Merge pull request #5791 from chengduoZH/fix_conv_op

remove vector::erase

paddle/operators/conv_op.h

@@ -38,7 +38,7 @@ inline bool IsExpand(std::vector<int64_t>& filter_dim,
                      std::vector<int>& dilations) {
   bool filter_1 = true, strides_1 = true, padding_0 = true, dilation_1 = true;
   for (size_t j = 0; j < strides.size(); ++j) {
-    filter_1 = filter_1 && (static_cast<int>(filter_dim[j]) == 1);
+    filter_1 = filter_1 && (static_cast<int>(filter_dim[j + 2]) == 1);
     strides_1 = strides_1 && (strides[j] == 1);
     padding_0 = padding_0 && (paddings[j] == 0);
     dilation_1 = dilation_1 && (dilations[j] == 1);
@@ -91,32 +91,28 @@ class GemmConvKernel : public framework::OpKernel<T> {
 
     const int batch_size = static_cast<int>(input->dims()[0]);
 
-    // filter_shape_vec: {k_h, k_w} or {k_d, k_h, k_w}
+    // filter_shape_vec: {k_o, k_i, k_h, k_w} or {k_o, k_i, k_d, k_h, k_w}
     std::vector<int64_t> filter_shape_vec(framework::vectorize(filter.dims()));
-    filter_shape_vec.erase(filter_shape_vec.begin(),
-                           filter_shape_vec.begin() + 2);
-
-    // output_shape_vec: {o_h, o_w} or {o_d, o_h, o_w}
+    // output_shape_vec: {o_n, o_c, o_h, o_w} or {o_n, o_c, o_d, o_h, o_w}
     std::vector<int64_t> output_shape_vec(framework::vectorize(output->dims()));
-    output_shape_vec.erase(output_shape_vec.begin(),
-                           output_shape_vec.begin() + 2);
 
     // use col_shape in the im2col calculation
     // col_shape_vec: {i_c/g, k_h, k_w, o_h, o_w} or {i_c/g, k_d, k_h, k_w, o_d,
     // o_h, o_w}
-    std::vector<int64_t> col_shape_vec;
-    col_shape_vec.push_back(input->dims()[1] / groups);
-    col_shape_vec.insert(col_shape_vec.end(), filter_shape_vec.begin(),
-                         filter_shape_vec.end());
-    col_shape_vec.insert(col_shape_vec.end(), output_shape_vec.begin(),
-                         output_shape_vec.end());
+    size_t data_dim = filter_shape_vec.size() - 2;
+    std::vector<int64_t> col_shape_vec(1 + 2 * data_dim);
+    col_shape_vec[0] = input->dims()[1] / groups;
+    for (size_t j = 0; j < data_dim; ++j) {
+      col_shape_vec[j + 1] = filter_shape_vec[j + 2];
+      col_shape_vec[j + 1 + data_dim] = output_shape_vec[j + 2];
+    }
     framework::DDim col_shape(framework::make_ddim(col_shape_vec));
 
     // use col_matrix_shape in the gemm calculation
     // size: (i_c/g * k_h * k_w, o_h * o_w) or (i_c/g * k_d * k_h * k_w, o_d *
     // o_h * o_w)
     framework::DDim col_matrix_shape =
-        framework::flatten_to_2d(col_shape, filter_shape_vec.size() + 1);
+        framework::flatten_to_2d(col_shape, data_dim + 1);
 
     bool is_expand = IsExpand(filter_shape_vec, strides, paddings, dilations);
     Tensor col;
@@ -159,13 +155,13 @@ class GemmConvKernel : public framework::OpKernel<T> {
           col.ShareDataWith(in_slice);
           col_matrix.ShareDataWith(col);
           col_matrix.Resize(col_matrix_shape);
-        } else if (filter_shape_vec.size() == 2) {
+        } else if (data_dim == 2U) {
           // im2col
           im2col(context.device_context(), in_slice, dilations, strides,
                  std::vector<int>{paddings[0], paddings[1], paddings[0],
                                   paddings[1]},
                  &col);
-        } else if (filter_shape_vec.size() == 3) {
+        } else if (data_dim == 3U) {
           // vol2col
           vol2col(context.device_context(), in_slice, dilations, strides,
                   paddings, &col);
@@ -206,26 +202,22 @@ class GemmConvGradKernel : public framework::OpKernel<T> {
 
     const int batch_size = static_cast<int>(input->dims()[0]);
 
-    // filter_shape_vec: {k_h, k_w} or {k_d, k_h, k_w}
+    // filter_shape_vec: {k_o, k_i, k_h, k_w} or {k_o, k_i, k_d, k_h, k_w}
     std::vector<int64_t> filter_shape_vec(framework::vectorize(filter.dims()));
-    filter_shape_vec.erase(filter_shape_vec.begin(),
-                           filter_shape_vec.begin() + 2);
-
-    // output_shape_vec: {o_h, o_w} or {o_d, o_h, o_w}
+    // output_shape_vec: {o_n, o_c, o_h, o_w} or {o_n, o_c, o_d, o_h, o_w}
     std::vector<int64_t> output_shape_vec(
         framework::vectorize(output_grad->dims()));
-    output_shape_vec.erase(output_shape_vec.begin(),
-                           output_shape_vec.begin() + 2);
 
     // use col_shape in the im2col calculation
     // col_shape_vec: {i_c/g, k_h, k_w, o_h, o_w} or {i_c/g, k_d, k_h, k_w, o_d,
     // o_h, o_w}
-    std::vector<int64_t> col_shape_vec;
-    col_shape_vec.push_back(input->dims()[1] / groups);
-    col_shape_vec.insert(col_shape_vec.end(), filter_shape_vec.begin(),
-                         filter_shape_vec.end());
-    col_shape_vec.insert(col_shape_vec.end(), output_shape_vec.begin(),
-                         output_shape_vec.end());
+    size_t data_dim = filter_shape_vec.size() - 2;
+    std::vector<int64_t> col_shape_vec(1 + 2 * data_dim);
+    col_shape_vec[0] = input->dims()[1] / groups;
+    for (size_t j = 0; j < data_dim; ++j) {
+      col_shape_vec[j + 1] = filter_shape_vec[j + 2];
+      col_shape_vec[j + 1 + data_dim] = output_shape_vec[j + 2];
+    }
     framework::DDim col_shape(framework::make_ddim(col_shape_vec));
 
     // use col_matrix_shape in the gemm calculation
@@ -233,7 +225,7 @@ class GemmConvGradKernel : public framework::OpKernel<T> {
     // or
     // (i_c/g * k_d * k_h * k_w, o_d * o_h * o_w)
     framework::DDim col_matrix_shape =
-        framework::flatten_to_2d(col_shape, filter_shape_vec.size() + 1);
+        framework::flatten_to_2d(col_shape, data_dim + 1);
 
     framework::DDim input_shape = framework::slice_ddim(
         input->dims(), 1, static_cast<int>(input->dims().size()));
@@ -294,12 +286,12 @@ class GemmConvGradKernel : public framework::OpKernel<T> {
                                  out_grad_slice, false, T(1.0), &col_matrix,
                                  T(0.0));
 
-          if (is_expand && filter_shape_vec.size() == 2) {
+          if (is_expand && data_dim == 2U) {
             col2im(context.device_context(), col, dilations, strides,
                    std::vector<int>{paddings[0], paddings[1], paddings[0],
                                     paddings[1]},
                    &in_grad_slice);
-          } else if (is_expand && filter_shape_vec.size() == 3) {
+          } else if (is_expand && data_dim == 3U) {
             col2vol(context.device_context(), col, dilations, strides, paddings,
                     &in_grad_slice);
           }
@@ -328,12 +320,12 @@ class GemmConvGradKernel : public framework::OpKernel<T> {
             col.ShareDataWith(in_slice);
             col_matrix.ShareDataWith(col);
             col_matrix.Resize(col_matrix_shape);
-          } else if (filter_shape_vec.size() == 2) {
+          } else if (data_dim == 2U) {
             im2col(context.device_context(), in_slice, dilations, strides,
                    std::vector<int>{paddings[0], paddings[1], paddings[0],
                                     paddings[1]},
                    &col);
-          } else if (filter_shape_vec.size() == 3) {
+          } else if (data_dim == 3U) {
             vol2col(context.device_context(), in_slice, dilations, strides,
                     paddings, &col);
           }

paddle/operators/conv_transpose_op.h

@@ -68,30 +68,26 @@ class GemmConvTransposeKernel : public framework::OpKernel<T> {
 
     const int batch_size = static_cast<int>(input->dims()[0]);
 
-    // input_shape_vec: {h, w} or {d, h, w}
+    // input_shape_vec: {n, c, h, w} or {n, c, d, h, w}
     std::vector<int64_t> input_shape_vec = framework::vectorize(input->dims());
-    input_shape_vec.erase(input_shape_vec.begin(), input_shape_vec.begin() + 2);
-
-    // filter_shape_vec: {k_h, k_w} or {k_d, k_h, k_w}
+    // filter_shape_vec: {k_o, k_c, k_h, k_w} or {k_o, k_c, k_d, k_h, k_w}
     std::vector<int64_t> filter_shape_vec = framework::vectorize(filter.dims());
-    filter_shape_vec.erase(filter_shape_vec.begin(),
-                           filter_shape_vec.begin() + 2);
 
     // use col_shape in the im2col and col2im (or vol2col and col2vol)
     // calculation
     // col_shape_vec: {c, k_h, k_w, h, w} or {c, k_d, k_h, k_w, d, h, w}
-    std::vector<int64_t> col_shape_vec;
-    col_shape_vec.push_back(output->dims()[1]);
-    col_shape_vec.insert(col_shape_vec.end(), filter_shape_vec.begin(),
-                         filter_shape_vec.end());
-    col_shape_vec.insert(col_shape_vec.end(), input_shape_vec.begin(),
-                         input_shape_vec.end());
+    size_t data_dim = filter_shape_vec.size() - 2;
+    std::vector<int64_t> col_shape_vec(1 + 2 * data_dim);
+    col_shape_vec[0] = output->dims()[1];
+    for (size_t j = 0; j < data_dim; ++j) {
+      col_shape_vec[j + 1] = filter_shape_vec[j + 2];
+      col_shape_vec[j + 1 + data_dim] = input_shape_vec[j + 2];
+    }
     DDim col_shape(framework::make_ddim(col_shape_vec));
 
     // use col_matrix_shape in the gemm calculation
     // size: (c * k_h * k_w, h * w) or (c * k_d * k_h * k_w, d * h * w)
-    DDim col_matrix_shape =
-        framework::flatten_to_2d(col_shape, filter_shape_vec.size() + 1);
+    DDim col_matrix_shape = framework::flatten_to_2d(col_shape, data_dim + 1);
 
     Tensor col;
     col.mutable_data<T>(col_shape, context.GetPlace());
@@ -136,7 +132,7 @@ class GemmConvTransposeKernel : public framework::OpKernel<T> {
                              input_batch, false, static_cast<T>(1.0),
                              &col_matrix, static_cast<T>(0.0));
 
-      if (filter_shape_vec.size() == 2) {
+      if (data_dim == 2U) {
         // col2im: col_matrix -> dy
         // from (c * k_h * k_w, h * w) to (c, o_h, o_w)
         col2im(context.device_context(), col,
@@ -144,7 +140,7 @@ class GemmConvTransposeKernel : public framework::OpKernel<T> {
                std::vector<int>{paddings[0], paddings[1], paddings[0],
                                 paddings[1]},
                &output_batch);
-      } else if (filter_shape_vec.size() == 3) {
+      } else if (data_dim == 3U) {
         // col2vol: col_matrix -> dy
         // from (c * k_d * k_h * k_w, d * h * w) to (c, o_d, o_h, o_w)
         col2vol(context.device_context(), col, dilations, strides, paddings,
@@ -176,30 +172,26 @@ class GemmConvTransposeGradKernel : public framework::OpKernel<T> {
 
     const int batch_size = static_cast<int>(input->dims()[0]);
 
-    // input_shape_vec: {h, w} or {d, h, w}
+    // input_shape_vec: {n, c, h, w} or {n, c, d, h, w}
     std::vector<int64_t> input_shape_vec = framework::vectorize(input->dims());
-    input_shape_vec.erase(input_shape_vec.begin(), input_shape_vec.begin() + 2);
-
-    // filter_shape_vec: {k_h, k_w} or {k_d, k_h, k_w}
+    // filter_shape_vec: {k_o, k_c, k_h, k_w} or {k_o, k_c, k_d, k_h, k_w}
     std::vector<int64_t> filter_shape_vec = framework::vectorize(filter.dims());
-    filter_shape_vec.erase(filter_shape_vec.begin(),
-                           filter_shape_vec.begin() + 2);
 
     // use col_shape in the im2col and col2im (or vol2col and col2vol)
     // calculation
     // col_shape_vec: {c, k_h, k_w, h, w} or {c, k_d, k_h, k_w, d, h, w}
-    std::vector<int64_t> col_shape_vec;
-    col_shape_vec.push_back(output_grad->dims()[1]);
-    col_shape_vec.insert(col_shape_vec.end(), filter_shape_vec.begin(),
-                         filter_shape_vec.end());
-    col_shape_vec.insert(col_shape_vec.end(), input_shape_vec.begin(),
-                         input_shape_vec.end());
+    size_t data_dim = filter_shape_vec.size() - 2;
+    std::vector<int64_t> col_shape_vec(1 + 2 * data_dim);
+    col_shape_vec[0] = output_grad->dims()[1];
+    for (size_t j = 0; j < data_dim; ++j) {
+      col_shape_vec[j + 1] = filter_shape_vec[j + 2];
+      col_shape_vec[j + 1 + data_dim] = input_shape_vec[j + 2];
+    }
     DDim col_shape(framework::make_ddim(col_shape_vec));
 
     // use col_matrix_shape in the gemm calculation
     // size: (c * k_h * k_w, h * w) or (c * k_d * k_h * k_w, d * h * w)
-    DDim col_matrix_shape =
-        framework::flatten_to_2d(col_shape, filter_shape_vec.size() + 1);
+    DDim col_matrix_shape = framework::flatten_to_2d(col_shape, data_dim + 1);
 
     // output size: (c, o_h, o_w) or (c, o_d, o_h, o_w)
     DDim output_shape = framework::slice_ddim(output_grad->dims(), 1,
@@ -248,7 +240,7 @@ class GemmConvTransposeGradKernel : public framework::OpKernel<T> {
         Tensor output_grad_batch =
             output_grad->Slice(i, i + 1).Resize(output_shape);
 
-        if (filter_shape_vec.size() == 2) {
+        if (data_dim == 2U) {
           // im2col: dy -> col matrix
           // from (c, o_h, o_w) to (c * k_h * k_w, h * w)
           im2col(context.device_context(), output_grad_batch,
@@ -256,7 +248,7 @@ class GemmConvTransposeGradKernel : public framework::OpKernel<T> {
                  std::vector<int>{paddings[0], paddings[1], paddings[0],
                                   paddings[1]},
                  &col);
-        } else if (filter_shape_vec.size() == 3) {
+        } else if (data_dim == 3U) {
           // vol2col: dy -> col_matrix
           // from (c, o_d, o_h, o_w) to (c * k_d * k_h * k_w, d * h * w)
           vol2col(context.device_context(), output_grad_batch, dilations,