def test_observed_agents_selection(self):
agent_limit = 10
params = ParameterServer()
params["ML"]["GraphObserver"]["AgentLimit"] = agent_limit
observer = GraphObserver(params=params)
obs, obs_world = self._get_observation(
observer=observer,
world=self.world,
eval_id=self.eval_id)
obs = tf.expand_dims(obs, 0) # add a batch dimension
nodes, _, _ = GraphObserver.graph(obs, graph_dims=observer.graph_dimensions)
nodes = nodes[0] # remove batch dim
ego_node = nodes[0]
ego_node_pos = Point2d(
ego_node[0].numpy(), # x coordinate
ego_node[1].numpy()) # y coordinate
# verify that the nodes are ordered by
# ascending distance to the ego node
max_distance_to_ego = 0
for node in nodes:
pos = Point2d(
node[0].numpy(), # x coordinate
node[1].numpy()) # y coordinate
distance_to_ego = Distance(pos, ego_node_pos)
self.assertGreaterEqual(distance_to_ego, max_distance_to_ego,
msg='Nodes are not sorted by distance relative to '\
+ 'the ego node in ascending order.')
max_distance_to_ego = distance_to_ego
def test_request_subset_of_available_edge_features(self):
params = ParameterServer()
requested_features = GraphObserver.available_edge_attributes()[0:2]
params["ML"]["GraphObserver"]["EnabledEdgeFeatures"] = requested_features
observer = GraphObserver(params=params)
self.assertEqual(
observer._enabled_edge_attribute_keys,
requested_features)
def test_request_partially_invalid_edge_features(self):
params = ParameterServer()
requested_features =\
GraphObserver.available_edge_attributes()[0:2] + ['invalid']
params["ML"]["GraphObserver"]["EnabledEdgeFeatures"] = requested_features
observer = GraphObserver(params=params)
# remove invalid feature from expected list
requested_features.pop(-1)
self.assertEqual(
observer._enabled_edge_attribute_keys,
requested_features)
def test_observe_without_self_loops(self):
num_agents = 4
params = ParameterServer()
params["ML"]["GraphObserver"]["AgentLimit"] = num_agents
params["ML"]["GraphObserver"]["SelfLoops"] = False
observer = GraphObserver(params=params)
obs, _ = self._get_observation(observer, self.world, self.eval_id)
obs = tf.expand_dims(obs, 0) # add a batch dimension
_, adjacency, _ = GraphObserver.graph(obs, graph_dims=observer.graph_dimensions)
adjacency_list_diagonal = (tf.linalg.tensor_diag_part(adjacency[0]))
# assert zeros on the diagonal of the adjacency matrix
tf.assert_equal(adjacency_list_diagonal, tf.zeros(num_agents))
def visualize_graph(data_point, ax, visible_distance, normalization_ref):
# Transform to nx.Graph
observation = data_point["graph"]
graph = GraphObserver.graph_from_observation(observation)
# Get node positions
pos = dict()
goal = dict()
for i in graph.nodes:
features = graph.nodes[i]
pos[i] = [features["x"].numpy(), features["y"].numpy()]
goal[i] = [features["goal_x"].numpy(), features["goal_y"].numpy()]
# Draw ellipse for visibility range of ego agent
width = 4*visible_distance/normalization_ref["dx"][1]
height = 4*visible_distance/normalization_ref["dy"][1]
ellipse = Ellipse(pos[0], width=width,height=height, facecolor='yellow',
zorder=-1)#,**kwargs)
ax.add_patch(ellipse)
goal_ellipse = Ellipse(goal[0], width= 0.2, height=0.2, facecolor="green",
zorder=-2)
ax.add_patch(goal_ellipse)
# Change color for ego agent
node_colors = ["blue" for i in range(len(graph.nodes))]
node_colors[0] = "red"
return nx.draw(graph, pos = pos, with_labels=True, ax=ax,
node_color=node_colors)
def configurable_setup(params, num_scenarios, graph_sac=True):
"""Configurable GNN setup depending on a given filename
Args:
params: ParameterServer instance
Returns:
observer: GraphObserver instance
actor: ActorNetwork of BehaviorGraphSACAgent
"""
observer = GraphObserver(params=params)
bp = ContinuousHighwayBlueprint(params,
number_of_senarios=num_scenarios,
random_seed=0)
env = SingleAgentRuntime(blueprint=bp, observer=observer,
render=False)
if graph_sac:
# Get GNN SAC actor net
sac_agent = BehaviorGraphSACAgent(environment=env, observer=observer,
params=params)
else:
sac_agent = BehaviorSACAgent(environment=env, params=params)
actor = sac_agent._agent._actor_network
return observer, actor
def test_gnn_parameters(self):
params = ParameterServer()
params["ML"]["BehaviorGraphSACAgent"]["GNN"]["NumMpLayers"] = 4
params["ML"]["BehaviorGraphSACAgent"]["GNN"]["MpLayerNumUnits"] = 64
params["ML"]["BehaviorGraphSACAgent"]["GNN"]["message_calculation_class"] = "gnn_edge_mlp"
params["ML"]["BehaviorGraphSACAgent"]["GNN"]["global_exchange_mode"] = "mean"
gnn_library = GNNWrapper.SupportedLibrary.spektral
params["ML"]["BehaviorGraphSACAgent"]["GNN"]["Library"] = gnn_library
bp = ContinuousHighwayBlueprint(params, number_of_senarios=2500, random_seed=0)
observer = GraphObserver(params=params)
env = SingleAgentRuntime(blueprint=bp, observer=observer, render=False)
sac_agent = BehaviorGraphSACAgent(environment=env, observer=observer, params=params)
actor_gnn = sac_agent._agent._actor_network._gnn
critic_gnn = sac_agent._agent._critic_network_1._gnn
for gnn in [actor_gnn, critic_gnn]:
self.assertEqual(gnn._params["NumMpLayers"], 4)
self.assertEqual(gnn._params["MpLayerNumUnits"], 64)
self.assertEqual(gnn._params["message_calculation_class"], "gnn_edge_mlp")
self.assertEqual(gnn._params["global_exchange_mode"], "mean")
self.assertEqual(gnn._params["Library"], gnn_library)
def _init_call_func(self, observations, training=False):
"""Graph nets implementation."""
node_vals, edge_indices, node_to_graph, edge_vals = GraphObserver.graph(
observations=observations,
graph_dims=self._graph_dims,
dense=True)
batch_size = tf.shape(observations)[0]
node_counts = tf.unique_with_counts(node_to_graph)[2]
edge_counts = tf.math.square(node_counts)
input_graph = GraphsTuple(
nodes=tf.cast(node_vals, tf.float32),
edges=tf.cast(edge_vals, tf.float32),
globals=tf.tile([[0.]], [batch_size, 1]),
receivers=tf.cast(edge_indices[:, 1], tf.int32),
senders=tf.cast(edge_indices[:, 0], tf.int32),
n_node=node_counts,
n_edge=edge_counts)
self._latent_trace = []
latent = input_graph
for gb in self._graph_blocks:
latent = gb(latent)
self._latent_trace.append(latent)
node_values = tf.reshape(latent.nodes, [batch_size, -1, self._embedding_size])
return node_values
def test_agent_pruning(self):
"""
Verify that the observer correctly handles the case where
there are less agents in the world than set as the limit.
tl;dr: check that all entries of the node features,
adjacency matrix, and edge features not corresponding to
actually existing agents are zeros.
"""
num_agents = 25
params = ParameterServer()
params["ML"]["GraphObserver"]["AgentLimit"] = num_agents
observer = GraphObserver(params=params)
obs, world = self._get_observation(observer, self.world, self.eval_id)
obs = tf.expand_dims(obs, 0) # add a batch dimension
nodes, adjacency_matrix, edge_features = GraphObserver.graph(
observations=obs,
graph_dims=observer.graph_dimensions)
self.assertEqual(nodes.shape, [1, num_agents, observer.feature_len])
expected_num_agents = len(world.agents)
# nodes that do not represent agents, but are contained
# to fill up the required observation space.
expected_n_fill_up_nodes = num_agents - expected_num_agents
fill_up_nodes = nodes[0, expected_num_agents:]
self.assertEqual(
fill_up_nodes.shape,
[expected_n_fill_up_nodes, observer.feature_len])
# verify that entries for non-existing agents are all zeros
self.assertEqual(tf.reduce_sum(fill_up_nodes), 0)
# the equivalent for edges: verify that for each zero entry
# in the adjacency matrix, the corresponding edge feature
# vector is a zero vector of correct length.
zero_indices = tf.where(tf.equal(adjacency_matrix, 0))
fill_up_edge_features = tf.gather_nd(edge_features, zero_indices)
edge_feature_len = observer.graph_dimensions[2]
zero_edge_feature_vectors = tf.zeros(
[zero_indices.shape[0], edge_feature_len])
self.assertTrue(tf.reduce_all(tf.equal(
fill_up_edge_features,
zero_edge_feature_vectors)))
def _call_spektral(self, observations, training=False):
embeddings, adj_matrix, edge_features = GraphObserver.graph(
observations=observations, graph_dims=self._graph_dims)
for conv in self._convolutions:
embeddings = conv([embeddings, adj_matrix, edge_features])
return embeddings
def setUp(self): """Setting up the test-case.""" params = ParameterServer() bp = ContinuousHighwayBlueprint(params, random_seed=0) self.env = SingleAgentRuntime(blueprint=bp, render=False) self.env.reset() self.world = self.env._world self.observer = GraphObserver(params) self.eval_id = self.env._scenario._eval_agent_ids[0]
def test_parameter_server_usage(self):
expected_num_agents = 15
expected_visibility_radius = 100
params = ParameterServer()
params["ML"]["GraphObserver"]["AgentLimit"] = expected_num_agents
params["ML"]["GraphObserver"]["VisibilityRadius"] = expected_visibility_radius
params["ML"]["GraphObserver"]["NormalizationEnabled"] = True
observer = GraphObserver(params=params)
self.assertEqual(observer._num_agents, expected_num_agents)
self.assertEqual(observer._visibility_radius, expected_visibility_radius)
# self.assertTrue(observer._add_self_loops)
self.assertTrue(observer._normalize_observations)
def __init__(self,
num_scenarios=3,
dump_dir=None,
render=False,
params=ParameterServer()):
"""Inits DataGenerator with the parameters (see class definition)."""
self._dump_dir = dump_dir
self._num_scenarios = num_scenarios
self._params = params
self._bp = ContinuousHighwayBlueprint(self._params,\
number_of_senarios=self._num_scenarios, random_seed=0)
self._observer = GraphObserver(params=self._params)
self._env = SingleAgentRuntime(blueprint=self._bp,
observer=self._observer,
render=render)
def run_configuration(argv):
# Uncomment one of the following default parameter filename definitions,
# depending on which GNN library you'd like to use.
# File with standard parameters for tf2_gnn use:
# param_filename = "examples/example_params/tfa_sac_gnn_tf2_gnn_default.json"
# File with standard parameters for spektral use:
param_filename = "examples/example_params/tfa_sac_gnn_spektral_default.json"
params = ParameterServer(filename=param_filename)
# NOTE: Modify these paths to specify your preferred path for checkpoints and summaries
# params["ML"]["BehaviorTFAAgents"]["CheckpointPath"] = "YOUR_PATH"
# params["ML"]["TFARunner"]["SummaryPath"] = "YOUR_PATH"
#viewer = MPViewer(
# params=params,
# x_range=[-35, 35],
# y_range=[-35, 35],
# follow_agent_id=True)
#viewer = VideoRenderer(
# renderer=viewer,
# world_step_time=0.2,
# fig_path="/your_path_here/training/video/")
# create environment
bp = ContinuousHighwayBlueprint(params,
number_of_senarios=2500,
random_seed=0)
observer = GraphObserver(params=params)
env = SingleAgentRuntime(blueprint=bp, observer=observer, render=False)
sac_agent = BehaviorGraphSACAgent(environment=env,
observer=observer,
params=params)
env.ml_behavior = sac_agent
runner = SACRunner(params=params, environment=env, agent=sac_agent)
if FLAGS.mode == "train":
runner.SetupSummaryWriter()
runner.Train()
elif FLAGS.mode == "visualize":
runner.Visualize(5)
elif FLAGS.mode == "evaluate":
runner.Evaluate()
def test_sac_graph_agent(self):
params = ParameterServer()
bp = ContinuousMergingBlueprint(params,
number_of_senarios=2500,
random_seed=0)
observer = GraphObserver(params=params)
env = SingleAgentRuntime(blueprint=bp, observer=observer, render=False)
sac_agent = BehaviorGraphSACAgent(environment=env,
observer=observer,
params=params)
env.ml_behavior = sac_agent
env.reset()
eval_id = env._scenario._eval_agent_ids[0]
self.assertEqual(env._world.agents[eval_id].behavior_model, sac_agent)
for _ in range(0, 5):
env._world.Step(0.2)
def _call_tf2_gnn(self, observations, training=False):
batch_size = tf.constant(observations.shape[0])
embeddings, adj_list, node_to_graph_map = GraphObserver.graph(
observations=observations, graph_dims=self._graph_dims, dense=True)
gnn_input = GNNInput(
node_features=embeddings,
adjacency_lists=(adj_list, ),
node_to_graph_map=node_to_graph_map,
num_graphs=batch_size,
)
# tf2_gnn outputs a flattened node embeddings vector, so we
# reshape it to have the embeddings of each node seperately.
flat_output = self._gnn(gnn_input, training=training)
output = tf.reshape(flat_output, [batch_size, -1, self.num_units])
return output
def test_observation_conforms_to_spec(self):
"""
Verify that the observation returned by the observer
is valid with respect to its defined observation space.
"""
num_agents = 4
params = ParameterServer()
params["ML"]["GraphObserver"]["AgentLimit"] = num_agents
observer = GraphObserver(params=params)
obs, _ = self._get_observation(observer, self.world, self.eval_id)
self.assertTrue(observer.observation_space.contains(obs))
# additionally check that the adjacency list is binary, since
# this can't be enforced by the observation space currently
adj_start_idx = num_agents * observer.feature_len
adj_end_idx = adj_start_idx + num_agents ** 2
adj_list = obs[adj_start_idx : adj_end_idx]
for element in adj_list: self.assertIn(element, [0, 1])
def _configurable_setup(self, params_filename):
"""Configurable GNN setup depending on a given filename
Args:
params_filename: str, corresponds to path of params file
Returns:
params: ParameterServer instance
observer: GraphObserver instance
actor: ActorNetwork of BehaviorGraphSACAgent
"""
params = ParameterServer(filename=params_filename)
observer = GraphObserver(params=params)
bp = ContinuousHighwayBlueprint(params,
number_of_senarios=2,
random_seed=0)
env = SingleAgentRuntime(blueprint=bp, observer=observer, render=False)
# Get GNN SAC actor net
sac_agent = BehaviorGraphSACAgent(environment=env,
observer=observer,
params=params)
actor = sac_agent._agent._actor_network
return params, observer, actor
def run_configuration(argv):
params = ParameterServer()
# NOTE: Modify these paths to specify your preferred path for checkpoints and summaries
# params["ML"]["BehaviorTFAAgents"]["CheckpointPath"] = "/Users/hart/Development/bark-ml/checkpoints_merge_spektral_att2/"
# params["ML"]["TFARunner"]["SummaryPath"] = "/Users/hart/Development/bark-ml/checkpoints_merge_spektral_att2/"
#viewer = MPViewer(
# params=params,
# x_range=[-35, 35],
# y_range=[-35, 35],
# follow_agent_id=True)
#viewer = VideoRenderer(
# renderer=viewer,
# world_step_time=0.2,
# fig_path="/your_path_here/training/video/")
# create environment
bp = ContinuousMergingBlueprint(params, num_scenarios=2500, random_seed=0)
observer = GraphObserver(params=params)
env = SingleAgentRuntime(blueprint=bp, observer=observer, render=False)
sac_agent = BehaviorGraphSACAgent(environment=env,
observer=observer,
params=params,
init_gnn='init_interaction_network')
env.ml_behavior = sac_agent
runner = SACRunner(params=params, environment=env, agent=sac_agent)
if FLAGS.mode == "train":
runner.SetupSummaryWriter()
runner.Train()
elif FLAGS.mode == "visualize":
runner.Run(num_episodes=10, render=True)
elif FLAGS.mode == "evaluate":
runner.Run(num_episodes=250, render=False)
def _test_graph_dim_validation_accepts_observer_dims(self):
observer = GraphObserver()
gnn = GNNWrapper(graph_dims=observer.graph_dimensions)
# verify no exception is raised and the dims are applied
self.assertEqual(gnn._graph_dims, observer.graph_dimensions)
def run_configuration(argv):
params = ParameterServer()
# NOTE: Modify these paths to specify your preferred path for checkpoints and summaries
# params["ML"]["BehaviorTFAAgents"]["CheckpointPath"] = "/Users/hart/Development/bark-ml/checkpoints/"
# params["ML"]["TFARunner"]["SummaryPath"] = "/Users/hart/Development/bark-ml/checkpoints/"
params["Visualization"]["Agents"]["Alpha"]["Other"] = 0.2
params["Visualization"]["Agents"]["Alpha"]["Controlled"] = 0.2
params["Visualization"]["Agents"]["Alpha"]["Controlled"] = 0.2
params["ML"]["VisualizeCfWorlds"] = False
params["ML"]["VisualizeCfHeatmap"] = False
# params["ML"]["ResultsFolder"] = "/Users/hart/Development/bark-ml/results/data/"
# viewer = MPViewer(
# params=params,
# x_range=[-35, 35],
# y_range=[-35, 35],
# follow_agent_id=True)
# create environment
bp = ContinuousMergingBlueprint(params,
num_scenarios=2500,
random_seed=0)
observer = GraphObserver(params=params)
behavior_model_pool = []
for count, a in enumerate([-5., 0., 5.]):
local_params = params.AddChild("local_"+str(count))
local_params["BehaviorConstantAcceleration"]["ConstAcceleration"] = a
behavior = BehaviorConstantAcceleration(local_params)
behavior_model_pool.append(behavior)
env = CounterfactualRuntime(
blueprint=bp,
observer=observer,
render=False,
params=params,
behavior_model_pool=behavior_model_pool)
sac_agent = BehaviorGraphSACAgent(environment=env,
observer=observer,
params=params)
env.ml_behavior = sac_agent
runner = SACRunner(params=params,
environment=env,
agent=sac_agent)
if FLAGS.mode == "train":
runner.SetupSummaryWriter()
runner.Train()
elif FLAGS.mode == "visualize":
runner._environment._max_col_rate = 0.
runner.Run(num_episodes=1, render=True)
elif FLAGS.mode == "evaluate":
for cr in np.arange(0, 1, 0.1):
runner._environment._max_col_rate = cr
runner.Run(num_episodes=250, render=False, max_col_rate=cr)
runner._environment._tracer.Save(
params["ML"]["ResultsFolder"] + "evaluation_results_runtime.pckl")
goal_reached = runner._tracer.success_rate
runner._tracer.Save(
params["ML"]["ResultsFolder"] + "evaluation_results_runner.pckl")
def test_gnn(self):
# Node features for graph 0.
nodes_0 = [
[10.1, 20., 30.], # Node 0
[11., 21., 31.], # Node 1
[12., 22., 32.], # Node 2
[13., 23., 33.], # Node 3
[14., 24., 34.]
] # Node 4
# Edge features for graph 0.
edges_0 = [
[100., 200.], # Edge 0
[101.2, 201.], # Edge 1
[102., 202.], # Edge 2
[103., 203.], # Edge 3
[104., 204.], # Edge 4
[105., 205.]
] # Edge 5
# The sender and receiver nodes associated with each edge for graph 0.
senders_0 = [
0, # Index of the sender node for edge 0
1, # Index of the sender node for edge 1
1, # Index of the sender node for edge 2
2, # Index of the sender node for edge 3
2, # Index of the sender node for edge 4
3
] # Index of the sender node for edge 5
receivers_0 = [
1, # Index of the receiver node for edge 0
2, # Index of the receiver node for edge 1
3, # Index of the receiver node for edge 2
0, # Index of the receiver node for edge 3
3, # Index of the receiver node for edge 4
4
] # Index of the receiver node for edge 5
data_dict_0 = {
"globals": [],
"nodes": nodes_0,
"edges": edges_0,
"senders": senders_0,
"receivers": receivers_0
}
input_graph = utils_tf.data_dicts_to_graphs_tuple(
[data_dict_0, data_dict_0])
num_nodes = len(nodes_0)
num_features = 3
num_edge_features = len(edges_0)
graph_dims = (num_nodes, num_features, num_edge_features)
# 6 edges x 2
# 5 nodes x 3
# adj matrix 5x5
obs = np.zeros(shape=(1, 52))
# NOTE: use dense
params = ParameterServer()
graph_observer = GraphObserver(params)
graph_observer.feature_len = 2
graph_observer.edge_feature_len = 3
_, _, _ = graph_observer.graph(obs, graph_dims=graph_dims)
print(input_graph)
def test_observation_to_graph_conversion(self):
params = ParameterServer()
params["ML"]["GraphObserver"]["SelfLoops"] = False
graph_observer = GraphObserver(params=params)
num_nodes = 5
num_features = 5
num_edge_features = 4
node_features = np.random.random_sample((num_nodes, num_features))
edge_features = np.random.random_sample((num_nodes, num_nodes, num_edge_features))
# note that edges are bidirectional, the
# the matrix is symmetric
adjacency_list = [
[0, 1, 1, 1, 0], # 1 connects with 2, 3, 4
[1, 0, 1, 1, 0], # 2 connects with 3, 4
[1, 1, 0, 1, 0], # 3 connects with 4
[1, 1, 1, 0, 0], # 4 has no links
[0, 0, 0, 0, 0] # empty slot -> all zeros
]
observation = np.array(node_features)
observation = np.append(observation, adjacency_list)
observation = np.append(observation, edge_features)
observation = observation.reshape(-1)
observations = np.array([observation, observation])
self.assertEqual(observations.shape, (2, 150))
expected_nodes = tf.constant([node_features, node_features])
expected_edge_features = tf.constant([edge_features, edge_features])
graph_dims = (num_nodes, num_features, num_edge_features)
nodes, edges, edge_features = graph_observer.graph(observations, graph_dims)
self.assertTrue(tf.reduce_all(tf.equal(nodes, expected_nodes)))
self.assertTrue(tf.reduce_all(tf.equal(edge_features, expected_edge_features)))
observations = np.array([observation, observation, observation])
# in dense mode, the nodes of all graphs are in a single list
expected_nodes = tf.constant([node_features, node_features, node_features])
expected_nodes = tf.reshape(expected_nodes, [-1, num_features])
# the edges encoded in the adjacency list above
expected_dense_edges = tf.constant([
# graph 1
[0, 1], [0, 2], [0, 3],
[1, 0], [1, 2], [1, 3],
[2, 0], [2, 1], [2, 3],
[3, 0], [3, 1], [3, 2],
# graph 2
[5, 6], [5, 7], [5, 8],
[6, 5], [6, 7], [6, 8],
[7, 5], [7, 6], [7, 8],
[8, 5], [8, 6], [8, 7],
# graph 3
[10, 11], [10, 12], [10, 13],
[11, 10], [11, 12], [11, 13],
[12, 10], [12, 11], [12, 13],
[13, 10], [13, 11], [13, 12]
], dtype=tf.int32)
expected_node_to_graph_map = tf.constant([
0, 0, 0, 0, 0,
1, 1, 1, 1, 1,
2, 2, 2, 2, 2
])
observations = tf.convert_to_tensor(observations)
print(observations)
nodes, edges, node_to_graph_map, E =\
GraphObserver.graph(observations, graph_dims, dense=True)
self.assertTrue(tf.reduce_all(tf.equal(nodes, expected_nodes)))
self.assertTrue(tf.reduce_all(tf.equal(edges, expected_dense_edges)))
