def _setup_graph(self, n_inp, n_out, drop_frac, start_iter=1, end_iter=4,
freq_iter=2):
"""Setups a trivial training procedure for sparse training."""
tf.reset_default_graph()
optim = tf.train.GradientDescentOptimizer(0.1)
sparse_optim = sparse_optimizers.SparseStaticOptimizer(
optim, start_iter, end_iter, freq_iter, drop_fraction=drop_frac)
x = tf.random.uniform((1, n_inp))
y = layers.masked_fully_connected(x, n_out, activation_fn=None)
global_step = tf.train.get_or_create_global_step()
weight = pruning.get_weights()[0]
# There is one masked layer to be trained.
mask = pruning.get_masks()[0]
# Around half of the values of the mask is set to zero with `mask_update`.
mask_update = tf.assign(
mask,
tf.constant(
np.random.choice([0, 1], size=(n_inp, n_out), p=[1./2, 1./2]),
dtype=tf.float32))
loss = tf.reduce_mean(y)
global_step = tf.train.get_or_create_global_step()
train_op = sparse_optim.minimize(loss, global_step)
# Init
sess = tf.Session()
init = tf.global_variables_initializer()
sess.run(init)
sess.run([mask_update])
return sess, train_op, mask, weight, global_step
def snip_op():
all_masks = pruning.get_masks()
assigner = sparse_utils.get_mask_init_fn(all_masks,
self._mask_init_method,
self._default_sparsity,
self._custom_sparsity_map,
mask_fn=snip_fn)
with ops.control_dependencies([assigner]):
assign_op = state_ops.assign(self.is_snipped,
True,
name='assign_true_after_snipped')
return assign_op
def check_global_sparsity():
"""Add a summary for the weight sparsity."""
weight_masks = magnitude_pruning.get_masks()
weights_per_layer = []
nonzero_per_layer = []
for mask in weight_masks:
nonzero_per_layer.append(tf.reduce_sum(mask))
weights_per_layer.append(tf.size(mask))
total_nonzero = tf.add_n(nonzero_per_layer)
total_weights = tf.add_n(weights_per_layer)
sparsity = (1.0 - (tf.cast(total_nonzero, tf.float32) /
tf.cast(total_weights, tf.float32)))
tf.summary.scalar("global_weight_sparsity", sparsity)
def scaffold_fn():
"""For initialization, passed to the estimator."""
if FLAGS.initial_value_checkpoint:
initialize_parameters_from_ckpt(FLAGS.initial_value_checkpoint)
all_masks = pruning.get_masks()
assigner = sparse_utils.get_mask_init_fn(
all_masks, FLAGS.mask_init_method, FLAGS.end_sparsity,
CUSTOM_SPARSITY_MAP)
def init_fn(scaffold, session):
"""A callable for restoring variable from a checkpoint."""
del scaffold # Unused.
session.run(assigner)
return tf.train.Scaffold(init_fn=init_fn)
def testMaskedLSTMCell(self):
expected_num_masks = 1
expected_num_rows = 2 * self.dim
expected_num_cols = 4 * self.dim
with self.cached_session():
inputs = variables.Variable(
random_ops.random_normal([self.batch_size, self.dim]))
c = variables.Variable(
random_ops.random_normal([self.batch_size, self.dim]))
h = variables.Variable(
random_ops.random_normal([self.batch_size, self.dim]))
state = tf_rnn_cells.LSTMStateTuple(c, h)
lstm_cell = rnn_cells.MaskedLSTMCell(self.dim)
lstm_cell(inputs, state)
self.assertEqual(len(pruning.get_masks()), expected_num_masks)
self.assertEqual(len(pruning.get_masked_weights()), expected_num_masks)
self.assertEqual(len(pruning.get_thresholds()), expected_num_masks)
self.assertEqual(len(pruning.get_weights()), expected_num_masks)
for mask in pruning.get_masks():
self.assertEqual(mask.shape, (expected_num_rows, expected_num_cols))
for weight in pruning.get_weights():
self.assertEqual(weight.shape, (expected_num_rows, expected_num_cols))
def metric_fn(labels, logits, cross_loss, reg_loss):
"""Calculate eval metrics."""
logging.info('In metric function')
eval_metrics = {}
predictions = tf.cast(tf.argmax(logits, axis=1), tf.int32)
in_top_5 = tf.cast(tf.nn.in_top_k(logits, labels, 5), tf.float32)
eval_metrics['top_5_eval_accuracy'] = tf.metrics.mean(in_top_5)
eval_metrics['cross_loss'] = tf.metrics.mean(cross_loss)
eval_metrics['reg_loss'] = tf.metrics.mean(reg_loss)
eval_metrics['eval_accuracy'] = tf.metrics.accuracy(
labels=labels, predictions=predictions)
# If evaluating once lets also calculate sparsities.
if FLAGS.mode == 'eval_once':
sparsity_summaries = utils.mask_summaries(pruning.get_masks())
# We call mean on a scalar to create tensor, update_op pairs.
sparsity_summaries = {k: tf.metrics.mean(v) for k, v
in sparsity_summaries.items()}
eval_metrics.update(sparsity_summaries)
return eval_metrics
def _setup_graph(self,
default_sparsity,
mask_init_method,
custom_sparsity_map,
n_inp=3,
n_out=5):
"""Setups a trivial training procedure for sparse training."""
tf.reset_default_graph()
optim = tf.train.GradientDescentOptimizer(1e-3)
sparse_optim = sparse_optimizers.SparseDNWOptimizer(
optim,
default_sparsity,
mask_init_method,
custom_sparsity_map=custom_sparsity_map)
inp_values = np.arange(1, n_inp + 1)
scale_vector_values = np.random.uniform(size=(n_out, )) - 0.5
# The gradient is the outer product of input and the output gradients.
# Since the loss is sample sum the output gradient is equal to the scale
# vector.
expected_grads = np.outer(inp_values, scale_vector_values)
x = tf.reshape(tf.constant(inp_values, dtype=tf.float32), (1, n_inp))
y = layers.masked_fully_connected(x, n_out, activation_fn=None)
scale_vector = tf.constant(scale_vector_values, dtype=tf.float32)
y = y * scale_vector
loss = tf.reduce_sum(y)
global_step = tf.train.get_or_create_global_step()
grads_and_vars = sparse_optim.compute_gradients(loss)
train_op = sparse_optim.apply_gradients(grads_and_vars,
global_step=global_step)
# Init
sess = tf.Session()
init = tf.global_variables_initializer()
sess.run(init)
mask = pruning.get_masks()[0]
weights = pruning.get_weights()[0]
return (sess, train_op, (expected_grads, grads_and_vars), mask,
weights)
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
def train_function(pruning_method, loss, output_dir, use_tpu):
"""Training script for resnet model.
Args:
pruning_method: string indicating pruning method used to compress model.
loss: tensor float32 of the cross entropy + regularization losses.
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)
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)
# 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)
if not use_tpu:
if FLAGS.num_workers > 0:
optimizer = tf.train.SyncReplicasOptimizer(
optimizer,
replicas_to_aggregate=FLAGS.num_workers,
total_num_replicas=FLAGS.num_workers)
optimizer.make_session_run_hook(True)
metrics = {
'global_step': tf.train.get_or_create_global_step(),
'loss': loss,
'learning_rate': learning_rate,
'current_epoch': current_epoch
}
if pruning_method == 'threshold':
# construct the necessary hparams string from the FLAGS
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=0,'
'use_tpu={4}'.format(
FLAGS.sparsity_begin_step,
FLAGS.sparsity_end_step,
FLAGS.end_sparsity,
FLAGS.pruning_frequency,
FLAGS.use_tpu,
))
# Parse pruning hyperparameters
pruning_hparams = pruning.get_pruning_hparams().parse(hparams_string)
# The first layer has so few parameters, we don't need to prune it, and
# pruning it a higher sparsity levels has very negative effects.
if FLAGS.prune_first_layer and FLAGS.first_layer_sparsity >= 0.:
pruning_hparams.set_hparam(
'weight_sparsity_map',
['resnet_model/initial_conv:%f' % FLAGS.first_layer_sparsity])
if FLAGS.prune_last_layer and FLAGS.last_layer_sparsity >= 0:
pruning_hparams.set_hparam(
'weight_sparsity_map',
['resnet_model/final_dense:%f' % FLAGS.last_layer_sparsity])
# Create a pruning object using the pruning hyperparameters
pruning_obj = pruning.Pruning(pruning_hparams, global_step=global_step)
# We override the train op to also update the mask.
with tf.control_dependencies([train_op]):
train_op = pruning_obj.conditional_mask_update_op()
masks = pruning.get_masks()
metrics.update(utils.mask_summaries(masks))
elif pruning_method == 'scratch':
masks = pruning.get_masks()
# make sure the masks have the sparsity we expect and that it doesn't change
metrics.update(utils.mask_summaries(masks))
elif pruning_method == 'variational_dropout':
masks = utils.add_vd_pruning_summaries(
threshold=FLAGS.log_alpha_threshold)
metrics.update(masks)
elif pruning_method == 'l0_regularization':
summaries = utils.add_l0_summaries()
metrics.update(summaries)
elif pruning_method == 'baseline':
pass
else:
raise ValueError('Unsupported pruning method', FLAGS.pruning_method)
host_call = (functools.partial(utils.host_call_fn,
output_dir), utils.format_tensors(metrics))
return host_call, train_op
def get_masks(self):
return pruning.get_masks()
def wide_resnet_w_pruning(features, labels, mode, params):
"""The model_fn for ResNet wide with pruning.
Args:
features: A float32 batch of images.
labels: A int32 batch of labels.
mode: Specifies whether training or evaluation.
params: Dictionary of parameters passed to the model.
Returns:
A EstimatorSpec for the model
Raises:
ValueError: if mode is not recognized as train or eval.
"""
if isinstance(features, dict):
features = features['feature']
train_dir = params['train_dir']
training_method = params['training_method']
global_step, accuracy, top_5_accuracy, logits = build_model(
mode=mode,
images=features,
labels=labels,
training_method=training_method,
num_classes=FLAGS.num_classes,
depth=FLAGS.resnet_depth,
width=FLAGS.resnet_width)
if mode == tf.estimator.ModeKeys.PREDICT:
predictions = {
'classes': tf.argmax(logits, axis=1),
'probabilities': tf.nn.softmax(logits, name='softmax_tensor')
}
return tf.estimator.EstimatorSpec(
mode=mode,
predictions=predictions,
export_outputs={
'classify': tf.estimator.export.PredictOutput(predictions)
})
with tf.name_scope('computing_cross_entropy_loss'):
entropy_loss = tf.losses.sparse_softmax_cross_entropy(
labels=labels, logits=logits)
tf.summary.scalar('cross_entropy_loss', entropy_loss)
with tf.name_scope('computing_total_loss'):
total_loss = tf.losses.get_total_loss(add_regularization_losses=True)
if mode == tf.estimator.ModeKeys.TRAIN:
hooks, eval_metrics, train_op = train_fn(training_method, global_step,
total_loss, train_dir, accuracy,
top_5_accuracy)
elif mode == tf.estimator.ModeKeys.EVAL:
hooks = None
train_op = None
with tf.name_scope('summaries'):
eval_metrics = create_eval_metrics(labels, logits)
else:
raise ValueError('mode not recognized as training or eval.')
if FLAGS.training_method in ('prune', 'snip', 'baseline'):
scaffold = None
tf.logging.info('No mask is set, starting dense.')
else:
all_masks = pruning.get_masks()
assigner = sparse_utils.get_mask_init_fn(
all_masks, FLAGS.mask_init_method, FLAGS.end_sparsity, {})
def init_fn(scaffold, session):
"""A callable for restoring variable from a checkpoint."""
del scaffold # Unused.
session.run(assigner)
scaffold = tf.train.Scaffold(init_fn=init_fn)
return tf.estimator.EstimatorSpec(
mode=mode,
training_hooks=hooks,
loss=total_loss,
train_op=train_op,
eval_metric_ops=eval_metrics,
scaffold=scaffold)
def train_fn(training_method, global_step, total_loss, train_dir, accuracy,
top_5_accuracy):
"""Training script for resnet model.
Args:
training_method: specifies the method used to sparsify networks.
global_step: the current step of training/eval.
total_loss: tensor float32 of the cross entropy + regularization losses.
train_dir: string specifying where directory where summaries are saved.
accuracy: tensor float32 batch classification accuracy.
top_5_accuracy: tensor float32 batch classification accuracy (top_5 classes).
Returns:
hooks: summary tensors to be computed at each training step.
eval_metrics: set to None during training.
train_op: the optimization term.
"""
# Rougly drops at every 30k steps.
boundaries = [30000, 60000, 90000]
if FLAGS.training_steps_multiplier != 1.0:
multiplier = FLAGS.training_steps_multiplier
boundaries = [int(x * multiplier) for x in boundaries]
tf.logging.info(
'Learning Rate boundaries are updated with multiplier:%.2f', multiplier)
learning_rate = tf.train.piecewise_constant(
global_step,
boundaries,
values=[0.1 / (5.**i) for i in range(len(boundaries) + 1)],
name='lr_schedule')
optimizer = tf.train.MomentumOptimizer(
learning_rate, momentum=FLAGS.momentum, use_nesterov=True)
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)
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)
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,
drop_fraction_anneal=FLAGS.drop_fraction_anneal, use_tpu=False)
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,
drop_fraction_anneal=FLAGS.drop_fraction_anneal,
initial_acc_scale=FLAGS.rigl_acc_scale, use_tpu=False)
elif training_method == 'snip':
optimizer = sparse_optimizers.SparseSnipOptimizer(
optimizer, mask_init_method=FLAGS.mask_init_method,
default_sparsity=FLAGS.end_sparsity, use_tpu=False)
elif training_method in ('scratch', 'baseline', 'prune'):
pass
else:
raise ValueError('Unsupported pruning method: %s' % FLAGS.training_method)
# Create the training op
update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS)
with tf.control_dependencies(update_ops):
train_op = optimizer.minimize(total_loss, global_step)
if training_method == 'prune':
# construct the necessary hparams string from the FLAGS
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=0,'
'use_tpu={4}'.format(
FLAGS.sparsity_begin_step,
FLAGS.sparsity_end_step,
FLAGS.end_sparsity,
FLAGS.pruning_frequency,
False,
))
# Parse pruning hyperparameters
pruning_hparams = pruning.get_pruning_hparams().parse(hparams_string)
# Create a pruning object using the pruning hyperparameters
pruning_obj = pruning.Pruning(pruning_hparams, global_step=global_step)
tf.logging.info('starting mask update op')
# We override the train op to also update the mask.
with tf.control_dependencies([train_op]):
train_op = pruning_obj.conditional_mask_update_op()
masks = pruning.get_masks()
mask_metrics = utils.mask_summaries(masks)
for name, tensor in mask_metrics.items():
tf.summary.scalar(name, tensor)
tf.summary.scalar('learning_rate', learning_rate)
tf.summary.scalar('accuracy', accuracy)
tf.summary.scalar('total_loss', total_loss)
tf.summary.scalar('top_5_accuracy', top_5_accuracy)
# Logging drop_fraction if dynamic sparse training.
if training_method in ('set', 'momentum', 'rigl', 'static'):
tf.summary.scalar('drop_fraction', optimizer.drop_fraction)
summary_op = tf.summary.merge_all()
summary_hook = tf.train.SummarySaverHook(
save_secs=300, output_dir=train_dir, summary_op=summary_op)
hooks = [summary_hook]
eval_metrics = None
return hooks, eval_metrics, train_op
