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108 lines
3.5 KiB
108 lines
3.5 KiB
# 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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import paddle.fluid as fluid
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import paddle.fluid.core as core
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import paddle.nn as nn
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import paddle.nn.functional as functional
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def stable_softmax(x):
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shiftx = (x - np.max(x))
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exps = np.exp(shiftx)
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return exps / np.sum(exps)
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def ref_log_softmax(x, axis=None, dtype=None):
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x_t = x.copy()
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if dtype is not None:
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x_t = x_t.astype(dtype)
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if axis is None:
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axis = -1
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out = np.apply_along_axis(stable_softmax, axis, x_t)
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return np.log(out)
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class TestNNLogSoftmaxAPI(unittest.TestCase):
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def setUp(self):
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self.init_data()
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def init_data(self):
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self.x_shape = [2, 3, 4, 5]
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self.x = np.random.uniform(-1, 1, self.x_shape).astype(np.float32)
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def check_api(self, place=fluid.CPUPlace(), axis=None):
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ref_out = ref_log_softmax(self.x, axis)
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main_program = fluid.Program()
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mylogsoftmax = nn.LogSoftmax(axis)
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with fluid.program_guard(main_program):
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x = fluid.data(name='x', shape=self.x_shape)
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y = mylogsoftmax(x)
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exe = fluid.Executor(place)
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out = exe.run(main_program, feed={'x': self.x}, fetch_list=[y])
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self.assertTrue(np.allclose(out[0], ref_out))
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with fluid.dygraph.guard(place):
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x = fluid.dygraph.to_variable(self.x)
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y = mylogsoftmax(x)
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self.assertTrue(np.allclose(y.numpy(), ref_out))
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def test_check_api(self):
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places = [fluid.CPUPlace()]
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if core.is_compiled_with_cuda():
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places.append(fluid.CUDAPlace(0))
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for place in places:
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for axis in [None, 2]:
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self.check_api(place, axis)
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class TestNNFunctionalLogSoftmaxAPI(unittest.TestCase):
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def setUp(self):
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self.init_data()
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def init_data(self):
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self.x_shape = [2, 3, 4, 5]
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self.x = np.random.uniform(-1, 1, self.x_shape).astype(np.float32)
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def check_api(self, place=fluid.CPUPlace(), axis=None, dtype=None):
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ref_out = ref_log_softmax(self.x, axis, dtype)
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main_program = fluid.Program()
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mylogsoftmax = nn.LogSoftmax(axis)
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with fluid.program_guard(main_program):
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x = fluid.data(name='x', shape=self.x_shape)
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y = functional.log_softmax(x, axis, dtype)
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exe = fluid.Executor(place)
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out = exe.run(main_program, feed={'x': self.x}, fetch_list=[y])
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self.assertTrue(np.allclose(out[0], ref_out))
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with fluid.dygraph.guard(place):
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x = fluid.dygraph.to_variable(self.x)
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y = functional.log_softmax(x, axis, dtype)
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self.assertTrue(np.allclose(y.numpy(), ref_out))
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def test_check_api(self):
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places = [fluid.CPUPlace()]
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if core.is_compiled_with_cuda():
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places.append(fluid.CUDAPlace(0))
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for place in places:
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self.check_api(place, None, None)
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self.check_api(place, None, np.float64)
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if __name__ == "__main__":
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unittest.main()
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