Pytorch Global Pruning is not reducing the size of the model

I am trying to Prune my Deep Learning model via Global Pruning. The original UnPruned model is about 77.5 MB. However after pruning, when I am saving the model, the size of the model is the same as the original. Can anyone help me with this issue?

Below is the Pruning code:-

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)