diff --git a/test/test_rangeify.py b/test/test_rangeify.py
index b47da8fb98..ab6018cfe8 100644
--- a/test/test_rangeify.py
+++ b/test/test_rangeify.py
@@ -300,6 +300,12 @@ class TestRangeify(unittest.TestCase):
     w2 = Tensor.empty(12, 8, 3, 3)
     x.conv2d(w1).conv2d(w2).realize()
 
+  def test_resnet_conv2d(self):
+    x = Tensor.empty(1, 8, 32, 32)
+    w1 = Tensor.empty(8, 8, 3, 3)
+    w2 = Tensor.empty(8, 8, 1, 1)
+    x.conv2d(w1).conv2d(w2).realize()
+
   def test_xception_conv2d(self):
     # NOTE: this fusion is bad, it's recomputing the inner many times
     x = Tensor.empty(1, 4, 32, 32)
diff --git a/test/test_schedule.py b/test/test_schedule.py
index 043c5e4631..5ac74c5d2c 100644
--- a/test/test_schedule.py
+++ b/test/test_schedule.py
@@ -1573,6 +1573,13 @@ class TestSchedule(unittest.TestCase):
   def test_conv2d(self): _test_conv2d(5 if SPLIT_REDUCEOP else 4)
   def test_conv2d_fused(self): _test_conv2d(5 if SPLIT_REDUCEOP else 4)
 
+  def test_resnet_conv2d(self):
+    x = Tensor.empty(1, 8, 32, 32)
+    w1 = Tensor.empty(8, 8, 3, 3)
+    w2 = Tensor.empty(8, 8, 1, 1)
+    out = x.conv2d(w1).conv2d(w2)
+    check_schedule(out, 2)
+
   @unittest.skipUnless(is_dtype_supported(dtypes.half) and is_dtype_supported(dtypes.ulong), "need half and ulong")
   def test_conv2d_half(self): _test_conv2d(5 if SPLIT_REDUCEOP else 4, dtype=dtypes.half)
   @unittest.skipUnless(is_dtype_supported(dtypes.half), "need half")
diff --git a/tinygrad/schedule/indexing.py b/tinygrad/schedule/indexing.py
index a896c2b5d8..3eacef8a3e 100644
--- a/tinygrad/schedule/indexing.py
+++ b/tinygrad/schedule/indexing.py
@@ -239,7 +239,7 @@ def run_rangeify(tsink:UOp, debug:bool=False) -> tuple[UOp, IndexingContext]:
     # if the EXPAND is used to inject a range, we don't mark it as ending_ranges. otherwise we do.
     # NOTE: this doesn't actually always end a range, but this is why convs are realized, so for now we need it
     if x.op is Ops.EXPAND and all(isinstance(y, int) or y.op is not Ops.RANGE for y in x.shape):
-      ending_ranges[x] = list(UOp.sink(*[ro for ri, ro in zip(rngs, out_rngs) if ri is not ro]).ranges.keys())
+      ending_ranges[x] += list(UOp.sink(*[ro for ri, ro in zip(rngs, out_rngs) if ri is not ro]).ranges.keys())
 
     # REDUCE_AXIS creates ranges for the axes it is reducing
     if x.op is Ops.REDUCE_AXIS:
@@ -247,15 +247,23 @@ def run_rangeify(tsink:UOp, debug:bool=False) -> tuple[UOp, IndexingContext]:
 
     if debug:
       realized_ranges = rctx.realize_map.get(x, None)
-      disp = []
-      for i, (ri, ro) in enumerate(zip([r.render() for r in rngs], [r.render() for r in out_rngs])):
-        rng = f"{ri}" if ri == ro else f"{ri} -> {ro}"
-        if realized_ranges is not None and i in realized_ranges: rng = colored(rng, "yellow")
-        disp.append("["+rng+"]")
-      print("***" if x in rctx.realize_map else "   ", len(consumer_map[x]), f"{str(x.op):20s}", ''.join(disp))
+      if x.op is Ops.RESHAPE or len(rngs) != len(out_rngs):
+        disp = render_ranges(rngs, realized=realized_ranges) + " -> " + render_ranges(out_rngs, realized=realized_ranges)
+      else:
+        disp = render_ranges(rngs, out_rngs, realized=realized_ranges)
+      print("***" if x in rctx.realize_map else "   ",
+            f"{len(consumer_map[x]):2d} {str(x.op):20s} {str(x.shape):35s} {len(ending_ranges[x]):2d}", disp)
 
     # assign to the range map. rngs are the input ranges, out_rngs are the output ranges, from the x op.
     rctx.range_map[x] = (rngs, out_rngs)
 
   tsink = graph_rewrite(tsink, pm_apply_rangeify, ctx=rctx, bottom_up=True, name="apply rangeify")
   return tsink, rctx
+
+def render_ranges(*rngs_list, realized) -> str:
+  disp = []
+  for i, rs in enumerate(zip(*[[r.render() for r in rngs] for rngs in rngs_list])):
+    rng = rs[0] if all_same(rs) else " -> ".join(rs)
+    if realized is not None and i in realized: rng = colored(rng, "yellow")
+    disp.append("["+rng+"]")
+  return ''.join(disp)