switch to not predict video, but training data
This commit is contained in:
Ruben van de Ven
parent
f3ac903555
commit
2171dd459a
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@ -214,13 +214,13 @@ class PredictionServer:
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prev_run_time = 0
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while self.is_running.is_set():
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timestep += 1
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this_run_time = time.time()
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logger.debug(f'test {prev_run_time - this_run_time}')
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time.sleep(max(0, prev_run_time - this_run_time + .5))
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prev_run_time = time.time()
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# for timestep in range(init_timestep + 1, eval_scene.timesteps):
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# input_dict = eval_scene.get_clipped_input_dict(timestep, hyperparams['state'])
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# this_run_time = time.time()
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# logger.debug(f'test {prev_run_time - this_run_time}')
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# time.sleep(max(0, prev_run_time - this_run_time + .5))
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# prev_run_time = time.time()
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# TODO: see process_data.py on how to create a node, the provide nodes + incoming data columns
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# data_columns = pd.MultiIndex.from_product([['position', 'velocity', 'acceleration'], ['x', 'y']])
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# x = node_values[:, 0]
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@ -239,62 +239,66 @@ class PredictionServer:
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# node_data = pd.DataFrame(data_dict, columns=data_columns)
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# node = Node(node_type=env.NodeType.PEDESTRIAN, node_id=node_id, data=node_data)
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if self.config.predict_training_data:
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input_dict = eval_scene.get_clipped_input_dict(timestep, hyperparams['state'])
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else:
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data = self.trajectory_socket.recv()
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frame: Frame = pickle.loads(data)
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trajectory_data = frame.trajectories # TODO: properly refractor
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# trajectory_data = json.loads(data)
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logger.debug(f"Receive {trajectory_data}")
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data = self.trajectory_socket.recv()
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frame: Frame = pickle.loads(data)
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trajectory_data = frame.trajectories # TODO: properly refractor
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# trajectory_data = json.loads(data)
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logger.debug(f"Receive {trajectory_data}")
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# class FakeNode:
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# def __init__(self, node_type: NodeType):
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# self.type = node_type
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# class FakeNode:
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# def __init__(self, node_type: NodeType):
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# self.type = node_type
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input_dict = {}
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for identifier, trajectory in trajectory_data.items():
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# if len(trajectory['history']) < 7:
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# # TODO: these trajectories should still be in the output, but without predictions
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# continue
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input_dict = {}
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for identifier, trajectory in trajectory_data.items():
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# if len(trajectory['history']) < 7:
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# # TODO: these trajectories should still be in the output, but without predictions
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# continue
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# TODO: modify this into a mapping function between JS data an the expected Node format
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# node = FakeNode(online_env.NodeType.PEDESTRIAN)
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history = [[h['x'], h['y']] for h in trajectory['history']]
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history = np.array(history)
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x = history[:, 0]
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y = history[:, 1]
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# TODO: calculate dt based on input
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vx = derivative_of(x, 0.2) #eval_scene.dt
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vy = derivative_of(y, 0.2)
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ax = derivative_of(vx, 0.2)
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ay = derivative_of(vy, 0.2)
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# TODO: modify this into a mapping function between JS data an the expected Node format
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# node = FakeNode(online_env.NodeType.PEDESTRIAN)
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history = [[h['x'], h['y']] for h in trajectory['history']]
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history = np.array(history)
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x = history[:, 0]
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y = history[:, 1]
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# TODO: calculate dt based on input
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vx = derivative_of(x, 0.2) #eval_scene.dt
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vy = derivative_of(y, 0.2)
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ax = derivative_of(vx, 0.2)
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ay = derivative_of(vy, 0.2)
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data_dict = {('position', 'x'): x[:],
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('position', 'y'): y[:],
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('velocity', 'x'): vx[:],
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('velocity', 'y'): vy[:],
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('acceleration', 'x'): ax[:],
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('acceleration', 'y'): ay[:]}
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data_columns = pd.MultiIndex.from_product([['position', 'velocity', 'acceleration'], ['x', 'y']])
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data_dict = {('position', 'x'): x[:],
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('position', 'y'): y[:],
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('velocity', 'x'): vx[:],
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('velocity', 'y'): vy[:],
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('acceleration', 'x'): ax[:],
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('acceleration', 'y'): ay[:]}
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data_columns = pd.MultiIndex.from_product([['position', 'velocity', 'acceleration'], ['x', 'y']])
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node_data = pd.DataFrame(data_dict, columns=data_columns)
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node = Node(
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node_type=online_env.NodeType.PEDESTRIAN,
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node_id=identifier,
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data=node_data,
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first_timestep=timestep
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)
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node_data = pd.DataFrame(data_dict, columns=data_columns)
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node = Node(
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node_type=online_env.NodeType.PEDESTRIAN,
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node_id=identifier,
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data=node_data,
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first_timestep=timestep
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)
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input_dict[node] = np.array([x[-1],y[-1],vx[-1],vy[-1],ax[-1],ay[-1]])
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input_dict[node] = np.array([x[-1],y[-1],vx[-1],vy[-1],ax[-1],ay[-1]])
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# print(input_dict)
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# print(input_dict)
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if not len(input_dict):
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# skip if our input is empty
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# TODO: we want to send out empty result...
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# And want to update the network
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if not len(input_dict):
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# skip if our input is empty
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# TODO: we want to send out empty result...
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data = json.dumps({})
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self.prediction_socket.send_string(data)
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data = json.dumps({})
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self.prediction_socket.send_string(data)
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continue
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continue
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maps = None
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if hyperparams['use_map_encoding']:
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@ -311,12 +315,14 @@ class PredictionServer:
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# robot_present_and_future += adjustment
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start = time.time()
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dists, preds = trajectron.incremental_forward(input_dict,
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maps,
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prediction_horizon=20, # TODO: make variable
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num_samples=2, # TODO: make variable
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robot_present_and_future=robot_present_and_future,
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full_dist=True)
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with warnings.catch_warnings():
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warnings.simplefilter('ignore') # prevent deluge of UserWarning from torch's rrn.py
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dists, preds = trajectron.incremental_forward(input_dict,
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maps,
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prediction_horizon=25, # TODO: make variable
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num_samples=20, # TODO: make variable
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robot_present_and_future=robot_present_and_future,
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full_dist=True)
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end = time.time()
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logger.debug("took %.2f s (= %.2f Hz) w/ %d nodes and %d edges" % (end - start,
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1. / (end - start), len(trajectron.nodes),
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@ -365,7 +371,10 @@ class PredictionServer:
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}
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data = json.dumps(response)
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logger.info(f"Total frame delay = {time.time()-frame.time}s ({len(trajectron.nodes)} nodes & {trajectron.scene_graph.get_num_edges()} edges. Trajectron: {end - start}s)")
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if self.config.predict_training_data:
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logger.info(f"Frame prediction: {len(trajectron.nodes)} nodes & {trajectron.scene_graph.get_num_edges()} edges. Trajectron: {end - start}s")
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else:
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logger.info(f"Total frame delay = {time.time()-frame.time}s ({len(trajectron.nodes)} nodes & {trajectron.scene_graph.get_num_edges()} edges. Trajectron: {end - start}s)")
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self.prediction_socket.send_string(data)
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logger.info('Stopping')
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