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train_teacher.py
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train_teacher.py
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import tensorflow as tf
from tensorflow.python.ops import control_flow_ops
from tensorflow import ConfigProto
slim = tf.contrib.slim
from datasets import dataset_factory
from nets import nets_factory
from preprocessing import preprocessing_factory
import numpy as np
import scipy.io as sio
train_dir = '/home/dmsl/Documents/tf/svd/VGG/VGG'
dataset_dir = '/home/dmsl/Documents/data/tf/cifar100'
dataset_name = 'cifar100'
model_name = 'VGG_teacher'
preprocessing_name = 'cifar100'
Optimizer = 'sgd' # 'adam' or 'sgd'
Learning_rate =1e-2
batch_size = 128
val_batch_size = 200
init_epoch = 0
num_epoch = 200+init_epoch
weight_decay = 1e-4
checkpoint_path = None
#checkpoint_path = '/home/dmsl/Documents/tf/svd/mobile/mobile'
ignore_missing_vars = True
### main
#%%
tf.logging.set_verbosity(tf.logging.INFO)
def _get_init_fn(checkpoint_path, ignore_missing_vars):
if checkpoint_path is None:
return None
variables_to_restore = slim.get_variables_to_restore()[1:]
for v in variables_to_restore:
print (v)
if tf.gfile.IsDirectory(checkpoint_path):
checkpoint_path = tf.train.latest_checkpoint(checkpoint_path)
return slim.assign_from_checkpoint_fn(checkpoint_path,
variables_to_restore,
ignore_missing_vars = ignore_missing_vars)
def GET_dataset(dataset_name, dataset_dir, batch_size, preprocessing_name, split):
if split == 'train':
sff = True
threads = 4
is_training = True
else:
sff = False
threads = 1
is_training = False
with tf.variable_scope('dataset_%s'%split):
dataset = dataset_factory.get_dataset(dataset_name, split, dataset_dir)
with tf.device('/device:CPU:0'):
if split == 'train':
global_step = slim.create_global_step()
p = tf.floor_div(tf.cast(global_step, tf.float32), tf.cast(int(dataset.num_samples / float(batch_size)), tf.float32))
else:
global_step = None
p = None
provider = slim.dataset_data_provider.DatasetDataProvider(dataset,
shuffle=sff,
num_readers = threads,
common_queue_capacity=dataset.num_samples,
common_queue_min=0)
images, labels = provider.get(['image', 'label'])
image_preprocessing_fn = preprocessing_factory.get_preprocessing(preprocessing_name, is_training)
images = image_preprocessing_fn(images)
if split == 'train':
batch_images, batch_labels = tf.train.shuffle_batch([images, labels],
batch_size = batch_size,
num_threads = threads,
capacity = dataset.num_samples,
min_after_dequeue = 0)
with tf.variable_scope('1-hot_encoding'):
batch_labels = slim.one_hot_encoding(batch_labels, dataset.num_classes,on_value=1.0)
batch_queue = slim.prefetch_queue.prefetch_queue([batch_images, batch_labels], capacity=40*batch_size)
image, label = batch_queue.dequeue()
else:
batch_images, batch_labels = tf.train.batch([images, labels],
batch_size = batch_size,
num_threads = threads,
capacity = dataset.num_samples)
with tf.variable_scope('1-hot_encoding'):
batch_labels = slim.one_hot_encoding(batch_labels, dataset.num_classes,on_value=1.0)
batch_queue = slim.prefetch_queue.prefetch_queue([batch_images, batch_labels], capacity=8*batch_size)
image, label = batch_queue.dequeue()
return p, global_step, dataset, image, label
def sigmoid(x,k):
return 1/(1+tf.exp(-(x-k)))
def MODEL(model_name, weight_decay, image, label, lr, epoch, is_training):
network_fn = nets_factory.get_network_fn(model_name, weight_decay = weight_decay)
end_points = network_fn(image, is_training=is_training, lr = lr, val=not(is_training))
losses = []
if is_training:
def scale_grad(x, scale):
return scale*x + tf.stop_gradient((1-scale)*x)
with tf.variable_scope('Student_loss'):
loss = tf.losses.softmax_cross_entropy(label,end_points['Logits'])
accuracy = slim.metrics.accuracy(tf.to_int32(tf.argmax(end_points['Logits'], 1)),
tf.to_int32(tf.argmax(label, 1)))
tf.summary.scalar('loss', loss)
tf.summary.scalar('accuracy', accuracy)
losses.append(loss+tf.add_n(tf.losses.get_regularization_losses()))
else:
losses = tf.losses.softmax_cross_entropy(label,end_points['Logits'])
accuracy = slim.metrics.accuracy(tf.to_int32(tf.argmax(end_points['Logits'], 1)),
tf.to_int32(tf.argmax(label, 1)))
return losses, accuracy
#%%
with tf.Graph().as_default() as graph:
## Load Dataset
epoch, global_step, dataset, image, label = GET_dataset(dataset_name, dataset_dir,
batch_size, preprocessing_name, 'train')
_, _, val_dataset, val_image, val_label = GET_dataset(dataset_name, dataset_dir,
val_batch_size, preprocessing_name, 'test')
with tf.device('/device:CPU:0'):
decay_steps = dataset.num_samples // batch_size
max_number_of_steps = int(dataset.num_samples/batch_size*(num_epoch))
total_loss, train_accuracy = MODEL(model_name, weight_decay, image, label, Learning_rate, epoch, True)
#%% Compute Loss & Gradient
def distillation_learning_rate(Learning_rate, epoch, init_epoch):
Learning_rate = tf.case([
(tf.less(epoch,100+init_epoch), lambda : Learning_rate),
(tf.less(epoch,150+init_epoch), lambda : Learning_rate*1e-1),
],
default = lambda : Learning_rate*1e-2)
tf.summary.scalar('learning_rate', Learning_rate)
return Learning_rate
variables = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
optimizer = tf.train.MomentumOptimizer(distillation_learning_rate(Learning_rate, epoch, init_epoch),
0.9, use_nesterov=True)
gradient0 = optimizer.compute_gradients(total_loss[0], var_list = variables)
update_ops.append(optimizer.apply_gradients(gradient0, global_step=global_step))
update_op = tf.group(*update_ops)
train_op = control_flow_ops.with_dependencies([update_op], tf.add_n(total_loss), name='train_op')
val_loss, val_accuracy = MODEL(model_name, weight_decay, val_image, val_label, Learning_rate, epoch, False)
summaries = set(tf.get_collection(tf.GraphKeys.SUMMARIES))
summary_op = tf.summary.merge(list(summaries), name='summary_op')
#%% for validation
def ts_fn(session, *args, **kwargs):
total_loss, should_stop = slim.learning.train_step(session, *args, **kwargs)
if ( ts_fn.step % (ts_fn.decay_steps) == 0):
accuracy = 0
itr = val_dataset.num_samples//val_batch_size
for i in range(itr):
accuracy += session.run(ts_fn.val_accuracy)
print ('Epoch %s Step %s - Loss: %.2f Accuracy: %.2f%%, Highest Accuracy : %.2f%%'
% (str((ts_fn.step-ts_fn.decay_steps*ts_fn.init_epoch)//ts_fn.decay_steps).rjust(3, '0'),
str(ts_fn.step-ts_fn.decay_steps*ts_fn.init_epoch).rjust(6, '0'),
total_loss, accuracy *100/itr, ts_fn.highest*100/itr))
acc = tf.Summary(value=[tf.Summary.Value(tag="Accuracy", simple_value=accuracy*100/itr)])
ts_fn.eval_writer.add_summary(acc, ts_fn.step-ts_fn.decay_steps*ts_fn.init_epoch)
var = {}
variables = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
for v in variables:
var[v.name[:-2]] = session.run(v)
sio.savemat(train_dir + '/trained_params.mat',var)
print ('save new parameters')
ts_fn.highest = accuracy
ts_fn.step += 1
return [total_loss, should_stop]
ts_fn.saver = tf.train.Saver()
ts_fn.eval_writer = tf.summary.FileWriter('%s/eval'%train_dir,graph,flush_secs=60)
ts_fn.step = 0
ts_fn.decay_steps = decay_steps
ts_fn.init_epoch = init_epoch
ts_fn.val_accuracy = val_accuracy
ts_fn.highest = 0
#%% training
config = ConfigProto()
config.gpu_options.allow_growth=True
slim.learning.train(train_op, logdir = train_dir, global_step = global_step,
session_config = config,
init_fn=_get_init_fn(checkpoint_path, ignore_missing_vars),
summary_op = summary_op,
train_step_fn=ts_fn,
number_of_steps = max_number_of_steps,
log_every_n_steps = 40, #'The frequency with which logs are print.'
save_summaries_secs = 120, #'The frequency with which summaries are saved, in seconds.'
save_interval_secs = 0) #'The frequency with which the model is saved, in seconds.'