本文整理匯總了Python中eval.evaluate方法的典型用法代碼示例。如果您正苦於以下問題:Python eval.evaluate方法的具體用法?Python eval.evaluate怎麽用?Python eval.evaluate使用的例子?那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類eval
的用法示例。
在下文中一共展示了eval.evaluate方法的5個代碼示例,這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚,您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: compute_validation_map
# 需要導入模塊: import eval [as 別名]
# 或者: from eval import evaluate [as 別名]
def compute_validation_map(epoch, iteration, yolact_net, dataset, log:Log=None):
with torch.no_grad():
yolact_net.eval()
start = time.time()
print()
print("Computing validation mAP (this may take a while)...", flush=True)
val_info = eval_script.evaluate(yolact_net, dataset, train_mode=True)
end = time.time()
if log is not None:
log.log('val', val_info, elapsed=(end - start), epoch=epoch, iter=iteration)
yolact_net.train()
示例2: train_and_eval
# 需要導入模塊: import eval [as 別名]
# 或者: from eval import evaluate [as 別名]
def train_and_eval(net, train_loader, val_loader, optimizer, loss_fn, metrics, params, model_dir, restore=None):
"""
Train and evaluate every epoch of a model.
net: The model.
train/val loader: The data loaders
params: The parameters parsed from JSON file
restore: if there is a checkpoint restore from that point.
"""
best_val_acc = 0.0
if restore is not None:
restore_file = os.path.join(args.param_path, args.resume_path + '_pth.tar')
logging.info("Loaded checkpoints from:{}".format(restore_file))
utils.load_checkpoint(restore_file, net, optimizer)
for ep in range(params.num_epochs):
logging.info("Running epoch: {}/{}".format(ep+1, params.num_epochs))
# train one epoch
train(net, train_loader, loss_fn, params, metrics, optimizer)
val_metrics = evaluate(net, val_loader, loss_fn, params, metrics)
val_acc = val_metrics['accuracy']
isbest = val_acc >= best_val_acc
utils.save_checkpoint({"epoch":ep, "state_dict":net.state_dict(), "optimizer":optimizer.state_dict()},
isBest=isbest, ckpt_dir=model_dir)
if isbest:
# if the accuracy is great save it to best.json
logging.info("New best accuracy found!")
best_val_acc = val_acc
best_json_path = os.path.join(model_dir, "best_model_params.json")
utils.save_dict_to_json(val_metrics, best_json_path)
last_acc_path = os.path.join(model_dir, 'last_acc_metrics.json')
utils.save_dict_to_json(val_metrics, last_acc_path)
示例3: evaluate_batch
# 需要導入模塊: import eval [as 別名]
# 或者: from eval import evaluate [as 別名]
def evaluate_batch(config, model_para, model_qg, sess_para, sess_qg, num_batches, eval_file, iterator,
id2word, map_to_orig, evaluate_func=evaluate):
answer_dict = {}
losses = []
next_element = iterator.get_next()
for _ in tqdm(range(1, num_batches + 1)):
para, para_unk, ques, labels, pos_tags, ner_tags, qa_id = sess_para.run(next_element)
para_emb = sess_para.run(model_para.bert_emb, feed_dict={model_para.input_ids: para_unk})
loss, symbols, probs = sess_qg.run([model_qg.loss, model_qg.symbols, model_qg.probs],
feed_dict={
model_qg.para: para, model_qg.bert_para: para_emb,
model_qg.que: ques, model_qg.labels: labels,
model_qg.pos_tags: pos_tags,
model_qg.ner_tags: ner_tags, model_qg.qa_id: qa_id,
model_qg.temperature: config.temperature,
model_qg.diverse_rate: config.diverse_rate
})
answer_dict_ = convert_tokens_seq(eval_file, qa_id, symbols, probs, id2word, map_to_orig)
for key in answer_dict_:
if key not in answer_dict:
answer_dict[key] = answer_dict_[key]
losses.append(loss)
loss = np.mean(losses)
metrics = evaluate_func(eval_file, answer_dict)
metrics["loss"] = loss
return metrics
示例4: main
# 需要導入模塊: import eval [as 別名]
# 或者: from eval import evaluate [as 別名]
def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
assert FLAGS.checkpoint_dir, "Flag 'checkpoint_dir' must be set."
assert FLAGS.eval_dir, "Flag 'eval_dir' must be set."
if FLAGS.config_file:
for config_file in FLAGS.config_file:
gin.parse_config_file(config_file)
if FLAGS.params:
gin.parse_config(FLAGS.params)
eval_.evaluate(FLAGS.checkpoint_dir, FLAGS.eval_dir)
示例5: run_evaluate
# 需要導入模塊: import eval [as 別名]
# 或者: from eval import evaluate [as 別名]
def run_evaluate(args, model, eval_features, device, eval_examples, tokenizer, best_dev_score=None):
logger.info("***** Running predictions *****")
logger.info(" Num orig examples = %d", len(eval_examples))
logger.info(" Num split examples = %d", len(eval_features))
logger.info(" Batch size = %d", args.predict_batch_size)
all_input_ids = torch.tensor([f.input_ids for f in eval_features], dtype=torch.long)
all_input_mask = torch.tensor([f.input_mask for f in eval_features], dtype=torch.long)
all_segment_ids = torch.tensor([f.segment_ids for f in eval_features], dtype=torch.long)
all_example_index = torch.arange(all_input_ids.size(0), dtype=torch.long)
eval_data = TensorDataset(all_input_ids, all_input_mask, all_segment_ids, all_example_index)
if args.local_rank == -1:
eval_sampler = SequentialSampler(eval_data)
else:
eval_sampler = DistributedSampler(eval_data)
eval_dataloader = DataLoader(eval_data, sampler=eval_sampler, batch_size=args.predict_batch_size)
model.eval()
all_results = []
logger.info("Start evaluating")
for input_ids, input_mask, segment_ids, example_indices in tqdm(eval_dataloader, desc="Evaluating"):
if len(all_results) % 1000 == 0:
logger.info("Processing example: %d" % (len(all_results)))
input_ids = input_ids.to(device)
input_mask = input_mask.to(device)
segment_ids = segment_ids.to(device)
with torch.no_grad():
batch_start_logits, batch_end_logits = model(input_ids, segment_ids, input_mask)
for i, example_index in enumerate(example_indices):
start_logits = batch_start_logits[i].detach().cpu().tolist()
end_logits = batch_end_logits[i].detach().cpu().tolist()
eval_feature = eval_features[example_index.item()]
unique_id = int(eval_feature.unique_id)
all_results.append(RawResult(unique_id=unique_id,
start_logits=start_logits,
end_logits=end_logits))
output_prediction_file = os.path.join(args.output_dir, "predictions.json")
output_nbest_file = os.path.join(args.output_dir, "nbest_predictions.json")
predict, nbest = write_predictions(eval_examples, eval_features, all_results,
args.n_best_size, args.max_answer_length,
args.do_lower_case, output_prediction_file,
output_nbest_file, args.verbose_logging)
with open(args.predict_file) as dataset_file:
dataset_json = json.load(dataset_file)
dataset = dataset_json['data']
dev_score = evaluate(dataset, predict, tokenizer)['f1']
logger.info('Dev score : {}'.format(dev_score))
if best_dev_score is not None and dev_score > best_dev_score:
logger.info('Saving model with dev score: {}'.format(dev_score))
best_dev_score = dev_score
write_to_file(predict, nbest, output_prediction_file, output_nbest_file)
torch.save(model.state_dict(), args.finetuned_checkpoint)
else:
write_to_file(predict, nbest, output_prediction_file, output_nbest_file)
return best_dev_score