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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import torch.nn as nn
from ray.rllib.models.pytorch.model import TorchModel
from ray.rllib.models.pytorch.misc import normc_initializer, valid_padding, \
SlimConv2d, SlimFC
from ray.rllib.models.visionnet import _get_filter_config
from ray.rllib.utils.annotations import override
class VisionNetwork(TorchModel):
"""Generic vision network."""
def __init__(self, obs_space, num_outputs, options):
TorchModel.__init__(self, obs_space, num_outputs, options)
filters = options.get("conv_filters")
if not filters:
filters = _get_filter_config(obs_space.shape)
layers = []
(w, h, in_channels) = obs_space.shape
in_size = [w, h]
for out_channels, kernel, stride in filters[:-1]:
padding, out_size = valid_padding(in_size, kernel,
[stride, stride])
layers.append(
SlimConv2d(in_channels, out_channels, kernel, stride, padding))
in_channels = out_channels
in_size = out_size
out_channels, kernel, stride = filters[-1]
layers.append(
SlimConv2d(in_channels, out_channels, kernel, stride, None))
self._convs = nn.Sequential(*layers)
self._logits = SlimFC(
out_channels, num_outputs, initializer=nn.init.xavier_uniform_)
self._value_branch = SlimFC(
out_channels, 1, initializer=normc_initializer())
@override(TorchModel)
def _forward(self, input_dict, hidden_state):
features = self._hidden_layers(input_dict["obs"])
logits = self._logits(features)
value = self._value_branch(features).squeeze(1)
return logits, features, value, hidden_state
def _hidden_layers(self, obs):
res = self._convs(obs.permute(0, 3, 1, 2)) # switch to channel-major
res = res.squeeze(3)
res = res.squeeze(2)
return res