from typing import Tuple, Optional from mlagents.trainers.exception import UnityTrainerException import torch from torch import nn class Normalizer(nn.Module): def __init__(self, vec_obs_size: int): super().__init__() self.normalization_steps = torch.tensor(1) self.running_mean = torch.zeros(vec_obs_size) self.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) self.running_mean = new_mean self.running_variance = new_variance self.normalization_steps = total_new_steps 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, kernel_size=1, stride=1, pad=0, dilation=1): from math import floor if type(kernel_size) is not tuple: kernel_size = (kernel_size, kernel_size) h = floor( ((h_w[0] + (2 * pad) - (dilation * (kernel_size[0] - 1)) - 1) / stride) + 1 ) w = floor( ((h_w[1] + (2 * pad) - (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]: 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 = [nn.Linear(input_size, hidden_size)] if normalize: self.normalizer = Normalizer(input_size) for _ in range(num_layers - 1): self.layers.append(nn.Linear(hidden_size, hidden_size)) self.layers.append(nn.LeakyReLU()) self.seq_layers = nn.Sequential(*self.layers) def forward(self, inputs: torch.Tensor) -> None: 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.conv1 = nn.Conv2d(initial_channels, 16, [8, 8], [4, 4]) self.conv2 = nn.Conv2d(16, 32, [4, 4], [2, 2]) self.dense = nn.Linear(self.final_flat, self.h_size) def forward(self, visual_obs: torch.Tensor) -> None: conv_1 = nn.functional.leaky_relu(self.conv1(visual_obs)) conv_2 = nn.functional.leaky_relu(self.conv2(conv_1)) # hidden = torch.relu(self.dense(conv_2.view([-1, self.final_flat]))) hidden = nn.functional.leaky_relu( self.dense(torch.reshape(conv_2, (-1, self.final_flat))) ) 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.conv1 = nn.Conv2d(initial_channels, 32, [8, 8], [4, 4]) self.conv2 = nn.Conv2d(32, 64, [4, 4], [2, 2]) self.conv3 = nn.Conv2d(64, 64, [3, 3], [1, 1]) self.dense = nn.Linear(self.final_flat, self.h_size) def forward(self, visual_obs): conv_1 = nn.functional.leaky_relu(self.conv1(visual_obs)) conv_2 = nn.functional.leaky_relu(self.conv2(conv_1)) conv_3 = nn.functional.leaky_relu(self.conv3(conv_2)) hidden = nn.functional.leaky_relu( self.dense(conv_3.view([-1, self.final_flat])) ) return hidden 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(self.make_block(channel)) last_channel = channel self.layers.append(nn.LeakyReLU()) self.dense = nn.Linear(n_channels[-1] * height * width, final_hidden) @staticmethod def make_block(channel): block_layers = [ nn.LeakyReLU(), nn.Conv2d(channel, channel, [3, 3], [1, 1], padding=1), nn.LeakyReLU(), nn.Conv2d(channel, channel, [3, 3], [1, 1], padding=1), ] return block_layers @staticmethod def forward_block(input_hidden, block_layers): hidden = input_hidden for layer in block_layers: hidden = layer(hidden) return hidden + input_hidden def forward(self, visual_obs): batch_size = visual_obs.shape[0] hidden = visual_obs for layer in self.layers: if isinstance(layer, nn.Module): hidden = layer(hidden) elif isinstance(layer, list): hidden = self.forward_block(hidden, layer) before_out = hidden.view(batch_size, -1) return torch.relu(self.dense(before_out))