Merge pull request #2686 from qingqing01/row_conv_fix

Fix bug for flowers dataset and row_conv.

python/paddle/trainer/config_parser.py

@@ -2082,10 +2082,10 @@ class MaxOutLayer(LayerBase):
 class RowConvLayer(LayerBase):
     def __init__(self, name, inputs, context_length, **xargs):
         super(RowConvLayer, self).__init__(
-            name, 'maxout', 0, inputs=inputs, **xargs)
+            name, 'row_conv', 0, inputs=inputs, **xargs)
         config_assert(
             len(self.inputs) == 1,
-            'TransLayer must have one and only one input')
+            'row convolution layer must have one and only one input.')
         input_layer = self.get_input_layer(0)
         row_conv_conf = self.config.inputs[0].row_conv_conf
         row_conv_conf.context_length = context_length

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

@@ -7,7 +7,7 @@ layers {
 }
 layers {
   name: "__row_conv_layer_0__"
-  type: "maxout"
+  type: "row_conv"
   size: 2560
   active_type: "relu"
   inputs {

python/paddle/v2/dataset/flowers.py

@@ -30,6 +30,7 @@ http://www.robots.ox.ac.uk/~vgg/publications/papers/nilsback08.{pdf,ps.gz}.
 """
 import cPickle
 import itertools
+import functools
 from common import download
 import tarfile
 import scipy.io as scio
@@ -54,21 +55,26 @@ TEST_FLAG = 'trnid'
 VALID_FLAG = 'valid'
 
 
-def default_mapper(sample):
+def default_mapper(is_train, sample):
     '''
     map image bytes data to type needed by model input layer
     '''
     img, label = sample
     img = load_image_bytes(img)
-    img = simple_transform(img, 256, 224, True)
+    img = simple_transform(
+        img, 256, 224, is_train, mean=[103.94, 116.78, 123.68])
     return img.flatten().astype('float32'), label
 
 
+train_mapper = functools.partial(default_mapper, True)
+test_mapper = functools.partial(default_mapper, False)
+
+
 def reader_creator(data_file,
                    label_file,
                    setid_file,
                    dataset_name,
-                   mapper=default_mapper,
+                   mapper,
                    buffered_size=1024,
                    use_xmap=True):
     '''
@@ -118,7 +124,7 @@ def reader_creator(data_file,
         return map_readers(mapper, reader)
 
 
-def train(mapper=default_mapper, buffered_size=1024, use_xmap=True):
+def train(mapper=train_mapper, buffered_size=1024, use_xmap=True):
     '''
     Create flowers training set reader.
     It returns a reader, each sample in the reader is
@@ -141,7 +147,7 @@ def train(mapper=default_mapper, buffered_size=1024, use_xmap=True):
         buffered_size, use_xmap)
 
 
-def test(mapper=default_mapper, buffered_size=1024, use_xmap=True):
+def test(mapper=test_mapper, buffered_size=1024, use_xmap=True):
     '''
     Create flowers test set reader.
     It returns a reader, each sample in the reader is
@@ -164,7 +170,7 @@ def test(mapper=default_mapper, buffered_size=1024, use_xmap=True):
         buffered_size, use_xmap)
 
 
-def valid(mapper=default_mapper, buffered_size=1024, use_xmap=True):
+def valid(mapper=test_mapper, buffered_size=1024, use_xmap=True):
     '''
     Create flowers validation set reader.
     It returns a reader, each sample in the reader is

python/paddle/v2/image.py

@@ -262,7 +262,12 @@ def left_right_flip(im):
         return im[:, ::-1, :]
 
 
-def simple_transform(im, resize_size, crop_size, is_train, is_color=True):
+def simple_transform(im,
+                     resize_size,
+                     crop_size,
+                     is_train,
+                     is_color=True,
+                     mean=None):
     """
     Simply data argumentation for training. These operations include
     resizing, croping and flipping.
@@ -288,8 +293,20 @@ def simple_transform(im, resize_size, crop_size, is_train, is_color=True):
             im = left_right_flip(im)
     else:
         im = center_crop(im, crop_size)
+    if len(im.shape) == 3:
         im = to_chw(im)
 
+    im = im.astype('float32')
+    if mean is not None:
+        mean = np.array(mean, dtype=np.float32)
+        # mean value, may be one value per channel 
+        if mean.ndim == 1:
+            mean = mean[:, np.newaxis, np.newaxis]
+        else:
+            # elementwise mean
+            assert len(mean.shape) == len(im)
+        im -= mean
+
     return im
 
 
@@ -297,7 +314,8 @@ def load_and_transform(filename,
                        resize_size,
                        crop_size,
                        is_train,
-                       is_color=True):
+                       is_color=True,
+                       mean=None):
     """
     Load image from the input file `filename` and transform image for
     data argumentation. Please refer to the `simple_transform` interface
@@ -318,5 +336,5 @@ def load_and_transform(filename,
     :type is_train: bool
     """
     im = load_image(filename)
-    im = simple_transform(im, resize_size, crop_size, is_train, is_color)
+    im = simple_transform(im, resize_size, crop_size, is_train, is_color, mean)
     return im