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Fix the API of bce loss and add functional API binary_cross_entropy #26012

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merged 8 commits into from
Aug 21, 2020
Merged

Fix the API of bce loss and add functional API binary_cross_entropy #26012

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02e5ffb
fix bceloss
ZHUI Aug 12, 2020
bec7b17
Merge remote-tracking branch 'upstream/develop' into bceloss
ZHUI Aug 13, 2020
b8fc165
change test case from x-> input
ZHUI Aug 13, 2020
9a8f6f3
fix docs
ZHUI Aug 13, 2020
d2d0ebb
Merge remote-tracking branch 'upstream/develop' into bceloss
ZHUI Aug 17, 2020
a8ffb85
update functional bce loss
ZHUI Aug 18, 2020
90d59cb
reslove merge conflicts
ZHUI Aug 18, 2020
ce37cb9
reslove merge conflict. refine the example code.
ZHUI Aug 20, 2020
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248 changes: 179 additions & 69 deletions python/paddle/fluid/tests/unittests/test_bce_loss.py
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Original file line number Diff line number Diff line change
Expand Up @@ -19,94 +19,204 @@
from op_test import OpTest


def test_static_layer(place,
input_np,
label_np,
reduction='mean',
weight_np=None):
prog = paddle.static.Program()
startup_prog = paddle.static.Program()
with paddle.static.program_guard(prog, startup_prog):
input = paddle.data(name='input', shape=input_np.shape, dtype='float64')
label = paddle.data(name='label', shape=label_np.shape, dtype='float64')
if weight_np is not None:
weight = paddle.data(
name='weight', shape=weight_np.shape, dtype='float64')
bce_loss = paddle.nn.loss.BCELoss(
weight=weight, reduction=reduction)
else:
bce_loss = paddle.nn.loss.BCELoss(reduction=reduction)
res = bce_loss(input, label)
exe = paddle.static.Executor(place)
static_result = exe.run(prog,
feed={"input": input_np,
"label": label_np}
if weight_np is None else {
"input": input_np,
"label": label_np,
"weight": weight_np
},
fetch_list=[res])
return static_result


def test_static_functional(place,
input_np,
label_np,
reduction='mean',
weight_np=None):
prog = paddle.static.Program()
startup_prog = paddle.static.Program()
with paddle.static.program_guard(prog, startup_prog):
input = paddle.data(name='input', shape=input_np.shape, dtype='float64')
label = paddle.data(name='label', shape=label_np.shape, dtype='float64')
if weight_np is not None:
weight = paddle.data(
name='weight', shape=weight_np.shape, dtype='float64')
res = paddle.nn.functional.binary_cross_entropy(
input, label, weight=weight, reduction=reduction)
else:
res = paddle.nn.functional.binary_cross_entropy(
input, label, reduction=reduction)
exe = paddle.static.Executor(place)
static_result = exe.run(prog,
feed={"input": input_np,
"label": label_np}
if weight_np is None else {
"input": input_np,
"label": label_np,
"weight": weight_np
},
fetch_list=[res])
return static_result


def test_dygraph_layer(place,
input_np,
label_np,
reduction='mean',
weight_np=None):
paddle.disable_static()
if weight_np is not None:
weight = paddle.to_tensor(weight_np)
bce_loss = paddle.nn.loss.BCELoss(weight=weight, reduction=reduction)
else:
bce_loss = paddle.nn.loss.BCELoss(reduction=reduction)
dy_res = bce_loss(paddle.to_tensor(input_np), paddle.to_tensor(label_np))
dy_result = dy_res.numpy()
paddle.enable_static()
return dy_result


def test_dygraph_functional(place,
input_np,
label_np,
reduction='mean',
weight_np=None):
paddle.disable_static()
input = paddle.to_tensor(input_np)
label = paddle.to_tensor(label_np)

if weight_np is not None:
weight = paddle.to_tensor(weight_np)
dy_res = paddle.nn.functional.binary_cross_entropy(
input, label, weight=weight, reduction=reduction)
else:
dy_res = paddle.nn.functional.binary_cross_entropy(
input, label, reduction=reduction)
dy_result = dy_res.numpy()
paddle.enable_static()
return dy_result


def calc_bceloss(input_np, label_np, reduction='mean', weight_np=None):
if weight_np is None:
expected = -1 * (label_np * np.log(input_np) +
(1. - label_np) * np.log(1. - input_np))
else:
expected = -1 * weight_np * (label_np * np.log(input_np) +
(1. - label_np) * np.log(1. - input_np))

if reduction == 'mean':
expected = np.mean(expected)
elif reduction == 'sum':
expected = np.sum(expected)
else:
expected = expected

return expected


class TestBCELoss(unittest.TestCase):
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def test_BCELoss(self):
input_np = np.random.random(size=(20, 30)).astype(np.float64)
label_np = np.random.random(size=(20, 30)).astype(np.float64)
prog = fluid.Program()
startup_prog = fluid.Program()
input_np = np.random.uniform(0.1, 0.8, size=(20, 30)).astype(np.float64)
label_np = np.random.randint(0, 2, size=(20, 30)).astype(np.float64)
places = [fluid.CPUPlace()]
if fluid.core.is_compiled_with_cuda():
places.append(fluid.CUDAPlace(0))
reductions = ['sum', 'mean', 'none']
for place in places:
for red in reductions:
with fluid.program_guard(prog, startup_prog):
input = fluid.data(
name='input', shape=[None, 30], dtype='float64')
label = fluid.data(
name='label', shape=[None, 30], dtype='float64')
bce_loss = paddle.nn.loss.BCELoss(reduction=red)
res = bce_loss(input, label)

exe = fluid.Executor(place)
static_result = exe.run(
prog,
feed={"input": input_np,
"label": label_np},
fetch_list=[res])

with fluid.dygraph.guard():
bce_loss = paddle.nn.loss.BCELoss(reduction=red)
dy_res = bce_loss(
fluid.dygraph.to_variable(input_np),
fluid.dygraph.to_variable(label_np))
dy_result = dy_res.numpy()

expected = -1 * (label_np * np.log(input_np) +
(1. - label_np) * np.log(1. - input_np))
if red == 'mean':
expected = np.mean(expected)
elif red == 'sum':
expected = np.sum(expected)
else:
expected = expected
for reduction in reductions:
static_result = test_static_layer(place, input_np, label_np,
reduction)
dy_result = test_dygraph_layer(place, input_np, label_np,
reduction)
expected = calc_bceloss(input_np, label_np, reduction)
self.assertTrue(np.allclose(static_result, expected))
self.assertTrue(np.allclose(static_result, dy_result))
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self.assertTrue(np.allclose(dy_result, expected))
static_functional = test_static_functional(place, input_np,
label_np, reduction)
dy_functional = test_dygraph_functional(place, input_np,
label_np, reduction)
self.assertTrue(np.allclose(static_functional, expected))
self.assertTrue(np.allclose(static_functional, dy_functional))
self.assertTrue(np.allclose(dy_functional, expected))

def test_BCELoss_weight(self):
input_np = np.random.random(size=(2, 3, 4, 10)).astype(np.float64)
label_np = np.random.random(size=(2, 3, 4, 10)).astype(np.float64)
input_np = np.random.uniform(
0.1, 0.8, size=(2, 3, 4, 10)).astype(np.float64)
label_np = np.random.randint(
0, 2, size=(2, 3, 4, 10)).astype(np.float64)
weight_np = np.random.random(size=(3, 4, 10)).astype(np.float64)
prog = fluid.Program()
startup_prog = fluid.Program()
place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda(
) else fluid.CPUPlace()
with fluid.program_guard(prog, startup_prog):
input = fluid.data(
name='input', shape=[None, 3, 4, 10], dtype='float64')
label = fluid.data(
name='label', shape=[None, 3, 4, 10], dtype='float64')
weight = fluid.data(
name='weight', shape=[3, 4, 10], dtype='float64')
bce_loss = paddle.nn.loss.BCELoss(weight=weight)
res = bce_loss(input, label)

exe = fluid.Executor(place)
static_result = exe.run(prog,
feed={
"input": input_np,
"label": label_np,
"weight": weight_np
},
fetch_list=[res])

with fluid.dygraph.guard():
bce_loss = paddle.nn.loss.BCELoss(
weight=fluid.dygraph.to_variable(weight_np))
dy_res = bce_loss(
fluid.dygraph.to_variable(input_np),
fluid.dygraph.to_variable(label_np))
dy_result = dy_res.numpy()

expected = np.mean(-1 * weight_np *
(label_np * np.log(input_np) +
(1. - label_np) * np.log(1. - input_np)))
for reduction in ['sum', 'mean', 'none']:
static_result = test_static_layer(
place, input_np, label_np, reduction, weight_np=weight_np)
dy_result = test_dygraph_layer(
place, input_np, label_np, reduction, weight_np=weight_np)
expected = calc_bceloss(
input_np, label_np, reduction, weight_np=weight_np)
self.assertTrue(np.allclose(static_result, expected))
self.assertTrue(np.allclose(static_result, dy_result))
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self.assertTrue(np.allclose(dy_result, expected))
static_functional = test_static_functional(
place, input_np, label_np, reduction, weight_np=weight_np)
dy_functional = test_dygraph_functional(
place, input_np, label_np, reduction, weight_np=weight_np)
self.assertTrue(np.allclose(static_functional, expected))
self.assertTrue(np.allclose(static_functional, dy_functional))
self.assertTrue(np.allclose(dy_functional, expected))

def test_BCELoss_boardcast(self):
input_np = np.random.uniform(
0.1, 0.8, size=(2, 3, 4, 10)).astype(np.float64)
label_np = np.random.randint(0, 2, size=(3, 4, 10)).astype(np.float64)
place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda(
) else fluid.CPUPlace()

static_result = test_static_layer(place, input_np, label_np)
dy_result = test_dygraph_layer(place, input_np, label_np)
expected = calc_bceloss(input_np, label_np)
self.assertTrue(np.allclose(static_result, expected))
self.assertTrue(np.allclose(static_result, dy_result))
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self.assertTrue(np.allclose(dy_result, expected))

def test_BCELoss_error(self):
paddle.disable_static()
self.assertRaises(
ValueError, paddle.nn.loss.BCELoss, reduction="unsupport reduction")
input = paddle.to_tensor([[0.1, 0.3]], dtype='float32')
label = paddle.to_tensor([[0.0, 1.0]], dtype='float32')
self.assertRaises(
ValueError,
paddle.nn.functional.binary_cross_entropy,
input=input,
label=label,
reduction="unsupport reduction")
paddle.enable_static()


def bce_loss(input, label):
return -1 * (label * np.log(input) + (1. - label) * np.log(1. - input))
Expand Down
1 change: 1 addition & 0 deletions python/paddle/nn/functional/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -119,6 +119,7 @@
# from .lod import dynamic_gru #DEFINE_ALIAS
# from .lod import dynamic_lstm #DEFINE_ALIAS
# from .lod import dynamic_lstmp #DEFINE_ALIAS
from .loss import binary_cross_entropy #DEFINE_ALIAS
from .loss import bpr_loss #DEFINE_ALIAS
from .loss import center_loss #DEFINE_ALIAS
from .loss import cross_entropy #DEFINE_ALIAS
Expand Down
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