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from typing import Optional, Sequence, Type
import gym
import torch
import torch.nn as nn
from rl_algo_impls.shared.encoder.cnn import FlattenedCnnEncoder
from rl_algo_impls.shared.module.utils import layer_init
class ResidualBlock(nn.Module):
def __init__(
self,
channels: int,
activation: Type[nn.Module] = nn.ReLU,
init_layers_orthogonal: bool = False,
) -> None:
super().__init__()
self.residual = nn.Sequential(
activation(),
layer_init(
nn.Conv2d(channels, channels, 3, padding=1), init_layers_orthogonal
),
activation(),
layer_init(
nn.Conv2d(channels, channels, 3, padding=1), init_layers_orthogonal
),
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return x + self.residual(x)
class ConvSequence(nn.Module):
def __init__(
self,
in_channels: int,
out_channels: int,
activation: Type[nn.Module] = nn.ReLU,
init_layers_orthogonal: bool = False,
) -> None:
super().__init__()
self.seq = nn.Sequential(
layer_init(
nn.Conv2d(in_channels, out_channels, 3, padding=1),
init_layers_orthogonal,
),
nn.MaxPool2d(3, stride=2, padding=1),
ResidualBlock(out_channels, activation, init_layers_orthogonal),
ResidualBlock(out_channels, activation, init_layers_orthogonal),
)
def forward(self, x: torch.Tensor) -> torch.Tensor:
return self.seq(x)
class ImpalaCnn(FlattenedCnnEncoder):
"""
IMPALA-style CNN architecture
"""
def __init__(
self,
obs_space: gym.Space,
activation: Type[nn.Module],
cnn_init_layers_orthogonal: Optional[bool],
linear_init_layers_orthogonal: bool,
cnn_flatten_dim: int,
impala_channels: Sequence[int] = (16, 32, 32),
**kwargs,
) -> None:
if cnn_init_layers_orthogonal is None:
cnn_init_layers_orthogonal = False
in_channels = obs_space.shape[0] # type: ignore
sequences = []
for out_channels in impala_channels:
sequences.append(
ConvSequence(
in_channels, out_channels, activation, cnn_init_layers_orthogonal
)
)
in_channels = out_channels
sequences.append(activation())
cnn = nn.Sequential(*sequences)
super().__init__(
obs_space,
activation,
linear_init_layers_orthogonal,
cnn_flatten_dim,
cnn,
**kwargs,
)
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