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132 lines
4.7 KiB
132 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 numpy as np
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from paddle import fluid, nn
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import paddle.fluid.dygraph as dg
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import paddle.fluid.initializer as I
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import paddle.nn.functional as F
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import unittest
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class RowConvTestCase(unittest.TestCase):
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def __init__(self,
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methodName='runTest',
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batch_size=4,
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num_channels=8,
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time_steps=12,
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context_size=3,
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act=None,
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dtype="float32"):
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super(RowConvTestCase, self).__init__(methodName=methodName)
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self.batch_size = batch_size
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self.num_channels = num_channels
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self.time_steps = time_steps
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self.context_size = context_size
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self.act = act
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self.dtype = dtype
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def setUp(self):
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input_shape = (self.batch_size, self.time_steps, self.num_channels)
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self.input = np.random.uniform(size=input_shape).astype(self.dtype)
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self.weight_shape = weight_shape = (self.context_size + 1,
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self.num_channels)
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self.weight = np.random.uniform(size=weight_shape).astype(self.dtype)
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def fluid_layer(self, place):
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main = fluid.Program()
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start = fluid.Program()
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with fluid.unique_name.guard():
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with fluid.program_guard(main, start):
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x = fluid.data(
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"input", [-1, -1, self.num_channels], dtype=self.dtype)
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y = fluid.layers.row_conv(
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x,
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self.context_size,
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param_attr=I.NumpyArrayInitializer(self.weight),
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act=self.act)
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exe = fluid.Executor(place)
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exe.run(start)
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y_np, = exe.run(main, feed={"input": self.input}, fetch_list=[y])
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return y_np
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def functional_declarative(self, place):
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main = fluid.Program()
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start = fluid.Program()
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with fluid.unique_name.guard():
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with fluid.program_guard(main, start):
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x = fluid.data(
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"input", [-1, -1, self.num_channels], dtype=self.dtype)
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w = fluid.data("weight", self.weight_shape, dtype=self.dtype)
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y = F.extension.row_conv(x, w, act=self.act)
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exe = fluid.Executor(place)
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exe.run(start)
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y_np, = exe.run(main,
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feed={"input": self.input,
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"weight": self.weight},
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fetch_list=[y])
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return y_np
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def functional_imperative(self, place):
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with dg.guard(place):
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x_var = dg.to_variable(self.input)
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w_var = dg.to_variable(self.weight)
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y_var = F.extension.row_conv(x_var, w_var, act=self.act)
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y_np = y_var.numpy()
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return y_np
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def nn_layer(self, place):
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with dg.guard(place):
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x_var = dg.to_variable(self.input)
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conv = nn.RowConv(
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self.num_channels,
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self.context_size,
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param_attr=I.NumpyArrayInitializer(self.weight),
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act=self.act,
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dtype=self.dtype)
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y_var = conv(x_var)
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y_np = y_var.numpy()
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return y_np
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def _test_equivalence(self, place):
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result1 = self.fluid_layer(place)
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result2 = self.functional_declarative(place)
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result3 = self.functional_imperative(place)
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result4 = self.nn_layer(place)
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np.testing.assert_array_almost_equal(result1, result2)
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np.testing.assert_array_almost_equal(result2, result3)
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np.testing.assert_array_almost_equal(result3, result4)
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def runTest(self):
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place = fluid.CPUPlace()
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self._test_equivalence(place)
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if fluid.core.is_compiled_with_cuda():
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palce = fluid.CUDAPlace(0)
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self._test_equivalence(place)
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def load_tests(loader, standard_tests, pattern):
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suite = unittest.TestSuite()
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suite.addTest(RowConvTestCase(methodName="runTest"))
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suite.addTest(RowConvTestCase(methodName="runTest", act="sigmoid"))
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suite.addTest(
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RowConvTestCase(
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methodName="runTest", context_size=5, act="sigmoid"))
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return suite
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if __name__ == "__main__":
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unittest.main()
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