model_names = ['GoogLeNet', 'Pretrained GoogLeNet']
# models = [googlenet_pre]
# model_names = ['Pretrained GoogLeNet']
#%%
criterion = nn.CrossEntropyLoss()
optimizers = []
for model in models:
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)
optimizers.append(optimizer)
#%%
from chosen_gpu import get_freer_gpu
device = torch.device(
get_freer_gpu()) if torch.cuda.is_available() else torch.device("cpu")
print("Configured device: ", device)
#%%
for model in models:
model = model.to(device)
criterion = criterion.to(device)
#%%
def count_parameters(model):
return sum(p.numel() for p in model.parameters() if p.requires_grad)
from torchvision.utils import save_image
from visdom import Visdom
from modules import VAE
from train_test import train
from train_test import test
import utils
#%%
log_interval = 100
seed = 1
torch.manual_seed(seed)
from chosen_gpu import get_freer_gpu
device = torch.device(get_freer_gpu())
print("Configured device: ", device)
#%%
compose = transforms.Compose([
transforms.Resize((64, 64)),
transforms.ToTensor(),
#transforms.Normalize((.5, .5, .5), (.5, .5, .5))
])
ds = torchvision.datasets.ImageFolder(root='dataset/', transform=compose)
ratio = [int(len(ds) * 0.7), len(ds) - int(len(ds) * 0.7)]
train_dataset, test_dataset = torch.utils.data.random_split(ds, ratio)