From 510f00800a43c8df18ac4551d2b35fba0a400d5d Mon Sep 17 00:00:00 2001 From: chengduoZH Date: Thu, 21 Sep 2017 15:49:16 +0800 Subject: [PATCH] Add pool3d unit test --- .../v2/framework/tests/test_pool3d_op.py | 108 ++++++++++++++++++ 1 file changed, 108 insertions(+) create mode 100644 python/paddle/v2/framework/tests/test_pool3d_op.py diff --git a/python/paddle/v2/framework/tests/test_pool3d_op.py b/python/paddle/v2/framework/tests/test_pool3d_op.py new file mode 100644 index 00000000000..f8e9a768e03 --- /dev/null +++ b/python/paddle/v2/framework/tests/test_pool3d_op.py @@ -0,0 +1,108 @@ +import unittest +import numpy as np +from op_test import OpTest + + +def max_pool3D_forward_naive(x, ksize, strides, paddings=[0, 0]): + + N, C, D, H, W = x.shape + D_out = (D - ksize[0] + 2 * paddings[0]) / strides[0] + 1 + H_out = (H - ksize[1] + 2 * paddings[1]) / strides[1] + 1 + W_out = (W - ksize[2] + 2 * paddings[2]) / strides[2] + 1 + out = np.zeros((N, C, D_out, H_out, W_out)) + for k in xrange(D_out): + d_start = np.max((k * strides[0] - paddings[0], 0)) + d_end = np.min((k * strides[0] + ksize[0] - paddings[0], D)) + for i in xrange(H_out): + h_start = np.max((i * strides[0] - paddings[0], 0)) + h_end = np.min((i * strides[0] + ksize[0] - paddings[0], H)) + for j in xrange(W_out): + w_start = np.max((j * strides[1] - paddings[1], 0)) + w_end = np.min((j * strides[1] + ksize[1] - paddings[1], W)) + + x_masked = x[:, :, d_start:d_end, h_start:h_end, w_start:w_end] + + out[:, :, k, i, j] = np.max(x_masked, axis=(2, 3, 4)) + return out + + +def ave_pool3D_forward_naive(x, ksize, strides, paddings=[0, 0]): + + N, C, D, H, W = x.shape + D_out = (D - ksize[0] + 2 * paddings[0]) / strides[0] + 1 + H_out = (H - ksize[1] + 2 * paddings[1]) / strides[1] + 1 + W_out = (W - ksize[2] + 2 * paddings[2]) / strides[2] + 1 + out = np.zeros((N, C, D_out, H_out, W_out)) + for k in xrange(D_out): + d_start = np.max((k * strides[0] - paddings[0], 0)) + d_end = np.min((k * strides[0] + ksize[0] - paddings[0], D)) + for i in xrange(H_out): + h_start = np.max((i * strides[0] - paddings[0], 0)) + h_end = np.min((i * strides[0] + ksize[0] - paddings[0], H)) + for j in xrange(W_out): + w_start = np.max((j * strides[1] - paddings[1], 0)) + w_end = np.min((j * strides[1] + ksize[1] - paddings[1], W)) + + x_masked = x[:, :, d_start:d_end, h_start:h_end, w_start:w_end] + + out[:, :, k, i, j] = np.sum(x_masked, axis=(2, 3, 4)) / ( + (d_end - d_start) * (h_end - h_start) * (w_end - w_start)) + return out + + +class TestPool3d_Op(OpTest): + def setUp(self): + self.initTestCase() + self.op_type = "pool3d" + input = np.random.random(self.shape).astype("float32") + output = self.pool3D_forward_naive(input, self.ksize, self.strides, + self.paddings) + self.inputs = {'Input': input} + + self.attrs = { + 'strides': self.strides, + 'paddings': self.paddings, + 'ksize': self.ksize, + 'pooling_type': self.pool_type, + } + + self.outputs = {'Output': output} + + def test_check_output(self): + self.check_output() + + def test_check_grad(self): + self.check_grad(set(['Input']), 'Output', max_relative_error=0.07) + + def initTestCase(self): + self.pool_type = "ave" + self.pool3D_forward_naive = ave_pool3D_forward_naive + self.shape = [2, 3, 5, 5, 5] + self.ksize = [3, 3, 3] + self.strides = [1, 1, 1] + self.paddings = [0, 0, 0] + + +class TestCase1(TestPool3d_Op): + def initTestCase(self): + self.op_type = "pool3d" + self.pool_type = "ave" + self.pool3D_forward_naive = ave_pool3D_forward_naive + self.shape = [2, 3, 7, 7, 7] + self.ksize = [3, 3, 3] + self.strides = [1, 1, 1] + self.paddings = [1, 1, 1] + + +# class TestCase2(TestPool3d_Op): +# def initTestCase(self): +# self.op_type = "pool3d" +# self.pool_type = "max" +# self.pool3D_forward_naive = max_pool3D_forward_naive +# self.shape = [2, 3, 5, 5, 5] +# self.ksize = [3, 3, 3] +# self.strides = [1, 1, 1] +# self.paddings = [1, 1, 1] + +if __name__ == '__main__': + unittest.main() -- GitLab