Change optimizer parameters group method (#1239)

* Change optimizer parameters group method

* Add torch nn

* Change isinstance method(torch.Tensor to nn.Parameter)

* parameter freeze fix, PEP8 reformat

* freeze bug fix

Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

train.py

@@ -10,6 +10,7 @@ from warnings import warn
 import math
 import numpy as np
 import torch.distributed as dist
+import torch.nn as nn
 import torch.nn.functional as F
 import torch.optim as optim
 import torch.optim.lr_scheduler as lr_scheduler
@@ -80,12 +81,12 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
         model = Model(opt.cfg, ch=3, nc=nc).to(device)  # create
 
     # Freeze
-    freeze = ['', ]  # parameter names to freeze (full or partial)
-    if any(freeze):
-        for k, v in model.named_parameters():
-            if any(x in k for x in freeze):
-                print('freezing %s' % k)
-                v.requires_grad = False
+    freeze = []  # parameter names to freeze (full or partial)
+    for k, v in model.named_parameters():
+        v.requires_grad = True  # train all layers
+        if any(x in k for x in freeze):
+            print('freezing %s' % k)
+            v.requires_grad = False
 
     # Optimizer
     nbs = 64  # nominal batch size
@@ -93,14 +94,13 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
     hyp['weight_decay'] *= total_batch_size * accumulate / nbs  # scale weight_decay
 
     pg0, pg1, pg2 = [], [], []  # optimizer parameter groups
-    for k, v in model.named_parameters():
-        v.requires_grad = True
-        if '.bias' in k:
-            pg2.append(v)  # biases
-        elif '.weight' in k and '.bn' not in k:
-            pg1.append(v)  # apply weight decay
-        else:
-            pg0.append(v)  # all else
+    for k, v in model.named_modules():
+        if hasattr(v, 'bias') and isinstance(v.bias, nn.Parameter):
+            pg2.append(v.bias)  # biases
+        if isinstance(v, nn.BatchNorm2d):
+            pg0.append(v.weight)  # no decay
+        elif hasattr(v, 'weight') and isinstance(v.weight, nn.Parameter):
+            pg1.append(v.weight)  # apply decay
 
     if opt.adam:
         optimizer = optim.Adam(pg0, lr=hyp['lr0'], betas=(hyp['momentum'], 0.999))  # adjust beta1 to momentum