Merge branch 'yuanshuai/enable-armv7' into 'incubate/lite'

[LITE] Fix bug of global max pooling

See merge request inference/paddlelite!36

paddle/fluid/lite/arm/math/pooling.cc

@@ -218,23 +218,8 @@ void pooling_global(const void* din, void* dout, int num, int chout, int hout,
   int size_channel_in = win * hin;
   float* data_out = static_cast<float*>(dout);
   const float* data_in = static_cast<const float*>(din);
-
   int cnt = size_channel_in / 8;
 
-#if 0
-    LOG(INFO) << "size_channel_in:" << size_channel_in;
-    LOG(INFO) << "cnt:" << cnt;
-    LOG(INFO) << "num:" << num;
-    LOG(INFO) << "chout:" << chout;
-    LOG(INFO) << "hout:" << hout;
-    LOG(INFO) << "wout:" << wout;
-
-    LOG(INFO) << "chin:" << chin;
-    LOG(INFO) << "hin:" << hin;
-    LOG(INFO) << "win:" << win;
-    LOG(INFO) << "pooling_type " << pooling_type;
-#endif
-
   for (int n = 0; n < num; ++n) {
     float* data_out_batch = data_out + n * chout;
     const float* data_in_batch = data_in + n * chin * size_channel_in;
@@ -254,24 +239,12 @@ void pooling_global(const void* din, void* dout, int num, int chout, int hout,
           data_in_channel += 8;
         }
 #else
-        int num = cnt;
-        if (num > 0) {
-          asm volatile(
-              "max_loop:                                        @main loop\n"
-              "vld1.f32   {d0-d1}, [%[data_in_channel]]!        @load q1, "
-              "data_in_channel\n"
-              "vmax.f32   %q[vmax], %q[vmax], q0                @max vmax, "
-              "vmax, data_in_channel\n"
-              "vld1.f32   {d2-d3}, [%[data_in_channel]]!        @ load 2nd 4 "
-              "data"
-              "vmax.f32   %q[vmax], %q[vmax], q1                @ compare 2nd "
-              "4 datas\n"
-              "subs       %[num], #1                            @subs num, 1\n"
-              "bne        max_loop                              @bne num\n"
-              : [data_in_channel] "+r"(data_in_channel), [num] "+r"(num),
-                [vmax] "+w"(vmax)
-              :
-              : "cc", "memory", "q0", "q1");
+        for (; i < cnt; i++) {
+          float32x4_t vdin1 = vld1q_f32(data_in_channel);
+          vmax = vmaxq_f32(vdin1, vmax);
+          float32x4_t vdin2 = vld1q_f32(data_in_channel + 4);
+          vmax = vmaxq_f32(vmax, vdin2);
+          data_in_channel += 8;
         }
 #endif  // __aarch64__
         float32x2_t vmax_tmp =

paddle/fluid/lite/kernels/arm/pool_compute_test.cc

@@ -25,6 +25,43 @@ namespace lite {
 namespace kernels {
 namespace arm {
 
+int PoolOutputSize(int input_size, int filter_size, int padding, int stride,
+                   bool ceil_mode) {
+  int output_size;
+  if (!ceil_mode) {
+    output_size = (input_size - filter_size + 2 * padding) / stride + 1;
+  } else {
+    output_size =
+        (input_size - filter_size + 2 * padding + stride - 1) / stride + 1;
+  }
+  return output_size;
+}
+
+std::vector<int64_t> compute_output_shape(operators::PoolParam* param_) {
+  const auto x_dims = param_->x->dims();
+  std::vector<int>& ksize = param_->ksize;
+  if (param_->global_pooling) {
+    ksize.resize(static_cast<size_t>(x_dims.size()) - 2);
+    for (size_t i = 0; i < ksize.size(); ++i) {
+      param_->paddings[i] = 0;
+      ksize[i] = static_cast<int>(x_dims[i + 2]);
+    }
+  }
+
+  std::vector<int64_t> output_shape({x_dims[0], x_dims[1]});
+  if (param_->adaptive) {
+    output_shape.insert(output_shape.end(), param_->ksize.begin(),
+                        param_->ksize.end());
+  } else {
+    for (size_t i = 0; i < param_->ksize.size(); ++i) {
+      output_shape.push_back(
+          PoolOutputSize(x_dims[i + 2], param_->ksize[i], param_->paddings[i],
+                         param_->strides[i], param_->ceil_mode));
+    }
+  }
+  return output_shape;
+}
+
 void pool_compute_ref(const operators::PoolParam& param) {
   auto& in_dims = param.x->dims();
   auto& out_dims = param.output->dims();
@@ -66,33 +103,28 @@ void pool_compute_ref(const operators::PoolParam& param) {
   if (global_pooling == true) {
     ksize[0] = in_h;
     ksize[1] = in_w;
-  }
 
-#if 0
-  for (int i = 0; i < ksize.size(); ++i) {
-    LOG(INFO) << "ksize[" << i << "]:" << ksize[i];
-  }
-  for (int i = 0; i < strides.size(); ++i) {
-    LOG(INFO) << "strides[" << i << "]:" << strides[i];
-  }
-  for (int i = 0; i < paddings.size(); ++i) {
-    LOG(INFO) << "paddings[" << i << "]:" << paddings[i];
+    for (int n = 0; n < in_n; ++n) {
+      for (int c = 0; c < in_c; ++c) {
+        const float* src = src_ptr + n * in_c * in_h * in_w + c * in_h * in_w;
+        float res = src[0];
+        if (pooling_type == "max") {
+          for (int i = 1; i < in_h * in_w; ++i) {
+            float cur_val = src[i];
+            res = cur_val > res ? cur_val : res;
+          }
+        } else if (pooling_type == "avg") {
+          for (int i = 1; i < in_h * in_w; ++i) {
+            float cur_val = src[i];
+            res += cur_val;
+          }
+          res /= (in_h * in_w);
+        }
+        dst_ptr[n * in_c * out_h * out_w + c] = res;
+      }
+    }
+    return;
   }
-  LOG(INFO) << "in nchw:" << in_n << ", " << in_c << ", " << in_h << ", "
-            << in_w;
-  LOG(INFO) << "size_in_n:" << size_in_n;
-  LOG(INFO) << "size_out_c:" << size_out_c;
-  LOG(INFO) << "out_h:" << out_h;
-  LOG(INFO) << "out_w:" << out_w;
-  LOG(INFO) << "size_out_n:" << size_out_n;
-  LOG(INFO) << "size_out_c:" << size_out_c;
-  LOG(INFO) << "window_h:" << window_h;
-  LOG(INFO) << "window_w:" << window_w;
-  LOG(INFO) << "stride_h:" << stride_h;
-  LOG(INFO) << "stride_w:" << stride_w;
-  LOG(INFO) << "pad_h:" << pad_h;
-  LOG(INFO) << "pad_w:" << pad_w;
-#endif
 
   for (int ind_n = 0; ind_n < in_n; ++ind_n) {
     for (int ind_c = 0; ind_c < in_c; ++ind_c) {
@@ -179,21 +211,21 @@ TEST(pool_arm, compute) {
 
   for (auto pooling_type : {"avg", "max"}) {
     for (auto global_pooling : {true}) {
-      for (auto stride : {2}) {
-        for (auto pad : {0}) {
+      // for (auto ksize: {3}) { // TODO(yuanshuai): ksize enable 2, 3
+      for (auto stride : {1, 2}) {
+        for (auto pad : {0, 1}) {
           for (auto n : {1, 3, 4, 11}) {
             for (auto c : {1, 3, 11 /* ,1024 */}) {  // speedup for ci
-              for (auto h : {3, 1, 11, 4, 1}) {
-                for (auto w : {1, 3, 4, 12, 1}) {
-                  VLOG(3) << "n:" << n << " c:" << c << " h:" << h << " w:" << w
-                          << " stride:" << stride << " pad:" << pad
-                          << " pooling_type:" << pooling_type
-                          << " global_pooling:" << global_pooling;
+              for (auto h : {2, 3, 4, 11}) {
+                for (auto w : {2, 3, 4, 11}) {
+                  LOG(INFO) << "n:" << n << " c:" << c << " h:" << h
+                            << " w:" << w  // << " ksize:" << ksize
+                            << " stride:" << stride << " pad:" << pad
+                            << " pooling_type:" << pooling_type
+                            << " global_pooling:" << global_pooling;
 
                   // init x, output
                   x.Resize(DDim(std::vector<int64_t>({n, c, h, w})));
-                  output.Resize(DDim(std::vector<int64_t>({n, c, 1, 1})));
-                  output_ref.Resize(DDim(std::vector<int64_t>({n, c, 1, 1})));
                   auto* x_data = x.mutable_data<float>();
                   for (int i = 0; i < x.dims().production(); ++i) {
                     x_data[i] = i;
@@ -203,6 +235,8 @@ TEST(pool_arm, compute) {
                   param.x = &x;
                   param.output = &output;
                   param.pooling_type = pooling_type;
+                  // param.ksize = {ksize, ksize}; //TODO(yuanshuai): ksize
+                  // enable
                   param.ksize = {h, w};
                   param.global_pooling = global_pooling;
                   param.strides = {stride, stride};
@@ -212,41 +246,40 @@ TEST(pool_arm, compute) {
                   param.ceil_mode = false;
                   param.use_quantizer = false;
 
+                  const std::vector<int64_t>& output_shape =
+                      compute_output_shape(&param);
+                  output.Resize(DDim(output_shape));
+                  output_ref.Resize(DDim(output_shape));
+
                   // compute
                   pool.SetParam(param);
                   pool.Run();
 
-#if 0
-          LOG(INFO) << "n:" << n << " c:" << c << " h:" << h << " w:" << w
-                    << " end";
-          std::cout << "n:" << n << " c:" << c << " h:" << h << " w:" << w
-                    << " end" << std::endl;
-          for (int i = 0; i < param.ksize.size(); ++i) {
-            std::cout << " ksize[" << i << "]:" << param.ksize[i];
-          }
-          std::cout << "\n";
-          for (int i = 0; i < param.strides.size(); ++i) {
-            std::cout << " strides[" << i << "]:" << param.strides[i];
-          }
-          std::cout << "\n";
-          for (int i = 0; i < param.paddings.size(); ++i) {
-            std::cout << " paddings[" << i << "]:" << param.paddings[i];
-          }
-          std::cout << "\n";
-#endif
-
                   // compute ref
-                  // output_ref.Resize(output.dims());
                   param.output = &output_ref;
                   pool_compute_ref(param);
-                  VLOG(3) << "pool_compute_ref(param) end";
 
                   // compare
                   auto* output_data = output.mutable_data<float>();
                   auto* output_ref_data = output_ref.mutable_data<float>();
                   for (int i = 0; i < output.dims().production(); i++) {
-                    EXPECT_NEAR(output_data[i], output_ref_data[i],
-                                1);  // 1e-5);
+                    EXPECT_NEAR(output_data[i], output_ref_data[i], 1e-5);
+                    float tmp = output_data[i] - output_ref_data[i];
+                    tmp = tmp < 0 ? -tmp : tmp;
+                    if (tmp > 1e-5) {
+                      std::cout << "output_data[0]:" << output_data[0]
+                                << std::endl;
+                      std::cout << "output_ref_data[0]:" << output_ref_data[0]
+                                << std::endl;
+                      std::cout
+                          << "x.dims().production():" << x.dims().production()
+                          << std::endl;
+                      for (int ii = 0; ii < x.dims().production(); ++ii) {
+                        std::cout << x_data[ii] << " ";
+                      }
+                      std::cout;
+                      exit(0);
+                    }
                   }
 
                   VLOG(3) << "compare pass";
@@ -256,6 +289,7 @@ TEST(pool_arm, compute) {
           }
         }  // pad
       }    // stride
+           //} // ksize TODO(yuanshuai): ksize enable
     }      // global_pooling
   }        // pooling_type
 }

paddle/fluid/lite/tools/build.sh

@@ -214,7 +214,7 @@ function test_arm {
         echo "android do not need armv7hf"
         return 0
     fi
-
+    
     # TODO(yuanshuai): enable armv7 on android
     if [[ ${abi} == "armv7" ]]; then
         echo "skip android v7 test yet"