Rename ndim() -> dim() - 6/6

Summary: Codemod generated with clangr shard mode, 50 files per diff, clangr code(ndim()->dim()): diffusion/FBS/browse/master/fbcode/caffe2/caffe2/fb/codemods/TensorMethodRename.cpp Reviewed By: ezyang Differential Revision: D12935827 fbshipit-source-id: 80ecb034c243dbfd267b9f131cee9d7afd5ef063
TakawaAkirayo · Nov 7, 2018 · 57ec8f1 · 57ec8f1
1 parent e60a7c2
commit 57ec8f1
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Showing 8 changed files with 19 additions and 18 deletions.
diff --git a/caffe2/sgd/adagrad_op.h b/caffe2/sgd/adagrad_op.h
@@ -166,7 +166,7 @@ class SparseAdagradOp final : public Operator<Context> {
     CAFFE_ENFORCE_EQ(Input(LR).numel(), 1);
     CAFFE_ENFORCE_EQ(
         Input(PARAM).size_from_dim(1),
-        Input(GRAD).size_from_dim(Input(INDICES).ndim()));
+        Input(GRAD).size_from_dim(Input(INDICES).dim()));
 
     return DispatchHelper<TensorTypes<int32_t, int64_t>>::call(
         this, Input(INDICES));
@@ -254,7 +254,7 @@ class RowWiseSparseAdagradOp final : public Operator<Context> {
     CAFFE_ENFORCE_EQ(Input(LR).numel(), 1);
     CAFFE_ENFORCE_EQ(
         Input(PARAM).size_from_dim(1),
-        Input(GRAD).size_from_dim(Input(INDICES).ndim()));
+        Input(GRAD).size_from_dim(Input(INDICES).dim()));
 
     return DispatchHelper<TensorTypes<int32_t, int64_t>>::call(
         this, Input(INDICES));

diff --git a/caffe2/sgd/adam_op.h b/caffe2/sgd/adam_op.h
@@ -166,7 +166,7 @@ class SparseAdamOp final : public Operator<Context> {
     CAFFE_ENFORCE_EQ(Input(PARAM).numel(), Input(MOMENT_2).numel());
     CAFFE_ENFORCE_EQ(
         Input(PARAM).size_from_dim(1),
-        Input(GRAD).size_from_dim(Input(INDICES).ndim()));
+        Input(GRAD).size_from_dim(Input(INDICES).dim()));
     CAFFE_ENFORCE_EQ(Input(LR).numel(), 1);
 
     return DispatchHelper<TensorTypes<int32_t, int64_t>>::call(
@@ -336,7 +336,7 @@ class RowWiseSparseAdamOp final : public Operator<Context> {
     CAFFE_ENFORCE_EQ(Input(PARAM).sizes()[0], Input(MOMENT_2).numel());
     CAFFE_ENFORCE_EQ(
         Input(PARAM).size_from_dim(1),
-        Input(GRAD).size_from_dim(Input(INDICES).ndim()));
+        Input(GRAD).size_from_dim(Input(INDICES).dim()));
     CAFFE_ENFORCE_EQ(Input(LR).numel(), 1);
 
     return DispatchHelper<TensorTypes<int32_t, int64_t>>::call(

diff --git a/caffe2/sgd/momentum_sgd_op.h b/caffe2/sgd/momentum_sgd_op.h
@@ -131,8 +131,9 @@ class SparseMomentumSGDUpdateOp final : public Operator<Context> {
     // Enforce shapes
     CAFFE_ENFORCE_EQ(Input(LR).numel(), 1);
     CAFFE_ENFORCE_EQ(Input(PARAM).numel(), Input(MOMENTUM).numel());
-    CAFFE_ENFORCE_EQ(Input(PARAM).size_from_dim(1),
-        Input(GRAD).size_from_dim(Input(INDICES).ndim()));
+    CAFFE_ENFORCE_EQ(
+        Input(PARAM).size_from_dim(1),
+        Input(GRAD).size_from_dim(Input(INDICES).dim()));
 
     return DispatchHelper<TensorTypes<int32_t, int64_t>>::call(
         this, Input(INDICES));

diff --git a/caffe2/sgd/wngrad_op.h b/caffe2/sgd/wngrad_op.h
@@ -170,7 +170,7 @@ class SparseWngradOp final : public Operator<Context> {
     CAFFE_ENFORCE_EQ(Input(LR).numel(), 1);
     CAFFE_ENFORCE_EQ(
         Input(PARAM).size_from_dim(1),
-        Input(GRAD).size_from_dim(Input(INDICES).ndim()));
+        Input(GRAD).size_from_dim(Input(INDICES).dim()));
 
     return DispatchHelper<TensorTypes<int32_t, int64_t>>::call(
         this, Input(INDICES));

diff --git a/caffe2/sgd/yellowfin_op.h b/caffe2/sgd/yellowfin_op.h
@@ -129,16 +129,16 @@ CAFFE2_YF_READ_INPUT(GRAD, grad)
 CAFFE_ENFORCE(OperatorBase::InputIsTensorType(ITER, CPU));
 CAFFE_ENFORCE_EQ(lr_avg_tensor.numel(), 1);
 CAFFE_ENFORCE_EQ(mu_avg_tensor.numel(), 1);
-CAFFE_ENFORCE_EQ(param_tensor.ndim(), moment_tensor.ndim());
-CAFFE_ENFORCE_EQ(param_tensor.ndim(), g_avg_tensor.ndim());
-CAFFE_ENFORCE_EQ(param_tensor.ndim(), g2_avg_tensor.ndim());
-CAFFE_ENFORCE_EQ(param_tensor.ndim(), grad_tensor.ndim());
-for (int i = 0; i < param_tensor.ndim(); ++i) {
+CAFFE_ENFORCE_EQ(param_tensor.dim(), moment_tensor.dim());
+CAFFE_ENFORCE_EQ(param_tensor.dim(), g_avg_tensor.dim());
+CAFFE_ENFORCE_EQ(param_tensor.dim(), g2_avg_tensor.dim());
+CAFFE_ENFORCE_EQ(param_tensor.dim(), grad_tensor.dim());
+for (int i = 0; i < param_tensor.dim(); ++i) {
   CAFFE_ENFORCE_EQ(param_tensor.dim32(i), moment_tensor.dim32(i));
   CAFFE_ENFORCE_EQ(param_tensor.dim32(i), g_avg_tensor.dim32(i));
   CAFFE_ENFORCE_EQ(param_tensor.dim32(i), g2_avg_tensor.dim32(i));
   CAFFE_ENFORCE_EQ(param_tensor.dim32(i), grad_tensor.dim32(i));
-    }
+}
 
     iter_ = OperatorBase::Input<Tensor>(ITER, CPU).template data<int64_t>()[0];
 

diff --git a/caffe2/share/contrib/zstd/quant_decomp_zstd_op.cc b/caffe2/share/contrib/zstd/quant_decomp_zstd_op.cc
@@ -96,7 +96,7 @@ bool QuantDecompZstdOp::RunOnDevice() {
 
   // op_compressed: compressed data, 1d
   if (op_compressed.template IsType<uint8_t>()) {
-    CAFFE_ENFORCE_EQ(op_compressed.ndim(), 1, op_compressed.ndim());
+    CAFFE_ENFORCE_EQ(op_compressed.dim(), 1, op_compressed.dim());
   } else {
     // string type has 0 dimension
     CAFFE_ENFORCE_EQ(op_compressed.numel(), 1, op_compressed.numel());

diff --git a/modules/detectron/batch_permutation_op.cc b/modules/detectron/batch_permutation_op.cc
@@ -80,7 +80,7 @@ bool BatchPermutationOp<float, CPUContext>::RunOnDevice() {
   const auto& indices = Input(1);
   auto* Y = Output(0);
 
-  CAFFE_ENFORCE_EQ(indices.ndim(), 1, "indices must be 1-d");
+  CAFFE_ENFORCE_EQ(indices.dim(), 1, "indices must be 1-d");
   CAFFE_ENFORCE_EQ(
     X.dim32(0), indices.dim32(0),
     "X.dim32(0) must be equal to indices.dim32(0)",

diff --git a/modules/detectron/upsample_nearest_op.h b/modules/detectron/upsample_nearest_op.h
@@ -38,14 +38,14 @@ class UpsampleNearestOp final : public Operator<Context> {
     auto& X = Input(0);
     auto* Y = Output(0);
     auto out_shape = X.sizes().vec();
-    out_shape[X.ndim() - 1] *= scale_;
-    out_shape[X.ndim() - 2] *= scale_;
+    out_shape[X.dim() - 1] *= scale_;
+    out_shape[X.dim() - 2] *= scale_;
     Y->Resize(out_shape);
 
     int d1;
     int d2;
     int d3;
-    if (X.ndim() == 3) {
+    if (X.dim() == 3) {
       d1 = Y->dim32(0);
       d2 = Y->dim32(1);
       d3 = Y->dim32(2);