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Add size information #21

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Merged
merged 1 commit into from
Aug 9, 2018
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Merged

Add size information #21

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23 changes: 18 additions & 5 deletions torchsummary/torchsummary.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,9 +3,9 @@
from torch.autograd import Variable

from collections import OrderedDict
import numpy as np


def summary(model, input_size, device="cuda"):
def summary(model, input_size, batch_size=-1,device="cuda"):
def register_hook(module):
def hook(module, input, output):
class_name = str(module.__class__).split('.')[-1].split("'")[0]
Expand All @@ -14,12 +14,12 @@ 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] = -1
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] = -1
summary[m_key]['output_shape'][0] = batch_size

params = 0
if hasattr(module, 'weight') and hasattr(module.weight, 'size'):
Expand Down Expand Up @@ -67,18 +67,31 @@ def hook(module, input, output):
print(line_new)
print('================================================================')
total_params = 0
total_output = 0
trainable_params = 0
for layer in summary:
# input_shape, output_shape, trainable, nb_params
line_new = '{:>20} {:>25} {:>15}'.format(layer, str(summary[layer]['output_shape']), '{0:,}'.format(summary[layer]['nb_params']))
total_params += summary[layer]['nb_params']
total_output += np.prod(summary[layer]['output_shape'])
if 'trainable' in summary[layer]:
if summary[layer]['trainable'] == True:
trainable_params += summary[layer]['nb_params']
print(line_new)
#assume 4 bytes/number (float on cuda).
total_input_size = abs(np.prod(input_size)*batch_size*4./(1024**2.))
total_output_size = abs(2.*total_output*4./(1024**2.)) #x2 for gradients
total_params_size = abs(total_params.numpy()*4./(1024**2.))
total_size = total_params_size + total_output_size + total_input_size

print('================================================================')
print('Total params: {0:,}'.format(total_params))
print('Trainable params: {0:,}'.format(trainable_params))
print('Non-trainable params: {0:,}'.format(total_params - trainable_params))
print('----------------------------------------------------------------')
# return summary
print('Input size (MB): %0.2f' % total_input_size)
print('Forward/backward pass size (MB): %0.2f' % total_output_size)
print('Params size (MB): %0.2f' % total_params_size)
print('Estimated Total Size (MB): %0.2f' % total_size)
print('----------------------------------------------------------------')
#return summary