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gqcnn_network.py
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gqcnn_network.py
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import numpy as np
<<<<<<< HEAD
import math
import json
import tensorflow as tf
import tensorflow.contrib.framework as tcf
=======
import json
import tensorflow as tf
>>>>>>> d50452e82653da6fc8a059471974df4f57f72e37
import os
EPS = 1e-8
def reset_graph():
if 'sess' in globals() and sess:
sess.close()
tf.reset_default_graph()
class GQCNN(object):
def __init__(self, batch_size=1, is_training=False, reuse=False, gpu_mode=False):
self.batch_size = batch_size
self.is_training = is_training
self.momentum_rate = 0.9
self.max_global_grad_norm = 100000000000
self.reuse = reuse
self.train_l2_regularizer = 0.0005
<<<<<<< HEAD
model_dir = "/home/scarab6/Desktop/gqcnn/models/gqcnn_example_pj"
#model_dir = "/home/cjg429/Desktop/gqcnn-master/data/training/data/training/mini-dexnet_fc_pj_10_02_18/grasps"
im_mean_filename = os.path.join(model_dir, 'im_mean.npy')
im_std_filename = os.path.join(model_dir, 'im_std.npy')
self._im_mean = np.load(im_mean_filename)
self._im_std = np.load(im_std_filename)
pose_mean_filename = os.path.join(model_dir, 'pose_mean.npy')
pose_std_filename = os.path.join(model_dir, 'pose_std.npy')
self._pose_mean = np.load(pose_mean_filename)
self._pose_std = np.load(pose_std_filename)
self._batch_size = 64
self._im_height = 96
self._im_width = 96
self._num_channels = 1
self._pose_dim = 1
self._weights = {}
self._load_model = True
self._pretrain = False
=======
model_dir = "/home/cjg429/Desktop/gqcnn-master/data/training/data/training/mini-dexnet_fc_pj_10_02_18/grasps"
im_mean_filename = os.path.join(model_dir, 'im_mean.npy')
im_std_filename = os.path.join(model_dir, 'im_std.npy')
self.im_mean = np.load(im_mean_filename)
self.im_std = np.load(im_std_filename)
pose_mean_filename = os.path.join(model_dir, 'pose_mean.npy')
pose_std_filename = os.path.join(model_dir, 'pose_std.npy')
self.pose_mean = np.load(pose_mean_filename)
self.pose_std = np.load(pose_std_filename)
>>>>>>> d50452e82653da6fc8a059471974df4f57f72e37
with tf.variable_scope('gq_cnn', reuse=self.reuse):
if not gpu_mode:
with tf.device('/cpu:0'):
tf.logging.info('Model using cpu.')
<<<<<<< HEAD
self.g = tf.Graph()
self._init_weights_file()
self._build_graph()
else:
tf.logging.info('Model using gpu.')
self.g = tf.Graph()
self._init_weights_file()
self._build_graph()
self._init_session()
def _build_graph(self):
#self.g = tf.Graph()
=======
self._build_graph()
else:
tf.logging.info('Model using gpu.')
self._build_graph()
self._init_session()
def _build_graph(self):
self.g = tf.Graph()
>>>>>>> d50452e82653da6fc8a059471974df4f57f72e37
with self.g.as_default():
self.input_im_node = tf.placeholder(tf.float32, shape=[None, 96, 96, 1])
self.input_pose_node = tf.placeholder(tf.float32, shape=[None, 1])
self.input_drop_rate_node = tf.placeholder_with_default(tf.constant(0.0), ())
self.input_label_node = tf.placeholder(tf.int32, shape=[None])
<<<<<<< HEAD
self.learning_rate = tf.placeholder_with_default(tf.constant(0.01), ())
self._var_list = []
def build_conv_layer(input_node, input_channels, filter_h, filter_w, num_filt, name, norm=False, pad='SAME'):
with tf.name_scope(name):
if '{}_weights'.format(name) in self._weights.keys():
convW = self._weights['{}_weights'.format(name)]
convb = self._weights['{}_bias'.format(name)]
else:
convW_shape = [filter_h, filter_w, input_channels, num_filt]
fan_in = filter_h * filter_w * input_channels
std = np.sqrt(2.0 / (fan_in))
convW = tf.Variable(tf.truncated_normal(convW_shape, stddev=std), name='{}_weights'.format(name))
convb = tf.Variable(tf.truncated_normal([num_filt], stddev=std), name='{}_bias'.format(name))
convh = tf.nn.conv2d(input_node, convW, strides=[1, 1, 1, 1], padding=pad) + convb
convh = tf.nn.relu(convh)
return convh
def build_fc_layer(input_node, fan_in, out_size, name, drop_rate, final_fc_layer=False):
if '{}_weights'.format(name) in self._weights.keys():
fcW = self._weights['{}_weights'.format(name)]
fcb = self._weights['{}_bias'.format(name)]
else:
std = np.sqrt(2.0 / (fan_in))
fcW = tf.Variable(tf.truncated_normal([fan_in, out_size], stddev=std), name='{}_weights'.format(name))
if final_fc_layer:
fcb = tf.Variable(tf.constant(0.0, shape=[out_size]), name='{}_bias'.format(name))
else:
fcb = tf.Variable(tf.truncated_normal([out_size], stddev=std), name='{}_bias'.format(name))
self._var_list.append(fcW)
self._var_list.append(fcb)
=======
self.learning_rate = tf.placeholder_with_default(tf.constant(0.0001), ())
def build_conv_layer(input_node, input_channels, filter_h, filter_w, num_filt, name, norm=False, pad='SAME'):
convW_shape = [filter_h, filter_w, input_channels, num_filt]
fan_in = filter_h * filter_w * input_channels
std = np.sqrt(2.0 / (fan_in))
convW = tf.Variable(tf.truncated_normal(convW_shape, stddev=std), name='{}_weights'.format(name))
convb = tf.Variable(tf.truncated_normal([num_filt], stddev=std), name='{}_bias'.format(name))
convh = tf.nn.conv2d(input_node, convW, strides=[1, 1, 1, 1], padding=pad) + convb
convh = tf.nn.relu(convh)
return convh
def build_fc_layer(input_node, fan_in, out_size, name, drop_rate, final_fc_layer=False):
std = np.sqrt(2.0 / (fan_in))
fcW = tf.Variable(tf.truncated_normal([fan_in, out_size], stddev=std), name='{}_weights'.format(name))
if final_fc_layer:
fcb = tf.Variable(tf.constant(0.0, shape=[out_size]), name='{}_bias'.format(name))
else:
fcb = tf.Variable(tf.truncated_normal([out_size], stddev=std), name='{}_bias'.format(name))
>>>>>>> d50452e82653da6fc8a059471974df4f57f72e37
if final_fc_layer:
fc = tf.matmul(input_node, fcW) + fcb
else:
fc = tf.nn.relu(tf.matmul(input_node, fcW) + fcb)
fc = tf.nn.dropout(fc, 1 - drop_rate)
return fc
def build_pc_layer(input_node, fan_in, out_size, name):
<<<<<<< HEAD
if '{}_weights'.format(name) in self._weights.keys():
pcW = self._weights['{}_weights'.format(name)]
pcb = self._weights['{}_bias'.format(name)]
else:
std = np.sqrt(2.0 / (fan_in))
pcW = tf.Variable(tf.truncated_normal([fan_in, out_size], stddev=std), name='{}_weights'.format(name))
pcb = tf.Variable(tf.truncated_normal([out_size], stddev=std), name='{}_bias'.format(name))
self._var_list.append(pcW)
self._var_list.append(pcb)
=======
std = np.sqrt(2.0 / (fan_in))
pcW = tf.Variable(tf.truncated_normal([fan_in, out_size], stddev=std), name='{}_weights'.format(name))
pcb = tf.Variable(tf.truncated_normal([out_size], stddev=std), name='{}_bias'.format(name))
>>>>>>> d50452e82653da6fc8a059471974df4f57f72e37
pc = tf.nn.relu(tf.matmul(input_node, pcW) + pcb)
return pc
def build_fc_merge(input_fc_node_1, input_fc_node_2, fan_in_1, fan_in_2, out_size, drop_rate, name):
<<<<<<< HEAD
if '{}_input_1_weights'.format(name) in self._weights.keys():
input1W = self._weights['{}_input_1_weights'.format(name)]
input2W = self._weights['{}_input_2_weights'.format(name)]
fcb = self._weights['{}_bias'.format(name)]
else:
std = np.sqrt(2.0 / (fan_in_1 + fan_in_2))
input1W = tf.Variable(tf.truncated_normal([fan_in_1, out_size], stddev=std), name='{}_input_1_weights'.format(name))
input2W = tf.Variable(tf.truncated_normal([fan_in_2, out_size], stddev=std), name='{}_input_2_weights'.format(name))
fcb = tf.Variable(tf.truncated_normal([out_size], stddev=std), name='{}_bias'.format(name))
self._var_list.append(input1W)
self._var_list.append(input2W)
self._var_list.append(fcb)
=======
std = np.sqrt(2.0 / (fan_in_1 + fan_in_2))
input1W = tf.Variable(tf.truncated_normal([fan_in_1, out_size], stddev=std), name='{}_input_1_weights'.format(name))
input2W = tf.Variable(tf.truncated_normal([fan_in_2, out_size], stddev=std), name='{}_input_2_weights'.format(name))
fcb = tf.Variable(tf.truncated_normal([out_size], stddev=std), name='{}_bias'.format(name))
>>>>>>> d50452e82653da6fc8a059471974df4f57f72e37
fc = tf.nn.relu(tf.matmul(input_fc_node_1, input1W) + tf.matmul(input_fc_node_2, input2W) + fcb)
fc = tf.nn.dropout(fc, 1 - drop_rate)
return fc
<<<<<<< HEAD
with tf.name_scope("im_stream"): # im_stream
im_stream = build_conv_layer(self.input_im_node, 1, 9, 9, 16, "conv1_1", pad="VALID")
im_stream = build_conv_layer(im_stream, 16, 5, 5, 16, "conv1_2", pad="VALID")
im_stream = tf.nn.max_pool(im_stream, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
im_stream = build_conv_layer(im_stream, 16, 5, 5, 16, "conv2_1", pad="VALID")
im_stream = build_conv_layer(im_stream, 16, 5, 5, 16, "conv2_2", pad="VALID")
im_stream = tf.nn.max_pool(im_stream, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
im_stream = tf.reshape(im_stream, (-1, 17 * 17 * 16))
im_stream = build_fc_layer(im_stream, 17 * 17 * 16, 64, "fc3", self.input_drop_rate_node, final_fc_layer=False)
with tf.name_scope("pose_stream"):# pose_stream
pose_stream = build_pc_layer(self.input_pose_node, 1, 16, "pc1")
with tf.name_scope("merge_stream"):# merge_stream
merge_stream = build_fc_merge(im_stream, pose_stream, 64, 16, 64, self.input_drop_rate_node, "fc4")
merge_stream = build_fc_layer(merge_stream, 64, 2, "fc5", 0, final_fc_layer=True)
self.output_tensor = tf.nn.softmax(merge_stream)
=======
# im_stream
im_stream = build_conv_layer(self.input_im_node, 1, 9, 9, 16, "conv1_1", pad="VALID")
im_stream = build_conv_layer(im_stream, 16, 5, 5, 16, "conv1_2", pad="VALID")
im_stream = tf.nn.max_pool(im_stream, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
im_stream = build_conv_layer(im_stream, 16, 5, 5, 16, "conv2_1", pad="VALID")
im_stream = build_conv_layer(im_stream, 16, 5, 5, 16, "conv2_2", pad="VALID")
im_stream = tf.nn.max_pool(im_stream, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
im_stream = tf.reshape(im_stream, (-1, 17 * 17 * 16))
im_stream = build_fc_layer(im_stream, 17 * 17 * 16, 128, "fc3", self.input_drop_rate_node, final_fc_layer=False)
# pose_stream
pose_stream = build_pc_layer(self.input_pose_node, 1, 16, "pc1")
# merge_stream
merge_stream = build_fc_merge(im_stream, pose_stream, 128, 16, 128, self.input_drop_rate_node, "fc4")
merge_stream = build_fc_layer(merge_stream, 128, 2, "fc5", 0, final_fc_layer=True)
self.output_tensor = tf.nn.softmax(merge_stream)
'''
h = tf.layers.conv2d(self.input_im_node, 16, 9, strides=1, padding='valid', activation=tf.nn.relu, name="conv1_1") # 88 X 88 X 16
h = tf.layers.conv2d(h, 16, 5, strides=1, padding='valid', activation=tf.nn.relu, name="conv1_2") # 84 X 84 X 16
h = tf.layers.max_pooling2d(h, (2, 2), (2, 2), padding='same') # 42 X 42 X 16
h = tf.layers.conv2d(h, 16, 5, strides=1, padding='valid', activation=tf.nn.relu, name="conv2_1") # 38 X 38 X 16
h = tf.layers.conv2d(h, 16, 5, strides=1, padding='valid', activation=tf.nn.relu, name="conv2_2") # 34 X 34 X 16
h = tf.layers.max_pooling2d(h, (2, 2), (2, 2), padding='same') # 17 X 17 X 16
h = tf.reshape(h, (-1, 17 * 17 * 16))
h = tf.layers.dense(h, 128, activation=tf.nn.relu, name="fc3")
h = tf.nn.dropout(h, 1 - self.input_drop_rate_node)
h_pose = tf.layers.dense(self.input_pose_node, 16, activation=tf.nn.relu, name="pc1")
#h_merge = tf.concat((h, h_pose), axis=1)
#h_merge = tf.layers.dense(h_merge, 128, activation=tf.nn.relu, name="fc4")
h_merge = tf.layers.dense(h, 128, name="fc4_input_1")
h_merge = tf.add(h_merge, tf.layers.dense(h_pose, 128, use_bias=False, name="fc4_input_2"))
h_merge = tf.nn.relu(h_merge)
h_merge = tf.nn.dropout(h_merge, 1 - self.input_drop_rate_node)
self.output_tensor = tf.layers.dense(h_merge, 2, activation=tf.nn.softmax, name="fc5")'''
>>>>>>> d50452e82653da6fc8a059471974df4f57f72e37
# train ops
if self.is_training:
self.global_step = tf.Variable(0, name='global_step', trainable=False)
self.unregularized_loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(_sentinel=None, labels=self.input_label_node, logits=self.output_tensor, name=None))
self.loss = self.unregularized_loss
t_vars = tf.trainable_variables()
self.regularizers = tf.nn.l2_loss(t_vars[0])
for var in t_vars[1:]:
self.regularizers += tf.nn.l2_loss(var)
self.loss += self.train_l2_regularizer * self.regularizers
<<<<<<< HEAD
self.optimizer = tf.train.MomentumOptimizer(self.learning_rate, self.momentum_rate)
gradients, variables = zip(*self.optimizer.compute_gradients(self.loss, var_list=self._var_list))
gradients, global_grad_norm = tf.clip_by_global_norm(gradients, self.max_global_grad_norm)
# generate op to apply gradients
self.train_op = self.optimizer.apply_gradients(zip(gradients, variables), global_step=self.global_step)
#self.optimizer = tf.train.MomentumOptimizer(self.learning_rate, self.momentum_rate)
#grads = self.optimizer.compute_gradients(self.loss) # can potentially clip gradients here.
#self.train_op = self.optimizer.apply_gradients(grads, global_step=self.global_step, name='train_step')
=======
#self.lr = tf.Variable(self.learning_rate, trainable=False)
self.optimizer = tf.train.MomentumOptimizer(self.learning_rate, self.momentum_rate)
#self.optimizer = tf.train.AdamOptimizer(self.learning_rate)
grads = self.optimizer.compute_gradients(self.loss) # can potentially clip gradients here.
self.train_op = self.optimizer.apply_gradients(grads, global_step=self.global_step, name='train_step')
>>>>>>> d50452e82653da6fc8a059471974df4f57f72e37
'''
# training
self.lr = tf.Variable(self.learning_rate, trainable=False)
self.optimizer = tf.train.MomentumOptimizer(self.lr, self.momentum_rate)
grads = self.optimizer.compute_gradients(self.loss)
grads, global_grad_norm = tf.clip_by_global_norm(grads, self.max_global_grad_norm)
self.train_op = self.optimizer.apply_gradients(grads, global_step=self.global_step, name='train_step')'''
# initialize vars
self.init = tf.global_variables_initializer()
t_vars = tf.trainable_variables()
self.assign_ops = {}
for var in t_vars:
#if var.name.startswith('conv_vae'):
pshape = var.get_shape()
pl = tf.placeholder(tf.float32, pshape, var.name[:-2]+'_placeholder')
assign_op = var.assign(pl)
self.assign_ops[var] = (assign_op, pl)
<<<<<<< HEAD
def _init_weights_file(self):
if self._load_model == False:
return
if self._pretrain:
self._base_layer_names = ["conv1_1_weights", "conv1_1_bias",
"conv1_2_weights", "conv1_2_bias",
"conv2_1_weights", "conv2_1_bias",
"conv2_2_weights", "conv2_2_bias"]
model_dir = "/home/scarab6/Desktop/gqcnn/models/gqcnn_example_pj"
ckpt_file = os.path.join(model_dir, 'model.ckpt')
with self.g.as_default():
reader = tf.train.NewCheckpointReader(ckpt_file)
ckpt_vars = tcf.list_variables(ckpt_file)
full_var_names = []
short_names = []
self._weights = {}
for variable, shape in ckpt_vars:
full_var_names.append(variable)
short_names.append(variable.split('/')[-1])
# load variables
if self._pretrain:
for full_var_name, short_name in zip(full_var_names, short_names):
if short_name in self._base_layer_names:
self._weights[short_name] = tf.Variable(reader.get_tensor(full_var_name), name=full_var_name)
return
for full_var_name, short_name in zip(full_var_names, short_names):
self._weights[short_name] = tf.Variable(reader.get_tensor(full_var_name), name=full_var_name)
=======
>>>>>>> d50452e82653da6fc8a059471974df4f57f72e37
def _init_session(self):
"""Launch TensorFlow session and initialize variables"""
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
self.sess = tf.Session(config=config, graph=self.g)
self.sess.run(self.init)
def close_session(self):
""" Close TensorFlow session """
self.sess.close()
<<<<<<< HEAD
'''def predict(self, image_arr, pose_arr):
# setup for prediction
num_batches = math.ceil(image_arr.shape[0] / self._batch_size)
num_images = image_arr.shape[0]
num_poses = pose_arr.shape[0]
output_arr = None
if num_images != num_poses:
raise ValueError('Must provide same number of images as poses!')
self._input_im_arr = (image_arr - self._im_mean) / self._im_std
self._input_pose_arr = (pose_arr - self._pose_mean) / self._pose_std
return self.sess.run(self.output_tensor, feed_dict={self.input_im_node: self._input_im_arr, self.input_pose_node: self._input_pose_arr})
#self._input_im_arr = np.zeros((self._batch_size, self._im_height, self._im_width, self._num_channels))
#self._input_pose_arr = np.zeros((self._batch_size, self._pose_dim))
# predict in batches
with self.g.as_default():
if self.sess is None:
raise RuntimeError('No TF Session open. Please call open_session() first.')
i = 0
batch_idx = 0
while i < num_images:
batch_idx += 1
dim = min(self._batch_size, num_images - i)
cur_ind = i
end_ind = cur_ind + dim
self._input_im_arr = (image_arr - self._im_mean) / self._im_std
self._input_pose_arr = (pose_arr - self._pose_mean) / self._pose_std
#self._input_im_arr[:dim, ...] = (image_arr[cur_ind:end_ind, ...] - self._im_mean) / self._im_std
#self._input_pose_arr[:dim, :] = (pose_arr[cur_ind:end_ind, :] - self._pose_mean) / self._pose_std
gqcnn_output = self.sess.run(self.output_tensor,
feed_dict={self.input_im_node: self._input_im_arr,
self.input_pose_node: self._input_pose_arr})
# allocate output tensor
if output_arr is None:
output_arr = np.zeros([num_images] + list(gqcnn_output.shape[1:]))
output_arr[cur_ind:end_ind, :] = gqcnn_output[:dim, :]
i = end_ind
return output_arr'''
def predict(self, image_arr, pose_arr):
input_im_arr = (image_arr - self._im_mean) / self._im_std
input_pose_arr = (pose_arr - self._pose_mean) / self._pose_std
return self.sess.run(self.output_tensor, feed_dict={self.input_im_node: input_im_arr, self.input_pose_node: input_pose_arr})
=======
def predict(self, image_arr, pose_arr):
input_im_arr = (image_arr - self.im_mean) / self.im_std
input_pose_arr = (pose_arr - self.pose_mean) / self.pose_std
return self.sess.run(self.output_tensor, feed_dict={self.input_im_node: input_im_arr, self.input_pose_node: pose_arr})
>>>>>>> d50452e82653da6fc8a059471974df4f57f72e37
def get_model_params(self):
# get trainable params.
model_names = []
model_params = []
model_shapes = []
with self.g.as_default():
t_vars = tf.trainable_variables()
for var in t_vars:
#if var.name.startswith('conv_vae'):
param_name = var.name
p = self.sess.run(var)
model_names.append(param_name)
params = np.round(p*10000).astype(np.int).tolist()
model_params.append(params)
model_shapes.append(p.shape)
return model_params, model_shapes, model_names
def get_random_model_params(self, stdev=0.5):
# get random params.
_, mshape, _ = self.get_model_params()
rparam = []
for s in mshape:
#rparam.append(np.random.randn(*s)*stdev)
rparam.append(np.random.standard_cauchy(s)*stdev) # spice things up
return rparam
<<<<<<< HEAD
=======
'''def set_model_params_with_ckpt(self, ckpt_dir):
with self.g.as_default():
t_vars = tf.trainable_variables()
idx = 0
for var in t_vars:
ckpt_file = os.path.join(ckpt_dir, 'model.ckpt')
reader = tf.train.NewCheckpointReader(ckpt_file)
ckpt_vars = tf.contrib.framework.list_variables(ckpt_file)
for variable, shape in ckpt_vars:
short_name = variable.split('/')[-1]
for var in t_vars:
if var.name[:-2] == short_name:
p = tf.Variable(reader.get_tensor(variable), name=variable)
var.assign(p)
idx += 1'''
>>>>>>> d50452e82653da6fc8a059471974df4f57f72e37
def set_model_params(self, params):
with self.g.as_default():
t_vars = tf.trainable_variables()
idx = 0
for var in t_vars:
#if var.name.startswith('conv_vae'):
pshape = tuple(var.get_shape().as_list())
p = np.array(params[idx])
assert pshape == p.shape, "inconsistent shape"
assign_op, pl = self.assign_ops[var]
self.sess.run(assign_op, feed_dict={pl.name: p/10000.})
idx += 1
def load_json(self, jsonfile='gqcnn.json'):
with open(jsonfile, 'r') as f:
params = json.load(f)
self.set_model_params(params)
def save_json(self, jsonfile='gqcnn.json'):
model_params, model_shapes, model_names = self.get_model_params()
qparams = []
for p in model_params:
qparams.append(p)
with open(jsonfile, 'wt') as outfile:
json.dump(qparams, outfile, sort_keys=True, indent=0, separators=(',', ': '))
def set_random_params(self, stdev=0.5):
rparam = self.get_random_model_params(stdev)
self.set_model_params(rparam)
def save_model(self, model_save_path):
sess = self.sess
with self.g.as_default():
saver = tf.train.Saver(tf.global_variables())
<<<<<<< HEAD
saver.save(sess, os.path.join(model_save_path, 'model.ckpt'))
'''checkpoint_path = os.path.join(model_save_path, 'model.ckpt')
tf.logging.info('saving model %s.', checkpoint_path)
saver.save(sess, checkpoint_path, 0) # just keep one'''
def load_model(self, model_save_path):
sess = self.sess
with self.g.as_default():
saver = tf.train.Saver(tf.global_variables())
saver.restore(sess, os.path.join(model_save_path, 'model.ckpt'))
=======
checkpoint_path = os.path.join(model_save_path, 'gqcnn')
tf.logging.info('saving model %s.', checkpoint_path)
saver.save(sess, checkpoint_path, 0) # just keep one
>>>>>>> d50452e82653da6fc8a059471974df4f57f72e37
def load_checkpoint(self, checkpoint_path):
sess = self.sess
with self.g.as_default():
saver = tf.train.Saver(tf.global_variables())
ckpt = tf.train.get_checkpoint_state(checkpoint_path)
print('loading model', ckpt.model_checkpoint_path)
tf.logging.info('Loading model %s.', ckpt.model_checkpoint_path)
saver.restore(sess, ckpt.model_checkpoint_path)