more augmentor

examples/Inception/inception-bn.py

@@ -57,7 +57,7 @@ class Model(ModelDesc):
                 outs.append(x4)
                 return tf.concat(3, outs, name='concat')
 
-        with argscope(Conv2D, nl=BNReLU(), use_bias=False):
+        with argscope(Conv2D, nl=BNReLU, use_bias=False):
             l = Conv2D('conv0', image, 64, 7, stride=2)
             l = MaxPooling('pool0', l, 3, 2, padding='SAME')
             l = Conv2D('conv1', l, 64, 1)

examples/Inception/inceptionv3.py

@@ -36,7 +36,7 @@ class Model(ModelDesc):
 
     def _build_graph(self, input_vars):
         image, label = input_vars
-        image = image / 128.0 - 1   # ?
+        image = image / 255.0   # ?
 
         def proj_kk(l, k, ch_r, ch, stride=1):
             l = Conv2D('conv{0}{0}r'.format(k), l, ch_r, 1)
@@ -70,8 +70,8 @@ class Model(ModelDesc):
                 .Conv2D('conv277ba', ch_r, [7,1])
                 .Conv2D('conv277bb', ch, [1,7])())
 
-        nl = BNReLU(decay=0.9997, epsilon=1e-3)
-        with argscope(Conv2D, nl=nl, use_bias=False):
+        with argscope(Conv2D, nl=BNReLU, use_bias=False),\
+                argscope(BatchNorm, decay=0.9997, epsilon=1e-3):
             l = (LinearWrap(image)
                 .Conv2D('conv0', 32, 3, stride=2, padding='VALID') #299
                 .Conv2D('conv1', 32, 3, padding='VALID') #149
@@ -269,8 +269,8 @@ def get_config():
         callbacks=Callbacks([
             StatPrinter(), ModelSaver(),
             InferenceRunner(dataset_val, [
-                ClassificationError('wrong-top1', 'val-top1-error'),
-                ClassificationError('wrong-top5', 'val-top5-error')]),
+                ClassificationError('wrong-top1', 'val-error-top1'),
+                ClassificationError('wrong-top5', 'val-error-top5')]),
             ScheduledHyperParamSetter('learning_rate',
                                       [(5, 0.03), (9, 0.01), (12, 0.006),
                                        (17, 0.003), (22, 1e-3), (36, 2e-4),

examples/cifar-convnet.py

@@ -41,7 +41,7 @@ class Model(ModelDesc):
             tf.image_summary("train_image", image, 10)
 
         image = image / 4.0     # just to make range smaller
-        with argscope(Conv2D, nl=BNReLU(), use_bias=False, kernel_shape=3):
+        with argscope(Conv2D, nl=BNReLU, use_bias=False, kernel_shape=3):
             logits = LinearWrap(image) \
                     .Conv2D('conv1.1', out_channel=64) \
                     .Conv2D('conv1.2', out_channel=64) \

tensorpack/dataflow/dataset/ilsvrc.py

@@ -156,7 +156,7 @@ class ILSVRC12(RNGDataFlow):
 
     def get_data(self):
         """
-        Produce original images of shape [h, w, 3], and label,
+        Produce original images of shape [h, w, 3(BGR)], and label,
         and optionally a bbox of [xmin, ymin, xmax, ymax]
         """
         idxs = np.arange(len(self.imglist))

tensorpack/dataflow/imgaug/base.py

@@ -6,10 +6,10 @@ from abc import abstractmethod, ABCMeta
 from ...utils import get_rng
 from six.moves import zip
 
-__all__ = ['ImageAugmentor', 'AugmentorList']
+__all__ = ['Augmentor', 'ImageAugmentor', 'AugmentorList']
 
-class ImageAugmentor(object):
-    """ Base class for an image augmentor"""
+class Augmentor(object):
+    """ Base class for an augmentor"""
     __metaclass__ = ABCMeta
 
     def __init__(self):
@@ -24,37 +24,32 @@ class ImageAugmentor(object):
     def reset_state(self):
         self.rng = get_rng(self)
 
-    def augment(self, img):
+    def augment(self, d):
         """
-        Perform augmentation on the image in-place.
-        :param img: an [h,w] or [h,w,c] image
-        :returns: the augmented image, always of type 'float32'
+        Perform augmentation on the data.
         """
-        img, params = self._augment_return_params(img)
-        return img
+        d, params = self._augment_return_params(d)
+        return d
 
-    def _augment_return_params(self, img):
+    def _augment_return_params(self, d):
         """
         Augment the image and return both image and params
         """
-        prms = self._get_augment_params(img)
-        return (self._augment(img, prms), prms)
+        prms = self._get_augment_params(d)
+        return (self._augment(d, prms), prms)
 
     @abstractmethod
-    def _augment(self, img, param):
+    def _augment(self, d, param):
         """
         augment with the given param and return the new image
         """
 
-    def _get_augment_params(self, img):
+    def _get_augment_params(self, d):
         """
         get the augmentor parameters
         """
         return None
 
-    def _fprop_coord(self, coord, param):
-        return coord
-
     def _rand_range(self, low=1.0, high=None, size=None):
         if high is None:
             low, high = 0, low
@@ -62,6 +57,19 @@ class ImageAugmentor(object):
             size = []
         return self.rng.uniform(low, high, size)
 
+class ImageAugmentor(Augmentor):
+    def augment(self, img):
+        """
+        Perform augmentation on the image in-place.
+        :param img: an [h,w] or [h,w,c] image
+        :returns: the augmented image, always of type 'float32'
+        """
+        img, params = self._augment_return_params(img)
+        return img
+
+    def _fprop_coord(self, coord, param):
+        return coord
+
 class AugmentorList(ImageAugmentor):
     """
     Augment by a list of augmentors

tensorpack/dataflow/imgaug/imgproc.py

@@ -7,7 +7,7 @@ import numpy as np
 import cv2
 
 __all__ = ['Brightness', 'Contrast', 'MeanVarianceNormalize', 'GaussianBlur',
-        'Gamma', 'Clip']
+        'Gamma', 'Clip', 'Saturation']
 
 class Brightness(ImageAugmentor):
     """
@@ -111,9 +111,22 @@ class Gamma(ImageAugmentor):
 
 class Clip(ImageAugmentor):
     def __init__(self, min=0, max=255):
-        assert delta > 0
         self._init(locals())
 
     def _augment(self, img, _):
         img = np.clip(img, self.min, self.max)
         return img
+
+class Saturation(ImageAugmentor):
+    """ Saturation, see 'fb.resnet.torch' https://github.com/facebook/fb.resnet.torch/blob/master/datasets/transforms.lua#L218"""
+    def __init__(self, alpha=0.4):
+        super(Saturation, self).__init__()
+        assert alpha < 1
+        self._init(locals())
+
+    def _get_augment_params(self, _):
+        return 1 + self._rand_range(-self.alpha, self.alpha)
+
+    def _augment(self, img, v):
+        grey = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+        return img * v + (grey * (1 - v))[:,:,np.newaxis]

tensorpack/dataflow/imgaug/meta.py

@@ -6,7 +6,8 @@
 
 from .base import ImageAugmentor
 
-__all__ = ['RandomChooseAug', 'MapImage', 'Identity', 'RandomApplyAug']
+__all__ = ['RandomChooseAug', 'MapImage', 'Identity', 'RandomApplyAug',
+        'RandomOrderAug']
 
 class Identity(ImageAugmentor):
     def _augment(self, img, _):
@@ -15,8 +16,8 @@ class Identity(ImageAugmentor):
 class RandomApplyAug(ImageAugmentor):
     """ Randomly apply the augmentor with a prob. Otherwise do nothing"""
     def __init__(self, aug, prob):
-        super(RandomApplyAug, self).__init__()
         self._init(locals())
+        super(RandomApplyAug, self).__init__()
 
     def _get_augment_params(self, img):
         p = self.rng.rand()
@@ -41,7 +42,6 @@ class RandomChooseAug(ImageAugmentor):
         """
         :param aug_lists: list of augmentor, or list of (augmentor, probability) tuple
         """
-        super(RandomChooseAug, self).__init__()
         if isinstance(aug_lists[0], (tuple, list)):
             prob = [k[1] for k in aug_lists]
             aug_lists = [k[0] for k in aug_lists]
@@ -49,6 +49,7 @@ class RandomChooseAug(ImageAugmentor):
         else:
             prob = 1.0 / len(aug_lists)
             self._init(locals())
+        super(RandomChooseAug, self).__init__()
 
     def reset_state(self):
         super(RandomChooseAug, self).reset_state()
@@ -64,6 +65,34 @@ class RandomChooseAug(ImageAugmentor):
         idx, prm = prm
         return self.aug_lists[idx]._augment(img, prm)
 
+class RandomOrderAug(ImageAugmentor):
+    def __init__(self, aug_lists):
+        """
+        Shuffle the augmentors into random order.
+        :param aug_lists: list of augmentor, or list of (augmentor, probability) tuple
+        """
+        self._init(locals())
+        super(RandomOrderAug, self).__init__()
+
+    def reset_state(self):
+        super(RandomOrderAug, self).reset_state()
+        for a in self.aug_lists:
+            a.reset_state()
+
+    def _get_augment_params(self, img):
+        # Note: If augmentors change the shape of image, get_augment_param might not work
+        # All augmentors should only rely on the shape of image
+        idxs = self.rng.permutation(len(self.aug_lists))
+        prms = [self.aug_lists[k]._get_augment_params(img)
+                for k in range(len(self.aug_lists))]
+        return idxs, prms
+
+    def _augment(self, img, prm):
+        idxs, prms = prm
+        for k in idxs:
+            img = self.aug_lists[k]._augment(img, prms[k])
+        return img
+
 class MapImage(ImageAugmentor):
     """
     Map the image array by a function.

tensorpack/models/nonlin.py

@@ -63,15 +63,7 @@ def LeakyReLU(x, alpha, name=None):
         name = 'output'
     return tf.mul(x, 0.5, name=name)
 
-# I'm not a layer, but I return a nonlinearity.
-def BNReLU(is_training=None, **kwargs):
-    """
-    :param is_traning: boolean
-    :param kwargs: args for BatchNorm
-    :returns: an activation function that performs BN + ReLU (a too common combination)
-    """
-    def BNReLU(x, name=None):
-        x = BatchNorm('bn', x, use_local_stat=is_training, **kwargs)
+def BNReLU(x, name=None):
+    x = BatchNorm('bn', x, use_local_stat=None)
     x = tf.nn.relu(x, name=name)
     return x
-    return BNReLU