def load_model(self,
save_model_dir,
ckpt_file,
sess,
saver,
is_finetuning=False):
if (os.path.exists(save_model_dir)
and os_utils.chkpt_exists(save_model_dir)):
# Try to restore everything if possible
saver.restore(sess, ckpt_file)
print('Model Loaded Normally')
return 'Model Loaded Normally'
else:
print('Failed to Model Loaded Normally from ', ckpt_file)
exclusions = [
scope.strip() for scope in 'resnet_v2_50/logits'.split(',')
]
# exclusions = [scope.strip() for scope in '**'.split(',')]
variables_to_restore = []
for var in tf.contrib.slim.get_model_variables():
for exclusion in exclusions:
if var.op.name.startswith(exclusion):
break
else:
variables_to_restore.append(var)
# print(variables_to_restore)
init_fn = tf.contrib.framework.assign_from_checkpoint_fn(
self.cfg.imagenet__weights_filepath,
variables_to_restore,
ignore_missing_vars=False)
# init_fn = tf.contrib.framework.assign_from_checkpoint_fn(config.imagenet__weights_filepath)
init_fn(sess)
print('Some variables loaded from imagenet')
return 'Failed to Model Loaded Normally from ' + ckpt_file
def load_model(self,
save_model_dir,
ckpt_file,
sess,
saver,
load_logits=False):
if (os.path.exists(save_model_dir)
and os_utils.chkpt_exists(save_model_dir)):
# Try to restore everything if possible
saver.restore(sess, ckpt_file)
return 'Model Loaded Normally'
else:
if load_logits:
exclusions = [scope.strip() for scope in '**'.split(',')]
else:
exclusions = [
scope.strip()
for scope in 'global_step,MobilenetV1/Logits'.split(',')
]
variables_to_restore = []
for var in tf.contrib.slim.get_model_variables():
for exclusion in exclusions:
if var.op.name.startswith(exclusion):
break
else:
variables_to_restore.append(var)
# print(variables_to_restore)
init_fn = tf.contrib.framework.assign_from_checkpoint_fn(
self.cfg.imagenet__weights_filepath,
variables_to_restore,
ignore_missing_vars=False)
init_fn(sess)
return 'Failed to Model Loaded Normally from ' + str(
ckpt_file
) + '. Thus, Loaded Some variables loaded from imagenet'
def load_model(self,
save_model_dir,
ckpt_file,
sess,
saver,
load_logits=False):
if (os.path.exists(save_model_dir)
and os_utils.chkpt_exists(save_model_dir)):
# Try to restore everything if possible
saver.restore(sess, ckpt_file)
return 'Model weights initialized from {}'.format(ckpt_file)
else:
if (load_logits):
exclusions = [scope.strip() for scope in '**'.split(',')]
else:
exclusions = [
scope.strip() for scope in
'Logits,InceptionV1/Logits,InceptionV1/AuxLogits'.split(
',')
]
# exclusions = [scope.strip() for scope in '**'.split(',')]
variables_to_restore = []
for var in tf.contrib.slim.get_model_variables():
for exclusion in exclusions:
if var.op.name.startswith(exclusion):
break
else:
variables_to_restore.append(var)
# print(variables_to_restore)
init_fn = tf.contrib.framework.assign_from_checkpoint_fn(
self.cfg.imagenet_weights_filepath,
variables_to_restore,
ignore_missing_vars=False)
# init_fn = tf.contrib.framework.assign_from_checkpoint_fn(config.imagenet__weights_filepath)
init_fn(sess)
return 'Model weights initialized from imageNet'
def load_model(self,
save_model_dir,
ckpt_file,
sess,
saver,
load_logits=False):
# Try to initialize the network from a custom model
if (os.path.exists(save_model_dir)
and os_utils.chkpt_exists(save_model_dir)):
saver.restore(sess, ckpt_file)
return 'Model weights initialized from {}'.format(ckpt_file)
else: # if custom model is not provided, initialize the network weights using imageNet weights
if (load_logits):
exclusions = [scope.strip() for scope in '**'.split(',')]
else:
exclusions = [
scope.strip()
for scope in 'global_step,densenet161/logits'.split(',')
]
variables_to_restore = []
for var in tf.contrib.slim.get_model_variables():
for exclusion in exclusions:
if var.op.name.startswith(exclusion):
break
else:
variables_to_restore.append(var)
init_fn = tf.contrib.framework.assign_from_checkpoint_fn(
self.cfg.imagenet_weights_filepath,
variables_to_restore,
ignore_missing_vars=False)
init_fn(sess)
return 'Model weights initialized from imageNet'