wehub-resource-sync
414 lines
12 KiB
Python
414 lines
12 KiB
Python
# Copyright (c) 2018 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 os
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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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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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from test_attribute_var import UnittestBase
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import paddle
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from paddle.base import Program, program_guard
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from paddle.framework import in_pir_mode
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class BaseTestCase(OpTest):
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def initTestCase(self):
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self.op_type = 'arg_min'
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self.python_api = paddle.Tensor.argmin
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self.dims = (3, 4, 5)
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self.dtype = 'float32'
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self.axis = 0
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def setUp(self):
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self.initTestCase()
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self.x = (1000 * np.random.random(self.dims)).astype(self.dtype)
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self.inputs = {'X': self.x}
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self.attrs = {'axis': self.axis}
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if self.op_type == "arg_min":
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self.outputs = {'Out': np.argmin(self.x, axis=self.axis)}
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else:
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self.outputs = {'Out': np.argmax(self.x, axis=self.axis)}
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def test_check_output(self):
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self.check_output(check_cinn=True, check_pir=True)
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class TestCase0(BaseTestCase):
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def initTestCase(self):
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self.op_type = 'arg_max'
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self.python_api = paddle.Tensor.argmax
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self.dims = (3, 4, 5)
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self.dtype = 'float32'
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self.axis = 0
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class TestCase1(BaseTestCase):
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def initTestCase(self):
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self.op_type = 'arg_min'
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self.python_api = paddle.Tensor.argmin
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self.dims = (3, 4)
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self.dtype = 'float64'
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self.axis = 1
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class TestCase2(BaseTestCase):
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def initTestCase(self):
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self.op_type = 'arg_max'
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self.python_api = paddle.Tensor.argmax
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self.dims = (3, 4)
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self.dtype = 'int64'
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self.axis = 0
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@unittest.skipIf(
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not (paddle.is_compiled_with_cuda() or is_custom_device()),
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"FP16 test runs only on GPU",
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)
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class TestCase0FP16(BaseTestCase):
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def initTestCase(self):
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self.op_type = 'arg_max'
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self.python_api = paddle.Tensor.argmax
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self.dims = (3, 4, 5)
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self.dtype = np.float16
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self.axis = 0
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@unittest.skipIf(
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not (paddle.is_compiled_with_cuda() or is_custom_device()),
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"FP16 test runs only on GPU",
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)
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class TestCase1FP16(BaseTestCase):
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def initTestCase(self):
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self.op_type = 'arg_min'
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self.python_api = paddle.Tensor.argmin
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self.dims = (3, 4)
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self.dtype = np.float16
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self.axis = 1
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@unittest.skipIf(
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not (paddle.is_compiled_with_cuda() or is_custom_device()),
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"BFP16 test runs only on GPU",
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)
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class TestArgMinBF16OP(OpTest):
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def initTestType(self):
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self.op_type = 'arg_min'
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self.python_api = paddle.Tensor.argmin
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def initTestCase(self):
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self.initTestType()
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self.dims = (3, 4, 5)
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self.axis = 0
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self.dtype = np.uint16
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def setUp(self):
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self.initTestCase()
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x = np.random.random(self.dims).astype("float32")
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self.x = convert_float_to_uint16(x)
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self.inputs = {'X': self.x}
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self.attrs = {'axis': self.axis}
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if self.op_type == "arg_min":
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self.outputs = {'Out': np.argmin(x, axis=self.axis)}
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else:
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self.outputs = {'Out': np.argmax(x, axis=self.axis)}
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def test_check_output(self):
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self.check_output_with_place(get_device_place(), check_pir=True)
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class TestArgMaxBF16OP(TestArgMinBF16OP):
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def initTestType(self):
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self.op_type = 'arg_max'
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self.python_api = paddle.Tensor.argmax
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class TestArgMinMaxTypeCheck(unittest.TestCase):
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def test_type_error(self):
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# in static mode
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with program_guard(Program(), Program()):
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x = paddle.static.data(name='x', shape=[100, 10], dtype='bool')
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self.assertRaises(TypeError, paddle.argmin, x)
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self.assertRaises(TypeError, paddle.argmax, x)
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def test_bfp16(self):
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# in static mode
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if not (paddle.is_compiled_with_cuda() or is_custom_device()):
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return
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with program_guard(Program(), Program()):
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x = paddle.zeros(name='x', shape=[100, 10], dtype='uint16')
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t1 = paddle.argmin(x)
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t2 = paddle.argmax(x)
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class TestCase2_1(BaseTestCase):
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def initTestCase(self):
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self.op_type = 'arg_max'
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self.python_api = paddle.Tensor.argmax
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self.dims = (3, 4)
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self.dtype = 'int64'
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self.axis = -1
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class TestCase3(BaseTestCase):
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def initTestCase(self):
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self.op_type = 'arg_max'
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self.python_api = paddle.Tensor.argmax
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self.dims = (3,)
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self.dtype = 'int64'
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self.axis = 0
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class TestCase4(BaseTestCase):
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def initTestCase(self):
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self.op_type = 'arg_min'
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self.python_api = paddle.Tensor.argmin
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self.dims = (1,)
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self.dtype = 'int32'
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self.axis = 0
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class TestCase3_(BaseTestCase):
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def initTestCase(self):
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self.op_type = 'arg_max'
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self.python_api = paddle.Tensor.argmax
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self.dims = (3,)
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self.axis = 0
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class BaseTestComplex1_1(OpTest):
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def initTestCase(self):
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self.op_type = 'arg_max'
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self.python_api = paddle.Tensor.argmax
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self.dims = (4, 5, 6)
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self.dtype = 'int32'
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self.axis = 2
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def setUp(self):
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self.initTestCase()
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self.x = (np.random.random(self.dims)).astype(self.dtype)
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self.inputs = {'X': self.x}
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self.attrs = {'axis': self.axis}
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self.attrs = {'dtype': paddle.int32}
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if self.op_type == "arg_min":
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self.outputs = {
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'Out': np.argmin(self.x, axis=self.axis).asdtype("int32")
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}
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else:
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self.outputs = {
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'Out': np.argmax(self.x, axis=self.axis).asdtype("int32")
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}
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class BaseTestComplex1_2(OpTest):
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def initTestCase(self):
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self.op_type = 'arg_min'
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self.python_api = paddle.Tensor.argmin
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self.dims = (4, 5, 6)
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self.dtype = 'int32'
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self.axis = 2
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def setUp(self):
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self.initTestCase()
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self.x = (np.random.random(self.dims)).astype(self.dtype)
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self.inputs = {'X': self.x}
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self.attrs = {'axis': self.axis}
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self.attrs = {'dtype': paddle.int32}
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if self.op_type == "arg_min":
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self.outputs = {
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'Out': np.argmin(self.x, axis=self.axis).asdtype("int32")
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}
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else:
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self.outputs = {
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'Out': np.argmax(self.x, axis=self.axis).asdtype("int32")
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}
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class BaseTestComplex2_1(OpTest):
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def initTestCase(self):
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self.op_type = 'arg_max'
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self.python_api = paddle.Tensor.argmax
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self.dims = (4, 5, 6)
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self.dtype = 'int32'
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self.axis = 2
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def setUp(self):
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self.initTestCase()
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self.x = (np.random.random(self.dims)).astype(self.dtype)
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self.inputs = {'X': self.x}
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self.attrs = {'axis': self.axis}
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self.attrs = {'dtype': paddle.int32}
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self.attrs = {'keep_dims': True}
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if self.op_type == "arg_min":
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self.outputs = {
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'Out': np.argmin(self.x, axis=self.axis)
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.asdtype("int32")
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.reshape(4, 5, 1)
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}
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else:
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self.outputs = {
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'Out': np.argmax(self.x, axis=self.axis)
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.asdtype("int32")
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.reshape(4, 5, 1)
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}
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class BaseTestComplex2_2(OpTest):
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def initTestCase(self):
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self.op_type = 'arg_min'
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self.python_api = paddle.Tensor.argmin
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self.dims = (4, 5, 6)
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self.dtype = 'int32'
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self.axis = 2
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def setUp(self):
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self.initTestCase()
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self.x = (np.random.random(self.dims)).astype(self.dtype)
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self.inputs = {'X': self.x}
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self.attrs = {'axis': self.axis}
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self.attrs = {'dtype': paddle.int32}
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self.attrs = {'keep_dims': True}
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if self.op_type == "arg_min":
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self.outputs = {
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'Out': np.argmin(self.x, axis=self.axis)
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.asdtype("int32")
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.reshape(4, 5, 1)
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}
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else:
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self.outputs = {
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'Out': np.argmax(self.x, axis=self.axis)
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.asdtype("int32")
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.reshape(4, 5, 1)
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}
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class TestArgMaxTensorAxis(UnittestBase):
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def init_info(self):
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self.shapes = [[2, 3, 4]]
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self.x = [np.random.randn(*shape) for shape in self.shapes]
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self.save_path = os.path.join(self.temp_dir.name, self.path_prefix())
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def test_static(self):
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main_prog = paddle.static.Program()
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startup_prog = paddle.static.Program()
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with paddle.static.program_guard(main_prog, startup_prog):
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fc = paddle.nn.Linear(4, 10)
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x = paddle.randn([2, 3, 4])
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x.stop_gradient = False
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feat = fc(x)
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out = self.call_func(feat)
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sgd = paddle.optimizer.SGD()
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sgd.minimize(paddle.mean(paddle.cast(out, 'float32')))
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if not in_pir_mode():
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self.assertTrue(self.var_prefix() in str(main_prog))
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exe = paddle.static.Executor()
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exe.run(startup_prog)
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res = exe.run(fetch_list=[feat, out])
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paddle.static.save_inference_model(
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self.save_path, [x], [feat, out], exe
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)
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gt = np.argmax(res[0], 0)
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np.testing.assert_allclose(res[1], gt)
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# Test for Inference Predictor
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infer_outs = self.infer_prog()
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gt = np.argmax(infer_outs[0], 0)
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np.testing.assert_allclose(infer_outs[1], gt)
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def path_prefix(self):
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return 'argmax_tensor_axis'
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def var_prefix(self):
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return "Var["
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def call_func(self, x):
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axis = paddle.assign(0)
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out = paddle.argmax(x, axis)
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return out
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class TestArgMinTensorAxis(TestArgMaxTensorAxis):
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def test_static(self):
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main_prog = paddle.base.Program()
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startup_prog = paddle.base.Program()
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with paddle.base.program_guard(main_prog, startup_prog):
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fc = paddle.nn.Linear(4, 10)
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x = paddle.randn([2, 3, 4])
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x.stop_gradient = False
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feat = fc(x)
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feat = paddle.cast(feat, 'int32')
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out = self.call_func(feat)
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sgd = paddle.optimizer.SGD()
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sgd.minimize(paddle.mean(paddle.cast(out, 'float32')))
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if not paddle.framework.use_pir_api():
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self.assertTrue(self.var_prefix() in str(main_prog))
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exe = paddle.static.Executor()
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exe.run(startup_prog)
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res = exe.run(fetch_list=[feat, out])
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paddle.static.save_inference_model(
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self.save_path, [x], [feat, out], exe
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)
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gt = np.argmin(res[0], 1)
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np.testing.assert_allclose(np.squeeze(res[1]), gt)
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# Test for Inference Predictor
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infer_outs = self.infer_prog()
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gt = np.argmin(infer_outs[0], 1)
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np.testing.assert_allclose(np.squeeze(infer_outs[1]), gt)
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def path_prefix(self):
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return 'argmin_tensor_axis'
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def call_func(self, x):
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axis = paddle.assign(1)
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out = paddle.argmin(x, axis, keepdim=True)
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return out
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class TestArgmax_ZeroSize(BaseTestCase):
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def initTestCase(self):
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self.op_type = 'arg_max'
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self.python_api = paddle.Tensor.argmax
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self.dims = (3, 0, 5)
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self.dtype = 'float32'
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self.axis = 0
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class TestArgmin_ZeroSize(BaseTestCase):
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def initTestCase(self):
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self.op_type = 'arg_min'
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self.python_api = paddle.Tensor.argmin
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self.dims = (3, 0, 5)
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self.dtype = 'float32'
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self.axis = 0
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if __name__ == '__main__':
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paddle.enable_static()
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unittest.main()
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