fix about box & imgaug

scripts/dump_train_config.py

@@ -26,6 +26,7 @@ parser.add_argument('--index',
 parser.add_argument('-n', '--number', help='number of images to dump',
                     default=10, type=int)
 args = parser.parse_args()
+logger.auto_set_dir(action='d')
 
 get_config_func = imp.load_source('config_script', args.config).get_config
 config = get_config_func()

tensorpack/dataflow/common.py

@@ -68,8 +68,15 @@ class BatchData(ProxyDataFlow):
                 tp = 'float32'
             else:
                 tp = dt.dtype
+            try:
                 result.append(
                     np.array([x[k] for x in data_holder], dtype=tp))
+            except KeyboardInterrupt:
+                raise
+            except:
+                logger.exception("Cannot batch data. Perhaps they are of inconsistent shape?")
+                import IPython as IP;
+                IP.embed(config=IP.terminal.ipapp.load_default_config())
         return result
 
 class FixedSizeData(ProxyDataFlow):

tensorpack/dataflow/dataset/ilsvrc.py

@@ -145,9 +145,10 @@ class ILSVRC12(RNGDataFlow):
         self.synset = meta.get_synset_1000()
 
         if include_bb:
+            bbdir = os.path.join(dir, 'bbox') if not \
+                    isinstance(include_bb, six.string_types) else 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.bblist = ILSVRC12.get_training_bbox(bbdir, self.imglist)
         self.include_bb = include_bb
 
     def size(self):
@@ -156,7 +157,7 @@ class ILSVRC12(RNGDataFlow):
     def get_data(self):
         """
         Produce original images of shape [h, w, 3], and label,
-        and optionally a bbox of [xmin, ymin, xmax, ymax] in [0, 1]
+        and optionally a bbox of [xmin, ymin, xmax, ymax]
         """
         idxs = np.arange(len(self.imglist))
         add_label_to_fname = (self.name != 'train' and self.dir_structure != 'original')
@@ -174,8 +175,8 @@ class ILSVRC12(RNGDataFlow):
                 im = np.expand_dims(im, 2).repeat(3,2)
             if self.include_bb:
                 bb = self.bblist[k]
-                if not bb:
-                    bb = [0, 0, 1, 1]
+                if bb is None:
+                    bb = [0, 0, im.shape[1]-1, im.shape[0]-1]
                 yield [im, label, bb]
             else:
                 yield [im, label]
@@ -191,10 +192,10 @@ class ILSVRC12(RNGDataFlow):
 
             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]
+            #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 ...'):

tensorpack/dataflow/imgaug/crop.py

@@ -8,7 +8,8 @@ from ...utils.rect import Rect
 from six.moves import range
 import numpy as np
 
-__all__ = ['RandomCrop', 'CenterCrop', 'FixedCrop', 'RandomCropRandomShape']
+__all__ = ['RandomCrop', 'CenterCrop', 'FixedCrop',
+        'RandomCropRandomShape', 'perturb_BB']
 
 class RandomCrop(ImageAugmentor):
     """ Randomly crop the image into a smaller one """

tensorpack/dataflow/imgaug/imgproc.py

@@ -98,7 +98,7 @@ class Gamma(ImageAugmentor):
         return self._rand_range(*self.range)
     def _augment(self, img, gamma):
         lut = ((np.arange(256, dtype='float32') / 255) ** (1. / (1. + gamma)) * 255).astype('uint8')
-        img = img.astype('uint8')
+        img = np.clip(img, 0, 255).astype('uint8')
         img = cv2.LUT(img, lut).astype('float32')
         return img
 

tensorpack/dataflow/imgaug/meta.py

@@ -25,6 +25,10 @@ class RandomApplyAug(ImageAugmentor):
         else:
             return (False, None)
 
+    def reset_state(self):
+        super(RandomApplyAug, self).reset_state()
+        self.aug.reset_state()
+
     def _augment(self, img, prm):
         if not prm[0]:
             return img
@@ -44,6 +48,11 @@ class RandomChooseAug(ImageAugmentor):
             prob = 1.0 / len(aug_lists)
             self._init(locals())
 
+    def reset_state(self):
+        super(RandomChooseAug, self).reset_state()
+        for a in self.aug_lists:
+            a.reset_state()
+
     def _get_augment_params(self, img):
         aug_idx = self.rng.choice(len(self.aug_lists), p=self.prob)
         aug_prm = self.aug_lists[aug_idx]._get_augment_params(img)

tensorpack/utils/logger.py

@@ -45,6 +45,7 @@ def get_time_str():
     return datetime.now().strftime('%m%d-%H%M%S')
 # logger file and directory:
 global LOG_FILE, LOG_DIR
+LOG_DIR = None
 def _set_file(path):
     if os.path.isfile(path):
         backup_name = path + '.' + get_time_str()
@@ -63,6 +64,7 @@ def set_logger_dir(dirname, action=None):
     """
     global LOG_FILE, LOG_DIR
     if os.path.isdir(dirname):
+        if not action:
             _logger.warn("""\
 Directory {} exists! Please either backup/delete it, or use a new directory. \
 If you're resuming from a previous run you can choose to keep it.""".format(dirname))
@@ -97,9 +99,11 @@ def disable_logger():
     for func in ['info', 'warning', 'error', 'critical', 'warn', 'exception', 'debug']:
         globals()[func] = lambda x: None
 
-def auto_set_dir(action=None):
+def auto_set_dir(action=None, overwrite_setting=False):
     """ set log directory to a subdir inside 'train_log', with the name being
     the main python file currently running"""
+    if LOG_DIR is not None and not overwrite_setting:
+        return
     mod = sys.modules['__main__']
     basename = os.path.basename(mod.__file__)
     set_logger_dir(

tensorpack/utils/rect.py

@@ -67,10 +67,11 @@ class Rect(object):
         return True
 
     def roi(self, img):
-        assert self.validate(img.shape[:2])
+        assert self.validate(img.shape[:2]), "{} vs {}".format(self, img.shape[:2])
         return img[self.y0:self.y1+1, self.x0:self.x1+1]
 
     def expand(self, frac):
+        assert frac > 1.0, frac
         neww = self.w * frac
         newh = self.h * frac
         newx = self.x - (neww - self.w) * 0.5