def test_prune_model_spec_with_path_dropout_rate_tensor(self):
model_spec = {
'op1':
schema.OneOf([
mobile_search_space_v3.ConvSpec(kernel_size=2, strides=2),
basic_specs.ZeroSpec(),
], basic_specs.OP_TAG),
'op2':
schema.OneOf([
mobile_search_space_v3.ConvSpec(kernel_size=3, strides=4),
], basic_specs.OP_TAG),
'filter':
schema.OneOf([32], basic_specs.FILTERS_TAG),
}
model_spec = search_space_utils.prune_model_spec(
model_spec, {basic_specs.OP_TAG: [0, 0]},
path_dropout_rate=tf.constant(2.0) / tf.constant(10.0),
training=True)
self.assertCountEqual(model_spec.keys(), ['op1', 'op2', 'filter'])
self.assertEqual(model_spec['op1'].mask.shape, tf.TensorShape([1]))
self.assertIsNone(model_spec['op2'].mask)
self.assertIsNone(model_spec['filter'].mask)
# The value should either be 0 or 1 / (1 - path_dropout_rate) = 1.25
op_mask_value = self.evaluate(model_spec['op1'].mask)
self.assertTrue(
abs(op_mask_value - 0) < 1e-6 or abs(op_mask_value - 1.25) < 1e-6,
msg='Unexpected op_mask_value: {}'.format(op_mask_value))
def test_get_strides_convolution(self):
self.assertEqual((1, 1),
mobile_search_space_v3.get_strides(
mobile_search_space_v3.ConvSpec(kernel_size=3,
strides=1)))
self.assertEqual((2, 2),
mobile_search_space_v3.get_strides(
mobile_search_space_v3.ConvSpec(kernel_size=3,
strides=2)))
def test_prune_model_spec_with_path_dropout_training(self):
model_spec = {
'op1':
schema.OneOf([
mobile_search_space_v3.ConvSpec(kernel_size=2, strides=2),
basic_specs.ZeroSpec(),
], basic_specs.OP_TAG),
'op2':
schema.OneOf([
mobile_search_space_v3.ConvSpec(kernel_size=3, strides=4),
], basic_specs.OP_TAG),
'filter':
schema.OneOf([32], basic_specs.FILTERS_TAG),
}
model_spec = search_space_utils.prune_model_spec(
model_spec, {basic_specs.OP_TAG: [0, 0]},
path_dropout_rate=0.2,
training=True)
self.assertCountEqual(model_spec.keys(), ['op1', 'op2', 'filter'])
self.assertEqual(model_spec['op1'].mask.shape, tf.TensorShape([1]))
self.assertIsNone(model_spec['op2'].mask)
self.assertIsNone(model_spec['filter'].mask)
self.assertEqual(
model_spec['op1'].choices,
[mobile_search_space_v3.ConvSpec(kernel_size=2, strides=2)])
self.assertEqual(
model_spec['op2'].choices,
[mobile_search_space_v3.ConvSpec(kernel_size=3, strides=4)])
self.assertEqual(model_spec['filter'].choices, [32])
self.assertEqual(model_spec['op1'].tag, basic_specs.OP_TAG)
self.assertEqual(model_spec['op2'].tag, basic_specs.OP_TAG)
self.assertEqual(model_spec['filter'].tag, basic_specs.FILTERS_TAG)
op_mask_sum = 0
for _ in range(100):
# The value should either be 0 or 1 / (1 - path_dropout_rate) = 1.25
op_mask_value = self.evaluate(model_spec['op1'].mask)
self.assertTrue(
abs(op_mask_value - 0) < 1e-6
or abs(op_mask_value - 1.25) < 1e-6,
msg='Unexpected op_mask_value: {}'.format(op_mask_value))
op_mask_sum += op_mask_value[0]
# The probability of this test failing by random chance is roughly 0.002%.
# Our random number generators are deterministically seeded, so the test
# shouldn't be flakey.
self.assertGreaterEqual(op_mask_sum, 75)
self.assertLessEqual(op_mask_sum, 113)
def test_v3_zero_or_conv_with_child(self):
kernel_size_mask = tf.placeholder(shape=[3], dtype=tf.float32)
kernel_size = schema.OneOf([3, 5, 7], basic_specs.OP_TAG,
kernel_size_mask)
layer_mask = tf.placeholder(shape=[2], dtype=tf.float32)
layer = schema.OneOf(choices=[
basic_specs.ZeroSpec(),
mobile_search_space_v3.ConvSpec(kernel_size=kernel_size,
strides=1),
],
tag=basic_specs.OP_TAG,
mask=layer_mask)
features = mobile_cost_model.coupled_tf_features(
_make_single_layer_model(layer))
with self.session() as sess:
self.assertAllClose([1.0, 0.0, 0.0, 0.0],
sess.run(
features, {
layer_mask: [1, 0],
kernel_size_mask: [1, 0, 0]
}))
self.assertAllClose([1.0, 0.0, 0.0, 0.0],
sess.run(
features, {
layer_mask: [1, 0],
kernel_size_mask: [0, 1, 0]
}))
self.assertAllClose([1.0, 0.0, 0.0, 0.0],
sess.run(
features, {
layer_mask: [1, 0],
kernel_size_mask: [0, 0, 1]
}))
self.assertAllClose([0.0, 1.0, 0.0, 0.0],
sess.run(
features, {
layer_mask: [0, 1],
kernel_size_mask: [1, 0, 0]
}))
self.assertAllClose([0.0, 0.0, 1.0, 0.0],
sess.run(
features, {
layer_mask: [0, 1],
kernel_size_mask: [0, 1, 0]
}))
self.assertAllClose([0.0, 0.0, 0.0, 1.0],
sess.run(
features, {
layer_mask: [0, 1],
kernel_size_mask: [0, 0, 1]
}))
def test_prune_model_spec_with_path_dropout_eval(self):
model_spec = {
'op1':
schema.OneOf([
mobile_search_space_v3.ConvSpec(kernel_size=2, strides=2),
basic_specs.ZeroSpec(),
], basic_specs.OP_TAG),
'op2':
schema.OneOf([
mobile_search_space_v3.ConvSpec(kernel_size=3, strides=4),
], basic_specs.OP_TAG),
'filter':
schema.OneOf([32], basic_specs.FILTERS_TAG),
}
model_spec = search_space_utils.prune_model_spec(
model_spec, {basic_specs.OP_TAG: [0, 0]},
path_dropout_rate=0.2,
training=False)
self.assertCountEqual(model_spec.keys(), ['op1', 'op2', 'filter'])
# Even though path_dropout_rate=0.2, the controller should not populate
# the mask for op1 because we called prune_model_spec() with training=False.
# In other words, path_dropout_rate should only affect the behavior during
# training, not during evaluation.
self.assertIsNone(model_spec['op1'].mask)
self.assertIsNone(model_spec['op2'].mask)
self.assertIsNone(model_spec['filter'].mask)
self.assertEqual(model_spec['op1'].tag, basic_specs.OP_TAG)
self.assertEqual(model_spec['op2'].tag, basic_specs.OP_TAG)
self.assertEqual(model_spec['filter'].tag, basic_specs.FILTERS_TAG)
self.assertEqual(
model_spec['op1'].choices,
[mobile_search_space_v3.ConvSpec(kernel_size=2, strides=2)])
self.assertEqual(
model_spec['op2'].choices,
[mobile_search_space_v3.ConvSpec(kernel_size=3, strides=4)])
self.assertEqual(model_spec['filter'].choices, [32])
def test_get_strides_oneof(self):
self.assertEqual(
(1, 1),
mobile_search_space_v3.get_strides(
schema.OneOf([
mobile_search_space_v3.ConvSpec(kernel_size=3, strides=1),
mobile_search_space_v3.ConvSpec(kernel_size=5, strides=1),
], basic_specs.OP_TAG)))
self.assertEqual(
(2, 2),
mobile_search_space_v3.get_strides(
schema.OneOf([
mobile_search_space_v3.ConvSpec(kernel_size=3, strides=2),
mobile_search_space_v3.ConvSpec(kernel_size=5, strides=2),
], basic_specs.OP_TAG)))
with self.assertRaisesRegex(ValueError, 'Stride mismatch'):
mobile_search_space_v3.get_strides(
schema.OneOf([
mobile_search_space_v3.ConvSpec(kernel_size=3, strides=1),
mobile_search_space_v3.ConvSpec(kernel_size=3, strides=2),
], basic_specs.OP_TAG))
def test_v3_two_children(self):
kernel_size1_mask = tf.placeholder(shape=[3], dtype=tf.float32)
kernel_size1 = schema.OneOf([3, 5, 7], basic_specs.OP_TAG,
kernel_size1_mask)
kernel_size2_mask = tf.placeholder(shape=[2], dtype=tf.float32)
kernel_size2 = schema.OneOf([3, 5], basic_specs.OP_TAG,
kernel_size2_mask)
layer_mask = tf.placeholder(shape=[2], dtype=tf.float32)
layer = schema.OneOf(choices=[
mobile_search_space_v3.SeparableConvSpec(kernel_size=kernel_size1,
strides=1),
mobile_search_space_v3.ConvSpec(kernel_size=kernel_size2,
strides=1),
],
tag=basic_specs.OP_TAG,
mask=layer_mask)
features = mobile_cost_model.coupled_tf_features(
_make_single_layer_model(layer))
with self.session() as sess:
self.assertAllClose(
[1.0, 0.0, 0.0, 0.0, 0.0],
sess.run(
features,
{
layer_mask: [1, 0], # select the first mask
kernel_size1_mask: [1, 0, 0],
kernel_size2_mask: [1, 0] # should be ignored
}))
self.assertAllClose(
[0.0, 1.0, 0.0, 0.0, 0.0],
sess.run(
features,
{
layer_mask: [1, 0], # select the first mask
kernel_size1_mask: [0, 1, 0],
kernel_size2_mask: [1, 0] # should be ignored
}))
self.assertAllClose(
[0.0, 0.0, 1.0, 0.0, 0.0],
sess.run(
features,
{
layer_mask: [1, 0], # select the first mask
kernel_size1_mask: [0, 0, 1],
kernel_size2_mask: [1, 0] # should be ignored
}))
self.assertAllClose(
[0.0, 0.0, 0.0, 1.0, 0.0], # select the second mask
sess.run(
features,
{
layer_mask: [0, 1],
kernel_size1_mask: [1, 0, 0], # should be ignored
kernel_size2_mask: [1, 0]
}))
self.assertAllClose(
[0.0, 0.0, 0.0, 0.0, 1.0], # select the second mask
sess.run(
features,
{
layer_mask: [0, 1],
kernel_size1_mask: [1, 0, 0], # should be ignored
kernel_size2_mask: [0, 1]
}))
