Create sotabench.py

sotabench.py

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+import argparse
+import glob
+import json
+import os
+import shutil
+from pathlib import Path
+
+import numpy as np
+import torch
+import yaml
+from tqdm import tqdm
+
+from models.experimental import attempt_load
+from utils.datasets import create_dataloader
+from utils.general import (
+    coco80_to_coco91_class, check_dataset, check_file, check_img_size, compute_loss, non_max_suppression, scale_coords,
+    xyxy2xywh, clip_coords, plot_images, xywh2xyxy, box_iou, output_to_target, ap_per_class, set_logging)
+from utils.torch_utils import select_device, time_synchronized
+
+
+from sotabencheval.object_detection import COCOEvaluator
+from sotabencheval.utils import is_server
+
+DATA_ROOT = './.data/vision/coco' if is_server() else '../coco'  # sotabench data dir
+
+
+def test(data,
+         weights=None,
+         batch_size=16,
+         imgsz=640,
+         conf_thres=0.001,
+         iou_thres=0.6,  # for NMS
+         save_json=False,
+         single_cls=False,
+         augment=False,
+         verbose=False,
+         model=None,
+         dataloader=None,
+         save_dir='',
+         merge=False,
+         save_txt=False):
+    # Initialize/load model and set device
+    training = model is not None
+    if training:  # called by train.py
+        device = next(model.parameters()).device  # get model device
+
+    else:  # called directly
+        set_logging()
+        device = select_device(opt.device, batch_size=batch_size)
+        merge, save_txt = opt.merge, opt.save_txt  # use Merge NMS, save *.txt labels
+        if save_txt:
+            out = Path('inference/output')
+            if os.path.exists(out):
+                shutil.rmtree(out)  # delete output folder
+            os.makedirs(out)  # make new output folder
+
+        # Remove previous
+        for f in glob.glob(str(Path(save_dir) / 'test_batch*.jpg')):
+            os.remove(f)
+
+        # Load model
+        model = attempt_load(weights, map_location=device)  # load FP32 model
+        imgsz = check_img_size(imgsz, s=model.stride.max())  # check img_size
+
+        # Multi-GPU disabled, incompatible with .half() https://github.com/ultralytics/yolov5/issues/99
+        # if device.type != 'cpu' and torch.cuda.device_count() > 1:
+        #     model = nn.DataParallel(model)
+
+    # Half
+    half = device.type != 'cpu'  # half precision only supported on CUDA
+    if half:
+        model.half()
+
+    # Configure
+    model.eval()
+    with open(data) as f:
+        data = yaml.load(f, Loader=yaml.FullLoader)  # model dict
+    check_dataset(data)  # check
+    nc = 1 if single_cls else int(data['nc'])  # number of classes
+    iouv = torch.linspace(0.5, 0.95, 10).to(device)  # iou vector for mAP@0.5:0.95
+    niou = iouv.numel()
+
+    # Dataloader
+    if not training:
+        img = torch.zeros((1, 3, imgsz, imgsz), device=device)  # init img
+        _ = model(img.half() if half else img) if device.type != 'cpu' else None  # run once
+        path = data['test'] if opt.task == 'test' else data['val']  # path to val/test images
+        dataloader = create_dataloader(path, imgsz, batch_size, model.stride.max(), opt,
+                                       hyp=None, augment=False, cache=True, pad=0.5, rect=True)[0]
+
+    seen = 0
+    names = model.names if hasattr(model, 'names') else model.module.names
+    coco91class = coco80_to_coco91_class()
+    s = ('%20s' + '%12s' * 6) % ('Class', 'Images', 'Targets', 'P', 'R', 'mAP@.5', 'mAP@.5:.95')
+    p, r, f1, mp, mr, map50, map, t0, t1 = 0., 0., 0., 0., 0., 0., 0., 0., 0.
+    loss = torch.zeros(3, device=device)
+    jdict, stats, ap, ap_class = [], [], [], []
+    evaluator = COCOEvaluator(root=DATA_ROOT, model_name=opt.weights.replace('.pt', ''))
+    for batch_i, (img, targets, paths, shapes) in enumerate(tqdm(dataloader, desc=s)):
+        img = img.to(device, non_blocking=True)
+        img = img.half() if half else img.float()  # uint8 to fp16/32
+        img /= 255.0  # 0 - 255 to 0.0 - 1.0
+        targets = targets.to(device)
+        nb, _, height, width = img.shape  # batch size, channels, height, width
+        whwh = torch.Tensor([width, height, width, height]).to(device)
+
+        # Disable gradients
+        with torch.no_grad():
+            # Run model
+            t = time_synchronized()
+            inf_out, train_out = model(img, augment=augment)  # inference and training outputs
+            t0 += time_synchronized() - t
+
+            # Compute loss
+            if training:  # if model has loss hyperparameters
+                loss += compute_loss([x.float() for x in train_out], targets, model)[1][:3]  # GIoU, obj, cls
+
+            # Run NMS
+            t = time_synchronized()
+            output = non_max_suppression(inf_out, conf_thres=conf_thres, iou_thres=iou_thres, merge=merge)
+            t1 += time_synchronized() - t
+
+        # Statistics per image
+        for si, pred in enumerate(output):
+            labels = targets[targets[:, 0] == si, 1:]
+            nl = len(labels)
+            tcls = labels[:, 0].tolist() if nl else []  # target class
+            seen += 1
+
+            if pred is None:
+                if nl:
+                    stats.append((torch.zeros(0, niou, dtype=torch.bool), torch.Tensor(), torch.Tensor(), tcls))
+                continue
+
+            # Append to text file
+            if save_txt:
+                gn = torch.tensor(shapes[si][0])[[1, 0, 1, 0]]  # normalization gain whwh
+                x = pred.clone()
+                x[:, :4] = scale_coords(img[si].shape[1:], x[:, :4], shapes[si][0], shapes[si][1])  # to original
+                for *xyxy, conf, cls in x:
+                    xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
+                    with open(str(out / Path(paths[si]).stem) + '.txt', 'a') as f:
+                        f.write(('%g ' * 5 + '\n') % (cls, *xywh))  # label format
+
+            # Clip boxes to image bounds
+            clip_coords(pred, (height, width))
+
+            # Append to pycocotools JSON dictionary
+            if save_json:
+                # [{"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}, ...
+                image_id = Path(paths[si]).stem
+                box = pred[:, :4].clone()  # xyxy
+                scale_coords(img[si].shape[1:], box, shapes[si][0], shapes[si][1])  # to original shape
+                box = xyxy2xywh(box)  # xywh
+                box[:, :2] -= box[:, 2:] / 2  # xy center to top-left corner
+                for p, b in zip(pred.tolist(), box.tolist()):
+                    result = {'image_id': int(image_id) if image_id.isnumeric() else image_id,
+                                  'category_id': coco91class[int(p[5])],
+                                  'bbox': [round(x, 3) for x in b],
+                                  'score': round(p[4], 5)}
+                    jdict.append(result)
+
+                    #evaluator.add([result])
+                    #if evaluator.cache_exists:
+                    #    break
+
+            # # Assign all predictions as incorrect
+            # correct = torch.zeros(pred.shape[0], niou, dtype=torch.bool, device=device)
+            # if nl:
+            #     detected = []  # target indices
+            #     tcls_tensor = labels[:, 0]
+            #
+            #     # target boxes
+            #     tbox = xywh2xyxy(labels[:, 1:5]) * whwh
+            #
+            #     # Per target class
+            #     for cls in torch.unique(tcls_tensor):
+            #         ti = (cls == tcls_tensor).nonzero(as_tuple=False).view(-1)  # prediction indices
+            #         pi = (cls == pred[:, 5]).nonzero(as_tuple=False).view(-1)  # target indices
+            #
+            #         # Search for detections
+            #         if pi.shape[0]:
+            #             # Prediction to target ious
+            #             ious, i = box_iou(pred[pi, :4], tbox[ti]).max(1)  # best ious, indices
+            #
+            #             # Append detections
+            #             detected_set = set()
+            #             for j in (ious > iouv[0]).nonzero(as_tuple=False):
+            #                 d = ti[i[j]]  # detected target
+            #                 if d.item() not in detected_set:
+            #                     detected_set.add(d.item())
+            #                     detected.append(d)
+            #                     correct[pi[j]] = ious[j] > iouv  # iou_thres is 1xn
+            #                     if len(detected) == nl:  # all targets already located in image
+            #                         break
+            #
+            # # Append statistics (correct, conf, pcls, tcls)
+            # stats.append((correct.cpu(), pred[:, 4].cpu(), pred[:, 5].cpu(), tcls))
+
+        # # Plot images
+        # if batch_i < 1:
+        #     f = Path(save_dir) / ('test_batch%g_gt.jpg' % batch_i)  # filename
+        #     plot_images(img, targets, paths, str(f), names)  # ground truth
+        #     f = Path(save_dir) / ('test_batch%g_pred.jpg' % batch_i)
+        #     plot_images(img, output_to_target(output, width, height), paths, str(f), names)  # predictions
+
+    evaluator.add(jdict)
+    evaluator.save()
+
+    # # Compute statistics
+    # stats = [np.concatenate(x, 0) for x in zip(*stats)]  # to numpy
+    # if len(stats) and stats[0].any():
+    #     p, r, ap, f1, ap_class = ap_per_class(*stats)
+    #     p, r, ap50, ap = p[:, 0], r[:, 0], ap[:, 0], ap.mean(1)  # [P, R, AP@0.5, AP@0.5:0.95]
+    #     mp, mr, map50, map = p.mean(), r.mean(), ap50.mean(), ap.mean()
+    #     nt = np.bincount(stats[3].astype(np.int64), minlength=nc)  # number of targets per class
+    # else:
+    #     nt = torch.zeros(1)
+    #
+    # # Print results
+    # pf = '%20s' + '%12.3g' * 6  # print format
+    # print(pf % ('all', seen, nt.sum(), mp, mr, map50, map))
+    #
+    # # Print results per class
+    # if verbose and nc > 1 and len(stats):
+    #     for i, c in enumerate(ap_class):
+    #         print(pf % (names[c], seen, nt[c], p[i], r[i], ap50[i], ap[i]))
+    #
+    # # Print speeds
+    # t = tuple(x / seen * 1E3 for x in (t0, t1, t0 + t1)) + (imgsz, imgsz, batch_size)  # tuple
+    # if not training:
+    #     print('Speed: %.1f/%.1f/%.1f ms inference/NMS/total per %gx%g image at batch-size %g' % t)
+    #
+    # # Save JSON
+    # if save_json and len(jdict):
+    #     f = 'detections_val2017_%s_results.json' % \
+    #         (weights.split(os.sep)[-1].replace('.pt', '') if isinstance(weights, str) else '')  # filename
+    #     print('\nCOCO mAP with pycocotools... saving %s...' % f)
+    #     with open(f, 'w') as file:
+    #         json.dump(jdict, file)
+    #
+    #     try:  # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb
+    #         from pycocotools.coco import COCO
+    #         from pycocotools.cocoeval import COCOeval
+    #
+    #         imgIds = [int(Path(x).stem) for x in dataloader.dataset.img_files]
+    #         cocoGt = COCO(glob.glob('../coco/annotations/instances_val*.json')[0])  # initialize COCO ground truth api
+    #         cocoDt = cocoGt.loadRes(f)  # initialize COCO pred api
+    #         cocoEval = COCOeval(cocoGt, cocoDt, 'bbox')
+    #         cocoEval.params.imgIds = imgIds  # image IDs to evaluate
+    #         cocoEval.evaluate()
+    #         cocoEval.accumulate()
+    #         cocoEval.summarize()
+    #         map, map50 = cocoEval.stats[:2]  # update results (mAP@0.5:0.95, mAP@0.5)
+    #     except Exception as e:
+    #         print('ERROR: pycocotools unable to run: %s' % e)
+    #
+    # # Return results
+    # model.float()  # for training
+    # maps = np.zeros(nc) + map
+    # for i, c in enumerate(ap_class):
+    #     maps[c] = ap[i]
+    # return (mp, mr, map50, map, *(loss.cpu() / len(dataloader)).tolist()), maps, t
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(prog='test.py')
+    parser.add_argument('--weights', nargs='+', type=str, default='yolov5s.pt', help='model.pt path(s)')
+    parser.add_argument('--data', type=str, default='data/coco.yaml', help='*.data path')
+    parser.add_argument('--batch-size', type=int, default=32, help='size of each image batch')
+    parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')
+    parser.add_argument('--conf-thres', type=float, default=0.001, help='object confidence threshold')
+    parser.add_argument('--iou-thres', type=float, default=0.65, help='IOU threshold for NMS')
+    parser.add_argument('--save-json', action='store_true', help='save a cocoapi-compatible JSON results file')
+    parser.add_argument('--task', default='val', help="'val', 'test', 'study'")
+    parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
+    parser.add_argument('--single-cls', action='store_true', help='treat as single-class dataset')
+    parser.add_argument('--augment', action='store_true', help='augmented inference')
+    parser.add_argument('--merge', action='store_true', help='use Merge NMS')
+    parser.add_argument('--verbose', action='store_true', help='report mAP by class')
+    parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
+    opt = parser.parse_args()
+    opt.save_json |= opt.data.endswith('coco.yaml')
+    opt.data = check_file(opt.data)  # check file
+    print(opt)
+
+    if opt.task in ['val', 'test']:  # run normally
+        test(opt.data,
+             opt.weights,
+             opt.batch_size,
+             opt.img_size,
+             opt.conf_thres,
+             opt.iou_thres,
+             opt.save_json,
+             opt.single_cls,
+             opt.augment,
+             opt.verbose)
+
+    elif opt.task == 'study':  # run over a range of settings and save/plot
+        for weights in ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt']:
+            f = 'study_%s_%s.txt' % (Path(opt.data).stem, Path(weights).stem)  # filename to save to
+            x = list(range(320, 800, 64))  # x axis
+            y = []  # y axis
+            for i in x:  # img-size
+                print('\nRunning %s point %s...' % (f, i))
+                r, _, t = test(opt.data, weights, opt.batch_size, i, opt.conf_thres, opt.iou_thres, opt.save_json)
+                y.append(r + t)  # results and times
+            np.savetxt(f, y, fmt='%10.4g')  # save
+        os.system('zip -r study.zip study_*.txt')
+        # utils.general.plot_study_txt(f, x)  # plot