def main(unused_args):
tf.set_random_seed(FLAGS.seed)
tf.get_variable_scope().set_use_resource(True)
np.random.seed(FLAGS.seed)
# Load the MNIST data and set up an iterator.
mnist_data = input_data.read_data_sets(FLAGS.mnist,
one_hot=False,
validation_size=0)
train_images = mnist_data.train.images
test_images = mnist_data.test.images
if FLAGS.input_mask_path:
reader = tf.train.load_checkpoint(FLAGS.input_mask_path)
input_mask = reader.get_tensor('layer1/mask')
indices = np.sum(input_mask, axis=1) != 0
train_images = train_images[:, indices]
test_images = test_images[:, indices]
dataset = tf.data.Dataset.from_tensor_slices(
(train_images, mnist_data.train.labels.astype(np.int32)))
num_batches = mnist_data.train.images.shape[0] // FLAGS.batch_size
dataset = dataset.shuffle(buffer_size=mnist_data.train.images.shape[0])
batched_dataset = dataset.repeat(FLAGS.num_epochs).batch(FLAGS.batch_size)
iterator = batched_dataset.make_one_shot_iterator()
test_dataset = tf.data.Dataset.from_tensor_slices(
(test_images, mnist_data.test.labels.astype(np.int32)))
num_test_images = mnist_data.test.images.shape[0]
test_dataset = test_dataset.repeat(FLAGS.num_epochs).batch(num_test_images)
test_iterator = test_dataset.make_one_shot_iterator()
# Set up loss function.
use_model_pruning = FLAGS.training_method != 'baseline'
if FLAGS.network_type == 'fc':
cross_entropy_train, _ = mnist_network_fc(
iterator.get_next(), model_pruning=use_model_pruning)
cross_entropy_test, accuracy_test = mnist_network_fc(
test_iterator.get_next(),
reuse=True,
model_pruning=use_model_pruning)
else:
raise RuntimeError(FLAGS.network + ' is an unknown network type.')
# Remove extra added ones. Current implementation adds the variables twice
# to the collection. Improve this hacky thing.
# TODO test the following with the convnet or any other network.
if use_model_pruning:
for k in ('masks', 'masked_weights', 'thresholds', 'kernel'):
# del tf.get_collection_ref(k)[2]
# del tf.get_collection_ref(k)[2]
collection = tf.get_collection_ref(k)
del collection[len(collection) // 2:]
print(tf.get_collection_ref(k))
# Set up optimizer and update ops.
global_step = tf.train.get_or_create_global_step()
batch_per_epoch = mnist_data.train.images.shape[0] // FLAGS.batch_size
if FLAGS.optimizer != 'adam':
if not use_model_pruning:
boundaries = [
int(round(s * batch_per_epoch)) for s in [60, 70, 80]
]
else:
boundaries = [
int(round(s * batch_per_epoch))
for s in [FLAGS.lr_drop_epoch, FLAGS.lr_drop_epoch + 20]
]
learning_rate = tf.train.piecewise_constant(
global_step,
boundaries,
values=[
FLAGS.learning_rate / (3.**i)
for i in range(len(boundaries) + 1)
])
else:
learning_rate = FLAGS.learning_rate
if FLAGS.optimizer == 'adam':
opt = tf.train.AdamOptimizer(FLAGS.learning_rate)
elif FLAGS.optimizer == 'momentum':
opt = tf.train.MomentumOptimizer(learning_rate,
FLAGS.momentum,
use_nesterov=FLAGS.use_nesterov)
elif FLAGS.optimizer == 'sgd':
opt = tf.train.GradientDescentOptimizer(learning_rate)
else:
raise RuntimeError(FLAGS.optimizer + ' is unknown optimizer type')
custom_sparsities = {
'layer2': FLAGS.end_sparsity * FLAGS.sparsity_scale,
'layer3': FLAGS.end_sparsity * 0
}
if FLAGS.training_method == 'set':
# We override the train op to also update the mask.
opt = sparse_optimizers.SparseSETOptimizer(
opt,
begin_step=FLAGS.maskupdate_begin_step,
end_step=FLAGS.maskupdate_end_step,
grow_init=FLAGS.grow_init,
frequency=FLAGS.maskupdate_frequency,
drop_fraction=FLAGS.drop_fraction,
drop_fraction_anneal=FLAGS.drop_fraction_anneal)
elif FLAGS.training_method == 'static':
# We override the train op to also update the mask.
opt = sparse_optimizers.SparseStaticOptimizer(
opt,
begin_step=FLAGS.maskupdate_begin_step,
end_step=FLAGS.maskupdate_end_step,
grow_init=FLAGS.grow_init,
frequency=FLAGS.maskupdate_frequency,
drop_fraction=FLAGS.drop_fraction,
drop_fraction_anneal=FLAGS.drop_fraction_anneal)
elif FLAGS.training_method == 'momentum':
# We override the train op to also update the mask.
opt = sparse_optimizers.SparseMomentumOptimizer(
opt,
begin_step=FLAGS.maskupdate_begin_step,
end_step=FLAGS.maskupdate_end_step,
momentum=FLAGS.s_momentum,
frequency=FLAGS.maskupdate_frequency,
drop_fraction=FLAGS.drop_fraction,
grow_init=FLAGS.grow_init,
drop_fraction_anneal=FLAGS.drop_fraction_anneal,
use_tpu=False)
elif FLAGS.training_method == 'rigl':
# We override the train op to also update the mask.
opt = sparse_optimizers.SparseRigLOptimizer(
opt,
begin_step=FLAGS.maskupdate_begin_step,
end_step=FLAGS.maskupdate_end_step,
grow_init=FLAGS.grow_init,
frequency=FLAGS.maskupdate_frequency,
drop_fraction=FLAGS.drop_fraction,
drop_fraction_anneal=FLAGS.drop_fraction_anneal,
initial_acc_scale=FLAGS.rigl_acc_scale,
use_tpu=False)
elif FLAGS.training_method == 'snip':
opt = sparse_optimizers.SparseSnipOptimizer(
opt,
mask_init_method=FLAGS.mask_init_method,
default_sparsity=FLAGS.end_sparsity,
custom_sparsity_map=custom_sparsities,
use_tpu=False)
elif FLAGS.training_method in ('scratch', 'baseline', 'prune'):
pass
else:
raise ValueError('Unsupported pruning method: %s' %
FLAGS.training_method)
train_op = opt.minimize(cross_entropy_train, global_step=global_step)
if FLAGS.training_method == 'prune':
hparams_string = (
'begin_pruning_step={0},sparsity_function_begin_step={0},'
'end_pruning_step={1},sparsity_function_end_step={1},'
'target_sparsity={2},pruning_frequency={3},'
'threshold_decay={4}'.format(FLAGS.prune_begin_step,
FLAGS.prune_end_step,
FLAGS.end_sparsity,
FLAGS.pruning_frequency,
FLAGS.threshold_decay))
pruning_hparams = pruning.get_pruning_hparams().parse(hparams_string)
pruning_hparams.set_hparam(
'weight_sparsity_map',
['{0}:{1}'.format(k, v) for k, v in custom_sparsities.items()])
print(pruning_hparams)
pruning_obj = pruning.Pruning(pruning_hparams, global_step=global_step)
with tf.control_dependencies([train_op]):
train_op = pruning_obj.conditional_mask_update_op()
weight_sparsity_levels = pruning.get_weight_sparsity()
global_sparsity = sparse_utils.calculate_sparsity(pruning.get_masks())
tf.summary.scalar('test_accuracy', accuracy_test)
tf.summary.scalar('global_sparsity', global_sparsity)
for k, v in zip(pruning.get_masks(), weight_sparsity_levels):
tf.summary.scalar('sparsity/%s' % k.name, v)
if FLAGS.training_method in ('prune', 'snip', 'baseline'):
mask_init_op = tf.no_op()
tf.logging.info('No mask is set, starting dense.')
else:
all_masks = pruning.get_masks()
mask_init_op = sparse_utils.get_mask_init_fn(all_masks,
FLAGS.mask_init_method,
FLAGS.end_sparsity,
custom_sparsities)
if FLAGS.save_model:
saver = tf.train.Saver()
init_op = tf.global_variables_initializer()
hyper_params_string = '_'.join([
FLAGS.network_type,
str(FLAGS.batch_size),
str(FLAGS.learning_rate),
str(FLAGS.momentum), FLAGS.optimizer,
str(FLAGS.l2_scale), FLAGS.training_method,
str(FLAGS.prune_begin_step),
str(FLAGS.prune_end_step),
str(FLAGS.end_sparsity),
str(FLAGS.pruning_frequency),
str(FLAGS.seed)
])
tf.io.gfile.makedirs(FLAGS.save_path)
filename = os.path.join(FLAGS.save_path, hyper_params_string + '.txt')
merged_summary_op = tf.summary.merge_all()
# Run session.
if not use_model_pruning:
with tf.Session() as sess:
summary_writer = tf.summary.FileWriter(
FLAGS.save_path, graph=tf.get_default_graph())
print('Epoch', 'Epoch time', 'Test loss', 'Test accuracy')
sess.run([init_op])
tic = time.time()
with tf.io.gfile.GFile(filename, 'w') as outputfile:
for i in range(FLAGS.num_epochs * num_batches):
sess.run([train_op])
if (i % num_batches) == (-1 % num_batches):
epoch_time = time.time() - tic
loss, accuracy, summary = sess.run([
cross_entropy_test, accuracy_test,
merged_summary_op
])
# Write logs at every test iteration.
summary_writer.add_summary(summary, i)
log_str = '%d, %.4f, %.4f, %.4f' % (
i // num_batches, epoch_time, loss, accuracy)
print(log_str)
print(log_str, file=outputfile)
tic = time.time()
if FLAGS.save_model:
saver.save(sess, os.path.join(FLAGS.save_path, 'model.ckpt'))
else:
with tf.Session() as sess:
summary_writer = tf.summary.FileWriter(
FLAGS.save_path, graph=tf.get_default_graph())
log_str = ','.join([
'Epoch', 'Iteration', 'Test loss', 'Test accuracy',
'G_Sparsity', 'Sparsity Layer 0', 'Sparsity Layer 1'
])
sess.run(init_op)
sess.run(mask_init_op)
tic = time.time()
mask_records = {}
with tf.io.gfile.GFile(filename, 'w') as outputfile:
print(log_str)
print(log_str, file=outputfile)
for i in range(FLAGS.num_epochs * num_batches):
if (FLAGS.mask_record_frequency > 0
and i % FLAGS.mask_record_frequency == 0):
mask_vals = sess.run(pruning.get_masks())
# Cast into bool to save space.
mask_records[i] = [
a.astype(np.bool) for a in mask_vals
]
sess.run([train_op])
weight_sparsity, global_sparsity_val = sess.run(
[weight_sparsity_levels, global_sparsity])
if (i % num_batches) == (-1 % num_batches):
epoch_time = time.time() - tic
loss, accuracy, summary = sess.run([
cross_entropy_test, accuracy_test,
merged_summary_op
])
# Write logs at every test iteration.
summary_writer.add_summary(summary, i)
log_str = '%d, %d, %.4f, %.4f, %.4f, %.4f, %.4f' % (
i // num_batches, i, loss, accuracy,
global_sparsity_val, weight_sparsity[0],
weight_sparsity[1])
print(log_str)
print(log_str, file=outputfile)
mask_vals = sess.run(pruning.get_masks())
if FLAGS.network_type == 'fc':
sparsities, sizes = get_compressed_fc(mask_vals)
print('[COMPRESSED SPARSITIES/SHAPE]: %s %s' %
(sparsities, sizes))
print('[COMPRESSED SPARSITIES/SHAPE]: %s %s' %
(sparsities, sizes),
file=outputfile)
tic = time.time()
if FLAGS.save_model:
saver.save(sess, os.path.join(FLAGS.save_path, 'model.ckpt'))
if mask_records:
np.save(os.path.join(FLAGS.save_path, 'mask_records'),
mask_records)
def train_function(training_method, loss, cross_loss, reg_loss, output_dir,
use_tpu):
"""Training script for resnet model.
Args:
training_method: string indicating pruning method used to compress model.
loss: tensor float32 of the cross entropy + regularization losses.
cross_loss: tensor, only cross entropy loss, passed for logging.
reg_loss: tensor, only regularization loss, passed for logging.
output_dir: string tensor indicating the directory to save summaries.
use_tpu: boolean indicating whether to run script on a tpu.
Returns:
host_call: summary tensors to be computed at each training step.
train_op: the optimization term.
"""
global_step = tf.train.get_global_step()
steps_per_epoch = FLAGS.num_train_images / FLAGS.train_batch_size
current_epoch = (tf.cast(global_step, tf.float32) / steps_per_epoch)
learning_rate = lr_schedule(current_epoch)
if FLAGS.use_adam:
# We don't use step decrease for the learning rate.
learning_rate = FLAGS.base_learning_rate * (FLAGS.train_batch_size /
256.0)
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
else:
optimizer = tf.train.MomentumOptimizer(learning_rate=learning_rate,
momentum=FLAGS.momentum,
use_nesterov=True)
if use_tpu:
# use CrossShardOptimizer when using TPU.
optimizer = contrib_tpu.CrossShardOptimizer(optimizer)
if training_method == 'set':
# We override the train op to also update the mask.
optimizer = sparse_optimizers.SparseSETOptimizer(
optimizer,
begin_step=FLAGS.maskupdate_begin_step,
end_step=FLAGS.maskupdate_end_step,
grow_init=FLAGS.grow_init,
frequency=FLAGS.maskupdate_frequency,
drop_fraction=FLAGS.drop_fraction,
drop_fraction_anneal=FLAGS.drop_fraction_anneal,
stateless_seed_offset=FLAGS.seed)
elif training_method == 'static':
# We override the train op to also update the mask.
optimizer = sparse_optimizers.SparseStaticOptimizer(
optimizer,
begin_step=FLAGS.maskupdate_begin_step,
end_step=FLAGS.maskupdate_end_step,
grow_init=FLAGS.grow_init,
frequency=FLAGS.maskupdate_frequency,
drop_fraction=FLAGS.drop_fraction,
drop_fraction_anneal=FLAGS.drop_fraction_anneal,
stateless_seed_offset=FLAGS.seed)
elif training_method == 'momentum':
# We override the train op to also update the mask.
optimizer = sparse_optimizers.SparseMomentumOptimizer(
optimizer,
begin_step=FLAGS.maskupdate_begin_step,
end_step=FLAGS.maskupdate_end_step,
momentum=FLAGS.s_momentum,
frequency=FLAGS.maskupdate_frequency,
drop_fraction=FLAGS.drop_fraction,
grow_init=FLAGS.grow_init,
stateless_seed_offset=FLAGS.seed,
drop_fraction_anneal=FLAGS.drop_fraction_anneal,
use_tpu=use_tpu)
elif training_method == 'rigl':
# We override the train op to also update the mask.
optimizer = sparse_optimizers.SparseRigLOptimizer(
optimizer,
begin_step=FLAGS.maskupdate_begin_step,
end_step=FLAGS.maskupdate_end_step,
grow_init=FLAGS.grow_init,
frequency=FLAGS.maskupdate_frequency,
drop_fraction=FLAGS.drop_fraction,
stateless_seed_offset=FLAGS.seed,
drop_fraction_anneal=FLAGS.drop_fraction_anneal,
initial_acc_scale=FLAGS.rigl_acc_scale,
use_tpu=use_tpu)
elif training_method == 'snip':
optimizer = sparse_optimizers.SparseSnipOptimizer(
optimizer,
mask_init_method=FLAGS.mask_init_method,
custom_sparsity_map=CUSTOM_SPARSITY_MAP,
default_sparsity=FLAGS.end_sparsity,
use_tpu=use_tpu)
elif training_method in ('scratch', 'baseline'):
pass
else:
raise ValueError('Unsupported pruning method: %s' %
FLAGS.training_method)
# UPDATE_OPS needs to be added as a dependency due to batch norm
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops), tf.name_scope('train'):
train_op = optimizer.minimize(loss, global_step)
metrics = {
'global_step': tf.train.get_or_create_global_step(),
'loss': loss,
'cross_loss': cross_loss,
'reg_loss': reg_loss,
'learning_rate': learning_rate,
'current_epoch': current_epoch,
}
# Logging drop_fraction if dynamic sparse training.
if training_method in ('set', 'momentum', 'rigl', 'static'):
metrics['drop_fraction'] = optimizer.drop_fraction
# Let's log some statistics from a single parameter-mask couple.
# This is useful for debugging.
test_var = pruning.get_weights()[0]
test_var_mask = pruning.get_masks()[0]
metrics.update({
'fw_nz_weight': tf.count_nonzero(test_var),
'fw_nz_mask': tf.count_nonzero(test_var_mask),
'fw_l1_weight': tf.reduce_sum(tf.abs(test_var))
})
masks = pruning.get_masks()
global_sparsity = sparse_utils.calculate_sparsity(masks)
metrics['global_sparsity'] = global_sparsity
metrics.update(
utils.mask_summaries(masks[:4] + masks[-1:],
with_img=FLAGS.log_mask_imgs_each_iteration))
host_call = (functools.partial(utils.host_call_fn,
output_dir), utils.format_tensors(metrics))
return host_call, train_op