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()