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aim_va_agent.py
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aim_va_agent.py
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import os
import json
import datetime
import pathlib
import time
import carla
from collections import deque
import torch
import carla
import numpy as np
from PIL import Image
from leaderboard.autoagents import autonomous_agent
from team_code.planner import RoutePlanner
from aim_va.model import MultiTaskImageNetwork
from aim_va.class_converter import sub_classes
from aim_va.data import seg_to_one_hot
from mmseg.apis import inference_segmentor, init_segmentor
SAVE_PATH = os.environ.get('SAVE_PATH', None)
def get_entry_point():
return 'WaypointSegmentationAgent'
def crop_image(image, crop_width_factor):
"""
crop a PIL image, returning a channels-first numpy array.
"""
image = Image.fromarray(image)
(width, height) = (image.width, image.height)
image = np.asarray(image)
crop = int(crop_width_factor * width)
start_x = height//2 - crop//2
start_y = width//2 - crop//2
cropped_image = image[start_x:start_x+crop, start_y:start_y+crop]
return cropped_image[:,:]
def scale_image(image, scale):
"""
Scale an image
"""
image = Image.fromarray(image)
(width, height) = (image.width // scale, image.height // scale)
im_resized = image.resize((width, height), resample=Image.NEAREST)
image = np.asarray(im_resized)
return image
class WaypointSegmentationAgent(autonomous_agent.AutonomousAgent):
def setup(self, path_to_conf_file):
self.track = autonomous_agent.Track.SENSORS
self.config_path = path_to_conf_file
self.step = -1
self.wall_start = time.time()
self.initialized = False
args_file = open(os.path.join(path_to_conf_file, 'args.txt'), 'r')
self.args = json.load(args_file)
args_file.close()
self.input_buffer = {'seg_center': deque()}
self.converter = sub_classes[self.args['classes']]
num_segmentation_classes = len(np.unique(self.converter))
self.net = MultiTaskImageNetwork('cuda', num_segmentation_classes, self.args['pred_len'], 1)
self.net.load_state_dict(torch.load(os.path.join(path_to_conf_file, 'best_model.pth')))
self.net.cuda()
self.net.eval()
seg_checkpoint_path = os.path.join(path_to_conf_file, 'iter_80000.pth')
seg_conf_path = os.path.join(path_to_conf_file, 'fcn_r50-d8_512x1024_80k_carla_full.py')
self.segmentation_net = init_segmentor(seg_conf_path, seg_checkpoint_path)
self.save_path = None
if SAVE_PATH is not None:
now = datetime.datetime.now()
string = pathlib.Path(os.environ['ROUTES']).stem + '_'
string += '_'.join(map(lambda x: '%02d' % x, (now.month, now.day, now.hour, now.minute, now.second)))
self.save_path = pathlib.Path(os.environ['SAVE_PATH']) / string
self.save_path.mkdir(parents=True, exist_ok=False)
(self.save_path / 'seg_center').mkdir()
def _init(self):
self._route_planner = RoutePlanner(4.0, 50.0)
self._route_planner.set_route(self._global_plan, True)
self.initialized = True
def _get_position(self, tick_data):
gps = tick_data['gps']
gps = (gps - self._route_planner.mean) * self._route_planner.scale
return gps
def sensors(self):
return [
{
'type': 'sensor.camera.rgb',
'x': 1.3, 'y': 0.0, 'z':2.3,
'roll': 0.0, 'pitch': 0.0, 'yaw': 0.0,
'width': 800, 'height': 600, 'fov': 100,
'id': 'rgb_center'
},
{
'type': 'sensor.other.imu',
'x': 0.0, 'y': 0.0, 'z': 0.0,
'roll': 0.0, 'pitch': 0.0, 'yaw': 0.0,
'sensor_tick': 0.05,
'id': 'imu'
},
{
'type': 'sensor.other.gnss',
'x': 0.0, 'y': 0.0, 'z': 0.0,
'roll': 0.0, 'pitch': 0.0, 'yaw': 0.0,
'sensor_tick': 0.01,
'id': 'gps'
},
{
'type': 'sensor.speedometer',
'reading_frequency': 20,
'id': 'speed'
}
]
def tick(self, input_data):
self.step += 1
_rgb = crop_image(input_data['rgb_center'][1][:, :, :3], self.args['input_crop']) # from 800 * 600 to 512 * 512
result = inference_segmentor(self.segmentation_net, _rgb)
seg = result[0].astype(np.uint8)
gps = input_data['gps'][1][:2]
speed = input_data['speed'][1]['speed']
compass = input_data['imu'][1][-1]
self.speed = speed
result = {
'seg_center': scale_image(seg, 2), # from 512 * 512 to 256 * 256
'gps': gps,
'speed': speed,
'compass': compass,
}
pos = self._get_position(result)
next_wp, next_cmd = self._route_planner.run_step(pos)
result['next_command'] = next_cmd.value
theta = compass + np.pi/2
R = np.array([
[np.cos(theta), -np.sin(theta)],
[np.sin(theta), np.cos(theta)]
])
local_command_point = np.array([next_wp[0]-pos[0], next_wp[1]-pos[1]])
local_command_point = R.T.dot(local_command_point)
result['target_point'] = tuple(local_command_point)
return result
@torch.no_grad()
def run_step(self, input_data, timestamp):
if not self.initialized:
self._init()
tick_data = self.tick(input_data)
if self.step < self.args['seq_len']:
seg_center = seg_to_one_hot(np.array((Image.fromarray(tick_data['seg_center'], 'L'))), self.converter).unsqueeze(0)
self.input_buffer['seg_center'].append(seg_center.to('cuda', dtype=torch.float32))
control = carla.VehicleControl()
control.steer = 0.0
control.throttle = 0.0
control.brake = 0.0
return control
gt_velocity = torch.FloatTensor([tick_data['speed']]).to('cuda', dtype=torch.float32)
tick_data['target_point'] = [torch.FloatTensor([tick_data['target_point'][0]]),
torch.FloatTensor([tick_data['target_point'][1]])]
target_point = torch.stack(tick_data['target_point'], dim=1).to('cuda', dtype=torch.float32)
encoding = []
seg_center = seg_to_one_hot(np.array((Image.fromarray(tick_data['seg_center']))), self.converter).unsqueeze(0)
self.input_buffer['seg_center'].popleft()
self.input_buffer['seg_center'].append(seg_center.to('cuda', dtype=torch.float32))
encoding.append(self.net.image_encoder(list(self.input_buffer['seg_center'])))
pred_wp = self.net(encoding, target_point)
steer, throttle, brake = self.net.control_pid(pred_wp, gt_velocity)
if brake < 0.05: brake = 0.0
if throttle > brake: brake = 0.0
control = carla.VehicleControl()
control.steer = float(steer)
control.throttle = float(throttle)
control.brake = float(brake)
if SAVE_PATH is not None and self.step % 10 == 0:
self.save(tick_data)
return control
def save(self, tick_data):
frame = self.step // 10
Image.fromarray(tick_data['seg_center']).save(self.save_path / 'seg_center' / ('%04d.png' % frame))
def destroy(self):
del self.net
del self.segmentation_net