--- a
+++ b/darkflow/net/ops/convolution.py
@@ -0,0 +1,120 @@
+import tf_slim as slim
+from .baseop import BaseOp
+import tensorflow as tf
+import numpy as np
+
+
+class reorg(BaseOp):
+    def _forward(self):
+        inp = self.inp.out
+        shape = inp.get_shape().as_list()
+        _, h, w, c = shape
+        s = self.lay.stride
+        out = list()
+        for i in range(int(h / s)):
+            row_i = list()
+            for j in range(int(w / s)):
+                si, sj = s * i, s * j
+                boxij = inp[:, si: si + s, sj: sj + s, :]
+                flatij = tf.reshape(boxij, [-1, 1, 1, c * s * s])
+                row_i += [flatij]
+            out += [tf.concat(row_i, 2)]
+
+        self.out = tf.concat(out, 1)
+
+    def forward(self):
+        inp = self.inp.out
+        s = self.lay.stride
+        self.out = tf.extract_image_patches(
+            inp, [1, s, s, 1], [1, s, s, 1], [1, 1, 1, 1], 'VALID')
+
+    def speak(self):
+        args = [self.lay.stride] * 2
+        msg = 'local flatten {}x{}'
+        return msg.format(*args)
+
+
+class local(BaseOp):
+    def forward(self):
+        pad = [[self.lay.pad, self.lay.pad]] * 2;
+        temp = tf.pad(self.inp.out, [[0, 0]] + pad + [[0, 0]])
+
+        k = self.lay.w['kernels']
+        ksz = self.lay.ksize
+        half = int(ksz / 2)
+        out = list()
+        for i in range(self.lay.h_out):
+            row_i = list()
+            for j in range(self.lay.w_out):
+                kij = k[i * self.lay.w_out + j]
+                i_, j_ = i + 1 - half, j + 1 - half
+                tij = temp[:, i_: i_ + ksz, j_: j_ + ksz, :]
+                row_i.append(
+                    tf.nn.conv2d(tij, kij,
+                                 padding='VALID',
+                                 strides=[1] * 4))
+            out += [tf.concat(row_i, 2)]
+
+        self.out = tf.concat(out, 1)
+
+    def speak(self):
+        l = self.lay
+        args = [l.ksize] * 2 + [l.pad] + [l.stride]
+        args += [l.activation]
+        msg = 'loca {}x{}p{}_{}  {}'.format(*args)
+        return msg
+
+
+class convolutional(BaseOp):
+    def forward(self):
+        pad = [[self.lay.pad, self.lay.pad]] * 2;
+        temp = tf.pad(self.inp.out, [[0, 0]] + pad + [[0, 0]])
+        temp = tf.nn.conv2d(temp, self.lay.w['kernel'], padding='VALID',
+                            name=self.scope, strides=[1] + [self.lay.stride] * 2 + [1])
+        if self.lay.batch_norm:
+            temp = self.batchnorm(self.lay, temp)
+        self.out = tf.nn.bias_add(temp, self.lay.w['biases'])
+
+    def batchnorm(self, layer, inp):
+        if not self.var:
+            temp = (inp - layer.w['moving_mean'])
+            temp /= (np.sqrt(layer.w['moving_variance']) + 1e-5)
+            temp *= layer.w['gamma']
+            return temp
+        else:
+            args = dict({
+                'center': False, 'scale': True,
+                'epsilon': 1e-5, 'scope': self.scope,
+                'updates_collections': None,
+                'is_training': layer.h['is_training'],
+                'param_initializers': layer.w
+            })
+            return slim.batch_norm(inp, **args)
+
+    def speak(self):
+        l = self.lay
+        args = [l.ksize] * 2 + [l.pad] + [l.stride]
+        args += [l.batch_norm * '+bnorm']
+        args += [l.activation]
+        msg = 'conv {}x{}p{}_{}  {}  {}'.format(*args)
+        return msg
+
+
+class conv_select(convolutional):
+    def speak(self):
+        l = self.lay
+        args = [l.ksize] * 2 + [l.pad] + [l.stride]
+        args += [l.batch_norm * '+bnorm']
+        args += [l.activation]
+        msg = 'sele {}x{}p{}_{}  {}  {}'.format(*args)
+        return msg
+
+
+class conv_extract(convolutional):
+    def speak(self):
+        l = self.lay
+        args = [l.ksize] * 2 + [l.pad] + [l.stride]
+        args += [l.batch_norm * '+bnorm']
+        args += [l.activation]
+        msg = 'extr {}x{}p{}_{}  {}  {}'.format(*args)
+        return msg