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 layers = [] last_channel = initial_channels for _, channel in enumerate(n_channels): layers.append(nn.Conv2d(last_channel, channel, [3, 3], [1, 1], padding=1)) layers.append(nn.MaxPool2d([3, 3], [2, 2])) height, width = pool_out_shape((height, width), 3) for _ in range(n_blocks): layers.append(ResNetBlock(channel)) last_channel = channel layers.append(Swish()) self.dense = linear_layer( n_channels[-1] * height * width, final_hidden, kernel_init=Initialization.KaimingHeNormal, kernel_gain=1.0, ) self.sequential = nn.Sequential(*layers) def forward(self, visual_obs): batch_size = visual_obs.shape[0] hidden = self.sequential(visual_obs) before_out = hidden.view(batch_size, -1) return torch.relu(self.dense(before_out))