ray/rllib/models/tf/visionnet_v1.py

from ray.rllib.models.model import Model
from ray.rllib.models.tf.misc import get_activation_fn, flatten
from ray.rllib.utils.annotations import override
from ray.rllib.utils import try_import_tf

tf = try_import_tf()


# Deprecated: see as an alternative models/tf/visionnet_v2.py
class VisionNetwork(Model):
    """Generic vision network."""

    @override(Model)
    def _build_layers_v2(self, input_dict, num_outputs, options):
        inputs = input_dict["obs"]
        filters = options.get("conv_filters")
        if not filters:
            filters = _get_filter_config(inputs.shape.as_list()[1:])

        activation = get_activation_fn(options.get("conv_activation"))

        with tf.name_scope("vision_net"):
            for i, (out_size, kernel, stride) in enumerate(filters[:-1], 1):
                inputs = tf.layers.conv2d(
                    inputs,
                    out_size,
                    kernel,
                    stride,
                    activation=activation,
                    padding="same",
                    name="conv{}".format(i))
            out_size, kernel, stride = filters[-1]

            # skip final linear layer
            if options.get("no_final_linear"):
                fc_out = tf.layers.conv2d(
                    inputs,
                    num_outputs,
                    kernel,
                    stride,
                    activation=activation,
                    padding="valid",
                    name="fc_out")
                return flatten(fc_out), flatten(fc_out)

            fc1 = tf.layers.conv2d(
                inputs,
                out_size,
                kernel,
                stride,
                activation=activation,
                padding="valid",
                name="fc1")
            fc2 = tf.layers.conv2d(
                fc1,
                num_outputs, [1, 1],
                activation=None,
                padding="same",
                name="fc2")
            return flatten(fc2), flatten(fc1)


def _get_filter_config(shape):
    shape = list(shape)
    filters_84x84 = [
        [16, [8, 8], 4],
        [32, [4, 4], 2],
        [256, [11, 11], 1],
    ]
    filters_42x42 = [
        [16, [4, 4], 2],
        [32, [4, 4], 2],
        [256, [11, 11], 1],
    ]
    if len(shape) == 3 and shape[:2] == [84, 84]:
        return filters_84x84
    elif len(shape) == 3 and shape[:2] == [42, 42]:
        return filters_42x42
    else:
        raise ValueError(
            "No default configuration for obs shape {}".format(shape) +
            ", you must specify `conv_filters` manually as a model option. "
            "Default configurations are only available for inputs of shape "
            "[42, 42, K] and [84, 84, K]. You may alternatively want "
            "to use a custom model or preprocessor.")