Hello, I am new to PyTorch and I have been running into several problems when trying to run the file main.py from XNOR-Net-PyTorch/main.py at master · jiecaoyu/XNOR-Net-PyTorch · GitHub. I have the newest stable version of PyTorch with Cuda 11.7.
I get the following error
Traceback (most recent call last):
File “C:\Users\saft_\miniconda3\envs\XNOR\lib\site-packages\spyder_kernels\py3compat.py”, line 356, in compat_exec
exec(code, globals, locals)
File “c:\users\saft_\machinelearning\xnor-net-pytorch\mnist\main.py”, line 189, in
train(epoch)
File “c:\users\saft_\machinelearning\xnor-net-pytorch\mnist\main.py”, line 39, in train
output = model(data)
File “C:\Users\saft_\miniconda3\envs\XNOR\lib\site-packages\torch\nn\modules\module.py”, line 1190, in _call_impl
return forward_call(*input, **kwargs)
File “C:\Users\saft_\MachineLearning\XNOR-Net-PyTorch\MNIST\models\LeNet_5.py”, line 93, in forward
x = self.bin_conv2(x)
File “C:\Users\saft_\miniconda3\envs\XNOR\lib\site-packages\torch\nn\modules\module.py”, line 1190, in _call_impl
return forward_call(*input, **kwargs)
File “C:\Users\saft_\MachineLearning\XNOR-Net-PyTorch\MNIST\models\LeNet_5.py”, line 53, in forward
x = BinActive()(x)
File “C:\Users\saft_\miniconda3\envs\XNOR\lib\site-packages\torch\autograd\function.py”, line 330, in call
raise RuntimeError(
RuntimeError: Legacy autograd function with non-static forward method is deprecated. Please use new-style autograd function with static forward method. (Example: Automatic differentiation package - torch.autograd — PyTorch 1.13 documentation)
The entire code reads
from __future__ import print_function
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import os
import sys
import models
import util
from torchvision import datasets, transforms
from torch.autograd import Variable
import util
def save_state(model, acc):
print('==> Saving model ...')
state = {
'acc': acc,
'state_dict': model.state_dict(),
}
for key in state['state_dict'].keys():
if 'module' in key:
state['state_dict'][key.replace('module.', '')] = \
state['state_dict'].pop(key)
torch.save(state, 'models/'+args.arch+'.best.pth.tar')
def train(epoch):
model.train()
for batch_idx, (data, target) in enumerate(train_loader):
if args.cuda:
data, target = data.cuda(), target.cuda()
data, target = Variable(data), Variable(target)
optimizer.zero_grad()
# process the weights including binarization
bin_op.binarization()
output = model(data)
loss = criterion(output, target)
loss.backward()
# restore weights
bin_op.restore()
bin_op.updateBinaryGradWeight()
optimizer.step()
if batch_idx % args.log_interval == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, batch_idx * len(data), len(train_loader.dataset),
100. * batch_idx / len(train_loader), loss.data.item()))
return
def test(evaluate=False):
global best_acc
model.eval()
test_loss = 0
correct = 0
bin_op.binarization()
for data, target in test_loader:
if args.cuda:
data, target = data.cuda(), target.cuda()
data, target = Variable(data, volatile=True), Variable(target)
output = model(data)
test_loss += criterion(output, target).data.item()
pred = output.data.max(1, keepdim=True)[1]
correct += pred.eq(target.data.view_as(pred)).cpu().sum()
bin_op.restore()
acc = 100. * float(correct) / len(test_loader.dataset)
if (acc > best_acc):
best_acc = acc
if not evaluate:
save_state(model, best_acc)
test_loss /= len(test_loader.dataset)
print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.2f}%)'.format(
test_loss * args.batch_size, correct, len(test_loader.dataset),
100. * float(correct) / len(test_loader.dataset)))
print('Best Accuracy: {:.2f}%\n'.format(best_acc))
return
def adjust_learning_rate(optimizer, epoch):
"""Sets the learning rate to the initial LR decayed by 10 every 15 epochs"""
lr = args.lr * (0.1 ** (epoch // args.lr_epochs))
print('Learning rate:', lr)
for param_group in optimizer.param_groups:
param_group['lr'] = lr
return lr
if __name__=='__main__':
# Training settings
parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
parser.add_argument('--batch-size', type=int, default=128, metavar='N',
help='input batch size for training (default: 128)')
parser.add_argument('--test-batch-size', type=int, default=128, metavar='N',
help='input batch size for testing (default: 128)')
parser.add_argument('--epochs', type=int, default=60, metavar='N',
help='number of epochs to train (default: 60)')
parser.add_argument('--lr-epochs', type=int, default=15, metavar='N',
help='number of epochs to decay the lr (default: 15)')
parser.add_argument('--lr', type=float, default=0.01, metavar='LR',
help='learning rate (default: 0.01)')
parser.add_argument('--momentum', type=float, default=0.9, metavar='M',
help='SGD momentum (default: 0.9)')
parser.add_argument('--weight-decay', '--wd', default=1e-5, type=float,
metavar='W', help='weight decay (default: 1e-5)')
parser.add_argument('--no-cuda', action='store_true', default=False,
help='disables CUDA training')
parser.add_argument('--seed', type=int, default=1, metavar='S',
help='random seed (default: 1)')
parser.add_argument('--log-interval', type=int, default=100, metavar='N',
help='how many batches to wait before logging training status')
parser.add_argument('--arch', action='store', default='LeNet_5',
help='the MNIST network structure: LeNet_5')
parser.add_argument('--pretrained', action='store', default=None,
help='pretrained model')
parser.add_argument('--evaluate', action='store_true', default=False,
help='whether to run evaluation')
args = parser.parse_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()
print(args)
torch.manual_seed(args.seed)
if args.cuda:
torch.cuda.manual_seed(args.seed)
# load data
kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}
train_loader = torch.utils.data.DataLoader(
datasets.MNIST('data', train=True, download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=args.batch_size, shuffle=True, **kwargs)
test_loader = torch.utils.data.DataLoader(
datasets.MNIST('data', train=False, transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])),
batch_size=args.test_batch_size, shuffle=True, **kwargs)
# generate the model
if args.arch == 'LeNet_5':
model = models.LeNet_5()
else:
print('ERROR: specified arch is not suppported')
exit()
if not args.pretrained:
best_acc = 0.0
else:
pretrained_model = torch.load(args.pretrained)
best_acc = pretrained_model['acc']
model.load_state_dict(pretrained_model['state_dict'])
if args.cuda:
model.cuda()
print(model)
param_dict = dict(model.named_parameters())
params = []
base_lr = 0.1
for key, value in param_dict.items():
params += [{'params':[value], 'lr': args.lr,
'weight_decay': args.weight_decay,
'key':key}]
optimizer = optim.Adam(params, lr=args.lr,
weight_decay=args.weight_decay)
criterion = nn.CrossEntropyLoss()
# define the binarization operator
bin_op = util.BinOp(model)
if args.evaluate:
test(evaluate=True)
exit()
for epoch in range(1, args.epochs + 1):
adjust_learning_rate(optimizer, epoch)
train(epoch)
test()
I am aware that Variable is deprecated, but I really don’t know how to solve this issue.