def testDropout(self, X):
input_record = self.new_record(schema.Scalar((np.float32, (1,))))
schema.FeedRecord(input_record, [X])
d_output = self.model.Dropout(input_record)
self.assertEqual(schema.Scalar((np.float32, (1,))), d_output)
self.model.output_schema = schema.Struct()
train_init_net, train_net = self.get_training_nets()
input_blob = input_record.field_blobs()[0]
output_blob = d_output.field_blobs()[0]
train_d_spec = OpSpec(
"Dropout",
[input_blob],
[output_blob, None],
{'is_test': 0, 'ratio': 0.5}
)
test_d_spec = OpSpec(
"Dropout",
[input_blob],
[output_blob, None],
{'is_test': 1, 'ratio': 0.5}
)
self.assertNetContainOps(
train_net,
[train_d_spec]
)
eval_net = self.get_eval_net()
self.assertNetContainOps(
eval_net,
[test_d_spec]
)
predict_net = self.get_predict_net()
self.assertNetContainOps(
predict_net,
[test_d_spec]
)
workspace.RunNetOnce(train_init_net)
workspace.RunNetOnce(train_net)
schema.FeedRecord(input_record, [X])
workspace.RunNetOnce(eval_net)
schema.FeedRecord(input_record, [X])
workspace.RunNetOnce(predict_net)
def testMapToRange(self):
input_record = self.new_record(schema.Scalar(np.int32))
indices_blob = self.model.MapToRange(input_record,
max_index=100).indices
self.model.output_schema = schema.Struct()
train_init_net, train_net = self.get_training_nets()
schema.FeedRecord(
input_record,
[np.array([10, 3, 20, 99, 15, 11, 3, 11], dtype=np.int32)])
workspace.RunNetOnce(train_init_net)
workspace.RunNetOnce(train_net)
indices = workspace.FetchBlob(indices_blob())
np.testing.assert_array_equal(
np.array([1, 2, 3, 4, 5, 6, 2, 6], dtype=np.int32), indices)
schema.FeedRecord(
input_record,
[np.array([10, 3, 23, 35, 60, 15, 10, 15], dtype=np.int32)])
workspace.RunNetOnce(train_net)
indices = workspace.FetchBlob(indices_blob())
np.testing.assert_array_equal(
np.array([1, 2, 7, 8, 9, 5, 1, 5], dtype=np.int32), indices)
eval_net = self.get_eval_net()
schema.FeedRecord(
input_record,
[np.array([10, 3, 23, 35, 60, 15, 200], dtype=np.int32)])
workspace.RunNetOnce(eval_net)
indices = workspace.FetchBlob(indices_blob())
np.testing.assert_array_equal(
np.array([1, 2, 7, 8, 9, 5, 0], dtype=np.int32), indices)
schema.FeedRecord(
input_record,
[np.array([10, 3, 23, 15, 101, 115], dtype=np.int32)])
workspace.RunNetOnce(eval_net)
indices = workspace.FetchBlob(indices_blob())
np.testing.assert_array_equal(
np.array([1, 2, 7, 5, 0, 0], dtype=np.int32), indices)
predict_net = self.get_predict_net()
schema.FeedRecord(
input_record,
[np.array([3, 3, 20, 23, 151, 35, 60, 15, 200], dtype=np.int32)])
workspace.RunNetOnce(predict_net)
indices = workspace.FetchBlob(indices_blob())
np.testing.assert_array_equal(
np.array([2, 2, 3, 7, 0, 8, 9, 5, 0], dtype=np.int32), indices)
def testSelectRecordByContext(self):
float_features = self.model.input_feature_schema.float_features
float_array = np.array([1.0, 2.0], dtype=np.float32)
schema.FeedRecord(float_features, [float_array])
with Tags(Tags.EXCLUDE_FROM_PREDICTION):
log_float_features = self.model.Log(float_features, 1)
joined = self.model.SelectRecordByContext(
schema.Struct(
(InstantiationContext.PREDICTION, float_features),
(InstantiationContext.TRAINING, log_float_features),
# TODO: TRAIN_ONLY layers are also generated in eval
(InstantiationContext.EVAL, log_float_features),
))
# model.output_schema has to a struct
self.model.output_schema = schema.Struct(('joined', joined))
predict_net = layer_model_instantiator.generate_predict_net(self.model)
workspace.RunNetOnce(predict_net)
predict_output = schema.FetchRecord(predict_net.output_record())
npt.assert_array_equal(float_array, predict_output['joined']())
eval_net = layer_model_instantiator.generate_eval_net(self.model)
workspace.RunNetOnce(eval_net)
eval_output = schema.FetchRecord(eval_net.output_record())
npt.assert_array_equal(np.log(float_array), eval_output['joined']())
_, train_net = (
layer_model_instantiator.generate_training_nets_forward_only(
self.model))
workspace.RunNetOnce(train_net)
train_output = schema.FetchRecord(train_net.output_record())
npt.assert_array_equal(np.log(float_array), train_output['joined']())
def testConv(self):
batch_size = 50
H = 1
W = 10
C = 50
output_dims = 32
kernel_h = 1
kernel_w = 3
stride_h = 1
stride_w = 1
pad_t = 0
pad_b = 0
pad_r = None
pad_l = None
input_record = self.new_record(schema.Scalar((np.float32, (H, W, C))))
X = np.random.random((batch_size, H, W, C)).astype(np.float32)
schema.FeedRecord(input_record, [X])
conv = self.model.Conv(input_record,
output_dims,
kernel_h=kernel_h,
kernel_w=kernel_w,
stride_h=stride_h,
stride_w=stride_w,
pad_t=pad_t,
pad_b=pad_b,
pad_r=pad_r,
pad_l=pad_l,
order='NHWC')
self.assertEqual(schema.Scalar((np.float32, (output_dims, ))), conv)
self.run_train_net_forward_only()
output_record = schema.FetchRecord(conv)
# check the number of output channels is the same as input in this example
assert output_record.field_types()[0].shape == (H, W, output_dims)
assert output_record().shape == (batch_size, H, W, output_dims)
train_init_net, train_net = self.get_training_nets()
# Init net assertions
init_ops = self.assertNetContainOps(train_init_net, [
OpSpec("XavierFill", None, None),
OpSpec("ConstantFill", None, None),
])
conv_spec = OpSpec("Conv", [
input_record.field_blobs()[0],
init_ops[0].output[0],
init_ops[1].output[0],
], conv.field_blobs())
# Train net assertions
self.assertNetContainOps(train_net, [conv_spec])
# Predict net assertions
predict_net = self.get_predict_net()
self.assertNetContainOps(predict_net, [conv_spec])
# Eval net assertions
eval_net = self.get_eval_net()
self.assertNetContainOps(eval_net, [conv_spec])
def testLastNWindowCollector(self, X, num_to_collect):
input_record = self.new_record(schema.Scalar(np.float32))
schema.FeedRecord(input_record, [X])
last_n = self.model.LastNWindowCollector(input_record, num_to_collect)
self.run_train_net_forward_only()
output_record = schema.FetchRecord(last_n)
start = max(0, 5 - num_to_collect)
npt.assert_array_equal(X[start:], output_record())
def testRandomFourierFeatures(self, batch_size, input_dims, output_dims, bandwidth):
X = np.random.random((batch_size, input_dims)).astype(np.float32)
scale = np.sqrt(2.0 / output_dims)
input_record = self.new_record(schema.Scalar((np.float32, (input_dims,))))
schema.FeedRecord(input_record, [X])
input_blob = input_record.field_blobs()[0]
rff_output = self.model.RandomFourierFeatures(input_record,
output_dims,
bandwidth)
self.model.output_schema = schema.Struct()
self.assertEqual(
schema.Scalar((np.float32, (output_dims, ))),
rff_output
)
train_init_net, train_net = self.get_training_nets()
# Init net assertions
init_ops_list = [
OpSpec("GaussianFill", None, None),
OpSpec("UniformFill", None, None),
]
init_ops = self._test_net(train_init_net, init_ops_list)
W = workspace.FetchBlob(self.model.layers[0].w)
b = workspace.FetchBlob(self.model.layers[0].b)
# Operation specifications
fc_spec = OpSpec("FC", [input_blob, init_ops[0].output[0],
init_ops[1].output[0]], None)
cosine_spec = OpSpec("Cos", None, None)
scale_spec = OpSpec("Scale", None, rff_output.field_blobs(),
{'scale': scale})
ops_list = [
fc_spec,
cosine_spec,
scale_spec
]
# Train net assertions
self._test_net(train_net, ops_list)
self._rff_hypothesis_test(rff_output(), X, W, b, scale)
# Eval net assertions
eval_net = self.get_eval_net()
self._test_net(eval_net, ops_list)
self._rff_hypothesis_test(rff_output(), X, W, b, scale)
# Predict net assertions
predict_net = self.get_predict_net()
self._test_net(predict_net, ops_list)
self._rff_hypothesis_test(rff_output(), X, W, b, scale)
def testMarginRankLoss(self):
input_record = self.new_record(
schema.Struct(
('pos_prediction', schema.Scalar((np.float32, (1, )))),
('neg_prediction', schema.List(np.float32)),
))
pos_items = np.array([0.1, 0.2, 0.3], dtype=np.float32)
neg_lengths = np.array([1, 2, 3], dtype=np.int32)
neg_items = np.array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6], dtype=np.float32)
schema.FeedRecord(input_record, [pos_items, neg_lengths, neg_items])
loss = self.model.MarginRankLoss(input_record)
self.run_train_net_forward_only()
self.assertEqual(schema.Scalar((np.float32, tuple())), loss)
def testUniformSampling(self):
input_record = self.new_record(schema.Scalar(np.int32))
input_array = np.array([3, 10, 11, 15, 20, 99], dtype=np.int32)
schema.FeedRecord(input_record, [input_array])
num_samples = 20
num_elements = 100
uniform_sampling_output = self.model.UniformSampling(
input_record, num_samples, num_elements)
self.model.loss = uniform_sampling_output
self.run_train_net()
samples = workspace.FetchBlob(uniform_sampling_output.samples())
sampling_prob = workspace.FetchBlob(
uniform_sampling_output.sampling_prob())
self.assertEqual(num_samples, len(samples))
np.testing.assert_array_equal(input_array, samples[:len(input_array)])
np.testing.assert_almost_equal(
np.array([float(num_samples) / num_elements] * num_samples,
dtype=np.float32), sampling_prob)
def testMergeIdListsLayer(self, num_inputs, batch_size):
inputs = []
for _ in range(num_inputs):
lengths = np.random.randint(5, size=batch_size).astype(np.int32)
size = lengths.sum()
values = np.random.randint(1, 10, size=size).astype(np.int64)
inputs.append(lengths)
inputs.append(values)
input_schema = schema.Tuple(*[
schema.List(
schema.Scalar(dtype=np.int64,
metadata=schema.Metadata(categorical_limit=20)))
for _ in range(num_inputs)
])
input_record = schema.NewRecord(self.model.net, input_schema)
schema.FeedRecord(input_record, inputs)
output_schema = self.model.MergeIdLists(input_record)
assert schema.equal_schemas(output_schema,
IdList,
check_field_names=False)
def test_regularizer_context(self, X):
weight_reg_out = L1Norm(0.2)
bias_reg_out = L1Norm(0)
regularizers = {"WEIGHT": weight_reg_out, "BIAS": bias_reg_out}
output_dims = 2
input_record = self.new_record(schema.Scalar((np.float32, (5, ))))
schema.FeedRecord(input_record, [X])
with UseRegularizer(regularizers):
weight_reg = RegularizerContext.current().get_regularizer("WEIGHT")
bias_reg = RegularizerContext.current().get_regularizer("BIAS")
optim = SgdOptimizer(0.15)
assert (weight_reg == weight_reg_out
), "fail to get correct weight reg from context"
assert bias_reg == bias_reg_out, "fail to get correct bias reg from context"
fc_output = self.model.FC(
input_record,
output_dims,
weight_optim=optim,
bias_optim=optim,
weight_reg=weight_reg,
bias_reg=bias_reg,
)
# model.output_schema has to a struct
self.model.output_schema = schema.Struct(("fc_output", fc_output))
self.assertEqual(schema.Scalar((np.float32, (output_dims, ))),
fc_output)
_, train_net = layer_model_instantiator.generate_training_nets(
self.model)
ops = train_net.Proto().op
ops_type_list = [ops[i].type for i in range(len(ops))]
assert ops_type_list.count("LpNorm") == 2
assert ops_type_list.count("Scale") == 4
assert ops_type_list.count("LpNormGradient") == 2
def testSemiRandomFeatures(self, batch_size, input_dims, output_dims, s,
scale, set_weight_as_global_constant,
use_struct_input):
def _semi_random_hypothesis_test(srf_output, X_full, X_random, rand_w,
rand_b, s):
"""
Runs hypothesis test for Semi Random Features layer.
Inputs:
srf_output -- output of net after running semi random features layer
X_full -- full input data
X_random -- random-output input data
rand_w -- random-initialized weight parameter from train_init_net
rand_b -- random-initialized bias parameter from train_init_net
s -- degree parameter
"""
# Get output from net
net_output = workspace.FetchBlob(srf_output)
# Fetch learned parameter blobs
learned_w = workspace.FetchBlob(self.model.layers[0].learned_w)
learned_b = workspace.FetchBlob(self.model.layers[0].learned_b)
# Computing output directly
x_rand = np.matmul(X_random, np.transpose(rand_w)) + rand_b
x_learn = np.matmul(X_full, np.transpose(learned_w)) + learned_b
x_pow = np.power(x_rand, s)
if s > 0:
h_rand_features = np.piecewise(x_rand,
[x_rand <= 0, x_rand > 0],
[0, 1])
else:
h_rand_features = np.piecewise(x_rand,
[x_rand <= 0, x_rand > 0],
[0, lambda x: x / (1 + x)])
output_ref = np.multiply(np.multiply(x_pow, h_rand_features),
x_learn)
# Comparing net output and computed output
npt.assert_allclose(net_output, output_ref, rtol=1e-4)
X_full = np.random.normal(size=(batch_size,
input_dims)).astype(np.float32)
if use_struct_input:
X_random = np.random.normal(size=(batch_size, input_dims)).\
astype(np.float32)
input_data = [X_full, X_random]
input_record = self.new_record(
schema.Struct(
('full', schema.Scalar((np.float32, (input_dims, )))),
('random', schema.Scalar((np.float32, (input_dims, ))))))
else:
X_random = X_full
input_data = [X_full]
input_record = self.new_record(
schema.Scalar((np.float32, (input_dims, ))))
schema.FeedRecord(input_record, input_data)
srf_output = self.model.SemiRandomFeatures(
input_record,
output_dims,
s=s,
scale_random=scale,
scale_learned=scale,
set_weight_as_global_constant=set_weight_as_global_constant)
self.model.output_schema = schema.Struct()
self.assertEqual(
schema.Struct(
('full', schema.Scalar((np.float32, (output_dims, )))),
('random', schema.Scalar((np.float32, (output_dims, ))))),
srf_output)
init_ops_list = [
OpSpec("GaussianFill", None, None),
OpSpec("UniformFill", None, None),
OpSpec("GaussianFill", None, None),
OpSpec("UniformFill", None, None),
]
train_init_net, train_net = self.get_training_nets()
# Need to run to initialize the global constants for layer
workspace.RunNetOnce(self.model.create_init_net(name='init_net'))
if set_weight_as_global_constant:
# If weight params are global constants, they won't be in train_init_net
init_ops = self._test_net(train_init_net, init_ops_list[:2])
rand_w = workspace.FetchBlob(
self.model.
global_constants['semi_random_features_fixed_rand_W'])
rand_b = workspace.FetchBlob(
self.model.
global_constants['semi_random_features_fixed_rand_b'])
# Operation specifications
fc_random_spec = OpSpec("FC", [None, None, None], None)
fc_learned_spec = OpSpec(
"FC", [None, init_ops[0].output[0], init_ops[1].output[0]],
None)
else:
init_ops = self._test_net(train_init_net, init_ops_list)
rand_w = workspace.FetchBlob(self.model.layers[0].random_w)
rand_b = workspace.FetchBlob(self.model.layers[0].random_b)
# Operation specifications
fc_random_spec = OpSpec(
"FC", [None, init_ops[0].output[0], init_ops[1].output[0]],
None)
fc_learned_spec = OpSpec(
"FC", [None, init_ops[2].output[0], init_ops[3].output[0]],
None)
softsign_spec = OpSpec("Softsign", None, None)
relu_spec = OpSpec("Relu", None, None)
relu_output_spec = OpSpec("Relu", None,
srf_output.random.field_blobs())
pow_spec = OpSpec("Pow", None, None, {'exponent': float(s - 1)})
mul_interim_spec = OpSpec("Mul", None, srf_output.random.field_blobs())
mul_spec = OpSpec("Mul", None, srf_output.full.field_blobs())
if s == 0:
ops_list = [
fc_learned_spec,
fc_random_spec,
softsign_spec,
relu_output_spec,
mul_spec,
]
elif s == 1:
ops_list = [
fc_learned_spec,
fc_random_spec,
relu_output_spec,
mul_spec,
]
else:
ops_list = [
fc_learned_spec,
fc_random_spec,
relu_spec,
pow_spec,
mul_interim_spec,
mul_spec,
]
# Train net assertions
self._test_net(train_net, ops_list)
_semi_random_hypothesis_test(srf_output.full(), X_full, X_random,
rand_w, rand_b, s)
# Eval net assertions
eval_net = self.get_eval_net()
self._test_net(eval_net, ops_list)
_semi_random_hypothesis_test(srf_output.full(), X_full, X_random,
rand_w, rand_b, s)
# Predict net assertions
predict_net = self.get_predict_net()
self._test_net(predict_net, ops_list)
_semi_random_hypothesis_test(srf_output.full(), X_full, X_random,
rand_w, rand_b, s)
def testArcCosineFeatureMap(self, batch_size, input_dims, output_dims, s,
scale, set_weight_as_global_constant):
def _arc_cosine_hypothesis_test(ac_output, X, W, b, s):
"""
Runs hypothesis test for Arc Cosine layer.
Inputs:
ac_output -- output of net after running arc cosine layer
X -- input data
W -- weight parameter from train_init_net
b -- bias parameter from train_init_net
s -- degree parameter
"""
# Get output from net
net_output = workspace.FetchBlob(ac_output)
# Computing output directly
x_rand = np.matmul(X, np.transpose(W)) + b
x_pow = np.power(x_rand, s)
if s > 0:
h_rand_features = np.piecewise(x_rand,
[x_rand <= 0, x_rand > 0],
[0, 1])
else:
h_rand_features = np.piecewise(x_rand,
[x_rand <= 0, x_rand > 0],
[0, lambda x: x / (1 + x)])
output_ref = np.multiply(x_pow, h_rand_features)
# Comparing net output and computed output
npt.assert_allclose(net_output, output_ref, rtol=1e-4)
X = np.random.normal(size=(batch_size, input_dims)).astype(np.float32)
input_record = self.new_record(
schema.Scalar((np.float32, (input_dims, ))))
schema.FeedRecord(input_record, [X])
input_blob = input_record.field_blobs()[0]
ac_output = self.model.ArcCosineFeatureMap(
input_record,
output_dims,
s=s,
scale=scale,
set_weight_as_global_constant=set_weight_as_global_constant)
self.model.output_schema = schema.Struct()
self.assertEqual(schema.Scalar((np.float32, (output_dims, ))),
ac_output)
train_init_net, train_net = self.get_training_nets()
# Run create_init_net to initialize the global constants, and W and b
workspace.RunNetOnce(train_init_net)
workspace.RunNetOnce(self.model.create_init_net(name='init_net'))
if set_weight_as_global_constant:
W = workspace.FetchBlob(
self.model.
global_constants['arc_cosine_feature_map_fixed_rand_W'])
b = workspace.FetchBlob(
self.model.
global_constants['arc_cosine_feature_map_fixed_rand_b'])
else:
W = workspace.FetchBlob(self.model.layers[0].random_w)
b = workspace.FetchBlob(self.model.layers[0].random_b)
# Operation specifications
fc_spec = OpSpec("FC", [input_blob, None, None], None)
softsign_spec = OpSpec("Softsign", None, None)
relu_spec = OpSpec("Relu", None, None)
relu_spec_output = OpSpec("Relu", None, ac_output.field_blobs())
pow_spec = OpSpec("Pow", None, None, {'exponent': float(s - 1)})
mul_spec = OpSpec("Mul", None, ac_output.field_blobs())
if s == 0:
ops_list = [
fc_spec,
softsign_spec,
relu_spec_output,
]
elif s == 1:
ops_list = [
fc_spec,
relu_spec_output,
]
else:
ops_list = [
fc_spec,
relu_spec,
pow_spec,
mul_spec,
]
# Train net assertions
self._test_net(train_net, ops_list)
_arc_cosine_hypothesis_test(ac_output(), X, W, b, s)
# Eval net assertions
eval_net = self.get_eval_net()
self._test_net(eval_net, ops_list)
_arc_cosine_hypothesis_test(ac_output(), X, W, b, s)
# Predict net assertions
predict_net = self.get_predict_net()
self._test_net(predict_net, ops_list)
_arc_cosine_hypothesis_test(ac_output(), X, W, b, s)
def testRandomFourierFeatures(self, batch_size, input_dims, output_dims,
bandwidth):
def _rff_hypothesis_test(rff_output, X, W, b, scale):
"""
Runs hypothesis test for Semi Random Features layer.
Inputs:
rff_output -- output of net after running random fourier features layer
X -- input data
W -- weight parameter from train_init_net
b -- bias parameter from train_init_net
scale -- value by which to scale the output vector
"""
output = workspace.FetchBlob(rff_output)
output_ref = scale * np.cos(np.dot(X, np.transpose(W)) + b)
npt.assert_allclose(output, output_ref, rtol=1e-4)
X = np.random.random((batch_size, input_dims)).astype(np.float32)
scale = np.sqrt(2.0 / output_dims)
input_record = self.new_record(
schema.Scalar((np.float32, (input_dims, ))))
schema.FeedRecord(input_record, [X])
input_blob = input_record.field_blobs()[0]
rff_output = self.model.RandomFourierFeatures(input_record,
output_dims, bandwidth)
self.model.output_schema = schema.Struct()
self.assertEqual(schema.Scalar((np.float32, (output_dims, ))),
rff_output)
train_init_net, train_net = self.get_training_nets()
# Init net assertions
init_ops_list = [
OpSpec("GaussianFill", None, None),
OpSpec("UniformFill", None, None),
]
init_ops = self._test_net(train_init_net, init_ops_list)
W = workspace.FetchBlob(self.model.layers[0].w)
b = workspace.FetchBlob(self.model.layers[0].b)
# Operation specifications
fc_spec = OpSpec(
"FC", [input_blob, init_ops[0].output[0], init_ops[1].output[0]],
None)
cosine_spec = OpSpec("Cos", None, None)
scale_spec = OpSpec("Scale", None, rff_output.field_blobs(),
{'scale': scale})
ops_list = [fc_spec, cosine_spec, scale_spec]
# Train net assertions
self._test_net(train_net, ops_list)
_rff_hypothesis_test(rff_output(), X, W, b, scale)
# Eval net assertions
eval_net = self.get_eval_net()
self._test_net(eval_net, ops_list)
_rff_hypothesis_test(rff_output(), X, W, b, scale)
# Predict net assertions
predict_net = self.get_predict_net()
self._test_net(predict_net, ops_list)
_rff_hypothesis_test(rff_output(), X, W, b, scale)
def testRandomFourierFeatures(self, batch_size, input_dims, output_dims,
bandwidth):
X = np.random.random((batch_size, input_dims)).astype(np.float32)
scale = np.sqrt(2.0 / output_dims)
input_record = self.new_record(
schema.Scalar((np.float32, (input_dims, ))))
schema.FeedRecord(input_record, [X])
input_blob = input_record.field_blobs()[0]
rff_output = self.model.RandomFourierFeatures(input_record,
output_dims, bandwidth)
self.model.output_schema = schema.Struct()
self.assertEqual(schema.Scalar((np.float32, (output_dims, ))),
rff_output)
train_init_net, train_net = self.get_training_nets()
# Init net assertions
init_ops = self.assertNetContainOps(train_init_net, [
OpSpec("GaussianFill", None, None),
OpSpec("UniformFill", None, None),
])
# Operation specifications
mat_mul_spec = OpSpec("MatMul", [input_blob, init_ops[0].output[0]],
None)
add_spec = OpSpec("Add", [None, init_ops[1].output[0]], None, {
'broadcast': 1,
'axis': 1
})
cosine_spec = OpSpec("Cos", None, None)
scale_spec = OpSpec("Scale", None, rff_output.field_blobs(),
{'scale': scale})
# Train net assertions
self.assertNetContainOps(
train_net, [mat_mul_spec, add_spec, cosine_spec, scale_spec])
workspace.RunNetOnce(train_init_net)
W = workspace.FetchBlob(self.model.layers[0].w)
b = workspace.FetchBlob(self.model.layers[0].b)
workspace.RunNetOnce(train_net)
train_output = workspace.FetchBlob(rff_output())
train_ref = scale * np.cos(np.dot(X, W) + b)
npt.assert_almost_equal(train_output, train_ref)
# Eval net assertions
eval_net = self.get_eval_net()
self.assertNetContainOps(
eval_net, [mat_mul_spec, add_spec, cosine_spec, scale_spec])
schema.FeedRecord(input_record, [X])
workspace.RunNetOnce(eval_net)
eval_output = workspace.FetchBlob(rff_output())
eval_ref = scale * np.cos(np.dot(X, W) + b)
npt.assert_almost_equal(eval_output, eval_ref)
# Predict net assertions
predict_net = self.get_predict_net()
self.assertNetContainOps(
predict_net, [mat_mul_spec, add_spec, cosine_spec, scale_spec])
schema.FeedRecord(input_record, [X])
workspace.RunNetOnce(predict_net)
predict_output = workspace.FetchBlob(rff_output())
predict_ref = scale * np.cos(np.dot(X, W) + b)
npt.assert_almost_equal(predict_output, predict_ref)
def testArcCosineFeatureMap(self, batch_size, input_dims, output_dims, s,
scale):
X = np.random.normal(size=(batch_size, input_dims)).astype(np.float32)
input_record = self.new_record(
schema.Scalar((np.float32, (input_dims, ))))
schema.FeedRecord(input_record, [X])
input_blob = input_record.field_blobs()[0]
ac_output = self.model.ArcCosineFeatureMap(input_record,
output_dims,
s=s,
scale=scale)
self.model.output_schema = schema.Struct()
self.assertEqual(schema.Scalar((np.float32, (output_dims, ))),
ac_output)
init_ops_list = [
OpSpec("GaussianFill", None, None),
OpSpec("UniformFill", None, None),
]
train_init_net, train_net = self.get_training_nets()
# Init net assertions
init_ops = self._test_net(train_init_net, init_ops_list)
workspace.RunNetOnce(self.model.param_init_net)
W = workspace.FetchBlob(self.model.layers[0].random_w)
b = workspace.FetchBlob(self.model.layers[0].random_b)
# Operation specifications
fc_spec = OpSpec(
"FC", [input_blob, init_ops[0].output[0], init_ops[1].output[0]],
None)
gt_spec = OpSpec("GT", None, None, {'broadcast': 1})
cast_spec = OpSpec("Cast", None, ac_output.field_blobs())
relu_spec = OpSpec("Relu", None, None)
relu_spec_output = OpSpec("Relu", None, ac_output.field_blobs())
pow_spec = OpSpec("Pow", None, None, {'exponent': float(s - 1)})
mul_spec = OpSpec("Mul", None, ac_output.field_blobs())
if s == 0:
ops_list = [
fc_spec,
gt_spec,
cast_spec,
]
elif s == 1:
ops_list = [
fc_spec,
relu_spec_output,
]
else:
ops_list = [
fc_spec,
relu_spec,
pow_spec,
mul_spec,
]
# Train net assertions
self._test_net(train_net, ops_list)
self._arc_cosine_hypothesis_test(ac_output(), X, W, b, s)
# Eval net assertions
eval_net = self.get_eval_net()
self._test_net(eval_net, ops_list)
self._arc_cosine_hypothesis_test(ac_output(), X, W, b, s)
# Predict net assertions
predict_net = self.get_predict_net()
self._test_net(predict_net, ops_list)
self._arc_cosine_hypothesis_test(ac_output(), X, W, b, s)
def testSemiRandomFeatures(self, batch_size, input_dims, output_dims, s,
scale):
X = np.random.normal(size=(batch_size, input_dims)).astype(np.float32)
input_record = self.new_record(
schema.Scalar((np.float32, (input_dims, ))))
schema.FeedRecord(input_record, [X])
input_blob = input_record.field_blobs()[0]
srf_output = self.model.SemiRandomFeatures(input_record,
output_dims,
s=s,
scale=scale)
self.model.output_schema = schema.Struct()
self.assertEqual(schema.Scalar((np.float32, (output_dims, ))),
srf_output)
init_ops_list = [
OpSpec("GaussianFill", None, None),
OpSpec("UniformFill", None, None),
OpSpec("GaussianFill", None, None),
OpSpec("UniformFill", None, None),
]
train_init_net, train_net = self.get_training_nets()
# Init net assertions
init_ops = self._test_net(train_init_net, init_ops_list)
# Need to run to initialize the global constants for layer
workspace.RunNetOnce(self.model.param_init_net)
rand_w = workspace.FetchBlob(self.model.layers[0].random_w)
rand_b = workspace.FetchBlob(self.model.layers[0].random_b)
# Operation specifications
fc_random_spec = OpSpec(
"FC", [input_blob, init_ops[0].output[0], init_ops[1].output[0]],
None)
fc_learned_spec = OpSpec(
"FC", [input_blob, init_ops[2].output[0], init_ops[3].output[0]],
None)
gt_spec = OpSpec("GT", None, None)
cast_spec = OpSpec("Cast", None, None)
relu_spec = OpSpec("Relu", None, None)
pow_spec = OpSpec("Pow", None, None, {'exponent': float(s - 1)})
mul_interim_spec = OpSpec("Mul", None, None)
mul_spec = OpSpec("Mul", None, srf_output.field_blobs())
if s == 0:
ops_list = [
fc_random_spec,
fc_learned_spec,
gt_spec,
cast_spec,
mul_spec,
]
elif s == 1:
ops_list = [
fc_random_spec,
fc_learned_spec,
relu_spec,
mul_spec,
]
else:
ops_list = [
fc_random_spec,
fc_learned_spec,
relu_spec,
pow_spec,
mul_interim_spec,
mul_spec,
]
# Train net assertions
self._test_net(train_net, ops_list)
self._semi_random_hypothesis_test(srf_output(), X, rand_w, rand_b, s)
# Eval net assertions
eval_net = self.get_eval_net()
self._test_net(eval_net, ops_list)
self._semi_random_hypothesis_test(srf_output(), X, rand_w, rand_b, s)
# Predict net assertions
predict_net = self.get_predict_net()
self._test_net(predict_net, ops_list)
self._semi_random_hypothesis_test(srf_output(), X, rand_w, rand_b, s)
def testArcCosineFeatureMap(self, batch_size, input_dims, output_dims, s, scale,
set_weight_as_global_constant):
X = np.random.normal(size=(batch_size, input_dims)).astype(np.float32)
input_record = self.new_record(schema.Scalar((np.float32, (input_dims,))))
schema.FeedRecord(input_record, [X])
input_blob = input_record.field_blobs()[0]
ac_output = self.model.ArcCosineFeatureMap(
input_record,
output_dims,
s=s,
scale=scale,
set_weight_as_global_constant=set_weight_as_global_constant
)
self.model.output_schema = schema.Struct()
self.assertEqual(
schema.Scalar((np.float32, (output_dims, ))),
ac_output
)
train_init_net, train_net = self.get_training_nets()
# Run create_init_net to initialize the global constants, and W and b
workspace.RunNetOnce(train_init_net)
workspace.RunNetOnce(self.model.create_init_net(name='init_net'))
if set_weight_as_global_constant:
W = workspace.FetchBlob(
self.model.global_constants['arc_cosine_feature_map_fixed_rand_W']
)
b = workspace.FetchBlob(
self.model.global_constants['arc_cosine_feature_map_fixed_rand_b']
)
else:
W = workspace.FetchBlob(self.model.layers[0].random_w)
b = workspace.FetchBlob(self.model.layers[0].random_b)
# Operation specifications
fc_spec = OpSpec("FC", [input_blob, None, None], None)
gt_spec = OpSpec("GT", None, None, {'broadcast': 1})
cast_spec = OpSpec("Cast", None, ac_output.field_blobs())
relu_spec = OpSpec("Relu", None, None)
relu_spec_output = OpSpec("Relu", None, ac_output.field_blobs())
pow_spec = OpSpec("Pow", None, None, {'exponent': float(s - 1)})
mul_spec = OpSpec("Mul", None, ac_output.field_blobs())
if s == 0:
ops_list = [
fc_spec,
gt_spec,
cast_spec,
]
elif s == 1:
ops_list = [
fc_spec,
relu_spec_output,
]
else:
ops_list = [
fc_spec,
relu_spec,
pow_spec,
mul_spec,
]
# Train net assertions
self._test_net(train_net, ops_list)
self._arc_cosine_hypothesis_test(ac_output(), X, W, b, s)
# Eval net assertions
eval_net = self.get_eval_net()
self._test_net(eval_net, ops_list)
self._arc_cosine_hypothesis_test(ac_output(), X, W, b, s)
# Predict net assertions
predict_net = self.get_predict_net()
self._test_net(predict_net, ops_list)
self._arc_cosine_hypothesis_test(ac_output(), X, W, b, s)
def testGatherRecord(self):
indices = np.array([1, 3, 4], dtype=np.int32)
dense = np.array(range(20), dtype=np.float32).reshape(10, 2)
lengths = np.array(range(10), dtype=np.int32)
items = np.array(range(lengths.sum()), dtype=np.int64)
items_lengths = np.array(range(lengths.sum()), dtype=np.int32)
items_items = np.array(range(items_lengths.sum()), dtype=np.int64)
record = self.new_record(
schema.Struct(
('dense', schema.Scalar(np.float32)),
('sparse',
schema.Struct(
('list', schema.List(np.int64)),
('list_of_list', schema.List(schema.List(np.int64))),
)), ('empty_struct', schema.Struct())))
indices_record = self.new_record(schema.Scalar(np.int32))
input_record = schema.Struct(
('indices', indices_record),
('record', record),
)
schema.FeedRecord(input_record, [
indices, dense, lengths, items, lengths, items_lengths, items_items
])
gathered_record = self.model.GatherRecord(input_record)
self.assertTrue(schema.equal_schemas(gathered_record, record))
self.run_train_net_forward_only()
gathered_dense = workspace.FetchBlob(gathered_record.dense())
np.testing.assert_array_equal(
np.concatenate([dense[i:i + 1] for i in indices]), gathered_dense)
gathered_lengths = workspace.FetchBlob(
gathered_record.sparse.list.lengths())
np.testing.assert_array_equal(
np.concatenate([lengths[i:i + 1] for i in indices]),
gathered_lengths)
gathered_items = workspace.FetchBlob(
gathered_record.sparse.list.items())
offsets = lengths.cumsum() - lengths
np.testing.assert_array_equal(
np.concatenate(
[items[offsets[i]:offsets[i] + lengths[i]] for i in indices]),
gathered_items)
gathered_items_lengths = workspace.FetchBlob(
gathered_record.sparse.list_of_list.items.lengths())
np.testing.assert_array_equal(
np.concatenate([
items_lengths[offsets[i]:offsets[i] + lengths[i]]
for i in indices
]), gathered_items_lengths)
nested_offsets = []
nested_lengths = []
nested_offset = 0
j = 0
for l in lengths:
nested_offsets.append(nested_offset)
nested_length = 0
for _i in range(l):
nested_offset += items_lengths[j]
nested_length += items_lengths[j]
j += 1
nested_lengths.append(nested_length)
gathered_items_items = workspace.FetchBlob(
gathered_record.sparse.list_of_list.items.items())
np.testing.assert_array_equal(
np.concatenate([
items_items[nested_offsets[i]:nested_offsets[i] +
nested_lengths[i]] for i in indices
]), gathered_items_items)
def testBatchNormalization(self, X):
input_record = self.new_record(schema.Scalar((np.float32, (5, ))))
schema.FeedRecord(input_record, [X])
bn_output = self.model.BatchNormalization(input_record)
self.assertEqual(schema.Scalar((np.float32, (5, ))), bn_output)
self.model.output_schema = schema.Struct()
train_init_net, train_net = self.get_training_nets()
init_ops = self.assertNetContainOps(train_init_net, [
OpSpec("ConstantFill", None, None),
OpSpec("ConstantFill", None, None),
OpSpec("ConstantFill", None, None),
OpSpec("ConstantFill", None, None),
])
input_blob = input_record.field_blobs()[0]
output_blob = bn_output.field_blobs()[0]
expand_dims_spec = OpSpec(
"ExpandDims",
[input_blob],
None,
)
train_bn_spec = OpSpec(
"SpatialBN",
[
None, init_ops[0].output[0], init_ops[1].output[0],
init_ops[2].output[0], init_ops[3].output[0]
],
[
output_blob, init_ops[2].output[0], init_ops[3].output[0],
None, None
],
{
'is_test': 0,
'order': 'NCHW',
'momentum': 0.9
},
)
test_bn_spec = OpSpec(
"SpatialBN",
[
None, init_ops[0].output[0], init_ops[1].output[0],
init_ops[2].output[0], init_ops[3].output[0]
],
[output_blob],
{
'is_test': 1,
'order': 'NCHW',
'momentum': 0.9
},
)
squeeze_spec = OpSpec(
"Squeeze",
[output_blob],
[output_blob],
)
self.assertNetContainOps(
train_net, [expand_dims_spec, train_bn_spec, squeeze_spec])
eval_net = self.get_eval_net()
self.assertNetContainOps(
eval_net, [expand_dims_spec, test_bn_spec, squeeze_spec])
predict_net = self.get_predict_net()
self.assertNetContainOps(
predict_net, [expand_dims_spec, test_bn_spec, squeeze_spec])
workspace.RunNetOnce(train_init_net)
workspace.RunNetOnce(train_net)
schema.FeedRecord(input_record, [X])
workspace.RunNetOnce(eval_net)
schema.FeedRecord(input_record, [X])
workspace.RunNetOnce(predict_net)