ok pytorch doesn’t like what I’m trying to do:
    for name, module in self._modules.items():
RuntimeError: OrderedDict mutated during iteration

but I am ok with mutating it…I am doing this on purpose to build off the resnet models given…
        module.track_running_stats = False
        model.__setattr__(name, module)

how is this supposed to be done properly?

this deosn’t seem to work either as now there are a bunch of extra fields that probably shouldn’t be there…
for name, module in copy.deepcopy(model).named_modules():
    # if type(module) == torch.nn.BatchNorm2d:
    if 'bn' in name:
        # module.track_running_stats = '123'
        #moddule = f'torch.nn.{module}'
        # module = BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
        module.track_running_stats = False
        # model.__setattr__(name, module)
        # eval(f'model.{name} = module')
model.fc = torch.nn.Linear(in_features=512, out_features=fc_out_features, bias=True)

but the above looks wrong, there are so many attributes…and the old ones seem to be there still too!!!

model
Out[80]: 
ResNet(
  (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
  (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
  (relu): ReLU(inplace=True)
  (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
  (layer1): Sequential(
    (0): BasicBlock(
      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    )
    (1): BasicBlock(
      (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
      (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    )
  )
  (layer2): Sequential(
    (0): BasicBlock(
      (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (downsample): Sequential(
        (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)
        (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (1): BasicBlock(
      (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    )
  )
  (layer3): Sequential(
    (0): BasicBlock(
      (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (downsample): Sequential(
        (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)
        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (1): BasicBlock(
      (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    )
  )
  (layer4): Sequential(
    (0): BasicBlock(
      (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (downsample): Sequential(
        (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)
        (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (1): BasicBlock(
      (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): ReLU(inplace=True)
      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)
      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
    )
  )
  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))
  (fc): Linear(in_features=512, out_features=5, bias=True)
  (layer1.0.bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
  (layer1.0.bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
  (layer1.1.bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
  (layer1.1.bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
  (layer2.0.bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
  (layer2.0.bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
  (layer2.1.bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
  (layer2.1.bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
  (layer3.0.bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
  (layer3.0.bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
  (layer3.1.bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
  (layer3.1.bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
  (layer4.0.bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
  (layer4.0.bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
  (layer4.1.bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
  (layer4.1.bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=False)
)


I guess if python was more like C and I could get the original pointer to module to point to the new one, that would work. The issue is that doing:
module =  new_module

has the local variable module point to new_module instead of getting the address of the actual module inside the resnet to point to the new module which is what I am trying to do.
Seems that the “best” idea I have is modify the actual pointer to the object/layer but make sure everything is modified properly. Including meta-data and weights if needed…the issue is idk how to make this 100% robust. I already tried to change tracking_stats to nonesense and it didn’t lead to errors as I wish it would have.

oh well that was close 
for name, module in model.named_modules():
    # if type(module) == torch.nn.BatchNorm2d:
    if 'bn' in name:
        print(name)
        print(module)
        new_module = eval('torch.nn.'+str(module).replace('track_running_stats=True', 'track_running_stats=False'))
        module.load_state_dict(new_module.state_dict())
model.fc = torch.nn.Linear(in_features=512, out_features=fc_out_features, bias=True)

but it complained:
RuntimeError: Error(s) in loading state_dict for BatchNorm2d:
	Missing key(s) in state_dict: "running_mean", "running_var", "num_batches_tracked". 

but that is ok. It SHOULD be missing…

Ok I found a list of potentially relevant answers:

Module.children() vs Module.modules() 22
Replacing convs modules with custom convs, then NotImplementedError 15
How to replace all ReLU activations in a pretrained network? 27
https://stackoverflow.com/questions/58297197/how-to-change-activation-layer-in-pytorch-pretrained-module 4

will go through and report my solution.