def test_svd_automatic_rank_selection_mem_top_percent(self):
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
# Allocate the generator you wish to use to provide the network with data
generator = tf_gen.TfRecordGenerator(
tfrecords=[os.path.join('data', 'mnist', 'validation.tfrecords')],
parser=MnistParser(data_inputs=['reshape_input']))
# Allocate the SVD instance and compress the network
svd = s.Svd(graph=os.path.join('models', 'mnist_save.meta'),
checkpoint=os.path.join('models', 'mnist_save'),
output_file=os.path.join('svd', 'svd_graph'),
layers=[],
num_ranks=5,
layer_selection_threshold=0.90,
metric=s.CostMetric.mac)
stats = svd.compress_net(generator=generator,
iterations=10,
error_margin=2)
stats.pretty_print(logger=logger)
self.assertTrue(1.0 > stats.compressed_model_accuracy)
self.assertTrue(
stats.baseline_model_accuracy >= stats.compressed_model_accuracy)
self.assertEqual(
2,
len(stats.per_rank_index[
stats.best_rank_index].per_selected_layer))
shutil.rmtree(str('./svd'))
def test_svd_automatic_rank_selection_mac_top_percent(self):
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
# Allocate the generator you wish to use to provide the network with data
generator = tf_gen.TfRecordGenerator(
tfrecords=[os.path.join('data', 'mnist', 'validation.tfrecords')],
parser=MnistParser(data_inputs=['reshape_input']))
# error_margin 85 % : forces to go through all 20 rank indices
error_margin = 85
# Allocate the SVD instance and compress the network
svd = s.Svd(graph=os.path.join('models', 'mnist_save.meta'),
checkpoint=os.path.join('models', 'mnist_save'),
output_file=os.path.join('svd', 'svd_graph'),
layers=[],
num_ranks=5,
layer_selection_threshold=0.90,
metric=s.CostMetric.mac)
stats = svd.compress_net(generator=generator,
iterations=10,
error_margin=error_margin)
stats.pretty_print(logger=logger)
shutil.rmtree(str('./svd'))
def test_svd_manual_rank_selection(self):
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
# Allocate the generator you wish to use to provide the network with data
generator = tf_gen.TfRecordGenerator(
tfrecords=[os.path.join('data', 'mnist', 'validation.tfrecords')],
parser=MnistParser(data_inputs=['reshape_input']))
# Only Compress Conv2d_1 and MatMul_1 with ranks 31 and 9 respectively
# no_evaluation should be True in Manual mode
layers = ['conv2/Conv2D', 'dense_1/MatMul']
layer_ranks = [('conv2/Conv2D', 31), ('dense_1/MatMul', 9)]
svd = s.Svd(graph=os.path.join('models', 'mnist_save.meta'),
checkpoint=os.path.join('models', 'mnist_save'),
output_file=os.path.join('svd', 'svd_graph'),
layers=layers,
layer_ranks=layer_ranks,
num_ranks=20,
no_evaluation=True,
metric=s.CostMetric.memory)
stats = svd.compress_net(generator=generator,
iterations=10,
error_margin=2)
stats.pretty_print(logger=logger)
self.assertTrue(1.0 >= stats.compressed_model_accuracy)
self.assertTrue(
stats.baseline_model_accuracy >= stats.compressed_model_accuracy)
shutil.rmtree(str('./svd'))
def weight_svd_manual_mode(self):
# Allocate the generator you wish to use to provide the network with data
generator = tf_gen.TfRecordGenerator(
tfrecords=[os.path.join('data', 'mnist', 'validation.tfrecords')],
parser=MnistParser())
# Only Compress Conv2d_1 and MatMul_1 with ranks 31 and 9 respectively
# no_evaluation should be True in Manual mode
layers = ['Conv2D_1', 'MatMul_1']
layer_ranks = [('Conv2D_1', 31), ('MatMul_1', 9)]
svd = s.Svd(graph=os.path.join('models', 'mnist_save.meta'),
checkpoint=os.path.join('models', 'mnist_save'),
output_file=os.path.join('svd', 'svd_graph'),
layers=layers,
layer_ranks=layer_ranks,
num_ranks=20,
no_evaluation=True,
metric=s.CostMetric.memory)
stats = svd.compress_net(generator=generator, iterations=10)
stats.pretty_print() # Print the stats for Weight SVD compression
def test_svd_layer_selection_without_mo(self):
tf.compat.v1.reset_default_graph()
svd = s.Svd(None, None, s.CostMetric.memory)
svd._svd = create_autospec(pymo.Svd, instance=True)
x = tf.compat.v1.placeholder(tf.float32, [None, 784], 'data')
y_hat = model(x)
sess = tf.compat.v1.Session()
sess.run(tf.compat.v1.global_variables_initializer())
svd._svd.GetCompressionType.return_value = pymo.SVD_COMPRESS_TYPE.TYPE_SINGLE
svd._store_net_stats(sess)
self.assertTrue(svd._svd.GetCompressionType.called)
def test_svd_layer_selection_with_mo(self):
tf.compat.v1.reset_default_graph()
svd = s.Svd(None, None, s.CostMetric.memory)
x = tf.compat.v1.placeholder(tf.float32, [None, 784], 'data')
y_hat = model(x)
sess = tf.compat.v1.Session()
sess.run(tf.compat.v1.global_variables_initializer())
svd._store_net_stats(sess)
layers = svd._svd.GetLayerNames()
print("Layers added: ", layers)
def weight_svd_auto_mode(self):
# Allocate the generator you wish to use to provide the network with data
generator = tf_gen.TfRecordGenerator(
tfrecords=[os.path.join('data', 'mnist', 'validation.tfrecords')],
parser=MnistParser())
# Allocate the SVD instance and compress the network
svd = s.Svd(graph=os.path.join('models', 'mnist_save.meta'),
checkpoint=os.path.join('models', 'mnist_save'),
output_file=os.path.join('svd', 'svd_graph'),
layers=[],
num_ranks=20,
layer_selection_threshold=0.95,
metric=s.CostMetric.memory)
stats = svd.compress_net(generator=generator, iterations=10)
stats.pretty_print() # Print the stats for Weight SVD compression