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测试代码逻辑优化

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Yunhao Meng
2025-10-23 13:07:34 +08:00
parent 2fbb741d3f
commit f9588b74a8
1 changed files with 30 additions and 28 deletions
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42
testcode.py
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@@ -8,39 +8,41 @@ from fed_algo_cs.server_base import FedYoloServer # FedYoloServer
from utils import Dataset # Dataset
if __name__ == "__main__":
if not os.path.exists("model.txt"):
# model structure test
model = init_model("yolo_v11_n", num_classes=1)
with open("model.txt", "w", encoding="utf-8") as f:
print(model, file=f)
if not os.path.exists("model_key_value.txt"):
# loop over model key and values
with open("model_key_value.txt", "w", encoding="utf-8") as f:
for k, v in model.state_dict().items():
print(f"{k}: {v.shape}", file=f)
# test agg function
from fed_algo_cs.server_base import FedYoloServer
import torch
import yaml
# from fed_algo_cs.server_base import FedYoloServer
# import torch
# import yaml
with open("./config/coco128_cfg.yaml", "r", encoding="utf-8") as f:
cfg = yaml.safe_load(f)
params = dict(cfg)
# with open("./config/coco128_cfg.yaml", "r", encoding="utf-8") as f:
# cfg = yaml.safe_load(f)
# # params = dict(cfg)
server = FedYoloServer(client_list=["c1", "c2", "c3"], model_name="yolo_v11_n", params=params)
state1 = {k: torch.ones_like(v) for k, v in server.model.state_dict().items()}
state2 = {k: torch.ones_like(v) * 2 for k, v in server.model.state_dict().items()}
state3 = {k: torch.ones_like(v) * 3 for k, v in server.model.state_dict().items()}
server.rec("c1", state1, n_data=20, loss=0.1)
server.rec("c2", state2, n_data=30, loss=0.2)
server.rec("c3", state3, n_data=50, loss=0.3)
server.select_clients(connection_ratio=1.0)
model_state, avg_loss, n_data = server.agg()
with open("agg_model.txt", "w", encoding="utf-8") as f:
for k, v in model_state.items():
print(f"{k}: {v.float().mean()}", file=f)
print(f"avg_loss: {avg_loss}, n_data: {n_data}")
# server = FedYoloServer(client_list=["c1", "c2", "c3"], model_name="yolo_v11_n", params=cfg)
# state1 = {k: torch.ones_like(v) for k, v in server.model.state_dict().items()}
# state2 = {k: torch.ones_like(v) * 2 for k, v in server.model.state_dict().items()}
# state3 = {k: torch.ones_like(v) * 3 for k, v in server.model.state_dict().items()}
# server.rec("c1", state1, n_data=20, loss=0.1)
# server.rec("c2", state2, n_data=30, loss=0.2)
# server.rec("c3", state3, n_data=50, loss=0.3)
# server.select_clients(connection_ratio=1.0)
# model_state, avg_loss, n_data = server.agg()
# with open("agg_model.txt", "w", encoding="utf-8") as f:
# for k, v in model_state.items():
# print(f"{k}: {v.float().mean()}", file=f)
# print(f"avg_loss: {avg_loss}, n_data: {n_data}")
# test single client training (should be the same as standalone training)
args = args_parser()
@@ -50,7 +52,7 @@ if __name__ == "__main__":
client = FedYoloClient(name="c1", params=cfg, model_name="yolo_v11_n")
filenames = []
data_dir = "/home/image1325/ssd1/dataset/COCO128"
data_dir = "/mnt/DATA/COCO128"
with open(f"{data_dir}/train.txt") as f:
for filename in f.readlines():
filename = os.path.basename(filename.rstrip())