[TOPI] Fix the filter width parameter in depthwise_conv2d (apache#6081)

* [TOPI] Fix the filter width parameter in depthwise_conv2d * Retrigger build Co-authored-by: Venkat Rasagna Reddy Komatireddy <[email protected]>
trevor-m · Sep 2, 2020 · b98a83a · b98a83a
1 parent f40ad6c
commit b98a83a
Showing 1 changed file with 2 additions and 2 deletions.
diff --git a/topi/python/topi/testing/depthwise_conv2d_python.py b/topi/python/topi/testing/depthwise_conv2d_python.py
@@ -58,7 +58,7 @@ def depthwise_conv2d_python_nchw(input_np, filter_np, stride, padding):
             for j in range(out_channel):
                 output_np[i, j, :, :] = signal.convolve2d(input_np[i, j//channel_multiplier, :, :], \
                                                           np.rot90(filter_np[j//channel_multiplier, j%channel_multiplier, :, :], 2), \
-                                                          mode='valid')[0:(in_height - filter_height + 1):stride_h, 0:(in_width - filter_height + 1):stride_w]
+                                                          mode='valid')[0:(in_height - filter_height + 1):stride_h, 0:(in_width - filter_width + 1):stride_w]
     if padding == 'SAME':
         out_channel = in_channel * channel_multiplier
         out_height = np.int(np.ceil(float(in_height) / float(stride_h)))
@@ -119,7 +119,7 @@ def depthwise_conv2d_python_nhwc(input_np, filter_np, stride, padding):
             for j in range(out_channel):
                 output_np[i, :, :, j] = signal.convolve2d(input_np[i, :, :, j//channel_multiplier], \
                                                           np.rot90(filter_np[:, :, j//channel_multiplier, j%channel_multiplier], 2), \
-                                                          mode='valid')[0:(in_height - filter_height + 1):stride_h, 0:(in_width - filter_height + 1):stride_w]
+                                                          mode='valid')[0:(in_height - filter_height + 1):stride_h, 0:(in_width - filter_width + 1):stride_w]
     if padding == 'SAME':
         out_channel = in_channel * channel_multiplier
         out_height = np.int(np.ceil(float(in_height) / float(stride_h)))