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【Paddle Hackathon No.11】 #45595

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merged 14 commits into from
Oct 13, 2022
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【Paddle Hackathon No.11】 #45595

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373d598
2022-08-30_update nn.layer.loss nn.functional.loss, test_file
yangguohao Aug 22, 2022
f573b02
Merge branch 'PaddlePaddle:develop' into multi_margin_loss
yangguohao Sep 5, 2022
9355a43
2022-08-30_update nn.layer.loss nn.functional.loss, test_file
yangguohao Aug 22, 2022
a317e41
fix: test_file
yangguohao Sep 6, 2022
eceeaab
fix: test_file, docs, multi_margin_loss
yangguohao Sep 14, 2022
66d1b4e
Merge branch 'PaddlePaddle:develop' into multi_margin_loss
yangguohao Sep 14, 2022
f49bfc3
fix: doc weight function
yangguohao Sep 22, 2022
0a042c8
fix: test_multi_margin_loss
yangguohao Sep 23, 2022
8cb585b
fix: weight np.testing.assert_allclose
yangguohao Sep 27, 2022
ec06e40
Merge branch 'PaddlePaddle:develop' into multi_margin_loss
yangguohao Sep 27, 2022
43c6226
fix: test_file
yangguohao Sep 28, 2022
518c53c
fix: en_doc
yangguohao Sep 29, 2022
ef91530
2022-10-10
yangguohao Oct 10, 2022
a6eb056
Merge branch 'PaddlePaddle:develop' into multi_margin_loss
yangguohao Oct 12, 2022
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348 changes: 348 additions & 0 deletions python/paddle/fluid/tests/unittests/test_multimarginloss.py
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@@ -0,0 +1,348 @@
# -*- coding: utf-8 -*
# Copyright (c) 2022 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.

import paddle
import numpy as np
import unittest


def call_MultiMarginLoss_layer(
input,
label,
p=1,
margin=1.0,
weight=None,
reduction='mean',
):
triplet_margin_loss = paddle.nn.MultiMarginLoss(p=p,
margin=margin,
weight=weight,
reduction=reduction)
res = triplet_margin_loss(
input=input,
label=label,
)
return res


def call_MultiMarginLoss_functional(
input,
label,
p=1,
margin=1.0,
weight=None,
reduction='mean',
):
res = paddle.nn.functional.multi_margin_loss(input=input,
label=label,
p=p,
margin=margin,
weight=weight,
reduction=reduction)
return res


def test_static(place,
input_np,
label_np,
p=1,
margin=1.0,
weight_np=None,
reduction='mean',
functional=False):
prog = paddle.static.Program()
startup_prog = paddle.static.Program()
with paddle.static.program_guard(prog, startup_prog):
input = paddle.static.data(name='input',
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For test cases in static mode, you'd better add some cases that paddle.static.data creates data layer that has dynamic shape to ensure that it works as expected.

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I add the new test cases of checking data shape in static mode. but I'm not quite sure about it.

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I mean, you can create an data layer with shape [-1, -1] and feed it with an array with shape [3, 4] or [5, 6].
Or create a data layer with shape [-1, 3,4] and feed it with an array with shape [7, 3, 4]

I the rank and all the static size matches, it is compatible.

shape=input_np.shape,
dtype=input_np.dtype)
label = paddle.static.data(name='label',
shape=label_np.shape,
dtype=label_np.dtype)
feed_dict = {
"input": input_np,
"label": label_np,
}
weight = None
if weight_np is not None:
weight = paddle.static.data(name='weight',
shape=weight_np.shape,
dtype=weight_np.dtype)
feed_dict['weight'] = weight_np
if functional:
res = call_MultiMarginLoss_functional(input=input,
label=label,
p=p,
margin=margin,
weight=weight,
reduction=reduction)
else:
res = call_MultiMarginLoss_layer(input=input,
label=label,
p=p,
margin=margin,
weight=weight,
reduction=reduction)

exe = paddle.static.Executor(place)
static_result = exe.run(prog, feed=feed_dict, fetch_list=[res])
return static_result


def test_dygraph(place,
input,
label,
p=1,
margin=1.0,
weight=None,
reduction='mean',
functional=False):
paddle.disable_static()
input = paddle.to_tensor(input)
label = paddle.to_tensor(label)

if weight is not None:
weight = paddle.to_tensor(weight)
if functional:
dy_res = call_MultiMarginLoss_functional(input=input,
label=label,
p=p,
margin=margin,
weight=weight,
reduction=reduction)
else:
dy_res = call_MultiMarginLoss_layer(input=input,
label=label,
p=p,
margin=margin,
weight=weight,
reduction=reduction)
dy_result = dy_res.numpy()
paddle.enable_static()
return dy_result


def calc_multi_margin_loss(
input,
label,
p=1,
margin=1.0,
weight=None,
reduction='mean',
):
label = label.reshape(-1, 1)
index_sample = []
for i in range(len(label)):
index_sample.append(input[i, label[i]])
index_sample = np.array(index_sample).reshape(-1, 1)

if weight is None:
expected = np.mean(np.maximum(margin + input - index_sample, 0.0)**p,
axis=1) - margin**p / input.shape[1]
else:
weight = weight.reshape(-1, 1)
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expected = np.mean(np.maximum(weight * (margin + input - index_sample), 0.0) ** p, axis=1) - margin ** p / \
input.shape[1]

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

return expected


class TestMultiMarginLoss(unittest.TestCase):

def test_MultiMarginLoss(self):
shape = (2, 2)
input = np.random.uniform(0.1, 0.8, size=shape).astype(np.float64)
label = np.random.uniform(0, input.shape[1],
size=(2, )).astype(np.int32)

places = [paddle.CPUPlace()]
if paddle.device.is_compiled_with_cuda():
places.append(paddle.CUDAPlace(0))
reductions = ['sum', 'mean', 'none']
for place in places:
for reduction in reductions:
expected = calc_multi_margin_loss(input=input,
label=label,
reduction=reduction)

dy_result = test_dygraph(
place=place,
input=input,
label=label,
reduction=reduction,
)

static_result = test_static(
place=place,
input_np=input,
label_np=label,
reduction=reduction,
)
self.assertTrue(np.allclose(static_result, expected))
self.assertTrue(np.allclose(static_result, dy_result))
self.assertTrue(np.allclose(dy_result, expected))
static_functional = test_static(place=place,
input_np=input,
label_np=label,
reduction=reduction,
functional=True)
dy_functional = test_dygraph(place=place,
input=input,
label=label,
reduction=reduction,
functional=True)
self.assertTrue(np.allclose(static_functional, expected))
self.assertTrue(np.allclose(static_functional, dy_functional))
self.assertTrue(np.allclose(dy_functional, expected))

def test_MultiMarginLoss_error(self):
paddle.disable_static()
self.assertRaises(ValueError,
paddle.nn.MultiMarginLoss,
reduction="unsupport reduction")
input = paddle.to_tensor([[0.1, 0.3]], dtype='float32')
label = paddle.to_tensor([0], dtype='int32')
self.assertRaises(ValueError,
paddle.nn.functional.multi_margin_loss,
input=input,
label=label,
reduction="unsupport reduction")
paddle.enable_static()

def test_MultiMarginLoss_dimension(self):
paddle.disable_static()

input = paddle.to_tensor([[0.1, 0.3], [1, 2]], dtype='float32')
label = paddle.to_tensor([0, 1, 1], dtype='int32')

self.assertRaises(
ValueError,
paddle.nn.functional.multi_margin_loss,
input=input,
label=label,
)
MMLoss = paddle.nn.MultiMarginLoss()
self.assertRaises(
ValueError,
MMLoss,
input=input,
label=label,
)
paddle.enable_static()

def test_MultiMarginLoss_p(self):
p = 2
shape = (2, 2)
reduction = 'mean'
place = paddle.CPUPlace()
input = np.random.uniform(0.1, 0.8, size=shape).astype(np.float64)
label = np.random.uniform(0, input.shape[1],
size=(2, )).astype(np.int64)
expected = calc_multi_margin_loss(input=input,
p=p,
label=label,
reduction=reduction)

dy_result = test_dygraph(
place=place,
p=p,
input=input,
label=label,
reduction=reduction,
)

static_result = test_static(
place=place,
p=p,
input_np=input,
label_np=label,
reduction=reduction,
)
self.assertTrue(np.allclose(static_result, expected))
self.assertTrue(np.allclose(static_result, dy_result))
self.assertTrue(np.allclose(dy_result, expected))
static_functional = test_static(place=place,
p=p,
input_np=input,
label_np=label,
reduction=reduction,
functional=True)
dy_functional = test_dygraph(place=place,
p=p,
input=input,
label=label,
reduction=reduction,
functional=True)
self.assertTrue(np.allclose(static_functional, expected))
self.assertTrue(np.allclose(static_functional, dy_functional))
self.assertTrue(np.allclose(dy_functional, expected))

def test_MultiMarginLoss_weight(self):
shape = (2, 2)
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reduction = 'mean'
place = paddle.CPUPlace()
input = np.random.uniform(0.1, 0.8, size=shape).astype(np.float64)
label = np.random.uniform(0, input.shape[1],
size=(2, )).astype(np.int64)
weight = np.random.uniform(0, 2, size=(2, )).astype(np.float64)
expected = calc_multi_margin_loss(input=input,
label=label,
weight=weight,
reduction=reduction)

dy_result = test_dygraph(
place=place,
input=input,
label=label,
weight=weight,
reduction=reduction,
)

static_result = test_static(
place=place,
input_np=input,
label_np=label,
weight_np=weight,
reduction=reduction,
)
self.assertTrue(np.allclose(static_result, expected))
self.assertTrue(np.allclose(static_result, dy_result))
self.assertTrue(np.allclose(dy_result, expected))
static_functional = test_static(place=place,
input_np=input,
label_np=label,
weight_np=weight,
reduction=reduction,
functional=True)
dy_functional = test_dygraph(place=place,
input=input,
label=label,
weight=weight,
reduction=reduction,
functional=True)
self.assertTrue(np.allclose(static_functional, expected))
self.assertTrue(np.allclose(static_functional, dy_functional))
self.assertTrue(np.allclose(dy_functional, expected))


if __name__ == "__main__":
unittest.main()
2 changes: 2 additions & 0 deletions python/paddle/nn/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -109,6 +109,7 @@
from .layer.loss import SmoothL1Loss # noqa: F401
from .layer.loss import HingeEmbeddingLoss # noqa: F401
from .layer.loss import CosineEmbeddingLoss # noqa: F401
from .layer.loss import MultiMarginLoss
from .layer.loss import TripletMarginWithDistanceLoss
from .layer.loss import TripletMarginLoss
from .layer.loss import SoftMarginLoss
Expand Down Expand Up @@ -319,6 +320,7 @@ def weight_norm(*args):
'Identity',
'CosineEmbeddingLoss',
'RReLU',
'MultiMarginLoss',
'TripletMarginWithDistanceLoss',
'TripletMarginLoss',
'SoftMarginLoss',
Expand Down
2 changes: 2 additions & 0 deletions python/paddle/nn/functional/__init__.py
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Expand Up @@ -92,6 +92,7 @@
from .loss import ctc_loss # noqa: F401
from .loss import hinge_embedding_loss # noqa: F401
from .loss import cosine_embedding_loss # noqa: F401
from .loss import multi_margin_loss
from .loss import multi_label_soft_margin_loss
from .loss import triplet_margin_with_distance_loss
from .loss import triplet_margin_loss
Expand Down Expand Up @@ -241,5 +242,6 @@
'rrelu',
'triplet_margin_with_distance_loss',
'triplet_margin_loss',
'multi_margin_loss',
'soft_margin_loss',
]
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