modify for code review by qingqing 2nd

paddle/operators/math/unpooling.cu

@@ -29,19 +29,19 @@ __global__ void KernelUnpool2dMax(const int nthreads,
                                   T* output_data,
                                   const int output_height,
                                   const int output_width) {
-    int bsize = input_height * input_width * channels;
-    int csize = input_height * input_width;
-    int out_bsize = output_height * output_width * channels;
-    int out_csize = output_height * output_width;
+    int in_n_stride = input_height * input_width * channels;
+    int in_c_stride = input_height * input_width;
+    int out_n_stride = output_height * output_width * channels;
+    int out_c_stride = output_height * output_width;
     int index = blockIdx.x * blockDim.x + threadIdx.x;
     int offset = blockDim.x * gridDim.x;
     for (int i = index; i < nthreads; i += offset) {
-      int bidx = i / bsize;
-      int boffset = i % bsize;
-      int cidx = boffset / csize;
-      int out_offset = bidx * out_bsize + cidx * out_csize;
+      int bidx = i / in_n_stride;
+      int boffset = i % in_n_stride;
+      int cidx = boffset / in_c_stride;
+      int out_offset = bidx * out_n_stride + cidx * out_c_stride;
       int out_index = indices_data[i];
-      PADDLE_ASSERT(out_index < (output_height * output_width));
+      PADDLE_ASSERT(out_index < out_c_stride);
       output_data[out_offset + out_index] = input_data[i];
     }
 }
@@ -57,19 +57,19 @@ __global__ void KernelUnpool2dMaxGrad(const int nthreads,
                                       const int output_height,
                                       const int output_width,
                                       T* input_grad) {
-    int bsize = input_height * input_width * channels;
-    int csize = input_height * input_width;
-    int out_bsize = output_height * output_width * channels;
-    int out_csize = output_height * output_width;
+    int in_n_stride = input_height * input_width * channels;
+    int in_c_stride = input_height * input_width;
+    int out_n_stride = output_height * output_width * channels;
+    int out_c_stride = output_height * output_width;
     int index = blockIdx.x * blockDim.x + threadIdx.x;
     int offset = blockDim.x * gridDim.x;
     for (int i = index; i < nthreads; i += offset) {
-      int bidx = i / bsize;
-      int boffset = i % bsize;
-      int cidx = boffset / csize;
-      int out_offset = bidx * out_bsize + cidx * out_csize;
+      int bidx = i / in_n_stride;
+      int boffset = i % in_n_stride;
+      int cidx = boffset / in_c_stride;
+      int out_offset = bidx * out_n_stride + cidx * out_c_stride;
       int out_index = indices_data[i];
-      PADDLE_ASSERT(out_index < (output_height * output_width));
+      PADDLE_ASSERT(out_index < out_c_stride);
       input_grad[i] = output_grad[out_offset + out_index];
     }
 }
@@ -93,10 +93,8 @@ class Unpool2dMaxFunctor<platform::GPUPlace, T, T2> {
     const T2 * indices_data = indices.data<T2>();
     T* output_data = output->mutable_data<T>(context.GetPlace());
     int nthreads = batch_size * output_channels * input_height * input_width;
-    int blocks = (nthreads + 1024 - 1) / 1024;
-    dim3 threads(1024, 1);
-    dim3 grid(blocks, 1);
-
+    int threads = 1024;
+    int grid =  (input.numel() + threads - 1) / threads;
     KernelUnpool2dMax<
         T, T2><<<grid, threads, 0,
              reinterpret_cast<const platform::CUDADeviceContext&>(context)
@@ -129,10 +127,8 @@ class Unpool2dMaxGradFunctor<platform::GPUPlace, T, T2> {
     const T* output_grad_data = output_grad.data<T>();
     T* input_grad_data = input_grad->mutable_data<T>(context.GetPlace());
     int nthreads = batch_size * output_channels * input_height * input_width;
-    int blocks = (nthreads + 1024 - 1) / 1024;
-    dim3 threads(1024, 1);
-    dim3 grid(blocks, 1);
-
+    int threads = 1024;
+    int grid =  (input.numel() + threads - 1) / threads;
     KernelUnpool2dMaxGrad<
         T, T2><<<grid, threads, 0,
              reinterpret_cast<const platform::CUDADeviceContext&>(context)

paddle/operators/unpool_op.cc

 /* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
- *
- * Licensed under the Apache License, Version 2.0 (the "License");
- * you may not use this file except in compliance with the License.
- * You may obtain a copy of the License at
- *
- *     http://www.apache.org/licenses/LICENSE-2.0
- *
- *     Unless required by applicable law or agreed to in writing, software
- *     distributed under the License is distributed on an "AS IS" BASIS,
- *     WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- *     See the License for the specific language governing permissions and
- *     limitations under the License. */
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+Indicesou may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
 
 #include "paddle/operators/unpool_op.h"
 namespace paddle {
@@ -25,7 +25,7 @@ class Unpool2dOpMaker : public framework::OpProtoAndCheckerMaker {
         "(Tensor) The input tensor of unpool operator. "
         "The format of input tensor is NCHW. Where N is batch size, C is the "
         "number of channels, H and W is the height and width of feature.");
-    AddInput("Y",
+    AddInput("Indices",
         "(Tensor) The input tensor of the indices given out by MaxPool2d. "
         "The format of input tensor is NCHW. Where N is batch size, C is the "
         "number of channels, H and W is the height and width of feature.");
@@ -50,12 +50,10 @@ class Unpool2dOpMaker : public framework::OpProtoAndCheckerMaker {
         "(string), unpooling type, can be \"max\" for max-unpooling ")
         .InEnum({"max"});
     AddComment(R"DOC(
-          "input: the input Tensor to invert
-          indices: the indices given out by MaxPool2d
-          ksize  – Size of the max pooling window.
-          stride – Stride of the max pooling window.
-                   "It is set to kernel_size by default.
-          padding – Padding that was added to the input"
+          "Paper: http://www.matthewzeiler.com/wp-content/uploads/2017
+          /07/iccv2011.pdf
+          PyTorch: http://pytorch.org/docs/master/nn.html?highlight=unpool#
+          torch.nn.MaxUnpool2d"
         )DOC");
   }
 };
@@ -79,27 +77,20 @@ public:
   void InferShape(framework::InferShapeContext* ctx) const override {
     PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) of UnpoolOp"
                    "should not be null.");
-    PADDLE_ENFORCE(ctx->HasInput("Y"), "Input(Y) of UnpoolOp"
+    PADDLE_ENFORCE(ctx->HasInput("Indices"), "Input(Indices) of UnpoolOp"
                    "should not be null.");
     PADDLE_ENFORCE(ctx->HasOutput("Out"),
                    "Output(Out) of UnpoolOp should not be null.");
-
     auto in_x_dims = ctx->GetInputDim("X");
-    auto in_y_dims = ctx->GetInputDim("Y");
+    auto in_y_dims = ctx->GetInputDim("Indices");
     std::string unpooling_type =
       ctx->Attrs().Get<std::string>("unpooling_type");
     std::vector<int> ksize = ctx->Attrs().Get<std::vector<int>>("ksize");
     std::vector<int> strides = ctx->Attrs().Get<std::vector<int>>("strides");
     std::vector<int> paddings = ctx->Attrs().Get<std::vector<int>>("paddings");
-
     PADDLE_ENFORCE(in_x_dims.size() == 4,
                     "Unpooling intput must be of 4-dimensional.");
-    for (int i = 0; i < 4; ++i) {
-      PADDLE_ENFORCE(in_x_dims[i] == in_y_dims[i],
-                     "X size must be eq Y size!");
-    }
-
-
+    PADDLE_ENFORCE_EQ(in_x_dims, in_y_dims);
     std::vector<int64_t> output_shape({in_x_dims[0], in_x_dims[1]});
     for (size_t i = 0; i < ksize.size(); ++i) {
       output_shape.push_back(

paddle/operators/unpool_op.cu.cc

 /* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
 
-   Licensed under the Apache License, Version 2.0 (the "License");
-   you may not use this file except in compliance with the License.
-   You may obtain a copy of the License at
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+Indicesou may obtain a copy of the License at
 
     http://www.apache.org/licenses/LICENSE-2.0
 
-   Unless required by applicable law or agreed to in writing, software
-   distributed under the License is distributed on an "AS IS" BASIS,
-   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   See the License for the specific language governing permissions and
-   limitations under the License. */
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
 
 #include "paddle/operators/unpool_op.h"
 

paddle/operators/unpool_op.h

@@ -2,7 +2,7 @@
 
 Licensed under the Apache License, Version 2.0 (the "License");
 you may not use this file except in compliance with the License.
-You may obtain a copy of the License at
+Indicesou may obtain a copy of the License at
 
     http://www.apache.org/licenses/LICENSE-2.0
 
@@ -26,7 +26,7 @@ class UnpoolKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
     const framework::Tensor* in_x = context.Input<framework::Tensor>("X");
-    const framework::Tensor* in_y = context.Input<framework::Tensor>("Y");
+    const framework::Tensor* in_y = context.Input<framework::Tensor>("Indices");
     auto * out = context.Output<framework::Tensor>("Out");
     std::string unpooling_type = context.Attr<std::string>("unpooling_type");
     std::vector<int> ksize = context.Attr<std::vector<int>>("ksize");
@@ -47,7 +47,7 @@ class UnpoolGradKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& context) const override {
     const framework::Tensor* in_x = context.Input<framework::Tensor>("X");
-    const framework::Tensor* in_y = context.Input<framework::Tensor>("Y");
+    const framework::Tensor* in_y = context.Input<framework::Tensor>("Indices");
     const framework::Tensor* out = context.Input<framework::Tensor>("Out");
     const framework::Tensor* out_grad =
         context.Input<framework::Tensor>(framework::GradVarName("Out"));

python/paddle/v2/fluid/tests/test_unpool_op.py

@@ -5,16 +5,16 @@ from op_test import OpTest
 
 def unpool2dmax_forward_naive(input, indices, ksize, strides, paddings):
     s0, s1, s2, s3 = input.shape
-    out_H=(s2 - 1) * strides[0] - 2 * paddings[0] + ksize[0]
-    out_W=(s2 - 1) * strides[1] - 2 * paddings[1] + ksize[1]
-    out = np.zeros((s0, s1, out_H, out_W))
+    out_hsize = (s2 - 1) * strides[0] - 2 * paddings[0] + ksize[0]
+    out_wsize = (s2 - 1) * strides[1] - 2 * paddings[1] + ksize[1]
+    out = np.zeros((s0, s1, out_hsize, out_wsize))
     for nidx in xrange(s0):
         for cidx in xrange(s1):
             for h in xrange(s2):
                 for w in xrange(s3):
                     index = indices[nidx, cidx, h, w]
-                    hidx = (index - index % out_W) / out_W
-                    widx = index % out_W
+                    hidx = (index - index % out_wsize) / out_wsize
+                    widx = index % out_wsize
                     out[nidx, cidx, int(hidx), int(widx)] = \
                             input[nidx, cidx, h, w]
 
@@ -26,34 +26,34 @@ class TestUnpoolOp(OpTest):
         self.op_type = "unpool"
         self.init_test_case()
         pre_input = np.random.random(self.shape).astype("float32")
-        N, C, H, W = pre_input.shape
-        H_out = (H - self.ksize[0] + 2 * self.paddings[0]) / \
+        nsize, csize, hsize, wsize = pre_input.shape
+        hsize_out = (hsize - self.ksize[0] + 2 * self.paddings[0]) / \
                 self.strides[0] + 1
-        W_out = (W - self.ksize[1] + 2 * self.paddings[1]) / \
+        wsize_out = (wsize - self.ksize[1] + 2 * self.paddings[1]) / \
                 self.strides[1] + 1
-        input = np.zeros((N, C, H_out, W_out))
-        indices = np.zeros((N, C, H_out, W_out))
-        for i in xrange(H_out):
-            for j in xrange(W_out):
+        input = np.zeros((nsize, csize, hsize_out, wsize_out))
+        indices = np.zeros((nsize, csize, hsize_out, wsize_out))
+        for i in xrange(hsize_out):
+            for j in xrange(wsize_out):
                 r_start = np.max((i * self.strides[0] - self.paddings[0], 0))
                 r_end = np.min((i * self.strides[0] + self.ksize[0] - \
-                        self.paddings[0], H))
+                        self.paddings[0], hsize))
                 c_start = np.max((j * self.strides[1] - self.paddings[1], 0))
                 c_end = np.min((j * self.strides[1] + self.ksize[1] - \
-                        self.paddings[1], W))
-                for nidx in xrange(N):
-                    for cidx in xrange(C):
+                        self.paddings[1], wsize))
+                for nidx in xrange(nsize):
+                    for cidx in xrange(csize):
                         x_masked = pre_input[nidx, cidx, r_start:r_end, \
                                 c_start:c_end]
                         input[nidx, cidx, i, j] = x_masked.max()
                         arg = x_masked.argmax()
                         indices[nidx, cidx, i, j] = \
-                                (r_start + arg / self.ksize[1]) * W + \
+                                (r_start + arg / self.ksize[1]) * wsize + \
                                 c_start + arg % self.ksize[1]
         output = self.Unpool2d_forward_naive(input, indices, self.ksize, \
                 self.strides, self.paddings).astype("float32")
         self.inputs = {'X': input.astype('float32'),
-                       'Y': indices.astype('int32')}
+                       'Indices': indices.astype('int32')}
         self.attrs = {
                  'strides': self.strides,
                  'paddings': self.paddings,