From 60eaf967eb6fa5273e268a72dc2c260ae3d348aa Mon Sep 17 00:00:00 2001 From: "xiaoli.liu@intel.com" Date: Sat, 29 Dec 2018 20:15:00 +0800 Subject: [PATCH] Clean unittest code. test=develop --- .../unittests/test_pool2d_int8_mkldnn_op.py | 216 ++++-------------- .../tests/unittests/test_pool2d_mkldnn_op.py | 45 ++-- .../fluid/tests/unittests/test_pool2d_op.py | 5 +- 3 files changed, 65 insertions(+), 201 deletions(-) diff --git a/python/paddle/fluid/tests/unittests/test_pool2d_int8_mkldnn_op.py b/python/paddle/fluid/tests/unittests/test_pool2d_int8_mkldnn_op.py index e73ac7c0a..f4495d0bc 100644 --- a/python/paddle/fluid/tests/unittests/test_pool2d_int8_mkldnn_op.py +++ b/python/paddle/fluid/tests/unittests/test_pool2d_int8_mkldnn_op.py @@ -20,217 +20,91 @@ import numpy as np import paddle.fluid.core as core from op_test import OpTest +from test_pool2d_op import TestPool2D_Op, avg_pool2D_forward_naive, max_pool2D_forward_naive -def adaptive_start_index(index, input_size, output_size): - return int(np.floor(index * input_size / output_size)) - - -def adaptive_end_index(index, input_size, output_size): - return int(np.ceil((index + 1) * input_size / output_size)) - - -def max_pool2D_forward_naive(x, - ksize, - strides, - paddings, - global_pool=0, - ceil_mode=False, - exclusive=True, - adaptive=False): - N, C, H, W = x.shape - if global_pool == 1: - ksize = [H, W] - if adaptive: - H_out, W_out = ksize - else: - H_out = (H - ksize[0] + 2 * paddings[0] + strides[0] - 1 - ) // strides[0] + 1 if ceil_mode else ( - H - ksize[0] + 2 * paddings[0]) // strides[0] + 1 - W_out = (W - ksize[1] + 2 * paddings[1] + strides[1] - 1 - ) // strides[1] + 1 if ceil_mode else ( - W - ksize[1] + 2 * paddings[1]) // strides[1] + 1 - out = np.zeros((N, C, H_out, W_out)) - for i in range(H_out): - for j in range(W_out): - if adaptive: - r_start = adaptive_start_index(i, H, ksize[0]) - r_end = adaptive_end_index(i, H, ksize[0]) - c_start = adaptive_start_index(j, W, ksize[1]) - c_end = adaptive_end_index(j, W, ksize[1]) - else: - r_start = np.max((i * strides[0] - paddings[0], 0)) - r_end = np.min((i * strides[0] + ksize[0] - paddings[0], H)) - c_start = np.max((j * strides[1] - paddings[1], 0)) - c_end = np.min((j * strides[1] + ksize[1] - paddings[1], W)) - x_masked = x[:, :, r_start:r_end, c_start:c_end] - - out[:, :, i, j] = np.max(x_masked, axis=(2, 3)) - return out - - -def avg_pool2D_forward_naive(x, - ksize, - strides, - paddings, - global_pool=0, - ceil_mode=False, - exclusive=True, - adaptive=False): - N, C, H, W = x.shape - if global_pool == 1: - ksize = [H, W] - if adaptive: - H_out, W_out = ksize - else: - H_out = (H - ksize[0] + 2 * paddings[0] + strides[0] - 1 - ) // strides[0] + 1 if ceil_mode else ( - H - ksize[0] + 2 * paddings[0]) // strides[0] + 1 - W_out = (W - ksize[1] + 2 * paddings[1] + strides[1] - 1 - ) // strides[1] + 1 if ceil_mode else ( - W - ksize[1] + 2 * paddings[1]) // strides[1] + 1 - out = np.zeros((N, C, H_out, W_out)) - for i in range(H_out): - for j in range(W_out): - if adaptive: - r_start = adaptive_start_index(i, H, ksize[0]) - r_end = adaptive_end_index(i, H, ksize[0]) - c_start = adaptive_start_index(j, W, ksize[1]) - c_end = adaptive_end_index(j, W, ksize[1]) - else: - r_start = np.max((i * strides[0] - paddings[0], 0)) - r_end = np.min((i * strides[0] + ksize[0] - paddings[0], H)) - c_start = np.max((j * strides[1] - paddings[1], 0)) - c_end = np.min((j * strides[1] + ksize[1] - paddings[1], W)) - x_masked = x[:, :, r_start:r_end, c_start:c_end] - - field_size = ((r_end - r_start) * (c_end - c_start)) \ - if (exclusive or adaptive) else (ksize[0] * ksize[1]) - out[:, :, i, j] = np.sum(x_masked, axis=(2, 3)) / field_size - return out - - -class TestPool2D_Op(OpTest): - def setUp(self): - self.op_type = "pool2d" - self.use_cudnn = False +class TestPool2dMKLDNNInt8_Op(TestPool2D_Op): + def init_kernel_type(self): self.use_mkldnn = True - self.dtype = np.int8 - self.init_test_case() - self.init_global_pool() - self.init_pool_type() - self.init_ceil_mode() - self.init_exclusive() - self.init_adaptive() - if self.global_pool: - self.paddings = [0 for _ in range(len(self.paddings))] - input = np.random.random(self.shape).astype(self.dtype) - output = self.pool2D_forward_naive( - input, self.ksize, self.strides, self.paddings, self.global_pool, - self.ceil_mode, self.exclusive, self.adaptive).astype(self.dtype) - self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(input)} - - self.attrs = { - 'strides': self.strides, - 'paddings': self.paddings, - 'ksize': self.ksize, - 'pooling_type': self.pool_type, - 'global_pooling': self.global_pool, - 'use_cudnn': self.use_cudnn, - 'use_mkldnn': self.use_mkldnn, - 'ceil_mode': self.ceil_mode, - 'data_format': - 'AnyLayout', # TODO(dzhwinter) : should be fix latter - 'exclusive': self.exclusive, - 'adaptive': self.adaptive - } - - self.outputs = {'Out': output} - - def test_check_output(self): - self.check_output_with_place(core.CPUPlace(), atol=1e-5) - def init_test_case(self): - self.shape = [2, 3, 5, 5] - self.ksize = [3, 3] - self.strides = [1, 1] - self.paddings = [0, 0] + def init_data_type(self): self.dtype = np.int8 - def init_pool_type(self): - self.pool_type = "avg" - self.pool2D_forward_naive = avg_pool2D_forward_naive - - def init_global_pool(self): - self.global_pool = True - - def init_ceil_mode(self): - self.ceil_mode = False + def setUp(self): + TestPool2D_Op.setUp(self) + assert self.dtype in [np.int8, np.uint8 + ], 'Dtype should be int8 or uint8' - def init_exclusive(self): - self.exclusive = True + def test_check_output(self): + self.check_output_with_place(core.CPUPlace(), atol=1e-5) - def init_adaptive(self): - self.adaptive = False + def test_check_grad(self): + pass -class TestCase1(TestPool2D_Op): +class TestCase1Avg(TestPool2dMKLDNNInt8_Op): def init_test_case(self): self.shape = [2, 3, 7, 7] self.ksize = [3, 3] self.strides = [1, 1] self.paddings = [0, 0] - self.dtype = np.int8 - - def init_pool_type(self): - self.pool_type = "avg" - self.pool2D_forward_naive = avg_pool2D_forward_naive def init_global_pool(self): self.global_pool = False -class TestCase2(TestPool2D_Op): +class TestCase2Avg(TestPool2dMKLDNNInt8_Op): def init_test_case(self): self.shape = [2, 3, 7, 7] self.ksize = [3, 3] self.strides = [1, 1] self.paddings = [1, 1] - self.dtype = np.uint8 - - def init_pool_type(self): - self.pool_type = "avg" - self.pool2D_forward_naive = avg_pool2D_forward_naive def init_global_pool(self): self.global_pool = False -class TestCase3(TestPool2D_Op): - def init_test_case(self): - self.shape = [2, 3, 7, 7] - self.ksize = [3, 3] - self.strides = [1, 1] - self.paddings = [0, 0] - self.dtype = np.int8 - +class TestCase0Max(TestPool2dMKLDNNInt8_Op): def init_pool_type(self): self.pool_type = "max" self.pool2D_forward_naive = max_pool2D_forward_naive -class TestCase4(TestCase1): - def init_test_case(self): - self.shape = [2, 3, 7, 7] - self.ksize = [3, 3] - self.strides = [1, 1] - self.paddings = [1, 1] - self.dtype = np.uint8 +class TestCase1Max(TestCase1Avg): + def init_pool_type(self): + self.pool_type = "max" + self.pool2D_forward_naive = max_pool2D_forward_naive + +class TestCase2Max(TestCase2Avg): def init_pool_type(self): self.pool_type = "max" self.pool2D_forward_naive = max_pool2D_forward_naive +def create_test_s8_u8_class(parent): + class TestS8Case(parent): + def init_data_type(self): + self.dtype = np.int8 + + class TestU8Case(parent): + def init_data_type(self): + self.dtype = np.uint8 + + cls_name_s8 = "{0}_{1}".format(parent.__name__, "mkldnn_s8") + cls_name_u8 = "{0}_{1}".format(parent.__name__, "mkldnn_u8") + TestS8Case.__name__ = cls_name_s8 + TestU8Case.__name__ = cls_name_u8 + globals()[cls_name_s8] = TestS8Case + globals()[cls_name_u8] = TestU8Case + + +create_test_s8_u8_class(TestPool2dMKLDNNInt8_Op) +create_test_s8_u8_class(TestCase1Avg) +create_test_s8_u8_class(TestCase2Avg) +create_test_s8_u8_class(TestCase0Max) +create_test_s8_u8_class(TestCase1Max) +create_test_s8_u8_class(TestCase2Max) + if __name__ == '__main__': unittest.main() diff --git a/python/paddle/fluid/tests/unittests/test_pool2d_mkldnn_op.py b/python/paddle/fluid/tests/unittests/test_pool2d_mkldnn_op.py index 19f29c782..7de5fefc1 100644 --- a/python/paddle/fluid/tests/unittests/test_pool2d_mkldnn_op.py +++ b/python/paddle/fluid/tests/unittests/test_pool2d_mkldnn_op.py @@ -18,35 +18,22 @@ import unittest from test_pool2d_op import TestPool2D_Op, TestCase1, TestCase2, TestCase3, TestCase4, TestCase5 -class TestMKLDNNCase1(TestPool2D_Op): - def init_kernel_type(self): - self.use_mkldnn = True - - -class TestMKLDNNCase2(TestCase1): - def init_kernel_type(self): - self.use_mkldnn = True - - -class TestMKLDNNCase3(TestCase2): - def init_kernel_type(self): - self.use_mkldnn = True - - -class TestMKLDNNCase4(TestCase3): - def init_kernel_type(self): - self.use_mkldnn = True - - -class TestMKLDNNCase5(TestCase4): - def init_kernel_type(self): - self.use_mkldnn = True - - -class TestMKLDNNCase6(TestCase5): - def init_kernel_type(self): - self.use_mkldnn = True - +def create_test_mkldnn_class(parent): + class TestMKLDNNCase(parent): + def init_kernel_type(self): + self.use_mkldnn = True + + cls_name = "{0}_{1}".format(parent.__name__, "MKLDNNOp") + TestMKLDNNCase.__name__ = cls_name + globals()[cls_name] = TestMKLDNNCase + + +create_test_mkldnn_class(TestPool2D_Op) +create_test_mkldnn_class(TestCase1) +create_test_mkldnn_class(TestCase2) +create_test_mkldnn_class(TestCase3) +create_test_mkldnn_class(TestCase4) +create_test_mkldnn_class(TestCase5) if __name__ == '__main__': unittest.main() diff --git a/python/paddle/fluid/tests/unittests/test_pool2d_op.py b/python/paddle/fluid/tests/unittests/test_pool2d_op.py index 5ccdf082e..92515add5 100644 --- a/python/paddle/fluid/tests/unittests/test_pool2d_op.py +++ b/python/paddle/fluid/tests/unittests/test_pool2d_op.py @@ -115,7 +115,7 @@ class TestPool2D_Op(OpTest): self.op_type = "pool2d" self.use_cudnn = False self.use_mkldnn = False - self.dtype = np.float32 + self.init_data_type() self.init_test_case() self.init_global_pool() self.init_kernel_type() @@ -177,6 +177,9 @@ class TestPool2D_Op(OpTest): def init_kernel_type(self): pass + def init_data_type(self): + self.dtype = np.float32 + def init_pool_type(self): self.pool_type = "avg" self.pool2D_forward_naive = avg_pool2D_forward_naive -- GitLab