from typing import Dict, List import gymnasium as gym from ray.rllib.models.tf.misc import normc_initializer from ray.rllib.models.tf.tf_modelv2 import TFModelV2 from ray.rllib.models.utils import get_activation_fn, get_filter_config from ray.rllib.utils.annotations import OldAPIStack from ray.rllib.utils.framework import try_import_tf from ray.rllib.utils.typing import ModelConfigDict, TensorType tf1, tf, tfv = try_import_tf() @OldAPIStack 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])