ml-agents/ml-agents/mlagents/trainers/torch/encoders.py

from typing import Tuple, Optional, Union

from mlagents.trainers.exception import UnityTrainerException
from mlagents.trainers.torch.layers import linear_layer, Initialization, Swish

import torch
from torch import nn


class Normalizer(nn.Module):
    def __init__(self, vec_obs_size: int):
        super().__init__()
        self.register_buffer("normalization_steps", torch.tensor(1))
        self.register_buffer("running_mean", torch.zeros(vec_obs_size))
        self.register_buffer("running_variance", torch.ones(vec_obs_size))

    def forward(self, inputs: torch.Tensor) -> torch.Tensor:
        normalized_state = torch.clamp(
            (inputs - self.running_mean)
            / torch.sqrt(self.running_variance / self.normalization_steps),
            -5,
            5,
        )
        return normalized_state

    def update(self, vector_input: torch.Tensor) -> None:
        steps_increment = vector_input.size()[0]
        total_new_steps = self.normalization_steps + steps_increment

        input_to_old_mean = vector_input - self.running_mean
        new_mean = self.running_mean + (input_to_old_mean / total_new_steps).sum(0)

        input_to_new_mean = vector_input - new_mean
        new_variance = self.running_variance + (
            input_to_new_mean * input_to_old_mean
        ).sum(0)
        # Update in-place
        self.running_mean.data.copy_(new_mean.data)
        self.running_variance.data.copy_(new_variance.data)
        self.normalization_steps.data.copy_(total_new_steps.data)

    def copy_from(self, other_normalizer: "Normalizer") -> None:
        self.normalization_steps.data.copy_(other_normalizer.normalization_steps.data)
        self.running_mean.data.copy_(other_normalizer.running_mean.data)
        self.running_variance.copy_(other_normalizer.running_variance.data)


def conv_output_shape(
    h_w: Tuple[int, int],
    kernel_size: Union[int, Tuple[int, int]] = 1,
    stride: int = 1,
    padding: int = 0,
    dilation: int = 1,
) -> Tuple[int, int]:
    """
    Calculates the output shape (height and width) of the output of a convolution layer.
    kernel_size, stride, padding and dilation correspond to the inputs of the
    torch.nn.Conv2d layer (https://pytorch.org/docs/stable/generated/torch.nn.Conv2d.html)
    :param h_w: The height and width of the input.
    :param kernel_size: The size of the kernel of the convolution (can be an int or a
    tuple [width, height])
    :param stride: The stride of the convolution
    :param padding: The padding of the convolution
    :param dilation: The dilation of the convolution
    """
    from math import floor

    if not isinstance(kernel_size, tuple):
        kernel_size = (int(kernel_size), int(kernel_size))
    h = floor(
        ((h_w[0] + (2 * padding) - (dilation * (kernel_size[0] - 1)) - 1) / stride) + 1
    )
    w = floor(
        ((h_w[1] + (2 * padding) - (dilation * (kernel_size[1] - 1)) - 1) / stride) + 1
    )
    return h, w


def pool_out_shape(h_w: Tuple[int, int], kernel_size: int) -> Tuple[int, int]:
    """
    Calculates the output shape (height and width) of the output of a max pooling layer.
    kernel_size corresponds to the inputs of the
    torch.nn.MaxPool2d layer (https://pytorch.org/docs/stable/generated/torch.nn.MaxPool2d.html)
    :param kernel_size: The size of the kernel of the convolution
    """
    height = (h_w[0] - kernel_size) // 2 + 1
    width = (h_w[1] - kernel_size) // 2 + 1
    return height, width


class VectorEncoder(nn.Module):
    def __init__(
        self,
        input_size: int,
        hidden_size: int,
        num_layers: int,
        normalize: bool = False,
    ):
        self.normalizer: Optional[Normalizer] = None
        super().__init__()
        self.layers = [
            linear_layer(
                input_size,
                hidden_size,
                kernel_init=Initialization.KaimingHeNormal,
                kernel_gain=1.0,
            )
        ]
        self.layers.append(Swish())
        if normalize:
            self.normalizer = Normalizer(input_size)

        for _ in range(num_layers - 1):
            self.layers.append(
                linear_layer(
                    hidden_size,
                    hidden_size,
                    kernel_init=Initialization.KaimingHeNormal,
                    kernel_gain=1.0,
                )
            )
            self.layers.append(Swish())
        self.seq_layers = nn.Sequential(*self.layers)

    def forward(self, inputs: torch.Tensor) -> torch.Tensor:
        if self.normalizer is not None:
            inputs = self.normalizer(inputs)
        return self.seq_layers(inputs)

    def copy_normalization(self, other_encoder: "VectorEncoder") -> None:
        if self.normalizer is not None and other_encoder.normalizer is not None:
            self.normalizer.copy_from(other_encoder.normalizer)

    def update_normalization(self, inputs: torch.Tensor) -> None:
        if self.normalizer is not None:
            self.normalizer.update(inputs)


class VectorAndUnnormalizedInputEncoder(VectorEncoder):
    """
    Encoder for concatenated vector input (can be normalized) and unnormalized vector input.
    This is used for passing inputs to the network that should not be normalized, such as
    actions in the case of a Q function or task parameterizations. It will result in an encoder with
    this structure:
    ____________       ____________       ____________
    | Vector     |     | Normalize  |     | Fully      |
    |            | --> |            | --> | Connected  |      ___________
    |____________|     |____________|     |            |     | Output    |
    ____________                          |            | --> |           |
    |Unnormalized|                        |            |     |___________|
    |   Input    | ---------------------> |            |
    |____________|                        |____________|
    """

    def __init__(
        self,
        input_size: int,
        hidden_size: int,
        unnormalized_input_size: int,
        num_layers: int,
        normalize: bool = False,
    ):
        super().__init__(
            input_size + unnormalized_input_size,
            hidden_size,
            num_layers,
            normalize=False,
        )
        if normalize:
            self.normalizer = Normalizer(input_size)
        else:
            self.normalizer = None

    def forward(  # pylint: disable=W0221
        self, inputs: torch.Tensor, unnormalized_inputs: Optional[torch.Tensor] = None
    ) -> None:
        if unnormalized_inputs is None:
            raise UnityTrainerException(
                "Attempted to call an VectorAndUnnormalizedInputEncoder without an unnormalized input."
            )  # Fix mypy errors about method parameters.
        if self.normalizer is not None:
            inputs = self.normalizer(inputs)
        return self.seq_layers(torch.cat([inputs, unnormalized_inputs], dim=-1))


class SimpleVisualEncoder(nn.Module):
    def __init__(
        self, height: int, width: int, initial_channels: int, output_size: int
    ):
        super().__init__()
        self.h_size = output_size
        conv_1_hw = conv_output_shape((height, width), 8, 4)
        conv_2_hw = conv_output_shape(conv_1_hw, 4, 2)
        self.final_flat = conv_2_hw[0] * conv_2_hw[1] * 32

        self.conv_layers = nn.Sequential(
            nn.Conv2d(initial_channels, 16, [8, 8], [4, 4]),
            nn.LeakyReLU(),
            nn.Conv2d(16, 32, [4, 4], [2, 2]),
            nn.LeakyReLU(),
        )
        self.dense = nn.Sequential(
            linear_layer(
                self.final_flat,
                self.h_size,
                kernel_init=Initialization.KaimingHeNormal,
                kernel_gain=1.0,
            ),
            nn.LeakyReLU(),
        )

    def forward(self, visual_obs: torch.Tensor) -> None:
        hidden = self.conv_layers(visual_obs)
        hidden = torch.reshape(hidden, (-1, self.final_flat))
        hidden = self.dense(hidden)
        return hidden


class NatureVisualEncoder(nn.Module):
    def __init__(self, height, width, initial_channels, output_size):
        super().__init__()
        self.h_size = output_size
        conv_1_hw = conv_output_shape((height, width), 8, 4)
        conv_2_hw = conv_output_shape(conv_1_hw, 4, 2)
        conv_3_hw = conv_output_shape(conv_2_hw, 3, 1)
        self.final_flat = conv_3_hw[0] * conv_3_hw[1] * 64

        self.conv_layers = nn.Sequential(
            nn.Conv2d(initial_channels, 32, [8, 8], [4, 4]),
            nn.LeakyReLU(),
            nn.Conv2d(32, 64, [4, 4], [2, 2]),
            nn.LeakyReLU(),
            nn.Conv2d(64, 64, [3, 3], [1, 1]),
            nn.LeakyReLU(),
        )
        self.dense = nn.Sequential(
            linear_layer(
                self.final_flat,
                self.h_size,
                kernel_init=Initialization.KaimingHeNormal,
                kernel_gain=1.0,
            ),
            nn.LeakyReLU(),
        )

    def forward(self, visual_obs: torch.Tensor) -> None:
        hidden = self.conv_layers(visual_obs)
        hidden = hidden.view([-1, self.final_flat])
        hidden = self.dense(hidden)
        return hidden


class ResNetBlock(nn.Module):
    def __init__(self, channel: int):
        """
        Creates a ResNet Block.
        :param channel: The number of channels in the input (and output) tensors of the
        convolutions
        """
        super().__init__()
        self.layers = nn.Sequential(
            Swish(),
            nn.Conv2d(channel, channel, [3, 3], [1, 1], padding=1),
            Swish(),
            nn.Conv2d(channel, channel, [3, 3], [1, 1], padding=1),
        )

    def forward(self, input_tensor: torch.Tensor) -> torch.Tensor:
        return input_tensor + self.layers(input_tensor)


class ResNetVisualEncoder(nn.Module):
    def __init__(self, height, width, initial_channels, final_hidden):
        super().__init__()
        n_channels = [16, 32, 32]  # channel for each stack
        n_blocks = 2  # number of residual blocks
        self.layers = []
        last_channel = initial_channels
        for _, channel in enumerate(n_channels):
            self.layers.append(
                nn.Conv2d(last_channel, channel, [3, 3], [1, 1], padding=1)
            )
            self.layers.append(nn.MaxPool2d([3, 3], [2, 2]))
            height, width = pool_out_shape((height, width), 3)
            for _ in range(n_blocks):
                self.layers.append(ResNetBlock(channel))
            last_channel = channel
        self.layers.append(Swish())
        self.dense = linear_layer(
            n_channels[-1] * height * width,
            final_hidden,
            kernel_init=Initialization.KaimingHeNormal,
            kernel_gain=1.0,
        )

    def forward(self, visual_obs):
        batch_size = visual_obs.shape[0]
        hidden = visual_obs
        for layer in self.layers:
            hidden = layer(hidden)
        before_out = hidden.view(batch_size, -1)
        return torch.relu(self.dense(before_out))