1 /**
2 * Copyright 2021 Huawei Technologies Co., Ltd
3 *
4 * Licensed under the Apache License, Version 2.0 (the "License");
5 * you may not use this file except in compliance with the License.
6 * You may obtain a copy of the License at
7 *
8 * http://www.apache.org/licenses/LICENSE-2.0
9 *
10 * Unless required by applicable law or agreed to in writing, software
11 * distributed under the License is distributed on an "AS IS" BASIS,
12 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13 * See the License for the specific language governing permissions and
14 * limitations under the License.
15 */
16
17 #include "nnacl/infer/full_connection_infer.h"
18 #include "nnacl/infer/infer_register.h"
19
FullConnectionInferPreJudge(const MatMulParameter * param,size_t inputs_size,const TensorC * input0)20 int FullConnectionInferPreJudge(const MatMulParameter *param, size_t inputs_size, const TensorC *input0) {
21 if ((param->has_bias_ && inputs_size != 3) || (!param->has_bias_ && inputs_size != 2)) {
22 return NNACL_INPUT_TENSOR_ERROR;
23 }
24 if (param->use_axis_ && (param->axis_ < 1 || param->axis_ > (int)(input0->shape_size_))) {
25 return NNACL_ERR;
26 }
27 return NNACL_OK;
28 }
29
FullConnectionInferShape(const TensorC * const * inputs,size_t inputs_size,TensorC ** outputs,size_t outputs_size,OpParameter * parameter)30 int FullConnectionInferShape(const TensorC *const *inputs, size_t inputs_size, TensorC **outputs, size_t outputs_size,
31 OpParameter *parameter) {
32 int check_ret = CheckAugmentWithMinSize(inputs, inputs_size, outputs, outputs_size, parameter, 2, 1);
33 if (check_ret != NNACL_OK) {
34 return check_ret;
35 }
36
37 const TensorC *input0 = inputs[0];
38 const TensorC *input1 = inputs[1];
39 TensorC *output = outputs[0];
40 MatMulParameter *param = (MatMulParameter *)parameter;
41 SetDataTypeFormat(output, input0);
42 if (!InferFlag(inputs, inputs_size)) {
43 return NNACL_INFER_INVALID;
44 }
45 int pre_judge = FullConnectionInferPreJudge(param, inputs_size, input0);
46 if (pre_judge != NNACL_OK) {
47 return pre_judge;
48 }
49 int new_k = 1;
50 if (param->use_axis_) {
51 for (size_t i = (size_t)(param->axis_); i < input0->shape_size_; ++i) {
52 new_k *= input0->shape_[i];
53 }
54 if (new_k != input1->shape_[1]) {
55 return NNACL_INPUT_TENSOR_ERROR;
56 }
57 } else {
58 new_k = input1->shape_[1];
59 }
60 if (param->has_bias_) {
61 if (inputs[2]->shape_[0] != input1->shape_[0]) {
62 return NNACL_INPUT_TENSOR_ERROR;
63 }
64 }
65 if (inputs[0]->shape_size_ > MAX_SHAPE_SIZE) {
66 return NNACL_INPUT_TENSOR_ERROR;
67 }
68 int out_shape[MAX_SHAPE_SIZE];
69 size_t out_shape_size = 0;
70 ShapeSet(out_shape, &out_shape_size, inputs[0]->shape_, inputs[0]->shape_size_);
71 if (param->use_axis_) {
72 out_shape_size = (size_t)(param->axis_) + 1;
73 out_shape[param->axis_] = input1->shape_[0];
74 } else {
75 int total = 1;
76 for (size_t i = 0; i < input0->shape_size_; ++i) {
77 total *= input0->shape_[i];
78 }
79 out_shape_size = 2;
80 if (new_k == 0) {
81 return NNACL_ERR;
82 }
83 int batch_size = total / new_k;
84 out_shape[0] = batch_size;
85 out_shape[1] = input1->shape_[0];
86 }
87 SetShapeArray(output, out_shape, out_shape_size);
88
89 return NNACL_OK;
90 }
91
92 REG_INFER(FullConnection, PrimType_FullConnection, FullConnectionInferShape)
93