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add option for multiple inputs when writing weight summary #543

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Dec 9, 2019
Merged

add option for multiple inputs when writing weight summary #543

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21 changes: 16 additions & 5 deletions pytorch_lightning/core/memory.py
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Original file line number Diff line number Diff line change
Expand Up @@ -50,20 +50,31 @@ def get_variable_sizes(self):
input_ = self.model.example_input_array

if self.model.on_gpu:
input_ = input_.cuda(0)
device = next(self.model.parameters()).get_device()
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Not very sure about this getting device...
Probably also check if there is a next one?

# test if input is a list or a tuple
if isinstance(input_, (list, tuple)):
input_ = [input_i.cuda(device) if torch.is_tensor(input_i) else input_i
for input_i in input_]
else:
input_ = input_.cuda(device)

if self.model.trainer.use_amp:
input_ = input_.half()
# test if it is not a list or a tuple
if isinstance(input_, (list, tuple)):
input_ = [input_i.half() if torch.is_tensor(input_i) else input_i
for input_i in input_]
else:
input_ = input_.half()

with torch.no_grad():

for _, m in mods:
if type(input_) is list or type(input_) is tuple: # pragma: no cover
if isinstance(input_, (list, tuple)): # pragma: no cover
out = m(*input_)
else:
out = m(input_)

if type(input_) is tuple or type(input_) is list: # pragma: no cover
if isinstance(input_, (list, tuple)): # pragma: no cover
in_size = []
for x in input_:
if type(x) is list:
Expand All @@ -75,7 +86,7 @@ def get_variable_sizes(self):

in_sizes.append(in_size)

if type(out) is tuple or type(out) is list: # pragma: no cover
if isinstance(out, (list, tuple)): # pragma: no cover
out_size = np.asarray([x.size() for x in out])
else:
out_size = np.array(out.size())
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