如何通过预先训练的骨干网络来更快地训练火炬视觉的RPN
如何通过预先训练的骨干网络来更快地训练火炬视觉的RPN
提问于 2021-08-07 16:29:59
正如标题所提到的，如果我已经预先训练了骨干，并且我想只训练RPN，而不是使用torchvision的更快的R-CNN来训练分类器。
是否有可以传递给create_model函数的参数，或者是否要停止训练我的train()函数中的分类器？
我在用手机，所以请原谅我的编辑
这是我的create model函数
Create your backbone from timm
backbone = timm.create_model(
“resnet50”,
pretrained=True,
num_classes=0, # this is important to remove fc layers
global_pool="" # 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,
)回答 1
Stack Overflow用户
回答已采纳
发布于 2021-08-07 16:54:05
您可以执行以下操作
# 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页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/68694221
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