--- a
+++ b/darkflow/net/yolov2/data.py
@@ -0,0 +1,86 @@
+from ...utils.pascal_voc_clean_xml import pascal_voc_clean_xml
+from numpy.random import permutation as perm
+from ..yolo.predict import preprocess
+from ..yolo.data import shuffle
+from copy import deepcopy
+import pickle
+import numpy as np
+import os
+
+
+def _batch(self, chunk):
+    """
+    Takes a chunk of parsed annotations
+    returns value for placeholders of net's 
+    input & loss layer correspond to this chunk
+    """
+    meta = self.meta
+    labels = meta['labels']
+
+    H, W, _ = meta['out_size']
+    C, B = meta['classes'], meta['num']
+    anchors = meta['anchors']
+
+    # preprocess
+    jpg = chunk[0];
+    w, h, allobj_ = chunk[1]
+    allobj = deepcopy(allobj_)
+    path = os.path.join(self.FLAGS.dataset, jpg)
+    img = self.preprocess(path, allobj)
+
+    # Calculate regression target
+    cellx = 1. * w / W
+    celly = 1. * h / H
+    for obj in allobj:
+        centerx = .5 * (obj[1] + obj[3])  # xmin, xmax
+        centery = .5 * (obj[2] + obj[4])  # ymin, ymax
+        cx = centerx / cellx
+        cy = centery / celly
+        if cx >= W or cy >= H: return None, None
+        obj[3] = float(obj[3] - obj[1]) / w
+        obj[4] = float(obj[4] - obj[2]) / h
+        obj[3] = np.sqrt(obj[3])
+        obj[4] = np.sqrt(obj[4])
+        obj[1] = cx - np.floor(cx)  # centerx
+        obj[2] = cy - np.floor(cy)  # centery
+        obj += [int(np.floor(cy) * W + np.floor(cx))]
+
+    # show(im, allobj, S, w, h, cellx, celly) # unit test
+
+    # Calculate placeholders' values
+    probs = np.zeros([H * W, B, C])
+    confs = np.zeros([H * W, B])
+    coord = np.zeros([H * W, B, 4])
+    proid = np.zeros([H * W, B, C])
+    prear = np.zeros([H * W, 4])
+    for obj in allobj:
+        probs[obj[5], :, :] = [[0.] * C] * B
+        probs[obj[5], :, labels.index(obj[0])] = 1.
+        proid[obj[5], :, :] = [[1.] * C] * B
+        coord[obj[5], :, :] = [obj[1:5]] * B
+        prear[obj[5], 0] = obj[1] - obj[3] ** 2 * .5 * W  # xleft
+        prear[obj[5], 1] = obj[2] - obj[4] ** 2 * .5 * H  # yup
+        prear[obj[5], 2] = obj[1] + obj[3] ** 2 * .5 * W  # xright
+        prear[obj[5], 3] = obj[2] + obj[4] ** 2 * .5 * H  # ybot
+        confs[obj[5], :] = [1.] * B
+
+    # Finalise the placeholders' values
+    upleft = np.expand_dims(prear[:, 0:2], 1)
+    botright = np.expand_dims(prear[:, 2:4], 1)
+    wh = botright - upleft;
+    area = wh[:, :, 0] * wh[:, :, 1]
+    upleft = np.concatenate([upleft] * B, 1)
+    botright = np.concatenate([botright] * B, 1)
+    areas = np.concatenate([area] * B, 1)
+
+    # value for placeholder at input layer
+    inp_feed_val = img
+    # value for placeholder at loss layer 
+    loss_feed_val = {
+        'probs': probs, 'confs': confs,
+        'coord': coord, 'proid': proid,
+        'areas': areas, 'upleft': upleft,
+        'botright': botright
+    }
+
+    return inp_feed_val, loss_feed_val