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Paddle-Lite
提交 bfadcc5e 编写于 作者: Z zhangyang
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Merge remote-tracking branch 'upstream/develop' into develop

上级 34a7a0e0 54b6fd3f
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Showing with 32 addition and 28 deletion
src/operators/math/depthwise_conv_3x3.cpp
浏览文件 @ bfadcc5e
......@@ -1275,7 +1275,8 @@ void DepthwiseConv3x3s2p1v2(const Tensor *input, const Tensor *filter,
const int inhxw = in_h * in_w;
const int outhxw = out_h * out_w;
/// todo : fix if_pad when w != h
const int if_pad = in_w - 1 == (out_w - 1) * 2 ? 1 : 0;
const int if_pad_r = in_w - 1 == (out_w - 1) * 2 ? 1 : 0;
const int if_pad_b = in_h - 1 == (out_h - 1) * 2 ? 1 : 0;
const int batch_size = static_cast<int>(input->dims()[0]);
const int c = static_cast<int>(input->dims()[1]);
const float *input_row_ptr;
......@@ -1366,7 +1367,7 @@ void DepthwiseConv3x3s2p1v2(const Tensor *input, const Tensor *filter,
elewise_res0 = vmlaq_n_f32(elewise_res0, input_buff_mid.val[0], w10);
elewise_res2 = vmlaq_n_f32(elewise_res2, input_buff_mid.val[0], w12);
if (!if_pad) {
if (!if_pad_b) {
elewise_res1 =
vmlaq_n_f32(elewise_res1, input_buff_bottom[w4].val[1], w21);
elewise_res0 =
......@@ -1401,8 +1402,8 @@ void DepthwiseConv3x3s2p1v2(const Tensor *input, const Tensor *filter,
w10 * input_const[out2in_mid - 1] + w11 * input_const[out2in_mid] +
w20 * input_const[out2in_mid + in_w - 1] +
w21 * input_const[out2in_mid + in_w] +
(1 - if_pad) * (w12 * input_const[out2in_mid + 1] +
w22 * input_const[out2in_mid + in_w + 1]);
(1 - if_pad_r) * (w12 * input_const[out2in_mid + 1] +
w22 * input_const[out2in_mid + in_w + 1]);
out2in_mid = (out_h - 1) * 2 * in_w;
......@@ -1410,19 +1411,20 @@ void DepthwiseConv3x3s2p1v2(const Tensor *input, const Tensor *filter,
w01 * input_const[out2in_mid - in_w] +
w02 * input_const[out2in_mid - in_w + 1] +
w11 * input_const[out2in_mid] + w12 * input_const[out2in_mid + 1] +
(1 - if_pad) * (w21 * input_const[out2in_mid + in_w] +
w22 * input_const[out2in_mid + in_w + 1]);
(1 - if_pad_b) * (w21 * input_const[out2in_mid + in_w] +
w22 * input_const[out2in_mid + in_w + 1]);
out2in_mid = (out_h - 1) * 2 * in_w + (out_w - 1) * 2;
output_data_tmp[out_h * out_w - 1] =
w00 * input_const[out2in_mid - in_w - 1] +
w01 * input_const[out2in_mid - in_w] +
w10 * input_const[out2in_mid - 1] + w11 * input_const[out2in_mid] +
(1 - if_pad) * (w20 * input_const[out2in_mid + in_w - 1] +
w21 * input_const[out2in_mid + in_w] +
w02 * input_const[out2in_mid - in_w + 1] +
w12 * input_const[out2in_mid + 1] +
w22 * input_const[out2in_mid + in_w + 1]);
(1 - if_pad_r) * (w20 * input_const[out2in_mid + in_w - 1] +
w21 * input_const[out2in_mid + in_w]) +
(1 - if_pad_b) * (w02 * input_const[out2in_mid - in_w + 1] +
w12 * input_const[out2in_mid + 1]) +
(1 - if_pad_r) * (1 - if_pad_b) * w22 *
input_const[out2in_mid + in_w + 1];
if (if_bias) {
output_data_tmp[0] += bias_data[j];
output_data_tmp[out_w - 1] += bias_data[j];
......@@ -1445,9 +1447,9 @@ void DepthwiseConv3x3s2p1v2(const Tensor *input, const Tensor *filter,
w10 * input_const[out2in_mid - 1] + w11 * input_const[out2in_mid] +
w20 * input_const[out2in_mid + in_w - 1] +
w21 * input_const[out2in_mid + in_w] +
(1 - if_pad) * (w02 * input_const[out2in_mid - in_w + 1] +
w12 * input_const[out2in_mid + 1] +
w22 * input_const[out2in_mid + in_w + 1]);
(1 - if_pad_r) * (w02 * input_const[out2in_mid - in_w + 1] +
w12 * input_const[out2in_mid + 1] +
w22 * input_const[out2in_mid + in_w + 1]);
if (if_bias) {
output_data_tmp[i * out_w] += bias_data[j];
output_data_tmp[i * out_w + out_w - 1] += bias_data[j];
......@@ -1662,7 +1664,8 @@ void DepthwiseConvAddBNRelu3x3s2p1v2(const Tensor *input, const Tensor *filter,
const int inhxw = in_h * in_w;
const int outhxw = out_h * out_w;
/// todo : fix if_pad when w != h
const int if_pad = in_w - 1 == (out_w - 1) * 2 ? 1 : 0;
const int if_pad_r = in_w - 1 == (out_w - 1) * 2 ? 1 : 0;
const int if_pad_b = in_h - 1 == (out_h - 1) * 2 ? 1 : 0;
const int batch_size = static_cast<int>(input->dims()[0]);
const int c = static_cast<int>(input->dims()[1]);
const int w_times = (out_w - 2) / 3;
......@@ -1756,7 +1759,7 @@ void DepthwiseConvAddBNRelu3x3s2p1v2(const Tensor *input, const Tensor *filter,
elewise_res0 = vmlaq_n_f32(elewise_res0, input_buff_mid.val[0], w10);
elewise_res2 = vmlaq_n_f32(elewise_res2, input_buff_mid.val[0], w12);
if (!if_pad) {
if (!if_pad_b) {
elewise_res1 =
vmlaq_n_f32(elewise_res1, input_buff_bottom[w4].val[1], w21);
elewise_res0 =
......@@ -1796,8 +1799,8 @@ void DepthwiseConvAddBNRelu3x3s2p1v2(const Tensor *input, const Tensor *filter,
w10 * input_const[out2in_mid - 1] + w11 * input_const[out2in_mid] +
w20 * input_const[out2in_mid + in_w - 1] +
w21 * input_const[out2in_mid + in_w] +
(1 - if_pad) * (w12 * input_const[out2in_mid + 1] +
w22 * input_const[out2in_mid + in_w + 1]);
(1 - if_pad_r) * (w12 * input_const[out2in_mid + 1] +
w22 * input_const[out2in_mid + in_w + 1]);
out2in_mid = (out_h - 1) * 2 * in_w;
......@@ -1805,19 +1808,20 @@ void DepthwiseConvAddBNRelu3x3s2p1v2(const Tensor *input, const Tensor *filter,
w01 * input_const[out2in_mid - in_w] +
w02 * input_const[out2in_mid - in_w + 1] +
w11 * input_const[out2in_mid] + w12 * input_const[out2in_mid + 1] +
(1 - if_pad) * (w21 * input_const[out2in_mid + in_w] +
w22 * input_const[out2in_mid + in_w + 1]);
(1 - if_pad_b) * (w21 * input_const[out2in_mid + in_w] +
w22 * input_const[out2in_mid + in_w + 1]);
out2in_mid = (out_h - 1) * 2 * in_w + (out_w - 1) * 2;
output_data_tmp[out_h * out_w - 1] =
w00 * input_const[out2in_mid - in_w - 1] +
w01 * input_const[out2in_mid - in_w] +
w10 * input_const[out2in_mid - 1] + w11 * input_const[out2in_mid] +
(1 - if_pad) * (w20 * input_const[out2in_mid + in_w - 1] +
w21 * input_const[out2in_mid + in_w] +
w02 * input_const[out2in_mid - in_w + 1] +
w12 * input_const[out2in_mid + 1] +
w22 * input_const[out2in_mid + in_w + 1]);
(1 - if_pad_r) * (w20 * input_const[out2in_mid + in_w - 1] +
w21 * input_const[out2in_mid + in_w]) +
(1 - if_pad_b) * (w02 * input_const[out2in_mid - in_w + 1] +
w12 * input_const[out2in_mid + 1]) +
(1 - if_pad_r) * (1 - if_pad_b) * w22 *
input_const[out2in_mid + in_w + 1];
output_data_tmp[0] =
output_data_tmp[0] * newscale_data[j] + newbias_data[j];
output_data_tmp[out_w - 1] =
......@@ -1857,9 +1861,9 @@ void DepthwiseConvAddBNRelu3x3s2p1v2(const Tensor *input, const Tensor *filter,
w10 * input_const[out2in_mid - 1] + w11 * input_const[out2in_mid] +
w20 * input_const[out2in_mid + in_w - 1] +
w21 * input_const[out2in_mid + in_w] +
(1 - if_pad) * (w02 * input_const[out2in_mid - in_w + 1] +
w12 * input_const[out2in_mid + 1] +
w22 * input_const[out2in_mid + in_w + 1]);
(1 - if_pad_r) * (w02 * input_const[out2in_mid - in_w + 1] +
w12 * input_const[out2in_mid + 1] +
w22 * input_const[out2in_mid + in_w + 1]);
output_data_tmp[i * out_w] =
output_data_tmp[i * out_w] * newscale_data[j] + newbias_data[j];
output_data_tmp[i * out_w + out_w - 1] =
......
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