def testWeightSparsityTiebreaker(self):
param_list = [
"begin_pruning_step=1", "pruning_frequency=1", "end_pruning_step=100",
"target_sparsity=0.5",
"threshold_decay=0.0"
]
test_spec = ",".join(param_list)
pruning_hparams = pruning.get_pruning_hparams().parse(test_spec)
with tf.variable_scope("layer1"):
w1 = tf.Variable(np.ones([100], dtype=np.float32),
name="weights")
_ = pruning.apply_mask(w1)
p = pruning.Pruning(pruning_hparams)
mask_update_op = p.conditional_mask_update_op()
increment_global_step = tf.assign_add(self.global_step, 1)
with self.cached_session() as session:
tf.global_variables_initializer().run()
for _ in range(110):
session.run(mask_update_op)
session.run(increment_global_step)
self.assertAllClose(
session.run(pruning.get_weight_sparsity()), [0.5])
def testWeightSpecificSparsity(self):
param_list = [
"begin_pruning_step=1", "pruning_frequency=1", "end_pruning_step=100",
"target_sparsity=0.5",
"weight_sparsity_map=[layer1:0.6,layer2/weights:0.75,.*kernel:0.6]",
"threshold_decay=0.0"
]
test_spec = ",".join(param_list)
pruning_hparams = pruning.get_pruning_hparams().parse(test_spec)
with tf.variable_scope("layer1"):
w1 = tf.Variable(tf.linspace(1.0, 100.0, 100), name="weights")
_ = pruning.apply_mask(w1)
with tf.variable_scope("layer2"):
w2 = tf.Variable(tf.linspace(1.0, 100.0, 100), name="weights")
_ = pruning.apply_mask(w2)
with tf.variable_scope("layer3"):
w3 = tf.Variable(tf.linspace(1.0, 100.0, 100), name="kernel")
_ = pruning.apply_mask(w3)
p = pruning.Pruning(pruning_hparams)
mask_update_op = p.conditional_mask_update_op()
increment_global_step = tf.assign_add(self.global_step, 1)
with self.cached_session() as session:
tf.global_variables_initializer().run()
for _ in range(110):
session.run(mask_update_op)
session.run(increment_global_step)
self.assertAllClose(
session.run(pruning.get_weight_sparsity()), [0.6, 0.75, 0.6])
def testPerLayerBlockSparsity(self):
param_list = [
"block_dims_map=[layer1/weights:1x1,layer2/weights:1x2]",
"block_pooling_function=AVG", "threshold_decay=0.0"
]
test_spec = ",".join(param_list)
pruning_hparams = pruning.get_pruning_hparams().parse(test_spec)
with tf.variable_scope("layer1"):
w1 = tf.Variable([[-0.1, 0.1], [-0.2, 0.2]], name="weights")
pruning.apply_mask(w1)
with tf.variable_scope("layer2"):
w2 = tf.Variable([[0.1, 0.1, 0.3, 0.3], [0.2, 0.2, 0.4, 0.4]],
name="weights")
pruning.apply_mask(w2)
sparsity = tf.Variable(0.5, name="sparsity")
p = pruning.Pruning(pruning_hparams, sparsity=sparsity)
mask_update_op = p.mask_update_op()
with self.cached_session() as session:
tf.global_variables_initializer().run()
session.run(mask_update_op)
mask1_eval = session.run(pruning.get_masks()[0])
mask2_eval = session.run(pruning.get_masks()[1])
self.assertAllEqual(
session.run(pruning.get_weight_sparsity()), [0.5, 0.5])
self.assertAllEqual(mask1_eval, [[0.0, 0.0], [1., 1.]])
self.assertAllEqual(mask2_eval, [[0, 0, 1., 1.], [0, 0, 1., 1.]])
def testGroupSpecificBlockSparsity(self):
param_list = [
"begin_pruning_step=1",
"pruning_frequency=1",
"end_pruning_step=100",
"target_sparsity=0.5",
"group_sparsity_map=[group1:0.6,group2:0.75]",
"group_block_dims_map=[group1:2x2,group2:2x4]",
"threshold_decay=0.0",
"group_pruning=True",
]
test_spec = ",".join(param_list)
pruning_hparams = pruning.get_pruning_hparams().parse(test_spec)
stacked_tensor_1 = pruning_utils.expand_tensor(
tf.reshape(tf.linspace(1.0, 100.0, 100), [1, 100]), [2, 2])
stacked_tensor_2 = pruning_utils.expand_tensor(
tf.reshape(tf.linspace(1.0, 100.0, 100), [1, 100]), [2, 4])
stacked_tensor_3 = pruning_utils.expand_tensor(
tf.reshape(tf.linspace(1.0, 200.0, 100), [1, 100]), [2, 4])
with tf.variable_scope("layer1"):
w1 = tf.Variable(stacked_tensor_1, name="weights")
_ = pruning.apply_mask_with_group(w1, group_name="group1")
with tf.variable_scope("layer2"):
w2 = tf.Variable(stacked_tensor_2, name="weights")
_ = pruning.apply_mask_with_group(w2, group_name="group2")
with tf.variable_scope("layer3"):
w3 = tf.Variable(stacked_tensor_2, name="kernel")
_ = pruning.apply_mask_with_group(w3, group_name="group2")
with tf.variable_scope("layer4"):
w4 = tf.Variable(stacked_tensor_3, name="kernel")
_ = pruning.apply_mask_with_group(w4, group_name="group2")
p = pruning.Pruning(pruning_hparams)
mask_update_op = p.conditional_mask_update_op()
increment_global_step = tf.assign_add(self.global_step, 1)
with self.cached_session() as session:
tf.global_variables_initializer().run()
for _ in range(110):
session.run(mask_update_op)
session.run(increment_global_step)
self.assertAllClose(session.run(pruning.get_weight_sparsity()),
[0.6, 0.9, 0.9, 0.45])
