def get_model(args):
''' define model '''
model = ConvNet(use_batch_norm=True, use_resnet=False)
print('---Model Information---')
print('Net:', model)
print('Use GPU:', args.use_cuda)
return model.to(args.device)
# unpack args
device = args.device
epoch = 1
lmbda = args.lmbda
lr = args.lr
criterion = make_criterion(args)
train_loss_tracker, train_acc_tracker = [], []
test_loss_tracker, test_acc_tracker = [], []
# ADD FILENAMES FOR MODEL WEIGHTS TO QUANTIZE AND EVALUATE THEM
filenames = ['control']
experiment_net = ConvNet()
experiment_net = experiment_net.to(device)
base_accuracies = []
for h in range(len(filenames)):
experiment_net.load_state_dict(torch.load(filenames[h] + '.pt'))
print('Test Accuracy without Quantization for ' + filenames[h] + '.pt')
acc = test(experiment_net, testloader, criterion, epoch, lmbda,
test_loss_tracker, test_acc_tracker)
base_accuracies.append(acc)
# CHANGE FOR LOOP RANGE TO QUANTIZE FOR DIFFERENT BITWIDTHS
for n_bits in range(4, 9):
print('{} BITWIDTH'.format(n_bits))
# L1 AND L2
for n in range(len(filenames)):
experiment_net.load_state_dict(torch.load(filenames[n] + '.pt'))
from models import ConvNet
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# Load the dataset
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
dataset = torchvision.datasets.ImageFolder("D:/PokeRapper/Pokemon",
transform=transform)
dataloader = torch.utils.data.DataLoad(dataset,
batch_size=1024,
shuffle=True,
num_workers=4)
# build the model
# TODO: Add support for loading different models
model = ConvNet()
model.to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)
# train the network
for epoch in range(100):
for i, data in enumerate(dataloader, 0):
print('')
