How to freeze selected layers of a model in Pytorch?

Question

I am using the mobileNetV2 and I only want to freeze part of the model. I know I can use the following code to freeze the entire model

MobileNet = models.mobilenet_v2(pretrained = True)

    for param in MobileNet.parameters():
        param.requires_grad = False

but I want everything from (15) onward to remain unfrozen. How can I selectively freeze everything before the desired layer is frozen?

    (15): InvertedResidual(
      (conv): Sequential(
        (0): ConvBNReLU(
          (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): ReLU6(inplace=True)
        )
        (1): ConvBNReLU(
          (0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)
          (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): ReLU6(inplace=True)
        )
        (2): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (16): InvertedResidual(
      (conv): Sequential(
        (0): ConvBNReLU(
          (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): ReLU6(inplace=True)
        )
        (1): ConvBNReLU(
          (0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)
          (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): ReLU6(inplace=True)
        )
        (2): Conv2d(960, 160, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (3): BatchNorm2d(160, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (17): InvertedResidual(
      (conv): Sequential(
        (0): ConvBNReLU(
          (0): Conv2d(160, 960, kernel_size=(1, 1), stride=(1, 1), bias=False)
          (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): ReLU6(inplace=True)
        )
        (1): ConvBNReLU(
          (0): Conv2d(960, 960, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=960, bias=False)
          (1): BatchNorm2d(960, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
          (2): ReLU6(inplace=True)
        )
        (2): Conv2d(960, 320, kernel_size=(1, 1), stride=(1, 1), bias=False)
        (3): BatchNorm2d(320, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      )
    )
    (18): ConvBNReLU(
      (0): Conv2d(320, 1280, kernel_size=(1, 1), stride=(1, 1), bias=False)
      (1): BatchNorm2d(1280, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU6(inplace=True)
    )   )   (classifier): Sequential(
    (0): Dropout(p=0.2, inplace=False)
    (1): Linear(in_features=1280, out_features=1000, bias=True)   ) )

Answer 1

Pytorch's model implementation is in good modularization, so like you do

for param in MobileNet.parameters():
    param.requires_grad = False

, you may also do

for param in MobileNet.features[15].parameters():
    param.requires_grad = True

afterwards to unfreeze parameters in (15).

Loop from 15 to 18 to unfreeze the last several layers.

Answer 2

Just adding this here for completeness. You can also freeze parameters in place without iterating over them with requires_grad_ (API).

For example say you have a RetinaNet and want to just fine-tune on the heads

class RetinaNet(torch.nn.Module):
    def __init__(self, ...):
        self.backbone = ResNet(...)
        self.fpn = FPN(...)
        self.box_head = torch.nn.Sequential(...)
        self.cls_head = torch.nn.Sequential(...)

Then you could freeze the backbone and FPN like this:

# Getting the model
retinanet = RetinaNet(...)

# Freezing backbone and FPN
retinanet.backbone.requires_grad_(False)
retinanet.fpn.requires_grad_(False)