/usr/local/lib64/python3.6/site-packages/caffe2/python/operator_test
Edit: /usr/local/lib64/python3.6/site-packages/caffe2/python/operator_test/cross_entropy_ops_test.py (10085B)
from caffe2.python import core
from hypothesis import given
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np
import unittest
def sigmoid(x):
return 1.0 / (1.0 + np.exp(-x))
def sigmoid_cross_entropy_with_logits(x, z):
return np.maximum(x, 0) - x * z + np.log(1 + np.exp(-np.abs(x)))
def sigmoid_cross_entropy_with_logits_grad(x, z):
return z - sigmoid(x)
def sigmoid_cross_entropy_with_logits_with_log_D_trick(x, z):
return -(2 * z - 1.) * np.log(sigmoid(x))
def sigmoid_cross_entropy_with_logits_with_log_D_trick_grad(x, z):
return (2 * z - 1.) * (1 - sigmoid(x))
def unjoined_sigmoid_cross_entropy(x, z):
return -z * x + (1. - z) * np.maximum(x, 0) \
+ (1. - z) * np.log(1 + np.exp(-np.abs(x)))
def unjoined_sigmoid_cross_entropy_grad(x, z):
return z - (1. - z) / (1. + np.exp(-x))
class TestCrossEntropyOps(hu.HypothesisTestCase):
@given(
inputs=st.lists(
elements=st.integers(min_value=1, max_value=5),
min_size=1,
max_size=2,
).flatmap(
lambda shape: st.tuples(
hu.arrays(
dims=shape,
elements=st.one_of(
hu.floats(min_value=-1.0, max_value=-0.1),
hu.floats(min_value=0.1, max_value=1.0),
)),
hu.arrays(
dims=shape,
elements=st.sampled_from([0.0, 1.0]),
),
)
),
options=st.one_of(
st.tuples(st.just(True), st.just(False)),
st.tuples(st.just(False), st.just(True)),
st.tuples(st.just(False), st.just(False))
),
**hu.gcs
)
def test_sigmoid_cross_entropy_with_logits(
self, inputs, options, gc, dc
):
logits, targets = inputs
log_D_trick, unjoined_lr_loss = options
def sigmoid_xentr_logit_ref(logits, targets):
if unjoined_lr_loss:
s = unjoined_sigmoid_cross_entropy(logits, targets)
else:
s = (
sigmoid_cross_entropy_with_logits(logits, targets)
if not log_D_trick else
sigmoid_cross_entropy_with_logits_with_log_D_trick(
logits, targets
)
)
m = np.mean(s, axis=len(logits.shape) - 1)
return (m, )
def sigmoid_xentr_logit_grad_ref(g_out, outputs, fwd_inputs):
fwd_logits, fwd_targets = fwd_inputs
inner_size = fwd_logits.shape[-1]
if unjoined_lr_loss:
m = unjoined_sigmoid_cross_entropy_grad(logits, targets)
else:
m = (
sigmoid_cross_entropy_with_logits_grad(fwd_logits, fwd_targets)
if not log_D_trick else
sigmoid_cross_entropy_with_logits_with_log_D_trick_grad(
fwd_logits, fwd_targets
)
)
# m = fwd_targets - sigmoid(fwd_logits)
g_in = -np.expand_dims(g_out, axis=-1) * m / inner_size
return (g_in, None)
op = core.CreateOperator(
'SigmoidCrossEntropyWithLogits', ['logits', 'targets'],
['xentropy'],
log_D_trick=log_D_trick,
unjoined_lr_loss=unjoined_lr_loss
)
self.assertReferenceChecks(
device_option=gc,
op=op,
inputs=[logits, targets],
reference=sigmoid_xentr_logit_ref,
output_to_grad='xentropy',
grad_reference=sigmoid_xentr_logit_grad_ref)
@given(
log_D_trick=st.just(False),
**hu.gcs_cpu_only
)
def test_cross_entropy_and_unjoied_cross_entropy_relation(
self, log_D_trick, gc, dc
):
logits = np.array([1.4720, 0.3500, -0.6529, -1.1908, 0.8357,
-1.0774, -0.3395, -0.2469, 0.6708, -1.8332], dtype='f')
targets = np.array([1., 1., 1., 1., 1., 1., 0., 0., 0., 0.], dtype='f')
lr_size = targets.size
unjoined_lr_loss = False
def sigmoid_xentr_logit_ref(logits, targets):
if unjoined_lr_loss:
s = unjoined_sigmoid_cross_entropy(logits, targets)
else:
s = sigmoid_cross_entropy_with_logits(logits, targets)
m = np.mean(s, axis=len(logits.shape) - 1)
return (m, )
def sigmoid_xentr_logit_grad_ref(g_out, outputs, fwd_inputs):
fwd_logits, fwd_targets = fwd_inputs
inner_size = fwd_logits.shape[-1]
if unjoined_lr_loss:
m = unjoined_sigmoid_cross_entropy_grad(logits, targets)
else:
m = sigmoid_cross_entropy_with_logits_grad(
fwd_logits, fwd_targets)
# m = fwd_targets - sigmoid(fwd_logits)
g_in = -np.expand_dims(g_out, axis=-1) * m / inner_size
return (g_in, None)
op = core.CreateOperator(
'SigmoidCrossEntropyWithLogits', ['logits', 'targets'],
['xentropy'],
log_D_trick=log_D_trick,
unjoined_lr_loss=unjoined_lr_loss
)
output_lr = self.assertReferenceChecks(
device_option=gc,
op=op,
inputs=[logits, targets],
reference=sigmoid_xentr_logit_ref,
output_to_grad='xentropy',
grad_reference=sigmoid_xentr_logit_grad_ref)
# Unjoined dataset where labels change later
logits = np.array([1.4720, 0.3500, -0.6529, -1.1908, 0.8357,
-1.0774, -0.3395, -0.2469, 0.6708, -1.8332, 1.4720, 0.3500,
-0.6529, -1.1908, 0.8357, -1.0774], dtype='f')
targets = np.array([0., 0., 0., 0., 0., 0., 0., 0., 0.,
0., 1., 1., 1., 1., 1., 1.], dtype='f')
unjoined_lr_loss = True
unjoined_lr_size = targets.size
op = core.CreateOperator(
'SigmoidCrossEntropyWithLogits', ['logits', 'targets'],
['xentropy'],
log_D_trick=log_D_trick,
unjoined_lr_loss=unjoined_lr_loss
)
outputs_unjoined_lr = self.assertReferenceChecks(
device_option=gc,
op=op,
inputs=[logits, targets],
reference=sigmoid_xentr_logit_ref,
output_to_grad='xentropy',
grad_reference=sigmoid_xentr_logit_grad_ref)
self.assertAlmostEqual(
output_lr[0].item(0) * lr_size / unjoined_lr_size,
outputs_unjoined_lr[0].item(0),
delta=0.0001)
@given(
inputs=st.lists(
elements=st.integers(min_value=1, max_value=5),
min_size=1,
max_size=2,
).flatmap(
lambda shape: st.tuples(
hu.arrays(
dims=shape,
elements=st.one_of(
hu.floats(min_value=-1.0, max_value=-0.1),
hu.floats(min_value=0.1, max_value=1.0),
)),
hu.arrays(
dims=shape,
elements=st.sampled_from([0.0, 1.0]),
),
hu.arrays(
dims=shape,
elements=hu.floats(min_value=0.1, max_value=1.0),
),
)
),
**hu.gcs
)
def test_weighted_sigmoid_cross_entropy_with_logits(self, inputs, gc, dc):
logits, targets, weights = inputs
def weighted_sigmoid_xentr_logit_ref(logits, targets, weights):
s = sigmoid_cross_entropy_with_logits(logits, targets)
s = np.multiply(s, weights)
m = np.mean(s, axis=len(logits.shape) - 1)
return (m, )
def weighted_sigmoid_xentr_logit_grad_ref(g_out, outputs, fwd_inputs):
fwd_logits, fwd_targets, fwd_weights = fwd_inputs
inner_size = fwd_logits.shape[-1]
m = fwd_targets - sigmoid(fwd_logits)
m = np.multiply(m, weights)
g_in = -np.expand_dims(g_out, axis=-1) * m / inner_size
return (g_in, None, None)
op = core.CreateOperator(
'WeightedSigmoidCrossEntropyWithLogits',
['logits', 'targets', 'weights'],
['xentropy'])
self.assertReferenceChecks(
device_option=gc,
op=op,
inputs=[logits, targets, weights],
reference=weighted_sigmoid_xentr_logit_ref,
output_to_grad='xentropy',
grad_reference=weighted_sigmoid_xentr_logit_grad_ref)
@given(n=st.integers(2, 10),
b=st.integers(1, 5),
**hu.gcs_cpu_only)
def test_soft_label_cross_entropy(self, n, b, gc, dc):
# Initialize X and add 1e-2 for numerical stability
X = np.random.rand(b, n).astype(np.float32)
X = X + 1e-2
for i in range(b):
X[i] = X[i] / np.sum(X[i])
# Initialize label
label = np.random.rand(b, n).astype(np.float32)
for i in range(b):
label[i] = label[i] / np.sum(label[i])
# Reference implementation of cross entropy with soft labels
def soft_label_xentr_ref(X, label):
xent = [np.sum((-label[j][i] * np.log(max(X[j][i], 1e-20))
for i in range(len(X[0])))) for j in range(b)]
return (xent,)
op = core.CreateOperator("CrossEntropy", ["X", "label"], ["Y"])
# TODO(surya) Once CrossEntropyOp is ported to GPU, add the respective
# tests to this unit test.
self.assertReferenceChecks(
device_option=gc,
op=op,
inputs=[X, label],
reference=soft_label_xentr_ref,
)
self.assertGradientChecks(
gc, op, [X, label], 0, [0], stepsize=1e-4, threshold=1e-2)
if __name__ == "__main__":
import unittest
unittest.main()