def load_model(dataset_name, algo_name, dataset):
if algo_name == "vbpr":
model = VBPR(dataset.n_users, dataset.n_items, dataset.corpus.image_features)
elif algo_name == "deepstyle":
model = DeepStyle(
dataset.n_users, dataset.n_items, dataset.n_categories,
dataset.corpus.image_features, dataset.corpus.item_category
)
model.load(f'../data/dataset/{dataset_name}/models/{algo_name}_resnet50.pth')
return model
from dataset import RecSysDataset
from train import Trainer
from models import VBPR
import torch
if __name__ == '__main__':
k=10
k2=20
batch_size=128
n_epochs=20
dataset = RecSysDataset()
vbpr = VBPR(
dataset.n_users, dataset.n_items,
dataset.corpus.image_features, k, k2)
tr = Trainer(vbpr, dataset)
tr.train(n_epochs, batch_size)
torch.save(vbpr, 'vbpr_resnet50_v1.pth')
parser.add_argument('--lambda_e', type=float, default=0.0001)
parser.add_argument('--algorithm', type=str,
default='deepstyle') # vbpr, deepstyle
parser.add_argument('--dataset', type=str, default='Electronics')
args = parser.parse_args()
print(args)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
dataset = RecSysDataset(args.dataset)
if args.algorithm == "vbpr":
model = VBPR(dataset.n_users, dataset.n_items,
dataset.corpus.image_features, args.k, args.k2,
args.lambda_w, args.lambda_b, args.lambda_e)
elif args.algorithm == "deepstyle":
model = DeepStyle(dataset.n_users, dataset.n_items,
dataset.n_categories, dataset.corpus.image_features,
dataset.corpus.item_category, args.k, args.lambda_w,
args.lambda_e)
elif args.algorithm == "bpr":
model = BPR(dataset.n_users, dataset.n_items, args.k, args.lambda_w,
args.lambda_b)
if torch.cuda.is_available():
model = model.cuda()
if __name__ == '__main__':
parser = ArgumentParser(description="Experiments")
parser.add_argument('--k', type=int, default=10)
parser.add_argument('--k2', type=int, default=10)
parser.add_argument('--algorithm', type=str,
default='deepstyle') # bpr, vbpr, vbprc, deepstyle
parser.add_argument('--dataset', type=str, default='Electronics')
args = parser.parse_args()
print(args)
dataset = RecSysDataset(args.dataset)
if args.algorithm == "vbpr":
model = VBPR(dataset.n_users, dataset.n_items,
dataset.corpus.image_features, args.k, args.k2)
elif args.algorithm == "vbprc":
model = VBPRC(dataset.n_users, dataset.n_items, dataset.n_categories,
dataset.corpus.image_features,
dataset.corpus.item_category, args.k, args.k2)
elif args.algorithm == "deepstyle":
model = DeepStyle(dataset.n_users, dataset.n_items,
dataset.n_categories, dataset.corpus.image_features,
dataset.corpus.item_category, args.k)
elif args.algorithm == "bpr":
model = BPR(dataset.n_users, dataset.n_items, args.k)
model.load(