wehub-resource-sync
117 lines
3.8 KiB
Python
117 lines
3.8 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 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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paddle.enable_static()
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class XPUTestLabelSmoothOp(XPUOpTestWrapper):
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def __init__(self):
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self.op_name = 'label_smooth'
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self.use_dynamic_create_class = True
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def dynamic_create_class(self):
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base_class = self.TestLabelSmoothOp
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classes = []
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batch_sizes = [1, 5, 1024]
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label_dims = [1, 7, 12]
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for bs in batch_sizes:
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for label_dim in label_dims:
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class_name = (
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'XPUTestLabelSmooth_' + str(bs) + "_" + str(label_dim)
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)
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attr_dict = {'batch_size': bs, 'label_dim': label_dim}
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classes.append([class_name, attr_dict])
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classes.append(['XPUTestLabelSmooth_3d', {'is_3d': True}])
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return base_class, classes
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class TestLabelSmoothOp(XPUOpTest):
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def setUp(self):
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self.op_type = "label_smooth"
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self.epsilon = 0.1
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self.use_xpu = True
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if not hasattr(self, 'batch_size'):
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self.batch_size = 10
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self.label_dim = 12
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self.label = np.zeros((self.batch_size, self.label_dim)).astype(
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"float32"
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)
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nonzero_index = np.random.randint(
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self.label_dim, size=(self.batch_size)
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)
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self.label[np.arange(self.batch_size), nonzero_index] = 1
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smoothed_label = (
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1 - self.epsilon
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) * self.label + self.epsilon / self.label_dim
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self.inputs = {'X': self.label}
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self.attrs = {'epsilon': self.epsilon}
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self.outputs = {'Out': smoothed_label}
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if hasattr(self, 'is_3d') and self.is_3d:
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self.inputs['X'] = self.inputs['X'].reshape(
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[2, -1, self.inputs['X'].shape[-1]]
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)
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self.outputs['Out'] = self.outputs['Out'].reshape(
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self.inputs['X'].shape
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)
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def test_check_output(self):
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if not paddle.is_compiled_with_xpu():
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return
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self.check_output_with_place(paddle.XPUPlace(0), atol=1e-6)
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def test_check_grad(self):
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return
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class XPUTestLabelSmoothOp_ZeroSize(XPUOpTest):
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def setUp(self):
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self.op_type = "label_smooth"
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self.epsilon = 0.1
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self.use_xpu = True
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self.label_dim = 1000
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self.label = np.zeros((0, 1, self.label_dim)).astype("float32")
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smoothed_label = (
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1 - self.epsilon
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) * self.label + self.epsilon / self.label_dim
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self.inputs = {'X': self.label}
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self.attrs = {'epsilon': self.epsilon}
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self.outputs = {'Out': smoothed_label}
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def test_check_output(self):
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if not paddle.is_compiled_with_xpu():
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return
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self.check_output_with_place(paddle.XPUPlace(0))
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def test_check_grad(self):
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return
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support_types = get_xpu_op_support_types('label_smooth')
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for stype in support_types:
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create_test_class(globals(), XPUTestLabelSmoothOp, stype)
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if __name__ == '__main__':
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unittest.main()
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