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154 lines
4.5 KiB
154 lines
4.5 KiB
# 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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"""
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Unit testing for affine_channel_op
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"""
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from __future__ import print_function
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import unittest
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import numpy as np
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from op_test import OpTest
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import paddle.fluid.core as core
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import paddle.fluid as fluid
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def affine_channel(x, scale, bias, layout):
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C = x.shape[1] if layout == 'NCHW' else x.shape[-1]
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if len(x.shape) == 4:
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new_shape = (1, C, 1, 1) if layout == 'NCHW' else (1, 1, 1, C)
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else:
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new_shape = (1, C)
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scale = scale.reshape(new_shape)
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bias = bias.reshape(new_shape)
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return x * scale + bias
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class TestAffineChannelOp(OpTest):
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def setUp(self):
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self.op_type = "affine_channel"
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self.init_test_case()
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x = np.random.random(self.shape).astype("float64")
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scale = np.random.random(self.C).astype("float64")
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bias = np.random.random(self.C).astype("float64")
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y = affine_channel(x, scale, bias, self.layout)
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self.inputs = {'X': x, 'Scale': scale, 'Bias': bias}
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self.attrs = {'data_layout': self.layout}
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self.outputs = {'Out': y}
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def test_check_output(self):
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self.check_output()
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def test_check_grad(self):
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self.check_grad(['X', 'Scale', 'Bias'], 'Out')
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def test_check_grad_stopgrad_dx(self):
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self.check_grad(['Scale', 'Bias'], 'Out', no_grad_set=set('X'))
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def test_check_grad_stopgrad_dscale_dbias(self):
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self.check_grad(['X'], 'Out', no_grad_set=set(['Scale', 'Bias']))
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def init_test_case(self):
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self.shape = [2, 100, 3, 3]
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self.C = 100
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self.layout = 'NCHW'
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class TestAffineChannelOpError(unittest.TestCase):
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def test_errors(self):
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with fluid.program_guard(fluid.Program()):
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def test_x_type():
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input_data = np.random.random(2, 1, 2, 2).astype("float32")
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fluid.layers.affine_channel(input_data)
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self.assertRaises(TypeError, test_x_type)
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def test_x_dtype():
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x2 = fluid.layers.data(
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name='x2', shape=[None, 1, 2, 2], dtype='int32')
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fluid.layers.affine_channel(x2)
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self.assertRaises(TypeError, test_x_dtype)
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def test_scale_type():
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x3 = fluid.layers.data(
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name='x3', shape=[None, 1, 2, 2], dtype='float32')
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fluid.layers.affine_channel(x3, scale=1)
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self.assertRaises(TypeError, test_scale_type)
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def test_bias_type():
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x4 = fluid.layers.data(
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name='x4', shape=[None, 1, 2, 2], dtype='float32')
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fluid.layers.affine_channel(x4, bias=1)
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self.assertRaises(TypeError, test_bias_type)
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class TestAffineChannelNHWC(TestAffineChannelOp):
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def init_test_case(self):
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self.shape = [2, 3, 3, 100]
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self.C = 100
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self.layout = 'NHWC'
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def test_check_grad_stopgrad_dx(self):
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return
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def test_check_grad_stopgrad_dscale_dbias(self):
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return
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class TestAffineChannel2D(TestAffineChannelOp):
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def init_test_case(self):
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self.shape = [2, 100]
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self.C = 100
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self.layout = 'NCHW'
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def test_check_grad_stopgrad_dx(self):
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return
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def test_check_grad_stopgrad_dscale_dbias(self):
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return
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# TODO(qingqing): disable unit testing for large shape
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#class TestAffineChannelNCHWLargeShape(TestAffineChannelOp):
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# def init_test_case(self):
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# self.shape = [4, 128, 112, 112]
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# self.C = 128
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# self.layout = 'NCHW'
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#
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# # since the gradient check is very slow in large shape, so skip check_grad
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# def test_check_grad(self):
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# pass
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#
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# def test_check_grad_stopgrad_dx(self):
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# pass
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#
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# def test_check_grad_stopgrad_dscale_dbias(self):
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# pass
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#class TestAffineChannelNHWCLargeShape(TestAffineChannelNCHWLargeShape):
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# def init_test_case(self):
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# self.shape = [64, 32, 32, 128]
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# self.C = 128
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# self.layout = 'NHWC'
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if __name__ == '__main__':
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unittest.main()
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