1234567891011121314151617181920212223242526272829303132333435363738394041424344454647484950515253545556575859606162636465666768697071727374757677 |
- from typing import Tuple
- from ray.rllib.models.torch.misc import Reshape
- from ray.rllib.models.utils import get_activation_fn, get_initializer
- from ray.rllib.utils.framework import try_import_torch
- torch, nn = try_import_torch()
- if torch:
- import torch.distributions as td
- class ConvTranspose2DStack(nn.Module):
- """ConvTranspose2D decoder generating an image distribution from a vector.
- """
- 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 (int): 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 (float): The initial bias values to use.
- activation_fn (str): 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)
- def forward(self, x):
- # x is [batch, hor_length, input_size]
- batch_dims = x.shape[:-1]
- model_out = self._model(x)
- # Equivalent to making a multivariate diag.
- reshape_size = batch_dims + self.output_shape
- mean = model_out.view(*reshape_size)
- return td.Independent(td.Normal(mean, 1.0), len(self.output_shape))
|