#Run 8 v2 monolith map 64 Bit
TWO AI TRAINS MEET AT THE WORLD FAMOUS TEHACHAPI LOOP (WALONG SIDING) ON THE UP MOJAVE SUBħ or newer 64 bit operating systems. eval-options: if specified, the key-value pair optional eval cfg will be kwargs for dataset.(includes UP-BNSF Mojave Sub (plus the new Palmdale Cutoff), Barstow-Yermo, and the BNSF Needles Sub) cfg-options: if specified, the key-value pair optional cfg will be merged into config file show-score-thr: If specified, detections with scores below this threshold will be removed. You do NOT need a GUI available in your environment for using this option. It is only applicable to single GPU testing and used for debugging and visualization. show-dir: If specified, detection results will be plotted on the images and saved to the specified directory. Otherwise, you may encounter an error like cannot connect to X server. Please make sure that GUI is available in your environment. show: If specified, detection results will be plotted on the images and shown in a new window. Cityscapes could be evaluated by cityscapes as well as all COCO metrics.
Allowed values depend on the dataset, e.g., proposal_fast, proposal, bbox, segm are available for COCO, mAP, recall for PASCAL VOC. If not specified, the results will not be saved to a file.ĮVAL_METRICS: Items to be evaluated on the results. RESULT_FILE: Filename of the output results in pickle format. Tools/dist_test.sh also supports multi-node testing, but relies on PyTorch’s launch utility. show_result ( img, result, out_file = 'result.jpg' ) asyncio. show_result ( img, result ) # or save the visualization results to image files model. Stream ( device = device )) # test a single image and show the results img = 'test.jpg' # or img = mmcv.imread(img), which will only load it once async with concurrent ( streamqueue ): result = await async_inference_detector ( model, img ) # visualize the results in a new window model. Queue () # queue size defines concurrency level streamqueue_size = 3 for _ in range ( streamqueue_size ): streamqueue. Import asyncio import torch from mmdet.apis import init_detector, async_inference_detector from import concurrent async def main (): config_file = 'configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' checkpoint_file = 'checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth' device = 'cuda:0' model = init_detector ( config_file, checkpoint = checkpoint_file, device = device ) # queue is used for concurrent inference of multiple images streamqueue = asyncio. Note: inference_detector only supports single-image inference for now. show_result ( frame, result, wait_time = 1 )Ī notebook demo can be found in demo/inference_demo.ipynb. VideoReader ( 'video.mp4' ) for frame in video : result = inference_detector ( model, frame ) model. show_result ( img, result, out_file = 'result.jpg' ) # test a video and show the results video = mmcv.
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