wehub-resource-sync
301 lines
9.9 KiB
Python
301 lines
9.9 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 sys
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import unittest
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sys.path.append("../../legacy_test")
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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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check_out_dtype,
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get_device_place,
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get_places,
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is_custom_device,
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)
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from test_sum_op import TestReduceOPTensorAxisBase
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from utils import dygraph_guard, static_guard
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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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class ApiMinTest(unittest.TestCase):
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def setUp(self):
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if core.is_compiled_with_cuda() or is_custom_device():
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self.place = get_device_place()
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else:
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self.place = core.CPUPlace()
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def test_api(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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data = paddle.static.data("data", shape=[10, 10], dtype="float32")
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result_min = paddle.min(x=data, axis=1)
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exe = paddle.static.Executor(self.place)
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input_data = np.random.rand(10, 10).astype(np.float32)
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(res,) = exe.run(feed={"data": input_data}, fetch_list=[result_min])
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self.assertEqual((res == np.min(input_data, axis=1)).all(), True)
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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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data = paddle.static.data("data", shape=[10, 10], dtype="int64")
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result_min = paddle.min(x=data, axis=0)
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exe = paddle.static.Executor(self.place)
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input_data = np.random.randint(10, size=(10, 10)).astype(np.int64)
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(res,) = exe.run(feed={"data": input_data}, fetch_list=[result_min])
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self.assertEqual((res == np.min(input_data, axis=0)).all(), True)
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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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data = paddle.static.data("data", shape=[10, 10], dtype="int64")
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result_min = paddle.min(x=data, axis=(0, 1))
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exe = paddle.static.Executor(self.place)
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input_data = np.random.randint(10, size=(10, 10)).astype(np.int64)
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(res,) = exe.run(feed={"data": input_data}, fetch_list=[result_min])
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self.assertEqual((res == np.min(input_data, axis=(0, 1))).all(), True)
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def test_errors(self):
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paddle.enable_static()
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def test_input_type():
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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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data = np.random.rand(10, 10)
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result_min = paddle.min(x=data, axis=0)
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self.assertRaises(TypeError, test_input_type)
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def test_imperative_api(self):
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paddle.disable_static()
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np_x = np.array([10, 10]).astype('float64')
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x = paddle.to_tensor(np_x)
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z = paddle.min(x, axis=0)
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np_z = z.numpy()
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z_expected = np.array(np.min(np_x, axis=0))
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self.assertEqual((np_z == z_expected).all(), True)
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def test_support_tuple(self):
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paddle.disable_static()
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np_x = np.array([10, 10]).astype('float64')
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x = paddle.to_tensor(np_x)
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z = paddle.min(x, axis=(0,))
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np_z = z.numpy()
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z_expected = np.array(np.min(np_x, axis=0))
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self.assertEqual((np_z == z_expected).all(), True)
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class TestOutDtype(unittest.TestCase):
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def test_min(self):
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api_fn = paddle.min
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shape = [10, 16]
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check_out_dtype(
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api_fn,
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in_specs=[(shape,)],
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expect_dtypes=['float32', 'float64', 'int32', 'int64'],
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)
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class TestMinWithTensorAxis1(TestReduceOPTensorAxisBase):
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def init_data(self):
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self.pd_api = paddle.min
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self.np_api = np.min
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self.x = paddle.randn([10, 5, 9, 9], dtype='float64')
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self.np_axis = np.array([1, 2], dtype='int64')
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self.tensor_axis = paddle.to_tensor([1, 2], dtype='int64')
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class TestMinWithTensorAxis2(TestReduceOPTensorAxisBase):
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def init_data(self):
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self.pd_api = paddle.min
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self.np_api = np.min
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self.x = paddle.randn([10, 10, 9, 9], dtype='float64')
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self.np_axis = np.array([0, 1, 2], dtype='int64')
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self.tensor_axis = [
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0,
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paddle.to_tensor([1], 'int64'),
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paddle.to_tensor([2], 'int64'),
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]
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self.keepdim = True
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class TestMinZeroSize1(unittest.TestCase):
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def init_data(self):
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self.shape = [0, 1, 2, 3]
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self.axis = [1, 2, 3]
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self.keepdims = False
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def setUp(self):
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self.init_data()
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self.data = np.random.random(self.shape).astype(np.float64)
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self.expect_res = np.min(
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self.data, axis=tuple(self.axis), keepdims=self.keepdims
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)
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self.places = get_places()
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def test_static(self):
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with static_guard():
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for place in self.places:
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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 = paddle.static.data(
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"x", shape=self.shape, dtype="float64"
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)
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res = paddle.min(x, axis=self.axis, keepdim=self.keepdims)
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exe = paddle.static.Executor(place)
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(res,) = exe.run(feed={"x": self.data}, fetch_list=[res])
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np.testing.assert_equal(res, self.expect_res)
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def test_dygraph(self):
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with dygraph_guard():
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x = paddle.to_tensor(self.data)
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res = paddle.min(x, axis=self.axis, keepdim=self.keepdims)
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np.testing.assert_equal(res, self.expect_res)
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class TestMinZeroSize2(TestMinZeroSize1):
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def init_data(self):
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self.shape = [0, 0, 2]
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self.axis = [2]
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self.keepdims = False
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class TestMinZeroSize3(TestMinZeroSize1):
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def init_data(self):
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self.shape = [0, 0, 2]
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self.axis = [2]
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self.keepdims = True
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class TestMinOp(OpTest):
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def setUp(self):
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self.op_type = "reduce_min"
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self.python_api = paddle.min
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self.init_data()
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self.prepare_data()
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def init_data(self):
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self.shape = [0, 1, 2]
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self.axis = [1]
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self.keepdims = False
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self.dtype = np.float64
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def prepare_data(self):
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self._input_data = np.random.random(self.shape).astype(self.dtype)
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self._output_data = np.min(
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self._input_data, keepdims=self.keepdims, axis=tuple(self.axis)
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)
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self.inputs = {'X': self._input_data}
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self.outputs = {'Out': self._output_data}
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self.attrs = {"dim": self.axis, "keep_dim": self.keepdims}
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def test_check_output(self):
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self.check_output(check_pir=True)
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def test_check_grad(self):
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self.check_grad(
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['X'],
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['Out'],
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check_pir=True,
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)
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@unittest.skipIf(
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not core.is_bfloat16_supported(get_device_place()),
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"place does not support BF16 evaluation",
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)
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class TestMinBfloat16(unittest.TestCase):
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def init_data(self):
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self.shape = [0, 1, 2]
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self.axis = [1]
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self.keepdims = False
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def setUp(self):
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self.init_data()
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data = np.random.random(self.shape).astype(np.float64)
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res = np.min(data, axis=tuple(self.axis), keepdims=self.keepdims)
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self.expect_shape = res.shape
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def test_shape(self):
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with dygraph_guard():
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x = paddle.zeros(self.shape, dtype=paddle.bfloat16)
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res = paddle.min(x, axis=self.axis, keepdim=self.keepdims)
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res = res.numpy()
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np.testing.assert_equal(res.shape, self.expect_shape)
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class TestMinAPIWithEmptyTensor(unittest.TestCase):
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def test_empty_tensor(self):
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with (
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base.dygraph.guard(),
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self.assertRaises(ValueError),
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):
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data = np.array([], dtype=np.float32)
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data = np.reshape(data, [0, 0, 0, 0, 0, 0, 0])
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x = paddle.to_tensor(data, dtype='float64')
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np_axis = np.array([0], dtype='int64')
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tensor_axis = paddle.to_tensor(np_axis, dtype='int64')
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out = paddle.min(x, tensor_axis)
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class TestMinWithNan(unittest.TestCase):
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def _get_places(self):
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return get_places()
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def _test_with_nan_static(
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self, func, shape, dtype=np.float32, place=paddle.CPUPlace()
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):
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with (
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static_guard(),
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paddle.static.program_guard(
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paddle.static.Program(), paddle.static.Program()
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),
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):
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x_np = np.arange(np.prod(shape), dtype=dtype).reshape(shape)
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x_np[0, 0] = np.nan
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x = paddle.static.data(name='x', shape=shape, dtype=dtype)
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out = func(x)
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exe = paddle.static.Executor(place)
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res = exe.run(feed={'x': x_np}, fetch_list=[out])
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self.assertTrue(np.isnan(res[0]), "Result should be NaN")
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def _test_with_nan_dynamic(
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self, func, shape, dtype=np.float32, place=paddle.CPUPlace()
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):
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with dygraph_guard():
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x_np = np.arange(np.prod(shape), dtype=dtype).reshape(shape)
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x_np[0, 0] = np.nan
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x = paddle.to_tensor(x_np, place=place)
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out = func(x)
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self.assertTrue(paddle.isnan(out), "Result should be NaN")
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def test_with_nan(self):
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places = self._get_places()
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for place in places:
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self._test_with_nan_dynamic(paddle.min, (2, 3), place=place)
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self._test_with_nan_static(paddle.min, (2, 3), place=place)
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if __name__ == '__main__':
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unittest.main()
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