def draw_RR_curve():
class_num = 3
vaild_ttrans = Transforms.Compose([
# Transforms.RandomVerticalFlip(p=0.5),
# transforms.RandomRotation(30),
# transforms.RandomCrop(100),
# transforms.RandomResizedCrop(112),
# Transforms.ColorJitter(brightness=0.5),
# transforms.RandomErasing(p=0.2, scale=(0.02, 0.03), ratio=(0.3, 0.3), value=0, ),
Transforms.Resize((24, 24)),
Transforms.ToTensor(),
Transforms.Normalize((0.45, 0.448, 0.455), (0.082, 0.082, 0.082)),
])
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
txt_path = '/media/omnisky/D4T/JSH/faceFenlei/eye/mbhlk_hl_0128/mix_train.txt'
vaild_txt = '/media/omnisky/D4T/JSH/faceFenlei/eye/mbhlk_hl_0128/mix_valid.txt'
# model
mixnet = MixNet(input_size=(24,24), num_classes=3)
weight_dict = torch.load("/media/omnisky/D4T/JSH/faceFenlei/Projects/hul_eye_class/weight/relabel_04_mix_SGD_mutillabel_24_24_20210302/Mixnet_epoch_49.pth")
new_state_dict = OrderedDict()
for k, v in weight_dict.items():
name = k[7:]
new_state_dict[name] = v
mixnet.load_state_dict(new_state_dict)
# stat(net,(3,48,48))
mixnet.to('cuda:0')
mixnet.eval()
vaild_data = mbhk_get_signal_eye(vaild_txt,vaild_ttrans)
valid_data_loader = DataLoader(vaild_data,batch_size=128,shuffle=False,num_workers=12)
score_list = [] #存储预测得分
label_list = [] #存储真实标签
with torch.no_grad():
for imgs,labels,_ in valid_data_loader:
for timg in imgs:
test_result = mixnet(timg.cuda())
# result = torch.max(test_result,1)[1]
result = torch.nn.functional.softmax(test_result,dim=1)
score_list.extend(result.cpu().numpy())
label_list.extend(labels.cpu().numpy())
label_list = np.array(label_list).reshape((-1,1))
score_list = np.array(score_list).reshape((-1,3))
plot_RR(label_list,score_list,['0','1','2'])
def main():
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
data_trans = Transforms.Compose([
Transforms.Resize((24, 24)),
Transforms.ToTensor(),
Transforms.Normalize((0.45, 0.448, 0.455), (0.082, 0.082, 0.082)),
])
test_data = mbhk_get_signal_eye(img_path, transform=data_trans)
test_data_loader = torch.utils.data.DataLoader(test_data,
batch_size=128,
shuffle=False,
num_workers=4)
# 定义模型 输入图像24*48
# net = MixNet(input_size=(24,48), num_classes=3)
# weight_dict = torch.load("weight/change_mix_data_0202/Mixnet_epoch_59.pth")
net = MixNet(input_size=(24, 24), num_classes=3)
weight_dict = torch.load(
"weight/mix_mbhk_change_signal_eye_24_24/Mixnet_epoch_59.pth")
new_state_dict = OrderedDict()
for k, v in weight_dict.items():
name = k[7:]
new_state_dict[name] = v
net.load_state_dict(new_state_dict)
# stat(net,(3,48,48))
net.to(device)
net.eval()
acc = 0.0
val_num = len(test_data)
#创建文件夹存储错误识别的图片
class_img = ["close_eye", "open_eye", "other"]
error_class_mbhk_img_path = "./error_class/mbhk_img"
error_class_change_img_path = "./error_class/change_img"
if not os.path.exists(error_class_mbhk_img_path):
os.makedirs(error_class_mbhk_img_path)
if not os.path.exists(error_class_change_img_path):
os.makedirs(error_class_change_img_path)
for tp in class_img:
if not os.path.exists(os.path.join(error_class_mbhk_img_path, tp)):
os.mkdir(os.path.join(error_class_mbhk_img_path, tp))
if not os.path.exists(os.path.join(error_class_change_img_path, tp)):
os.mkdir(os.path.join(error_class_change_img_path, tp))
#创建错误日志保存到txt文件
error_lod_mbhk = open(
os.path.join(error_class_mbhk_img_path, "error_mbhk_log.txt"), 'w')
error_lod_change = open(
os.path.join(error_class_change_img_path, "error_change_log.txt"), 'w')
with torch.no_grad():
for i in tqdm(range(len(test_data))):
# img,res_label,[img_path,json_path]
timg, label, tpath = test_data.__getitem__(i)
# timg = timg.unsqueeze(0)
#用于计数，防止重复操作
count = 0
for img in timg:
#增加维度
img = img.unsqueeze(0)
# label = label.unsqueeze(0)
outputs = net(img.to(device))
result = torch.max(outputs, 1)[1]
if result.item() != label and count == 0:
count += 1
if result == 0:
if "/imge" in tpath[0]:
#则说明是mbhk数据
shutil.copy(
tpath[0],
os.path.join(error_class_mbhk_img_path,
"open_eye"))
shutil.copy(
tpath[1],
os.path.join(error_class_mbhk_img_path,
"open_eye"))
error_lod_mbhk.write("{} {} {}\n".format(
tpath[0], label_idx[result.item()],
label_idx[label]))
else:
error_lod_change.write("{} {} {}\n".format(
tpath[0], label_idx[result.item()],
label_idx[label]))
shutil.copy(
tpath[0],
os.path.join(error_class_change_img_path,
"open_eye"))
shutil.copy(
tpath[1],
os.path.join(error_class_change_img_path,
"open_eye"))
elif result == 1:
if "/imge" in tpath[0]:
#则说明是mbhk数据
shutil.copy(
tpath[1],
os.path.join(error_class_mbhk_img_path,
"close_eye"))
shutil.copy(
tpath[0],
os.path.join(error_class_mbhk_img_path,
"close_eye"))
error_lod_mbhk.write("{} {} {}\n".format(
tpath[0], label_idx[result.item()],
label_idx[label]))
else:
error_lod_change.write("{} {} {}\n".format(
tpath[0], label_idx[result.item()],
label_idx[label]))
shutil.copy(
tpath[0],
os.path.join(error_class_change_img_path,
"close_eye"))
shutil.copy(
tpath[1],
os.path.join(error_class_change_img_path,
"close_eye"))
elif result == 2:
if "/imge" in tpath[0]:
#则说明是mbhk数据
shutil.copy(
tpath[1],
os.path.join(error_class_mbhk_img_path,
"other"))
shutil.copy(
tpath[0],
os.path.join(error_class_mbhk_img_path,
"other"))
error_lod_mbhk.write("{} {} {}\n".format(
tpath[0], label_idx[result.item()],
label_idx[label]))
else:
error_lod_change.write("{} {} {}\n".format(
tpath[0], label_idx[result.item()],
label_idx[label]))
shutil.copy(
tpath[0],
os.path.join(error_class_change_img_path,
"other"))
shutil.copy(
tpath[1],
os.path.join(error_class_change_img_path,
"other"))
error_lod_mbhk.close()
error_lod_change.close()
def main():
class_num = 3
vaild_ttrans = Transforms.Compose([
# Transforms.RandomVerticalFlip(p=0.5),
# transforms.RandomRotation(30),
# transforms.RandomCrop(100),
# transforms.RandomResizedCrop(112),
# Transforms.ColorJitter(brightness=0.5),
# transforms.RandomErasing(p=0.2, scale=(0.02, 0.03), ratio=(0.3, 0.3), value=0, ),
Transforms.Resize((24, 24)),
Transforms.ToTensor(),
Transforms.Normalize((0.45, 0.448, 0.455), (0.082, 0.082, 0.082)),
])
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
txt_path = '/media/omnisky/D4T/JSH/faceFenlei/eye/mbhlk_hl_0128/mix_train.txt'
vaild_txt = '/media/omnisky/D4T/JSH/faceFenlei/eye/mbhlk_hl_0128/mix_valid.txt'
# model
mixnet = MixNet(input_size=(24,24), num_classes=3)
weight_dict = torch.load("/media/omnisky/D4T/JSH/faceFenlei/Projects/hul_eye_class/weight/relabel_04_mix_SGD_mutillabel_24_24_20210302/Mixnet_epoch_49.pth")
new_state_dict = OrderedDict()
for k, v in weight_dict.items():
name = k[7:]
new_state_dict[name] = v
mixnet.load_state_dict(new_state_dict)
# stat(net,(3,48,48))
mixnet.to('cuda:0')
mixnet.eval()
vaild_data = mbhk_get_signal_eye(vaild_txt,vaild_ttrans)
valid_data_loader = DataLoader(vaild_data,batch_size=128,shuffle=False,num_workers=12)
score_list = [] #存储预测得分
label_list = [] #存储真实标签
with torch.no_grad():
for imgs,labels,_ in valid_data_loader:
for timg in imgs:
test_result = mixnet(timg.cuda())
# result = torch.max(test_result,1)[1]
result = torch.nn.functional.softmax(test_result,dim=1)
score_list.extend(result.cpu().numpy())
label_list.extend(torch.nn.functional.one_hot(labels,num_classes=3).numpy())
tlabel_list = np.array(label_list).reshape((-1,3))
tscore_list = np.array(score_list).reshape((-1,3))
# 调用sklearn,计算每个类别对应的fpr和tpr
fpr_dict = dict()
tpr_dict = dict()
roc_auc_dict = dict()
for i in range(class_num):
fpr_dict[i],tpr_dict[i],_ = roc_curve(tlabel_list[:,i],tscore_list[:,i])
roc_auc_dict[i] = auc(fpr_dict[i],tpr_dict[i])
# Compute micro-average ROC curve and ROC area
fpr_dict["micro"],tpr_dict["micro"],_ = roc_curve(tlabel_list.ravel(),tscore_list.ravel())
roc_auc_dict["micro"] = auc(fpr_dict["micro"],tpr_dict["micro"])
#绘制所有类别平均的roc曲线
# macro
# First aggregate all false positive rates
all_fpr = np.unique(np.concatenate([fpr_dict[i] for i in range(class_num)]))
# Then interpolate all ROC curves at this points
mean_ptr = np.zeros_like(all_fpr)
for i in range(class_num):
mean_ptr += interp(all_fpr,fpr_dict[i],tpr_dict[i])
# Finally average it and compute AUC
mean_ptr /= class_num
fpr_dict["macro"] = all_fpr
tpr_dict["macro"] = mean_ptr
roc_auc_dict["macro"] = auc(fpr_dict['macro'],tpr_dict["macro"])
plt.figure()
lw = 2
plt.plot(fpr_dict["micro"], tpr_dict["micro"],
label='micro-average ROC curve (area = {0:0.2f})'
''.format(roc_auc_dict["micro"]),
color='deeppink', linestyle=':', linewidth=4)
plt.plot(fpr_dict["macro"], tpr_dict["macro"],
label='macro-average ROC curve (area = {0:0.2f})'
''.format(roc_auc_dict["macro"]),
color='navy', linestyle=':', linewidth=4)
colors = cycle(['aqua', 'darkorange', 'cornflowerblue'])
for i, color in zip(range(class_num), colors):
plt.plot(fpr_dict[i], tpr_dict[i], color=color, lw=lw,
label='ROC curve of class {0} (area = {1:0.2f})'
''.format(i, roc_auc_dict[i]))
plt.plot([0, 1], [0, 1], 'k--', lw=lw)
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Some extension of Receiver operating characteristic to multi-class')
plt.legend(loc="lower right")
plt.savefig('set113_roc.jpg')
plt.show()