256, 128, 128], dropout_ratio=0.0, batch_norm=False, scale_mlp_units=1.0, act_fn=<class 'torch.nn.modules.activation.ELU'>, layer_norm=False, nerf=False)[source]¶ Bases:
torch.nn.modules.module.Module
Create an instance of mlp nn model
- Parameters
input_dims (int) – number of channels
output_dims (int) – output channel size
mlp_layers (list, optional) – perceptrons in each layer. Defaults to [256, 128, 128].
dropout_ratio (float, optional) – dropout after every layer. Defaults to 0.0.
batch_norm (bool, optional) – batch norm after every layer. Defaults to False.
scale_mlp_units (float, optional) – Quick way to scale up and down the number of perceptrons, as this gets multiplied with values in mlp_layers. Defaults to 1.0.
act_fn ([type], optional) – activation function after every layer. Defaults to ELU.
layer_norm (bool, optional) – layer norm after every layer. Defaults to False.
nerf (bool, optional) – use positional encoding (x->[sin(x),cos(x)]). Defaults to False.
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_is_full_backward_hook
: Optional[bool]¶
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training
: bool¶
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he_init
(param)[source]¶ initialize weights with he.
- Parameters
param (network params) – params to initialize.
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scale_to_base
(data, norm_dict, key)[source]¶ Scale the tensor back to the orginal units.
- Parameters
data (tensor) – input tensor to scale
norm_dict (Dict) – normalization dictionary of the form dict={key:{‘mean’:,’std’:}}
key (str) – key of the data
- Returns
output scaled tensor
- Return type
tensor
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scale_to_net
(data, norm_dict, key)[source]¶ Scale the tensor network range
- Parameters
data (tensor) – input tensor to scale
norm_dict (Dict) – normalization dictionary of the form dict={key:{‘mean’:,’std’:}}
key (str) – key of the data
- Returns
output scaled tensor
- Return type
tensor