Chengfang Lu
e72bc061c5
Major changes: - New frontend (platform/web/): Vite + React 18 + TypeScript + Tailwind - 4-module navigation: 数据送标 / 模型管理 / 车队管理 / 系统管理 - Data catalog with charts (DMS/ADAS/Lane 3-tab view) - Quality review workflow (标注质检): Good/Fine/Bad scoring with auto-advance - Audit enhancements: batch operations, rejection categories, Feishu notifications - Operation audit log (操作日志) - World model simulation studio (仿真工坊) - Dataset version management with snapshots and diff - ADAS 7-class dataset integration (138K images organized + compressed) - User management with Feishu integration and pagination - CRUD/search/filter on all pages, card layout redesign - PIL-optimized image overlay rendering - Auto-snapshot on build, in_review workflow stage - Removed embedded algorithm code (now in workspace)
226 lines
7.7 KiB
Python
226 lines
7.7 KiB
Python
# encoding: utf-8
|
||
|
||
import time
|
||
import tkinter.filedialog
|
||
|
||
import cv2
|
||
import numpy as np
|
||
import onnx
|
||
import onnxruntime
|
||
import scipy.special
|
||
from scipy.optimize import curve_fit
|
||
import matplotlib.pyplot as plt
|
||
|
||
import torch
|
||
import torch.backends.cudnn
|
||
import torchvision.transforms as transforms
|
||
from PIL import Image as imim
|
||
from torch import nn
|
||
|
||
from data.constant import tusimple_row_anchor
|
||
from model.model import parsingNet
|
||
from utils.common import merge_config
|
||
|
||
import glob
|
||
|
||
# 一些预定设置
|
||
torch.backends.cudnn.benchmark = True
|
||
args, cfg = merge_config()
|
||
griding_num = 100
|
||
img_w, img_h = 1280, 720
|
||
row_anchor = tusimple_row_anchor
|
||
cls_num_per_lane = 56
|
||
|
||
model_path = "/home/ljk/桌面/ClrNet_onnx_modify/UFLD/ep327_ufld.pth"
|
||
# model_path = "/home/ljk/桌面/ep015_2ch_res18.pth"
|
||
folder_path = "/home/ljk/桌面/pic_1228_lane/pic1_720/*.jpg"
|
||
|
||
|
||
def func(x, a, b, c, d):
|
||
return a * pow(x, 3) + b * pow(x, 2) + c * x + d
|
||
|
||
|
||
class ufld_2(nn.Module):
|
||
def __init__(self):
|
||
super().__init__()
|
||
|
||
# self.path = "/home/ljk/桌面/semantic_code/1.lane_detect/3.model_weight/1.pth/shuangyujing_model_SpeedUp.pth"
|
||
# print(self.path)
|
||
# self.net = torch.load(self.path)
|
||
|
||
backbone_ = '18'
|
||
self.net = parsingNet(pretrained=False, backbone=backbone_, cls_dim=(cfg.griding_num + 1, 56, 2),
|
||
use_aux=False).cuda()
|
||
|
||
self.path = model_path
|
||
print(self.path)
|
||
|
||
state_dict = torch.load(self.path, map_location='cpu')['model']
|
||
compatible_state_dict = {}
|
||
for k, v in state_dict.items():
|
||
if 'module.' in k:
|
||
compatible_state_dict[k[7:]] = v
|
||
else:
|
||
compatible_state_dict[k] = v
|
||
self.net.load_state_dict(compatible_state_dict, strict=False)
|
||
|
||
self.net.eval()
|
||
|
||
# self.onnx_model_name = self.path[:-4] + ".onnx"
|
||
self.onnx_model_name = "/home/ljk/桌面/ClrNet_onnx_modify/UFLD/ep327_ufld.onnx"
|
||
|
||
def export_onnx(self):
|
||
x = torch.randn(1, 3, 288, 800).cuda()
|
||
|
||
# torch.onnx.export 可以检查剪枝模型出了什么错
|
||
with torch.no_grad():
|
||
torch.onnx.export(self.net, x, self.onnx_model_name, export_params=True, opset_version=11,
|
||
input_names=['input'],
|
||
output_names=['output'])
|
||
|
||
print('\n', "start check...")
|
||
onnx_model = onnx.load(self.onnx_model_name)
|
||
try:
|
||
onnx.checker.check_model(onnx_model)
|
||
except:
|
||
print("Model incorrect")
|
||
else:
|
||
print("Model correct")
|
||
|
||
def output_cmp(self, img_path):
|
||
start = time.time()
|
||
img = imim.open(img_path)
|
||
img_transforms = transforms.Compose([
|
||
transforms.Resize((288, 800)),
|
||
transforms.ToTensor(),
|
||
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
|
||
])
|
||
img = img_transforms(img).unsqueeze_(0).cuda()
|
||
|
||
# 原模型的输出-------------------
|
||
with torch.no_grad():
|
||
out = self.net(img)
|
||
pth_output = to_numpy(out)
|
||
end = time.time()
|
||
cost_time = end - start
|
||
print("pth_cost_time:", cost_time * 1000, " ms")
|
||
print("原模型前5*4个向量输出：\n", pth_output[0][0][:5])
|
||
|
||
start = time.time()
|
||
ort_session = onnxruntime.InferenceSession(self.onnx_model_name)
|
||
end = time.time()
|
||
cost_time = end - start
|
||
print("onnx_cost_time:", cost_time * 1000, " ms")
|
||
ort_inputs = {'input': to_numpy(img)}
|
||
ort_out = ort_session.run(['output'], ort_inputs)[0]
|
||
print("onnx的输出大小：", ort_out.shape)
|
||
print("onnx前5*4个向量输出：\n", ort_out[0][0][:5])
|
||
|
||
# np.testing.assert_allclose(pth_output, ort_out, rtol=1e-01, atol=1e-03)
|
||
print("*****************************")
|
||
print("精度对比结束，满足精度！")
|
||
print("*****************************")
|
||
|
||
# _ = pro_process(pth_output, img_path)
|
||
print(img_path)
|
||
_ = pro_process(ort_out, img_path)
|
||
# cv2.namedWindow('vis_onnx')
|
||
# cv2.imshow("vis_onnx", pro_process(ort_out, img_path))
|
||
|
||
|
||
def get_path():
|
||
filename = tkinter.filedialog.askopenfilename()
|
||
return filename
|
||
|
||
|
||
# 工具函数：tensor->numpy
|
||
def to_numpy(tensor):
|
||
return tensor.detach().cpu().numpy() if tensor.requires_grad else tensor.cpu().numpy()
|
||
|
||
|
||
# 工具函数：后处理与可视化
|
||
def pro_process(out, path):
|
||
col_sample = np.linspace(0, 800 - 1, griding_num)
|
||
col_sample_w = col_sample[1] - col_sample[0]
|
||
|
||
out_j = out[0]
|
||
out_j = out_j[:, ::-1, :]
|
||
prob = scipy.special.softmax(out_j[:-1, :, :], axis=0)
|
||
idx = np.arange(griding_num) + 1
|
||
idx = idx.reshape(-1, 1, 1)
|
||
loc = np.sum(prob * idx, axis=0)
|
||
out_j = np.argmax(out_j, axis=0)
|
||
loc[out_j == griding_num] = 0
|
||
out_j = loc
|
||
|
||
vis = cv2.imread(path)
|
||
left_points = []
|
||
right_points = []
|
||
for i in range(out_j.shape[1]):
|
||
if np.sum(out_j[:, i] != 0) > 2:
|
||
if i == 1:
|
||
print("Left Line")
|
||
else:
|
||
print("Right Line")
|
||
count = 0
|
||
for k in range(out_j.shape[0]):
|
||
if out_j[k][i] > 0:
|
||
x = int(out_j[k][i] * col_sample_w * img_w / 800) - 1
|
||
y = int(img_h * (row_anchor[cls_num_per_lane - 1 - k] / 288)) - 1
|
||
ppp = (x, y)
|
||
# print(ppp)
|
||
if i == 0:
|
||
left_points.append(ppp)
|
||
else:
|
||
right_points.append(ppp)
|
||
cv2.circle(vis, ppp, 2, (255, 0, 0), -1)
|
||
count += 1
|
||
print(count)
|
||
print("\n")
|
||
# if len(left_points) == 0 or len(right_points) == 0:
|
||
# return vis
|
||
# left_points_x = np.reshape(left_points, (len(left_points), -1))[:, 1]
|
||
# left_points_y = np.reshape(left_points, (len(left_points), -1))[:, 0]
|
||
# right_points_x = np.reshape(right_points, (len(right_points), -1))[:, 1]
|
||
# right_points_y = np.reshape(right_points, (len(right_points), -1))[:, 0]
|
||
# popt_left, _ = curve_fit(func, left_points_x, left_points_y)
|
||
# popt_right, _ = curve_fit(func, right_points_x, right_points_y)
|
||
# right_points_y_func = []
|
||
# left_points_y_func = []
|
||
# for i in range(len(left_points_x)):
|
||
# ppp = (int(func(left_points_x[i], popt_left[0], popt_left[1], popt_left[2], popt_left[3])),
|
||
# left_points_x[i])
|
||
# # cv2.circle(vis, ppp, 5, (0, 255, 0), -1)
|
||
# left_points_y_func.append(
|
||
# int(func(left_points_x[i], popt_left[0], popt_left[1], popt_left[2], popt_left[3])))
|
||
# for i in range(len(right_points_x)):
|
||
# ppp = (
|
||
# int(func(right_points_x[i], popt_right[0], popt_right[1], popt_right[2], popt_right[3])),
|
||
# right_points_x[i])
|
||
# # cv2.circle(vis, ppp, 5, (0, 255, 0), -1)
|
||
# right_points_y_func.append(
|
||
# int(func(right_points_x[i], popt_right[0], popt_right[1], popt_right[2], popt_right[3])))
|
||
# left_points_y_func = np.reshape(left_points_y_func, (len(left_points_y_func), -1))
|
||
# right_points_y_func = np.reshape(right_points_y_func, (len(right_points_y_func), -1))
|
||
# left_points_y_func = np.squeeze(left_points_y_func)
|
||
# right_points_y_func = np.squeeze(right_points_y_func)
|
||
cv2.namedWindow('vis_onnx', 0)
|
||
cv2.imshow("vis_onnx", vis)
|
||
cv2.waitKey(0)
|
||
# cv2.destroyAllWindows()
|
||
# cv2.imwrite(path, vis)
|
||
return vis
|
||
|
||
|
||
def main():
|
||
files = glob.glob(folder_path)
|
||
files = sorted(files)
|
||
model = ufld_2()
|
||
model.export_onnx()
|
||
for i in range(len(files)):
|
||
model.output_cmp(files[i])
|
||
|
||
|
||
if __name__ == '__main__':
|
||
main()
|