121 lines
4.0 KiB
Python
121 lines
4.0 KiB
Python
# Copyright (c) 2021-2025, ETH Zurich and NVIDIA CORPORATION
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# All rights reserved.
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#
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# SPDX-License-Identifier: BSD-3-Clause
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from __future__ import annotations
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import torch
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import torch.nn as nn
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from functools import reduce
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from rsl_rl.utils import resolve_nn_activation
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class MLP(nn.Sequential):
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"""Multi-layer perceptron.
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The MLP network is a sequence of linear layers and activation functions. The
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last layer is a linear layer that outputs the desired dimension unless the
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last activation function is specified.
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It provides additional conveniences:
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- If the hidden dimensions have a value of ``-1``, the dimension is inferred
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from the input dimension.
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- If the output dimension is a tuple, the output is reshaped to the desired
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shape.
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"""
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def __init__(
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self,
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input_dim: int,
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output_dim: int | tuple[int] | list[int],
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hidden_dims: tuple[int] | list[int],
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activation: str = "elu",
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last_activation: str | None = None,
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):
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"""Initialize the MLP.
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Args:
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input_dim: Dimension of the input.
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output_dim: Dimension of the output.
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hidden_dims: Dimensions of the hidden layers. A value of ``-1`` indicates
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that the dimension should be inferred from the input dimension.
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activation: Activation function. Defaults to "elu".
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last_activation: Activation function of the last layer. Defaults to None,
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in which case the last layer is linear.
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"""
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super().__init__()
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# resolve activation functions
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activation_mod = resolve_nn_activation(activation)
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last_activation_mod = resolve_nn_activation(last_activation) if last_activation is not None else None
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# resolve number of hidden dims if they are -1
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hidden_dims_processed = [input_dim if dim == -1 else dim for dim in hidden_dims]
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# create layers sequentially
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layers = []
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layers.append(nn.Linear(input_dim, hidden_dims_processed[0]))
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layers.append(activation_mod)
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for layer_index in range(len(hidden_dims_processed) - 1):
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layers.append(nn.Linear(hidden_dims_processed[layer_index], hidden_dims_processed[layer_index + 1]))
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layers.append(activation_mod)
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# add last layer
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if isinstance(output_dim, int):
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layers.append(nn.Linear(hidden_dims_processed[-1], output_dim))
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else:
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# compute the total output dimension
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total_out_dim = reduce(lambda x, y: x * y, output_dim)
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# add a layer to reshape the output to the desired shape
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layers.append(nn.Linear(hidden_dims_processed[-1], total_out_dim))
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layers.append(nn.Unflatten(dim=-1, unflattened_size=output_dim))
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# add last activation function if specified
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if last_activation_mod is not None:
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layers.append(last_activation_mod)
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# register the layers
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for idx, layer in enumerate(layers):
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self.add_module(f"{idx}", layer)
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def init_weights(self, scales: float | tuple[float]):
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"""Initialize the weights of the MLP.
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Args:
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scales: Scale factor for the weights.
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"""
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def get_scale(idx) -> float:
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"""Get the scale factor for the weights of the MLP.
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Args:
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idx: Index of the layer.
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"""
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return scales[idx] if isinstance(scales, (list, tuple)) else scales
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# initialize the weights
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for idx, module in enumerate(self):
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if isinstance(module, nn.Linear):
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nn.init.orthogonal_(module.weight, gain=get_scale(idx))
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nn.init.zeros_(module.bias)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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"""Forward pass of the MLP.
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Args:
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x: Input tensor.
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"""
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for layer in self:
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x = layer(x)
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return x
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def reset(self, dones=None, hidden_states=None):
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pass
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def detach_hidden_states(self, dones=None):
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pass
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