let it work with modules with are layers

torchsummaryX/torchsummaryX.py

@@ -3,7 +3,10 @@ import numpy as np
 import pandas as pd
 import torch
 
-def summary(model, x, *args, **kwargs):
+# Some modules do the computation themselves using parameters or the parameters of children, treat these as layers
+layer_modules = (torch.nn.MultiheadAttention, )
+
+def summary(model, x, *args, layer_modules=layer_modules, print_summary=True, **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)
@@ -19,9 +22,12 @@ def summary(model, x, *args, **kwargs):
             module_idx = len(summary)
 
             # Lookup name in a dict that includes parents
+            module_name = str(module_idx)
             for name, item in module_names.items():
                 if item == module:
-                    key = "{}_{}".format(module_idx, name)
+                    module_name = name
+                    break
+            key = "{}_{}".format(module_idx, name)
 
             info = OrderedDict()
             info["id"] = id(module)
@@ -71,8 +77,8 @@ def summary(model, x, *args, **kwargs):
 
             summary[key] = info
 
-        # ignore Sequential and ModuleList
-        if not module._modules:
+        # ignore Sequential and ModuleList and other containers
+        if isinstance(module, layer_modules) or not module._modules:
             hooks.append(module.register_forward_hook(hook))
 
     module_names = get_names_dict(model)
@@ -84,6 +90,12 @@ def summary(model, x, *args, **kwargs):
     try:
         with torch.no_grad():
             model(x) if not (kwargs or args) else model(x, *args, **kwargs)
+    except Exception:
+        # This can be usefull for debugging
+        print("Failed to run torchsummaryX.summary, printing sizes of executed layers:")
+        df = pd.DataFrame(summary).T
+        print(df)
+        raise
     finally:
         for hook in hooks:
             hook.remove()
@@ -104,27 +116,29 @@ def summary(model, x, *args, **kwargs):
     df = df[["Kernel Shape", "Output Shape", "Params", "Mult-Adds"]]
     max_repr_width = max([len(row) for row in df.to_string().split("\n")])
 
-    option = pd.option_context(
-        "display.max_rows", 600,
-        "display.max_columns", 10,
-        "display.float_format", pd.io.formats.format.EngFormatter(use_eng_prefix=True)
-    )
-    with option:
-        print("="*max_repr_width)
-        print(df.replace(np.nan, "-"))
-        print("-"*max_repr_width)
-        df_total = pd.DataFrame(
-            {"Total params": (df_sum["Params"] + df_sum["params_nt"]),
-            "Trainable params": df_sum["Params"],
-            "Non-trainable params": df_sum["params_nt"],
-            "Mult-Adds": df_sum["Mult-Adds"]
-            },
-            index=['Totals']
-        ).T
-        print(df_total)
-        print("="*max_repr_width)
+    df_total = pd.DataFrame(
+        {"Total params": (df_sum["Params"] + df_sum["params_nt"]),
+        "Trainable params": df_sum["Params"],
+        "Non-trainable params": df_sum["params_nt"],
+        "Mult-Adds": df_sum["Mult-Adds"]
+        },
+        index=['Totals']
+    ).T
+    
+    if print_summary:
+        option = pd.option_context(
+            "display.max_rows", 600,
+            "display.max_columns", 10,
+            "display.float_format", pd.io.formats.format.EngFormatter(use_eng_prefix=True)
+        )
+        with option:
+            print("="*max_repr_width)
+            print(df.replace(np.nan, "-"))
+            print("-"*max_repr_width)
+            print(df_total)
+            print("="*max_repr_width)
 
-    return df
+    return df, df_total
 
 def get_names_dict(model):
     """Recursive walk to get names including path."""