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