diff --git a/forwardOnBigImages.py b/forwardOnBigImages.py
new file mode 100644
index 0000000..aea5867
--- /dev/null
+++ b/forwardOnBigImages.py
@@ -0,0 +1,30 @@
+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())
+    print("osize",o.size())
+    print("vc",vc)
+    print("outchunksize",outChunk.shape)
+    print("tc",tc)
+    outChunk[tuple(tc)]=o.cpu().data.numpy()[tuple(vc)]
+  return outChunk
diff --git a/myCrop.py b/myCrop.py
new file mode 100644
index 0000000..8f98da3
--- /dev/null
+++ b/myCrop.py
@@ -0,0 +1,18 @@
+import numpy as np
+
+def myCrop(img,inds,fill=0):
+    outsize=[]
+    dstinds=[]
+    srcinds=[]
+    for k in range(len(inds)):
+        srcbeg=max(0,inds[k].start)
+        srcend=min(img.shape[k],inds[k].stop)
+        dstbeg=max(0,-inds[k].start)
+        dstend=dstbeg+srcend-srcbeg
+        dstinds.append(slice(dstbeg,dstend))
+        srcinds.append(slice(srcbeg,srcend))
+        outsize.append(inds[k].stop-inds[k].start)
+    crop=np.full(tuple(outsize),fill,img.dtype)
+    crop[tuple(dstinds)]=img[tuple(srcinds)]
+    return crop, dstinds
+