Add size information

Added information for estimating the total size of the model. Estimates taken from here: http://jacobkimmel.github.io/pytorch_estimating_model_size/
-calculates size of input, parameters, and forward/backward pass intermediate variables
-prints out these estimates and total
-batch_size optional input

torchsummary/torchsummary.py

@@ -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]
@@ -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'):
@@ -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