wehub-resource-sync
523 lines
15 KiB
Python
523 lines
15 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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from op_test import get_places
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import paddle
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from paddle import base
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def call_MultiMarginLoss_layer(
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input,
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label,
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p=1,
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margin=1.0,
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weight=None,
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reduction='mean',
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):
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triplet_margin_loss = paddle.nn.MultiMarginLoss(
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p=p, margin=margin, weight=weight, reduction=reduction
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)
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res = triplet_margin_loss(
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input=input,
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label=label,
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)
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return res
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def call_MultiMarginLoss_functional(
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input,
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label,
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p=1,
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margin=1.0,
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weight=None,
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reduction='mean',
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):
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res = paddle.nn.functional.multi_margin_loss(
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input=input,
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label=label,
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p=p,
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margin=margin,
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weight=weight,
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reduction=reduction,
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)
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return res
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def test_static(
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place,
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input_np,
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label_np,
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p=1,
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margin=1.0,
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weight_np=None,
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reduction='mean',
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functional=False,
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):
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prog = paddle.static.Program()
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startup_prog = paddle.static.Program()
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with paddle.static.program_guard(prog, startup_prog):
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input = paddle.static.data(
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name='input', shape=input_np.shape, dtype=input_np.dtype
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)
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label = paddle.static.data(
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name='label', shape=label_np.shape, dtype=label_np.dtype
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)
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feed_dict = {
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"input": input_np,
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"label": label_np,
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}
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weight = None
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if weight_np is not None:
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weight = paddle.static.data(
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name='weight', shape=weight_np.shape, dtype=weight_np.dtype
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)
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feed_dict['weight'] = weight_np
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if functional:
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res = call_MultiMarginLoss_functional(
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input=input,
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label=label,
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p=p,
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margin=margin,
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weight=weight,
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reduction=reduction,
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)
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else:
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res = call_MultiMarginLoss_layer(
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input=input,
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label=label,
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p=p,
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margin=margin,
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weight=weight,
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reduction=reduction,
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)
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exe = paddle.static.Executor(place)
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static_result = exe.run(prog, feed=feed_dict, fetch_list=[res])
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return static_result[0]
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def test_static_data_shape(
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place,
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input_np,
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label_np,
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wrong_label_shape=None,
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weight_np=None,
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wrong_weight_shape=None,
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functional=False,
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):
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prog = paddle.static.Program()
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startup_prog = paddle.static.Program()
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with paddle.static.program_guard(prog, startup_prog):
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input = paddle.static.data(
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name='input', shape=input_np.shape, dtype=input_np.dtype
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)
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if wrong_label_shape is None:
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label_shape = label_np.shape
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else:
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label_shape = wrong_label_shape
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label = paddle.static.data(
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name='label', shape=label_shape, dtype=label_np.dtype
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)
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feed_dict = {
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"input": input_np,
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"label": label_np,
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}
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weight = None
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if weight_np is not None:
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if wrong_weight_shape is None:
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weight_shape = weight_np.shape
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else:
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weight_shape = wrong_weight_shape
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weight = paddle.static.data(
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name='weight', shape=weight_shape, dtype=weight_np.dtype
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)
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feed_dict['weight'] = weight_np
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if functional:
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res = call_MultiMarginLoss_functional(
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input=input,
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label=label,
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weight=weight,
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)
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else:
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res = call_MultiMarginLoss_layer(
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input=input,
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label=label,
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weight=weight,
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)
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exe = paddle.static.Executor(place)
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static_result = exe.run(prog, feed=feed_dict, fetch_list=[res])
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return static_result
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def test_dygraph(
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place,
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input,
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label,
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p=1,
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margin=1.0,
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weight=None,
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reduction='mean',
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functional=False,
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):
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paddle.disable_static()
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input = paddle.to_tensor(input)
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label = paddle.to_tensor(label)
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if weight is not None:
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weight = paddle.to_tensor(weight)
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if functional:
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dy_res = call_MultiMarginLoss_functional(
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input=input,
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label=label,
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p=p,
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margin=margin,
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weight=weight,
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reduction=reduction,
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)
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else:
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dy_res = call_MultiMarginLoss_layer(
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input=input,
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label=label,
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p=p,
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margin=margin,
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weight=weight,
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reduction=reduction,
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)
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dy_result = dy_res.numpy()
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paddle.enable_static()
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return dy_result
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def calc_multi_margin_loss(
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input,
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label,
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p=1,
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margin=1.0,
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weight=None,
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reduction='mean',
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):
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index_sample = np.array(
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[input[i, label[i]] for i in range(label.size)]
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).reshape(-1, 1)
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if weight is None:
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expected = (
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np.mean(np.maximum(margin + input - index_sample, 0.0) ** p, axis=1)
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- margin**p / input.shape[1]
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)
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else:
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weight = np.array(
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[weight[label[i]] for i in range(label.size)]
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).reshape(-1, 1)
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expected = np.mean(
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weight * (np.maximum((margin + input - index_sample), 0.0) ** p),
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axis=1,
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) - weight * (margin**p / input.shape[1])
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if reduction == 'mean':
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expected = np.mean(expected)
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elif reduction == 'sum':
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expected = np.sum(expected)
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else:
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expected = expected
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return expected
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class TestMultiMarginLoss(unittest.TestCase):
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def test_MultiMarginLoss(self):
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batch_size = 5
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num_classes = 2
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shape = (batch_size, num_classes)
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input = np.random.uniform(0.1, 0.8, size=shape).astype(np.float64)
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label = np.random.uniform(0, input.shape[1], size=(batch_size,)).astype(
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np.int64
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)
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places = get_places()
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reductions = ['sum', 'mean', 'none']
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for place in places:
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for reduction in reductions:
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expected = calc_multi_margin_loss(
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input=input, label=label, reduction=reduction
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)
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dy_result = test_dygraph(
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place=place,
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input=input,
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label=label,
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reduction=reduction,
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)
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static_result = test_static(
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place=place,
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input_np=input,
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label_np=label,
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reduction=reduction,
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)
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np.testing.assert_allclose(static_result, expected)
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np.testing.assert_allclose(static_result, dy_result)
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np.testing.assert_allclose(dy_result, expected)
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static_functional = test_static(
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place=place,
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input_np=input,
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label_np=label,
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reduction=reduction,
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functional=True,
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)
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dy_functional = test_dygraph(
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place=place,
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input=input,
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label=label,
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reduction=reduction,
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functional=True,
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)
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np.testing.assert_allclose(static_functional, expected)
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np.testing.assert_allclose(static_functional, dy_functional)
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np.testing.assert_allclose(dy_functional, expected)
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def test_MultiMarginLoss_error(self):
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paddle.disable_static()
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self.assertRaises(
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ValueError,
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paddle.nn.MultiMarginLoss,
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reduction="unsupported reduction",
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)
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input = paddle.to_tensor([[0.1, 0.3]], dtype='float32')
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label = paddle.to_tensor([0], dtype='int32')
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self.assertRaises(
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ValueError,
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paddle.nn.functional.multi_margin_loss,
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input=input,
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label=label,
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reduction="unsupported reduction",
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)
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paddle.enable_static()
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def test_MultiMarginLoss_dimension(self):
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paddle.disable_static()
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input = paddle.to_tensor([[0.1, 0.3], [1, 2]], dtype='float32')
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label = paddle.to_tensor([0, 1, 1], dtype='int32')
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self.assertRaises(
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ValueError,
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paddle.nn.functional.multi_margin_loss,
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input=input,
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label=label,
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)
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MMLoss = paddle.nn.MultiMarginLoss()
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self.assertRaises(
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ValueError,
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MMLoss,
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input=input,
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label=label,
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)
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paddle.enable_static()
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def test_MultiMarginLoss_target_alias(self):
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with base.dygraph.guard():
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input = paddle.to_tensor(
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[[0.2, 1.5, 0.7], [1.1, 0.4, 0.9]], dtype='float32'
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)
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label = paddle.to_tensor([1, 0], dtype='int64')
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for reduction in ['none', 'mean', 'sum']:
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with self.subTest(reduction=reduction):
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out_with_label = paddle.nn.functional.multi_margin_loss(
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input=input, label=label, reduction=reduction
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)
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out_with_target = paddle.nn.functional.multi_margin_loss(
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input=input, target=label, reduction=reduction
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)
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np.testing.assert_allclose(
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out_with_label.numpy(), out_with_target.numpy()
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)
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def test_MultiMarginLoss_target_alias_conflict(self):
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with base.dygraph.guard():
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input = paddle.to_tensor([[0.2, 1.5, 0.7]], dtype='float32')
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label = paddle.to_tensor([1], dtype='int64')
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with self.assertRaises(ValueError):
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paddle.nn.functional.multi_margin_loss(
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input=input,
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label=label,
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target=label,
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)
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def test_MultiMarginLoss_p(self):
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p = 2
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batch_size = 5
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num_classes = 2
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shape = (batch_size, num_classes)
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reduction = 'mean'
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place = paddle.CPUPlace()
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input = np.random.uniform(0.1, 0.8, size=shape).astype(np.float64)
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label = np.random.uniform(0, input.shape[1], size=(batch_size,)).astype(
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np.int64
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)
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expected = calc_multi_margin_loss(
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input=input, p=p, label=label, reduction=reduction
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)
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dy_result = test_dygraph(
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place=place,
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p=p,
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input=input,
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label=label,
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reduction=reduction,
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)
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static_result = test_static(
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place=place,
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p=p,
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input_np=input,
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label_np=label,
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reduction=reduction,
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)
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np.testing.assert_allclose(static_result, expected)
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np.testing.assert_allclose(static_result, dy_result)
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np.testing.assert_allclose(dy_result, expected)
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static_functional = test_static(
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place=place,
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p=p,
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input_np=input,
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label_np=label,
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reduction=reduction,
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functional=True,
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)
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dy_functional = test_dygraph(
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place=place,
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p=p,
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input=input,
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label=label,
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reduction=reduction,
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functional=True,
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)
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np.testing.assert_allclose(static_functional, expected)
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np.testing.assert_allclose(static_functional, dy_functional)
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np.testing.assert_allclose(dy_functional, expected)
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def test_MultiMarginLoss_weight(self):
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batch_size = 5
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num_classes = 2
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shape = (batch_size, num_classes)
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reduction = 'mean'
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place = paddle.CPUPlace()
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input = np.random.uniform(0.1, 0.8, size=shape).astype(np.float64)
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label = np.random.uniform(0, input.shape[1], size=(batch_size,)).astype(
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np.int64
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)
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weight = np.random.uniform(0, 2, size=(num_classes,)).astype(np.float64)
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expected = calc_multi_margin_loss(
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input=input, label=label, weight=weight, reduction=reduction
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)
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dy_result = test_dygraph(
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place=place,
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input=input,
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label=label,
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weight=weight,
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reduction=reduction,
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)
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static_result = test_static(
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place=place,
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input_np=input,
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label_np=label,
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weight_np=weight,
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reduction=reduction,
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)
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np.testing.assert_allclose(static_result, expected)
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np.testing.assert_allclose(static_result, dy_result)
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np.testing.assert_allclose(dy_result, expected)
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static_functional = test_static(
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place=place,
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input_np=input,
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label_np=label,
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weight_np=weight,
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reduction=reduction,
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functional=True,
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)
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dy_functional = test_dygraph(
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place=place,
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input=input,
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label=label,
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weight=weight,
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reduction=reduction,
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functional=True,
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)
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np.testing.assert_allclose(static_functional, expected)
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np.testing.assert_allclose(static_functional, dy_functional)
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np.testing.assert_allclose(dy_functional, expected)
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def test_MultiMarginLoss_static_data_shape(self):
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batch_size = 5
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num_classes = 2
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shape = (batch_size, num_classes)
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place = paddle.CPUPlace()
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input = np.random.uniform(0.1, 0.8, size=shape).astype(np.float64)
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label = np.random.uniform(0, input.shape[1], size=(batch_size,)).astype(
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np.int64
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)
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weight = np.random.uniform(0, 2, size=(num_classes,)).astype(np.float64)
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self.assertRaises(
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ValueError,
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test_static_data_shape,
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place=place,
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input_np=input,
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label_np=label,
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wrong_label_shape=(10,),
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functional=True,
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)
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self.assertRaises(
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ValueError,
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test_static_data_shape,
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place=place,
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input_np=input,
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label_np=label,
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wrong_label_shape=(10,),
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functional=False,
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)
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self.assertRaises(
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ValueError,
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test_static_data_shape,
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place=place,
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input_np=input,
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label_np=label,
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weight_np=weight,
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wrong_weight_shape=(3,),
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functional=True,
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)
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self.assertRaises(
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ValueError,
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test_static_data_shape,
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place=place,
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input_np=input,
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label_np=label,
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weight_np=weight,
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wrong_weight_shape=(3,),
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functional=False,
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)
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if __name__ == "__main__":
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unittest.main()
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