def __init__(self):
self.model_parameters = get_parameters()
self.define_cuda()
self.set_model_data()
# -*- coding: utf-8 -*-
import torch
import torch.optim as optim
from common.globals_args import root, fn_graph_file
from grounding.ranking.path_match_nn.sequence_loader import SeqRankingLoader
from grounding.ranking.path_match_nn.model import PathRanking
from grounding.ranking.path_match_nn.parameters import get_parameters
model_parameters = get_parameters()
model_parameters.cuda=False
torch.manual_seed(model_parameters.seed)
if not model_parameters.cuda:
model_parameters.gpu = -1
if torch.cuda.is_available() and model_parameters.cuda:
print("Note: You are using GPU for training")
torch.cuda.set_device(model_parameters.gpu)
torch.cuda.manual_seed(model_parameters.seed)
if torch.cuda.is_available() and not model_parameters.cuda:
print("Warning: You have Cuda but do not use it. You are using CPU for training")
def train(train_file,val_file,model_file):
model = PathRanking(model_parameters=model_parameters)
train_loader = SeqRankingLoader(train_file,model_parameters, model_parameters.gpu)
val_loader = SeqRankingLoader(val_file,model_parameters, model_parameters.gpu)
if model_parameters.cuda:
model.cuda()
print("Shift model to GPU")
for name, param in model.named_parameters():
print(name, param.size())