Generate protostr automatically when adding a new test for trainer_config_helpers.

python/paddle/trainer_config_helpers/tests/configs/file_list.sh

@@ -5,6 +5,6 @@ last_first_seq test_expand_layer test_ntm_layers test_hsigmoid
 img_layers img_trans_layers util_layers simple_rnn_layers unused_layers test_cost_layers
 test_rnn_group shared_fc shared_lstm shared_gru test_cost_layers_with_weight
 test_spp_layer test_bilinear_interp test_maxout test_bi_grumemory math_ops
-test_seq_concat_reshape)
+test_seq_concat_reshape test_pad)
 
 export whole_configs=(test_split_datasource)

python/paddle/trainer_config_helpers/tests/configs/generate_protostr.sh

@@ -11,12 +11,18 @@ for conf in ${configs[*]}
 do
     echo "Generating " $conf
     $1 -m paddle.utils.dump_config $conf.py > $protostr/$conf.protostr.unittest
+    if [ ! -f "$protostr/$conf.protostr" ]; then 
+        cp $protostr/$conf.protostr.unittest $protostr/$conf.protostr
+    fi
     cat ${conf}.py |$1 test_config_parser_for_non_file_config.py > $protostr/$conf.protostr.non_file_config.unittest
 done
 
 for conf in ${whole_configs[*]}
 do
     echo "Generating " $conf
     $1 -m paddle.utils.dump_config $conf.py "" --whole > $protostr/$conf.protostr.unittest
+    if [ ! -f "$protostr/$conf.protostr" ]; then 
+        cp $protostr/$conf.protostr.unittest $protostr/$conf.protostr
+    fi
     cat ${conf}.py |$1 test_config_parser_for_non_file_config.py --whole > $protostr/$conf.protostr.non_file_config.unittest
 done

python/paddle/trainer_config_helpers/tests/configs/protostr/test_pad.protostr

@@ -0,0 +1,120 @@
+type: "nn"
+layers {
+  name: "data"
+  type: "data"
+  size: 2016
+  active_type: ""
+  height: 48
+  width: 42
+}
+layers {
+  name: "__conv_0__"
+  type: "exconv"
+  size: 32256
+  active_type: ""
+  inputs {
+    input_layer_name: "data"
+    input_parameter_name: "___conv_0__.w0"
+    conv_conf {
+      filter_size: 3
+      channels: 1
+      stride: 1
+      padding: 1
+      groups: 1
+      filter_channels: 1
+      output_x: 42
+      img_size: 42
+      caffe_mode: true
+      filter_size_y: 3
+      padding_y: 1
+      stride_y: 1
+      output_y: 48
+      img_size_y: 48
+    }
+  }
+  bias_parameter_name: "___conv_0__.wbias"
+  num_filters: 16
+  shared_biases: true
+  height: 48
+  width: 42
+}
+layers {
+  name: "__pool_0__"
+  type: "pool"
+  size: 8064
+  active_type: ""
+  inputs {
+    input_layer_name: "__conv_0__"
+    pool_conf {
+      pool_type: "max-projection"
+      channels: 16
+      size_x: 2
+      stride: 2
+      output_x: 21
+      img_size: 42
+      padding: 0
+      size_y: 2
+      stride_y: 2
+      output_y: 24
+      img_size_y: 48
+      padding_y: 0
+    }
+  }
+  height: 24
+  width: 21
+}
+layers {
+  name: "__pad_0__"
+  type: "pad"
+  size: 14175
+  active_type: ""
+  inputs {
+    input_layer_name: "__pool_0__"
+    pad_conf {
+      image_conf {
+        channels: 16
+        img_size: 21
+        img_size_y: 24
+      }
+      pad_c: 2
+      pad_c: 3
+      pad_h: 1
+      pad_h: 2
+      pad_w: 3
+      pad_w: 1
+    }
+  }
+  height: 27
+  width: 25
+}
+parameters {
+  name: "___conv_0__.w0"
+  size: 144
+  initial_mean: 0.0
+  initial_std: 0.471404520791
+  initial_strategy: 0
+  initial_smart: false
+}
+parameters {
+  name: "___conv_0__.wbias"
+  size: 16
+  initial_mean: 0.0
+  initial_std: 0.0
+  dims: 16
+  dims: 1
+  initial_strategy: 0
+  initial_smart: false
+}
+input_layer_names: "data"
+output_layer_names: "__pad_0__"
+sub_models {
+  name: "root"
+  layer_names: "data"
+  layer_names: "__conv_0__"
+  layer_names: "__pool_0__"
+  layer_names: "__pad_0__"
+  input_layer_names: "data"
+  output_layer_names: "__pad_0__"
+  is_recurrent_layer_group: false
+}
+

python/paddle/trainer_config_helpers/tests/configs/test_pad.py

@@ -2,7 +2,7 @@
 
 settings(batch_size=1000, learning_rate=1e-5)
 
-data = data_layer(name='data', size=2304, height=48, width=42)
+data = data_layer(name='data', size=2016, height=48, width=42)
 
 conv = img_conv_layer(
     input=data,
@@ -13,8 +13,7 @@
     act=LinearActivation(),
     bias_attr=True)
 
-pool = img_pool_layer(
-    input=conv, num_channels=8, pool_size=2, stride=2, pool_type=MaxPooling())
+pool = img_pool_layer(input=conv, pool_size=2, stride=2, pool_type=MaxPooling())
 
 pad = pad_layer(input=pool, pad_c=[2, 3], pad_h=[1, 2], pad_w=[3, 1])