mirror of
https://github.com/vale981/ray
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82 lines
3.1 KiB
Python
82 lines
3.1 KiB
Python
from typing import Tuple
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from ray.rllib.models.torch.misc import Reshape
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from ray.rllib.models.utils import get_activation_fn, get_initializer
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from ray.rllib.utils.framework import try_import_torch
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torch, nn = try_import_torch()
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if torch:
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import torch.distributions as td
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class ConvTranspose2DStack(nn.Module):
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"""ConvTranspose2D decoder generating an image distribution from a vector."""
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def __init__(
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self,
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*,
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input_size: int,
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filters: Tuple[Tuple[int]] = (
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(1024, 5, 2),
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(128, 5, 2),
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(64, 6, 2),
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(32, 6, 2),
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),
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initializer="default",
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bias_init=0,
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activation_fn: str = "relu",
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output_shape: Tuple[int] = (3, 64, 64)
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):
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"""Initializes a TransposedConv2DStack instance.
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Args:
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input_size (int): The size of the 1D input vector, from which to
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generate the image distribution.
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filters (Tuple[Tuple[int]]): Tuple of filter setups (1 for each
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ConvTranspose2D layer): [in_channels, kernel, stride].
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initializer (Union[str]):
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bias_init (float): The initial bias values to use.
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activation_fn (str): Activation function descriptor (str).
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output_shape (Tuple[int]): Shape of the final output image.
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"""
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super().__init__()
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self.activation = get_activation_fn(activation_fn, framework="torch")
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self.output_shape = output_shape
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initializer = get_initializer(initializer, framework="torch")
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in_channels = filters[0][0]
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self.layers = [
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# Map from 1D-input vector to correct initial size for the
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# Conv2DTransposed stack.
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nn.Linear(input_size, in_channels),
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# Reshape from the incoming 1D vector (input_size) to 1x1 image
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# format (channels first).
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Reshape([-1, in_channels, 1, 1]),
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]
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for i, (_, kernel, stride) in enumerate(filters):
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out_channels = (
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filters[i + 1][0] if i < len(filters) - 1 else output_shape[0]
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)
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conv_transp = nn.ConvTranspose2d(in_channels, out_channels, kernel, stride)
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# Apply initializer.
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initializer(conv_transp.weight)
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nn.init.constant_(conv_transp.bias, bias_init)
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self.layers.append(conv_transp)
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# Apply activation function, if provided and if not last layer.
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if self.activation is not None and i < len(filters) - 1:
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self.layers.append(self.activation())
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# num-outputs == num-inputs for next layer.
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in_channels = out_channels
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self._model = nn.Sequential(*self.layers)
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def forward(self, x):
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# x is [batch, hor_length, input_size]
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batch_dims = x.shape[:-1]
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model_out = self._model(x)
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# Equivalent to making a multivariate diag.
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reshape_size = batch_dims + self.output_shape
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mean = model_out.view(*reshape_size)
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return td.Independent(td.Normal(mean, 1.0), len(self.output_shape))
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