Comments (2)
The layers do not specifically allude to 'conv' layers. VGG16 has 31 layers, among which 13 are conv layers as you already know. The other layers consist of maxpools and relus. If you execute the following code:
from torchvision import models
model = models.vgg16()
print(model.features)
You will obtain a list of layers, from 0 to 30. Layer 28, in fact, is the last conv layer.
from pytorch-pruning.
thx
from pytorch-pruning.
Related Issues (20)
- Python 3.6, pytorch 0.4.1, getting RuntimeError: Dimension out of range (expected to be in range of [-2, 1], but got 3) HOT 4
- torch.load(pruned_model) HOT 7
- [CUDA Runtime Error] Assertion `t >= 0 && t < n_classes` failed. HOT 5
- Accuracy drops from 96.46% to 58.67% HOT 1
- train_path
- Time of 1 pruning iteration
- in finetune.py 117 Why "-i"?? HOT 2
- Getting Error in pruning HOT 1
- pruning conv layers with 'groups' > 1
- Batch Nomalization
- I wonder if this pruning approach can be adapted to net like resnet
- layer_index
- how Pruning the last conv layer affects the first linear layer of the classifier
- can u plz tell me how to resolve this issue of not getting training data path
- where is prune_conv_layer?
- Will the pruned weight reactivated after finetuning?
- Running project in google colab
- running project on Anaconda Jupyter Notebook
- Pruning model YOLOX using 'import torch.nn.utils.prune as prune'
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from pytorch-pruning.