def _eval_episode(self, file_name):
video_file = file_name[0]
info_file = file_name[1]
info = pd.read_csv(info_file)
nr_images = len(info)
previous_speed = info['linear_speed'][0]
general_mse = steer_mse = 0
# Determine steering angles and commands
helper = DatasetHelper(None, None, None, self.cfg.dataset)
frame_indices = range(len(info))
course = info['course']
linear_speed = info['linear_speed']
angles, cmds = helper.get_steer(frame_indices, course, linear_speed)
# Open video to read frames
vid = cv2.VideoCapture(video_file)
for index in range(nr_images):
ret, frame = vid.read()
if not ret:
print('Could not retrieve frame')
return None, None
gt_speed = linear_speed[index]
gt_steer = angles[index]
predicted_steer, predicted_speed = self.run_1step(
frame, previous_speed, cmds[index])
steer = (predicted_steer - gt_steer) * (predicted_steer - gt_steer)
speed = (predicted_speed - gt_speed) * (predicted_speed - gt_speed)
steer_mse += steer
general_mse += 0.05 * speed + 0.95 * steer
log.info("Frame number {}".format(index))
log.info("Steer: predicted {}, ground_truth {}".format(
predicted_steer, gt_steer))
log.info("Speed: predicted {}, ground_truth {}".format(
predicted_speed, gt_speed))
previous_speed = gt_speed
vid.release()
general_mse /= float(nr_images)
steer_mse /= float(nr_images)
return general_mse, steer_mse
"""
Attach a text label above each bar displaying its height
"""
for rect in rects:
height = rect.get_height()
ax.text(rect.get_x() + rect.get_width() / 2. - .13,
1.0 * height,
'{:10.2f}'.format(round(height, 2)),
ha='center',
va='bottom',
size=10)
mpl.rc('hatch', color="white")
with DatasetHelper() as dataset_helper:
figs, axs = plt.subplots(1, 2, False, True)
label_memory = ["Memória", "Arquivo"]
for j, mode in enumerate(["memory", "file"]):
ax = axs[j]
fig = figs
plans = [
x[0] for x in dataset_helper.select_data(
"SELECT distinct plan FROM result WHERE plan <> '{0}'".format(
"CPU_ORIGINAL"))
]
print(plans)
datasets = [
x[0] for x in dataset_helper.select_data(
'''SELECT distinct dataset FROM result''')
]
ha='center',
va='bottom',
**hfont2)
def format_name(name, threads):
if name == "CPU_SEQUENTIAL":
return "SPar(" + str(threads) + " THREAD" + (
"S)" if str(threads) != "1" else ")")
return name.replace("GPU_", "") + "(" + str(threads) + " GPU" + (
"S)" if threads > 1 else ")")
for dataset in datasets:
for mode in ["memory", "file"]:
with DatasetHelper() as db:
sql_formatted = sql.format(mode, dataset["dataset_name"])
print("SQL:{0}".format(sql_formatted))
data = [x for x in db.select_data(sql_formatted)]
labels = [format_name(x[1], x[2]) for x in data]
print(labels)
y_pos = np.arange(len(labels))
print(y_pos)
values = [x[3] / x[4] for x in data]
# tempo_medio_sequential / (tempo_medio_p + desvio_tempo_p) - tempo_medio_sequential / tempo_medio_p
y_error = [
(x[3] / (x[4] + x[5]) - x[3] / x[4]) if x[5] != None else 0
for x in data
]
print(y_error)