add config parse

proto/ModelConfig.proto

@@ -515,6 +515,8 @@ message LayerConfig {
 
   // for HuberRegressionLoss
   optional double delta = 57 [ default = 1.0 ];
+  // for 3D data
+  optional double depth = 58 [ default = 1 ];
 }
 
 message EvaluatorConfig {

python/paddle/trainer/config_parser.py

@@ -1172,6 +1172,20 @@ def get_img_size(input_layer_name, channels):
     return img_size, img_size_y
 
 
+def get_img3d_size(input_layer_name, channels):
+    input = g_layer_map[input_layer_name]
+    img_pixels = input.size / channels
+    img_size = input.width
+    img_size_y = input.height
+    img_size_z = input.depth
+
+    config_assert(
+        img_size * img_size_y * img_size_z == img_pixels,
+        "Input layer %s: Incorrect input image size %d * %d * %d for input image pixels %d"
+        % (input_layer_name, img_size, img_size_y, img_size_z, img_pixels))
+    return img_size, img_size_y, img_size_z
+
+
 def parse_bilinear(bilinear, input_layer_name, bilinear_conf):
     parse_image(bilinear, input_layer_name, bilinear_conf.image_conf)
     bilinear_conf.out_size_x = bilinear.out_size_x
@@ -1224,6 +1238,12 @@ def parse_image(image, input_layer_name, image_conf):
         get_img_size(input_layer_name, image_conf.channels)
 
 
+def parse_image3d(image, input_layer_name, image_conf):
+    image_conf.channels = image.channels
+    image_conf.img_size, image_conf.img_size_y, image_conf.img_size_z = \
+        get_img3d_size(input_layer_name, image_conf.channels)
+
+
 def parse_norm(norm, input_layer_name, norm_conf):
     norm_conf.norm_type = norm.norm_type
     config_assert(
@@ -1585,6 +1605,9 @@ class LayerBase(object):
         self.config.height = height
         self.config.width = width
 
+    def set_layer_depth(self, depth):
+        self.config.depth = depth
+
     def set_cnn_layer(self,
                       input_layer_name,
                       height,
@@ -1788,11 +1811,19 @@ class DetectionOutputLayer(LayerBase):
 
 @config_layer('data')
 class DataLayer(LayerBase):
-    def __init__(self, name, size, height=None, width=None, device=None):
+    def __init__(self,
+                 name,
+                 size,
+                 depth=None,
+                 height=None,
+                 width=None,
+                 device=None):
         super(DataLayer, self).__init__(
             name, 'data', size, inputs=[], device=device)
         if height and width:
             self.set_layer_height_width(height, width)
+        if depth:
+            self.set_layer_depth(depth)
 
 
 '''
@@ -2077,6 +2108,7 @@ class BatchNormLayer(LayerBase):
                  name,
                  inputs,
                  bias=True,
+                 img3D=False,
                  use_global_stats=True,
                  moving_average_fraction=0.9,
                  batch_norm_type=None,
@@ -2121,15 +2153,33 @@ class BatchNormLayer(LayerBase):
 
         input_layer = self.get_input_layer(0)
         image_conf = self.config.inputs[0].image_conf
-        parse_image(self.inputs[0].image, input_layer.name, image_conf)
-
-        # Only pass the width and height of input to batch_norm layer
-        # when either of it is non-zero.
-        if input_layer.width != 0 or input_layer.height != 0:
-            self.set_cnn_layer(name, image_conf.img_size_y, image_conf.img_size,
-                               image_conf.channels, False)
+        if img3D:
+            parse_image3d(self.inputs[0].image, input_layer.name, image_conf)
+            # Only pass the width and height of input to batch_norm layer
+            # when either of it is non-zero.
+            if input_layer.width != 0 or input_layer.height != 0:
+                self.set_cnn_layer(
+                    input_layer_name=name,
+                    depth=image_conf.img_size_z,
+                    height=image_conf.img_size_y,
+                    width=image_conf.img_size,
+                    channels=image_conf.channels,
+                    is_print=True)
+            else:
+                self.set_layer_size(input_layer.size)
         else:
-            self.set_layer_size(input_layer.size)
+            parse_image(self.inputs[0].image, input_layer.name, image_conf)
+            # Only pass the width and height of input to batch_norm layer
+            # when either of it is non-zero.
+            if input_layer.width != 0 or input_layer.height != 0:
+                self.set_cnn_layer(
+                    input_layer_name=name,
+                    height=image_conf.img_size_y,
+                    width=image_conf.img_size,
+                    channels=image_conf.channels,
+                    is_print=True)
+            else:
+                self.set_layer_size(input_layer.size)
 
         psize = self.calc_parameter_size(image_conf)
         dims = [1, psize]
@@ -2139,6 +2189,28 @@ class BatchNormLayer(LayerBase):
 
         self.create_bias_parameter(bias, psize)
 
+    def set_cnn_layer(self,
+                      input_layer_name,
+                      depth=None,
+                      height=None,
+                      width=None,
+                      channels=None,
+                      is_print=True):
+        depthIsNone = False
+        if depth is None:
+            depth = 1
+            depthIsNone = True
+        size = depth * height * width * channels
+        self.set_layer_size(size)
+        self.set_layer_height_width(height, width)
+        self.set_layer_depth(depth)
+        if is_print and depthIsNone:
+            print("output for %s: c = %d, h = %d, w = %d, size = %d" %
+                  (input_layer_name, channels, height, width, size))
+        elif is_print:
+            print("output for %s: c = %d, d = %d, h = %d, w = %d, size = %d" %
+                  (input_layer_name, channels, depth, height, width, size))
+
     def calc_parameter_size(self, image_conf):
         return image_conf.channels
 

python/paddle/trainer_config_helpers/layers.py

@@ -166,6 +166,7 @@ class LayerType(object):
     EXCONVTRANS_LAYER = 'exconvt'
     CUDNNCONV_LAYER = 'cudnn_conv'
     POOL_LAYER = 'pool'
+    POOL3D_LAYER = 'pool3d'
     BATCH_NORM_LAYER = 'batch_norm'
     NORM_LAYER = 'norm'
     SUM_TO_ONE_NORM_LAYER = 'sum_to_one_norm'
@@ -894,7 +895,8 @@ def mixed_layer(size=0,
 
 
 @layer_support()
-def data_layer(name, size, height=None, width=None, layer_attr=None):
+def data_layer(name, size, depth=None, height=None, width=None,
+               layer_attr=None):
     """
     Define DataLayer For NeuralNetwork.
 
@@ -921,15 +923,18 @@ def data_layer(name, size, height=None, width=None, layer_attr=None):
         type=LayerType.DATA,
         name=name,
         size=size,
+        depth=depth,
         height=height,
         width=width,
         **ExtraLayerAttribute.to_kwargs(layer_attr))
 
+    if depth is None:
+        depth = 1
     num_filters = None
     if height is not None and width is not None:
-        num_filters = size / (width * height)
-        assert num_filters * width * height == size, \
-            "size=%s width=%s height=%s" % (size, width, height)
+        num_filters = size / (width * height * depth)
+        assert num_filters * width * height * depth == size, \
+            "size=%s width=%s height=%s depth=%s" % (size, width, height, depth)
 
     return LayerOutput(name, LayerType.DATA, size=size, num_filters=num_filters)
 
@@ -2799,6 +2804,7 @@ def img_cmrnorm_layer(input,
 def batch_norm_layer(input,
                      act=None,
                      name=None,
+                     img3D=False,
                      num_channels=None,
                      bias_attr=None,
                      param_attr=None,
@@ -2885,6 +2891,7 @@ def batch_norm_layer(input,
            (batch_norm_type == "cudnn_batch_norm")
     l = Layer(
         name=name,
+        img3D=img3D,
         inputs=Input(
             input.name, image=Image(channels=num_channels), **param_attr.attr),
         active_type=act.name,

python/paddle/trainer_config_helpers/tests/configs/test_BatchNorm3D.py

+from paddle.trainer_config_helpers import *
+
+settings(batch_size=1000, learning_rate=1e-4)
+
+data = data_layer(name='data', size=180, width=30, height=6)
+#
+batchNorm = batch_norm_layer(data, num_channels=1)
+#
+outputs(batchNorm)
+
+# #
+data3D = data_layer(name='data3D22', size=120 * 3, width=20, height=6, depth=3)
+#
+print(data3D)
+batchNorm3D = batch_norm_layer(data3D, num_channels=1, img3D=True)
+#
+outputs(batchNorm3D)

python/paddle/trainer_config_helpers/tests/layers_test.py

@@ -16,4 +16,4 @@ from paddle.trainer.config_parser import parse_config_and_serialize
 
 if __name__ == '__main__':
     parse_config_and_serialize(
-        'trainer_config_helpers/tests/layers_test_config.py', '')
+        'trainer_config_helpers/tests/configs/test_BatchNorm3D.py', '')