consolidate img_conv.conf in test_NetworkCompare

Author: wangyang59

paddle/gserver/tests/img_conv2_b.conf

@@ -34,6 +34,7 @@ conv = img_conv_layer(input=data, filter_size=1, filter_size_y=1,
                       num_channels=8,
                       num_filters=16, stride=1,
                       bias_attr=True,
-                      act=LinearActivation())
+                      act=LinearActivation(),
+                      groups=2)
 
 outputs(concat, conv)

paddle/gserver/tests/img_conv_a.conf

@@ -24,7 +24,7 @@ proj2 = conv_projection(input=data, filter_size=1, filter_size_y=1,
 concat = concat_layer(input=[proj1, proj2], bias_attr=False, act=ReluActivation())
 
 proj = conv_projection(input=data, filter_size=1, filter_size_y=1,
-                       num_channels=8, num_filters=16, stride=1)
+                       num_channels=8, num_filters=16, stride=1, groups=2)
 
 with mixed_layer(bias_attr=True, act=LinearActivation()) as conv:
     conv += proj

paddle/gserver/tests/img_conv_b.conf

@@ -17,20 +17,27 @@ from paddle.trainer_config_helpers import *
 
 settings(batch_size=10)
 data = data_layer(name ="input", size=8*16*16)
+conv1 = img_conv_layer(input=data, filter_size=1, filter_size_y=1,
+                        num_channels=8,
+                        num_filters=16, stride=1,
+                        bias_attr=False,
+                        act=ReluActivation(),
+                        layer_type="exconv")
+conv2 = img_conv_layer(input=data, filter_size=1, filter_size_y=1,
+                       num_channels=8,
+                       num_filters=16, stride=1,
+                       bias_attr=False,
+                       act=ReluActivation(),
+                       layer_type="exconv")
 
-conv = img_conv_layer(input=data, filter_size=2, filter_size_y=2,
-                      num_channels=8,
-                      num_filters=16, stride=1,
-                      bias_attr=True,
-                      act=LinearActivation(),
-                      layer_type="exconv")
+concat = concat_layer(input=[conv1, conv2])
 
-conv2 = img_conv_layer(input=data, filter_size=3, filter_size_y=3,
+conv = img_conv_layer(input=data, filter_size=1, filter_size_y=1,
                       num_channels=8,
-                      num_filters=16, stride=2,
+                      num_filters=16, stride=1,
                       bias_attr=True,
                       act=LinearActivation(),
                       groups=2,
                       layer_type="exconv")
-                      
-outputs(conv, conv2)
+
+outputs(concat, conv)

paddle/gserver/tests/img_conv2_a.conf paddle/gserver/tests/img_conv_c.confpaddle/gserver/tests/img_conv2_a.conf renamed to paddle/gserver/tests/img_conv_c.conf

@@ -166,9 +166,8 @@ TEST(Projection, scaling) {
   }
 }
 
-#ifndef PADDLE_ONLY_CPU
-TEST(Projection, conv) {
-  const int NUM_FILTERS = 16;
+void testProjectionConv(size_t groups) {
+  const int NUM_FILTERS = 18;
   const int FILTER_SIZE = 2;
   const int FILTER_SIZE_Y = 3;
   const int CHANNELS = 3;
@@ -186,7 +185,7 @@ TEST(Projection, conv) {
   conv->set_padding_y(1);
   conv->set_stride(2);
   conv->set_stride_y(2);
-  conv->set_groups(1);
+  conv->set_groups(groups);
   conv->set_filter_channels(conv->channels() / conv->groups());
   conv->set_img_size(IMAGE_SIZE);
   int output_x = outputSize(conv->img_size(),
@@ -206,59 +205,19 @@ TEST(Projection, conv) {
   testProjectionGrad(
       conf,
       INPUT_DATA,
-      /* parameterSize */ NUM_FILTERS * CHANNELS * FILTER_SIZE * FILTER_SIZE_Y,
+      /* parameterSize */ NUM_FILTERS * CHANNELS * FILTER_SIZE * FILTER_SIZE_Y
+                          / groups,
       /* batchSize */ 100,
       true,
       false,
       NUM_FILTERS,
       true);
 }
 
-TEST(Projection, conv2) {
-  const int NUM_FILTERS = 18;
-  const int FILTER_SIZE = 2;
-  const int FILTER_SIZE_Y = 3;
-  const int CHANNELS = 3;
-  const int IMAGE_SIZE = 16;
-
-  ProjectionConfig conf;
-  conf.set_type("conv");
-  conf.set_num_filters(NUM_FILTERS);
-
-  ConvConfig* conv = conf.mutable_conv_conf();
-  conv->set_filter_size(FILTER_SIZE);
-  conv->set_filter_size_y(FILTER_SIZE_Y);
-  conv->set_channels(CHANNELS);
-  conv->set_padding(0);
-  conv->set_padding_y(1);
-  conv->set_stride(2);
-  conv->set_stride_y(2);
-  conv->set_groups(3);
-  conv->set_filter_channels(conv->channels() / conv->groups());
-  conv->set_img_size(IMAGE_SIZE);
-  int output_x = outputSize(conv->img_size(),
-                            conv->filter_size(),
-                            conv->padding(),
-                            conv->stride(),
-                            /* caffeMode */ true);
-  int output_y = outputSize(conv->img_size(),
-                            conv->filter_size_y(),
-                            conv->padding_y(),
-                            conv->stride_y(),
-                            /* caffeMode */ true);
-  conv->set_output_x(output_x);
-  conf.set_input_size(IMAGE_SIZE * IMAGE_SIZE * CHANNELS);
-  conf.set_output_size(output_x * output_y * NUM_FILTERS);
-
-  testProjectionGrad(
-      conf,
-      INPUT_DATA,
-      /* parameterSize */ NUM_FILTERS * FILTER_SIZE * FILTER_SIZE_Y,
-      /* batchSize */ 100,
-      true,
-      false,
-      NUM_FILTERS,
-      true);
+#ifndef PADDLE_ONLY_CPU
+TEST(Projection, conv) {
+  testProjectionConv(1);
+  testProjectionConv(3);
 }
 #endif
 

paddle/gserver/tests/test_LayerGrad.cpp

@@ -256,9 +256,10 @@ TEST(Compare, img_conv) {
   FLAGS_use_gpu = useGpu;
 }
 
+// Test cudnn_conv and exconv give the same result
 TEST(Compare, img_conv2) {
-  std::string config_file_a = "./gserver/tests/img_conv2_a.conf";
-  std::string config_file_b = "./gserver/tests/img_conv2_b.conf";
+  std::string config_file_a = "./gserver/tests/img_conv_a.conf";
+  std::string config_file_b = "./gserver/tests/img_conv_c.conf";
   bool useGpu = FLAGS_use_gpu;
   FLAGS_use_gpu = true;
   compareNetwork(config_file_a, config_file_b);

paddle/gserver/tests/test_NetworkCompare.cpp

@@ -698,7 +698,8 @@ def calc_parameter_size(self, input_size, output_size):
         ci = self.proj_conf.conv_conf.channels
         fh = self.proj_conf.conv_conf.filter_size
         fw = self.proj_conf.conv_conf.filter_size_y
-        return co * ci * fh * fw
+        gr = self.proj_conf.conv_conf.groups
+        return co * ci * fh * fw / gr
 
     def calc_bias_size(self):
         return self.proj_conf.num_filters