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未验证 提交 b988d1dd 编写于 作者: X xiebaiyuan 提交者： GitHub
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[LITE][OPENCL][Image]support multi batch conv2d 3x3 5x5 7x7 ,open lws,test=develop (#3258)

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.gitignore
浏览文件 @ b988d1dd
......@@ -105,3 +105,5 @@ metal/paddle-mobile-demo/paddle-mobile-demo/Resources
metal/paddle-mobile-demo/paddle-mobile-demo/Resources/images
metal/paddle-mobile-demo/paddle-mobile-demo/Resources/models
metal/MobileNetDemo/MobileNetDemo/Resources
build*
lite/backends/opencl/cl_kernel/image/conv2d_5x5_opt_kernel.cl
浏览文件 @ b988d1dd
......@@ -233,6 +233,258 @@ __kernel void conv2d_5x5_opt(__private const int item_ch,
output[3] = activation_type4(output[3]);
output[4] = activation_type4(output[4]);
WRITE_IMG_TYPE(CL_DTYPE_CHAR,
output_image,
(int2)(out_w_base_id + out_w_id0, item_h_id),
output[0]);
if (out_w_id1 < out_w) {
WRITE_IMG_TYPE(CL_DTYPE_CHAR,
output_image,
(int2)(out_w_base_id + out_w_id1, item_h_id),
output[1]);
}
if (out_w_id2 < out_w) {
WRITE_IMG_TYPE(CL_DTYPE_CHAR,
output_image,
(int2)(out_w_base_id + out_w_id2, item_h_id),
output[2]);
}
if (out_w_id3 < out_w) {
WRITE_IMG_TYPE(CL_DTYPE_CHAR,
output_image,
(int2)(out_w_base_id + out_w_id3, item_h_id),
output[3]);
}
if (out_w_id4 < out_w) {
WRITE_IMG_TYPE(CL_DTYPE_CHAR,
output_image,
(int2)(out_w_base_id + out_w_id4, item_h_id),
output[4]);
}
}
// support batch > 1
__kernel void conv2d_5x5_multi_batch(__private const int item_ch,
__private const int item_w,
__private const int item_h,
__read_only image2d_t input_image,
__read_only image2d_t filter_image,
#if defined(BIASE_CH) || defined(BIASE_ELE)
__read_only image2d_t bias,
#endif
__write_only image2d_t output_image,
__private const int stride,
__private const int pad,
__private const int dilation,
__private const int batch,
__private const int in_ch,
__private const int in_w,
__private const int in_h,
__private const int out_w,
__private const int out_h) {
const sampler_t sampler =
CLK_NORMALIZED_COORDS_TRUE | CLK_ADDRESS_CLAMP | CLK_FILTER_NEAREST;
// filter
const int filter_w = 5;
const int filter_h = 5;
// item_id
const int item_ch_id = get_global_id(0);
const int item_w_id = get_global_id(1);
const int item_h_id = get_global_id(2);
// out_width_id_per_blk and out_batch_id
int out_batch_id = item_h_id / in_h;
int out_w_base_id = item_ch_id * out_w;
int out_w_id0 = item_w_id;
int out_w_id1 = out_w_id0 + item_w;
int out_w_id2 = out_w_id1 + item_w;
int out_w_id3 = out_w_id2 + item_w;
int out_w_id4 = out_w_id3 + item_w;
// in_width_id_per_blk and in_height_id_per_batch
int in_h_id = (item_h_id % out_h) * stride - pad;
int in_w_id0 = item_w_id * stride - pad;
int in_w_id1 = in_w_id0 + item_w * stride;
int in_w_id2 = in_w_id1 + item_w * stride;
int in_w_id3 = in_w_id2 + item_w * stride;
int in_w_id4 = in_w_id3 + item_w * stride;
#ifdef BIASE_CH
CL_DTYPE4 output[5];
output[0] =
READ_IMG_TYPE(CL_DTYPE_CHAR, bias, sampler, (int2)(item_ch_id, 0));
output[1] = output[0];
output[2] = output[0];
output[3] = output[0];
output[4] = output[0];
#elif defined(BIASE_ELE)
CL_DTYPE4 output[5];
output[0] = READ_IMG_TYPE(CL_DTYPE_CHAR,
bias,
sampler,
(int2)(out_w_base_id + out_w_id0, item_h_id));
if (out_w_id1 < out_w) {
output[1] = READ_IMG_TYPE(CL_DTYPE_CHAR,
bias,
sampler,
(int2)(out_w_base_id + out_w_id1, item_h_id));
}
if (out_w_id2 < out_w) {
output[2] = READ_IMG_TYPE(CL_DTYPE_CHAR,
bias,
sampler,
(int2)(out_w_base_id + out_w_id2, item_h_id));
}
if (out_w_id3 < out_w) {
output[3] = READ_IMG_TYPE(CL_DTYPE_CHAR,
bias,
sampler,
(int2)(out_w_base_id + out_w_id3, item_h_id));
}
if (out_w_id4 < out_w) {
output[4] = READ_IMG_TYPE(CL_DTYPE_CHAR,
bias,
sampler,
(int2)(out_w_base_id + out_w_id4, item_h_id));
}
#else
CL_DTYPE4 output[5] = {0.0f};
#endif
CL_DTYPE4 filter[4] = {0.0f};
CL_DTYPE4 filter_trans[4] = {0.0f};
CL_DTYPE4 input[5] = {0.0f};
int filter_h_val0 = item_ch_id * 4 * filter_h;
int filter_h_val1 = filter_h_val0 + filter_h;
int filter_h_val2 = filter_h_val1 + filter_h;
int filter_h_val3 = filter_h_val2 + filter_h;
for (int ch = 0; ch < (in_ch + 3) / 4; ch++) {
int ch_surplus = (ch + 1) * 4 - in_ch > 0 ? (ch + 1) * 4 - in_ch : 0;
const int in_w_base_id = mul24(ch, in_w);
int filter_w_val = ch * filter_w;
for (int h = 0; h < filter_h; h++) {
int in_h_val = select(
out_batch_id * in_h + in_h_id + h,
-1,
(out_batch_id * in_h + in_h_id + h < out_batch_id * in_h ||
out_batch_id * in_h + in_h_id + h >= (out_batch_id + 1) * in_h));
for (int w = 0; w < filter_w; w++) {
int in_w_val0 = select(in_w_base_id + in_w_id0 + w,
-1,
(in_w_id0 + w < 0 || in_w_id0 + w >= in_w));
int in_w_val1 = select(in_w_base_id + in_w_id1 + w,
-1,
(in_w_id1 + w < 0 || in_w_id1 + w >= in_w));
int in_w_val2 = select(in_w_base_id + in_w_id2 + w,
-1,
(in_w_id2 + w < 0 || in_w_id2 + w >= in_w));
int in_w_val3 = select(in_w_base_id + in_w_id3 + w,
-1,
(in_w_id3 + w < 0 || in_w_id3 + w >= in_w));
int in_w_val4 = select(in_w_base_id + in_w_id4 + w,
-1,
(in_w_id4 + w < 0 || in_w_id4 + w >= in_w));
filter[0] =
READ_IMG_TYPE(CL_DTYPE_CHAR,
filter_image,
sampler,
(int2)(filter_w_val + w,
filter_h_val0 + h)); // in_ch:0-3,out_ch:0
filter[1] =
READ_IMG_TYPE(CL_DTYPE_CHAR,
filter_image,
sampler,
(int2)(filter_w_val + w,
filter_h_val1 + h)); // in_ch:0-3,out_ch:1
filter[2] =
READ_IMG_TYPE(CL_DTYPE_CHAR,
filter_image,
sampler,
(int2)(filter_w_val + w,
filter_h_val2 + h)); // in_ch:0-3,out_ch:2
filter[3] =
READ_IMG_TYPE(CL_DTYPE_CHAR,
filter_image,
sampler,
(int2)(filter_w_val + w,
filter_h_val3 + h)); // in_ch:0-3,out_ch:3
filter_trans[0] = (CL_DTYPE4)(filter[0].x,
filter[1].x,
filter[2].x,
filter[3].x); // in_ch:0,out_ch:0-3
filter_trans[1] = (CL_DTYPE4)(filter[0].y,
filter[1].y,
filter[2].y,
filter[3].y); // in_ch:1,out_ch:0-3
filter_trans[2] = (CL_DTYPE4)(filter[0].z,
filter[1].z,
filter[2].z,
filter[3].z); // in_ch:2,out_ch:0-3
filter_trans[3] = (CL_DTYPE4)(filter[0].w,
filter[1].w,
filter[2].w,
filter[3].w); // in_ch:3,out_ch:0-3
input[0] = READ_IMG_TYPE(
CL_DTYPE_CHAR, input_image, sampler, (int2)(in_w_val0, in_h_val));
input[1] = READ_IMG_TYPE(
CL_DTYPE_CHAR, input_image, sampler, (int2)(in_w_val1, in_h_val));
input[2] = READ_IMG_TYPE(
CL_DTYPE_CHAR, input_image, sampler, (int2)(in_w_val2, in_h_val));
input[3] = READ_IMG_TYPE(
CL_DTYPE_CHAR, input_image, sampler, (int2)(in_w_val3, in_h_val));
input[4] = READ_IMG_TYPE(
CL_DTYPE_CHAR, input_image, sampler, (int2)(in_w_val4, in_h_val));
output[0] = mad(input[0].x, filter_trans[0], output[0]);
output[1] = mad(input[1].x, filter_trans[0], output[1]);
output[2] = mad(input[2].x, filter_trans[0], output[2]);
output[3] = mad(input[3].x, filter_trans[0], output[3]);
output[4] = mad(input[4].x, filter_trans[0], output[4]);
if (ch_surplus < 3) {
output[0] = mad(input[0].y, filter_trans[1], output[0]);
output[1] = mad(input[1].y, filter_trans[1], output[1]);
output[2] = mad(input[2].y, filter_trans[1], output[2]);
output[3] = mad(input[3].y, filter_trans[1], output[3]);
output[4] = mad(input[4].y, filter_trans[1], output[4]);
}
if (ch_surplus < 2) {
output[0] = mad(input[0].z, filter_trans[2], output[0]);
output[1] = mad(input[1].z, filter_trans[2], output[1]);
output[2] = mad(input[2].z, filter_trans[2], output[2]);
output[3] = mad(input[3].z, filter_trans[2], output[3]);
output[4] = mad(input[4].z, filter_trans[2], output[4]);
}
if (ch_surplus < 1) {
output[0] = mad(input[0].w, filter_trans[3], output[0]);
output[1] = mad(input[1].w, filter_trans[3], output[1]);
output[2] = mad(input[2].w, filter_trans[3], output[2]);
output[3] = mad(input[3].w, filter_trans[3], output[3]);
output[4] = mad(input[4].w, filter_trans[3], output[4]);
}
}
}
}
output[0] = activation_type4(output[0]);
output[1] = activation_type4(output[1]);
output[2] = activation_type4(output[2]);
output[3] = activation_type4(output[3]);
output[4] = activation_type4(output[4]);
WRITE_IMG_TYPE(CL_DTYPE_CHAR,
output_image,
(int2)(out_w_base_id + out_w_id0, item_h_id),
......
lite/backends/opencl/cl_kernel/image/conv2d_7x7_opt_kernel.cl
浏览文件 @ b988d1dd
......@@ -233,6 +233,258 @@ __kernel void conv2d_7x7_opt(__private const int item_ch,
output[3] = activation_type4(output[3]);
output[4] = activation_type4(output[4]);
WRITE_IMG_TYPE(CL_DTYPE_CHAR,
output_image,
(int2)(out_w_base_id + out_w_id0, item_h_id),
output[0]);
if (out_w_id1 < out_w) {
WRITE_IMG_TYPE(CL_DTYPE_CHAR,
output_image,
(int2)(out_w_base_id + out_w_id1, item_h_id),
output[1]);
}
if (out_w_id2 < out_w) {
WRITE_IMG_TYPE(CL_DTYPE_CHAR,
output_image,
(int2)(out_w_base_id + out_w_id2, item_h_id),
output[2]);
}
if (out_w_id3 < out_w) {
WRITE_IMG_TYPE(CL_DTYPE_CHAR,
output_image,
(int2)(out_w_base_id + out_w_id3, item_h_id),
output[3]);
}
if (out_w_id4 < out_w) {
WRITE_IMG_TYPE(CL_DTYPE_CHAR,
output_image,
(int2)(out_w_base_id + out_w_id4, item_h_id),
output[4]);
}
}
// support batch > 1
__kernel void conv2d_7x7_multi_batch(__private const int item_ch,
__private const int item_w,
__private const int item_h,
__read_only image2d_t input_image,
__read_only image2d_t filter_image,
#if defined(BIASE_CH) || defined(BIASE_ELE)
__read_only image2d_t bias,
#endif
__write_only image2d_t output_image,
__private const int stride,
__private const int pad,
__private const int dilation,
__private const int batch,
__private const int in_ch,
__private const int in_w,
__private const int in_h,
__private const int out_w,
__private const int out_h) {
const sampler_t sampler =
CLK_NORMALIZED_COORDS_TRUE | CLK_ADDRESS_CLAMP | CLK_FILTER_NEAREST;
// filter
const int filter_w = 7;
const int filter_h = 7;
// item_id
const int item_ch_id = get_global_id(0);
const int item_w_id = get_global_id(1);
const int item_h_id = get_global_id(2);
// out_width_id_per_blk and out_batch_id
int out_batch_id = item_h_id / in_h;
int out_w_base_id = item_ch_id * out_w;
int out_w_id0 = item_w_id;
int out_w_id1 = out_w_id0 + item_w;
int out_w_id2 = out_w_id1 + item_w;
int out_w_id3 = out_w_id2 + item_w;
int out_w_id4 = out_w_id3 + item_w;
// in_width_id_per_blk and in_height_id_per_batch
int in_h_id = (item_h_id % out_h) * stride - pad;
int in_w_id0 = item_w_id * stride - pad;
int in_w_id1 = in_w_id0 + item_w * stride;
int in_w_id2 = in_w_id1 + item_w * stride;
int in_w_id3 = in_w_id2 + item_w * stride;
int in_w_id4 = in_w_id3 + item_w * stride;
#ifdef BIASE_CH
CL_DTYPE4 output[5];
output[0] =
READ_IMG_TYPE(CL_DTYPE_CHAR, bias, sampler, (int2)(item_ch_id, 0));
output[1] = output[0];
output[2] = output[0];
output[3] = output[0];
output[4] = output[0];
#elif defined(BIASE_ELE)
CL_DTYPE4 output[5];
output[0] = READ_IMG_TYPE(CL_DTYPE_CHAR,
bias,
sampler,
(int2)(out_w_base_id + out_w_id0, item_h_id));
if (out_w_id1 < out_w) {
output[1] = READ_IMG_TYPE(CL_DTYPE_CHAR,
bias,
sampler,
(int2)(out_w_base_id + out_w_id1, item_h_id));
}
if (out_w_id2 < out_w) {
output[2] = READ_IMG_TYPE(CL_DTYPE_CHAR,
bias,
sampler,
(int2)(out_w_base_id + out_w_id2, item_h_id));
}
if (out_w_id3 < out_w) {
output[3] = READ_IMG_TYPE(CL_DTYPE_CHAR,
bias,
sampler,
(int2)(out_w_base_id + out_w_id3, item_h_id));
}
if (out_w_id4 < out_w) {
output[4] = READ_IMG_TYPE(CL_DTYPE_CHAR,
bias,
sampler,
(int2)(out_w_base_id + out_w_id4, item_h_id));
}
#else
CL_DTYPE4 output[5] = {0.0f};
#endif
CL_DTYPE4 filter[4] = {0.0f};
CL_DTYPE4 filter_trans[4] = {0.0f};
CL_DTYPE4 input[5] = {0.0f};
int filter_h_val0 = item_ch_id * 4 * filter_h;
int filter_h_val1 = filter_h_val0 + filter_h;
int filter_h_val2 = filter_h_val1 + filter_h;
int filter_h_val3 = filter_h_val2 + filter_h;
for (int ch = 0; ch < (in_ch + 3) / 4; ch++) {
int ch_surplus = (ch + 1) * 4 - in_ch > 0 ? (ch + 1) * 4 - in_ch : 0;
const int in_w_base_id = mul24(ch, in_w);
int filter_w_val = ch * filter_w;
for (int h = 0; h < filter_h; h++) {
int in_h_val = select(
out_batch_id * in_h + in_h_id + h,
-1,
(out_batch_id * in_h + in_h_id + h < out_batch_id * in_h ||
out_batch_id * in_h + in_h_id + h >= (out_batch_id + 1) * in_h));
for (int w = 0; w < filter_w; w++) {
int in_w_val0 = select(in_w_base_id + in_w_id0 + w,
-1,
(in_w_id0 + w < 0 || in_w_id0 + w >= in_w));
int in_w_val1 = select(in_w_base_id + in_w_id1 + w,
-1,
(in_w_id1 + w < 0 || in_w_id1 + w >= in_w));
int in_w_val2 = select(in_w_base_id + in_w_id2 + w,
-1,
(in_w_id2 + w < 0 || in_w_id2 + w >= in_w));
int in_w_val3 = select(in_w_base_id + in_w_id3 + w,
-1,
(in_w_id3 + w < 0 || in_w_id3 + w >= in_w));
int in_w_val4 = select(in_w_base_id + in_w_id4 + w,
-1,
(in_w_id4 + w < 0 || in_w_id4 + w >= in_w));
filter[0] =
READ_IMG_TYPE(CL_DTYPE_CHAR,
filter_image,
sampler,
(int2)(filter_w_val + w,
filter_h_val0 + h)); // in_ch:0-3,out_ch:0
filter[1] =
READ_IMG_TYPE(CL_DTYPE_CHAR,
filter_image,
sampler,
(int2)(filter_w_val + w,
filter_h_val1 + h)); // in_ch:0-3,out_ch:1
filter[2] =
READ_IMG_TYPE(CL_DTYPE_CHAR,
filter_image,
sampler,
(int2)(filter_w_val + w,
filter_h_val2 + h)); // in_ch:0-3,out_ch:2
filter[3] =
READ_IMG_TYPE(CL_DTYPE_CHAR,
filter_image,
sampler,
(int2)(filter_w_val + w,
filter_h_val3 + h)); // in_ch:0-3,out_ch:3
filter_trans[0] = (CL_DTYPE4)(filter[0].x,
filter[1].x,
filter[2].x,
filter[3].x); // in_ch:0,out_ch:0-3
filter_trans[1] = (CL_DTYPE4)(filter[0].y,
filter[1].y,
filter[2].y,
filter[3].y); // in_ch:1,out_ch:0-3
filter_trans[2] = (CL_DTYPE4)(filter[0].z,
filter[1].z,
filter[2].z,
filter[3].z); // in_ch:2,out_ch:0-3
filter_trans[3] = (CL_DTYPE4)(filter[0].w,
filter[1].w,
filter[2].w,
filter[3].w); // in_ch:3,out_ch:0-3
input[0] = READ_IMG_TYPE(
CL_DTYPE_CHAR, input_image, sampler, (int2)(in_w_val0, in_h_val));
input[1] = READ_IMG_TYPE(
CL_DTYPE_CHAR, input_image, sampler, (int2)(in_w_val1, in_h_val));
input[2] = READ_IMG_TYPE(
CL_DTYPE_CHAR, input_image, sampler, (int2)(in_w_val2, in_h_val));
input[3] = READ_IMG_TYPE(
CL_DTYPE_CHAR, input_image, sampler, (int2)(in_w_val3, in_h_val));
input[4] = READ_IMG_TYPE(
CL_DTYPE_CHAR, input_image, sampler, (int2)(in_w_val4, in_h_val));
output[0] = mad(input[0].x, filter_trans[0], output[0]);
output[1] = mad(input[1].x, filter_trans[0], output[1]);
output[2] = mad(input[2].x, filter_trans[0], output[2]);
output[3] = mad(input[3].x, filter_trans[0], output[3]);
output[4] = mad(input[4].x, filter_trans[0], output[4]);
if (ch_surplus < 3) {
output[0] = mad(input[0].y, filter_trans[1], output[0]);
output[1] = mad(input[1].y, filter_trans[1], output[1]);
output[2] = mad(input[2].y, filter_trans[1], output[2]);
output[3] = mad(input[3].y, filter_trans[1], output[3]);
output[4] = mad(input[4].y, filter_trans[1], output[4]);
}
if (ch_surplus < 2) {
output[0] = mad(input[0].z, filter_trans[2], output[0]);
output[1] = mad(input[1].z, filter_trans[2], output[1]);
output[2] = mad(input[2].z, filter_trans[2], output[2]);
output[3] = mad(input[3].z, filter_trans[2], output[3]);
output[4] = mad(input[4].z, filter_trans[2], output[4]);
}
if (ch_surplus < 1) {
output[0] = mad(input[0].w, filter_trans[3], output[0]);
output[1] = mad(input[1].w, filter_trans[3], output[1]);
output[2] = mad(input[2].w, filter_trans[3], output[2]);
output[3] = mad(input[3].w, filter_trans[3], output[3]);
output[4] = mad(input[4].w, filter_trans[3], output[4]);
}
}
}
}
output[0] = activation_type4(output[0]);
output[1] = activation_type4(output[1]);
output[2] = activation_type4(output[2]);
output[3] = activation_type4(output[3]);
output[4] = activation_type4(output[4]);
WRITE_IMG_TYPE(CL_DTYPE_CHAR,
output_image,
(int2)(out_w_base_id + out_w_id0, item_h_id),
......
lite/kernels/opencl/conv_image_compute.cc
浏览文件 @ b988d1dd
......@@ -142,9 +142,10 @@ void ConvImageCompute::PrepareForRun() {
filter_image_dims[0], filter_image_dims[1], filter_image_v.data());
impl_ = &ConvImageCompute::DepthwiseConv2d;
} else if (kernel_h == 3 && kernel_h == 3) {
} else if (kernel_w == 3 && kernel_h == 3) {
// conv2d_3x3
kernel_func_names_.push_back("conv2d_3x3_opt");
kernel_func_names_.push_back(bs > 1 ? "conv2d_3x3_multi_batch"
: "conv2d_3x3_opt");
kernel_func_paths_.push_back("image/conv2d_3x3_opt_kernel.cl");
CLImageConverterFolder converter;
......@@ -174,7 +175,9 @@ void ConvImageCompute::PrepareForRun() {
impl_ = &ConvImageCompute::Conv2d5x5;
#else
// conv2d_5x5_opt
kernel_func_names_.push_back("conv2d_5x5_opt");
kernel_func_names_.push_back(bs > 1 ? "conv2d_5x5_multi_batch"
: "conv2d_5x5_opt");
kernel_func_paths_.push_back("image/conv2d_5x5_opt_kernel.cl");
CLImageConverterFolder converter;
......@@ -207,7 +210,8 @@ void ConvImageCompute::PrepareForRun() {
#else
// conv2d_7x7
kernel_func_names_.push_back("conv2d_7x7_opt");
kernel_func_names_.push_back(bs > 1 ? "conv2d_7x7_multi_batch"
: "conv2d_7x7_opt");
kernel_func_paths_.push_back("image/conv2d_7x7_opt_kernel.cl");
CLImageConverterFolder converter;
......
lite/kernels/opencl/conv_image_compute.h
浏览文件 @ b988d1dd
......@@ -59,7 +59,7 @@ class ConvImageCompute : public KernelLite<TARGET(kOpenCL),
std::shared_ptr<cl::Event> event_{new cl::Event};
Tensor filter_gpu_image_;
Tensor bias_gpu_image_;
bool use_lws{false};
bool use_lws{true};
};
} // namespace opencl
......
lite/kernels/opencl/conv_image_compute_test.cc
浏览文件 @ b988d1dd
......@@ -510,7 +510,7 @@ TEST(conv2d, compute_image2d_3x3) {
const int dilation = 1;
const int stride = 2;
const int group = 1;
for (int batch_size = 1; batch_size < 2; ++batch_size) {
for (int batch_size = 1; batch_size < 4; ++batch_size) {
for (int oc = 1; oc < 10; oc += 1) { // oc
for (int ih = 5; ih < 9; ih += 1) { // ih
int iw = ih;
......@@ -532,7 +532,7 @@ const int stride = 2;
#else // big scale with group
const int stride = 1;
const int group = 32 / 1;
const int batch_size = 1;
const int batch_size = 2;
const int ic = 32 / 1;
const int ih = 112 / 1;
const int iw = 112 / 1;
......@@ -558,7 +558,8 @@ const int stride = 2;
PRECISION(kFP16),
DATALAYOUT(kImageDefault));
ASSERT_FALSE(kernels.empty());
CHECK(batch_size == 1) << "conv3x3 only supprt batch_size == 1";
// CHECK(batch_size == 1) << "conv3x3 only supprt
// batch_size == 1";
auto kernel = std::move(kernels.front());
SHADOW_LOG << "created conv2d kernel";
......@@ -886,15 +887,16 @@ TEST(conv2d, compute_image2d_5x5) {
// int loop_cnt = 0;
#ifdef LOOP_TEST
for (int batch_size = 1; batch_size < 2; ++batch_size) {
for (int oc = 1; oc < 10; oc += 1) { // oc
for (int ih = 5; ih < 9; ih += 1) { // ih
for (int batch_size = 1; batch_size < 4; ++batch_size) {
for (int oc = 1; oc < 5; oc += 1) { // oc
for (int ih = 5; ih < 8; ih += 1) { // ih
int iw = ih;
for (int ic = 2; ic < 10; ic += 1) { // ic
for (int ic = 2; ic < 6; ic += 1) { // ic
for (bool bias_flag : {true, false}) {
for (std::string relu_flag : {/*true,*/ "relu"}) {
for (std::string relu_flag : {""
"relu"}) {
#else
const int batch_size = 1;
const int batch_size = 2;
const int oc = 1;
const int ih = 5;
const int iw = 5;
......@@ -1231,15 +1233,15 @@ TEST(conv2d, compute_image2d_7x7) {
// int loop_cnt = 0;
#ifdef LOOP_TEST
for (int batch_size = 1; batch_size < 2; ++batch_size) {
for (int oc = 1; oc < 10; oc += 1) { // oc
for (int batch_size = 1; batch_size < 4; ++batch_size) {
for (int oc = 1; oc < 6; oc += 1) { // oc
for (int ih = 7; ih < 8; ih += 1) { // ih
int iw = ih;
for (int ic = 2; ic < 4; ic += 1) { // ic
for (bool bias_flag : {false, true}) {
for (std::string relu_flag : {"", "relu"}) {
#else
const int batch_size = 1;
const int batch_size = 2;
const int oc = 1;
const int ih = 7;
const int iw = 7;
......
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