From be8002024eeba76526e60b9b5f211119fc090445 Mon Sep 17 00:00:00 2001
From: Mike Clark <github@wassname.org>
Date: Wed, 5 Jun 2019 05:27:28 +0000
Subject: [PATCH] include module path in name, return dataframe

---
 torchsummaryX/torchsummaryX.py | 77 +++++++++++++++++++---------------
 1 file changed, 43 insertions(+), 34 deletions(-)

diff --git a/torchsummaryX/torchsummaryX.py b/torchsummaryX/torchsummaryX.py
index db15310..666957f 100644
--- a/torchsummaryX/torchsummaryX.py
+++ b/torchsummaryX/torchsummaryX.py
@@ -1,12 +1,12 @@
 from collections import OrderedDict
 import numpy as np
+import pandas as pd
 import torch
 
 def summary(model, x, *args, **kwargs):
     """Summarize the given input model.
     Summarized information are 1) output shape, 2) kernel shape,
     3) number of the parameters and 4) operations (Mult-Adds)
-
     Args:
         model (Module): Model to summarize
         x (Tensor): Input tensor of the model with [N, C, H, W] shape
@@ -16,8 +16,11 @@ def summary(model, x, *args, **kwargs):
     def register_hook(module):
         def hook(module, inputs, outputs):
             cls_name = str(module.__class__).split(".")[-1].split("'")[0]
-            module_idx = len(summary)
-            key = "{}_{}".format(module_idx, cls_name)
+            
+            # Lookup name in a dict that includes parents
+            for name, item in module_names.items():
+                if item == module:
+                    key = name            
 
             info = OrderedDict()
             info["id"] = id(module)
@@ -65,45 +68,51 @@ def summary(model, x, *args, **kwargs):
         # ignore Sequential and ModuleList
         if not module._modules:
             hooks.append(module.register_forward_hook(hook))
+            
+    module_names = get_names_dict(model)
 
     hooks = []
     summary = OrderedDict()
 
     model.apply(register_hook)
+    
     with torch.no_grad():
         model(x) if not (kwargs or args) else model(x, *args, **kwargs)
 
     for hook in hooks:
         hook.remove()
+    
+    # Use pandas to align the columns
+    df = pd.DataFrame(summary).T
+    df['Mult-Adds (M)'] = pd.to_numeric(df['macs'], errors='coerce')/1e6
+    df['Mult-Adds (M)'] = df['Mult-Adds (M)'].replace(np.nan, '-')
+    df = df.rename(columns=dict(
+        ksize='Kernel Shape',
+        out='Output Shape',
+        params='Params (K)',
+    ))
+    df.index.name = 'Layer'
+    df = df[['Kernel Shape',  'Output Shape', 'Params (K)',   'Mult-Adds (M)']]
+    
+    return df
+    
+def get_names_dict(model):
+    """Recursive walk to get names including path."""
+    names = {}
 
-    print("-"*100)
-    print("{:<15} {:>20} {:>20} {:>20} {:>20}"
-        .format("Layer", "Kernel Shape", "Output Shape", 
-                "# Params (K)", "# Mult-Adds (M)"))
-    print("="*100)
-
-    total_params, total_macs = 0, 0
-    for layer, info in summary.items():
-        repr_ksize = str(info["ksize"])
-        repr_out = str(info["out"])
-        repr_params = info["params"]
-        repr_macs = info["macs"]
-
-        if isinstance(repr_params, (int, float)):
-            total_params += repr_params
-            repr_params = "{0:,.2f}".format(repr_params/1000)
-        if isinstance(repr_macs, (int, float)):
-            total_macs += repr_macs
-            repr_macs = "{0:,.2f}".format(repr_macs/1000000)
-
-        print("{:<15} {:>20} {:>20} {:>20} {:>20}"
-            .format(layer, repr_ksize, repr_out, repr_params, repr_macs))
-
-        # for RNN, describe inner weights (i.e. w_hh, w_ih)
-        for inner_name, inner_shape in info["inner"].items():
-            print("  {:<13} {:>20}".format(inner_name, str(inner_shape)))
-
-    print("="*100)
-    print("# Params:    {0:,.2f}K".format(total_params/1000))
-    print("# Mult-Adds: {0:,.2f}M".format(total_macs/1000000))
-    print("-"*100)
+    def _get_names(module, parent_name=''):
+        for key, module in module.named_children():
+            cls_name = str(module.__class__).split(".")[-1].split("'")[0]
+            num_named_children = len(list(module.named_children()))
+            if num_named_children>0:
+                name = parent_name + '.' + key if parent_name else key  
+            else:
+                name = parent_name + '.' + cls_name + '_'+ key if parent_name else key  
+#             print(f'{parent_name}, key={key}, nc={num_named_children}, name={name}')
+            names[name] = module
+            
+            if isinstance(module, torch.nn.Module):
+                _get_names(module, parent_name=name)  
+            
+    _get_names(model)
+    return names