307 lines
15 KiB
Python
307 lines
15 KiB
Python
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import torch
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import numpy as np
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from collections import Counter
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from model.trajectron import Trajectron
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from model.online.online_mgcvae import OnlineMultimodalGenerativeCVAE
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from model.model_utils import ModeKeys
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from data import RingBuffer, TemporalSceneGraph, SceneGraph, derivative_of
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class OnlineTrajectron(Trajectron):
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def __init__(self, model_registrar,
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hyperparams, device):
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super(OnlineTrajectron, self).__init__(model_registrar=model_registrar,
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hyperparams=hyperparams,
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log_writer=False,
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device=device)
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self.node_data = dict()
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self.scene_graph = None
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self.RING_CAPACITY = max(len(self.hyperparams['edge_removal_filter']),
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len(self.hyperparams['edge_addition_filter']),
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self.hyperparams['maximum_history_length']) + 1
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self.rel_states = dict()
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self.removed_nodes = Counter()
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def __repr__(self):
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return f"OnlineTrajectron(# nodes: {len(self.nodes)}, device: {self.device}, hyperparameters: {str(self.hyperparams)}) "
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def _add_node_model(self, node):
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if node in self.nodes:
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raise ValueError('%s was already added to this graph!' % str(node))
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self.nodes.add(node)
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self.node_models_dict[node] = OnlineMultimodalGenerativeCVAE(self.env,
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node,
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self.model_registrar,
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self.hyperparams,
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self.device)
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def update_removed_nodes(self):
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for node in list(self.removed_nodes.keys()):
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if self.removed_nodes[node] >= len(self.hyperparams['edge_removal_filter']):
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del self.node_data[node]
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del self.removed_nodes[node]
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def _remove_node_model(self, node):
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if node not in self.nodes:
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raise ValueError('%s is not in this graph!' % str(node))
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self.nodes.remove(node)
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del self.node_models_dict[node]
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def set_environment(self, env, init_timestep=0):
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self.env = env
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self.scene_graph = SceneGraph(edge_radius=self.env.attention_radius)
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self.nodes.clear()
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self.node_data.clear()
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self.node_models_dict.clear()
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# Fast-forwarding ourselves to the initial timestep, without running any of the underlying models.
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for timestep in range(init_timestep + 1):
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self.incremental_forward(self.env.scenes[0].get_clipped_pos_dict(timestep, self.hyperparams['state']),
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run_models=False)
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def incremental_forward(self, new_inputs_dict,
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prediction_horizon=0,
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num_samples=0,
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robot_present_and_future=None,
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z_mode=False,
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gmm_mode=False,
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full_dist=False,
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all_z_sep=False,
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run_models=True):
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# The way this function works is by appending the new datapoints to the
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# ends of each of the LSTMs in the graph. Then, we recalculate the
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# encoder's output vector h_x and feed that into the decoder to sample new outputs.
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mode = ModeKeys.PREDICT
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# No grad since we're predicting always, as evidenced by the line above.
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with torch.no_grad():
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for node, new_input in new_inputs_dict.items():
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if node not in self.node_data:
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self.node_data[node] = RingBuffer(capacity=self.RING_CAPACITY,
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dtype=(float, len(self.state[node.type]['position'])))
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self.node_data[node].append(new_input)
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if node in self.removed_nodes:
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del self.removed_nodes[node]
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# Nodes in self.node_data that aren't in new_inputs_dict were just removed.
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newly_removed_nodes = (set(self.node_data.keys()) - set(self.removed_nodes.keys())) - set(
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new_inputs_dict.keys())
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# We update self.removed_nodes with the newly removed nodes as well as all existing removed nodes to get
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# the time since their last removal increased by one.
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self.removed_nodes.update(newly_removed_nodes | set(self.removed_nodes.keys()))
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# For any nodes that are older than the length of the edge_removal_filter, we can safely clear their data.
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self.update_removed_nodes()
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# Any remaining removed nodes that aren't yet old enough for data clearing simply have NaNs appended so
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# that when it's passed through the LSTMs, the hidden state keeps propagating but the input plays no role
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# (the NaNs get converted to zeros later on).
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for node in self.removed_nodes:
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self.node_data[node].append(np.full((1, 2), np.nan))
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for node in self.node_data:
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x = self.node_data[node][:, 0]
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y = self.node_data[node][:, 1]
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vx = derivative_of(x, self.env.scenes[0].dt)
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vy = derivative_of(y, self.env.scenes[0].dt)
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ax = derivative_of(vx, self.env.scenes[0].dt)
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ay = derivative_of(vy, self.env.scenes[0].dt)
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new_node_data = np.stack([x, y, vx, vy, ax, ay], axis=-1)
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node.overwrite_data(new_node_data,
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forward_in_time_on_next_overwrite=(self.node_data[node].shape[0]
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== self.RING_CAPACITY))
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temp_scene_dict = {k: v[:, 0:2] for k, v in self.node_data.items()}
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if not temp_scene_dict:
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new_scene_graph = SceneGraph(edge_radius=self.env.attention_radius)
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else:
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new_scene_graph = TemporalSceneGraph.create_from_temp_scene_dict(
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temp_scene_dict,
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self.env.attention_radius,
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duration=self.RING_CAPACITY,
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edge_addition_filter=self.hyperparams['edge_addition_filter'],
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edge_removal_filter=self.hyperparams['edge_removal_filter'],
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online=True).to_scene_graph(t=self.RING_CAPACITY - 1)
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if self.hyperparams['dynamic_edges'] == 'yes':
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new_nodes, removed_nodes, new_neighbors, removed_neighbors = new_scene_graph - self.scene_graph
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# Aside from updating the scene graph, this for loop updates the graph model
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# structure of all affected nodes.
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not_removed_nodes = [node for node in self.nodes if node not in removed_nodes]
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self.scene_graph = new_scene_graph
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for node in not_removed_nodes:
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self.node_models_dict[node].update_graph(new_scene_graph, new_neighbors, removed_neighbors)
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# These next 2 for loops add or remove entire node models.
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for node in new_nodes:
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if node.is_robot:
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continue
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self._add_node_model(node)
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self.node_models_dict[node].update_graph(new_scene_graph, new_neighbors, removed_neighbors)
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for node in removed_nodes:
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if node.is_robot:
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continue
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self._remove_node_model(node)
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# This actually updates the node models with the newly observed data.
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if run_models:
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inputs = dict()
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inputs_st = dict()
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inputs_np = dict()
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iter_list = list(self.node_models_dict.keys())
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if self.env.scenes[0].robot is not None:
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iter_list.append(self.env.scenes[0].robot)
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for node in iter_list:
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input_np = node.get(np.array([node.last_timestep, node.last_timestep]), self.state[node.type])
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_, std = self.env.get_standardize_params(self.state[node.type.name], node.type)
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std[0:2] = self.env.attention_radius[(node.type, node.type)]
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rel_state = np.zeros_like(input_np)
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rel_state[:, 0:2] = input_np[:, 0:2]
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input_st = self.env.standardize(input_np,
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self.state[node.type.name],
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node.type,
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mean=rel_state)
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self.rel_states[node] = rel_state
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# Converting NaNs to zeros.
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input_np[np.isnan(input_np)] = 0
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input_st[np.isnan(input_st)] = 0
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# Convert to torch tensors
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inputs[node] = torch.tensor(input_np, dtype=torch.float, device=self.device)
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inputs_st[node] = torch.tensor(input_st, dtype=torch.float, device=self.device)
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inputs_np[node] = input_np
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# We want tensors of shape (1, ph + 1, state_dim) where the first 1 is the batch size.
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if (self.hyperparams['incl_robot_node']
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and self.env.scenes[0].robot is not None
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and robot_present_and_future is not None):
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if len(robot_present_and_future.shape) == 2:
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robot_present_and_future = robot_present_and_future[np.newaxis, :]
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assert robot_present_and_future.shape[1] == prediction_horizon + 1
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for node in self.node_models_dict:
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self.node_models_dict[node].encoder_forward(inputs,
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inputs_st,
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inputs_np,
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robot_present_and_future)
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# If num_predicted_timesteps or num_samples == 0 then do not run the decoder at all,
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# just update the encoder LSTMs.
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if prediction_horizon == 0 or num_samples == 0:
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return
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return self.sample_model(prediction_horizon,
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num_samples,
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robot_present_and_future=robot_present_and_future,
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z_mode=z_mode,
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gmm_mode=gmm_mode,
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full_dist=full_dist,
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all_z_sep=all_z_sep)
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def _run_decoder(self, node,
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num_predicted_timesteps,
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num_samples,
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robot_present_and_future=None,
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z_mode=False,
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gmm_mode=False,
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full_dist=False,
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all_z_sep=False):
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model = self.node_models_dict[node]
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predictions = model.decoder_forward(num_predicted_timesteps,
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num_samples,
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robot_present_and_future=robot_present_and_future,
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z_mode=z_mode,
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gmm_mode=gmm_mode,
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full_dist=full_dist,
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all_z_sep=all_z_sep)
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predictions_uns = self.env.unstandardize(predictions.cpu().detach().numpy(),
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self.pred_state[node.type.name],
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node.type,
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mean=self.rel_states[node][..., 0:2])
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# Return will be of shape (batch_size, num_samples, num_predicted_timesteps, 2)
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return np.transpose(predictions_uns, (1, 0, 2, 3))
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def sample_model(self, num_predicted_timesteps,
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num_samples,
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robot_present_and_future=None,
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z_mode=False,
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gmm_mode=False,
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full_dist=False,
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all_z_sep=False):
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# Just start from the encoder output (minus the
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# robot future) and get num_samples of
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# num_predicted_timesteps-length trajectories.
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if num_predicted_timesteps == 0 or num_samples == 0:
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return
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mode = ModeKeys.PREDICT
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# We want tensors of shape (1, ph + 1, state_dim) where the first 1 is the batch size.
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if self.hyperparams['incl_robot_node'] and self.env.scenes[
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0].robot is not None and robot_present_and_future is not None:
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if len(robot_present_and_future.shape) == 2:
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robot_present_and_future = robot_present_and_future[np.newaxis, :]
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assert robot_present_and_future.shape[1] == num_predicted_timesteps + 1
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# No grad since we're predicting always, as evidenced by the line above.
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with torch.no_grad():
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predictions_dict = dict()
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for node in set(self.nodes) - set(self.removed_nodes.keys()):
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if node.is_robot:
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continue
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predictions_dict[node] = self._run_decoder(node, num_predicted_timesteps,
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num_samples,
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robot_present_and_future,
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z_mode,
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gmm_mode,
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full_dist,
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all_z_sep)
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return predictions_dict
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def forward(self, init_env,
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init_timestep,
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pos_dicts, # After the initial environment
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num_predicted_timesteps,
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num_samples,
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robot_present_and_future=None,
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z_mode=False,
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gmm_mode=False,
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full_dist=False,
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all_z_sep=False):
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# This is the standard forward prediction function,
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# if you have some historical data and just want to
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# predict forward some number of timesteps.
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# Setting us back to the initial scene graph we had.
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self.set_environment(init_env, init_timestep)
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# Looping through and applying updates to the model.
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for i in range(len(pos_dicts)):
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self.incremental_forward(pos_dicts[i])
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return self.sample_model(num_predicted_timesteps,
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num_samples,
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robot_present_and_future=robot_present_and_future,
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z_mode=z_mode,
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gmm_mode=gmm_mode,
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full_dist=full_dist,
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all_z_sep=all_z_sep)
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