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Running
on
Zero
File size: 1,880 Bytes
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"""
https://github.com/pytorch/vision/blob/main/torchvision/models/_utils.py
Copyright(c) 2023 lyuwenyu. All Rights Reserved.
"""
from collections import OrderedDict
from typing import Dict, List
import torch.nn as nn
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`.
"""
_version = 3
def __init__(self, model: nn.Module, return_layers: List[str]) -> None:
if not set(return_layers).issubset([name for name, _ in model.named_children()]):
raise ValueError(
"return_layers are not present in model. {}".format(
[name for name, _ in model.named_children()]
)
)
orig_return_layers = return_layers
return_layers = {str(k): str(k) for k in return_layers}
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().__init__(layers)
self.return_layers = orig_return_layers
def forward(self, x):
outputs = []
for name, module in self.items():
x = module(x)
if name in self.return_layers:
outputs.append(x)
return outputs
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