本文整理汇总了Python中predictor.Predictor方法的典型用法代码示例。如果您正苦于以下问题:Python predictor.Predictor方法的具体用法?Python predictor.Predictor怎么用?Python predictor.Predictor使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类predictor
的用法示例。
在下文中一共展示了predictor.Predictor方法的4个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test
# 需要导入模块: import predictor [as 别名]
# 或者: from predictor import Predictor [as 别名]
def test(cf, logger, max_fold=None):
"""performs testing for a given fold (or held out set). saves stats in evaluator.
"""
logger.time("test_fold")
logger.info('starting testing model of fold {} in exp {}'.format(cf.fold, cf.exp_dir))
net = model.net(cf, logger).cuda()
batch_gen = data_loader.get_test_generator(cf, logger)
test_predictor = Predictor(cf, net, logger, mode='test')
test_results_list = test_predictor.predict_test_set(batch_gen, return_results = not hasattr(
cf, "eval_test_separately") or not cf.eval_test_separately)
if test_results_list is not None:
test_evaluator = Evaluator(cf, logger, mode='test')
test_evaluator.evaluate_predictions(test_results_list)
test_evaluator.score_test_df(max_fold=max_fold)
logger.info('Testing of fold {} took {}.\n'.format(cf.fold, logger.get_time("test_fold", reset=True, format="hms")))
示例2: test
# 需要导入模块: import predictor [as 别名]
# 或者: from predictor import Predictor [as 别名]
def test(logger):
"""
perform testing for a given fold (or hold out set). save stats in evaluator.
"""
logger.info('starting testing model of fold {} in exp {}'.format(cf.fold, cf.exp_dir))
net = model.net(cf, logger).cuda()
test_predictor = Predictor(cf, net, logger, mode='test')
test_evaluator = Evaluator(cf, logger, mode='test')
batch_gen = data_loader.get_test_generator(cf, logger)
test_results_list = test_predictor.predict_test_set(batch_gen, return_results=True)
test_evaluator.evaluate_predictions(test_results_list)
test_evaluator.score_test_df()
示例3: train_epoches
# 需要导入模块: import predictor [as 别名]
# 或者: from predictor import Predictor [as 别名]
def train_epoches(t_dataset, v_dataset, model, n_epochs, teacher_forcing_ratio):
eval_f = Evaluate_test()
best_dev = 0
train_loader = t_dataset.corpus
len_batch = len(train_loader)
epoch_examples_total = t_dataset.len
for epoch in range(1, n_epochs + 1):
model.train(True)
torch.set_grad_enabled(True)
epoch_loss = 0
for batch_idx in range(len_batch):
loss, num_examples = train_batch(t_dataset, batch_idx, model, teacher_forcing_ratio)
epoch_loss += loss * num_examples
sys.stdout.write(
'%d batches processed. current batch loss: %f\r' %
(batch_idx, loss)
)
sys.stdout.flush()
epoch_loss /= epoch_examples_total
log_msg = "Finished epoch %d with losses: %.4f" % (epoch, epoch_loss)
print(log_msg)
predictor = Predictor(model, v_dataset.vocab, args.cuda)
print("Start Evaluating")
cand, ref = predictor.preeval_batch(v_dataset)
print('Result:')
print('ref: ', ref[1][0])
print('cand: ', cand[1])
final_scores = eval_f.evaluate(live=True, cand=cand, ref=ref)
epoch_score = 2*final_scores['ROUGE_L']*final_scores['Bleu_4']/(final_scores['Bleu_4']+ final_scores['ROUGE_L'])
if epoch_score > best_dev:
torch.save(model.state_dict(), args.save)
print("model saved")
best_dev = epoch_score
示例4: __init__
# 需要导入模块: import predictor [as 别名]
# 或者: from predictor import Predictor [as 别名]
def __init__(self, config):
self.config = config
# model
self.model_dir = config.get('model', 'model_dir')
self.model_prefix = config.get('model', 'model_prefix')
self.model_epoch = config.getint('model', 'model_epoch')
self.label_num = config.getint('model', 'label_num')
self.ctx = mx.gpu(config.getint('model', 'gpu'))
# data
self.ds_rate = int(config.get('data', 'ds_rate'))
self.cell_width = int(config.get('data', 'cell_width'))
self.test_shape = [int(f) for f in config.get('data', 'test_shape').split(',')]
self.result_shape = [int(f) for f in config.get('data', 'result_shape').split(',')]
self.rgb_mean = [float(f) for f in config.get('data', 'rgb_mean').split(',')]
# rescale for test
self.test_scales = [float(f) for f in config.get('data', 'test_scales').split(',')]
self.cell_shapes = [[math.ceil(l * s / self.ds_rate)*self.ds_rate for l in self.test_shape]
for s in self.test_scales]
self.modules = []
for i, test_scale in enumerate(self.test_scales):
predictor = mx.module.Module.load(
prefix=os.path.join(self.model_dir, self.model_prefix),
epoch=self.model_epoch,
context=self.ctx)
data_shape = (1, 3, int(self.cell_shapes[i][0]), int(self.cell_shapes[i][1]))
predictor.bind(data_shapes=[('data', data_shape)], for_training=False)
self.modules.append(predictor)
self.predictor = Predictor(
modules=self.modules,
label_num=self.label_num,
ds_rate=self.ds_rate,
cell_width=self.cell_width,
result_shape=self.result_shape,
test_scales=self.test_scales
)