wehub-resource-sync
212 lines
7.2 KiB
Python
212 lines
7.2 KiB
Python
# Copyright (c) 2023 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 get_test_cover_info import (
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XPUOpTestWrapper,
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create_test_class,
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get_xpu_op_support_types,
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)
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from op_test_xpu import XPUOpTest
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import paddle
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from paddle import base
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paddle.enable_static()
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class XPUTestPReluOp(XPUOpTestWrapper):
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def __init__(self):
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self.op_name = "prelu"
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self.use_dynamic_create_class = False
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class TestPReluOp(XPUOpTest):
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def setUp(self):
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self.set_xpu()
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self.op_type = "prelu"
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self.init_dtype()
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# override
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self.init_input_shape()
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self.init_attr()
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self.x = np.random.uniform(-10.0, 10.0, self.x_shape).astype(
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self.dtype
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)
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# Since zero point in prelu is not differentiable, avoid randomize zero.
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self.x[np.abs(self.x) < 0.005] = 0.02
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if self.attrs == {
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'mode': "all",
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"data_format": "NCHW",
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} or self.attrs == {'mode': "all", "data_format": "NHWC"}:
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self.alpha = np.random.uniform(-1, -0.5, (1))
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elif self.attrs == {'mode': "channel", "data_format": "NCHW"}:
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self.alpha = np.random.uniform(
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-1, -0.5, [1, self.x_shape[1], 1, 1]
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)
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elif self.attrs == {'mode': "channel", "data_format": "NHWC"}:
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self.alpha = np.random.uniform(
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-1, -0.5, [1, 1, 1, self.x_shape[-1]]
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)
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else:
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self.alpha = np.random.uniform(-1, -0.5, [1, *self.x_shape[1:]])
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self.alpha = self.alpha.astype(self.dtype)
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self.inputs = {'X': self.x, 'Alpha': self.alpha}
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reshaped_alpha = self.inputs['Alpha']
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if self.attrs == {'mode': "channel", "data_format": "NCHW"}:
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reshaped_alpha = np.reshape(
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self.inputs['Alpha'],
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[1, self.x_shape[1]] + [1] * len(self.x_shape[2:]),
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)
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elif self.attrs == {'mode': "channel", "data_format": "NHWC"}:
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reshaped_alpha = np.reshape(
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self.inputs['Alpha'],
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[1] + [1] * len(self.x_shape[1:-1]) + [self.x_shape[-1]],
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)
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self.alpha = np.random.uniform(
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-10.0, 10.0, [1, self.x_shape[1], 1, 1]
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).astype(self.dtype)
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out_np = np.maximum(self.inputs['X'], 0.0)
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out_np = out_np + np.minimum(self.inputs['X'], 0.0) * reshaped_alpha
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assert out_np is not self.inputs['X']
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self.outputs = {'Out': out_np}
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def init_input_shape(self):
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self.x_shape = [2, 3, 5, 6]
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def init_attr(self):
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self.attrs = {'mode': "channel", 'data_format': "NCHW"}
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def set_xpu(self):
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self.__class__.no_need_check_grad = False
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self.place = paddle.XPUPlace(0)
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def init_dtype(self):
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self.dtype = self.in_type
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def test_check_output(self):
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self.check_output_with_place(self.place)
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def test_check_grad(self):
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self.check_grad_with_place(
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self.place, ['X', 'Alpha'], 'Out', check_dygraph=False
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)
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class TestModeChannelNHWC(TestPReluOp):
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def init_input_shape(self):
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self.x_shape = [2, 3, 4, 5]
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def init_attr(self):
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self.attrs = {'mode': "channel", "data_format": "NHWC"}
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class TestModeAll(TestPReluOp):
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def init_input_shape(self):
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self.x_shape = [2, 3, 4, 5]
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def init_attr(self):
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self.attrs = {'mode': "all", "data_format": "NCHW"}
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class TestModeAllNHWC(TestPReluOp):
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def init_input_shape(self):
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self.x_shape = [2, 3, 4, 50]
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def init_attr(self):
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self.attrs = {'mode': "all", "data_format": "NHWC"}
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class TestModeElt(TestPReluOp):
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def init_input_shape(self):
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self.x_shape = [3, 2, 5, 10]
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def init_attr(self):
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self.attrs = {'mode': "element", "data_format": "NCHW"}
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class TestModeEltNHWC(TestPReluOp):
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def init_input_shape(self):
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self.x_shape = [3, 2, 5, 10]
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def init_attr(self):
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self.attrs = {'mode': "element", "data_format": "NHWC"}
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def prelu_t(x, mode, param_attr=None, name=None, data_format='NCHW'):
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helper = base.layer_helper.LayerHelper('prelu', **locals())
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alpha_shape = [1, x.shape[1], 1, 1]
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dtype = helper.input_dtype(input_param_name='x')
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alpha = helper.create_parameter(
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attr=helper.param_attr,
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shape=alpha_shape,
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dtype='float32',
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is_bias=False,
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default_initializer=paddle.nn.initializer.Constant(0.25),
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)
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out = helper.create_variable_for_type_inference(dtype)
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helper.append_op(
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type="prelu",
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inputs={"X": x, 'Alpha': alpha},
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attrs={"mode": mode, 'data_format': data_format},
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outputs={"Out": out},
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)
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return out
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# error message test if mode is not one of 'all', 'channel', 'element'
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class TestModeError(unittest.TestCase):
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def setUp(self):
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self.place = paddle.XPUPlace(0)
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self.x_np = np.ones([1, 2, 3, 4]).astype('float32')
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def test_mode_error(self):
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with paddle.pir_utils.OldIrGuard():
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main_program = paddle.base.Program()
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with base.program_guard(main_program, paddle.base.Program()):
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x = paddle.static.data(name='x', shape=[2, 3, 4, 5])
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try:
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y = prelu_t(x, 'any')
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except Exception as e:
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assert e.args[0].find('InvalidArgument') != -1
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def test_data_format_error1(self):
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with paddle.pir_utils.OldIrGuard():
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main_program = paddle.base.Program()
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with base.program_guard(main_program, paddle.base.Program()):
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x = paddle.static.data(name='x', shape=[2, 3, 4, 5])
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try:
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y = prelu_t(x, 'channel', data_format='N')
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except Exception as e:
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assert e.args[0].find('InvalidArgument') != -1
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def test_data_format_error2(self):
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main_program = paddle.base.Program()
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with base.program_guard(main_program, paddle.base.Program()):
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x = paddle.static.data(name='x', shape=[2, 3, 4, 5])
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try:
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y = paddle.nn.PReLU(3, data_format='N')(x)
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except ValueError as e:
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pass
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support_types = get_xpu_op_support_types("prelu")
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for stype in support_types:
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create_test_class(globals(), XPUTestPReluOp, stype)
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if __name__ == "__main__":
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unittest.main()
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