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utils.py
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utils.py
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import os
import sys
import glob
import yaml
import torch
import model_
import pandas as pd
def get_yaml_value(config_path):
f = open(config_path, 'r', encoding="utf-8")
t_value = yaml.load(f, Loader=yaml.FullLoader)
f.close()
# params = t_value[key_name]
return t_value
def save_network(network, dir_model_name, epoch_label):
param_dict = get_yaml_value("settings.yaml")
save_path = param_dict['weight_save_path']
# save_path = "/home/sues/media/disk2/save_model_weight"
# with open("settings.yaml", "r", encoding="utf-8") as f:
# dict = yaml.load(f, Loader=yaml.FullLoader)
# dict['name'] = dir_model_name
# with open("settings.yaml", "w", encoding="utf-8") as f:
# yaml.dump(dict, f)
# if not os.path.isdir(os.path.join(save_path, dir_model_name)):
# os.mkdir(os.path.join(save_path, dir_model_name))
if isinstance(epoch_label, int):
save_filename = 'net_%03d.pth' % epoch_label
else:
save_filename = 'net_%s.pth' % epoch_label
save_path = os.path.join(save_path, dir_model_name, save_filename)
torch.save(network.state_dict(), save_path)
def fliplr(img):
'''flip horizontal'''
inv_idx = torch.arange(img.size(3) - 1, -1, -1).long() # N x C x H x W
img_flip = img.index_select(3, inv_idx)
return img_flip
def which_view(name):
if 'satellite' in name:
return 1
elif 'drone' in name:
return 2
else:
print('unknown view')
return -1
def get_model_list(dirname, key, seq):
if os.path.exists(dirname) is False:
print('no dir: %s' % dirname)
return None
gen_models = [os.path.join(dirname, f) for f in os.listdir(dirname) if
os.path.isfile(os.path.join(dirname, f)) and key in f and ".pth" in f]
if gen_models is None:
return None
gen_models.sort()
last_model_name = gen_models[seq]
return last_model_name
def load_network(model_name, name, weight_save_path, classes, drop_rate, seq):
# model_name = get_yaml_value("model")
# name = get_yaml_value("name")
# weight_save_path = get_yaml_value("weight_save_path")
dirname = os.path.join(weight_save_path, name)
last_model_name = os.path.basename(get_model_list(dirname, 'net', seq))
print(get_model_list(dirname, 'net', seq) + " " + "seq: " + str(seq))
# print(os.path.join(dirname,last_model_name))
# classes = get_yaml_value("classes")
# drop_rate = get_yaml_value("drop_rate")
model = model_.model_dict[model_name](classes, drop_rate)
# model = model_.ResNet(classes, drop_rate)
# model.load_state_dict(torch.load(os.path.join(dirname, last_model_name)))
return model, last_model_name
def get_id(img_path):
camera_id = []
labels = []
paths = []
for path, v in img_path:
folder_name = os.path.basename(os.path.dirname(path))
labels.append(int(folder_name))
paths.append(path)
return labels, paths
def create_dir(path):
if not os.path.exists(path):
os.mkdir(path)
def select_best_weight(model_name, csv_path):
# csv_path = "./save_model_weight"
model_csv_list = glob.glob(os.path.join(csv_path, model_name + "*.csv"))
drone_list = []
satellite_list = []
csv_150_list = list(filter(lambda i: "150" in i, model_csv_list))
csv_200_list = list(filter(lambda i: "200" in i, model_csv_list))
csv_250_list = list(filter(lambda i: "250" in i, model_csv_list))
csv_300_list = list(filter(lambda i: "300" in i, model_csv_list))
csv_lists = [csv_150_list, csv_200_list, csv_250_list, csv_300_list]
for csv_list in csv_lists:
drone_recall1_max = 0
drone_csv_index = None
satellite_recall1_max = 0
satellite_csv_index = None
for csv in csv_list:
table = pd.read_csv(csv, index_col=0)
drone_recall1 = table.at["recall@1", "drone_max"]
satellite_recall1 = table.at["recall@1", "satellite_max"]
if satellite_recall1 > satellite_recall1_max:
satellite_recall1_max = satellite_recall1
satellite_csv_index = csv
if drone_recall1 > drone_recall1_max:
drone_recall1_max = drone_recall1
drone_csv_index = csv
drone_list.append(drone_csv_index)
satellite_list.append(satellite_csv_index)
return drone_list, satellite_list
def get_best_weight(query_name, model_name, height, csv_path):
drone_best_list, satellite_best_list = select_best_weight(model_name, csv_path)
# print(drone_best_list, satellite_best_list)
net_path = None
if "drone" in query_name:
for weight in drone_best_list:
if str(height) in weight:
drone_best_weight = weight.split(".")[0]
table = pd.read_csv(weight, index_col=0)
query_number = len(list(filter(lambda x: "drone" in x, table.columns))) - 1
values = list(table.loc["recall@1", :])[:query_number]
indexes = list(table.loc["recall@1", :].index)[:query_number]
net_name = indexes[values.index(max(values))]
net = net_name.split("_")[2] + "_" + net_name.split("_")[3]
net_path = os.path.join(drone_best_weight, net)
# print(values, indexes)
if "satellite" in query_name:
for weight in satellite_best_list:
if str(height) in weight:
satellite_best_weight = weight.split(".")[0]
table = pd.read_csv(weight, index_col=0)
query_number = len(list(filter(lambda x: "drone" in x, table.columns))) - 1
values = list(table.loc["recall@1", :])[query_number:query_number*2]
indexes = list(table.loc["recall@1", :].index)[query_number:query_number*2]
net_name = indexes[values.index(max(values))]
net = net_name.split("_")[2] + "_" + net_name.split("_")[3]
net_path = os.path.join(satellite_best_weight, net)
return net_path
def parameter(index_name, index_number):
with open("settings.yaml", "r", encoding="utf-8") as f:
setting_dict = yaml.load(f, Loader=yaml.FullLoader)
setting_dict[index_name] = index_number
# print(setting_dict)
f.close()
with open("settings.yaml", "w", encoding="utf-8") as f:
yaml.dump(setting_dict, f)
f.close()
if __name__ == '__main__':
# param = get_yaml_value("settings.yaml")
# print(param['height'])
# for height in [150, 200, 250, 300]:
# print("----")
# parameter("height", height)
# param = get_yaml_value("settings.yaml")
# print(param['height'])
for height in [150, 200, 250, 300]:
print(height)
for i in ["satellite", "drone"]:
print(i)
result = get_best_weight(i, "vit", str(height), "/home/sues/media/disk2/save_model_weight")
print(result)