From 12858baa6c31f646500d9dab26053f5a340cfd0e Mon Sep 17 00:00:00 2001
From: Dong Zhihong <dzhwinter@gmail.com>
Date: Tue, 14 Nov 2017 00:26:43 -0800
Subject: [PATCH] "relauch ci"

---
 paddle/operators/accuracy_op.cu         | 29 ++++++++++++++++++++-----
 python/paddle/v2/framework/evaluator.py |  8 +++----
 2 files changed, 28 insertions(+), 9 deletions(-)

diff --git a/paddle/operators/accuracy_op.cu b/paddle/operators/accuracy_op.cu
index 1776f331053..b575c682f0d 100644
--- a/paddle/operators/accuracy_op.cu
+++ b/paddle/operators/accuracy_op.cu
@@ -24,7 +24,8 @@ using platform::PADDLE_CUDA_NUM_THREADS;
 template <int BlockSize>
 __global__ void AccuracyCudaKernel(const int N, const int D,
                                    const int64_t* Xdata,
-                                   const int64_t* labeldata, float* accuracy) {
+                                   const int64_t* labeldata, int* correct_data,
+                                   float* accuracy) {
   int count = 0;
   __shared__ int total[BlockSize];
 
@@ -43,6 +44,7 @@ __global__ void AccuracyCudaKernel(const int N, const int D,
   // reduce the count with init value 0, and output accuracy.
   int result = thrust::reduce(thrust::device, total, total + BlockSize, 0);
   if (threadIdx.x == 0) {
+    *correct_data = result;
     *accuracy = static_cast<float>(result) / static_cast<float>(N);
   }
 }
@@ -56,31 +58,48 @@ class AccuracyOpCUDAKernel : public framework::OpKernel<T> {
     auto* inference = ctx.Input<Tensor>("Out");
     auto* indices = ctx.Input<Tensor>("Indices");
     auto* label = ctx.Input<Tensor>("Label");
+
     auto* accuracy = ctx.Output<Tensor>("Accuracy");
+    auto* correct = ctx.Output<Tensor>("Correct");
+    auto* total = ctx.Output<Tensor>("Total");
     // FIXME(typhoonzero): only support indices currently
     // if add support for output values, how to detect the data type?
     const int64_t* indices_data = indices->data<int64_t>();
     const int64_t* label_data = label->data<int64_t>();
+
+    int* correct_data = correct->mutable_data<int>(ctx.GetPlace());
+    int* total_data = total->mutable_data<int>(ctx.GetPlace());
     float* accuracy_data = accuracy->mutable_data<float>(ctx.GetPlace());
 
-    size_t num_samples = inference->dims()[0];
+    int num_samples = static_cast<int>(inference->dims()[0]);
     size_t infer_width = inference->dims()[1];
     PADDLE_ENFORCE(cudaMemset(accuracy_data, 0, sizeof(float)));
+    // cudaMemset((void**)&correct_data, 0, sizeof(float));
 
     if (num_samples == 0) {
       return;
     }
+    cudaMemcpy(total_data, &num_samples, sizeof(int), cudaMemcpyHostToDevice);
 
     AccuracyCudaKernel<PADDLE_CUDA_NUM_THREADS><<<
         1, PADDLE_CUDA_NUM_THREADS, 0, ctx.cuda_device_context().stream()>>>(
-        num_samples, infer_width, indices_data, label_data, accuracy_data);
+        num_samples, infer_width, indices_data, label_data, correct_data,
+        accuracy_data);
+
+    int d_num_samples, d_num_correct;
+    float d_accuracy;
+    cudaMemcpy(&d_num_correct, correct_data, sizeof(int),
+               cudaMemcpyDeviceToHost);
+    cudaMemcpy(&d_num_samples, total_data, sizeof(int), cudaMemcpyDeviceToHost);
+    cudaMemcpy(&d_accuracy, accuracy_data, sizeof(float),
+               cudaMemcpyDeviceToHost);
   }
 };
 
 }  // namespace operators
 }  // namespace paddle
 
-// FIXME(typhoonzero): types of T is for infernece data.
-// label data is always int
+// FIXME(typhoonzero): types of T is for inference data.
+// label data is always int64
 REGISTER_OP_GPU_KERNEL(accuracy, paddle::operators::AccuracyOpCUDAKernel<float>,
                        paddle::operators::AccuracyOpCUDAKernel<double>);
diff --git a/python/paddle/v2/framework/evaluator.py b/python/paddle/v2/framework/evaluator.py
index 89290abb830..ffff25b3461 100644
--- a/python/paddle/v2/framework/evaluator.py
+++ b/python/paddle/v2/framework/evaluator.py
@@ -43,7 +43,7 @@ class Evaluator(object):
         """
         Clear metric states at the begin of each pass/user specified batch
         """
-        if program == None:
+        if reset_program == None:
             reset_program = Program()
         else:
             reset_program = program
@@ -147,9 +147,9 @@ class Accuracy(Evaluator):
 
         return acc_out
 
-    def eval(self, executor, program=None):
-        if program != None:
-            eval_program = program
+    def eval(self, executor, eval_program=None):
+        if eval_program != None:
+            eval_program = eval_program
         else:
             eval_program = Program()
         block = eval_program.global_block()
-- 
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