wehub-resource-sync
331 lines
11 KiB
Python
331 lines
11 KiB
Python
# 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 convert_uint16_to_float, get_device_place, is_custom_device
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from utils import dygraph_guard, static_guard
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import paddle
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from paddle.base import core
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from paddle.base.data_feeder import convert_dtype
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class TestEmptyLikeAPICommon(unittest.TestCase):
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def __check_out__(self, out):
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data_type = convert_dtype(out.dtype)
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self.assertEqual(
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data_type,
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self.dst_dtype,
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f'dtype should be {self.dst_dtype}, but get {data_type}',
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)
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shape = out.shape
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self.assertTupleEqual(
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shape,
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self.dst_shape,
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f'shape should be {self.dst_shape}, but get {shape}',
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)
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if data_type in [
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'float16',
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'float32',
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'float64',
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'int32',
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'int64',
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'uint16',
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]:
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max_value = np.nanmax(out)
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min_value = np.nanmin(out)
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always_non_full_zero = max_value >= min_value
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always_full_zero = max_value == 0.0 and min_value == 0.0
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self.assertTrue(
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always_full_zero or always_non_full_zero,
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'always_full_zero or always_non_full_zero.',
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)
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elif data_type in ['uint16']:
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uout = convert_uint16_to_float(out)
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max_value = np.nanmax(uout)
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min_value = np.nanmin(uout)
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always_non_full_zero = max_value >= min_value
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always_full_zero = max_value == 0.0 and min_value == 0.0
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self.assertTrue(
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always_full_zero or always_non_full_zero,
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'always_full_zero or always_non_full_zero.',
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)
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elif data_type in ['bool']:
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total_num = out.size
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true_num = np.sum(out)
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false_num = np.sum(~out)
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self.assertTrue(
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total_num == true_num + false_num,
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'The value should always be True or False.',
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)
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else:
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self.assertTrue(False, 'invalid data type')
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class TestEmptyLikeAPI(TestEmptyLikeAPICommon):
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def setUp(self):
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self.init_config()
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def test_dygraph_api_out(self):
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with dygraph_guard():
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out = paddle.empty_like(self.x, self.dtype)
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self.__check_out__(out.numpy())
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def init_config(self):
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self.x = np.random.random((200, 3)).astype("float32")
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self.dtype = self.x.dtype
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self.dst_shape = self.x.shape
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self.dst_dtype = self.dtype
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self.x = paddle.to_tensor(self.x)
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class TestEmptyLikeAPI2(TestEmptyLikeAPI):
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def init_config(self):
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self.x = np.random.random((200, 3)).astype("float64")
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self.dtype = self.x.dtype
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self.dst_shape = self.x.shape
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self.dst_dtype = self.dtype
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self.x = paddle.to_tensor(self.x)
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class TestEmptyLikeAPI3(TestEmptyLikeAPI):
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def init_config(self):
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self.x = np.random.random((200, 3)).astype("int")
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self.dtype = self.x.dtype
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self.dst_shape = self.x.shape
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self.dst_dtype = self.dtype
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self.x = paddle.to_tensor(self.x)
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class TestEmptyLikeAPI4(TestEmptyLikeAPI):
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def init_config(self):
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self.x = np.random.random((200, 3)).astype("int64")
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self.dtype = self.x.dtype
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self.dst_shape = self.x.shape
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self.dst_dtype = self.dtype
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self.x = paddle.to_tensor(self.x)
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class TestEmptyLikeAPI5(TestEmptyLikeAPI):
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def init_config(self):
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self.x = np.random.random((200, 3)).astype("bool")
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self.dtype = self.x.dtype
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self.dst_shape = self.x.shape
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self.dst_dtype = self.dtype
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self.x = paddle.to_tensor(self.x)
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class TestEmptyLikeAPI6(TestEmptyLikeAPI):
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def init_config(self):
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self.x = np.random.random((200, 3)).astype("float64")
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self.dtype = "float32"
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self.dst_shape = self.x.shape
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self.dst_dtype = self.dtype
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self.x = paddle.to_tensor(self.x)
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class TestEmptyLikeAPI7(TestEmptyLikeAPI):
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def init_config(self):
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self.x = np.random.random((200, 3)).astype("int")
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self.dtype = "float32"
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self.dst_shape = self.x.shape
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self.dst_dtype = self.dtype
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self.x = paddle.to_tensor(self.x)
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class TestEmptyLikeAPI8(TestEmptyLikeAPI):
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def init_config(self):
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self.x = np.random.random((200, 3)).astype("int64")
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self.dtype = "float32"
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self.dst_shape = self.x.shape
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self.dst_dtype = self.dtype
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self.x = paddle.to_tensor(self.x)
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class TestEmptyLikeAPI9(TestEmptyLikeAPI):
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def init_config(self):
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self.x = np.random.random((200, 3)).astype("bool")
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self.dtype = "float32"
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self.dst_shape = self.x.shape
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self.dst_dtype = self.dtype
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self.x = paddle.to_tensor(self.x)
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class TestEmptyLikeAPI10(TestEmptyLikeAPI):
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def init_config(self):
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self.x = np.random.random((200, 3)).astype("float32")
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self.dtype = "bool"
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self.dst_shape = self.x.shape
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self.dst_dtype = self.dtype
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self.x = paddle.to_tensor(self.x)
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class TestEmptyLikeAPI_Static(TestEmptyLikeAPICommon):
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def setUp(self):
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self.init_config()
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def test_static_graph(self):
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with static_guard():
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train_program = paddle.static.Program()
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startup_program = paddle.static.Program()
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with paddle.static.program_guard(train_program, startup_program):
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x = np.random.random(self.x_shape).astype(self.dtype)
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data_x = paddle.static.data(
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'x', shape=self.data_x_shape, dtype=self.dtype
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)
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out = paddle.empty_like(data_x)
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place = (
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get_device_place()
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if (core.is_compiled_with_cuda() or is_custom_device())
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else paddle.CPUPlace()
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)
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exe = paddle.static.Executor(place)
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res = exe.run(train_program, feed={'x': x}, fetch_list=[out])
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self.dst_dtype = self.dtype
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self.dst_shape = x.shape
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self.__check_out__(res[0])
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def init_config(self):
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self.x_shape = (200, 3)
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self.data_x_shape = [200, 3]
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self.dtype = 'float32'
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class TestEmptyLikeAPI_Static2(TestEmptyLikeAPI_Static):
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def init_config(self):
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self.x_shape = (3, 200, 3)
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self.data_x_shape = [-1, 200, 3]
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self.dtype = 'float32'
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class TestEmptyLikeAPI_StaticForFP16Op(TestEmptyLikeAPICommon):
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def setUp(self):
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self.init_config()
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def init_config(self):
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self.x_shape = (200, 3)
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self.data_x_shape = [200, 3]
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self.dtype = 'float16'
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def test_static_graph(self):
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with static_guard():
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if paddle.base.core.is_compiled_with_cuda() or is_custom_device():
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place = get_device_place()
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with paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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):
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x = np.random.random([200, 3]).astype(self.dtype)
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data_x = paddle.static.data(
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name="x", shape=[200, 3], dtype=self.dtype
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)
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out = paddle.empty_like(data_x)
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exe = paddle.static.Executor(place)
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res = exe.run(
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paddle.static.default_main_program(),
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feed={'x': x},
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fetch_list=[out],
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)
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self.dst_dtype = self.dtype
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self.dst_shape = x.shape
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self.__check_out__(res[0])
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class TestEmptyLikeAPI_StaticForBF16Op(TestEmptyLikeAPICommon):
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def setUp(self):
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self.init_config()
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def init_config(self):
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self.x_shape = (200, 3)
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self.data_x_shape = [200, 3]
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self.dtype = 'uint16'
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def test_static_graph(self):
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with static_guard():
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if paddle.base.core.is_compiled_with_cuda() or is_custom_device():
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place = get_device_place()
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with paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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):
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x = np.random.random([200, 3]).astype(np.uint16)
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data_x = paddle.static.data(
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name="x", shape=[200, 3], dtype=np.uint16
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)
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out = paddle.empty_like(data_x)
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exe = paddle.static.Executor(place)
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res = exe.run(
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paddle.static.default_main_program(),
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feed={'x': x},
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fetch_list=[out],
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)
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self.dst_dtype = self.dtype
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self.dst_shape = x.shape
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self.__check_out__(res[0])
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class TestEmptyLikeAPI_Alias(unittest.TestCase):
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def setUp(self):
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paddle.disable_static()
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def test_check_output(self):
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"""
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Test the alias of empty_like function.
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``empty_like(x=x)`` is equivalent to ``empty_like(input=x)``
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"""
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shape_cases = [
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[2],
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[2, 4],
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[2, 4, 8],
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]
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dtype_cases = [
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None, # test default dtype
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"float32",
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"float64",
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"int32",
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"int64",
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"bool",
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]
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for shape in shape_cases:
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for dtype in dtype_cases:
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x = paddle.rand(shape)
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for param_alias in ["x", "input"]:
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if dtype is None:
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out = paddle.empty_like(**{param_alias: x})
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expected_shape = x.shape
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expected_dtype = x.dtype
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else:
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out = paddle.empty_like(**{param_alias: x}, dtype=dtype)
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expected_shape = x.shape
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expected_dtype = paddle.to_tensor(
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[1], dtype=dtype
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).dtype
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# Verify shape and dtype
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self.assertEqual(out.shape, expected_shape)
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self.assertEqual(out.dtype, expected_dtype)
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if __name__ == '__main__':
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unittest.main()
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