show non trainable params in summary, use pandas to display engineering units

This shows a trainable parameter in the full data frame and trainable vs non-trainable params in the summary. This should be like keras [example](https://github.com/keras-team/keras/pull/11795#issue-235955201).

I also changed the pandas display so that instead of putting (M) or (K) manually in the label, pandas appends it to the number automatically.

torchsummaryX/torchsummaryX.py

@@ -32,9 +32,10 @@ def summary(model, x, *args, **kwargs):
 
             info["ksize"] = "-"
             info["inner"] = OrderedDict()
-            info["params"], info["macs"] = 0, 0
+            info["params_nt"],  info["params"], info["macs"] = 0, 0, 0
             for name, param in module.named_parameters():
                 info["params"] += param.nelement() * param.requires_grad
+                info["params_nt"] += param.nelement() * (not param.requires_grad)
 
                 if name == "weight":
                     ksize = list(param.size())
@@ -76,33 +77,45 @@ def summary(model, x, *args, **kwargs):
     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()
+    try:
+        with torch.no_grad():
+            model(x) if not (kwargs or args) else model(x, *args, **kwargs)
+    finally:
+        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["Params (K)"] = pd.to_numeric(df["params"], errors="coerce")/1e3
+    
+    df["Mult-Adds"] = pd.to_numeric(df["macs"], errors="coerce")
+    df["Params"] = pd.to_numeric(df["params"], errors="coerce")
+    df["Non-trainable params"] = pd.to_numeric(df["params_nt"], errors="coerce")
     df = df.rename(columns=dict(
         ksize="Kernel Shape",
         out="Output Shape",
     ))
-    df.index.name = "Layer"
-    df = df[["Kernel Shape", "Output Shape", "Params (K)", "Mult-Adds (M)"]]
     df_sum = df.sum()
+    df.index.name = "Layer"
+    
+    df = df[["Kernel Shape", "Output Shape", "Params", "Mult-Adds"]]
+    
 
     max_repr_width = max([len(row) for row in df.to_string().split("\n")])
 
-    print("="*max_repr_width)
-    print(df.replace(np.nan, "-"))
-    print("-"*max_repr_width)
-    print("Params (K): ", df_sum["Params (K)"])
-    print("Mult-Adds (M): ", df_sum["Mult-Adds (M)"])
-    print("="*max_repr_width)
+    with pd.option_context("display.max_rows", 10, "display.max_columns", 5, 'display.float_format', pd.io.formats.format.EngFormatter(use_eng_prefix=True)):
+        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)
 
     return df