wehub-resource-sync
349 lines
10 KiB
Python
349 lines
10 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 (
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OpTest,
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convert_float_to_uint16,
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get_device_place,
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is_custom_device,
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)
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import paddle
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from paddle.base import core
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from paddle.static import Program, program_guard
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def arange_wrapper(start, end, step, dtype="float32"):
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return paddle.arange(start, end, step, dtype)
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class TestArangeOp(OpTest):
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def setUp(self):
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self.op_type = "range"
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self.init_config()
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self.inputs = {
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'Start': np.array([self.case[0]]).astype(self.dtype),
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'End': np.array([self.case[1]]).astype(self.dtype),
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'Step': np.array([self.case[2]]).astype(self.dtype),
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}
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self.outputs = {
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'Out': np.arange(self.case[0], self.case[1], self.case[2]).astype(
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self.dtype
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)
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}
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def init_config(self):
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self.dtype = np.float32
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self.python_api = arange_wrapper
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self.case = (0, 1, 0.2)
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def test_check_output(self):
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self.check_output(check_pir=True, check_symbol_infer=False)
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class TestFloatArangeOp(TestArangeOp):
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def init_config(self):
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self.dtype = np.float32
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self.python_api = paddle.arange
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self.case = (0, 5, 1)
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class TestFloat16ArangeOp(TestArangeOp):
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def init_config(self):
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self.dtype = np.float16
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self.python_api = paddle.arange
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self.case = (0, 5, 1)
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@unittest.skipIf(
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not (core.is_compiled_with_cuda() or is_custom_device())
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or not core.is_bfloat16_supported(get_device_place()),
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"core is not compiled with CUDA and not support the bfloat16",
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)
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class TestBFloat16ArangeOp(OpTest):
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def setUp(self):
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self.op_type = "range"
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self.init_config()
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self.inputs = {
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'Start': convert_float_to_uint16(self.start),
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'End': convert_float_to_uint16(self.end),
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'Step': convert_float_to_uint16(self.step),
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}
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self.outputs = {
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'Out': convert_float_to_uint16(
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np.arange(self.start, self.end, self.step)
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)
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}
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def init_config(self):
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self.dtype = np.uint16
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self.python_api = arange_wrapper
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self.case = (0, 5, 1)
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self.start = np.array([self.case[0]]).astype(np.float32)
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self.end = np.array([self.case[1]]).astype(np.float32)
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self.step = np.array([self.case[2]]).astype(np.float32)
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def test_check_output(self):
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place = get_device_place()
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self.check_output_with_place(
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place, check_pir=True, check_symbol_infer=False
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)
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class TestInt32ArangeOp(TestArangeOp):
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def init_config(self):
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self.dtype = np.int32
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self.python_api = paddle.arange
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self.case = (0, 5, 2)
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class TestFloat64ArangeOp(TestArangeOp):
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def init_config(self):
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self.dtype = np.float64
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self.python_api = paddle.arange
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self.case = (10, 1, -2)
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class TestInt64ArangeOp(TestArangeOp):
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def init_config(self):
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self.dtype = np.int64
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self.python_api = paddle.arange
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self.case = (-1, -10, -2)
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class TestZeroSizeArangeOp(TestArangeOp):
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def init_config(self):
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self.dtype = np.int32
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self.python_api = paddle.arange
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self.case = (0, 0, 1)
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class TestArangeOpError(unittest.TestCase):
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def test_static_errors(self):
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with program_guard(Program(), Program()):
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paddle.enable_static()
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self.assertRaises(TypeError, paddle.arange, 10, dtype='int8')
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def test_unisfinite_start_errors(self):
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paddle.disable_static()
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start = paddle.to_tensor(np.array([np.nan], 'float32'))
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end = paddle.to_tensor(np.array([100], 'float32'))
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self.assertRaises(
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ValueError,
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paddle.arange,
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start=start,
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end=end,
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step=1,
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dtype='int32',
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)
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self.assertRaises(
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ValueError,
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paddle.arange,
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start=start,
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end=end,
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step=1,
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dtype='float32',
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)
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start = float('nan')
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self.assertRaises(
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ValueError,
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paddle.arange,
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start=start,
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end=end,
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step=1,
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dtype='int32',
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)
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start = float('nan')
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self.assertRaises(
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ValueError,
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paddle.arange,
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start=start,
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end=end,
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step=1,
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dtype='float32',
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)
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def test_unisfinite_end_errors(self):
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paddle.disable_static()
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start = paddle.to_tensor(np.array([0.0], 'float32'))
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end = paddle.to_tensor(np.array([np.inf], 'float32'))
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self.assertRaises(
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ValueError,
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paddle.arange,
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start=start,
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end=end,
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step=1,
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dtype='int32',
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)
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self.assertRaises(
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ValueError,
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paddle.arange,
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start=start,
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end=end,
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step=1,
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dtype='float32',
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)
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end = float('inf')
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self.assertRaises(
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ValueError,
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paddle.arange,
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start=start,
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end=end,
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step=1,
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dtype='int32',
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)
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end = float('inf')
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self.assertRaises(
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ValueError,
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paddle.arange,
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start=start,
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end=end,
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step=1,
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dtype='float32',
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)
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class TestArangeAPI(unittest.TestCase):
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def test_out(self):
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paddle.enable_static()
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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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x1 = paddle.arange(0, 5, 1, 'float32')
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place = get_device_place()
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exe = paddle.static.Executor(place)
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out = exe.run(fetch_list=[x1])
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expected_data = np.arange(0, 5, 1).astype(np.float32)
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self.assertEqual((out == expected_data).all(), True)
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self.assertListEqual(list(x1.shape), [5])
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paddle.disable_static(place)
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class TestArangeImperative(unittest.TestCase):
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def test_out(self):
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place = get_device_place()
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paddle.disable_static(place)
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x1 = paddle.arange(0, 5, 1)
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x2 = paddle.tensor.arange(5)
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x3 = paddle.tensor.creation.arange(5)
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start = paddle.to_tensor(np.array([0], 'float32'))
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end = paddle.to_tensor(np.array([5], 'float32'))
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step = paddle.to_tensor(np.array([1], 'float32'))
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x4 = paddle.arange(start, end, step, 'int64')
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expected_data = np.arange(0, 5, 1).astype(np.int64)
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for x in [x1, x2, x3, x4]:
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np.testing.assert_array_equal(x.numpy(), expected_data)
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start_float = paddle.to_tensor(np.array([0.5], 'float32'))
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end_float = paddle.to_tensor(np.array([1.5], 'float32'))
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step_float = paddle.to_tensor(np.array([0.5], 'float32'))
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# all [start, end, step] is float
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x5 = paddle.arange(start_float, end_float, step_float)
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x5_expected_data = np.arange(0.5, 1.5, 0.5).astype(np.float32)
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np.testing.assert_array_equal(x5.numpy(), x5_expected_data)
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self.assertEqual(x5.numpy().dtype, np.float32)
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# [start, end] is float , [step] is int
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x6 = paddle.arange(start_float, end_float, 1)
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x6_expected_data = np.arange(0.5, 1.5, 1).astype(np.float32)
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np.testing.assert_array_equal(x6.numpy(), x6_expected_data)
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self.assertEqual(x6.numpy().dtype, np.float32)
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# [start] is float , [end] is int
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x7 = paddle.arange(start_float, 1)
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x7_expected_data = np.arange(0.5, 1).astype(np.float32)
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np.testing.assert_array_equal(x7.numpy(), x7_expected_data)
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self.assertEqual(x7.numpy().dtype, np.float32)
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# [start] is float
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x8 = paddle.arange(start_float)
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x8_expected_data = np.arange(0.5).astype(np.float32)
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np.testing.assert_array_equal(x8.numpy(), x8_expected_data)
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self.assertEqual(x8.numpy().dtype, np.float32)
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# [start] is int
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x9 = paddle.arange(1)
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x9_expected_data = np.arange(1).astype(np.int64)
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np.testing.assert_array_equal(x9.numpy(), x9_expected_data)
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self.assertEqual(x9.numpy().dtype, np.int64)
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# [start] is float
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x10 = paddle.arange(1.0)
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x10_expected_data = np.arange(1).astype(np.float32)
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np.testing.assert_array_equal(x10.numpy(), x10_expected_data)
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self.assertEqual(x10.numpy().dtype, np.float32)
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# [start] is np.int
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x11 = paddle.arange(np.int64(10))
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x11_expected_data = np.arange(10).astype(np.int64)
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np.testing.assert_array_equal(x11.numpy(), x11_expected_data)
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self.assertEqual(x11.numpy().dtype, np.int64)
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# [start] is a big integer
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x12 = paddle.arange(
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start=0,
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end=-9007199254740994,
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step=-9007199254740993,
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)
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# numpy give wrong result here, so we generate 'x12_expected_data' manually
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# x12_expected_data = np.arange(start=0, stop=-9007199254740994, step=-9007199254740993, dtype=np.int64)
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x12_expected_data = np.array([0, -9007199254740993])
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np.testing.assert_array_equal(x12.numpy(), x12_expected_data)
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self.assertEqual(x12.numpy().dtype, np.int64)
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# [start<end step<0]
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x13 = paddle.arange(start=0, end=10, step=-1)
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x13_expected_data = np.array([])
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np.testing.assert_array_equal(x13.numpy(), x13_expected_data)
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# [start>end step>0]
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x14 = paddle.arange(start=10, end=0, step=1)
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x14_expected_data = np.array([])
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np.testing.assert_array_equal(x14.numpy(), x14_expected_data)
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paddle.enable_static()
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class TestArangeStatic(unittest.TestCase):
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def test_infermeta(self):
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paddle.enable_static()
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x = paddle.arange(0, 1 + 0.005, 0.005)
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self.assertEqual(x.shape, [201])
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paddle.disable_static()
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if __name__ == "__main__":
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unittest.main()
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