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eval.py
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eval.py
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import numpy as np
import torch
import torch.nn.functional as F
import logging
from sklearn.preprocessing import label_binarize
from sklearn.metrics import roc_auc_score
def test(step, dataset_test, name, n_share, G, Cs,
open=False, entropy=False, thr=None):
G.eval()
for c in Cs:
c.eval()
## Known Score Calculation.
correct = 0
correct_close = 0
size = 0
per_class_num = np.zeros((n_share + 1))
per_class_correct = np.zeros((n_share + 1)).astype(np.float32)
class_list = np.unique(dataset_test.dataset.labels)
open_class = 1000
for batch_idx, data in enumerate(dataset_test):
with torch.no_grad():
img_t, label_t = data[0].cuda(), data[1].cuda()
feat = G(img_t)
out_t = Cs[0](feat)
pred = out_t.data.max(1)[1]
correct_close += pred.eq(label_t.data).cpu().sum()
out_t = F.softmax(out_t)
if entropy:
pred_unk = -torch.sum(out_t * torch.log(out_t), 1)
else:
out_open = Cs[1](feat)
out_open = F.softmax(out_open.view(out_t.size(0), 2, -1),1)
tmp_range = torch.range(0, out_t.size(0)-1).long().cuda()
pred_unk = out_open[tmp_range, 0, pred]
correct += pred.eq(label_t.data).cpu().sum()
pred = pred.cpu().numpy()
k = label_t.data.size()[0]
for i, t in enumerate(class_list):
t_ind = np.where(label_t.data.cpu().numpy() == t)
correct_ind = np.where(pred[t_ind[0]] == t)
per_class_correct[i] += float(len(correct_ind[0]))
per_class_num[i] += float(len(t_ind[0]))
size += k
if batch_idx == 0:
prediction = pred
else:
prediction = np.r_[prediction, pred]
if open:
label_t = label_t.data.cpu().numpy()
if batch_idx == 0:
label_all = label_t
anomaly_score = pred_unk.data.cpu().numpy()
else:
anomaly_score = np.r_[anomaly_score, pred_unk.data.cpu().numpy()]
label_all = np.r_[label_all, label_t]
if open:
Y_test = label_binarize(label_all, classes=[i for i in class_list])
roc = roc_auc_score(Y_test[:, -1], anomaly_score)
else:
roc = 0.0
logger = logging.getLogger(__name__)
logging.basicConfig(filename=name, format="%(message)s")
logger.setLevel(logging.INFO)
close_count = float(per_class_num[:len(class_list) - 1].sum())
acc_close_all = 100. *float(correct_close) / close_count
output = ["step %s"%step,
"acc close all %s" % float(acc_close_all),
"roc %s"% float(roc)]
logger.info(output)
print(output)
gen_submission_files('./submission/sample_submit.txt', dataset_test.dataset.imgs, prediction, anomaly_score)
return acc_close_all, roc
def test_pretrained(step, dataset_test, name, n_share, G,
open=False, entropy=False, thr=None, prob=False, logit=False):
G.eval()
## Known Score Calculation.
correct = 0
correct_close = 0
size = 0
per_class_num = np.zeros((n_share + 1))
per_class_correct = np.zeros((n_share + 1)).astype(np.float32)
class_list = np.unique(dataset_test.dataset.labels)
for batch_idx, data in enumerate(dataset_test):
with torch.no_grad():
img_t, label_t = data[0].cuda(), data[1].cuda()
out_t = G(img_t)
pred = out_t.data.max(1)[1]
correct_close += pred.eq(label_t.data).cpu().sum()
logit_t = out_t
out_t = F.softmax(out_t)
if entropy:
pred_unk = -torch.sum(out_t * torch.log(out_t), 1)
elif prob:
pred_unk = -torch.max(out_t, dim=-1)[0]
else:
pred_unk = -torch.max(logit_t, dim=-1)[0]
correct += pred.eq(label_t.data).cpu().sum()
pred = pred.cpu().numpy()
k = label_t.data.size()[0]
for i, t in enumerate(class_list):
t_ind = np.where(label_t.data.cpu().numpy() == t)
correct_ind = np.where(pred[t_ind[0]] == t)
per_class_correct[i] += float(len(correct_ind[0]))
per_class_num[i] += float(len(t_ind[0]))
size += k
if batch_idx == 0:
prediction = pred
else:
prediction = np.r_[prediction, pred]
if open:
label_t = label_t.data.cpu().numpy()
if batch_idx == 0:
label_all = label_t
anomaly_score = pred_unk.data.cpu().numpy()
else:
anomaly_score = np.r_[anomaly_score, pred_unk.data.cpu().numpy()]
label_all = np.r_[label_all, label_t]
if open:
Y_test = label_binarize(label_all, classes=[i for i in class_list])
roc = roc_auc_score(Y_test[:, -1], anomaly_score)
else:
roc = 0.0
logger = logging.getLogger(__name__)
logging.basicConfig(filename=name, format="%(message)s")
logger.setLevel(logging.INFO)
close_count = float(per_class_num[:len(class_list) - 1].sum())
acc_close_all = 100. *float(correct_close) / close_count
output = ["step %s"%step,
"acc close all %s" % float(acc_close_all),
"roc %s"% float(roc)]
logger.info(output)
print(output)
gen_submission_files('./sample_submit.txt', dataset_test.dataset.imgs, prediction, anomaly_score)
return acc_close_all, roc
def gen_submission_files(outfile, image_names, prediction, anomaly_score):
f = open(outfile, 'w')
for img, pred, score in zip(image_names, prediction, anomaly_score):
f.write('{} {} {}\n'.format(img, pred, score))
f.close()