Merge pull request tensorflow#2137 from zheng-xq/conv_grad_stride_ksize

Issue 2066: Fix the conv for stride > ksize case.

Author: martinwicke

tensorflow/core/kernels/conv_grad_ops.cc

@@ -18,6 +18,7 @@ limitations under the License.
 #define USE_EIGEN_TENSOR
 #define EIGEN_USE_THREADS
 
+#include <algorithm>
 #include <vector>
 #include "tensorflow/core/framework/numeric_op.h"
 #include "tensorflow/core/framework/op_kernel.h"
@@ -838,13 +839,13 @@ class Conv2DSlowBackpropInputOp : public OpKernel {
                    context->allocate_output(0, input_shape, &in_backprop));
 
     const int padding_rows =
-        (padding_ == VALID)
-            ? 0
-            : (output_rows - 1) * stride_rows + filter_rows - input_rows;
+        (padding_ == VALID) ? 0
+                            : std::max<int>(0, (output_rows - 1) * stride_rows +
+                                                   filter_rows - input_rows);
     const int padding_cols =
-        (padding_ == VALID)
-            ? 0
-            : (output_cols - 1) * stride_cols + filter_cols - input_cols;
+        (padding_ == VALID) ? 0
+                            : std::max<int>(0, (output_cols - 1) * stride_cols +
+                                                   filter_cols - input_cols);
 
     // TODO(keveman): cuDNN only supports equal padding on both sides, so only
     // calling it when that is true. Remove this check when (if?) cuDNN starts
@@ -1137,13 +1138,13 @@ class Conv2DSlowBackpropFilterOp : public OpKernel {
                    context->allocate_output(0, filter_shape, &filter_backprop));
 
     const int padding_rows =
-        (padding_ == VALID)
-            ? 0
-            : (output_rows - 1) * stride_rows + filter_rows - input_rows;
+        (padding_ == VALID) ? 0
+                            : std::max<int>(0, (output_rows - 1) * stride_rows +
+                                                   filter_rows - input_rows);
     const int padding_cols =
-        (padding_ == VALID)
-            ? 0
-            : (output_cols - 1) * stride_cols + filter_cols - input_cols;
+        (padding_ == VALID) ? 0
+                            : std::max<int>(0, (output_cols - 1) * stride_cols +
+                                                   filter_cols - input_cols);
 
     // TODO(zhengxq): cuDNN only supports equal padding on both sides, so only
     // calling it when that is true. Remove this check when (if?) cuDNN starts

tensorflow/python/kernel_tests/conv_ops_test.py

@@ -465,6 +465,21 @@ def testConv2D2x2Depth3ValidBackpropInputStride1x2(self):
                                       data_format=data_format,
                                       use_gpu=use_gpu)
 
+  def testConv2DStrideTwoFilterOneSameBackpropInput(self):
+    expected_output = [1.0, 0.0, 2.0, 0.0,
+                       0.0, 0.0, 0.0, 0.0,
+                       3.0, 0.0, 4.0, 0.0,
+                       0.0, 0.0, 0.0, 0.0]
+    for (data_format, use_gpu) in GetTestConfigs():
+      self._RunAndVerifyBackpropInput(input_sizes=[1, 4, 4, 1],
+                                      filter_sizes=[1, 1, 1, 1],
+                                      output_sizes=[1, 2, 2, 1],
+                                      strides=[2, 2],
+                                      padding="SAME",
+                                      expected=expected_output,
+                                      data_format=data_format,
+                                      use_gpu=use_gpu)
+
   # Testing for backprops
   def _RunAndVerifyBackpropFilter(self, input_sizes, filter_sizes, output_sizes,
                                   strides, padding, expected, data_format,
@@ -568,6 +583,18 @@ def testConv2D2x2Depth3ValidBackpropFilterStride1x2(self):
                                        data_format=data_format,
                                        use_gpu=use_gpu)
 
+  def testConv2DStrideTwoFilterOneSameBackpropFilter(self):
+    expected_output = [78.]
+    for (data_format, use_gpu) in GetTestConfigs():
+      self._RunAndVerifyBackpropFilter(input_sizes=[1, 4, 4, 1],
+                                      filter_sizes=[1, 1, 1, 1],
+                                      output_sizes=[1, 2, 2, 1],
+                                      strides=[2, 2],
+                                      padding="SAME",
+                                      expected=expected_output,
+                                      data_format=data_format,
+                                      use_gpu=use_gpu)
+
   # Gradient checkers
   def ConstructAndTestGradient(self, batch, input_rows, input_cols, filter_rows,
                                filter_cols, in_depth, out_depth, stride_rows,