diff --git a/forwardOnBigImages.py b/forwardOnBigImages.py
index 1cb92a7..22c0e63 100644
--- a/forwardOnBigImages.py
+++ b/forwardOnBigImages.py
@@ -1,28 +1,64 @@
 import numpy as np
 import networkTraining.cropRoutines as cropRoutines
 import torch
 
 def targetCoords(sourceCoords,validCoords):
   cc=sourceCoords
   vc=validCoords
   tc=[]
   for i in range(len(cc)):
     tc.append(slice(cc[i].start+vc[i].start,cc[i].start+vc[i].stop))
   return tc
 
 def processChunk(inChunk,cropSize,marginSize,startDim,net,outChannels=None):
   nc=cropRoutines.noCrops(inChunk.shape,cropSize,marginSize,startDim)
   size=np.array(inChunk.shape)
   if outChannels:
     size[1]=outChannels
   outChunk=np.zeros(tuple(size))
   net.eval()
   for i in range(nc):
     cc,vc=cropRoutines.cropCoords(i,cropSize,marginSize,inChunk.shape,startDim)
     tc=targetCoords(cc,vc)
     crop=inChunk[tuple(cc)]
     o=net.forward(torch.from_numpy(crop).cuda())
     tc[1]=slice(0,size[1])
     vc[1]=slice(0,size[1])
     outChunk[tuple(tc)]=o.cpu().data.numpy()[tuple(vc)]
   return outChunk
+
+def processChunk_v2(inChunk,cropSize,marginSize,startDim,net,outDims=None):
+  # outDims can be a tuple of sizes...
+  nc=cropRoutines.noCrops(inChunk.shape,cropSize,marginSize,startDim)
+  size=inChunk.shape
+  if outDims:
+    size=[]
+    for k in range(len(outDims)):
+      size.append(outDims[k])
+    for k in range(startDim,inChunk.ndim):
+      size.append(inChunk.shape[k])
+  outChunk=np.zeros(tuple(size))
+  net.eval()
+  for i in range(nc):
+    cc,vc=cropRoutines.cropCoords(i,cropSize,marginSize,inChunk.shape,startDim)
+    tc=targetCoords(cc,vc)
+    crop=inChunk[tuple(cc)]
+    o=net.forward(torch.from_numpy(crop).cuda())
+    if outDims:
+      ttc=[]
+      tvc=[]
+      for k in range(len(outDims)):
+        ttc.append(slice(0,outDims[k]))
+        tvc.append(slice(0,outDims[k]))
+      for k in range(startDim,inChunk.ndim):
+        ttc.append(tc[k])
+        tvc.append(vc[k])
+      tc=ttc
+      vc=tvc
+    print("osize",o.size())
+    print("cropshape",crop.shape)
+    print("tc,vc",tc,vc)
+    print("outchanksjape",outChunk.shape)
+    print("oshape",o.cpu().data.numpy().shape)
+    outChunk[tuple(tc)]=o.cpu().data.numpy()[tuple(vc)]
+  return outChunk