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predict.py
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predict.py
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import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from sklearn import metrics
import time
from utils_1 import get_time_dif
from pytorch_pretrained.optimization import BertAdam
def evaluate(config, model, data_iter, test=False):
model.eval()
loss_total = 0
predict_all = np.array([], dtype=int)
labels_all = np.array([], dtype=int)
with torch.no_grad():
for texts, labels in data_iter:
outputs = model(texts)
loss = F.cross_entropy(outputs, labels)
loss_total += loss
labels = labels.data.cpu().numpy()
predic = torch.max(outputs.data, 1)[1].cpu().numpy()
labels_all = np.append(labels_all, labels)
predict_all = np.append(predict_all, predic)
acc = metrics.accuracy_score(labels_all, predict_all)
if test:
report = metrics.classification_report(labels_all, predict_all, target_names=config.class_list, digits=4)
confusion = metrics.confusion_matrix(labels_all, predict_all)
return acc, loss_total / len(data_iter), report, confusion
return acc, loss_total / len(data_iter)
def test(config, model, test_iter):
# test
model.load_state_dict(torch.load(config.save_path))
model.eval()
start_time = time.time()
test_acc, test_loss, test_report, test_confusion = evaluate(config, model, test_iter, test=True)
msg = 'Test Loss: {0:>5.2}, Test Acc: {1:>6.2%}'
print(msg.format(test_loss, test_acc))
print("Precision, Recall and F1-Score...")
print(test_report)
print("Confusion Matrix...")
print(test_confusion)
time_dif = get_time_dif(start_time)
print("Time usage:", time_dif)