From d80ac2fd2df4e7fb8a28acfa512bb11472b5cc99 Mon Sep 17 00:00:00 2001
From: liugz18 <57401541+liugz18@users.noreply.github.com>
Date: 星期四, 18 七月 2024 21:34:55 +0800
Subject: [PATCH] Rename 'res' in line 514 to avoid with naming conflict with line 365

---
 funasr/train_utils/model_summary.py |   26 ++++++++++++++------------
 1 files changed, 14 insertions(+), 12 deletions(-)

diff --git a/funasr/train_utils/model_summary.py b/funasr/train_utils/model_summary.py
index 8d7f14f..842cd21 100644
--- a/funasr/train_utils/model_summary.py
+++ b/funasr/train_utils/model_summary.py
@@ -1,4 +1,3 @@
-import humanfriendly
 import numpy as np
 import torch
 
@@ -48,23 +47,26 @@
 def model_summary(model: torch.nn.Module) -> str:
     message = "Model structure:\n"
     message += str(model)
-    tot_params = sum(p.numel() for p in model.parameters())
-    num_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
+
+    tot_params, num_params = 0, 0
+    for name, param in model.named_parameters():
+        print(
+            "name: {}, dtype: {}, device: {}, trainable: {}, shape: {}, numel: {}".format(
+                name, param.dtype, param.device, param.requires_grad, param.shape, param.numel()
+            )
+        )
+        tot_params += param.numel()
+        if param.requires_grad:
+            num_params += param.numel()
+
     percent_trainable = "{:.1f}".format(num_params * 100.0 / tot_params)
     tot_params = get_human_readable_count(tot_params)
     num_params = get_human_readable_count(num_params)
     message += "\n\nModel summary:\n"
     message += f"    Class Name: {model.__class__.__name__}\n"
     message += f"    Total Number of model parameters: {tot_params}\n"
-    message += (
-        f"    Number of trainable parameters: {num_params} ({percent_trainable}%)\n"
-    )
-    num_bytes = humanfriendly.format_size(
-        sum(
-            p.numel() * to_bytes(p.dtype) for p in model.parameters() if p.requires_grad
-        )
-    )
-    message += f"    Size: {num_bytes}\n"
+    message += f"    Number of trainable parameters: {num_params} ({percent_trainable}%)\n"
+
     dtype = next(iter(model.parameters())).dtype
     message += f"    Type: {dtype}"
     return message

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