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)