wehub-resource-sync
412 lines
14 KiB
Python
412 lines
14 KiB
Python
# Copyright (c) 2020 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 OpTest, get_device_place, get_places, is_custom_device
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import paddle
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from paddle.base import core
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def test_static_layer(
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place, input_np, label_np, reduction='mean', weight_np=None
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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='float64'
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)
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label = paddle.static.data(
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name='label', shape=label_np.shape, dtype='float64'
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)
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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='float64'
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)
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bce_loss = paddle.nn.loss.BCELoss(
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weight=weight, reduction=reduction
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)
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else:
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bce_loss = paddle.nn.loss.BCELoss(reduction=reduction)
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res = bce_loss(input, label)
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exe = paddle.static.Executor(place)
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(static_result,) = exe.run(
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prog,
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feed=(
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{"input": input_np, "label": label_np}
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if weight_np is None
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else {"input": input_np, "label": label_np, "weight": weight_np}
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),
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fetch_list=[res],
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)
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return static_result
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def test_static_functional(
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place, input_np, label_np, reduction='mean', weight_np=None
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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='float64'
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)
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label = paddle.static.data(
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name='label', shape=label_np.shape, dtype='float64'
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)
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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='float64'
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)
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res = paddle.nn.functional.binary_cross_entropy(
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input, label, weight=weight, reduction=reduction
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)
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else:
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res = paddle.nn.functional.binary_cross_entropy(
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input, label, reduction=reduction
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)
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exe = paddle.static.Executor(place)
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(static_result,) = exe.run(
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prog,
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feed=(
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{"input": input_np, "label": label_np}
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if weight_np is None
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else {"input": input_np, "label": label_np, "weight": weight_np}
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),
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fetch_list=[res],
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)
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return static_result
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def test_dygraph_layer(
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place, input_np, label_np, reduction='mean', weight_np=None
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):
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paddle.disable_static()
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if weight_np is not None:
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weight = paddle.to_tensor(weight_np)
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bce_loss = paddle.nn.loss.BCELoss(weight=weight, reduction=reduction)
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else:
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bce_loss = paddle.nn.loss.BCELoss(reduction=reduction)
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dy_res = bce_loss(paddle.to_tensor(input_np), paddle.to_tensor(label_np))
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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 test_dygraph_functional(
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place, input_np, label_np, reduction='mean', weight_np=None
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):
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paddle.disable_static()
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input = paddle.to_tensor(input_np)
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label = paddle.to_tensor(label_np)
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if weight_np is not None:
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weight = paddle.to_tensor(weight_np)
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dy_res = paddle.nn.functional.binary_cross_entropy(
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input, label, weight=weight, reduction=reduction
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)
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else:
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dy_res = paddle.nn.functional.binary_cross_entropy(
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input, label, 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_bceloss(input_np, label_np, reduction='mean', weight_np=None):
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if weight_np is None:
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expected = -1 * (
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label_np * np.log(input_np)
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+ (1.0 - label_np) * np.log(1.0 - input_np)
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)
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else:
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expected = (
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-1
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* weight_np
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* (
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label_np * np.log(input_np)
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+ (1.0 - label_np) * np.log(1.0 - input_np)
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)
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)
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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 TestBCELoss(unittest.TestCase):
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def test_BCELoss(self):
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input_np = np.random.uniform(0.1, 0.8, size=(20, 30)).astype(np.float64)
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label_np = np.random.randint(0, 2, size=(20, 30)).astype(np.float64)
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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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static_result = test_static_layer(
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place, input_np, label_np, reduction
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)
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dy_result = test_dygraph_layer(
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place, input_np, label_np, reduction
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)
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expected = calc_bceloss(input_np, label_np, reduction)
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np.testing.assert_allclose(static_result, expected, rtol=1e-05)
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np.testing.assert_allclose(static_result, dy_result, rtol=1e-05)
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np.testing.assert_allclose(dy_result, expected, rtol=1e-05)
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static_functional = test_static_functional(
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place, input_np, label_np, reduction
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)
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dy_functional = test_dygraph_functional(
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place, input_np, label_np, reduction
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)
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np.testing.assert_allclose(
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static_functional, expected, rtol=1e-05
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)
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np.testing.assert_allclose(
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static_functional, dy_functional, rtol=1e-05
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)
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np.testing.assert_allclose(dy_functional, expected, rtol=1e-05)
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def test_BCELoss_weight(self):
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input_np = np.random.uniform(0.1, 0.8, size=(2, 3, 4, 10)).astype(
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np.float64
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)
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label_np = np.random.randint(0, 2, size=(2, 3, 4, 10)).astype(
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np.float64
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)
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weight_np = np.random.random(size=(3, 4, 10)).astype(np.float64)
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place = get_device_place()
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for reduction in ['sum', 'mean', 'none']:
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static_result = test_static_layer(
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place, input_np, label_np, reduction, weight_np=weight_np
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)
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dy_result = test_dygraph_layer(
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place, input_np, label_np, reduction, weight_np=weight_np
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)
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expected = calc_bceloss(
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input_np, label_np, reduction, weight_np=weight_np
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)
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np.testing.assert_allclose(static_result, expected, rtol=1e-05)
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np.testing.assert_allclose(static_result, dy_result, rtol=1e-05)
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np.testing.assert_allclose(dy_result, expected, rtol=1e-05)
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static_functional = test_static_functional(
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place, input_np, label_np, reduction, weight_np=weight_np
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)
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dy_functional = test_dygraph_functional(
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place, input_np, label_np, reduction, weight_np=weight_np
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)
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np.testing.assert_allclose(static_functional, expected, rtol=1e-05)
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np.testing.assert_allclose(
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static_functional, dy_functional, rtol=1e-05
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)
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np.testing.assert_allclose(dy_functional, expected, rtol=1e-05)
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def test_BCELoss_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.loss.BCELoss,
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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([[0.0, 1.0]], dtype='float32')
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self.assertRaises(
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ValueError,
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paddle.nn.functional.binary_cross_entropy,
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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_BCELoss_target_alias(self):
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paddle.disable_static()
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self.addCleanup(paddle.enable_static)
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input_np = np.random.uniform(0.1, 0.8, size=(4, 5)).astype(np.float32)
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label_np = np.random.randint(0, 2, size=(4, 5)).astype(np.float32)
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input = paddle.to_tensor(input_np)
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label = paddle.to_tensor(label_np)
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for reduction in ["none", "mean", "sum"]:
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out = paddle.nn.functional.binary_cross_entropy(
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input=input, label=label, reduction=reduction
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)
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out_alias = paddle.nn.functional.binary_cross_entropy(
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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.numpy(), out_alias.numpy(), rtol=1e-6, atol=1e-6
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)
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with self.assertRaises(ValueError):
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paddle.nn.functional.binary_cross_entropy(
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input=input, label=label, target=label, reduction="none"
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)
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def test_BCELoss_target_alias_static(self):
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paddle.enable_static()
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input_np = np.random.uniform(0.1, 0.8, size=(4, 5)).astype(np.float32)
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label_np = np.random.randint(0, 2, size=(4, 5)).astype(np.float32)
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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='float32'
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)
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label = paddle.static.data(
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name='label', shape=label_np.shape, dtype='float32'
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)
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out = paddle.nn.functional.binary_cross_entropy(
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input=input, label=label, reduction="sum"
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)
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out_alias = paddle.nn.functional.binary_cross_entropy(
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input=input, target=label, reduction="sum"
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)
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exe = paddle.static.Executor(paddle.CPUPlace())
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exe.run(startup_prog)
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static_result, static_alias_result = exe.run(
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prog,
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feed={"input": input_np, "label": label_np},
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fetch_list=[out, out_alias],
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)
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np.testing.assert_allclose(
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static_result,
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static_alias_result,
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rtol=1e-6,
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atol=1e-6,
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)
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def bce_loss(input, label):
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return -1 * (label * np.log(input) + (1.0 - label) * np.log(1.0 - input))
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def bce_wrapper(x, label):
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return paddle._C_ops.bce_loss(x, label)
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class TestBceLossOp(OpTest):
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def setUp(self):
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self.init_test_dtype()
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self.init_test_case()
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self.op_type = "bce_loss"
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self.prim_op_type = "comp"
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self.python_api = bce_wrapper
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self.public_python_api = bce_wrapper
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input_np = np.random.uniform(0.1, 0.8, self.shape).astype(self.dtype)
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label_np = np.random.randint(0, 2, self.shape).astype(self.dtype)
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output_np = bce_loss(input_np, label_np)
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self.inputs = {'X': input_np, 'Label': label_np}
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self.outputs = {'Out': output_np}
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def test_check_output(self):
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self.check_output(check_pir=True, check_prim_pir=True)
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def test_check_grad(self):
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self.check_grad(['X'], 'Out', check_pir=True)
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def init_test_case(self):
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self.shape = [10, 10]
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def init_test_dtype(self):
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self.dtype = "float64"
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class TestBceLossOpCase1(OpTest):
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def init_test_cast(self):
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self.shape = [2, 3, 4, 5]
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class TestBceLossOpCase2(OpTest):
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def init_test_cast(self):
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self.shape = [2, 3, 20]
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class TestBceLossOpFP16(TestBceLossOp):
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def test_check_output(self):
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self.check_output(check_pir=True, check_prim_pir=True)
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def test_check_grad(self):
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self.check_grad(['X'], 'Out', check_pir=True)
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def init_test_dtype(self):
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self.dtype = np.float16
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class TestBceLossOpStaticFP16(unittest.TestCase):
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def test_fp16(self):
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if not (core.is_compiled_with_cuda() or is_custom_device()):
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return
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paddle.enable_static()
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shape = [2, 3, 20]
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x_data = np.random.uniform(0.1, 0.8, shape).astype("float16")
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y_data = np.random.randint(0, 2, shape).astype("float16")
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with paddle.static.program_guard(paddle.static.Program()):
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x = paddle.static.data(shape=shape, name='x', dtype='float16')
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y = paddle.static.data(shape=shape, name='y', dtype='float16')
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out = paddle.nn.functional.binary_cross_entropy(
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x, y, reduction="none"
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)
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if core.is_compiled_with_cuda() or is_custom_device():
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place = get_device_place()
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exe = paddle.static.Executor(place)
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exe.run(paddle.static.default_startup_program())
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output_pd = exe.run(
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feed={'x': x_data, 'y': y_data}, fetch_list=[out]
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)[0]
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paddle.disable_static()
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class TestBceLossOp_ZeroSize(TestBceLossOp):
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def init_test_cast(self):
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self.shape = [0, 1, 2]
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class TestBceLossOp_ZeroSize2(TestBceLossOp):
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def init_test_cast(self):
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self.shape = [0]
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class TestBCELossWithZeroSizeTensor(unittest.TestCase):
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def test_bce_loss_with_zero_size_tensor(self):
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paddle.disable_static()
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input = paddle.to_tensor([], dtype='float32').reshape([0, 13125, 1])
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label = paddle.to_tensor([], dtype='float32').reshape([0, 13125, 1])
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input.stop_gradient = False
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out = paddle.nn.functional.binary_cross_entropy(
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input, label, reduction='sum'
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)
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loss = out.sum()
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loss.backward()
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self.assertEqual(loss.shape, [])
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self.assertEqual(float(loss), 0.0)
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paddle.enable_static()
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if __name__ == "__main__":
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paddle.enable_static()
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unittest.main()
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