def __init__(self,
input_size: int,
depth: int = 32,
act: ActFunc = None,
shape: Tuple[int] = (3, 64, 64)):
"""Initializes a ConvDecoder instance.
Args:
input_size (int): Input size, usually feature size output from
RSSM.
depth (int): Number of channels in the first conv layer
act (Any): Activation for Encoder, default ReLU
shape (List): Shape of observation input
"""
super().__init__()
self.act = act
if not act:
self.act = nn.ReLU
self.depth = depth
self.shape = shape
self.layers = [
Linear(input_size, 32 * self.depth),
Reshape([-1, 32 * self.depth, 1, 1]),
ConvTranspose2d(32 * self.depth, 4 * self.depth, 5, stride=2),
self.act(),
ConvTranspose2d(4 * self.depth, 2 * self.depth, 5, stride=2),
self.act(),
ConvTranspose2d(2 * self.depth, self.depth, 6, stride=2),
self.act(),
ConvTranspose2d(self.depth, self.shape[0], 6, stride=2),
]
self.model = nn.Sequential(*self.layers)
def __init__(
self,
*,
input_size: int,
filters: Tuple[Tuple[int]] = (
(1024, 5, 2),
(128, 5, 2),
(64, 6, 2),
(32, 6, 2),
),
initializer="default",
bias_init=0,
activation_fn: str = "relu",
output_shape: Tuple[int] = (3, 64, 64)
):
"""Initializes a TransposedConv2DStack instance.
Args:
input_size: The size of the 1D input vector, from which to
generate the image distribution.
filters (Tuple[Tuple[int]]): Tuple of filter setups (1 for each
ConvTranspose2D layer): [in_channels, kernel, stride].
initializer (Union[str]):
bias_init: The initial bias values to use.
activation_fn: Activation function descriptor (str).
output_shape (Tuple[int]): Shape of the final output image.
"""
super().__init__()
self.activation = get_activation_fn(activation_fn, framework="torch")
self.output_shape = output_shape
initializer = get_initializer(initializer, framework="torch")
in_channels = filters[0][0]
self.layers = [
# Map from 1D-input vector to correct initial size for the
# Conv2DTransposed stack.
nn.Linear(input_size, in_channels),
# Reshape from the incoming 1D vector (input_size) to 1x1 image
# format (channels first).
Reshape([-1, in_channels, 1, 1]),
]
for i, (_, kernel, stride) in enumerate(filters):
out_channels = (
filters[i + 1][0] if i < len(filters) - 1 else output_shape[0]
)
conv_transp = nn.ConvTranspose2d(in_channels, out_channels, kernel, stride)
# Apply initializer.
initializer(conv_transp.weight)
nn.init.constant_(conv_transp.bias, bias_init)
self.layers.append(conv_transp)
# Apply activation function, if provided and if not last layer.
if self.activation is not None and i < len(filters) - 1:
self.layers.append(self.activation())
# num-outputs == num-inputs for next layer.
in_channels = out_channels
self._model = nn.Sequential(*self.layers)