How to train only RPN for torch vision Faster RCNN with pretrained backbone
As per the title mentioned, if I have already pretrained backbone, and I want to train only the RPN instead of the classifier using the Faster R-CNN from torchvision.
Is there any parameters I can pass in to the create_model function or would I stop the classifier training in my train() function?
I’m on mobile so olease excuse my editting
This is my create model function
Create your backbone from timm
backbone = timm.create_model(
“resnet50”,
pretrained=True,
num_classes=0, # this is important to remove fc layers
global_pool=&quot;&quot; # this is important to remove fc layers
)

backbone.out_channels = backbone.feature_info[-1][“num_chs”]

anchor_generator = AnchorGenerator(
sizes=((16, 32, 64, 128, 256),), aspect_ratios=((0.25, 0.5, 1.0, 2.0),)
)
roi_pooler = torchvision.ops.MultiScaleRoIAlign(
featmap_names=[“0”], output_size=7, sampling_ratio=2
)
fastercnn_model = FasterRCNN(
backbone=backbone,
num_classes=1000,
rpn_anchor_generator=anchor_generator,
box_roi_pool=roi_pooler,
)


You can do the following
# First you can use model.children() method to see the idx of the backbone
for idx, child in enumerate(fastercnn_model.children()):
    if idx == 1:
        # Now set requires_grad for that idx to False
        for param in child.parameters():
            param.requires_grad = False
        break

# ===============  UPDATED  ========================
# This will train only the box_predictor not even the RPN. You can try out
# Different strategies and find the best for you.
# setting everything to false

for child in fastercnn_model.children():
    for param in child.parameters():
        param.requires_grad = False
        
for idx, child in enumerate(fastercnn_model.children()):
    if idx == 3:
        for i, param in enumerate(child.parameters()):
            if i==1:
                param.requires_grad = True
        break

