some more docs

tensorpack/callbacks/__init__.py

@@ -5,7 +5,7 @@
 from pkgutil import walk_packages
 import os
 
-def global_import(name):
+def _global_import(name):
     p = __import__(name, globals(), locals(), level=1)
     lst = p.__all__ if '__all__' in dir(p) else dir(p)
     for k in lst:
@@ -14,5 +14,5 @@ def global_import(name):
 for _, module_name, _ in walk_packages(
         [os.path.dirname(__file__)]):
     if not module_name.startswith('_'):
-        global_import(module_name)
+        _global_import(module_name)
 

tensorpack/callbacks/summary.py

@@ -73,7 +73,7 @@ class StatPrinter(Callback):
     """
     def __init__(self, print_tag=None):
         """
-        :param print_tag : a list of regex to match scalar summary to print.
+        :param print_tag: a list of regex to match scalar summary to print.
             If None, will print all scalar tags
         """
         self.print_tag = print_tag

tensorpack/dataflow/__init__.py

@@ -9,7 +9,7 @@ import os.path
 from . import dataset
 from . import imgaug
 
-def global_import(name):
+def _global_import(name):
     p = __import__(name, globals(), locals(), level=1)
     lst = p.__all__ if '__all__' in dir(p) else dir(p)
     for k in lst:
@@ -20,5 +20,5 @@ for _, module_name, _ in walk_packages(
         [os.path.dirname(__file__)]):
     if not module_name.startswith('_') and \
         module_name not in __SKIP:
-        global_import(module_name)
+        _global_import(module_name)
 

tensorpack/dataflow/base.py

@@ -9,12 +9,13 @@ from abc import abstractmethod, ABCMeta
 __all__ = ['DataFlow', 'ProxyDataFlow']
 
 class DataFlow(object):
+    """ Base class for all DataFlow """
     __metaclass__ = ABCMeta
 
     @abstractmethod
     def get_data(self):
         """
-        A generator to generate data as tuple.
+        A generator to generate data as a list.
         """
 
     def size(self):
@@ -30,10 +31,17 @@ class DataFlow(object):
         pass
 
 class ProxyDataFlow(DataFlow):
+    """ Base class for DataFlow that proxies another"""
     def __init__(self, ds):
+        """
+        :param ds: a :mod:`DataFlow` instance to proxy
+        """
         self.ds = ds
 
     def reset_state(self):
+        """
+        Will reset state of the proxied DataFlow
+        """
         self.ds.reset_state()
 
     def size(self):

tensorpack/dataflow/common.py

@@ -14,10 +14,12 @@ __all__ = ['BatchData', 'FixedSizeData', 'FakeData', 'MapData',
 class BatchData(ProxyDataFlow):
     def __init__(self, ds, batch_size, remainder=False):
         """
-        Group data in ds into batches
-        ds: a DataFlow instance
-        remainder: whether to return the remaining data smaller than a batch_size.
-            if set True, will possibly return a data point of a smaller 1st dimension
+        Group data in `ds` into batches.
+
+        :param ds: a DataFlow instance
+        :param remainder: whether to return the remaining data smaller than a batch_size.
+            If set True, will possibly return a data point of a smaller 1st dimension.
+            Otherwise, all generated data are guranteed to have the same size.
         """
         super(BatchData, self).__init__(ds)
         if not remainder:
@@ -34,17 +36,20 @@ class BatchData(ProxyDataFlow):
         return div + int(self.remainder)
 
     def get_data(self):
+        """
+        :returns: produce batched data by tiling data on an extra 0th dimension.
+        """
         holder = []
         for data in self.ds.get_data():
             holder.append(data)
             if len(holder) == self.batch_size:
-                yield BatchData.aggregate_batch(holder)
+                yield BatchData._aggregate_batch(holder)
                 holder = []
         if self.remainder and len(holder) > 0:
-            yield BatchData.aggregate_batch(holder)
+            yield BatchData._aggregate_batch(holder)
 
     @staticmethod
-    def aggregate_batch(data_holder):
+    def _aggregate_batch(data_holder):
         size = len(data_holder[0])
         result = []
         for k in range(size):
@@ -60,16 +65,23 @@ class BatchData(ProxyDataFlow):
         return result
 
 class FixedSizeData(ProxyDataFlow):
-    """ generate data from another dataflow, but with a fixed epoch size"""
+    """ Generate data from another DataFlow, but with a fixed epoch size"""
     def __init__(self, ds, size):
+        """
+        :param ds: a :mod:`DataFlow` to produce data
+        :param size: a int
+        """
         super(FixedSizeData, self).__init__(ds)
-        self._size = size
+        self._size = int(size)
         self.itr = None
 
     def size(self):
         return self._size
 
     def get_data(self):
+        """
+        Produce data from ds, stop at size
+        """
         if self.itr is None:
             self.itr = self.ds.get_data()
         cnt = 0
@@ -86,10 +98,15 @@ class FixedSizeData(ProxyDataFlow):
                 return
 
 class RepeatedData(ProxyDataFlow):
-    """ repeat another dataflow for certain times
-        if nr == -1, repeat infinitely many times
+    """ Take data points from another `DataFlow` and produce them until
+        it's exhausted for certain amount of times.
     """
     def __init__(self, ds, nr):
+        """
+        :param ds: a :mod:`DataFlow` instance.
+        :param nr: number of times to repeat ds.
+            If nr == -1, repeat ds infinitely many times.
+        """
         self.nr = nr
         super(RepeatedData, self).__init__(ds)
 
@@ -109,13 +126,14 @@ class RepeatedData(ProxyDataFlow):
                     yield dp
 
 class FakeData(DataFlow):
-    """ Build fake random data of given shapes"""
+    """ Generate fake random data of given shapes"""
     def __init__(self, shapes, size):
         """
-        shapes: list of list/tuple
+        :param shapes: a list of lists/tuples
+        :param size: size of this DataFlow
         """
         self.shapes = shapes
-        self._size = size
+        self._size = int(size)
         self.rng = get_rng(self)
 
     def size(self):
@@ -126,8 +144,12 @@ class FakeData(DataFlow):
             yield [self.rng.random_sample(k) for k in self.shapes]
 
 class MapData(ProxyDataFlow):
-    """ Map a function to the datapoint"""
+    """ Map a function on the datapoint"""
     def __init__(self, ds, func):
+        """
+        :param ds: a :mod:`DataFlow` instance.
+        :param func: a function that takes a original datapoint, returns a new datapoint
+        """
         super(MapData, self).__init__(ds)
         self.func = func
 
@@ -138,6 +160,10 @@ class MapData(ProxyDataFlow):
 class MapDataComponent(ProxyDataFlow):
     """ Apply a function to the given index in the datapoint"""
     def __init__(self, ds, func, index=0):
+        """
+        :param ds: a :mod:`DataFlow` instance.
+        :param func: a function that takes a datapoint dp[index], returns a new value of dp[index]
+        """
         super(MapDataComponent, self).__init__(ds)
         self.func = func
         self.index = index
@@ -150,11 +176,12 @@ class MapDataComponent(ProxyDataFlow):
 
 class RandomChooseData(DataFlow):
     """
-    Randomly choose from several dataflow. Stop producing when any of its dataflow stops.
+    Randomly choose from several DataFlow. Stop producing when any of them is
+    exhausted.
     """
     def __init__(self, df_lists):
         """
-        df_lists: list of dataflow, or list of (dataflow, probability) tuple
+        :param df_lists: list of dataflow, or list of (dataflow, probability) tuple
         """
         if isinstance(df_lists[0], (tuple, list)):
             assert sum([v[1] for v in df_lists]) == 1.0
@@ -184,13 +211,12 @@ class RandomChooseData(DataFlow):
 
 class RandomMixData(DataFlow):
     """
-    Randomly choose from several dataflow, will eventually exhaust all dataflow.
-    So it's a perfect mix.
+    Randomly choose from several dataflow, and will eventually exhaust all dataflow.  So it's a perfect mix.
     """
     def __init__(self, df_lists):
         """
-        df_lists: list of dataflow
-        all DataFlow in df_lists must have size() implemented
+        :param df_lists: list of dataflow.
+            All DataFlow in `df_lists` must have :func:`size()` implemented
         """
         self.df_lists = df_lists
         self.sizes = [k.size() for k in self.df_lists]
@@ -217,11 +243,11 @@ class RandomMixData(DataFlow):
 
 class JoinData(DataFlow):
     """
-    Concatenate several dataflows
+    Concatenate several dataflows.
     """
     def __init__(self, df_lists):
         """
-        df_lists: list of dataflow
+        :param df_lists: list of :mod:`DataFlow` instances
         """
         self.df_lists = df_lists
 

tensorpack/dataflow/dftools.py

@@ -9,8 +9,12 @@ from ..utils.fs import mkdir_p
 
 # TODO name_func to write label?
 def dump_dataset_images(ds, dirname, max_count=None, index=0):
-    """ dump images to a folder
-        index: the index of the image in a data point
+    """ Dump images from a `DataFlow` to a directory.
+
+        :param ds: a `DataFlow` instance.
+        :param dirname: name of the directory.
+        :param max_count: max number of images to dump
+        :param index: the index of the image component in a data point.
     """
     mkdir_p(dirname)
     if max_count is None:

tensorpack/dataflow/format.py

@@ -22,9 +22,14 @@ __all__ = ['HDF5Data']
 
 class HDF5Data(DataFlow):
     """
-    Zip data from different paths in this HDF5 data file
+    Zip data from different paths in an HDF5 file. Will load all data into memory.
     """
     def __init__(self, filename, data_paths, shuffle=True):
+        """
+        :param filename: h5 data file.
+        :param data_paths: list of h5 paths to zipped. For example ['images', 'labels']
+        :param shuffle: shuffle the order of all data.
+        """
         self.f = h5py.File(filename, 'r')
         logger.info("Loading {} to memory...".format(filename))
         self.dps = [self.f[k].value for k in data_paths]

tensorpack/dataflow/image.py

@@ -12,11 +12,11 @@ from .imgaug import AugmentorList, Image
 __all__ = ['ImageFromFile', 'AugmentImageComponent']
 
 class ImageFromFile(DataFlow):
-    """ generate rgb images from files """
+    """ Generate rgb images from list of files """
     def __init__(self, files, channel=3, resize=None):
-        """ files: list of file path
-            channel: 1 or 3 channel
-            resize: a (h, w) tuple. If given, will force a resize
+        """ :param files: list of file paths
+            :param channel: 1 or 3 channel
+            :param resize: a (h, w) tuple. If given, will force a resize
         """
         assert len(files)
         self.files = files
@@ -39,13 +39,14 @@ class ImageFromFile(DataFlow):
 
 class AugmentImageComponent(MapDataComponent):
     """
-    Augment image in each data point
-    Args:
-        ds: a DataFlow dataset instance
-        augmentors: a list of ImageAugmentor instance
-        index: the index of image in each data point. default to be 0
+    Augment the image component of datapoints
     """
     def __init__(self, ds, augmentors, index=0):
+        """
+        :param ds: a `DataFlow` instance.
+        :param augmentors: a list of `ImageAugmentor` instance to be applied in order.
+        :param index: the index of the image component in the produced datapoints by `ds`. default to be 0
+        """
         self.augs = AugmentorList(augmentors)
         super(AugmentImageComponent, self).__init__(
             ds, lambda x: self.augs.augment(Image(x)).arr, index)

tensorpack/dataflow/imgaug/base.py

@@ -8,14 +8,20 @@ from ...utils import get_rng
 __all__ = ['Image', 'ImageAugmentor', 'AugmentorList']
 
 class Image(object):
-    """ An image with attributes, for augmentor to operate on
-        Attributes (such as coordinates) have to be augmented acoordingly, if necessary
+    """ An image class with attributes, for augmentor to operate on.
+        Attributes (such as coordinates) have to be augmented acoordingly by
+        the augmentor, if necessary.
     """
     def __init__(self, arr, coords=None):
+        """
+        :param arr: the image array. Expected to be of [h, w, c] or [h, w]
+        :param coords: keypoint coordinates.
+        """
         self.arr = arr
         self.coords = coords
 
 class ImageAugmentor(object):
+    """ Base class for an image augmentor"""
     __metaclass__ = ABCMeta
 
     def __init__(self):
@@ -33,19 +39,17 @@ class ImageAugmentor(object):
 
     def augment(self, img):
         """
-        Note: will both modify `img` in-place and return `img`
+        Perform augmentation on the image in-place.
+        :param img: an `Image` instance.
+        :returns: the augmented `Image` instance. arr will always be of type
+        'float32' after augmentation.
         """
         self._augment(img)
         return img
 
     @abstractmethod
     def _augment(self, img):
-        """
-        Augment the image in-place. Will always make it float32 array.
-        Args:
-            img: the input Image instance
-                img.arr must be of shape [h, w] or [h, w, c]
-        """
+        pass
 
     def _rand_range(self, low=1.0, high=None, size=None):
         if high is None:
@@ -59,6 +63,9 @@ class AugmentorList(ImageAugmentor):
     Augment by a list of augmentors
     """
     def __init__(self, augmentors):
+        """
+        :param augmentors: list of `ImageAugmentor` instance to be applied
+        """
         self.augs = augmentors
 
     def _augment(self, img):
@@ -68,5 +75,6 @@ class AugmentorList(ImageAugmentor):
             aug.augment(img)
 
     def reset_state(self):
+        """ Will reset state of each augmentor """
         for a in self.augs:
             a.reset_state()

tensorpack/dataflow/imgaug/crop.py

@@ -14,8 +14,7 @@ class RandomCrop(ImageAugmentor):
     """ Randomly crop the image into a smaller one """
     def __init__(self, crop_shape):
         """
-        Args:
-            crop_shape: shape in (h, w)
+        :param crop_shape: a shape like (h, w)
         """
         self._init(locals())
 
@@ -28,8 +27,11 @@ class RandomCrop(ImageAugmentor):
             raise NotImplementedError()
 
 class CenterCrop(ImageAugmentor):
-    """ Crop the image in the center"""
+    """ Crop the image at the center"""
     def __init__(self, crop_shape):
+        """
+        :param crop_shape: a shape like (h, w)
+        """
         self._init(locals())
 
     def _augment(self, img):
@@ -43,6 +45,12 @@ class CenterCrop(ImageAugmentor):
 class FixedCrop(ImageAugmentor):
     """ Crop a rectangle at a given location"""
     def __init__(self, rangex, rangey):
+        """
+        Two arguments defined the range in both axes to crop, min inclued, max excluded.
+
+        :param rangex: like (xmin, xmax).
+        :param rangey: like (ymin, ymax).
+        """
         self._init(locals())
 
     def _augment(self, img):
@@ -53,17 +61,30 @@ class FixedCrop(ImageAugmentor):
             raise NotImplementedError()
 
 class BackgroundFiller(object):
-    @abstractmethod
-    def fill(background_shape, img):
+    """ Base class for all BackgroundFiller"""
+    def fill(self, background_shape, img):
         """
-        return a proper background image of background_shape, given img
+        Return a proper background image of background_shape, given img
+
+        :param background_shape: a shape of [h, w]
+        :param img: an image
+        :returns: a background image
         """
+        return self._fill(background_shape, img)
+
+    @abstractmethod
+    def _fill(self, background_shape, img):
+        pass
 
 class ConstantBackgroundFiller(BackgroundFiller):
+    """ Fill the background by a constant """
     def __init__(self, value):
+        """
+        :param value: the value to fill the background.
+        """
         self.value = value
 
-    def fill(self, background_shape, img):
+    def _fill(self, background_shape, img):
         assert img.ndim in [3, 1]
         if img.ndim == 3:
             return_shape = background_shape + (3,)
@@ -73,9 +94,13 @@ class ConstantBackgroundFiller(BackgroundFiller):
 
 class CenterPaste(ImageAugmentor):
     """
-    Paste the image onto center of a background
+    Paste the image onto the center of a background canvas.
     """
     def __init__(self, background_shape, background_filler=None):
+        """
+        :param background_shape: shape of the background canvas.
+        :param background_filler: a `BackgroundFiller` instance. Default to zero-filler.
+        """
         if background_filler is None:
             background_filler = ConstantBackgroundFiller(0)
 

tensorpack/dataflow/imgaug/deform.py

@@ -10,6 +10,7 @@ __all__ = ['GaussianDeform', 'GaussianMap']
 # TODO really needs speedup
 
 class GaussianMap(object):
+    """ Generate gaussian weighted deformation map"""
     def __init__(self, image_shape, sigma=0.5):
         assert len(image_shape) == 2
         self.shape = image_shape
@@ -53,14 +54,14 @@ def np_sample(img, coords):
 # TODO input/output with different shape
 class GaussianDeform(ImageAugmentor):
     """
-    Some kind of deformation
+    Some kind of deformation. Quite slow.
     """
-    #TODO docs
     def __init__(self, anchors, shape, sigma=0.5, randrange=None):
         """
-        anchors: in [0,1] coordinate
-        shape: 2D image shape
-        randrange: default to shape[0] / 8
+        :param anchors: in [0,1] coordinate
+        :param shape: image shape in [h, w]
+        :param sigma: sigma for Gaussian weight
+        :param randrange: default to shape[0] / 8
         """
         super(GaussianDeform, self).__init__()
         self.anchors = anchors

tensorpack/dataflow/imgaug/imgproc.py

@@ -9,9 +9,12 @@ __all__ = ['BrightnessAdd', 'Contrast', 'MeanVarianceNormalize']
 
 class BrightnessAdd(ImageAugmentor):
     """
-    Randomly add a value within [-delta,delta], and clip in [0,255]
+    Random adjust brightness.
     """
     def __init__(self, delta, clip=True):
+        """
+        Randomly add a value within [-delta,delta], and clip in [0,255] if clip is True.
+        """
         assert delta > 0
         self._init(locals())
 
@@ -27,6 +30,10 @@ class Contrast(ImageAugmentor):
     and clip to [0, 255]
     """
     def __init__(self, factor_range, clip=True):
+        """
+        :param factor_range: an interval to random sample the `contrast_factor`.
+        :param clip: boolean.
+        """
         self._init(locals())
 
     def _augment(self, img):
@@ -44,6 +51,9 @@ class MeanVarianceNormalize(ImageAugmentor):
     where adjusted_stddev = max(stddev, 1.0/sqrt(num_pixels * channels))
     """
     def __init__(self, all_channel=True):
+        """
+        :param all_channel: if True, normalize all channels together. else separately.
+        """
         self.all_channel = all_channel
 
     def _augment(self, img):

tensorpack/dataflow/imgaug/noname.py

@@ -9,11 +9,16 @@ import cv2
 __all__ = ['Flip', 'MapImage', 'Resize']
 
 class Flip(ImageAugmentor):
-    def __init__(self, horiz=False, vert=False, prob=0.5):
     """
     Random flip.
-        Args:
-            horiz, vert: True/False
+    """
+    def __init__(self, horiz=False, vert=False, prob=0.5):
+        """
+        Only one of horiz, vert can be set.
+
+        :param horiz: whether or not apply horizontal flip.
+        :param vert: whether or not apply vertical flip.
+        :param prob: probability of flip.
         """
         if horiz and vert:
             raise ValueError("Please use two Flip, with both 0.5 prob")
@@ -34,7 +39,13 @@ class Flip(ImageAugmentor):
 
 
 class MapImage(ImageAugmentor):
+    """
+    Map the image array by a function.
+    """
     def __init__(self, func):
+        """
+        :param func: a function which takes a image array and return a augmented one
+        """
         self.func = func
 
     def _augment(self, img):
@@ -42,11 +53,10 @@ class MapImage(ImageAugmentor):
 
 
 class Resize(ImageAugmentor):
-    """Resize image to a target size"""
+    """ Resize image to a target size"""
     def __init__(self, shape):
         """
-        Args:
-            shape: (h, w)
+        :param shape: shape in (h, w)
         """
         self._init(locals())
 

tensorpack/dataflow/prefetch.py

@@ -15,8 +15,8 @@ class Sentinel:
 class PrefetchProcess(multiprocessing.Process):
     def __init__(self, ds, queue):
         """
-        ds: ds to take data from
-        queue: output queue to put results in
+        :param ds: ds to take data from
+        :param queue: output queue to put results in
         """
         super(PrefetchProcess, self).__init__()
         self.ds = ds
@@ -30,11 +30,15 @@ class PrefetchProcess(multiprocessing.Process):
         finally:
             self.queue.put(Sentinel())
 
-
 class PrefetchData(DataFlow):
+    """
+    Prefetch data from a `DataFlow` using multiprocessing
+    """
     def __init__(self, ds, nr_prefetch, nr_proc=1):
         """
-        use multiprocess
+        :param ds: a `DataFlow` instance.
+        :param nr_prefetch: size of the queue to hold prefetched datapoints.
+        :param nr_proc: number of processes to use.
         """
         self.ds = ds
         self._size = self.ds.size()