提交 667bc256 编写于 作者: L lidanqing 提交者: ceci3

UT for conv2d_mkldnn_op with fuse_bias and fuse_residual (#16016)

test=develop
上级 742839f8
...@@ -15,44 +15,139 @@ ...@@ -15,44 +15,139 @@
from __future__ import print_function from __future__ import print_function
import unittest import unittest
import numpy as np
from paddle.fluid.tests.unittests.test_conv2d_op import TestConv2dOp, TestWithPad, TestWithStride, TestWithGroup, TestWith1x1, TestWithInput1x1Filter1x1 import paddle.fluid.core as core
from paddle.fluid.tests.unittests.op_test import OpTest
from paddle.fluid.tests.unittests.test_conv2d_op import TestConv2dOp
class TestMKLDNN(TestConv2dOp): def conv2d_bias_naive(out, bias):
def init_kernel_type(self): _, out_c, _, _ = out.shape
self.use_mkldnn = True
self.data_format = "NCHW"
for l in range(out_c):
out[:, l, :, :] = out[:, l, :, :] + bias[l]
return out
class TestMKLDNNWithPad(TestWithPad):
def init_kernel_type(self):
self.use_mkldnn = True
self.data_format = "NCHW"
def conv2d_residual_naive(out, residual):
assert out.shape == residual.shape
out = np.add(out, residual)
return out
class TestMKLDNNWithStride(TestWithStride):
def init_kernel_type(self):
self.use_mkldnn = True
self.data_format = "NCHW"
class TestConv2dMKLDNNOp(TestConv2dOp):
def init_group(self):
self.groups = 1
class TestMKLDNNWithGroup(TestWithGroup):
def init_kernel_type(self): def init_kernel_type(self):
self.use_mkldnn = True
self.data_format = "NCHW" self.data_format = "NCHW"
self.use_mkldnn = True
self._cpu_only = True
def init_test_case(self):
self.pad = [0, 0]
self.stride = [1, 1]
self.input_size = [2, 3, 5, 5] # NCHW
assert np.mod(self.input_size[1], self.groups) == 0
f_c = self.input_size[1] // self.groups
self.filter_size = [6, f_c, 3, 3]
class TestMKLDNNWith1x1(TestWith1x1): def setUp(self):
def init_kernel_type(self): self.fuse_bias = False
self.use_mkldnn = True self.bias_size = None
self.data_format = "NCHW" self.fuse_relu = False
self.fuse_residual_connection = False
self.input_residual_size = None
TestConv2dOp.setUp(self)
output = self.outputs['Output']
class TestMKLDNNWithInput1x1Filter1x1(TestWithInput1x1Filter1x1): #mkldnn only support either conv-sum-relu, or conv-relu.
def init_kernel_type(self): if self.fuse_bias and self.bias_size is not None:
self.use_mkldnn = True bias = np.random.random(self.bias_size).astype(self.dtype)
self.data_format = "NCHW" output = conv2d_bias_naive(output, bias)
output = output.astype(self.dtype)
self.attrs['fuse_bias'] = self.fuse_bias
self.inputs['Bias'] = OpTest.np_dtype_to_fluid_dtype(bias)
if self.fuse_residual_connection and self.input_residual_size is not None:
input_residual = np.random.random(self.input_residual_size).astype(
self.dtype)
output = conv2d_residual_naive(output, input_residual)
self.attrs[
'fuse_residual_connection'] = self.fuse_residual_connection
self.inputs['ResidualData'] = OpTest.np_dtype_to_fluid_dtype(
input_residual)
if self.fuse_relu:
output = np.maximum(output, 0).astype(self.dsttype)
output = output.astype(self.dtype)
self.attrs['fuse_bias'] = self.fuse_bias
self.attrs['fuse_relu'] = self.fuse_relu
self.attrs['fuse_residual_connection'] = self.fuse_residual_connection
self.outputs['Output'] = output
class TestWithFuse(TestConv2dMKLDNNOp):
def init_test_case(self):
TestConv2dMKLDNNOp.init_test_case(self)
self.pad = [1, 1]
self.fuse_bias = True
self.bias_size = [6]
self.fuse_residual_connection = True
self.input_residual_size = [2, 6, 5, 5]
def test_check_grad(self):
pass
def test_check_grad_no_filter(self):
pass
def test_check_grad_no_input(self):
pass
class TestWithPadWithBias(TestConv2dMKLDNNOp):
def init_test_case(self):
TestConv2dMKLDNNOp.init_test_case(self)
self.pad = [1, 1]
self.input_size = [2, 3, 6, 6]
class TestWithStride(TestConv2dMKLDNNOp):
def init_test_case(self):
TestConv2dMKLDNNOp.init_test_case(self)
self.pad = [1, 1]
self.stride = [2, 2]
self.input_size = [2, 3, 6, 6]
class TestWithGroup(TestConv2dMKLDNNOp):
def init_group(self):
self.groups = 3
class TestWith1x1(TestConv2dMKLDNNOp):
def init_test_case(self):
TestConv2dMKLDNNOp.init_test_case(self)
self.filter_size = [6, 3, 1, 1]
class TestWithInput1x1Filter1x1(TestConv2dMKLDNNOp):
def init_test_case(self):
TestConv2dMKLDNNOp.init_test_case(self)
self.input_size = [2, 3, 1, 1] # NCHW
assert np.mod(self.input_size[1], self.groups) == 0
f_c = self.input_size[1] // self.groups
self.filter_size = [6, f_c, 1, 1]
def init_group(self):
self.groups = 3
if __name__ == '__main__': if __name__ == '__main__':
......
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