Merge branch 'conv1x1' into 'master'

Optimize conv1x1 using 2x4 block kernel and add openmp to batch_norm kernel.

See merge request !68

mace/examples/BUILD

@@ -6,26 +6,26 @@ cc_binary(
     srcs = [
         "helloworld.cc",
     ],
+    copts = ["-std=c++11"],
+    linkopts = ["-fopenmp"] + if_android(["-ldl"]),
     deps = [
         "//mace/core",
         "//mace/ops",
         "@org_tensorflow//tensorflow/core:android_tensorflow_lib",
     ],
-    copts = ["-std=c++11"],
-    linkopts = ["-fopenmp",] + if_android(["-ldl"]),
 )
 
 cc_test(
     name = "benchmark_example",
+    testonly = 1,
     srcs = ["benchmark_example.cc"],
+    copts = ["-std=c++11"],
+    linkopts = ["-fopenmp"] + if_android(["-ldl"]),
+    linkstatic = 1,
     deps = [
         "//mace/core",
         "//mace/core:test_benchmark_main",
     ],
-    copts = ["-std=c++11"],
-    linkopts = ["-fopenmp",] + if_android(["-ldl"]),
-    linkstatic = 1,
-    testonly = 1,
 )
 
 cc_binary(
@@ -33,12 +33,12 @@ cc_binary(
     srcs = [
         "mace_run.cc",
     ],
+    copts = ["-std=c++11"],
+    linkopts = ["-fopenmp"] + if_android(["-ldl"]),
+    linkstatic = 1,
     deps = [
         "//mace/core",
-        "//mace/utils",
         "//mace/ops",
+        "//mace/utils:command_line_flags",
     ],
-    copts = ["-std=c++11",],
-    linkopts = ["-fopenmp",] + if_android(["-ldl"]),
-    linkstatic = 1,
 )

mace/kernels/BUILD

@@ -5,7 +5,6 @@ package(
     default_visibility = ["//visibility:public"],
 )
 
-
 licenses(["notice"])  # Apache 2.0
 
 load("//mace:mace.bzl", "if_android")
@@ -14,36 +13,40 @@ cc_library(
     name = "kernels",
     srcs = glob(["*.cc"]) + if_android(glob(["neon/*.cc"])),
     hdrs = glob(["*.h"]) + if_android(glob(["neon/*.h"])),
-    deps = [
-        "//mace/core:core",
+    copts = [
+        "-std=c++11",
+        "-fopenmp",
     ],
-    copts = ['-std=c++11', "-fopenmp",],
     linkopts = if_android(["-lm"]),
+    deps = [
+        "//mace/core",
+        "//mace/utils:utils",
+    ],
 )
 
 cc_test(
     name = "kernel_test",
+    testonly = 1,
     srcs = glob(["test/*.cc"]),
+    copts = ["-std=c++11"],
+    linkopts = if_android(["-pie"]),
+    linkstatic = 1,
     deps = [
-        "@gtest//:gtest_main",
         ":kernels",
-        "//mace/core:core",
+        "//mace/core",
+        "@gtest//:gtest_main",
     ],
-    copts = ['-std=c++11'],
-    linkopts = if_android(["-pie"]),
-    linkstatic = 1,
-    testonly = 1,
 )
 
 cc_test(
     name = "benchmark",
+    testonly = 1,
     srcs = glob(["benchmark/*.cc"]),
+    copts = ["-std=c++11"],
+    linkstatic = 1,
     deps = [
         ":kernels",
-        "//mace/core:core",
+        "//mace/core",
         "//mace/core:test_benchmark_main",
     ],
-    copts = ['-std=c++11'],
-    linkstatic = 1,
-    testonly = 1,
 )

mace/kernels/batch_norm.h

@@ -33,6 +33,7 @@ struct BatchNormFunctor {
     // new_offset = \offset - mean * common_val;
     // Y = new_scale * X + new_offset;
     T new_scale, new_offset;
+#pragma omp parallel for
     for (index_t c = 0; c < channel; ++c) {
       new_scale = scale[c] / std::sqrt(var[c] + variance_epsilon);
       new_offset = offset[c] - mean[c] * new_scale;

mace/kernels/concat.h

@@ -19,7 +19,7 @@ struct ConcatFunctor {
                   T *output) {
     const size_t input_count = input_list.size();
     for (int inner_idx = 0; inner_idx < inner_dim; ++inner_idx) {
-      for (int i = 0; i < input_count; ++i) {
+      for (size_t i = 0; i < input_count; ++i) {
         if (DataTypeCanUseMemcpy(DataTypeToEnum<T>::v())) {
           memcpy(output, input_list[i], outer_dims[i] * sizeof(T));
           output += outer_dims[i];

mace/kernels/neon/batch_norm_neon.cc

@@ -31,6 +31,7 @@ void BatchNormFunctor<DeviceType::NEON, float>::operator()(
   float new_scale, new_offset;
   index_t count = sample_size >> 2;
   index_t remain_count = sample_size - (count << 2);
+#pragma omp parallel for
   for (index_t c = 0; c < channel; ++c) {
     new_scale = scale[c] / std::sqrt(var[c] + variance_epsilon);
     new_offset = offset[c] - mean[c] * new_scale;

mace/kernels/neon/conv_2d_neon_1x1.cc

@@ -4,11 +4,297 @@
 
 #include <arm_neon.h>
 #include "mace/core/common.h"
+#include "mace/utils/utils.h"
 
 namespace mace {
 namespace kernels {
+static constexpr index_t kInputChannelBlockSize = 2;
+static constexpr index_t kOutputChannelBlockSize = 4;
+static __attribute__((__aligned__(64))) int32_t mask_array[8] = {
+    0, 0, 0, 0, -1, -1, -1, -1
+};
+
+static inline void NeonConv2x4Kernel(index_t input_channels,
+                                     index_t pixel_size,
+                                     const float *input,
+                                     const float *filter,
+                                     float *output) {
+  const float *input0 = input;
+  const float *input1 = input + pixel_size;
+
+  const float32x2_t vfilter0x = vld1_f32(filter);
+  filter += input_channels;
+  const float32x2_t vfilter1x = vld1_f32(filter);
+  filter += input_channels;
+  const float32x2_t vfilter2x = vld1_f32(filter);
+  filter += input_channels;
+  const float32x2_t vfilter3x = vld1_f32(filter);
+
+  float *output0 = output;
+  float *output1 = output0 + pixel_size;
+  float *output2 = output1 + pixel_size;
+  float *output3 = output2 + pixel_size;
+  while (pixel_size >= 4) {
+    float32x4_t voutput0 = vld1q_f32(output0);
+    float32x4_t voutput1 = vld1q_f32(output1);
+    float32x4_t voutput2 = vld1q_f32(output2);
+    float32x4_t voutput3 = vld1q_f32(output3);
+
+    const float32x4_t vinput0 = vld1q_f32(input0);
+    input0 += 4;
+    voutput0 = vfmaq_lane_f32(voutput0, vinput0, vfilter0x, 0);
+    voutput1 = vfmaq_lane_f32(voutput1, vinput0, vfilter1x, 0);
+    voutput2 = vfmaq_lane_f32(voutput2, vinput0, vfilter2x, 0);
+    voutput3 = vfmaq_lane_f32(voutput3, vinput0, vfilter3x, 0);
+
+    const float32x4_t vinput1 = vld1q_f32(input1);
+    input1 += 4;
+    voutput0 = vfmaq_lane_f32(voutput0, vinput1, vfilter0x, 1);
+    voutput1 = vfmaq_lane_f32(voutput1, vinput1, vfilter1x, 1);
+    voutput2 = vfmaq_lane_f32(voutput2, vinput1, vfilter2x, 1);
+    voutput3 = vfmaq_lane_f32(voutput3, vinput1, vfilter3x, 1);
+
+    vst1q_f32(output0, voutput0);
+    output0 += 4;
+    vst1q_f32(output1, voutput1);
+    output1 += 4;
+    vst1q_f32(output2, voutput2);
+    output2 += 4;
+    vst1q_f32(output3, voutput3);
+    output3 += 4;
+
+    pixel_size -= 4;
+  }
+  if (pixel_size != 0) {
+    const int32x4_t vmask = vld1q_s32(&mask_array[pixel_size]);
+
+    output0 = output0 + pixel_size - 4;
+    float32x4_t voutput0 = vld1q_f32(output0);
+    output1 = output1 + pixel_size - 4;
+    float32x4_t voutput1 = vld1q_f32(output1);
+    output2 = output2 + pixel_size - 4;
+    float32x4_t voutput2 = vld1q_f32(output2);
+    output3 = output3 + pixel_size - 4;
+    float32x4_t voutput3 = vld1q_f32(output3);
+
+    const float32x4_t vinput0 = vreinterpretq_f32_s32(
+        vandq_s32(vmask, vreinterpretq_s32_f32(vld1q_f32(&input0[pixel_size - 4]))));
+    voutput0 = vfmaq_lane_f32(voutput0, vinput0, vfilter0x, 0);
+    voutput1 = vfmaq_lane_f32(voutput1, vinput0, vfilter1x, 0);
+    voutput2 = vfmaq_lane_f32(voutput2, vinput0, vfilter2x, 0);
+    voutput3 = vfmaq_lane_f32(voutput3, vinput0, vfilter3x, 0);
+
+    const float32x4_t vinput1 = vreinterpretq_f32_s32(
+        vandq_s32(vmask, vreinterpretq_s32_f32(vld1q_f32(&input1[pixel_size - 4]))));
+    voutput0 = vfmaq_lane_f32(voutput0, vinput1, vfilter0x, 1);
+    voutput1 = vfmaq_lane_f32(voutput1, vinput1, vfilter1x, 1);
+    voutput2 = vfmaq_lane_f32(voutput2, vinput1, vfilter2x, 1);
+    voutput3 = vfmaq_lane_f32(voutput3, vinput1, vfilter3x, 1);
+
+    vst1q_f32(output0, voutput0);
+    vst1q_f32(output1, voutput1);
+    vst1q_f32(output2, voutput2);
+    vst1q_f32(output3, voutput3);
+  }
+}
+
+static inline void NeonConv2x4SubBlockKernel(index_t input_channels_subblock_size,
+                                             index_t output_channels_subblock_size,
+                                             index_t input_channels,
+                                             index_t pixel_size,
+                                             const float *input,
+                                             const float *filter,
+                                             float *output) {
+  const float *input0 = input;
+  const float *input1 = input + pixel_size;
+
+  float32x2_t vfilter0x, vfilter1x, vfilter2x, vfilter3x;
+  vfilter0x = vld1_dup_f32(&filter[0]);
+  if (input_channels_subblock_size > 1) {
+    vfilter0x = vld1_lane_f32(&filter[1], vfilter0x, 1);
+  }
+  if (output_channels_subblock_size > 1) {
+    filter += input_channels;
+    vfilter1x = vld1_dup_f32(&filter[0]);
+    if (input_channels_subblock_size > 1) {
+      vfilter1x = vld1_lane_f32(&filter[1], vfilter1x, 1);
+    }
+    if (output_channels_subblock_size > 2) {
+      filter += input_channels;
+      vfilter2x = vld1_dup_f32(&filter[0]);
+      if (input_channels_subblock_size > 1) {
+        vfilter2x = vld1_lane_f32(&filter[1], vfilter2x, 1);
+      }
+      if (output_channels_subblock_size > 3) {
+        filter += input_channels;
+        vfilter3x = vld1_dup_f32(&filter[0]);
+        if (input_channels_subblock_size > 1) {
+          vfilter3x = vld1_lane_f32(&filter[1], vfilter3x, 1);
+        }
+      }
+    }
+  }
+
+  float *output0 = output;
+  float *output1 = output0 + pixel_size;
+  float *output2 = output1 + pixel_size;
+  float *output3 = output2 + pixel_size;
+  while (pixel_size >= 4) {
+    float32x4_t voutput0, voutput1, voutput2, voutput3;
+    voutput0 = vld1q_f32(output0);
+    if (output_channels_subblock_size > 1) {
+      voutput1 = vld1q_f32(output1);
+      if (output_channels_subblock_size > 2) {
+        voutput2 = vld1q_f32(output2);
+        if (output_channels_subblock_size > 3) {
+          voutput3 = vld1q_f32(output3);
+        }
+      }
+    }
+
+    const float32x4_t vinput0 = vld1q_f32(input0);
+    input0 += 4;
+    voutput0 = vfmaq_lane_f32(voutput0, vinput0, vfilter0x, 0);
+    voutput1 = vfmaq_lane_f32(voutput1, vinput0, vfilter1x, 0);
+    voutput2 = vfmaq_lane_f32(voutput2, vinput0, vfilter2x, 0);
+    voutput3 = vfmaq_lane_f32(voutput3, vinput0, vfilter3x, 0);
+
+    if (input_channels_subblock_size > 1) {
+      const float32x4_t vinput1 = vld1q_f32(input1);
+      input1 += 4;
+      voutput0 = vfmaq_lane_f32(voutput0, vinput1, vfilter0x, 1);
+      voutput1 = vfmaq_lane_f32(voutput1, vinput1, vfilter1x, 1);
+      voutput2 = vfmaq_lane_f32(voutput2, vinput1, vfilter2x, 1);
+      voutput3 = vfmaq_lane_f32(voutput3, vinput1, vfilter3x, 1);
+    }
+
+    vst1q_f32(output0, voutput0);
+    output0 += 4;
+    if (output_channels_subblock_size > 1) {
+      vst1q_f32(output1, voutput1);
+      output1 += 4;
+      if (output_channels_subblock_size > 2) {
+        vst1q_f32(output2, voutput2);
+        output2 += 4;
+        if (output_channels_subblock_size > 3) {
+          vst1q_f32(output3, voutput3);
+          output3 += 4;
+        }
+      }
+    }
+
+    pixel_size -= 4;
+  }
+  if (pixel_size != 0) {
+    const int32x4_t vmask = vld1q_s32(&mask_array[pixel_size]);
+
+    float32x4_t voutput0, voutput1, voutput2, voutput3;
+    output0 += pixel_size - 4;
+    voutput0 = vld1q_f32(output0);
+    if (output_channels_subblock_size > 1) {
+      output1 += pixel_size - 4;
+      voutput1 = vld1q_f32(output1);
+      if (output_channels_subblock_size > 2) {
+        output2 += pixel_size - 4;
+        voutput2 = vld1q_f32(output2);
+        if (output_channels_subblock_size > 3) {
+          output3 += pixel_size - 4;
+          voutput3 = vld1q_f32(output3);
+        }
+      }
+    }
+
+    const float32x4_t vinput0 = vreinterpretq_f32_s32(
+        vandq_s32(vmask, vreinterpretq_s32_f32(vld1q_f32(&input0[pixel_size - 4]))));
+    voutput0 = vfmaq_lane_f32(voutput0, vinput0, vfilter0x, 0);
+    voutput1 = vfmaq_lane_f32(voutput1, vinput0, vfilter1x, 0);
+    voutput2 = vfmaq_lane_f32(voutput2, vinput0, vfilter2x, 0);
+    voutput3 = vfmaq_lane_f32(voutput3, vinput0, vfilter3x, 0);
+
+    if (input_channels_subblock_size > 1) {
+      const float32x4_t vinput1 = vreinterpretq_f32_s32(
+          vandq_s32(vmask, vreinterpretq_s32_f32(vld1q_f32(&input1[pixel_size - 4]))));
+      voutput0 = vfmaq_lane_f32(voutput0, vinput1, vfilter0x, 1);
+      voutput1 = vfmaq_lane_f32(voutput1, vinput1, vfilter1x, 1);
+      voutput2 = vfmaq_lane_f32(voutput2, vinput1, vfilter2x, 1);
+      voutput3 = vfmaq_lane_f32(voutput3, vinput1, vfilter3x, 1);
+    }
+
+    vst1q_f32(output0, voutput0);
+    if (output_channels_subblock_size > 1) {
+      vst1q_f32(output1, voutput1);
+      if (output_channels_subblock_size > 2) {
+        vst1q_f32(output2, voutput2);
+        if (output_channels_subblock_size > 3) {
+          vst1q_f32(output3, voutput3);
+        }
+      }
+    }
+  }
+}
 
 void Conv2dNeonK1x1S1(const float *input,  // NCHW
+                      const index_t *input_shape,
+                      const float *filter,  // c_out, c_in, filter_h, filter_w
+                      const index_t *filter_shape,
+                      const float *bias,    // c_out
+                      float *output,        // NCHW
+                      const index_t *output_shape) {
+  const index_t batch = output_shape[0];
+  const index_t channels = output_shape[1];
+  const index_t height = output_shape[2];
+  const index_t width = output_shape[3];
+
+  const index_t input_batch = input_shape[0];
+  const index_t input_channels = input_shape[1];
+  const index_t input_height = input_shape[2];
+  const index_t input_width = input_shape[3];
+
+  MACE_CHECK(input_batch == batch && input_height == height &&
+      input_width == width);
+
+  const index_t total_pixels = height * width;
+  const index_t round_up_channels = RoundUp(channels, kOutputChannelBlockSize);
+
+#pragma omp parallel for collapse(2)
+  for (index_t n = 0; n < batch; ++n) {
+    for (int i = 0; i < channels; ++i) {
+      float *output_ptr_base = output + n * channels * total_pixels + i * total_pixels;
+      std::fill(output_ptr_base, output_ptr_base + total_pixels, bias ? bias[i] : 0);
+    }
+  }
+  // benchmark omp collapsed(2)
+#pragma omp parallel for collapse(2)
+  for (index_t n = 0; n < batch; ++n) {
+    for (index_t c = 0; c < round_up_channels; c += kOutputChannelBlockSize) {
+      const float *input_ptr = input + n * input_channels * total_pixels;
+      const float *filter_ptr = filter + c * input_channels;
+      float *output_ptr = output + n * channels * total_pixels + c * total_pixels;
+      const index_t output_channel_block_size = std::min(channels - c, kOutputChannelBlockSize);
+      index_t remain_input_channels = input_channels;
+      if (c + kOutputChannelBlockSize <= channels) {
+        while (remain_input_channels >= kInputChannelBlockSize) {
+          NeonConv2x4Kernel(input_channels, total_pixels, input_ptr, filter_ptr, output_ptr);
+
+          input_ptr += kInputChannelBlockSize * total_pixels;
+          filter_ptr += kInputChannelBlockSize;
+          remain_input_channels -= kInputChannelBlockSize;
+        }
+      }
+      while (remain_input_channels != 0) {
+        const index_t input_channel_block_size = std::min(remain_input_channels, kInputChannelBlockSize);
+        NeonConv2x4SubBlockKernel(input_channel_block_size, output_channel_block_size,
+                                  input_channels, total_pixels, input_ptr, filter_ptr, output_ptr);
+        input_ptr += kInputChannelBlockSize * total_pixels;
+        filter_ptr += kInputChannelBlockSize;
+        remain_input_channels -= input_channel_block_size;
+      }
+
+    }
+  }
+};
+
+void Conv2dNeonPixelK1x1S1(const float *input,  // NCHW
                       const index_t *input_shape,
                       const float *filter,  // c_out, c_in, kernel_h, kernel_w
                       const index_t *filter_shape,

mace/ops/BUILD

@@ -34,7 +34,10 @@ cc_library(
         ["*.h"],
         exclude = ["ops_test_util.h"],
     ),
-    copts = ["-std=c++11", "-fopenmp",],
+    copts = [
+        "-std=c++11",
+        "-fopenmp",
+    ],
     deps = [
         "//mace/core",
         "//mace/kernels",
@@ -50,7 +53,7 @@ cc_test(
         ["*_test.cc"],
     ),
     copts = ["-std=c++11"],
-    linkopts = ["-fopenmp",] + if_android(["-ldl"]),
+    linkopts = ["-fopenmp"] + if_android(["-ldl"]),
     linkstatic = 1,
     deps = [
         ":ops",
@@ -64,7 +67,7 @@ cc_test(
     testonly = 1,
     srcs = glob(["*_benchmark.cc"]),
     copts = ["-std=c++11"],
-    linkopts = ["-fopenmp",] + if_android(["-ldl"]),
+    linkopts = ["-fopenmp"] + if_android(["-ldl"]),
     linkstatic = 1,
     deps = [
         ":ops",

mace/ops/batch_norm_benchmark.cc

@@ -57,14 +57,16 @@ static void BatchNorm(
   BM_BATCH_NORM_MACRO(N, C, H, W, TYPE, NEON);
 
 BM_BATCH_NORM(1, 1, 512, 512, float);
-BM_BATCH_NORM(1, 1, 1024, 1024, float);
 BM_BATCH_NORM(1, 3, 128, 128, float);
 BM_BATCH_NORM(1, 3, 512, 512, float);
-BM_BATCH_NORM(1, 3, 1024, 1024, float);
+BM_BATCH_NORM(1, 32, 112, 112, float);
 BM_BATCH_NORM(1, 64, 256, 256, float);
 BM_BATCH_NORM(1, 64, 512, 512, float);
+BM_BATCH_NORM(1, 128, 56, 56, float);
 BM_BATCH_NORM(1, 128, 256, 256, float);
-BM_BATCH_NORM(1, 128, 512, 512, float);
+BM_BATCH_NORM(1, 256, 14, 14, float);
+BM_BATCH_NORM(1, 512, 14, 14, float);
+BM_BATCH_NORM(1, 1024, 7, 7, float);
 BM_BATCH_NORM(32, 1, 256, 256, float);
 BM_BATCH_NORM(32, 3, 256, 256, float);
 }  //  namespace mace
\ No newline at end of file

mace/ops/conv_2d_benchmark.cc

@@ -72,6 +72,11 @@ static void Conv2d(int iters,
 
 BM_CONV_2D(1, 64, 32, 32, 1, 1, 1, VALID, 128, float);
 BM_CONV_2D(1, 64, 33, 31, 1, 1, 1, VALID, 128, float);  // Test bad alignments
+BM_CONV_2D(1, 3, 512, 512, 1, 1, 1, VALID, 3, float);
+BM_CONV_2D(1, 32, 112, 112, 1, 1, 1, VALID, 64, float);
+BM_CONV_2D(1, 64, 56, 56, 1, 1, 1, VALID, 128, float);
+BM_CONV_2D(1, 256, 28, 28, 1, 1, 1, VALID, 256, float);
+BM_CONV_2D(1, 1024, 7, 7, 1, 1, 1, VALID, 1024, float);
 BM_CONV_2D(1, 64, 32, 32, 3, 3, 1, VALID, 128, float);
 BM_CONV_2D(1, 64, 33, 31, 3, 3, 1, VALID, 128, float);
 BM_CONV_2D(1, 3, 512, 512, 3, 3, 1, VALID, 3, float);
@@ -86,5 +91,4 @@ BM_CONV_2D(1, 64, 32, 32, 5, 5, 1, VALID, 128, float);
 BM_CONV_2D(1, 64, 32, 31, 5, 5, 1, VALID, 128, float);
 BM_CONV_2D(1, 64, 32, 32, 5, 5, 1, SAME, 128, float);
 BM_CONV_2D(1, 64, 32, 31, 5, 5, 1, SAME, 128, float);
-
 }  //  namespace mace

mace/ops/conv_2d_test.cc

@@ -165,18 +165,69 @@ TEST_F(Conv2dOpTest, Conv1x1) {
 }
 
 // TODO we need more tests
-TEST_F(Conv2dOpTest, ConvNxNS12) {
+TEST_F(Conv2dOpTest, IdleConvNxNS12) {
   testing::internal::LogToStderr();
   auto func = [&](int kernel_h, int kernel_w, int stride_h, int stride_w,
                   Padding type) {
     srand(time(NULL));
 
     // generate random input
-    index_t batch = 1 + rand() % 10;
-    index_t input_channels = 1 + rand() % 10;
+    index_t batch = 3 ;
+    index_t input_channels = 64;
+    index_t height = 32;
+    index_t width = 32;
+    index_t output_channels = 128;
+    // Construct graph
+    auto& net = test_net();
+    OpDefBuilder("Conv2D", "Conv2dTest")
+        .Input("Input")
+        .Input("Filter")
+        .Input("Bias")
+        .Output("Output")
+        .Finalize(net.operator_def());
+
+    // Add args
+    net.AddIntsArg("strides", {stride_h, stride_w});
+    net.AddIntArg("padding", type);
+    net.AddIntsArg("dilations", {1, 1});
+
+    // Add input data
+    net.AddRandomInput<float>("Input", {batch, input_channels, height, width});
+    net.AddRandomInput<float>(
+        "Filter", {output_channels, input_channels, kernel_h, kernel_w});
+    net.AddRandomInput<float>("Bias", {output_channels});
+    // run cpu
+    net.RunOp();
+
+    // Check
+    Tensor expected;
+    expected.Copy(*net.GetOutput("Output"));
+
+    // Run NEON
+    net.RunOp(DeviceType::NEON);
+    ExpectTensorNear<float>(expected, *net.GetOutput("Output"), 0.001);
+  };
+
+  for (int kernel_size : {1}) {
+    for (int stride : {1}) {
+      func(kernel_size, kernel_size, stride, stride, VALID);
+      func(kernel_size, kernel_size, stride, stride, SAME);
+    }
+  }
+}
+
+TEST_F(Conv2dOpTest, DisgustConvNxNS12) {
+  testing::internal::LogToStderr();
+  auto func = [&](int kernel_h, int kernel_w, int stride_h, int stride_w,
+                  Padding type) {
+    srand(time(NULL));
+
+    // generate random input
+    index_t batch = 3 + rand() % 10;
+    index_t input_channels = 3 + rand() % 10;
     index_t height = 107;
     index_t width = 113;
-    index_t output_channels = 1 + rand() % 10;
+    index_t output_channels = 3 + rand() % 10;
     // Construct graph
     auto& net = test_net();
     OpDefBuilder("Conv2D", "Conv2dTest")

mace/utils/BUILD

@@ -8,15 +8,23 @@ package(
 licenses(["notice"])  # Apache 2.0
 
 cc_library(
-    name = "utils",
-    srcs = glob([
-        "*.cc",
-    ]),
-    hdrs = glob([
-        "*.h",
-    ]),
+    name = "command_line_flags",
+    srcs = [
+        "command_line_flags.cc",
+    ],
+    hdrs = [
+        "command_line_flags.h",
+    ],
     copts = ["-std=c++11"],
     deps = [
-        "//mace/core:core",
+        "//mace/core",
+    ],
+)
+
+cc_library(
+    name = "utils",
+    hdrs = [
+        "utils.h",
     ],
-)
\ No newline at end of file
+    copts = ["-std=c++11"],
+)

mace/utils/utils.h

+//
+// Copyright (c) 2017 XiaoMi All rights reserved.
+//
+
+#ifndef MACE_UTILS_UTILS_H_
+#define MACE_UTILS_UTILS_H_
+namespace mace {
+template <typename Integer>
+Integer RoundUp(Integer i, Integer factor) {
+  return (i + factor - 1) / factor * factor;
+}
+
+template <typename Integer>
+Integer CeilQuotient(Integer a, Integer b) {
+  return (a + b - 1) / b;
+}
+} //  namespace mace
+#endif //  MACE_UTILS_UTILS_H_