def eval_model(cls,
master_gpu_id,
model,
eval_dataset,
eval_batch_size=1,
use_cuda=False,
num_workers=1):
model.eval()
eval_dataloader = DataLoader(dataset=eval_dataset,
pin_memory=use_cuda,
batch_size=eval_batch_size,
num_workers=num_workers,
shuffle=False)
predicted_probs = []
true_labels = []
batch_count = 1
for batch in tqdm(eval_dataloader, unit="batch", ncols=100, desc="Evaluating process: "):
labels = batch["label"].cuda(master_gpu_id) if use_cuda and master_gpu_id is not None else batch["label"]
tokens = batch['tokens'].cuda(master_gpu_id) if use_cuda and master_gpu_id is not None else batch['tokens']
segment_ids = batch['segment_ids'].cuda(master_gpu_id) if use_cuda and master_gpu_id is not None else batch[
'segment_ids']
attention_mask = batch["attention_mask"].cuda(master_gpu_id) if use_cuda and master_gpu_id is not None else \
batch["attention_mask"]
with torch.no_grad():
output = model(tokens, segment_ids, attention_mask)
# 将模型输出转为列表
output = torch.softmax(output, dim=1).cpu().tolist()
# 获取正例结果
output = np.array(output)[:, 1]
# 将该Batch的正例预测值列表拼接至全局正例预测值列表中
predicted_probs.extend(output.tolist())
# 将真实label列表拼接至全局真实label列表
true_labels.extend(labels.tolist())
LoggerHelper.info("Batch: " + str(batch_count))
batch_count += 1
predicted_probs = [round(prob, 2) for prob in predicted_probs]
precision, recall, _thresholds = precision_recall_curve(true_labels, predicted_probs)
auc = roc_auc_score(true_labels, predicted_probs)
logloss = log_loss(true_labels, predicted_probs)
for i in range(len(_thresholds)):
log_str_th = 'VAL => Thresholds: {0:>2}, Precision: {1:>7.2%}, Recall: {2:>7.2%}, F1: {3:>7.2%}'.format(
_thresholds[i], precision[i], recall[i], f1_score(precision[i], recall[i]))
LoggerHelper.info(log_str_th)
LoggerHelper.info("AUC: " + str(auc))
LoggerHelper.info("Logloss: " + str(logloss))
LoggerHelper.info("Total Evaluation Samples: " + str(len(true_labels)))
LoggerHelper.info("Total Positive Evaluation Samples: " + str(len([x for x in true_labels if x == 1])))
LoggerHelper.info("Total Negtive Evaluation Samples: " + str(len([x for x in true_labels if x == 0])))
return
def eval_model(cls,
master_gpu_id,
model,
eval_dataset,
eval_batch_size=1,
use_cuda=False,
num_workers=1):
model.eval()
eval_dataloader = DataLoader(dataset=eval_dataset,
pin_memory=use_cuda,
batch_size=eval_batch_size,
num_workers=num_workers,
shuffle=False,
collate_fn=DiDiDatasetAudio.collate)
predicted_probs = []
true_labels = []
batch_count = 1
for batch in tqdm(eval_dataloader, unit="batch", ncols=100, desc="Evaluating process: "):
audio_inputs, label_inputs, _, _ = batch
label_inputs = label_inputs.cuda(master_gpu_id) if use_cuda and master_gpu_id is not None else label_inputs
audio_inputs, audio_length = audio_inputs
audio_inputs = audio_inputs.cuda(master_gpu_id) if use_cuda and master_gpu_id is not None else audio_inputs
audio_length = audio_length.cuda(master_gpu_id) if use_cuda and master_gpu_id is not None else audio_length
with torch.no_grad():
main_output = model(audio_inputs, audio_length)
# 将模型输出转为列表
main_output = torch.softmax(main_output, dim=1).cpu().tolist()
# 获取正例结果
prob = np.array(main_output)[:, 1]
# 将该Batch的正例预测值列表拼接至全局正例预测值列表中
predicted_probs.extend(prob.tolist())
# 将真实label列表拼接至全局真实label列表
true_labels.extend(label_inputs.tolist())
LoggerHelper.info("Batch: " + str(batch_count))
batch_count += 1
predicted_probs = [round(prob, 2) for prob in predicted_probs]
precision, recall, _thresholds = precision_recall_curve(true_labels, predicted_probs)
auc = roc_auc_score(true_labels, predicted_probs)
logloss = log_loss(true_labels, predicted_probs)
for i in range(len(_thresholds)):
log_str_th = 'VAL => Thresholds: {0:>2}, Precision: {1:>7.2%}, Recall: {2:>7.2%}, F1: {3:>7.2%}'.format(
_thresholds[i], precision[i], recall[i], f1_score(precision[i], recall[i]))
LoggerHelper.info(log_str_th)
LoggerHelper.info("AUC: " + str(auc))
LoggerHelper.info("Logloss: " + str(logloss))
return