saver = tf.train.Saver()
# If the model is new initialize variables, else restore the session
if init:
sess.run(tf.global_variables_initializer())
print("Initializing model...")
else:
# if we are initializing with the weights from another model load it
if init_model is not None:
# initialize the global variables
sess.run(tf.global_variables_initializer())
# create the initializer function to initialize the weights
init_fn = load_weights(init_model,
exclude=[
"conv5", "bn5", "fc1", "fc1_bb",
"fc2_bn", "fc3", "fc3_bn", "fc2",
"fc_logits", "global_step"
])
# run the initializer
init_fn(sess)
# reset the global step
initial_global_step = global_step.assign(0)
sess.run(initial_global_step)
print("Initializing weights from model", init_model)
# reset init model so we don't do this again
init_model = None
# otherwise load this model
# create the saver
saver = tf.train.Saver()
# If the model is new initialize variables, else restore the session
if init:
sess.run(tf.global_variables_initializer())
print("Initializing model...")
else:
# if we are initializing with the weights from another model load it
if init_model is not None:
# initialize the global variables
sess.run(tf.global_variables_initializer())
# create the initializer function to initialize the weights
init_fn = load_weights(init_model, exclude=["up_conv5", "accuracy", "up_conv4", "up_conv3","up_conv2","up_conv1","conv9", "bn9", "conv8", "bn8","conv6", "bn6", "conv7", "bn7","fcn_logits", "logits", "global_step"])
# init_fn = load_weights(init_model, include=["conv1", "conv1.1", "conv1.2", "conv2.1", "conv2.2", "conv3.1", "conv3.2", "conv4", "conv5"], exclude=["conv1.1/bias","fc1", "up_conv5", "up_conv4", "up_conv3","up_conv2","up_conv1","conv9", "bn9", "conv8", "bn8","conv6", "bn6", "conv7", "bn7","fcn_logits", "logits", "bn_fc2", "bn_fc1", "fc2", "global_step"])
# run the initializer
init_fn(sess)
# reset the global step
initial_global_step = global_step.assign(0)
sess.run(initial_global_step)
print("Initializing weights from model", init_model)
# reset init model so we don't do this again
init_model = None
elif restore_model is not None:
saver.restore(sess, './model/' + restore_model + '.ckpt')
print("Restoring model", restore_model)
# create the saver
saver = tf.train.Saver()
# If the model is new initialize variables, else restore the session
if init:
sess.run(tf.global_variables_initializer())
print("Initializing model...")
else:
# if we are initializing with the weights from another model load it
if init_model is not None:
# initialize the global variables
sess.run(tf.global_variables_initializer())
# create the initializer function to initialize the weights
init_fn = load_weights(init_model,
exclude=["fc1", "fc2", "global_step"])
# run the initializer
init_fn(sess)
# reset the global step
initial_global_step = global_step.assign(0)
sess.run(initial_global_step)
print("Initializing weights from model", init_model)
# reset init model so we don't do this again
init_model = None
# otherwise load this model
else:
saver.restore(sess, './model/' + model_name + '.ckpt')
saver = tf.train.Saver()
sess.run(tf.local_variables_initializer())
# If the model is new initialize variables, else restore the session
if init:
sess.run(tf.global_variables_initializer())
print("Initializing model...")
else:
# if we are initializing with the weights from another model load it
if init_model is not None:
# initialize the global variables
sess.run(tf.global_variables_initializer())
# create the initializer function to initialize the weights
# init_fn = load_weights(init_model, exclude=['bottleneck_5.1',"up_conv6", "conv_up_conv6", "bn_up_conv6", "bn_unpool5.1", "up_conv5",'bn_unpool4.1', "up_conv4", "bn_bottleneck_5.1", 'bottleneck_5.2', 'bn_bottleneck_5.2', 'bn_bottleneck_4.1', 'bottleneck_4.2', 'bn_bottleneck_4.2', 'bottleneck_4.1', 'bn_bottleneck_4.1', 'bn_unpool1.1', "up_conv3", "bn_upsample_3", "upsample_3", "up_conv6", "up_conv5", "bn_unpool4.1", 'up_conv4', 'bn_up_conv3', 'up_conv3', 'bn_upsample_2', 'upsample_2', 'bn_bottleneck_4.2', 'bottleneck_4.2', 'bn_bottleneck_4.1', 'bottleneck_4.1', 'bn_bottleneck_5.2', 'bn_bottleneck_5.1', 'bottleneck_5.2', 'bottleneck_5.1', 'bn_upsample_1', 'upsample_1'])
init_fn = load_weights(init_model, exclude=['conv5.3', 'bn_conv5.3', "upsample_5", 'conv5.3',"bn5.3",'conv5.2',"bn5.2", "bn_fc_fc_1","bn_fc_fc_2","fc_fc_1","fc_fc_2",'up_conv3', 'bn_up_conv3', "bn_unpool_4.1", "unpool_4.1", 'upsample_1', 'bn_upsample_1', 'up_conv1', 'bn_up_conv1', 'bottleneck_5.1', 'bn_bottleneck_5.1', 'bottleneck_5.2', 'bn_bottleneck_5.2', 'upsample_2', 'bn_upsample_2', 'up_conv2', 'bn_up_conv2', 'bottleneck_4.1', 'bn_bottleneck_4.1', 'bottleneck_4.2', 'bn_bottleneck_4.2', 'up_conv3', 'bn_up_conv3', 'up_conv4', 'bn_up_conv4', "conv_up_conv5", "bn_up_conv5", "conv_up_conv6", "bn_up_conv6", "conv_up_conv7", "bn_up_conv7", 'upsample_4', 'bn_upsample_4', "logits"])
# init_fn = load_weights(init_model, exclude=['up_conv1', "bn_unpool1.1", "conv_fc_1", "bn_fc_1"])
# run the initializer
init_fn(sess)
# reset the global step
initial_global_step = global_step.assign(0)
sess.run(initial_global_step)
print("Initializing weights from model", init_model)
# reset init model so we don't do this again
init_model = None
elif restore_model is not None:
# create the saver
saver = tf.train.Saver()
# If the model is new initialize variables, else restore the session
if init:
sess.run(tf.global_variables_initializer())
print("Initializing model...")
else:
# if we are initializing with the weights from another model load it
if init_model is not None:
# initialize the global variables
sess.run(tf.global_variables_initializer())
# create the initializer function to initialize the weights
init_fn = load_weights(init_model, exclude=["fc1", "fc1_bn", "fc2_bn", "fc2", "fc_logits", "global_step"])
# run the initializer
init_fn(sess)
# reset the global step
initial_global_step = global_step.assign(0)
sess.run(initial_global_step)
print("Initializing weights from model", init_model)
# reset init model so we don't do this again
init_model = None
# otherwise load this model
else:
saver.restore(sess, './model/' + model_name + '.ckpt')
# create the saver
saver = tf.train.Saver()
# If the model is new initialize variables, else restore the session
if init:
sess.run(tf.global_variables_initializer())
print("Initializing model...")
else:
# if we are initializing with the weights from another model load it
if init_model is not None:
# initialize the global variables
sess.run(tf.global_variables_initializer())
# create the initializer function to initialize the weights
init_fn = load_weights(init_model, exclude=["fc3", "bn_conv6", "up_conv7", "bn_up_conv7", "conv_conv6","up_conv2","up_conv5","up_conv6", "accuracy", "up_conv4", "up_conv3", "global_step"])
# run the initializer
init_fn(sess)
## reload some weights from one checkpoint and some from a different one
# init_fn = load_weights("model_s3.2.1.48m.12", exclude=["conv_up_conv7", "bn_up_conv7", "fc3", "conv5", "accuracy", "bn5"])
# init_fn(sess)
#
# init_fn = load_weights("model_s3.2.0.47m.12", include=["conv5", "bn5"])
# init_fn(sess)
#
# # reset the global step
# initial_global_step = global_step.assign(0)
# sess.run(initial_global_step)
# create the saver
saver = tf.train.Saver()
sess.run(tf.local_variables_initializer())
# If the model is new initialize variables, else restore the session
if init:
sess.run(tf.global_variables_initializer())
print("Initializing model...")
else:
# if we are initializing with the weights from another model load it
if init_model is not None:
# initialize the global variables
sess.run(tf.global_variables_initializer())
# create the initializer function to initialize the weights
init_fn = load_weights(init_model, exclude=['bottleneck_5.1',"up_conv6", "conv_up_conv6", "bn_up_conv6", "bn_unpool5.1", "up_conv5",'bn_unpool4.1', "up_conv4", "bn_bottleneck_5.1", 'bottleneck_5.2', 'bn_bottleneck_5.2', 'bn_bottleneck_4.1', 'bottleneck_4.2', 'bn_bottleneck_4.2', 'bottleneck_4.1', 'bn_bottleneck_4.1', 'bn_unpool1.1', "up_conv3"])
# run the initializer
init_fn(sess)
# reset the global step
initial_global_step = global_step.assign(0)
sess.run(initial_global_step)
print("Initializing weights from model", init_model)
# reset init model so we don't do this again
init_model = None
elif restore_model is not None:
saver.restore(sess, './model/' + restore_model + '.ckpt')
print("Restoring model", restore_model)
# create the saver
saver = tf.train.Saver()
sess.run(tf.local_variables_initializer())
# If the model is new initialize variables, else restore the session
if init:
sess.run(tf.global_variables_initializer())
print("Initializing model...")
else:
# if we are initializing with the weights from another model load it
if init_model is not None:
# initialize the global variables
sess.run(tf.global_variables_initializer())
# create the initializer function to initialize the weights
init_fn = load_weights(init_model, exclude=["bottleneck_3.1"])
# run the initializer
init_fn(sess)
# reset the global step
initial_global_step = global_step.assign(0)
sess.run(initial_global_step)
print("Initializing weights from model", init_model)
# reset init model so we don't do this again
init_model = None
elif restore_model is not None:
saver.restore(sess, './model/' + restore_model + '.ckpt')
print("Restoring model", restore_model)
# create the saver
saver = tf.train.Saver()
# If the model is new initialize variables, else restore the session
if init:
sess.run(tf.global_variables_initializer())
print("Initializing model...")
else:
# if we are initializing with the weights from another model load it
if init_model is not None:
# initialize the global variables
sess.run(tf.global_variables_initializer())
# create the initializer function to initialize the weights
init_fn = load_weights(init_model, exclude=["fc1", "bn_fc1", "bn_fc2", "fc3", "bn_fc3", "fc2", "logits", "global_step"])
# run the initializer
init_fn(sess)
# reset the global step
initial_global_step = global_step.assign(0)
sess.run(initial_global_step)
print("Initializing weights from model", init_model)
# reset init model so we don't do this again
init_model = None
# otherwise load this model
else:
saver.restore(sess, './model/' + model_name + '.ckpt')
saver = tf.train.Saver()
sess.run(tf.local_variables_initializer())
# If the model is new initialize variables, else restore the session
if init:
sess.run(tf.global_variables_initializer())
print("Initializing model...")
else:
# if we are initializing with the weights from another model load it
if init_model is not None:
# initialize the global variables
sess.run(tf.global_variables_initializer())
# create the initializer function to initialize the weights
# init_fn = load_weights(init_model, exclude=['bottleneck_5.1',"up_conv6", "conv_up_conv6", "bn_up_conv6", "bn_unpool5.1", "up_conv5",'bn_unpool4.1', "up_conv4", "bn_bottleneck_5.1", 'bottleneck_5.2', 'bn_bottleneck_5.2', 'bn_bottleneck_4.1', 'bottleneck_4.2', 'bn_bottleneck_4.2', 'bottleneck_4.1', 'bn_bottleneck_4.1', 'bn_unpool1.1', "up_conv3"])
init_fn = load_weights(init_model, exclude=['up_conv1', "bn_unpool1.1", "conv_fc_1", "bn_fc_1"])
# run the initializer
init_fn(sess)
# reset the global step
initial_global_step = global_step.assign(0)
sess.run(initial_global_step)
print("Initializing weights from model", init_model)
# reset init model so we don't do this again
init_model = None
elif restore_model is not None:
saver.restore(sess, './model/' + restore_model + '.ckpt')
print("Restoring model", restore_model)
