Add groups test.

python/paddle/v2/framework/tests/test_conv2d_op.py

@@ -15,28 +15,36 @@ class TestConv2dOp(OpTest):
         filter_width = 3
         stride = 1
         padding = 0
+        groups = 3
         output_height = (input_height - filter_height + 2 * padding
                          ) / stride + 1
         output_width = (input_width - filter_width + 2 * padding) / stride + 1
         input = np.random.random((batch_size, input_channels, input_height,
                                   input_width)).astype("float32")
+
         filter = np.random.random(
-            (output_channels, input_channels, filter_height,
+            (output_channels, input_channels / groups, filter_height,
              filter_width)).astype("float32")
         output = np.ndarray(
             (batch_size, output_channels, output_height, output_width))
 
         self.inputs = {'Input': input, 'Filter': filter}
-        self.attrs = {'strides': [1, 1], 'paddings': [0, 0]}
+        self.attrs = {'strides': [1, 1], 'paddings': [0, 0], 'groups': groups}
 
+        output_group_channels = output_channels / groups
+        input_group_channels = input_channels / groups
         for batchid in xrange(batch_size):
-            for channelid in xrange(output_channels):
+            for group in xrange(groups):
+                for outchannelid in range(group * output_group_channels,
+                                          (group + 1) * output_group_channels):
                     for rowid in xrange(output_height):
                         for colid in xrange(output_width):
                             start_h = (rowid * stride) - padding
                             start_w = (colid * stride) - padding
                             output_value = 0.0
-                        for inchannelid in xrange(input_channels):
+                            for inchannelid in range(
+                                    group * input_group_channels,
+                                (group + 1) * input_group_channels):
                                 for frowid in xrange(filter_height):
                                     for fcolid in xrange(filter_width):
                                         input_value = 0.0
@@ -48,10 +56,12 @@ class TestConv2dOp(OpTest):
                                              incolid < input_width)):
                                             input_value = input[batchid][
                                                 inchannelid][inrowid][incolid]
-                                    filter_value = filter[channelid][
+                                        filter_value = filter[outchannelid][
-                                        inchannelid][frowid][fcolid]
+                                            inchannelid % input_group_channels][
+                                                frowid][fcolid]
                                         output_value += input_value * filter_value
-                        output[batchid][channelid][rowid][colid] = output_value
+                            output[batchid][outchannelid][rowid][
+                                colid] = output_value
 
         self.outputs = {'Output': output}