def eval_one_batch(preds):
dataset = Dataset()
model = Model(dataset)
try:
x_test, y_test = dataset.evaluate_data_no_processor("test.csv")
print('eval.py use test.csv')
except:
x_test, y_test = dataset.evaluate_data_no_processor("dev.csv")
print('eval.py use dev.csv')
randnum = random.randint(0, 100)
random.seed(randnum)
random.shuffle(x_test)
random.seed(randnum)
random.shuffle(y_test)
# 通过模型得到预测的结果，格式为：[[<image id> <confidence> <left> <top> <right> <bottom>], ...]
preds = model.predict_all(x_test)
# 加载标签 [{'boxes':[], 'labels':[], 'image_id':[]}, ...]
targets = []
for i in range(len(y_test)):
label_path = y_test[i]['label_path'] # label/019646.jpg.txt
boxes = []
labels = []
image_id = []
image_id.append(x_test[i]['image_path'])
with open(os.path.join(DATA_PATH, label_path)) as f:
for line in f.readlines():
# 1954.7443195924375,695.1497671989313,1984.659514688955,738.4779589540301,1933
temp = line.strip().split(',')
xmin = int(float(temp[0]))
ymin = int(float(temp[1]))
xmax = int(float(temp[2]))
ymax = int(float(temp[3]))
boxes.append([xmin, ymin, xmax, ymax])
labels.append(int(temp[4]))
target = {}
target["boxes"] = boxes
target["labels"] = labels
target["image_id"] = image_id
targets.append(target)
'''
if不需要修改
'''
if len(y_test) != len(x_test):
result = dict()
result['score'] = 0
result['label'] = "评估违规"
result['info'] = ""
print(json.dumps(result))
else:
'''
在下面实现不同的评估算法
'''
# 开始计算最后得分
sum_ap = 0
all_labels = [i for i in range(2)] # 所有目标类别
# 以下是我自己加的
for label in all_labels: # 逐个类别计算ap
prediction1 = [] # 在计算 ap 的时候，需要把prediction按照最后预测的类别进行筛选
for pred in preds:
if pred[3] == label:
prediction1.append([
pred[0], pred[1], pred[2][0], pred[2][1], pred[2][2],
pred[2][3]
])
if len(prediction1) != 0: # 当包含预测框的时候，进行计算ap值
rec, prec, ap = voc_eval(targets, prediction1, label)
else:
ap = 0
sum_ap += ap
map = sum_ap / len(all_labels)
result = dict()
result['score'] = round(map * 100, 2)
result['label'] = "The Score is MAP."
result['info'] = ""
print(json.dumps(result))
return map
# -*- coding: utf-8 -*
'''
实现模型的调用
'''
from flyai.dataset import Dataset
from model import Model
data = Dataset()
model = Model(data)
dataset = Dataset()
x_test, y_test = dataset.evaluate_data_no_processor('dev.csv')
preds = model.predict_all(x_test)
map = sum_ap / len(all_labels)
result = dict()
result['score'] = round(map * 100, 2)
result['label'] = "The Score is MAP."
result['info'] = ""
print(json.dumps(result))
return map
if __name__ == "__main__":
dataset = Dataset()
model = Model(dataset)
try:
x_test, y_test = dataset.evaluate_data_no_processor("test.csv")
print('eval.py use test.csv')
except:
x_test, y_test = dataset.evaluate_data_no_processor("dev.csv")
print('eval.py use dev.csv')
randnum = random.randint(0, 100)
random.seed(randnum)
random.shuffle(x_test)
random.seed(randnum)
random.shuffle(y_test)
# 通过模型得到预测的结果，格式为：[[<image id> <confidence> <left> <top> <right> <bottom>], ...]
preds = model.predict_all(x_test)
# 加载标签 [{'boxes':[], 'labels':[], 'image_id':[]}, ...]
targets = []
import sys
import json
import random
from flyai.dataset import Dataset
from model import Model
dataset = Dataset()
x_test, y_test = dataset.evaluate_data_no_processor(sys.argv[1])
randnum = random.randint(0, 100)
random.seed(randnum)
random.shuffle(x_test)
random.seed(randnum)
random.shuffle(y_test)
model = Model(dataset)
labels = model.predict_all(x_test)
eval = 0
if len(y_test) != len(labels):
result = dict()
result['score'] = 0
result['label'] = "评估违规"
result['info'] = ""
print(json.dumps(result))
else:
for index in range(len(labels)):
label = labels[index]
