这不是加载模型的权重。我正在尝试查看火炬中加载的
efficientnet-b3os.system('pip install efficientnet_pytorch')
from efficientnet_pytorch import EfficientNet
MODEL_NAME = 'efficientnet-b3'
effnet = EfficientNet.from_pretrained(MODEL_NAME)
effnet.modules # this works, but only gives the module names
effnet.weights # doesn't work
effnet.layers # doesn't work
effnet.modules[1] # doesn't work, second module is batch norm ._bc0
我想要一些功能来在 torch 中复制以下 TF 代码，第一批规范层的访问权重
from tensorflow.keras.applications import EfficientNetB3
base_model = EfficientNetB3(weights="imagenet")
base_model.trainable_variables[1] # indexing with 1 gives weights of conv layer
上述代码的输出：
<tf.Variable 'stem_bn/gamma:0' shape=(40,) dtype=float32, numpy=
array([ 0.1913209 , 2.7074034 , 9.623442 , 2.5562265 , 3.127593 ,
4.348222 , 2.4381876 , 3.4623973 , 3.6115906 , 4.1241236 ,
2.18851 , 8.9716835 , 0.7232651 , 0.6261555 , 9.050293 ,
7.9233327 , 0.47725916, 3.4991856 , 5.334402 , 4.843143 ,
1.4122163 , 1.953061 , 8.150878 , 5.0044165 , 2.3806598 ,
4.2976685 , 2.2239766 , 0.551327 , 7.799995 , 3.3823645 ,
1.8910869 , 4.0793633 , 0.73215246, 3.4526935 , 10.874565 ,
2.0920732 , 6.272054 , 3.6823177 , 4.2152214 , 3.4319222 ],
dtype=float32)>
您可以通过调用
model.named_parameters()在您的情况下，调用
effnet.named_parameters()这是一个示例摘录，这样您就可以明白我的意思：
[('_conv_head.weight', Parameter containing:
tensor([[[[ 0.0074]],
[[-0.0904]],
[[-0.0091]],
...,
[[-0.0504]],
[[ 0.1140]],
[[-0.0710]]],
[[[ 0.0635]],
[[ 0.0290]],
[[-0.0583]],
...,
[[-0.0813]],
[[-0.0230]],
[[-0.1120]]],
[[[ 0.0873]],
[[-0.0174]],
[[-0.0170]],
...,
[[-0.0014]],
[[ 0.0323]],
[[ 0.0569]]],
...,
[[[-0.0218]],
[[ 0.0292]],
[[ 0.0267]],
...,
[[-0.0044]],
[[-0.0463]],
[[-0.0257]]],
[[[-0.0209]],
[[ 0.0279]],
[[ 0.0094]],
...,
[[-0.1759]],
[[-0.0702]],
[[-0.0902]]],
[[[-0.0488]],
[[ 0.0276]],
[[-0.0174]],
...,
[[-0.0391]],
[[-0.0268]],
[[-0.0205]]]], requires_grad=True)),
('_bn1.weight', Parameter containing:
tensor([2.3115, 2.0343, 2.0015, ..., 1.7868, 2.3552, 1.8885],
requires_grad=True)),
('_bn1.bias', Parameter containing:
tensor([-1.8066, -1.4178, -1.3111, ..., -1.0494, -1.8520, -1.1798],
requires_grad=True)),
('_fc.weight', Parameter containing:
tensor([[-0.0157, -0.0483, 0.0139, ..., -0.0201, -0.0092, -0.0752],
[-0.0100, -0.0575, 0.0328, ..., -0.0362, -0.0534, -0.0041],
[-0.0083, 0.0142, -0.0006, ..., 0.0151, -0.0033, 0.0500],
...,
[-0.0840, -0.0217, -0.0354, ..., -0.0620, 0.0143, 0.0786],
[-0.0956, -0.0169, 0.0738, ..., 0.1063, -0.0742, 0.0036],
[ 0.0485, 0.0470, 0.1002, ..., -0.0832, 0.1081, 0.0145]],
requires_grad=True)),
('_fc.bias', Parameter containing:
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