def setup(opts): with open(opts["checkpoint"], 'rb') as f: var_dict = pickle.load(f) image_placeholder = tf.placeholder(dtype=tf.float32, shape=[None,227,227,3]) global_feature_placeholder = network.vfn_rl(image_placeholder, var_dict) h_placeholder = tf.placeholder(dtype=tf.float32, shape=[None,1024]) c_placeholder = tf.placeholder(dtype=tf.float32, shape=[None,1024]) action, h, c = network.vfn_rl(image_placeholder, var_dict, global_feature=global_feature_placeholder, h=h_placeholder, c=c_placeholder) sess = tf.Session() return {"sess" : sess, "action" : action, "h" : h, "c" : c, "img_ph" : image_placeholder, "global_f_ph" : global_feature_placeholder, "h_ph" : h_placeholder, "c_ph" : c_placeholder }
import numpy as np import tensorflow as tf import skimage.io as io import network from actions import command2action, generate_bbox, crop_input global_dtype = tf.float32 with open('vfn_rl.pkl', 'rb') as f: var_dict = pickle.load(f) image_placeholder = tf.placeholder(dtype=global_dtype, shape=[None, 227, 227, 3]) global_feature_placeholder = network.vfn_rl(image_placeholder, var_dict) h_placeholder = tf.placeholder(dtype=global_dtype, shape=[None, 1024]) c_placeholder = tf.placeholder(dtype=global_dtype, shape=[None, 1024]) action, h, c = network.vfn_rl(image_placeholder, var_dict, global_feature=global_feature_placeholder, h=h_placeholder, c=c_placeholder) sess = tf.Session() def auto_cropping(origin_image): batch_size = len(origin_image) terminals = np.zeros(batch_size)