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feat: allow more input shapes and weight locations #189

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d24da94
feat: allow more input shapes and weight locations
ClashLuke Aug 5, 2022
fc6667d
fix: remove trailing bracket
ClashLuke Aug 5, 2022
784f1e1
fix: initialize trainable
ClashLuke Aug 5, 2022
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36 changes: 20 additions & 16 deletions torchsummary/torchsummary.py
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Original file line number Diff line number Diff line change
@@ -1,9 +1,12 @@
import typing
from collections import OrderedDict

import numpy as np
import torch
import torch.nn as nn
from torch.autograd import Variable
from torch.utils._pytree import tree_map

from collections import OrderedDict
import numpy as np


def summary(model, input_size, batch_size=-1, device=torch.device('cuda:0'), dtypes=None):
Expand All @@ -14,6 +17,12 @@ def summary(model, input_size, batch_size=-1, device=torch.device('cuda:0'), dty
return params_info


def tensor_size(tensor: typing.Any) -> typing.Optional[typing.List[int]]:
if not isinstance(tensor, torch.Tensor):
return None
return list(tensor.size())


def summary_string(model, input_size, batch_size=-1, device=torch.device('cuda:0'), dtypes=None):
if dtypes == None:
dtypes = [torch.FloatTensor]*len(input_size)
Expand All @@ -27,22 +36,17 @@ def hook(module, input, output):

m_key = "%s-%i" % (class_name, module_idx + 1)
summary[m_key] = OrderedDict()
summary[m_key]["input_shape"] = list(input[0].size())
summary[m_key]["input_shape"][0] = batch_size
if isinstance(output, (list, tuple)):
summary[m_key]["output_shape"] = [
[-1] + list(o.size())[1:] for o in output
]
else:
summary[m_key]["output_shape"] = list(output.size())
summary[m_key]["output_shape"][0] = batch_size
input_shape = tree_map(tensor_size, input)
if len(input_shape) == 1: # backwards compatibility
input_shape = input_shape[0]
summary[m_key]["input_shape"] = input_shape
summary[m_key]["output_shape"] = tree_map(tensor_size, input)

params = 0
if hasattr(module, "weight") and hasattr(module.weight, "size"):
params += torch.prod(torch.LongTensor(list(module.weight.size())))
summary[m_key]["trainable"] = module.weight.requires_grad
if hasattr(module, "bias") and hasattr(module.bias, "size"):
params += torch.prod(torch.LongTensor(list(module.bias.size())))
summary[m_key]["trainable"] = False
for p in module.parameters(recurse=False):
params += np.prod(list(p.size()))
summary[m_key]["trainable"] |= p.requires_grad
summary[m_key]["nb_params"] = params

if (
Expand Down