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From wang...@apache.org
Subject incubator-singa git commit: SINGA-190 - Add prelu layer and flatten layer
Date Tue, 14 Jun 2016 05:31:27 GMT
Repository: incubator-singa
Updated Branches:
  refs/heads/dev f488070e3 -> a4d9aab52


SINGA-190 - Add prelu layer and flatten layer

Add test for PReLU using cudagpu device.


Project: http://git-wip-us.apache.org/repos/asf/incubator-singa/repo
Commit: http://git-wip-us.apache.org/repos/asf/incubator-singa/commit/a4d9aab5
Tree: http://git-wip-us.apache.org/repos/asf/incubator-singa/tree/a4d9aab5
Diff: http://git-wip-us.apache.org/repos/asf/incubator-singa/diff/a4d9aab5

Branch: refs/heads/dev
Commit: a4d9aab52db083f18b42f6231dda93e835dba609
Parents: f488070
Author: jixin <jixin@comp.nus.edu.sg>
Authored: Tue Jun 14 13:27:31 2016 +0800
Committer: jixin <jixin@comp.nus.edu.sg>
Committed: Tue Jun 14 13:27:31 2016 +0800

----------------------------------------------------------------------
 src/model/layer/prelu.cc |   7 +--
 src/model/layer/prelu.h  |   1 +
 test/singa/test_prelu.cc | 109 ++++++++++++++++++++++++++++++++++++++++++
 3 files changed, 114 insertions(+), 3 deletions(-)
----------------------------------------------------------------------


http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/a4d9aab5/src/model/layer/prelu.cc
----------------------------------------------------------------------
diff --git a/src/model/layer/prelu.cc b/src/model/layer/prelu.cc
index b916bed..6766d75 100644
--- a/src/model/layer/prelu.cc
+++ b/src/model/layer/prelu.cc
@@ -41,7 +41,7 @@ const Tensor PReLU::Forward(int flag, const Tensor &input) {
         h = temp.shape(2);
         w = temp.shape(3);
         temp.Reshape(Shape{n * c, h * w});
-        Tensor temp_a(Shape{n, c});
+        Tensor temp_a(Shape{n, c}, input.device(), input.data_type());
         Uniform(1.f, 1.f, &temp_a);
         MultRow(a_, &temp_a);
         temp_a.Reshape(Shape{n * c});
@@ -87,7 +87,7 @@ const std::pair<Tensor, vector<Tensor> > PReLU::Backward(int
flag,
         h = temp1.shape(2);
         w = temp1.shape(3);
         temp1.Reshape(Shape{n * c, h * w});
-        Tensor temp_a(Shape{n, c});
+        Tensor temp_a(Shape{n, c}, grad.device(), grad.data_type());
         Uniform(1.f, 1.f, &temp_a);
         MultRow(a_, &temp_a);
         temp_a.Reshape(Shape{n * c});
@@ -108,8 +108,9 @@ const std::pair<Tensor, vector<Tensor> > PReLU::Backward(int
flag,
       LOG(FATAL) << "Incorrect input format for prelu layer.";
     }
     input_grad = grad * input * ((input > 0.f) + temp1);
-    Tensor temp2 = grad * input * (input <= 0.f), temp3(Shape{n * c});
+    Tensor temp2 = grad * input * (input <= 0.f);
     if (format_ == "NCHW") {
+      Tensor temp3(Shape{n * c}, grad.device(), grad.data_type());
       temp2.Reshape(Shape{n * c, h * w});
       SumColumns(temp2, &temp3);
       temp3.Reshape(Shape{n, c});

http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/a4d9aab5/src/model/layer/prelu.h
----------------------------------------------------------------------
diff --git a/src/model/layer/prelu.h b/src/model/layer/prelu.h
index d165fe2..2ee5e9f 100644
--- a/src/model/layer/prelu.h
+++ b/src/model/layer/prelu.h
@@ -21,6 +21,7 @@
 #include <string>
 #include <vector>
 #include "singa/model/layer.h"
+#include "singa_config.h"
 
 namespace singa {
 class PReLU : public Layer {

http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/a4d9aab5/test/singa/test_prelu.cc
----------------------------------------------------------------------
diff --git a/test/singa/test_prelu.cc b/test/singa/test_prelu.cc
index 6fc372b..883935d 100644
--- a/test/singa/test_prelu.cc
+++ b/test/singa/test_prelu.cc
@@ -135,3 +135,112 @@ TEST(PReLU, BackwardCPU) {
   for (size_t i = 0; i < n; i++) EXPECT_FLOAT_EQ(dx[i], xptr[i]);
   for (size_t i = 0; i < params; i++) EXPECT_FLOAT_EQ(da[i], aptr[i]);
 }
+
+#ifdef USE_CUDA
+TEST(PReLU, ForwardGPU) {
+  const float x[] = {1.f,  2.f, 3.f,  -2.f, -3.f, -1.f,
+                         -1.f, 2.f, -1.f, -2.f, -2.f, -1.f};
+  size_t n = sizeof(x) / sizeof(float);
+  size_t batchsize = 2, c = 3, h = 2, w = 1;
+  singa::CudaGPU cuda(0, 1);
+  singa::Tensor in(singa::Shape{batchsize, h, w, c}, &cuda);
+  in.CopyDataFromHostPtr<float>(x, n);
+
+  PReLU prelu;
+  singa::LayerConf conf;
+  singa::PReLUConf *preluconf = conf.mutable_prelu_conf();
+  preluconf->set_channel_shared(false);
+  preluconf->set_format("NHWC");
+  prelu.Setup(conf);
+
+  const float neg_slope[] = {0.25f, 0.5f, 0.75f};
+  singa::Tensor a(singa::Shape{c}, &cuda);
+  a.CopyDataFromHostPtr<float>(neg_slope, c);
+  prelu.Set_a(a);
+
+  singa::Tensor out = prelu.Forward(singa::kTrain, in);
+  singa::CppCPU host(0, 1);
+  out.ToDevice(&host);
+  const float *yptr = out.data<const float *>();
+  EXPECT_EQ(n, out.Size());
+
+  float *y = new float[n];
+  size_t div_factor = prelu.Channel_shared() ? c : 1;
+  if (prelu.Format() == "NCHW") {
+    for (size_t i = 0; i < n; i++) {
+      size_t pos = i / (h * w) % c / div_factor;
+      y[i] = std::max(x[i], 0.f) + neg_slope[pos] * std::min(x[i], 0.f);
+    }
+  } else if (prelu.Format() == "NHWC") {
+    for (size_t i = 0; i < n; i++) {
+      size_t pos = i % c / div_factor;
+      y[i] = std::max(x[i], 0.f) + neg_slope[pos] * std::min(x[i], 0.f);
+    }
+  }
+  for (size_t i = 0; i < n; i++) EXPECT_FLOAT_EQ(y[i], yptr[i]);
+}
+
+TEST(PReLU, BackwardGPU) {
+  const float x[] = {1.f,  2.f, 3.f,  -2.f, -3.f, -1.f,
+                           -1.f, 2.f, -1.f, -2.f, -2.f, -1.f};
+  size_t n = sizeof(x) / sizeof(float);
+  size_t batchsize = 2, c = 3, h = 2, w = 1;
+  singa::CudaGPU cuda(0, 1);
+  singa::Tensor in(singa::Shape{batchsize, c, h, w}, &cuda);
+  in.CopyDataFromHostPtr<float>(x, n);
+
+  PReLU prelu;
+  singa::LayerConf conf;
+  singa::PReLUConf *preluconf = conf.mutable_prelu_conf();
+  preluconf->set_channel_shared(false);
+  preluconf->set_format("NCHW");
+  prelu.Setup(conf);
+
+  const float neg_slope[] = {0.25f, 0.5f, 0.75f};
+  singa::Tensor a(singa::Shape{c}, &cuda);
+  a.CopyDataFromHostPtr<float>(neg_slope, c);
+  prelu.Set_a(a);
+
+  singa::Tensor out = prelu.Forward(singa::kTrain, in);
+  const float grad[] = {1.f, 2.f,  -2.f, -1.f, -1.f, -3.f,
+                          2.f, -2.f, 1.f,  1.f,  -2.f, 0.f};
+  singa::Tensor out_diff(singa::Shape{batchsize, c, h, w}, &cuda);
+  out_diff.CopyDataFromHostPtr<float>(grad, n);
+  const auto ret = prelu.Backward(singa::kTrain, out_diff);
+
+  singa::Tensor in_diff = ret.first;
+  singa::CppCPU host(0, 1);
+  in_diff.ToDevice(&host);
+  const float *xptr = in_diff.data<const float *>();
+  singa::Tensor a_diff = ret.second.at(0);
+  a_diff.ToDevice(&host);
+  const float *aptr = a_diff.data<const float *>();
+  float *dx = new float[n];
+  size_t div_factor = prelu.Channel_shared() ? c : 1;
+  size_t params = prelu.Channel_shared() ? 1 : c;
+  float da[] = {0.f, 0.f, 0.f};
+  if (prelu.Format() == "NCHW") {
+    for (size_t i = 0; i < n; i++) {
+      size_t pos = i / (h * w) % c / div_factor;
+      dx[i] = grad[i] *
+                (std::max(x[i], 0.f) + neg_slope[pos] * std::min(x[i], 0.f));
+    }
+    for (size_t i = 0; i < n; i++) {
+      size_t pos = i / (h * w) % c / div_factor;
+      da[pos] += grad[i] * std::min(x[i], 0.f);
+    }
+  } else if (prelu.Format() == "NHWC") {
+    for (size_t i = 0; i < n; i++) {
+      size_t pos = i % c / div_factor;
+      dx[i] = grad[i] *
+        (std::max(x[i], 0.f) + neg_slope[pos] * std::min(x[i], 0.f));
+  }
+    for (size_t i = 0; i < n; i++) {
+      size_t pos = i % c / div_factor;
+      da[pos] += grad[i] * std::min(x[i], 0.f);
+    }
+  }
+  for (size_t i = 0; i < n; i++) EXPECT_FLOAT_EQ(dx[i], xptr[i]);
+  for (size_t i = 0; i < params; i++) EXPECT_FLOAT_EQ(da[i], aptr[i]);
+}
+#endif


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