取出预训练模型中间层的输出(pytorch)

from torchvision.models import resnet18
import torch

model = resnet18(pretrained=True)
print("model:", model)
out = []
x = torch.randn(1, 3, 224, 224)
return_layer = "layer1"
for name, module in model.named_children():
    x = module(x)
    if name == return_layer:
        out.append(x.data)
        break
print(out[0].shape)  # torch.Size([1, 64, 56, 56])

from torchvision.models._utils import IntermediateLayerGetter

class IntermediateLayerGetter(nn.ModuleDict):
    """
    Module wrapper that returns intermediate layers from a model

    It has a strong assumption that the modules have been registered
    into the model in the same order as they are used.
    This means that one should **not** reuse the same nn.Module
    twice in the forward if you want this to work.

    Additionally, it is only able to query submodules that are directly
    assigned to the model. So if `model` is passed, `model.feature1` can
    be returned, but not `model.feature1.layer2`.

    Args:
        model (nn.Module): model on which we will extract the features
        return_layers (Dict[name, new_name]): a dict containing the names
            of the modules for which the activations will be returned as
            the key of the dict, and the value of the dict is the name
            of the returned activation (which the user can specify).
    """
    _version = 2
    __annotations__ = {
        "return_layers": Dict[str, str],
    }

    def __init__(self, model: nn.Module, return_layers: Dict[str, str]) -> None:
        if not set(return_layers).issubset([name for name, _ in model.named_children()]):
            raise ValueError("return_layers are not present in model")
        orig_return_layers = return_layers
        return_layers = {str(k): str(v) for k, v in return_layers.items()}

        # 重新构建backbone，将没有使用到的模块全部删掉
        layers = OrderedDict()
        for name, module in model.named_children():
            layers[name] = module
            if name in return_layers:
                del return_layers[name]
            if not return_layers:
                break

        super(IntermediateLayerGetter, self).__init__(layers)
        self.return_layers = orig_return_layers

    def forward(self, x: Tensor) -> Dict[str, Tensor]:
        out = OrderedDict()
        for name, module in self.items():
            x = module(x)
            if name in self.return_layers:
                out_name = self.return_layers[name]
                out[out_name] = x
        return out

import torch
from torchvision.models import resnet18, vgg16_bn, resnet34
from torchvision.models._utils import IntermediateLayerGetter

model = resnet34()
stage_indices = ['relu', 'layer1', 'layer2', 'layer3', 'layer4']
return_layers = dict([(str(j), f"stage{i}") for i, j in enumerate(stage_indices)])
model= IntermediateLayerGetter(model, return_layers=return_layers)
input = torch.randn(1, 3, 224, 224)
output = model(input)
print([(k, v.shape) for k, v in output.items()])

import torch
from torchvision.models import vgg16_bn
from torchvision.models.feature_extraction import create_feature_extractor

model = vgg16_bn()
model = create_feature_extractor(model, {"features.43": "pool5", "features.33": "pool4", "features.23": "pool3"})
input = torch.randn(1, 3, 224, 224)
output = model(input)
print([(k, v.shape) for k, v in output.items()])

import torch
from torchvision.models import resnet18

model = resnet18()
fmap_block = dict()  # 装feature map
def forward_hook(module, input, output):
    fmap_block['input'] = input
    fmap_block['output'] = output

layer_name = 'avgpool'
for (name, module) in model.named_modules():
    if name == layer_name:
        module.register_forward_hook(hook=forward_hook)

input = torch.randn(64, 3, 224, 224)
output = model(input)
print(fmap_block['input'][0].shape)
print(fmap_block['output'].shape)