def kitti_model_config():
"""Specify the parameters to tune below."""
mc = base_model_config('KITTI')
# mc.IMAGE_WIDTH = 1864 # half width 621
# mc.IMAGE_HEIGHT = 562 # half height 187
mc.IMAGE_WIDTH = 1248 # half width 621
mc.IMAGE_HEIGHT = 384 # half height 187
# mc.IMAGE_WIDTH = 621
# mc.IMAGE_HEIGHT = 187
mc.WEIGHT_DECAY = 0.0001
mc.PROB_THRESH = 0.005
mc.TOP_N_DETECTION = 64
mc.PLOT_PROB_THRESH = 0.4
mc.NMS_THRESH = 0.4
mc.LEARNING_RATE = 0.01
mc.MOMENTUM = 0.9
mc.DECAY_STEPS = 10000
mc.LR_DECAY_FACTOR = 0.5
mc.BATCH_SIZE = 20
mc.LOSS_COEF_BBOX = 5.0
mc.LOSS_COEF_CONF_POS = 75.0
mc.LOSS_COEF_CONF_NEG = 100.0
mc.LOSS_COEF_CLASS = 1.0
mc.MAX_GRAD_NORM = 1.0
mc.DATA_AUGMENTATION = True
mc.DRIFT_X = 150
mc.DRIFT_Y = 100
mc.ANCHOR_BOX = set_anchors(mc)
mc.ANCHORS = len(mc.ANCHOR_BOX)
mc.ANCHOR_PER_GRID = 9
mc.USE_DECONV = False
mc.EXCLUDE_HARD_EXAMPLES = False
return mc
def coco_config():
"""Specify the parameters to tune below."""
mc = base_model_config('COCO')
mc.IMAGE_WIDTH = 256 #1248
mc.IMAGE_HEIGHT = 256 #384
mc.BATCH_SIZE = 15
mc.WEIGHT_DECAY = 0.0001
mc.LEARNING_RATE = 0.001
mc.DECAY_STEPS = 10000
mc.MAX_GRAD_NORM = 1.0
mc.MOMENTUM = 0.9
mc.LR_DECAY_FACTOR = 0.5
mc.LOSS_COEF_BBOX = 5.0
mc.LOSS_COEF_CONF_POS = 75.0
mc.LOSS_COEF_CONF_NEG = 100.0
mc.LOSS_COEF_CLASS = 1.0
mc.PLOT_PROB_THRESH = 0.4
mc.NMS_THRESH = 0.4
mc.PROB_THRESH = 0.005
mc.TOP_N_DETECTION = 64
mc.DATA_AUGMENTATION = True
mc.DRIFT_X = 150
mc.DRIFT_Y = 100
mc.EXCLUDE_HARD_EXAMPLES = False
mc.ANCHOR_BOX = set_anchors(mc)
mc.ANCHORS = len(mc.ANCHOR_BOX)
mc.ANCHOR_PER_GRID = 9
return mc
import logging
import os
import math
import tensorflow as tf
import numpy as np
from config.config import base_model_config
from data.kitti_raw_manager import load_raw_forward_data, get_spherical_data
from plot.plot import plot_points
cfg = base_model_config()
def main(args=None):
if tf.gfile.Exists(cfg.log_dir):
tf.gfile.DeleteRecursively(cfg.log_dir)
tf.gfile.MakeDirs(cfg.log_dir)
drives = os.listdir(cfg.basedir)
frame = load_raw_forward_data('0001')[0]
spherical = get_spherical_data(frame)
plot_points(np.array(spherical))
if __name__ == '__main__':
tf.app.run()
