[英]Pytorch Global Pruning is not reducing the size of the model
我正在嘗試通過全局修剪修剪我的深度學習 model。 原始的未修剪 model 約為 77.5 MB。 但是修剪后,當我保存 model 時,model 的大小與原始大小相同。 誰能幫我解決這個問題?
以下是修剪代碼:-
import torch.nn.utils.prune as prune
parameters_to_prune = (
(model.encoder[0], ‘weight’),
(model.up_conv1[0], ‘weight’),
(model.up_conv2[0], ‘weight’),
(model.up_conv3[0], ‘weight’),
)
print(parameters_to_prune)
prune.global_unstructured(
parameters_to_prune,
pruning_method=prune.L1Unstructured,
amount=0.2,
)
print(
“Sparsity in Encoder.weight: {:.2f}%”.format(
100. * float(torch.sum(model.encoder[0].weight == 0))
/ float(model.encoder[0].weight.nelement())
)
)
print(
“Sparsity in up_conv1.weight: {:.2f}%”.format(
100. * float(torch.sum(model.up_conv1[0].weight == 0))
/ float(model.up_conv1[0].weight.nelement())
)
)
print(
“Sparsity in up_conv2.weight: {:.2f}%”.format(
100. * float(torch.sum(model.up_conv2[0].weight == 0))
/ float(model.up_conv2[0].weight.nelement())
)
)
print(
“Sparsity in up_conv3.weight: {:.2f}%”.format(
100. * float(torch.sum(model.up_conv3[0].weight == 0))
/ float(model.up_conv3[0].weight.nelement())
)
)
print(
“Global sparsity: {:.2f}%”.format(
100. * float(
torch.sum(model.encoder[0].weight == 0)
+ torch.sum(model.up_conv1[0].weight == 0)
+ torch.sum(model.up_conv2[0].weight == 0)
+ torch.sum(model.up_conv3[0].weight == 0)
)
/ float(
model.encoder[0].weight.nelement()
+ model.up_conv1[0].weight.nelement()
+ model.up_conv2[0].weight.nelement()
+ model.up_conv3[0].weight.nelement()
)
)
)
**Setting Pruning to Permanent**
prune.remove(model.encoder[0], “weight”)
prune.remove(model.up_conv1[0], “weight”)
prune.remove(model.up_conv2[0], “weight”)
prune.remove(model.up_conv3[0], “weight”)
**Saving the model**
PATH = “C:\PrunedNet.pt”
torch.save(model.state_dict(), PATH)
如果像這樣應用,修剪不會改變 model 大小。
如果你有張量,可以這樣說:
[1., 2., 3., 4., 5., 6., 7., 8.]
你修剪了50%
的數據,例如:
[1., 2., 0., 4., 0., 6., 0., 0.]
您仍然會有8
個浮點值,並且它們的大小相同。
Conv2d
,如果它的權重為零或可忽略不計,則可以將其刪除)否則它將無法正常工作。 查看一些相關項目,這些項目可以讓您無需自己編寫代碼就可以完成,例如Torch-Pruning 。
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