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utils.py
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utils.py
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from torch.utils.data import Dataset, DataLoader
from torchvision import datasets, transforms
import os
num_classes = {'cifar10': 10, 'cifar100': 100, 'mnist':10, 'imagenet':1000}
datapath = {
'cifar10': 'G:/dataset/cifar10',
'cifar100': 'G:/dataset/cifar100',
'mnist':'G:/dataset/mnist',
'imagenet': '/gdata/ImageNet2012'
}
def print_args(args):
print('ARGUMENTS:')
for arg in vars(args):
print(f">>> {arg}: {getattr(args, arg)}")
def load_cv_data(data_aug, batch_size, workers, dataset, data_target_dir):
if dataset == 'cifar10':
mean = [x / 255 for x in [125.3, 123.0, 113.9]]
std = [x / 255 for x in [63.0, 62.1, 66.7]]
elif dataset == 'cifar100':
mean = [x / 255 for x in [129.3, 124.1, 112.4]]
std = [x / 255 for x in [68.2, 65.4, 70.4]]
elif dataset == 'mnist':
mean = (0.1307,)
std = (0.3081,)
elif dataset == 'imagenet':
mean = (0.485, 0.456, 0.406)
std = (0.229, 0.224, 0.225)
else:
assert False, f"Unknown dataset : {dataset}"
if data_aug:
if dataset == 'svhn':
train_transform = transforms.Compose([
transforms.RandomCrop(32, padding=2),
transforms.ToTensor(),
transforms.Normalize(mean, std)
])
test_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean, std)
])
elif dataset == 'imagenet':
train_transform = transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean, std),
])
test_transform = transforms.Compose([
transforms.Resize(int(224 / 0.875)),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean, std),
])
else:
train_transform = transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.RandomCrop(32, padding=2),
transforms.ToTensor(),
transforms.Normalize(mean, std)
])
test_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean, std)
])
else:
if dataset == 'imagenet':
train_transform = transforms.Compose([
transforms.Resize(int(224 / 0.875)),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean, std),
])
test_transform = transforms.Compose([
transforms.Resize(int(224 / 0.875)),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean, std),
])
else:
train_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean, std)
])
test_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean, std)
])
if dataset == 'cifar10':
train_data = datasets.CIFAR10(data_target_dir, train=True, transform=train_transform, download=True)
test_data = datasets.CIFAR10(data_target_dir, train=False, transform=test_transform, download=True)
elif dataset == 'cifar100':
train_data = datasets.CIFAR100(data_target_dir, train=True, transform=train_transform, download=True)
test_data = datasets.CIFAR100(data_target_dir, train=False, transform=test_transform, download=True)
elif dataset == 'svhn':
train_data = datasets.SVHN(data_target_dir, split='train', transform=train_transform, download=True)
test_data = datasets.SVHN(data_target_dir, split='test', transform=test_transform, download=True)
elif dataset == 'mnist':
train_data = datasets.MNIST(data_target_dir, train=True, transform=train_transform, download=True)
test_data = datasets.MNIST(data_target_dir, train=False, transform=test_transform, download=True)
elif dataset == 'imagenet':
train_data = datasets.ImageFolder(root=os.path.join(data_target_dir, 'train'),transform=train_transform)
test_data = datasets.ImageFolder(root=os.path.join(data_target_dir, 'val'),transform=test_transform)
else:
assert False, 'Do not support dataset : {}'.format(dataset)
train_dataloader = DataLoader(train_data, batch_size=batch_size, shuffle=True, num_workers=workers, pin_memory=True)
test_dataloader = DataLoader(test_data, batch_size=batch_size, shuffle=False, num_workers=workers, pin_memory=True)
return train_dataloader, test_dataloader
if __name__ == "__main__":
import models
import modules
import torch
import numpy as np
import torch.nn as nn
train_dataloader, test_dataloader = load_cv_data(data_aug=False,
batch_size=50,
workers=0,
dataset='cifar10',
data_target_dir=datapath['cifar10']
)
percentage = 0.004 # load 0.004 of the data
norm_data_list = []
for idx, (imgs, targets) in enumerate(train_dataloader):
norm_data_list.append(imgs)
if idx == int(len(train_dataloader) * percentage) - 1:
break
norm_data = torch.cat(norm_data_list)
model = models.__dict__['vgg16'](num_classes=10, dropout=0.1)
model = modules.replace_maxpool2d_by_avgpool2d(model)
model = modules.replace_relu_by_spikingnorm(model, True)
v = []
model.load_state_dict(torch.load("C:/Users/dell/Desktop/Projects/Paper1_Figures/Figure6/vgg16_cifar10_[0.100_91.340_1.801].pth")['net'])
cnt = 0
for m in model.modules():
if isinstance(m,modules.SpikingNorm):
v.append(m.calc_v_th().data.item())
cnt+=1
x = model(norm_data)
v.append(torch.max(x).item())
print(len(v))
np.savetxt('vth_rnl.csv', np.array(v), delimiter=',')
model = modules.replace_spikingnorm_by_relu(model)
model.load_state_dict(torch.load("C:/Users/dell/Desktop/Projects/Paper1_Code/normal/pretrain/vgg16_cifar10.pth")['net'])
max_activation = []
def hook(module,input,output):
# print(torch.max(output))
max_activation.append(torch.max(output).item())
for m in model.modules():
if isinstance(m,nn.ReLU):
m.register_forward_hook(hook)
x = model(norm_data)
max_activation.append(torch.max(x).item())
print(len(max_activation))
np.savetxt('vth_original.csv', np.array(max_activation), delimiter=',')
np.savetxt('vth_x0.8.csv', np.array(max_activation) * 0.8, delimiter=',')