10 examples of 'tensorflow load checkpoint' in Python

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333def load_checkpoint(sess, saver):
334 #ckpt = tf.train.get_checkpoint_state('save')
335 #if ckpt and ckpt.model_checkpoint_path:
336 #saver.restore(sess, tf.train.latest_checkpoint('save'))
337 ckpt = 'pretrain_g'+str(config['PRE_GEN_EPOCH'])+'_d'+str(config['PRE_DIS_EPOCH'])+'.ckpt'
338 saver.restore(sess, './save/' + ckpt)
339 print 'checkpoint {} loaded'.format(ckpt)
340 return
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147def load(saver, sess, ckpt_path):
148 '''Load trained weights.
149
150 Args:
151 saver: TensorFlow saver object.
152 sess: TensorFlow session.
153 ckpt_path: path to checkpoint file with parameters.
154 '''
155 ckpt = tf.train.get_checkpoint_state(ckpt_path)
156 if ckpt and ckpt.model_checkpoint_path:
157 # ckpt_name = os.path.basename(ckpt.model_checkpoint_path)
158 # saver.restore(sess, os.path.join(ckpt_path, ckpt_name))
159 saver.restore(sess, ckpt.model_checkpoint_path)
160 # print("Restored model parameters from {}".format(ckpt_name))
161 print("Restored model parameters")
162 return True
163 else:
164 return False
30def load_ckpt(saver, sess, ckpt_dir="train"):
31 """Load checkpoint from the ckpt_dir (if unspecified, this is train dir) and restore it to saver and sess, waiting 10 secs in the case of failure. Also returns checkpoint name."""
32 while True:
33 try:
34 latest_filename = "checkpoint_best" if ckpt_dir=="eval" else None
35 ckpt_dir = os.path.join(FLAGS.log_root, ckpt_dir)
36 ckpt_state = tf.train.get_checkpoint_state(ckpt_dir, latest_filename=latest_filename)
37 tf.logging.info('Loading checkpoint %s', ckpt_state.model_checkpoint_path)
38 saver.restore(sess, ckpt_state.model_checkpoint_path)
39 return ckpt_state.model_checkpoint_path
40 except:
41 tf.logging.info("Failed to load checkpoint from %s. Sleeping for %i secs...", ckpt_dir, 10)
42 time.sleep(10)
48def load_latest_checkpoint(self):
49 self.saver.restore(self.sess, tf.train.latest_checkpoint('checkpoints'))
155def load(self, checkpoint_dir):
156 """
157 To load the checkpoint use to test or pretrain
158 """
159 print("\nReading Checkpoints.....\n\n")
160 model_dir = "%s_%s_%s" % ("espcn", self.image_size,self.scale)# give the model name by label_size
161 checkpoint_dir = os.path.join(checkpoint_dir, model_dir)
162 ckpt = tf.train.get_checkpoint_state(checkpoint_dir)
163
164 # Check the checkpoint is exist
165 if ckpt and ckpt.model_checkpoint_path:
166 ckpt_path = str(ckpt.model_checkpoint_path) # convert the unicode to string
167 self.saver.restore(self.sess, os.path.join(os.getcwd(), ckpt_path))
168 print("\n Checkpoint Loading Success! %s\n\n"% ckpt_path)
169 else:
170 print("\n! Checkpoint Loading Failed \n\n")
211def restore_from_checkpoint(self):
212 """Returns scaffold function to restore parameters from checkpoint."""
213 def scaffold_fn():
214 """Loads pretrained model through scaffold function."""
215 tf.train.init_from_checkpoint(self._checkpoint,
216 {'/': self._checkpoint_prefix,})
217 return tf.train.Scaffold()
218 return scaffold_fn if self._checkpoint else None
9def get_checkpoint(logdir):
10 ''' Get the first checkpoint '''
11 ckpt = tf.train.get_checkpoint_state(logdir)
12 if ckpt:
13 return ckpt.model_checkpoint_path
14 else:
15 print('No checkpoint found')
16 return None
138def load_chkpt(saver, sess, chkptdir):
139 ckpt = tf.train.get_checkpoint_state(chkptdir)
140 if ckpt and ckpt.model_checkpoint_path:
141 ckpt_fn = ckpt.model_checkpoint_path.replace('//', '/')
142 print('[DEBUG] Loading checkpoint from %s' % ckpt_fn)
143 saver.restore(sess, ckpt_fn)
144 else:
145 raise NameError('[ERROR] No checkpoint found at: %s' % chkptdir)
279def restore_variables(checkpoint):
280 if not checkpoint:
281 return tf.no_op("restore_op")
282
283 # Load checkpoints
284 tf.logging.info("Loading %s" % checkpoint)
285 var_list = tf.train.list_variables(checkpoint)
286 reader = tf.train.load_checkpoint(checkpoint)
287 values = {}
288
289 for (name, shape) in var_list:
290 tensor = reader.get_tensor(name)
291 name = name.split(":")[0]
292 values[name] = tensor
293
294 var_list = tf.trainable_variables()
295 ops = []
296
297 for var in var_list:
298 name = var.name.split(":")[0]
299
300 if name in values:
301 tf.logging.info("Restore %s" % var.name)
302 ops.append(tf.assign(var, values[name]))
303
304 return tf.group(*ops, name="restore_op")
221def restore_checkpoint_if_exists(saver, sess, logdir):
222 """Looks for a checkpoint and restores the session from it if found.
223 Args:
224 saver: A tf.train.Saver for restoring the session.
225 sess: A TensorFlow session.
226 logdir: The directory to look for checkpoints in.
227 Returns:
228 True if a checkpoint was found and restored, False otherwise.
229 """
230 checkpoint = tf.train.get_checkpoint_state(logdir)
231 if checkpoint:
232 checkpoint_name = os.path.basename(checkpoint.model_checkpoint_path)
233 full_checkpoint_path = os.path.join(logdir, checkpoint_name)
234 saver.restore(sess, full_checkpoint_path)
235 return True
236 return False

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