several bug fix and improvement

examples/Inception/inceptionv3.py

@@ -189,8 +189,7 @@ class Model(ModelDesc):
         wd_w = tf.train.exponential_decay(0.00004, get_global_step_var(),
                                           80000, 0.7, True)
         wd_cost = tf.mul(wd_w, regularize_cost('.*/W', tf.nn.l2_loss), name='l2_regularize_loss')
-        for k in [loss1, loss2, wd_cost]:
-            add_moving_summary(k)
+        add_moving_summary(loss1, loss2, wd_cost)
 
         self.cost = tf.add_n([0.4 * loss1, loss2, wd_cost], name='cost')
 

tensorpack/dataflow/dataset/bsds500.py

@@ -59,7 +59,7 @@ class BSDS500(RNGDataFlow):
         image_files = glob.glob(image_glob)
         gt_dir = os.path.join(self.data_root, 'groundTruth', name)
         self.data = np.zeros((len(image_files), IMG_H, IMG_W, 3), dtype='uint8')
-        self.label = np.zeros((len(image_files), IMG_H, IMG_W), dtype='bool')
+        self.label = np.zeros((len(image_files), IMG_H, IMG_W), dtype='float32')
 
         for idx, f in enumerate(image_files):
             im = cv2.imread(f, cv2.IMREAD_COLOR)
@@ -73,14 +73,15 @@ class BSDS500(RNGDataFlow):
             gt = loadmat(gt_file)['groundTruth'][0]
             n_annot = gt.shape[0]
             gt = sum(gt[k]['Boundaries'][0][0] for k in range(n_annot))
-            gt[gt < 3] = 0
-            gt[gt != 0] = 1
+            gt[gt > 3] = 3
+            gt = gt / 3.0
             if gt.shape[0] > gt.shape[1]:
                 gt = gt.transpose()
             assert gt.shape == (IMG_H, IMG_W)
 
             self.data[idx] = im
             self.label[idx] = gt
+        #self.label[self.label<0.9] = 0
 
     def size(self):
         return self.data.shape[0]

tensorpack/dataflow/image.py

@@ -57,6 +57,7 @@ class AugmentImageComponent(MapDataComponent):
 
 
 class AugmentImagesTogether(MapData):
+    """ Augment a list of images of the same shape, with the same parameters"""
     def __init__(self, ds, augmentors, index=(0,1)):
         """
         :param ds: a `DataFlow` instance.

tensorpack/dataflow/imgaug/geometry.py

@@ -11,7 +11,9 @@ __all__ = ['Rotation']
 
 class Rotation(ImageAugmentor):
     """ Random rotate the image w.r.t a random center"""
-    def __init__(self, max_deg, center_range=(0,1)):
+    def __init__(self, max_deg, center_range=(0,1),
+            interp=cv2.INTER_CUBIC,
+            border=cv2.BORDER_REPLICATE):
         """
         :param max_deg: max abs value of the rotation degree
         :param center_range: the location of the rotation center
@@ -25,6 +27,7 @@ class Rotation(ImageAugmentor):
         return cv2.getRotationMatrix2D(tuple(center), deg, 1)
 
     def _augment(self, img, rot_m):
-        return cv2.warpAffine(img, rot_m, img.shape[1::-1],
-                flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REPLICATE)
+        ret = cv2.warpAffine(img, rot_m, img.shape[1::-1],
+                flags=self.interp, borderMode=self.border)
+        return ret
 

tensorpack/dataflow/imgaug/noise.py

@@ -26,7 +26,7 @@ class GaussianNoise(ImageAugmentor):
         self._init(locals())
 
     def _get_augment_params(self, img):
-        return self.rng.randn(img.shape)
+        return self.rng.randn(*img.shape)
 
     def _augment(self, img, noise):
         ret = img + noise

tensorpack/models/pool.py

@@ -99,9 +99,9 @@ def FixedUnPooling(x, shape, unpool_mat=None):
     if unpool_mat is None:
         mat = np.zeros(shape, dtype='float32')
         mat[0][0] = 1
-        unpool_mat = tf.Variable(mat, trainable=False, name='unpool_mat')
+        unpool_mat = tf.constant(mat, name='unpool_mat')
     elif isinstance(unpool_mat, np.ndarray):
-        unpool_mat = tf.Variable(unpool_mat, trainable=False, name='unpool_mat')
+        unpool_mat = tf.constant(unpool_mat, name='unpool_mat')
     assert unpool_mat.get_shape().as_list() == list(shape)
 
     # perform a tensor-matrix kronecker product

tensorpack/tfutils/sessinit.py

@@ -150,7 +150,7 @@ class ParamRestore(SessionInit):
         variable_names = set([k.name for k in variables])
         param_names = set(six.iterkeys(self.prms))
 
-        intersect = variable_names and param_names
+        intersect = variable_names & param_names
 
         logger.info("Params to restore: {}".format(
             ', '.join(map(str, intersect))))
@@ -159,12 +159,13 @@ class ParamRestore(SessionInit):
         for k in param_names - variable_names:
             logger.warn("Variable {} in the dict not found in this graph!".format(k))
 
+
         upd = SessionUpdate(sess, [v for v in variables if v.name in intersect])
         logger.info("Restoring from dict ...")
         upd.update({name: value for name, value in six.iteritems(self.prms) if name in intersect})
 
 
-def ChainInit(SessionInit):
+class ChainInit(SessionInit):
     """ Init a session by a list of SessionInit instance."""
     def __init__(self, sess_inits, new_session=True):
         """

tensorpack/tfutils/summary.py

@@ -8,6 +8,7 @@ import re
 
 from ..utils import *
 from . import get_global_step_var
+from .symbolic_functions import rms
 
 __all__ = ['create_summary', 'add_param_summary', 'add_activation_summary',
            'add_moving_summary', 'summary_moving_average']
@@ -36,8 +37,7 @@ def add_activation_summary(x, name=None):
         tf.histogram_summary(name + '/activation', x)
         tf.scalar_summary(name + '/activation_sparsity', tf.nn.zero_fraction(x))
         tf.scalar_summary(
-                name + '/activation_rms',
-                tf.sqrt(tf.reduce_mean(tf.square(x))))
+                name + '/activation_rms', rms(x))
 
 def add_param_summary(summary_lists):
     """
@@ -64,12 +64,10 @@ def add_param_summary(summary_lists):
             tf.scalar_summary(name + '/mean', tf.reduce_mean(var))
             return
         if action == 'rms':
-            tf.scalar_summary(name + '/rms',
-                    tf.sqrt(tf.reduce_mean(tf.square(var))))
+            tf.scalar_summary(name + '/rms', rms(var))
             return
         raise RuntimeError("Unknown summary type: {}".format(action))
 
-    import re
     params = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)
     with tf.name_scope('param_summary'):
         for p in params:
@@ -84,6 +82,7 @@ def add_param_summary(summary_lists):
 def add_moving_summary(v, *args):
     """
     :param v: tensor or list of tensor to summary
+    :param args: tensors to summary
     """
     if not isinstance(v, list):
         v = [v]

tensorpack/utils/loadcaffe.py

@@ -64,11 +64,14 @@ def load_caffe(model_desc, model_file):
             prev_data_shape = net.blobs[prev_blob_name].data.shape[1:]
         except ValueError:
             prev_data_shape = None
+        logger.info("Processing layer {} of type {}".format(
+            layername, layer.type))
         if layer.type in param_processors:
             param_dict.update(param_processors[layer.type](
                 layername, layer.blobs, prev_data_shape))
         else:
-            assert len(layer.blobs) == 0, len(layer.blobs)
+            if len(layer.blobs) != 0:
+                logger.warn("Layer type {} not supported!".format(layer.type))
     logger.info("Model loaded from caffe. Params: " + \
                 " ".join(sorted(param_dict.keys())))
     return param_dict