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【Hackathon No.17】为 Paddle 新增 paddle.nn.CosineEmbeddingLoss 和 paddle.n…
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…n.functional.cosine_embedding_loss API (#41680)

* add cosine embedding loss API

* new version

* new version

* new version

* set label to int32

* new version

* new version-test

* new version

* new version

* new version

* new version

* new version

* new version

* new version

* new version

* new version

* new version

* new version

* new version

* new version

* new version

* new version

* new version

* aligning to Chinese document

* add name parameter

* activate CI

* fix format error

* unit test code format

* format code
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Patrick-Star125 committed Jun 8, 2022
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328 changes: 328 additions & 0 deletions python/paddle/fluid/tests/unittests/test_cosine_embedding_loss.py
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License

from __future__ import print_function

import paddle
import paddle.static as static
import numpy as np
import unittest


def cosine_embedding_loss(input1, input2, label, margin=0.5, reduction='mean'):
z = (input1 * input2).sum(axis=-1)
mag_square1 = np.square(input1).sum(axis=-1) + 10e-12
mag_square2 = np.square(input2).sum(axis=-1) + 10e-12
denom = np.sqrt(mag_square1 * mag_square2)
cos = z / denom
zeros = np.zeros_like(cos)
pos = 1 - cos
neg = np.clip(cos - margin, a_min=0, a_max=np.inf)
out_pos = np.where(label == 1, pos, zeros)
out_neg = np.where(label == -1, neg, zeros)
out = out_pos + out_neg
if reduction == 'none':
return out
if reduction == 'mean':
return np.mean(out)
elif reduction == 'sum':
return np.sum(out)


class TestFunctionCosineEmbeddingLoss(unittest.TestCase):

def setUp(self):
self.input1_np = np.random.random(size=(5, 3)).astype(np.float64)
self.input2_np = np.random.random(size=(5, 3)).astype(np.float64)
a = np.array([-1, -1, -1]).astype(np.int32)
b = np.array([1, 1]).astype(np.int32)
self.label_np = np.concatenate((a, b), axis=0)
np.random.shuffle(self.label_np)

def run_dynamic(self):
input1 = paddle.to_tensor(self.input1_np)
input2 = paddle.to_tensor(self.input2_np)
label = paddle.to_tensor(self.label_np)
dy_result = paddle.nn.functional.cosine_embedding_loss(input1,
input2,
label,
margin=0.5,
reduction='mean')
expected1 = cosine_embedding_loss(self.input1_np,
self.input2_np,
self.label_np,
margin=0.5,
reduction='mean')
self.assertTrue(np.allclose(dy_result.numpy(), expected1))
self.assertTrue(dy_result.shape, [1])

dy_result = paddle.nn.functional.cosine_embedding_loss(input1,
input2,
label,
margin=0.5,
reduction='sum')
expected2 = cosine_embedding_loss(self.input1_np,
self.input2_np,
self.label_np,
margin=0.5,
reduction='sum')

self.assertTrue(np.allclose(dy_result.numpy(), expected2))
self.assertTrue(dy_result.shape, [1])

dy_result = paddle.nn.functional.cosine_embedding_loss(input1,
input2,
label,
margin=0.5,
reduction='none')
expected3 = cosine_embedding_loss(self.input1_np,
self.input2_np,
self.label_np,
margin=0.5,
reduction='none')

self.assertTrue(np.allclose(dy_result.numpy(), expected3))
self.assertTrue(dy_result.shape, [5])

def run_static(self, use_gpu=False):
input1 = static.data(name='input1', shape=[5, 3], dtype='float64')
input2 = static.data(name='input2', shape=[5, 3], dtype='float64')
label = static.data(name='label', shape=[5], dtype='int32')
result0 = paddle.nn.functional.cosine_embedding_loss(input1,
input2,
label,
margin=0.5,
reduction='none')
result1 = paddle.nn.functional.cosine_embedding_loss(input1,
input2,
label,
margin=0.5,
reduction='sum')
result2 = paddle.nn.functional.cosine_embedding_loss(input1,
input2,
label,
margin=0.5,
reduction='mean')

place = paddle.CUDAPlace(0) if use_gpu else paddle.CPUPlace()
exe = static.Executor(place)
exe.run(static.default_startup_program())
static_result = exe.run(feed={
"input1": self.input1_np,
"input2": self.input2_np,
"label": self.label_np
},
fetch_list=[result0, result1, result2])
expected = cosine_embedding_loss(self.input1_np,
self.input2_np,
self.label_np,
margin=0.5,
reduction='none')

self.assertTrue(np.allclose(static_result[0], expected))
expected = cosine_embedding_loss(self.input1_np,
self.input2_np,
self.label_np,
margin=0.5,
reduction='sum')

self.assertTrue(np.allclose(static_result[1], expected))
expected = cosine_embedding_loss(self.input1_np,
self.input2_np,
self.label_np,
margin=0.5,
reduction='mean')

self.assertTrue(np.allclose(static_result[2], expected))

def test_cpu(self):
paddle.disable_static(place=paddle.CPUPlace())
self.run_dynamic()
paddle.enable_static()

with static.program_guard(static.Program()):
self.run_static()

def test_gpu(self):
if not paddle.is_compiled_with_cuda():
return

paddle.disable_static(place=paddle.CUDAPlace(0))
self.run_dynamic()
paddle.enable_static()

with static.program_guard(static.Program()):
self.run_static(use_gpu=True)

def test_errors(self):
paddle.disable_static()
input1 = paddle.to_tensor(self.input1_np)
input2 = paddle.to_tensor(self.input2_np)
label = paddle.to_tensor(self.label_np)

def test_label_shape_error():
label = paddle.to_tensor(
np.random.randint(low=0, high=2, size=(2, 3)))
paddle.nn.functional.cosine_embedding_loss(input1,
input2,
label,
margin=0.5,
reduction='mean')

self.assertRaises(ValueError, test_label_shape_error)

def test_input_different_shape_error():
input1 = paddle.to_tensor(self.input1_np[0])
label = paddle.to_tensor(np.ndarray([1]))
paddle.nn.functional.cosine_embedding_loss(input1,
input2,
label,
margin=0.5,
reduction='mean')

self.assertRaises(ValueError, test_input_different_shape_error)

def test_input_shape2D_error():
input1 = paddle.to_tensor(
np.random.random(size=(2, 3, 4)).astype(np.float64))
input2 = paddle.to_tensor(
np.random.random(size=(2, 3, 4)).astype(np.float64))
paddle.nn.functional.cosine_embedding_loss(input1,
input2,
label,
margin=0.5,
reduction='mean')

self.assertRaises(ValueError, test_input_shape2D_error)

def test_label_value_error():
label = paddle.to_tensor(np.ndarray([-1, -2]))
paddle.nn.functional.cosine_embedding_loss(input1,
input2,
label,
margin=0.5,
reduction='mean')

self.assertRaises(ValueError, test_label_value_error)

def test_input_type_error():
input1 = paddle.to_tensor(self.input1_np.astype(np.int64))
paddle.nn.functional.cosine_embedding_loss(input1,
input2,
label,
margin=0.5,
reduction='mean')

self.assertRaises(ValueError, test_input_type_error)

def test_label_type_error():
label = paddle.to_tensor(self.label_np.astype(np.int16))
paddle.nn.functional.cosine_embedding_loss(input1,
input2,
label,
margin=0.5,
reduction='mean')

self.assertRaises(ValueError, test_label_type_error)


class TestClassCosineEmbeddingLoss(unittest.TestCase):

def setUp(self):
self.input1_np = np.random.random(size=(10, 3)).astype(np.float32)
self.input2_np = np.random.random(size=(10, 3)).astype(np.float32)
a = np.array([-1, -1, -1, -1, -1]).astype(np.int64)
b = np.array([1, 1, 1, 1, 1]).astype(np.int64)
self.label_np = np.concatenate((a, b), axis=0)
np.random.shuffle(self.label_np)
self.input1_np_1D = np.random.random(size=10).astype(np.float32)
self.input2_np_1D = np.random.random(size=10).astype(np.float32)
self.label_np_1D = np.array([1]).astype(np.int64)

def run_dynamic(self):
input1 = paddle.to_tensor(self.input1_np)
input2 = paddle.to_tensor(self.input2_np)
label = paddle.to_tensor(self.label_np)
CosineEmbeddingLoss = paddle.nn.CosineEmbeddingLoss(margin=0.5,
reduction='mean')
dy_result = CosineEmbeddingLoss(input1, input2, label)
expected1 = cosine_embedding_loss(self.input1_np,
self.input2_np,
self.label_np,
margin=0.5,
reduction='mean')
self.assertTrue(np.allclose(dy_result.numpy(), expected1))
self.assertTrue(dy_result.shape, [1])

input1_1D = paddle.to_tensor(self.input1_np_1D)
input2_1D = paddle.to_tensor(self.input2_np_1D)
label_1D = paddle.to_tensor(self.label_np_1D)
dy_result = CosineEmbeddingLoss(input1_1D, input2_1D, label_1D)
expected2 = cosine_embedding_loss(self.input1_np_1D,
self.input2_np_1D,
self.label_np_1D,
margin=0.5,
reduction='mean')
self.assertTrue(np.allclose(dy_result.numpy(), expected2))

def run_static(self):
input1 = static.data(name='input1', shape=[10, 3], dtype='float32')
input2 = static.data(name='input2', shape=[10, 3], dtype='float32')
label = static.data(name='label', shape=[10], dtype='int64')
CosineEmbeddingLoss = paddle.nn.CosineEmbeddingLoss(margin=0.5,
reduction='mean')
result = CosineEmbeddingLoss(input1, input2, label)

place = paddle.CPUPlace()
exe = static.Executor(place)
exe.run(static.default_startup_program())
static_result = exe.run(feed={
"input1": self.input1_np,
"input2": self.input2_np,
"label": self.label_np
},
fetch_list=[result])
expected = cosine_embedding_loss(self.input1_np,
self.input2_np,
self.label_np,
margin=0.5,
reduction='mean')

self.assertTrue(np.allclose(static_result[0], expected))

def test_cpu(self):
paddle.disable_static(place=paddle.CPUPlace())
self.run_dynamic()
paddle.enable_static()

with static.program_guard(static.Program()):
self.run_static()

def test_errors(self):

def test_margin_error():
CosineEmbeddingLoss = paddle.nn.CosineEmbeddingLoss(
margin=2, reduction='mean')

self.assertRaises(ValueError, test_margin_error)

def test_reduction_error():
CosineEmbeddingLoss = paddle.nn.CosineEmbeddingLoss(
margin=2, reduction='reduce_mean')

self.assertRaises(ValueError, test_reduction_error)


if __name__ == "__main__":
unittest.main()
2 changes: 2 additions & 0 deletions python/paddle/nn/__init__.py
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Expand Up @@ -107,6 +107,7 @@
from .layer.loss import CTCLoss # noqa: F401
from .layer.loss import SmoothL1Loss # noqa: F401
from .layer.loss import HingeEmbeddingLoss # noqa: F401
from .layer.loss import CosineEmbeddingLoss # noqa: F401
from .layer.norm import BatchNorm # noqa: F401
from .layer.norm import SyncBatchNorm # noqa: F401
from .layer.norm import GroupNorm # noqa: F401
Expand Down Expand Up @@ -311,5 +312,6 @@ def weight_norm(*args):
'MaxUnPool3D',
'HingeEmbeddingLoss',
'Identity',
'CosineEmbeddingLoss',
'RReLU',
]
2 changes: 2 additions & 0 deletions python/paddle/nn/functional/__init__.py
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Expand Up @@ -90,6 +90,7 @@
from .loss import square_error_cost # noqa: F401
from .loss import ctc_loss # noqa: F401
from .loss import hinge_embedding_loss # noqa: F401
from .loss import cosine_embedding_loss # noqa: F401
from .norm import batch_norm # noqa: F401
from .norm import instance_norm # noqa: F401
from .norm import layer_norm # noqa: F401
Expand Down Expand Up @@ -229,5 +230,6 @@
'class_center_sample',
'sparse_attention',
'fold',
'cosine_embedding_loss',
'rrelu',
]

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