wehub-resource-sync
205 lines
6.4 KiB
Python
205 lines
6.4 KiB
Python
# Copyright (c) 2019 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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from paddle import base
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from paddle.base import core
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from paddle.nn import functional
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class TestOneHotOp(OpTest):
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def setUp(self):
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self.op_type = 'one_hot_v2'
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depth = 10
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depth_np = np.array(10).astype('int32')
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dimension = 12
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x_lod = [[4, 1, 3, 3]]
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x = [np.random.randint(0, depth - 1) for i in range(sum(x_lod[0]))]
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x = np.array(x).astype('int32').reshape([sum(x_lod[0])])
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out = np.zeros(shape=(np.prod(x.shape), depth)).astype('float32')
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for i in range(np.prod(x.shape)):
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out[i, x[i]] = 1.0
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self.inputs = {'X': (x, x_lod), 'depth_tensor': depth_np}
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self.attrs = {'dtype': int(core.VarDesc.VarType.FP32)}
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self.outputs = {'Out': (out, x_lod)}
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def test_check_output(self):
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self.check_output(check_dygraph=False)
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class TestOneHotOp_attr(OpTest):
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def setUp(self):
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self.op_type = 'one_hot_v2'
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depth = 10
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dimension = 12
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x_lod = [[4, 1, 3, 3]]
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x = [np.random.randint(0, depth - 1) for i in range(sum(x_lod[0]))]
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x = np.array(x).astype('int32').reshape([sum(x_lod[0]), 1])
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out = np.zeros(shape=(np.prod(x.shape[:-1]), 1, depth)).astype(
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'float32'
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)
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for i in range(np.prod(x.shape)):
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out[i, 0, x[i]] = 1.0
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self.inputs = {'X': (x, x_lod)}
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self.attrs = {'dtype': int(core.VarDesc.VarType.FP32), 'depth': depth}
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self.outputs = {'Out': (out, x_lod)}
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def test_check_output(self):
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self.check_output(check_dygraph=False)
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class TestOneHotOp_default_dtype(OpTest):
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def setUp(self):
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self.op_type = 'one_hot_v2'
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depth = 10
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depth_np = np.array(10).astype('int32')
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dimension = 12
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x_lod = [[4, 1, 3, 3]]
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x = [np.random.randint(0, depth - 1) for i in range(sum(x_lod[0]))]
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x = np.array(x).astype('int32').reshape([sum(x_lod[0])])
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out = np.zeros(shape=(np.prod(x.shape), depth)).astype('float32')
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for i in range(np.prod(x.shape)):
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out[i, x[i]] = 1.0
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self.inputs = {'X': (x, x_lod), 'depth_tensor': depth_np}
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self.attrs = {}
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self.outputs = {'Out': (out, x_lod)}
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def test_check_output(self):
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self.check_output(check_dygraph=False)
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class TestOneHotOp_default_dtype_attr(OpTest):
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def setUp(self):
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self.op_type = 'one_hot_v2'
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depth = 10
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dimension = 12
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x_lod = [[4, 1, 3, 3]]
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x = [np.random.randint(0, depth - 1) for i in range(sum(x_lod[0]))]
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x = np.array(x).astype('int32').reshape([sum(x_lod[0]), 1])
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out = np.zeros(shape=(np.prod(x.shape[:-1]), 1, depth)).astype(
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'float32'
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)
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for i in range(np.prod(x.shape)):
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out[i, 0, x[i]] = 1.0
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self.inputs = {'X': (x, x_lod)}
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self.attrs = {'depth': depth}
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self.outputs = {'Out': (out, x_lod)}
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def test_check_output(self):
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self.check_output(check_dygraph=False)
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class TestOneHotOpApi(unittest.TestCase):
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def test_api(self):
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main = paddle.static.Program()
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startup = paddle.static.Program()
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with paddle.static.program_guard(main, startup):
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num_classes = 10
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label = paddle.static.data(
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name="label", shape=[-1, 1], dtype="int64"
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)
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one_hot_label = functional.one_hot(x=label, num_classes=num_classes)
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place = base.CPUPlace()
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label_data = np.array(
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[np.random.randint(0, 10 - 1) for i in range(6)]
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).reshape([6, 1])
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label_data = label_data.astype('int64')
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exe = base.Executor(place)
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exe.run(startup)
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ret = exe.run(
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feed={
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'label': label_data,
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},
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fetch_list=[one_hot_label],
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return_numpy=False,
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)
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def test_api_with_depthTensor(self):
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main = paddle.static.Program()
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startup = paddle.static.Program()
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with paddle.static.program_guard(main, startup):
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num_classes = paddle.assign(np.array([10], dtype=np.int32))
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label = paddle.static.data(
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name="label", shape=[-1, 1], dtype="int64"
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)
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one_hot_label = functional.one_hot(x=label, num_classes=num_classes)
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place = base.CPUPlace()
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label_data = np.array(
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[np.random.randint(0, 10 - 1) for i in range(6)]
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).reshape([6, 1])
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label_data = label_data.astype('int64')
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exe = base.Executor(place)
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exe.run(startup)
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ret = exe.run(
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feed={
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'label': label_data,
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},
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fetch_list=[one_hot_label],
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return_numpy=False,
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)
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def test_api_with_dygraph(self):
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num_classes = 10
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label = np.array(
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[np.random.randint(0, num_classes - 1) for i in range(6)]
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).reshape([6, 1])
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with base.dygraph.guard():
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one_hot_label = functional.one_hot(
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x=paddle.to_tensor(label), num_classes=num_classes
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)
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class BadInputTestOnehotV2(unittest.TestCase):
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def test_error(self):
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with base.program_guard(base.Program()):
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def test_bad_x():
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label = paddle.static.data(
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name="label",
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shape=[4],
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dtype="float32",
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)
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if not paddle.framework.use_pir_api():
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label.desc.set_need_check_feed(False)
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one_hot_label = functional.one_hot(x=label, num_classes=4)
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self.assertRaises(TypeError, test_bad_x)
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if __name__ == '__main__':
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paddle.enable_static()
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unittest.main()
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