models/vq_behavior_transformer/utils.py

import torch
from typing import Callable, List, Optional


class MLP(torch.nn.Sequential):
    """This block implements the multi-layer perceptron (MLP) module.
    Adapted for backward compatibility from the torchvision library:
    https://pytorch.org/vision/0.14/generated/torchvision.ops.MLP.html

    LICENSE:

    From PyTorch:

    Copyright (c) 2016-     Facebook, Inc            (Adam Paszke)
    Copyright (c) 2014-     Facebook, Inc            (Soumith Chintala)
    Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert)
    Copyright (c) 2012-2014 Deepmind Technologies    (Koray Kavukcuoglu)
    Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu)
    Copyright (c) 2011-2013 NYU                      (Clement Farabet)
    Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston)
    Copyright (c) 2006      Idiap Research Institute (Samy Bengio)
    Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz)

    From Caffe2:

    Copyright (c) 2016-present, Facebook Inc. All rights reserved.

    All contributions by Facebook:
    Copyright (c) 2016 Facebook Inc.

    All contributions by Google:
    Copyright (c) 2015 Google Inc.
    All rights reserved.

    All contributions by Yangqing Jia:
    Copyright (c) 2015 Yangqing Jia
    All rights reserved.

    All contributions by Kakao Brain:
    Copyright 2019-2020 Kakao Brain

    All contributions by Cruise LLC:
    Copyright (c) 2022 Cruise LLC.
    All rights reserved.

    All contributions from Caffe:
    Copyright(c) 2013, 2014, 2015, the respective contributors
    All rights reserved.

    All other contributions:
    Copyright(c) 2015, 2016 the respective contributors
    All rights reserved.

    Caffe2 uses a copyright model similar to Caffe: each contributor holds
    copyright over their contributions to Caffe2. The project versioning records
    all such contribution and copyright details. If a contributor wants to further
    mark their specific copyright on a particular contribution, they should
    indicate their copyright solely in the commit message of the change when it is
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    All rights reserved.

    Redistribution and use in source and binary forms, with or without
    modification, are permitted provided that the following conditions are met:

    1. Redistributions of source code must retain the above copyright
    notice, this list of conditions and the following disclaimer.

    2. Redistributions in binary form must reproduce the above copyright
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    Args:
        in_channels (int): Number of channels of the input
        hidden_channels (List[int]): List of the hidden channel dimensions
        norm_layer (Callable[..., torch.nn.Module], optional): Norm layer that will be stacked on top of the linear layer. If ``None`` this layer won't be used. Default: ``None``
        activation_layer (Callable[..., torch.nn.Module], optional): Activation function which will be stacked on top of the normalization layer (if not None), otherwise on top of the linear layer. If ``None`` this layer won't be used. Default: ``torch.nn.ReLU``
        inplace (bool, optional): Parameter for the activation layer, which can optionally do the operation in-place.
            Default is ``None``, which uses the respective default values of the ``activation_layer`` and Dropout layer.
        bias (bool): Whether to use bias in the linear layer. Default ``True``
        dropout (float): The probability for the dropout layer. Default: 0.0
    """

    def __init__(
        self,
        in_channels: int,
        hidden_channels: List[int],
        activation_layer: Optional[Callable[..., torch.nn.Module]] = torch.nn.ReLU,
        inplace: Optional[bool] = None,
        bias: bool = True,
        dropout: float = 0.0,
    ):
        params = {} if inplace is None else {"inplace": inplace}

        layers = []
        in_dim = in_channels
        for hidden_dim in hidden_channels[:-1]:
            layers.append(torch.nn.Linear(in_dim, hidden_dim, bias=bias))
            layers.append(activation_layer(**params))
            layers.append(torch.nn.Dropout(dropout, **params))
            in_dim = hidden_dim

        layers.append(torch.nn.Linear(in_dim, hidden_channels[-1], bias=bias))
        layers.append(torch.nn.Dropout(dropout, **params))

        super().__init__(*layers)


def batch_idx(x: torch.Tensor, idx: torch.Tensor):
    assert (
        idx.shape == x.shape[: len(idx.shape)]
    ), f"Index shape must match a prefix of x.shape. Got idx: {idx.shape} and x: {x.shape}"
    # if there are multiple dimensions after the indexed dimension, flatten it and reshape back
    remaining_shape = x.shape[len(idx.shape) + 1 :]
    x = x.flatten(start_dim=idx.dim() + 1)
    indices = []
    for i in range(len(idx.shape)):
        raw = torch.arange(idx.shape[i], device=idx.device)
        desired_shape = [1] * len(idx.shape)
        desired_shape[i] = idx.shape[i]
        indices.append(raw.reshape(desired_shape))
    indices.append(idx)
    result = x[tuple(indices)]
    return result.reshape(idx.shape + remaining_shape)