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151 lines
4.7 KiB
151 lines
4.7 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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from op_test import OpTest
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import paddle
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import paddle.nn.functional as F
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np.random.seed(10)
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def ref_log_softmax(x):
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shiftx = (x - np.max(x))
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out = shiftx - np.log(np.exp(shiftx).sum())
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return out
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def ref_log_softmax_grad(x, axis):
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if axis < 0:
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axis += len(x.shape)
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out = np.apply_along_axis(ref_log_softmax, axis, x)
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axis_dim = x.shape[axis]
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dout = np.full_like(x, fill_value=1. / x.size)
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dx = dout - np.exp(out) * dout.copy().sum(axis=axis, keepdims=True).repeat(
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axis_dim, axis=axis)
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return dx
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class TestLogSoftmaxOp(OpTest):
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def setUp(self):
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self.op_type = 'log_softmax'
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self.dtype = 'float64'
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self.shape = [2, 3, 4, 5]
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self.axis = -1
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self.set_attrs()
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x = np.random.uniform(0.1, 1., self.shape).astype(self.dtype)
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out = np.apply_along_axis(ref_log_softmax, self.axis, x)
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self.x_grad = ref_log_softmax_grad(x, self.axis)
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self.inputs = {'X': x}
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self.outputs = {'Out': out}
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self.attrs = {'axis': self.axis}
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def set_attrs(self):
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pass
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def test_check_output(self):
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self.check_output()
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def test_check_grad(self):
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self.check_grad(['X'], ['Out'], user_defined_grads=[self.x_grad])
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class TestLogSoftmaxShape(TestLogSoftmaxOp):
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def set_attrs(self):
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self.shape = [12, 10]
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class TestLogSoftmaxAxis(TestLogSoftmaxOp):
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def set_attrs(self):
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self.axis = 1
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class TestNNLogSoftmaxAPI(unittest.TestCase):
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def setUp(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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self.place = paddle.CUDAPlace(0) \
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if paddle.fluid.core.is_compiled_with_cuda() \
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else paddle.CPUPlace()
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def check_api(self, axis=-1):
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ref_out = np.apply_along_axis(ref_log_softmax, axis, self.x)
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logsoftmax = paddle.nn.LogSoftmax(axis)
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# test static api
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with paddle.static.program_guard(paddle.static.Program()):
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x = paddle.data(name='x', shape=self.x_shape)
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y = logsoftmax(x)
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exe = paddle.static.Executor(self.place)
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out = exe.run(feed={'x': self.x}, fetch_list=[y])
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self.assertTrue(np.allclose(out[0], ref_out))
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# test dygrapg api
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paddle.disable_static()
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x = paddle.to_variable(self.x)
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y = logsoftmax(x)
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self.assertTrue(np.allclose(y.numpy(), ref_out))
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paddle.enable_static()
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def test_check_api(self):
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for axis in [-1, 1]:
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self.check_api(axis)
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class TestNNFunctionalLogSoftmaxAPI(unittest.TestCase):
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def setUp(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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self.place = paddle.CUDAPlace(0) \
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if paddle.fluid.core.is_compiled_with_cuda() \
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else paddle.CPUPlace()
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def check_api(self, axis=-1, dtype=None):
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x = self.x.copy()
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if dtype is not None:
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x = x.astype(dtype)
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ref_out = np.apply_along_axis(ref_log_softmax, axis, x)
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with paddle.static.program_guard(paddle.static.Program()):
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x = paddle.data(name='x', shape=self.x_shape)
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y = F.log_softmax(x, axis, dtype)
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exe = paddle.static.Executor(self.place)
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out = exe.run(feed={'x': self.x}, fetch_list=[y])
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self.assertTrue(np.allclose(out[0], ref_out))
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paddle.disable_static()
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x = paddle.to_variable(self.x)
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y = F.log_softmax(x, axis, dtype)
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self.assertTrue(np.allclose(y.numpy(), ref_out), True)
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paddle.enable_static()
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def test_check_api(self):
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for axis in [-1, 1]:
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self.check_api(axis)
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self.check_api(-1, 'float64')
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def test_errors(self):
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with paddle.static.program_guard(paddle.static.Program()):
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x = paddle.data(name='X1', shape=[100], dtype='int32')
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self.assertRaises(TypeError, F.log_softmax, x)
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x = paddle.data(name='X2', shape=[100], dtype='float32')
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self.assertRaises(TypeError, F.log_softmax, x, dtype='int32')
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if __name__ == "__main__":
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unittest.main()
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