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https://github.com/vale981/ray
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60d4d5e1aa
* Remove all __future__ imports from RLlib. * Remove (object) again from tf_run_builder.py::TFRunBuilder. * Fix 2xLINT warnings. * Fix broken appo_policy import (must be appo_tf_policy) * Remove future imports from all other ray files (not just RLlib). * Remove future imports from all other ray files (not just RLlib). * Remove future import blocks that contain `unicode_literals` as well. Revert appo_tf_policy.py to appo_policy.py (belongs to another PR). * Add two empty lines before Schedule class. * Put back __future__ imports into determine_tests_to_run.py. Fails otherwise on a py2/print related error.
62 lines
2.3 KiB
Python
62 lines
2.3 KiB
Python
from ray.rllib.models.torch.torch_modelv2 import TorchModelV2
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from ray.rllib.models.torch.misc import normc_initializer, valid_padding, \
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SlimConv2d, SlimFC
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from ray.rllib.models.tf.visionnet_v1 import _get_filter_config
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from ray.rllib.utils.annotations import override
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from ray.rllib.utils import try_import_torch
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_, nn = try_import_torch()
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class VisionNetwork(TorchModelV2, nn.Module):
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"""Generic vision network."""
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def __init__(self, obs_space, action_space, num_outputs, model_config,
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name):
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TorchModelV2.__init__(self, obs_space, action_space, num_outputs,
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model_config, name)
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nn.Module.__init__(self)
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filters = model_config.get("conv_filters")
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if not filters:
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filters = _get_filter_config(obs_space.shape)
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layers = []
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(w, h, in_channels) = obs_space.shape
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in_size = [w, h]
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for out_channels, kernel, stride in filters[:-1]:
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padding, out_size = valid_padding(in_size, kernel,
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[stride, stride])
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layers.append(
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SlimConv2d(in_channels, out_channels, kernel, stride, padding))
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in_channels = out_channels
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in_size = out_size
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out_channels, kernel, stride = filters[-1]
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layers.append(
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SlimConv2d(in_channels, out_channels, kernel, stride, None))
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self._convs = nn.Sequential(*layers)
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self._logits = SlimFC(
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out_channels, num_outputs, initializer=nn.init.xavier_uniform_)
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self._value_branch = SlimFC(
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out_channels, 1, initializer=normc_initializer())
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self._cur_value = None
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@override(TorchModelV2)
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def forward(self, input_dict, state, seq_lens):
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features = self._hidden_layers(input_dict["obs"].float())
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logits = self._logits(features)
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self._cur_value = self._value_branch(features).squeeze(1)
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return logits, state
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@override(TorchModelV2)
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def value_function(self):
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assert self._cur_value is not None, "must call forward() first"
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return self._cur_value
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def _hidden_layers(self, obs):
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res = self._convs(obs.permute(0, 3, 1, 2)) # switch to channel-major
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res = res.squeeze(3)
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res = res.squeeze(2)
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return res
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