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Fix edge cases on test_backbone_utils when the sampled node is buffer with integer value #6091

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Merged
merged 1 commit into from
May 26, 2022
Merged

Fix edge cases on test_backbone_utils when the sampled node is buffer with integer value #6091

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16 changes: 8 additions & 8 deletions test/test_backbone_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -183,12 +183,12 @@ def test_forward_backward(self, model_name):
out_agg = 0
for node_out in out.values():
if isinstance(node_out, Sequence):
out_agg += sum(o.mean() for o in node_out if o is not None)
out_agg += sum(o.float().mean() for o in node_out if o is not None)
elif isinstance(node_out, Mapping):
out_agg += sum(o.mean() for o in node_out.values() if o is not None)
out_agg += sum(o.float().mean() for o in node_out.values() if o is not None)
else:
# Assume that the only other alternative at this point is a Tensor
out_agg += node_out.mean()
out_agg += node_out.float().mean()
out_agg.backward()

def test_feature_extraction_methods_equivalence(self):
Expand Down Expand Up @@ -224,12 +224,12 @@ def test_jit_forward_backward(self, model_name):
out_agg = 0
for node_out in fgn_out.values():
if isinstance(node_out, Sequence):
out_agg += sum(o.mean() for o in node_out if o is not None)
out_agg += sum(o.float().mean() for o in node_out if o is not None)
elif isinstance(node_out, Mapping):
out_agg += sum(o.mean() for o in node_out.values() if o is not None)
out_agg += sum(o.float().mean() for o in node_out.values() if o is not None)
else:
# Assume that the only other alternative at this point is a Tensor
out_agg += node_out.mean()
out_agg += node_out.float().mean()
out_agg.backward()

def test_train_eval(self):
Expand All @@ -239,7 +239,7 @@ def __init__(self):
self.dropout = torch.nn.Dropout(p=1.0)

def forward(self, x):
x = x.mean()
x = x.float().mean()
x = self.dropout(x) # dropout
if self.training:
x += 100 # add
Expand Down Expand Up @@ -330,4 +330,4 @@ def forward(self, x):
# Check forward
out = model(self.inp)
# And backward
out["leaf_module"].mean().backward()
out["leaf_module"].float().mean().backward()