Rollback of: Add native depthwise_convolution op (forward pass).

The current depthwise_conv is very inefficient by calling slice() on each
input channel on input and filters, followed by a conv() on each input channel,
after which is a concat().
Change: 115617901

1 files changed, 53 insertions, 0 deletions

diff --git a/tensorflow/core/kernels/depthwise_conv_op.h b/tensorflow/core/kernels/depthwise_conv_op.h
new file mode 100644
index 0000000000..9733c5914d
--- /dev/null
+++ b/tensorflow/core/kernels/depthwise_conv_op.h
@@ -0,0 +1,53 @@
+/* Copyright 2015 Google Inc. All Rights Reserved.
+
+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.
+==============================================================================*/
+
+#ifndef THIRD_PARTY_TENSORFLOW_CORE_KERNELS_DEPTHWISE_CONV_OP_H_
+#define THIRD_PARTY_TENSORFLOW_CORE_KERNELS_DEPTHWISE_CONV_OP_H_
+
+struct DepthwiseArgs {
+  // Input layer dimensions
+  int batch;
+  int in_rows;
+  int in_cols;
+  int in_depth;
+  int filter_rows;
+  int filter_cols;
+  int depth_multiplier;
+  int stride;
+  int pad_rows;
+  int pad_cols;
+
+  // Output layer dimensions
+  int out_rows;
+  int out_cols;
+  int out_depth;
+
+  DepthwiseArgs()
+      : batch(0),
+        in_rows(0),
+        in_cols(0),
+        in_depth(0),
+        filter_rows(0),
+        filter_cols(0),
+        depth_multiplier(0),
+        stride(0),
+        pad_rows(0),
+        pad_cols(0),
+        out_rows(0),
+        out_cols(0),
+        out_depth(0) {}
+};
+
+#endif  // THIRD_PARTY_TENSORFLOW_CORE_KERNELS_DEPTHWISE_CONV_OP_H_