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)
47 lines
1.4 KiB
Python
Executable File
47 lines
1.4 KiB
Python
Executable File
import torch, os, cv2
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from model.model import parsingNet
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from utils.common import merge_config
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from utils.dist_utils import dist_print
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import torch
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import scipy.special, tqdm
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import numpy as np
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import torchvision.transforms as transforms
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from data.dataset import LaneTestDataset
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from data.constant import culane_row_anchor, tusimple_row_anchor
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from PIL import Image
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# Export to TorchScript that can be used for LibTorch
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torch.backends.cudnn.benchmark = True
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# From cuLANE, Change this line if you are using TuSimple
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cls_num_per_lane = 18
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griding_num = 200
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backbone =18
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net = parsingNet(pretrained = False,backbone='18', cls_dim = (griding_num+1,cls_num_per_lane,4),
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use_aux=False)
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# Change test_model where your model stored.
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test_model = '/data/Models/UltraFastLaneDetection/culane_18.pth'
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#state_dict = torch.load(test_model, map_location='cpu')['model'] # CPU
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state_dict = torch.load(test_model, map_location='cuda')['model'] # CUDA
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compatible_state_dict = {}
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for k, v in state_dict.items():
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if 'module.' in k:
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compatible_state_dict[k[7:]] = v
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else:
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compatible_state_dict[k] = v
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net.load_state_dict(compatible_state_dict, strict=False)
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net.eval()
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# Test Input Image
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img = torch.zeros(1, 3, 288, 800) # image size(1,3,320,192) iDetection
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y = net(img) # dry run
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ts = torch.jit.trace(net, img)
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#ts.save('UFLD.torchscript-cpu.pt') # CPU
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ts.save('UFLD.torchscript-cuda.pt') # CUDA |