测试

• 目标检测

• 笔记

• 微调分割

import torch
from torchvision.models import resnet50
from torchvision.models.feature_extraction import create_feature_extractor


x = torch.rand(1, 3, 224, 224)

model = resnet50()

return_nodes = {
"layer4.2.relu_2": "layer4"
}
model2 = create_feature_extractor(model, return_nodes=return_nodes)
intermediate_outputs = model2(x)

print(intermediate_outputs['layer4'].shape)

torch.Size([1, 2048, 7, 7])

参考：automatic augmentation transforms

from torchvision import transforms

t = transforms.RandAugment()
# t = transforms.TrivialAugmentWide()
transformed = t(image)

transform = transforms.Compose([
transforms.Resize(256),
transforms.RandAugment(), # transforms.TrivialAugmentWide()
transforms.ToTensor()])