hsigmoid -> hsigmoid_loss/HSigmoidLoss; refine docs (#27745)
zhupengyang
4 years ago
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GitHub
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b84d4ae31c
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659d04df2c
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# 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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from paddle import fluid, nn
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import paddle.fluid.dygraph as dg
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import paddle.nn.functional as F
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import paddle.fluid.initializer as I
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import numpy as np
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import unittest
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class HSigmoidTestCase(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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feature_size=6,
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num_classes=8,
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labels=None,
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path_code=None,
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path_table=None,
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is_sparse=False,
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dtype="float32"):
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super(HSigmoidTestCase, self).__init__()
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self.batch_size = batch_size
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self.feature_size = feature_size
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self.num_classes = num_classes
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self.dtype = dtype
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self.is_sparse = is_sparse
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self.labels = labels
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self.path_code = path_code
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self.path_table = path_table
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self.is_custom = path_code is not None and path_table is not None
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def setUp(self):
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input_shape = (self.batch_size, self.feature_size)
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self.input = np.random.uniform(
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-1, 1, size=input_shape).astype(self.dtype)
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if self.labels is None:
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self.labels = np.random.randint(
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0, self.num_classes, size=(self.batch_size, 1)).astype(np.int64)
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C = self.num_classes if self.is_custom else self.num_classes - 1
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self.weight_shape = (C, self.feature_size)
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self.weight = np.random.randn(*self.weight_shape).astype(self.dtype)
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self.bias_shape = (C, 1)
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self.bias = np.random.randn(*self.bias_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, self.feature_size], dtype=self.dtype)
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label = fluid.data("labels", [-1, 1], dtype="int64")
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if self.is_custom:
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path_table = fluid.data(
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"path_table", [-1, -1], dtype="int64")
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path_code = fluid.data("path_code", [-1, -1], dtype="int64")
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else:
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path_table = path_code = None
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y = fluid.layers.hsigmoid(
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x,
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label,
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self.num_classes,
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param_attr=I.NumpyArrayInitializer(self.weight),
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bias_attr=I.NumpyArrayInitializer(self.bias),
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path_table=path_table,
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path_code=path_code,
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is_custom=self.is_custom,
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is_sparse=self.is_sparse, )
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exe = fluid.Executor(place)
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exe.run(start)
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feed_dict = {"input": self.input, "labels": self.labels}
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if self.is_custom:
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feed_dict["path_code"] = self.path_code
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feed_dict["path_table"] = self.path_table
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y_np, = exe.run(main, feed=feed_dict, fetch_list=[y])
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return y_np
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def functional(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, self.feature_size], dtype=self.dtype)
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label = fluid.data("labels", [-1, 1], dtype="int64")
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if self.is_custom:
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path_table = fluid.data(
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"path_table", [-1, -1], dtype="int64")
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path_code = fluid.data("path_code", [-1, -1], dtype="int64")
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else:
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path_table = path_code = None
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w = fluid.data("weight", self.weight_shape, dtype=self.dtype)
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b = fluid.data("bias", self.bias_shape, dtype=self.dtype)
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y = F.hsigmoid(
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x,
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label,
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w,
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b,
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self.num_classes,
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is_sparse=self.is_sparse,
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path_table=path_table,
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path_code=path_code)
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exe = fluid.Executor(place)
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exe.run(start)
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feed_dict = {
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"input": self.input,
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"labels": self.labels,
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"weight": self.weight,
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"bias": self.bias
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}
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if self.is_custom:
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feed_dict["path_code"] = self.path_code
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feed_dict["path_table"] = self.path_table
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y_np, = exe.run(main, feed=feed_dict, fetch_list=[y])
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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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label_var = dg.to_variable(self.labels)
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if self.is_custom:
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path_code_var = dg.to_variable(self.path_code)
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path_table_var = dg.to_variable(self.path_table)
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else:
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path_code_var = path_table_var = None
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hierarchical_softmax = nn.HSigmoid(
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self.feature_size,
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self.num_classes,
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is_custom=self.is_custom,
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is_sparse=self.is_sparse,
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param_attr=I.NumpyArrayInitializer(self.weight),
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bias_attr=I.NumpyArrayInitializer(self.bias),
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dtype=self.dtype)
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y_var = hierarchical_softmax(
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x_var,
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label_var,
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path_table=path_table_var,
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path_code=path_code_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(place)
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result3 = 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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def runTest(self):
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place = fluid.CPUPlace()
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self._test_equivalence(place)
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class HSigmoidTestErrorCase(HSigmoidTestCase):
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def runTest(self):
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place = fluid.CPUPlace()
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with dg.guard(place):
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with self.assertRaises(ValueError):
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self.nn_layer()
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def nn_layer(self):
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x_var = dg.to_variable(self.input)
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label_var = dg.to_variable(self.labels)
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if self.is_custom:
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path_code_var = dg.to_variable(self.path_code)
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path_table_var = dg.to_variable(self.path_table)
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else:
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path_code_var = path_table_var = None
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hierarchical_softmax = nn.HSigmoid(
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self.feature_size,
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self.num_classes,
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is_custom=self.is_custom,
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param_attr=I.NumpyArrayInitializer(self.weight),
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bias_attr=I.NumpyArrayInitializer(self.bias),
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dtype=self.dtype)
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y_var = hierarchical_softmax(
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x_var,
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label_var,
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path_table=path_table_var,
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path_code=path_code_var)
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y_np = y_var.numpy()
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return y_np
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def load_tests(loader, standard_tests, pattern):
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suite = unittest.TestSuite()
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suite.addTest(HSigmoidTestCase(methodName="runTest"))
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suite.addTest(
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HSigmoidTestCase(
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methodName="runTest",
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batch_size=4,
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feature_size=6,
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num_classes=8,
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labels=np.array([0, 1, 4, 5]).astype(np.int64),
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path_table=np.array([(0, 2, -1, -1, -1), (0, 1, 3, -1, -1), (
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0, 1, 4, -1, -1), (0, 2, -1, -1, -1)]).astype(np.int64),
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path_code=np.array([(0, 0, -1, -1, -1), (1, 1, 1, -1, -1), (
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1, 0, 0, -1, -1), (0, 1, -1, -1, -1)]).astype(np.int64)))
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suite.addTest(HSigmoidTestErrorCase(methodName="runTest", num_classes=1))
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return suite
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if __name__ == "__main__":
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unittest.main()
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