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.
108 lines
4.2 KiB
Python
108 lines
4.2 KiB
Python
from ray.rllib.models.tf.tf_modelv2 import TFModelV2
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from ray.rllib.models.tf.visionnet_v1 import _get_filter_config
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from ray.rllib.models.tf.misc import normc_initializer, get_activation_fn
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from ray.rllib.utils import try_import_tf
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tf = try_import_tf()
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class VisionNetwork(TFModelV2):
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"""Generic vision network implemented in ModelV2 API."""
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def __init__(self, obs_space, action_space, num_outputs, model_config,
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name):
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super(VisionNetwork, self).__init__(obs_space, action_space,
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num_outputs, model_config, name)
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activation = get_activation_fn(model_config.get("conv_activation"))
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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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no_final_linear = model_config.get("no_final_linear")
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vf_share_layers = model_config.get("vf_share_layers")
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inputs = tf.keras.layers.Input(
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shape=obs_space.shape, name="observations")
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last_layer = inputs
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# Build the action layers
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for i, (out_size, kernel, stride) in enumerate(filters[:-1], 1):
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last_layer = tf.keras.layers.Conv2D(
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out_size,
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kernel,
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strides=(stride, stride),
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activation=activation,
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padding="same",
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name="conv{}".format(i))(last_layer)
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out_size, kernel, stride = filters[-1]
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if no_final_linear:
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# the last layer is adjusted to be of size num_outputs
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last_layer = tf.keras.layers.Conv2D(
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num_outputs,
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kernel,
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strides=(stride, stride),
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activation=activation,
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padding="valid",
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name="conv_out")(last_layer)
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conv_out = last_layer
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else:
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last_layer = tf.keras.layers.Conv2D(
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out_size,
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kernel,
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strides=(stride, stride),
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activation=activation,
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padding="valid",
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name="conv{}".format(i + 1))(last_layer)
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conv_out = tf.keras.layers.Conv2D(
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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="conv_out")(last_layer)
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# Build the value layers
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if vf_share_layers:
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last_layer = tf.keras.layers.Lambda(
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lambda x: tf.squeeze(x, axis=[1, 2]))(last_layer)
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value_out = tf.keras.layers.Dense(
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1,
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name="value_out",
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activation=None,
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kernel_initializer=normc_initializer(0.01))(last_layer)
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else:
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# build a parallel set of hidden layers for the value net
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last_layer = inputs
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for i, (out_size, kernel, stride) in enumerate(filters[:-1], 1):
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last_layer = tf.keras.layers.Conv2D(
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out_size,
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kernel,
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strides=(stride, stride),
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activation=activation,
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padding="same",
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name="conv_value_{}".format(i))(last_layer)
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out_size, kernel, stride = filters[-1]
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last_layer = tf.keras.layers.Conv2D(
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out_size,
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kernel,
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strides=(stride, stride),
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activation=activation,
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padding="valid",
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name="conv_value_{}".format(i + 1))(last_layer)
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last_layer = tf.keras.layers.Conv2D(
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1, [1, 1],
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activation=None,
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padding="same",
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name="conv_value_out")(last_layer)
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value_out = tf.keras.layers.Lambda(
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lambda x: tf.squeeze(x, axis=[1, 2]))(last_layer)
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self.base_model = tf.keras.Model(inputs, [conv_out, value_out])
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self.register_variables(self.base_model.variables)
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def forward(self, input_dict, state, seq_lens):
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# explicit cast to float32 needed in eager
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model_out, self._value_out = self.base_model(
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tf.cast(input_dict["obs"], tf.float32))
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return tf.squeeze(model_out, axis=[1, 2]), state
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def value_function(self):
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return tf.reshape(self._value_out, [-1])
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