minor changes on deconv implementation and add protostr test for deconv layer

paddle/gserver/layers/ExpandConvLayer.cpp

@@ -32,10 +32,7 @@ void ExpandConvLayer::forward(PassType passType) {
   Layer::forward(passType);
 
   /* malloc memory for the output_ if necessary */
-  /* note: one sample correspond to one colum, and the
+  int batchSize = inputLayers_[0]->getOutputValue()->getHeight();
-   *   transOutValue correspond sample to one row */
-  int batchSize = inputLayers_[0]->getOutputValue()->getWidth();
-  batchSize = inputLayers_[0]->getOutputValue()->getHeight();
   resetOutput(batchSize, getOutputSize());
 
   MatrixPtr image = nullptr;

paddle/gserver/layers/ExpandConvTransLayer.cpp

@@ -38,8 +38,6 @@ void ExpandConvTransLayer::forward(PassType passType) {
   Layer::forward(passType);
 
   /* malloc memory for the output_ if necessary */
-  /* note: one sample correspond to one colum, and the
-   *   transOutValue correspond sample to one row */
   int batchSize = inputLayers_[0]->getOutputValue()->getHeight();
   resetOutput(batchSize, getOutputSize());
 

paddle/gserver/layers/ExpandConvTransLayer.h

@@ -26,7 +26,7 @@ namespace paddle {
  * This layer expands input and use matrix multiplication to
  * calculate convolution transpose (deconv) operation.
  *
- * The config file api is img_convTrans_layer.
+ * The config file api is img_conv_layer with flag trans=True.
  */
 class ExpandConvTransLayer : public ExpandConvBaseLayer {
 public:

paddle/math/MathUtils.h

@@ -60,6 +60,10 @@ void sparseRand(int* major, int* minor, int nnz, int majorLen, int minorMax,
 int outputSize(int imageSize, int filterSize, int padding, int stride,
                bool caffeMode);
 
+/**
+ * Calculate image size based on output size and caffeMode_.
+ * It is the reverse function of outputSize()
+ */
 int imageSize(int outputSize, int filterSize, int padding, int stride,
               bool caffeMode);
 

python/paddle/trainer/config_parser.py

@@ -1017,6 +1017,17 @@ def cnn_output_size(img_size, filter_size, padding, stride, caffe_mode):
     else:
         return 1 + int(math.ceil(output))
 
+'''
+calcualte image_size based on output_size for convolution. 
+It is the reverse function of cnn_output_size
+'''
+def cnn_image_size(output_size, filter_size, padding, stride, caffe_mode):
+    if caffe_mode:
+        img_size = (output_size - 1) * stride + filter_size - 2 * padding
+    else:
+        img_size = (output_size - 2) * stride + filter_size - 2 * padding + 1 
+    return img_size
+
 def parse_pool(pool, input_layer_name, pool_conf):
     pool_conf.pool_type = pool.pool_type
     config_assert(pool.pool_type in ['max-projection', 'avg-projection',
@@ -1120,14 +1131,9 @@ def parse_conv(conv, input_layer_name, conv_conf, trans=False):
                       ("Input layer %s: Incorrect input image size %d for input "
                        + "image pixels %d")
                       % (input_layer_name, conv_conf.output_x, outputSize))
-        if conv.caffe_mode:
+        conv_conf.img_size = cnn_image_size(
-            conv_conf.img_size = \
+            conv_conf.output_x, conv_conf.filter_size, 
-                (conv_conf.output_x - 1) * conv.stride \
+            conv_conf.padding, conv_conf.stride, conv_conf.caffe_mode)
-                + conv.filter_size - 2 * conv.padding
-        else:
-            conv_conf.img_size = \
-                (conv_conf.output_x - 2) * conv.stride \
-                + conv.filter_size - 2 * conv.padding + 1 
 
 def parse_block_expand(block_expand, input_layer_name, block_expand_conf):
     block_expand_conf.channels = block_expand.channels
@@ -1656,12 +1662,6 @@ class ConvTransLayerBase(LayerBase):
         use_gpu = int(g_command_config_args.get("use_gpu", 0))
         parallel_nn = int(g_command_config_args.get("parallel_nn", 0))
 
-        # Automatically select cudnn_type for GPU and exconv for CPU
-        # if set type=conv, but still reserve the way user specify
-        # exconv or cudnn_conv manually.
-        if self.layer_type == "cudnn_convt":
-            config_assert(use_gpu, "cudnn_convt only support GPU")
-
         # cudnn_convt has not been implemented so use exconvt only
         self.layer_type = "exconvt"
         # need to specify layer in config

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

@@ -9,7 +9,7 @@ protostr=$PWD/protostr
 configs=(test_fc layer_activations projections test_print_layer
 test_sequence_pooling test_lstmemory_layer test_grumemory_layer
 last_first_seq test_expand_layer test_ntm_layers test_hsigmoid
-img_layers util_layers simple_rnn_layers unused_layers test_cost_layers
+img_layers img_trans_layers util_layers simple_rnn_layers unused_layers test_cost_layers
 test_rnn_group shared_fc shared_lstm test_cost_layers_with_weight
 test_maxout test_bi_grumemory math_ops)
 

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

@@ -0,0 +1,22 @@
+from paddle.trainer_config_helpers import *
+
+settings(
+    learning_rate=1e-3,
+    batch_size=1000
+)
+
+img = data_layer(name='image', size=227*227)
+
+# the parse_conv in config_parse.py is not strictly accurate when filter_size
+# is not square. So here set square filter_size.
+img_conv = img_conv_layer(input=img, num_channels=1, num_filters=64,
+                          filter_size=(32, 32), padding=(1, 1), stride=(1, 1),
+                          act=LinearActivation(), trans=True)
+img_bn = batch_norm_layer(input=img_conv, act=ReluActivation())
+
+img_norm = img_cmrnorm_layer(input=img_bn, size=32)
+
+img_pool = img_pool_layer(input=img_conv, pool_size=32, pool_type=MaxPooling())
+
+
+outputs(img_pool, img_norm)