mirror of
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.
85 lines
2.8 KiB
Python
85 lines
2.8 KiB
Python
from ray.rllib.models.model import Model
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from ray.rllib.models.tf.misc import get_activation_fn, flatten
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from ray.rllib.utils.annotations import override
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from ray.rllib.utils import try_import_tf
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tf = try_import_tf()
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# Deprecated: see as an alternative models/tf/visionnet_v2.py
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class VisionNetwork(Model):
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"""Generic vision network."""
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@override(Model)
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def _build_layers_v2(self, input_dict, num_outputs, options):
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inputs = input_dict["obs"]
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filters = options.get("conv_filters")
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if not filters:
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filters = _get_filter_config(inputs.shape.as_list()[1:])
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activation = get_activation_fn(options.get("conv_activation"))
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with tf.name_scope("vision_net"):
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for i, (out_size, kernel, stride) in enumerate(filters[:-1], 1):
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inputs = tf.layers.conv2d(
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inputs,
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out_size,
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kernel,
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stride,
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activation=activation,
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padding="same",
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name="conv{}".format(i))
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out_size, kernel, stride = filters[-1]
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# skip final linear layer
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if options.get("no_final_linear"):
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fc_out = tf.layers.conv2d(
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inputs,
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num_outputs,
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kernel,
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stride,
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activation=activation,
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padding="valid",
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name="fc_out")
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return flatten(fc_out), flatten(fc_out)
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fc1 = tf.layers.conv2d(
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inputs,
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out_size,
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kernel,
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stride,
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activation=activation,
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padding="valid",
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name="fc1")
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fc2 = tf.layers.conv2d(
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fc1,
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num_outputs, [1, 1],
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activation=None,
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padding="same",
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name="fc2")
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return flatten(fc2), flatten(fc1)
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def _get_filter_config(shape):
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shape = list(shape)
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filters_84x84 = [
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[16, [8, 8], 4],
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[32, [4, 4], 2],
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[256, [11, 11], 1],
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]
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filters_42x42 = [
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[16, [4, 4], 2],
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[32, [4, 4], 2],
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[256, [11, 11], 1],
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]
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if len(shape) == 3 and shape[:2] == [84, 84]:
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return filters_84x84
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elif len(shape) == 3 and shape[:2] == [42, 42]:
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return filters_42x42
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else:
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raise ValueError(
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"No default configuration for obs shape {}".format(shape) +
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", you must specify `conv_filters` manually as a model option. "
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"Default configurations are only available for inputs of shape "
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"[42, 42, K] and [84, 84, K]. You may alternatively want "
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"to use a custom model or preprocessor.")
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