mozga-intel
8 years ago
parent
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commit
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/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#pragma once
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#include "paddle/fluid/framework/op_registry.h"
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namespace paddle {
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namespace operators {
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using Tensor = framework::Tensor;
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class FCOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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void InferShape(framework::InferShapeContext* ctx) const override;
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protected:
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framework::OpKernelType GetExpectedKernelType(
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const framework::ExecutionContext& ctx) const override;
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};
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class FCOpGrad : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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void InferShape(framework::InferShapeContext* ctx) const override;
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protected:
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framework::OpKernelType GetExpectedKernelType(
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const framework::ExecutionContext& ctx) const override;
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};
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} // namespace operators
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} // namespace paddle
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# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import unittest
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import numpy as np
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from op_test import OpTest
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def fully_connected_naive(input, weights, bias_data=None):
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in_n, in_c, in_h, in_w = input.shape
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w_h, w_c = weights.shape
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x_data = np.reshape(input, [in_n, in_c * in_h * in_w])
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w_data = np.transpose(np.reshape(weights, (w_c, in_c * in_h * in_w)))
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result = None
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if not bias_data:
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result = np.dot(x_data, w_data)
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else:
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result = np.dot(x_data, w_data) + bias_data
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return result
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class MatrixGenerate:
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def __init__(self, mb, ic, oc, h, w):
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self.input = np.random.random((mb, ic, h, w)).astype("float32")
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self.weights = np.random.random((ic * h * w, oc)).astype("float32")
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class TestFCMKLDNNOp(OpTest):
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def setUp(self):
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self.op_type = "fc"
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self.use_mkldnn = True
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self.with_bias = True
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self.matrix = MatrixGenerate(1, 10, 15, 3, 3)
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self.inputs = {'Input': self.matrix.input, 'W': self.matrix.weights}
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self.attrs = {
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'use_mkldnn': self.use_mkldnn,
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'with_bias': self.with_bias
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}
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self.outputs = {
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'Out': fully_connected_naive(self.matrix.input, self.matrix.weights)
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}
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def test_check_output(self):
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self.check_output()
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def test_check_grad_normal(self):
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self.check_grad(set(['Input', 'W']), 'Out', max_relative_error=0.9)
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def test_check_grad_no_weight(self):
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self.check_grad(
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['Input'], 'Out', max_relative_error=0.5, no_grad_set=set('W'))
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class TestFCMKLDNNOp1(TestFCMKLDNNOp):
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def init_op_type(self):
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self.matrix = MatrixGenerate(2, 15, 48, 2, 2)
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class TestFCMKLDNNOp2(TestFCMKLDNNOp):
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def init_op_type(self):
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self.matrix = MatrixGenerate(2, 32, 40, 1, 1)
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class TestFCMKLDNNOp3(TestFCMKLDNNOp):
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def init_op_type(self):
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self.matrix = MatrixGenerate(2, 2, 4, 1, 1)
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class TestFCMKLDNNOp4(TestFCMKLDNNOp):
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def init_op_type(self):
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self.with_bias = False
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self.matrix = MatrixGenerate(2, 32, 48, 2, 2)
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class TestFCMKLDNNOp4(TestFCMKLDNNOp):
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def init_op_type(self):
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self.with_bias = False
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self.matrix = MatrixGenerate(2, 32, 1000, 6, 6)
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if __name__ == "__main__":
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unittest.main()
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