| # Licensed to the Apache Software Foundation (ASF) under one |
| # or more contributor license agreements. See the NOTICE file |
| # distributed with this work for additional information |
| # regarding copyright ownership. The ASF licenses this file |
| # to you under the Apache License, Version 2.0 (the |
| # "License"); you may not use this file except in compliance |
| # with the License. You may obtain a copy of the License at |
| # |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, software |
| # distributed under the License is distributed on an "AS IS" BASIS, |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| # See the License for the specific language governing permissions and |
| # limitations under the License. |
| # ============================================================================= |
| |
| import unittest |
| from singa import singa_wrap |
| |
| |
| class TestPythonOperation(unittest.TestCase): |
| |
| def test_conv2d(self): |
| print("TEST CONV2D FORWARD") |
| x_shape = [2, 1, 3, 3] |
| x = singa_wrap.Tensor(x_shape) |
| x.CopyFloatDataFromHostPtr( |
| [1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9]) |
| |
| W_shape = [1, 1, 3, 3] |
| W = singa_wrap.Tensor(W_shape) |
| W.CopyFloatDataFromHostPtr([1, 1, 0, 0, 0, -1, 0, 1, 0]) |
| |
| b_shape = [1] |
| b = singa_wrap.Tensor(b_shape) |
| b.CopyFloatDataFromHostPtr([1]) |
| |
| dy_shape = [2, 1, 2, 2] |
| dy = singa_wrap.Tensor(dy_shape) |
| dy.CopyFloatDataFromHostPtr([0.1, 0.2, 0.3, 0.4, 0.1, 0.2, 0.3, 0.4]) |
| |
| handle = singa_wrap.ConvHandle(x, (3, 3), (2, 2), (1, 1), 1, 1, True) |
| y = singa_wrap.CpuConvForward(x, W, b, handle) |
| |
| self.assertListEqual([2, 1, 2, 2], list(y.shape())) |
| |
| _y = y.GetFloatValue(int(y.Size())) |
| self.assertAlmostEqual(3.0, _y[0]) |
| self.assertAlmostEqual(7.0, _y[1]) |
| self.assertAlmostEqual(-3.0, _y[2]) |
| self.assertAlmostEqual(12.0, _y[3]) |
| self.assertAlmostEqual(3.0, _y[4]) |
| self.assertAlmostEqual(7.0, _y[5]) |
| self.assertAlmostEqual(-3.0, _y[6]) |
| self.assertAlmostEqual(12.0, _y[7]) |
| |
| print("TEST CONV2D DATA BACKWARD") |
| |
| dx = singa_wrap.CpuConvBackwardx(dy, W, x, handle) |
| self.assertListEqual([2, 1, 3, 3], list(dx.shape())) |
| |
| _dx = dx.GetFloatValue(int(dx.Size())) |
| self.assertAlmostEqual(0.0, _dx[0]) |
| self.assertAlmostEqual(-0.1, _dx[1]) |
| self.assertAlmostEqual(0.0, _dx[2]) |
| self.assertAlmostEqual(0.4, _dx[3]) |
| self.assertAlmostEqual(0.4, _dx[4]) |
| self.assertAlmostEqual(0.6, _dx[5]) |
| self.assertAlmostEqual(0.0, _dx[6]) |
| self.assertAlmostEqual(-0.3, _dx[7]) |
| |
| print("TEST CONV2D WEIGHT BACKWARD") |
| dW = singa_wrap.CpuConvBackwardW(dy, x, W, handle) |
| self.assertListEqual([1, 1, 3, 3], list(dW.shape())) |
| |
| _dW = dW.GetFloatValue(int(dW.Size())) |
| self.assertAlmostEqual(4.0, _dW[0], places=5) |
| self.assertAlmostEqual(7.2, _dW[1], places=5) |
| self.assertAlmostEqual(3.0, _dW[2], places=5) |
| self.assertAlmostEqual(7.2, _dW[3], places=5) |
| self.assertAlmostEqual(12.8, _dW[4], places=5) |
| self.assertAlmostEqual(5.2, _dW[5], places=5) |
| self.assertAlmostEqual(2.0, _dW[6], places=5) |
| self.assertAlmostEqual(3.2, _dW[7], places=5) |
| self.assertAlmostEqual(1.0, _dW[8], places=5) |
| |
| print("TEST CONV2D DATA BACKWARD") |
| db = singa_wrap.CpuConvBackwardb(dy, b, handle) |
| self.assertEqual(1, dW.shape()[0]) |
| |
| _db = db.GetFloatValue(int(db.Size())) |
| print(_db) |
| self.assertAlmostEqual(2.0, _db[0], places=5) |
| |
| def test_pooling(self): |
| x_shape = [2, 1, 3, 3] |
| x = singa_wrap.Tensor(x_shape) |
| x.CopyFloatDataFromHostPtr( |
| [1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9]) |
| |
| y_shape = [2, 1, 2, 2] |
| dy = singa_wrap.Tensor(y_shape) |
| dy.CopyFloatDataFromHostPtr([0.1, 0.2, 0.3, 0.4, 0.1, 0.2, 0.3, 0.4]) |
| |
| k_dim = [2, 2] |
| s_dim = [1, 1] |
| p_dim = [0, 0] |
| |
| # max pooling |
| handle = singa_wrap.PoolingHandle(x, k_dim, s_dim, p_dim, True) |
| y = singa_wrap.CpuPoolingForward(handle, x) |
| self.assertListEqual([2, 1, 2, 2], list(y.shape())) |
| dx = singa_wrap.CpuPoolingBackward(handle, dy, x, y) |
| self.assertListEqual([2, 1, 3, 3], list(dx.shape())) |
| |
| # avg pooling |
| handle = singa_wrap.PoolingHandle(x, k_dim, s_dim, p_dim, False) |
| y = singa_wrap.CpuPoolingForward(handle, x) |
| self.assertListEqual([2, 1, 2, 2], list(y.shape())) |
| dx = singa_wrap.CpuPoolingBackward(handle, dy, x, y) |
| self.assertListEqual([2, 1, 3, 3], list(dx.shape())) |
| |
| def test_batch_norm(self): |
| x_shape = [2, 2] |
| x = singa_wrap.Tensor(x_shape) |
| x.CopyFloatDataFromHostPtr([1, 2, 3, 4]) |
| |
| dy_shape = [2, 2] |
| dy = singa_wrap.Tensor(dy_shape) |
| dy.CopyFloatDataFromHostPtr([4, 3, 2, 1]) |
| |
| scale_shape = [2] |
| scale = singa_wrap.Tensor(scale_shape) |
| scale.CopyFloatDataFromHostPtr([1, 1]) |
| |
| bias_shape = [2] |
| bias = singa_wrap.Tensor(bias_shape) |
| bias.CopyFloatDataFromHostPtr([0, 0]) |
| |
| mean_shape = [2] |
| mean = singa_wrap.Tensor(mean_shape) |
| mean.CopyFloatDataFromHostPtr([1, 2]) |
| var = singa_wrap.Tensor(mean_shape) |
| var.CopyFloatDataFromHostPtr([1, 2]) |
| |
| handle = singa_wrap.BatchNormHandle(0.9, x) |
| |
| # 2D Forward Inference |
| y = singa_wrap.CpuBatchNormForwardInference(handle, x, scale, bias, |
| mean, var) |
| self.assertListEqual([2, 2], list(y.shape())) |
| |
| # 2D Forward Training |
| (y, mean_updated, var_updated) = singa_wrap.CpuBatchNormForwardTraining( |
| handle, x, scale, bias, mean, var) |
| self.assertListEqual([2, 2], list(y.shape())) |
| self.assertListEqual([2], list(mean_updated.shape())) |
| self.assertListEqual([2], list(var_updated.shape())) |
| |
| # 2D Backward dx |
| (dx, dscale, dbias) = singa_wrap.CpuBatchNormBackwardx(handle, y, dy, x, |
| scale, bias, |
| mean_updated, |
| var_updated) |
| self.assertListEqual([2, 2], list(dx.shape())) |
| self.assertListEqual([2], list(dscale.shape())) |
| self.assertListEqual([2], list(dbias.shape())) |
| |
| # 4D Forward Inference |
| |
| x2_shape = [1, 2, 4, 4] |
| x2 = singa_wrap.Tensor(x2_shape) |
| x2.CopyFloatDataFromHostPtr( |
| [0.0736655, 0.0459045, 0.0779517, 0.0771059, 0.0586862, 0.0561263, |
| 0.0708457, 0.0977273, 0.0405025, -0.170897, 0.0208982, 0.136865, |
| -0.0367905, -0.0618205, -0.0103908, -0.0522777, -0.122161, |
| -0.025427, -0.0718576, -0.185941, 0.0166533, 0.178679, -0.0576606, |
| -0.137817, 0.150676, 0.153442, -0.0929899, -0.148675, -0.112459, |
| -0.106284, -0.103074, -0.0668811]) |
| |
| handle = singa_wrap.BatchNormHandle(0.9, x) |
| y2 = singa_wrap.CpuBatchNormForwardInference(handle, x2, scale, bias, |
| mean, var) |
| self.assertListEqual([1, 2, 4, 4], list(y2.shape())) |
| |
| |
| if __name__ == '__main__': |
| unittest.main() |
