custom_mlp.py

from inspect import isclass

import torch
import torch.nn as nn

from pyro.distributions.util import broadcast_shape


class Exp(nn.Module):
    """
    a custom module for exponentiation of tensors
    """
    def __init__(self):
        super(Exp, self).__init__()

    def forward(self, val):
        return torch.exp(val)


class ConcatModule(nn.Module):
    """
    a custom module for concatenation of tensors
    """
    def __init__(self, allow_broadcast=False):
        self.allow_broadcast = allow_broadcast
        super(ConcatModule, self).__init__()

    def forward(self, *input_args):
        # we have a single object
        if len(input_args) == 1:
            # regardless of type,
            # we don't care about single objects
            # we just index into the object
            input_args = input_args[0]

        # don't concat things that are just single objects
        if torch.is_tensor(input_args):
            return input_args
        else:
            if self.allow_broadcast:
                shape = broadcast_shape(*[s.shape[:-1] for s in input_args]) + (-1,)
                input_args = [s.expand(shape) for s in input_args]
            return torch.cat(input_args, dim=-1)


class ListOutModule(nn.ModuleList):
    """
    a custom module for outputting a list of tensors from a list of nn modules
    """
    def __init__(self, modules):
        super(ListOutModule, self).__init__(modules)

    def forward(self, *args, **kwargs):
        # loop over modules in self, apply same args
        return [mm.forward(*args, **kwargs) for mm in self]


def call_nn_op(op):
    """
    a helper function that adds appropriate parameters when calling
    an nn module representing an operation like Softmax

    :param op: the nn.Module operation to instantiate
    :return: instantiation of the op module with appropriate parameters
    """
    if op in [nn.Softmax, nn.LogSoftmax]:
        return op(dim=1)
    else:
        return op()


class MLP(nn.Module):

    def __init__(self, mlp_sizes, activation=nn.ReLU, output_activation=None,
                 post_layer_fct=lambda layer_ix, total_layers, layer: None,
                 post_act_fct=lambda layer_ix, total_layers, layer: None,
                 allow_broadcast=False, use_cuda=False):
        # init the module object
        super(MLP, self).__init__()

        assert len(mlp_sizes) >= 2, "Must have input and output layer sizes defined"

        # get our inputs, outputs, and hidden
        input_size, hidden_sizes, output_size = mlp_sizes[0], mlp_sizes[1:-1], mlp_sizes[-1]

        # assume int or list
        assert isinstance(input_size, (int, list, tuple)), "input_size must be int, list, tuple"

        # everything in MLP will be concatted if it's multiple arguments
        last_layer_size = input_size if type(input_size) == int else sum(input_size)

        # everything sent in will be concatted together by default
        all_modules = [ConcatModule(allow_broadcast)]

        # loop over l
        for layer_ix, layer_size in enumerate(hidden_sizes):
            assert type(layer_size) == int, "Hidden layer sizes must be ints"

            # get our nn layer module (in this case nn.Linear by default)
            cur_linear_layer = nn.Linear(last_layer_size, layer_size)

            # for numerical stability -- initialize the layer properly
            cur_linear_layer.weight.data.normal_(0, 0.001)
            cur_linear_layer.bias.data.normal_(0, 0.001)

            # use GPUs to share data during training (if available)
            if use_cuda:
                cur_linear_layer = nn.DataParallel(cur_linear_layer)

            # add our linear layer
            all_modules.append(cur_linear_layer)

            # handle post_linear
            post_linear = post_layer_fct(layer_ix + 1, len(hidden_sizes), all_modules[-1])

            # if we send something back, add it to sequential
            # here we could return a batch norm for example
            if post_linear is not None:
                all_modules.append(post_linear)

            # handle activation (assumed no params -- deal with that later)
            all_modules.append(activation())

            # now handle after activation
            post_activation = post_act_fct(layer_ix + 1, len(hidden_sizes), all_modules[-1])

            # handle post_activation if not null
            # could add batch norm for example
            if post_activation is not None:
                all_modules.append(post_activation)

            # save the layer size we just created
            last_layer_size = layer_size

        # now we have all of our hidden layers
        # we handle outputs
        assert isinstance(output_size, (int, list, tuple)), "output_size must be int, list, tuple"

        if type(output_size) == int:
            all_modules.append(nn.Linear(last_layer_size, output_size))
            if output_activation is not None:
                all_modules.append(call_nn_op(output_activation)
                                   if isclass(output_activation) else output_activation)
        else:

            # we're going to have a bunch of separate layers we can spit out (a tuple of outputs)
            out_layers = []

            # multiple outputs? handle separately
            for out_ix, out_size in enumerate(output_size):

                # for a single output object, we create a linear layer and some weights
                split_layer = []

                # we have an activation function
                split_layer.append(nn.Linear(last_layer_size, out_size))

                # then we get our output activation (either we repeat all or we index into a same sized array)
                act_out_fct = output_activation if not isinstance(output_activation, (list, tuple)) \
                    else output_activation[out_ix]

                if(act_out_fct):
                    # we check if it's a class. if so, instantiate the object
                    # otherwise, use the object directly (e.g. pre-instaniated)
                    split_layer.append(call_nn_op(act_out_fct)
                                       if isclass(act_out_fct) else act_out_fct)

                # our outputs is just a sequential of the two
                out_layers.append(nn.Sequential(*split_layer))

            all_modules.append(ListOutModule(out_layers))

        # now we have all of our modules, we're ready to build our sequential!
        # process mlps in order, pretty standard here
        self.sequential_mlp = nn.Sequential(*all_modules)

    # pass through our sequential for the output!
    def forward(self, *args, **kwargs):
        return self.sequential_mlp.forward(*args, **kwargs)