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- import gym
- from typing import Dict, List, Optional, Sequence
- from ray.rllib.models.tf.tf_modelv2 import TFModelV2
- from ray.rllib.models.tf.misc import normc_initializer
- from ray.rllib.models.utils import get_activation_fn, get_filter_config
- from ray.rllib.policy.sample_batch import SampleBatch
- from ray.rllib.utils.framework import try_import_tf
- from ray.rllib.utils.typing import ModelConfigDict, TensorType
- tf1, tf, tfv = try_import_tf()
- # TODO: (sven) obsolete this class once we only support native keras models.
- class VisionNetwork(TFModelV2):
- """Generic vision network implemented in ModelV2 API.
- An additional post-conv fully connected stack can be added and configured
- via the config keys:
- `post_fcnet_hiddens`: Dense layer sizes after the Conv2D stack.
- `post_fcnet_activation`: Activation function to use for this FC stack.
- """
- def __init__(self, obs_space: gym.spaces.Space,
- action_space: gym.spaces.Space, num_outputs: int,
- model_config: ModelConfigDict, name: str):
- if not model_config.get("conv_filters"):
- model_config["conv_filters"] = get_filter_config(obs_space.shape)
- super(VisionNetwork, self).__init__(obs_space, action_space,
- num_outputs, model_config, name)
- activation = get_activation_fn(
- self.model_config.get("conv_activation"), framework="tf")
- filters = self.model_config["conv_filters"]
- assert len(filters) > 0,\
- "Must provide at least 1 entry in `conv_filters`!"
- # Post FC net config.
- post_fcnet_hiddens = model_config.get("post_fcnet_hiddens", [])
- post_fcnet_activation = get_activation_fn(
- model_config.get("post_fcnet_activation"), framework="tf")
- no_final_linear = self.model_config.get("no_final_linear")
- vf_share_layers = self.model_config.get("vf_share_layers")
- input_shape = obs_space.shape
- self.data_format = "channels_last"
- inputs = tf.keras.layers.Input(shape=input_shape, name="observations")
- last_layer = inputs
- # Whether the last layer is the output of a Flattened (rather than
- # a n x (1,1) Conv2D).
- self.last_layer_is_flattened = False
- # Build the action layers
- for i, (out_size, kernel, stride) in enumerate(filters[:-1], 1):
- last_layer = tf.keras.layers.Conv2D(
- out_size,
- kernel,
- strides=stride
- if isinstance(stride, (list, tuple)) else (stride, stride),
- activation=activation,
- padding="same",
- data_format="channels_last",
- name="conv{}".format(i))(last_layer)
- out_size, kernel, stride = filters[-1]
- # No final linear: Last layer has activation function and exits with
- # num_outputs nodes (this could be a 1x1 conv or a FC layer, depending
- # on `post_fcnet_...` settings).
- if no_final_linear and num_outputs:
- last_layer = tf.keras.layers.Conv2D(
- out_size if post_fcnet_hiddens else num_outputs,
- kernel,
- strides=stride
- if isinstance(stride, (list, tuple)) else (stride, stride),
- activation=activation,
- padding="valid",
- data_format="channels_last",
- name="conv_out")(last_layer)
- # Add (optional) post-fc-stack after last Conv2D layer.
- layer_sizes = post_fcnet_hiddens[:-1] + ([num_outputs]
- if post_fcnet_hiddens else
- [])
- feature_out = last_layer
- for i, out_size in enumerate(layer_sizes):
- feature_out = last_layer
- last_layer = tf.keras.layers.Dense(
- out_size,
- name="post_fcnet_{}".format(i),
- activation=post_fcnet_activation,
- kernel_initializer=normc_initializer(1.0))(last_layer)
- # Finish network normally (w/o overriding last layer size with
- # `num_outputs`), then add another linear one of size `num_outputs`.
- else:
- last_layer = tf.keras.layers.Conv2D(
- out_size,
- kernel,
- strides=stride
- if isinstance(stride, (list, tuple)) else (stride, stride),
- activation=activation,
- padding="valid",
- data_format="channels_last",
- name="conv{}".format(len(filters)))(last_layer)
- # num_outputs defined. Use that to create an exact
- # `num_output`-sized (1,1)-Conv2D.
- if num_outputs:
- if post_fcnet_hiddens:
- last_cnn = last_layer = tf.keras.layers.Conv2D(
- post_fcnet_hiddens[0], [1, 1],
- activation=post_fcnet_activation,
- padding="same",
- data_format="channels_last",
- name="conv_out")(last_layer)
- # Add (optional) post-fc-stack after last Conv2D layer.
- for i, out_size in enumerate(post_fcnet_hiddens[1:] +
- [num_outputs]):
- feature_out = last_layer
- last_layer = tf.keras.layers.Dense(
- out_size,
- name="post_fcnet_{}".format(i + 1),
- activation=post_fcnet_activation
- if i < len(post_fcnet_hiddens) - 1 else None,
- kernel_initializer=normc_initializer(1.0))(
- last_layer)
- else:
- feature_out = last_layer
- last_cnn = last_layer = tf.keras.layers.Conv2D(
- num_outputs, [1, 1],
- activation=None,
- padding="same",
- data_format="channels_last",
- name="conv_out")(last_layer)
- if last_cnn.shape[1] != 1 or last_cnn.shape[2] != 1:
- raise ValueError(
- "Given `conv_filters` ({}) do not result in a [B, 1, "
- "1, {} (`num_outputs`)] shape (but in {})! Please "
- "adjust your Conv2D stack such that the dims 1 and 2 "
- "are both 1.".format(self.model_config["conv_filters"],
- self.num_outputs,
- list(last_cnn.shape)))
- # num_outputs not known -> Flatten, then set self.num_outputs
- # to the resulting number of nodes.
- else:
- self.last_layer_is_flattened = True
- last_layer = tf.keras.layers.Flatten(
- data_format="channels_last")(last_layer)
- # Add (optional) post-fc-stack after last Conv2D layer.
- for i, out_size in enumerate(post_fcnet_hiddens):
- last_layer = tf.keras.layers.Dense(
- out_size,
- name="post_fcnet_{}".format(i),
- activation=post_fcnet_activation,
- kernel_initializer=normc_initializer(1.0))(last_layer)
- feature_out = last_layer
- self.num_outputs = last_layer.shape[1]
- logits_out = last_layer
- # Build the value layers
- if vf_share_layers:
- if not self.last_layer_is_flattened:
- feature_out = tf.keras.layers.Lambda(
- lambda x: tf.squeeze(x, axis=[1, 2]))(feature_out)
- value_out = tf.keras.layers.Dense(
- 1,
- name="value_out",
- activation=None,
- kernel_initializer=normc_initializer(0.01))(feature_out)
- else:
- # build a parallel set of hidden layers for the value net
- last_layer = inputs
- for i, (out_size, kernel, stride) in enumerate(filters[:-1], 1):
- last_layer = tf.keras.layers.Conv2D(
- out_size,
- kernel,
- strides=stride
- if isinstance(stride, (list, tuple)) else (stride, stride),
- activation=activation,
- padding="same",
- data_format="channels_last",
- name="conv_value_{}".format(i))(last_layer)
- out_size, kernel, stride = filters[-1]
- last_layer = tf.keras.layers.Conv2D(
- out_size,
- kernel,
- strides=stride
- if isinstance(stride, (list, tuple)) else (stride, stride),
- activation=activation,
- padding="valid",
- data_format="channels_last",
- name="conv_value_{}".format(len(filters)))(last_layer)
- last_layer = tf.keras.layers.Conv2D(
- 1, [1, 1],
- activation=None,
- padding="same",
- data_format="channels_last",
- name="conv_value_out")(last_layer)
- value_out = tf.keras.layers.Lambda(
- lambda x: tf.squeeze(x, axis=[1, 2]))(last_layer)
- self.base_model = tf.keras.Model(inputs, [logits_out, value_out])
- def forward(self, input_dict: Dict[str, TensorType],
- state: List[TensorType],
- seq_lens: TensorType) -> (TensorType, List[TensorType]):
- obs = input_dict["obs"]
- if self.data_format == "channels_first":
- obs = tf.transpose(obs, [0, 2, 3, 1])
- # Explicit cast to float32 needed in eager.
- model_out, self._value_out = self.base_model(tf.cast(obs, tf.float32))
- # Our last layer is already flat.
- if self.last_layer_is_flattened:
- return model_out, state
- # Last layer is a n x [1,1] Conv2D -> Flatten.
- else:
- return tf.squeeze(model_out, axis=[1, 2]), state
- def value_function(self) -> TensorType:
- return tf.reshape(self._value_out, [-1])
- class Keras_VisionNetwork(tf.keras.Model if tf else object):
- """Generic vision network implemented in tf keras.
- An additional post-conv fully connected stack can be added and configured
- via the config keys:
- `post_fcnet_hiddens`: Dense layer sizes after the Conv2D stack.
- `post_fcnet_activation`: Activation function to use for this FC stack.
- """
- def __init__(
- self,
- input_space: gym.spaces.Space,
- action_space: gym.spaces.Space,
- num_outputs: Optional[int] = None,
- *,
- name: str = "",
- conv_filters: Optional[Sequence[Sequence[int]]] = None,
- conv_activation: Optional[str] = None,
- post_fcnet_hiddens: Optional[Sequence[int]] = (),
- post_fcnet_activation: Optional[str] = None,
- no_final_linear: bool = False,
- vf_share_layers: bool = False,
- free_log_std: bool = False,
- **kwargs,
- ):
- super().__init__(name=name)
- if not conv_filters:
- conv_filters = get_filter_config(input_space.shape)
- assert len(conv_filters) > 0,\
- "Must provide at least 1 entry in `conv_filters`!"
- conv_activation = get_activation_fn(conv_activation, framework="tf")
- post_fcnet_activation = get_activation_fn(
- post_fcnet_activation, framework="tf")
- input_shape = input_space.shape
- self.data_format = "channels_last"
- inputs = tf.keras.layers.Input(shape=input_shape, name="observations")
- last_layer = inputs
- # Whether the last layer is the output of a Flattened (rather than
- # a n x (1,1) Conv2D).
- self.last_layer_is_flattened = False
- # Build the action layers
- for i, (out_size, kernel, stride) in enumerate(conv_filters[:-1], 1):
- last_layer = tf.keras.layers.Conv2D(
- out_size,
- kernel,
- strides=stride
- if isinstance(stride, (list, tuple)) else (stride, stride),
- activation=conv_activation,
- padding="same",
- data_format="channels_last",
- name="conv{}".format(i))(last_layer)
- out_size, kernel, stride = conv_filters[-1]
- # No final linear: Last layer has activation function and exits with
- # num_outputs nodes (this could be a 1x1 conv or a FC layer, depending
- # on `post_fcnet_...` settings).
- if no_final_linear and num_outputs:
- last_layer = tf.keras.layers.Conv2D(
- out_size if post_fcnet_hiddens else num_outputs,
- kernel,
- strides=stride
- if isinstance(stride, (list, tuple)) else (stride, stride),
- activation=conv_activation,
- padding="valid",
- data_format="channels_last",
- name="conv_out")(last_layer)
- # Add (optional) post-fc-stack after last Conv2D layer.
- layer_sizes = post_fcnet_hiddens[:-1] + ([num_outputs]
- if post_fcnet_hiddens else
- [])
- for i, out_size in enumerate(layer_sizes):
- last_layer = tf.keras.layers.Dense(
- out_size,
- name="post_fcnet_{}".format(i),
- activation=post_fcnet_activation,
- kernel_initializer=normc_initializer(1.0))(last_layer)
- # Finish network normally (w/o overriding last layer size with
- # `num_outputs`), then add another linear one of size `num_outputs`.
- else:
- last_layer = tf.keras.layers.Conv2D(
- out_size,
- kernel,
- strides=stride
- if isinstance(stride, (list, tuple)) else (stride, stride),
- activation=conv_activation,
- padding="valid",
- data_format="channels_last",
- name="conv{}".format(len(conv_filters)))(last_layer)
- # num_outputs defined. Use that to create an exact
- # `num_output`-sized (1,1)-Conv2D.
- if num_outputs:
- if post_fcnet_hiddens:
- last_cnn = last_layer = tf.keras.layers.Conv2D(
- post_fcnet_hiddens[0], [1, 1],
- activation=post_fcnet_activation,
- padding="same",
- data_format="channels_last",
- name="conv_out")(last_layer)
- # Add (optional) post-fc-stack after last Conv2D layer.
- for i, out_size in enumerate(post_fcnet_hiddens[1:] +
- [num_outputs]):
- last_layer = tf.keras.layers.Dense(
- out_size,
- name="post_fcnet_{}".format(i + 1),
- activation=post_fcnet_activation
- if i < len(post_fcnet_hiddens) - 1 else None,
- kernel_initializer=normc_initializer(1.0))(
- last_layer)
- else:
- last_cnn = last_layer = tf.keras.layers.Conv2D(
- num_outputs, [1, 1],
- activation=None,
- padding="same",
- data_format="channels_last",
- name="conv_out")(last_layer)
- if last_cnn.shape[1] != 1 or last_cnn.shape[2] != 1:
- raise ValueError(
- "Given `conv_filters` ({}) do not result in a [B, 1, "
- "1, {} (`num_outputs`)] shape (but in {})! Please "
- "adjust your Conv2D stack such that the dims 1 and 2 "
- "are both 1.".format(
- self.model_config["conv_filters"], num_outputs,
- list(last_cnn.shape)))
- # num_outputs not known -> Flatten.
- else:
- self.last_layer_is_flattened = True
- last_layer = tf.keras.layers.Flatten(
- data_format="channels_last")(last_layer)
- # Add (optional) post-fc-stack after last Conv2D layer.
- for i, out_size in enumerate(post_fcnet_hiddens):
- last_layer = tf.keras.layers.Dense(
- out_size,
- name="post_fcnet_{}".format(i),
- activation=post_fcnet_activation,
- kernel_initializer=normc_initializer(1.0))(last_layer)
- logits_out = last_layer
- # Build the value layers
- if vf_share_layers:
- if not self.last_layer_is_flattened:
- last_layer = tf.keras.layers.Lambda(
- lambda x: tf.squeeze(x, axis=[1, 2]))(last_layer)
- value_out = tf.keras.layers.Dense(
- 1,
- name="value_out",
- activation=None,
- kernel_initializer=normc_initializer(0.01))(last_layer)
- else:
- # build a parallel set of hidden layers for the value net
- last_layer = inputs
- for i, (out_size, kernel, stride) in enumerate(
- conv_filters[:-1], 1):
- last_layer = tf.keras.layers.Conv2D(
- out_size,
- kernel,
- strides=stride
- if isinstance(stride, (list, tuple)) else (stride, stride),
- activation=conv_activation,
- padding="same",
- data_format="channels_last",
- name="conv_value_{}".format(i))(last_layer)
- out_size, kernel, stride = conv_filters[-1]
- last_layer = tf.keras.layers.Conv2D(
- out_size,
- kernel,
- strides=stride
- if isinstance(stride, (list, tuple)) else (stride, stride),
- activation=conv_activation,
- padding="valid",
- data_format="channels_last",
- name="conv_value_{}".format(len(conv_filters)))(last_layer)
- last_layer = tf.keras.layers.Conv2D(
- 1, [1, 1],
- activation=None,
- padding="same",
- data_format="channels_last",
- name="conv_value_out")(last_layer)
- value_out = tf.keras.layers.Lambda(
- lambda x: tf.squeeze(x, axis=[1, 2]))(last_layer)
- self.base_model = tf.keras.Model(inputs, [logits_out, value_out])
- def call(self, input_dict: SampleBatch) -> \
- (TensorType, List[TensorType], Dict[str, TensorType]):
- obs = input_dict["obs"]
- if self.data_format == "channels_first":
- obs = tf.transpose(obs, [0, 2, 3, 1])
- # Explicit cast to float32 needed in eager.
- model_out, self._value_out = self.base_model(tf.cast(obs, tf.float32))
- state = [v for k, v in input_dict.items() if k.startswith("state_in_")]
- extra_outs = {SampleBatch.VF_PREDS: tf.reshape(self._value_out, [-1])}
- # Our last layer is already flat.
- if self.last_layer_is_flattened:
- return model_out, state, extra_outs
- # Last layer is a n x [1,1] Conv2D -> Flatten.
- else:
- return tf.squeeze(model_out, axis=[1, 2]), state, extra_outs
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