bbox and tf_func

examples/ResNet/svhn-resnet.py

@@ -38,13 +38,12 @@ def get_data(train_or_test):
     if isTrain:
         augmentors = [
             imgaug.CenterPaste((40, 40)),
-            imgaug.RandomCrop((32, 32)),
-            #imgaug.Flip(horiz=True),
             imgaug.Brightness(10),
             imgaug.Contrast((0.8,1.2)),
-            imgaug.GaussianDeform(  # this is slow
+            imgaug.GaussianDeform(  # this is slow. without it, can only reach 1.9% error
                 [(0.2, 0.2), (0.2, 0.8), (0.8,0.8), (0.8,0.2)],
-                (32, 32), 0.2, 3),
+                (40, 40), 0.2, 3),
+            imgaug.RandomCrop((32, 32)),
             imgaug.MapImage(lambda x: x - pp_mean),
         ]
     else:

tensorpack/dataflow/dataset/ilsvrc.py

@@ -7,10 +7,12 @@ import tarfile
 import cv2
 import numpy as np
 from six.moves import range
+import xml.etree.ElementTree as ET
 
 from ...utils import logger, get_rng, get_dataset_dir, memoized
 from ...utils.loadcaffe import get_caffe_pb
 from ...utils.fs import mkdir_p, download
+from ...utils.timer import timed_operation
 from ..base import RNGDataFlow
 
 __all__ = ['ILSVRCMeta', 'ILSVRC12']
@@ -20,7 +22,6 @@ def log_once(s): logger.warn(s)
 
 CAFFE_ILSVRC12_URL = "http://dl.caffe.berkeleyvision.org/caffe_ilsvrc12.tar.gz"
 
-# TODO move caffe_pb outside
 class ILSVRCMeta(object):
     """
     Some metadata for ILSVRC dataset.
@@ -90,15 +91,16 @@ class ILSVRCMeta(object):
 
 class ILSVRC12(RNGDataFlow):
     def __init__(self, dir, name, meta_dir=None, shuffle=True,
-            dir_structure='original'):
+            dir_structure='original', include_bb=False):
         """
         :param dir: A directory containing a subdir named `name`, where the
             original ILSVRC12_`name`.tar gets decompressed.
         :param name: 'train' or 'val' or 'test'
-        :param dir_structure: the dir structure of 'val' or 'test'.
-            if is 'original' then keep the original decompressed dir with list
-            of image files. if equals to 'train', use the `train/` dir
+        :param dir_structure: The dir structure of 'val' or 'test'.
+            If is 'original' then keep the original decompressed dir with list
+            of image files (as below). If equals to 'train', use the `train/` dir
             structure with class name as subdirectories.
+        :param include_bb: Include the bounding box. Useful in training.
 
         Dir should have the following structure:
 
@@ -116,6 +118,11 @@ class ILSVRC12(RNGDataFlow):
               test/
                 ILSVRC2012_test_00000001.JPEG
                 ...
+              bbox/
+                n02134418/
+                  n02134418_198.xml
+                  ...
+                ...
 
         After decompress ILSVRC12_img_train.tar, you can use the following
         command to build the above structure for `train/`:
@@ -125,6 +132,7 @@ class ILSVRC12(RNGDataFlow):
             find -type f -name '*.tar' | parallel -P 10 'echo {} && mkdir -p {/.} && tar xf {} -C {/.}'
             Or:
             for i in *.tar; do dir=${i%.tar}; echo $dir; mkdir -p $dir; tar xf $i -C $dir; done
+
         """
         assert name in ['train', 'test', 'val']
         self.full_dir = os.path.join(dir, name)
@@ -136,12 +144,19 @@ class ILSVRC12(RNGDataFlow):
         self.dir_structure = dir_structure
         self.synset = meta.get_synset_1000()
 
+        if include_bb:
+            assert name == 'train', 'Bounding box only available for training'
+            self.bblist = ILSVRC12.get_training_bbox(
+                    os.path.join(dir, 'bbox'), self.imglist)
+        self.include_bb = include_bb
+
     def size(self):
         return len(self.imglist)
 
     def get_data(self):
         """
-        Produce original images or shape [h, w, 3], and label
+        Produce original images of shape [h, w, 3], and label,
+        and optionally a bbox of [xmin, ymin, xmax, ymax] in [0, 1]
         """
         idxs = np.arange(len(self.imglist))
         add_label_to_fname = (self.name != 'train' and self.dir_structure != 'original')
@@ -157,15 +172,55 @@ class ILSVRC12(RNGDataFlow):
             assert im is not None, fname
             if im.ndim == 2:
                 im = np.expand_dims(im, 2).repeat(3,2)
-            yield [im, label]
-
+            if self.include_bb:
+                bb = self.bblist[k]
+                if not bb:
+                    bb = [0, 0, 1, 1]
+                yield [im, label, bb]
+            else:
+                yield [im, label]
+
+    @staticmethod
+    def get_training_bbox(bbox_dir, imglist):
+        ret = []
+
+        def parse_bbox(fname):
+            root = ET.parse(fname).getroot()
+            size = root.find('size').getchildren()
+            size = map(int, [size[0].text, size[1].text])
+
+            box = root.find('object').find('bndbox').getchildren()
+            box = map(lambda x: float(x.text), box)
+            box[0] /= size[0]
+            box[1] /= size[1]
+            box[2] /= size[0]
+            box[3] /= size[1]
+            return np.asarray(box, dtype='float32')
+
+        with timed_operation('Loading Bounding Boxes ...'):
+            cnt = 0
+            import tqdm
+            for k in tqdm.trange(len(imglist)):
+                fname = imglist[k][0]
+                fname = fname[:-4] + 'xml'
+                fname = os.path.join(bbox_dir, fname)
+                try:
+                    ret.append(parse_bbox(fname))
+                    cnt += 1
+                except KeyboardInterrupt:
+                    raise
+                except:
+                    ret.append(None)
+            logger.info("{}/{} images have bounding box.".format(cnt, len(imglist)))
+        return ret
 
 if __name__ == '__main__':
     meta = ILSVRCMeta()
-    print(meta.get_per_pixel_mean())
     #print(meta.get_synset_words_1000())
 
-    #ds = ILSVRC12('/home/wyx/data/imagenet', 'val')
+    ds = ILSVRC12('/home/wyx/data/fake_ilsvrc/', 'train', include_bb=True,
+            shuffle=False)
+    ds.reset_state()
 
     for k in ds.get_data():
         from IPython import embed; embed()

tensorpack/dataflow/imgaug/base.py

@@ -16,7 +16,6 @@ class ImageAugmentor(object):
         self.reset_state()
 
     def _init(self, params=None):
-        self.reset_state()
         if params:
             for k, v in params.items():
                 if k != 'self':

tensorpack/dataflow/imgaug/noname.py

@@ -22,7 +22,7 @@ class Flip(ImageAugmentor):
         :param prob: probability of flip.
         """
         if horiz and vert:
-            raise ValueError("Please use two Flip, with both 0.5 prob")
+            raise ValueError("Please use two Flip instead.")
         elif horiz:
             self.code = 1
         elif vert:

tensorpack/dataflow/tf_func.py

+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# File: tf_func.py
+# Author: Yuxin Wu <ppwwyyxxc@gmail.com>
+
+from .base import ProxyDataFlow
+from ..utils import logger
+
+try:
+    import tensorflow as tf
+except ImportError:
+    logger.warn("Cannot import tensorflow. TFFuncMapper won't be available.")
+    __all__ = []
+else:
+    __all__ = ['TFFuncMapper']
+
+class TFFuncMapper(ProxyDataFlow):
+    def __init__(self, ds,
+            get_placeholders, symbf, apply_symbf_on_dp, device='/cpu:0'):
+        """
+        :param get_placeholders: a function returning the placeholders
+        :param symbf: a symbolic function taking the placeholders
+        :param apply_symbf_on_dp: apply the above function to datapoint
+        """
+        super(TFFuncMapper, self).__init__(ds)
+        self.get_placeholders = get_placeholders
+        self.symbf = symbf
+        self.apply_symbf_on_dp = apply_symbf_on_dp
+        self.device = device
+
+    def reset_state(self):
+        super(TFFuncMapper, self).reset_state()
+        self.graph = tf.Graph()
+        with self.graph.as_default(), \
+                tf.device(self.device):
+            self.placeholders = self.get_placeholders()
+            self.output_vars = self.symbf(self.placeholders)
+            self.sess = tf.Session()
+
+            def run_func(vals):
+                return self.sess.run(self.output_vars,
+                        feed_dict=dict(zip(self.placeholders, vals)))
+            self.run_func = run_func
+
+    def get_data(self):
+        for dp in self.ds.get_data():
+            dp = self.apply_symbf_on_dp(dp, self.run_func)
+            if dp:
+                yield dp
+
+
+if __name__ == '__main__':
+    from .raw import FakeData
+    from .prefetch import PrefetchDataZMQ
+    from .image import AugmentImageComponent
+    from . import imgaug
+    ds = FakeData([[224, 224, 3]], 100000, random=False)
+
+    def tf_aug(v):
+        v = v[0]
+        v = tf.image.random_brightness(v, 0.1)
+        v = tf.image.random_contrast(v, 0.8, 1.2)
+        v = tf.image.random_flip_left_right(v)
+        return v
+    ds = TFFuncMapper(ds,
+            lambda: [tf.placeholder(tf.float32, [224, 224, 3], name='img')],
+            tf_aug,
+            lambda dp, f: [f([dp[0]])[0]]
+            )
+    #ds = AugmentImageComponent(ds,
+            #[imgaug.Brightness(0.1, clip=False),
+             #imgaug.Contrast((0.8, 1.2), clip=False),
+             #imgaug.Flip(horiz=True)
+            #])
+    #ds = PrefetchDataZMQ(ds, 4)
+    ds.reset_state()
+
+    import tqdm
+    itr = ds.get_data()
+    for k in tqdm.trange(100000):
+        next(itr)