test.py

import argparse
import json
import os
from pathlib import Path
from threading import Thread

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.datasets_segments import create_dataloader
from utils.general import (
    coco80_to_coco91_class,
    check_dataset,
    check_file,
    check_img_size,
    check_requirements,
    box_iou,
    non_max_suppression,
    scale_coords,
    xyxy2xywh,
    xywh2xyxy,
    set_logging,
    increment_path,
    colorstr,
    set_process_title
)
from utils.general_mask import mask_iou, process_mask, process_mask_upsample, scale_masks
from utils.metrics import ap_per_class, ConfusionMatrix
from utils.plots import plot_images, output_to_target, plot_study_txt
from utils.torch_utils import select_device, time_synchronized, TracedModel

from typing import Union, Tuple


def test(
        data,
        weights=None,
        batch_size: int = 32,
        imgsz: Union[int, Tuple[int, int]] = 640,
        conf_thres: float = 0.001,
        iou_thres: float = 0.6,  # for NMS
        save_json: bool = False,
        single_cls: bool = False,
        augment: bool = False,
        verbose: bool = False,
        opt = None,
        model=None,
        dataloader=None,
        save_dir: Union[str, Path] = Path(''),  # for saving images
        save_txt: bool = False,  # for auto-labelling
        save_hybrid: bool = False,  # for hybrid auto-labelling
        save_conf: bool = False,  # save auto-label confidences
        plots: bool = True,
        overlap: bool = False,
        wandb_logger=None,
        compute_loss=None,
        half_precision: bool = True,
        trace: bool = False,
        is_coco: bool = False,
        v5_metric: bool = False
):
    # limits number of examples plotted
    max_n_masks: int = 15

    if save_json:
        check_requirements(['pycocotools'])
        process = process_mask_upsample  # more accurate
    else:
        process = process_mask  # faster

    # 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)

        # Directories
        save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok))  # increment run
        (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True)  # make dir

        # Load model
        model = attempt_load(weights, map_location=device)  # load FP32 model
        gs = max(int(model.stride.max()), 32)  # grid size (max stride)
        imgsz = [check_img_size(x, gs) for x in imgsz]  # verify imgsz are gs-multiples
        if len(imgsz) == 1:
            # expand to 2-element tuple
            imgsz = (imgsz[0], imgsz[0])

        if trace:
            model = TracedModel(model, device, imgsz)

    # Half
    half = device.type != 'cpu' and half_precision  # half precision only supported on CUDA
    if half:
        model.half()

    # Configure
    model.eval()
    if isinstance(data, str):
        is_coco = data.endswith('coco.yaml')
        with open(data) as fid:
            data = yaml.load(fid, Loader=yaml.SafeLoader)
    check_dataset(data)  # check
    n_classes = 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()

    # Logging
    log_imgs = 0
    if wandb_logger and wandb_logger.wandb:
        log_imgs = min(wandb_logger.log_imgs, 100)

    # Dataloader
    if not training:
        if device.type != 'cpu':
            model(torch.zeros(1, 3, *imgsz).to(device).type_as(next(model.parameters())))  # run once
        task = opt.task if opt.task in ('train', 'val', 'test') else 'val'  # path to train/val/test images
        dataloader, _ = create_dataloader(
            data[task], imgsz, batch_size, gs, opt,
            # pad=0.5,
            rect=opt.rect if (opt is not None) and ("rect" in opt) else False,
            prefix=colorstr(f'{task}: '),
            overlap=overlap,
            augment=False
        )

    if v5_metric:
        print("Testing with YOLOv5 AP metric ...")
    
    seen = 0
    confusion_matrix = ConfusionMatrix(nc=n_classes)
    names = {k: v for k, v in enumerate(model.names if hasattr(model, 'names') else model.module.names)}
    coco91class = coco80_to_coco91_class()
    desc = ('%20s' + '%12s' * 6) % ('Class', 'Images', 'Labels', '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(4, device=device)
    jdict, stats, ap, ap_class, wandb_images = [], [], [], [], []
    for i_batch, (img, targets, paths, shapes, masks) in enumerate(tqdm(dataloader, desc=desc)):
        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)
        batch_size, _, height, width = img.shape  # batch size, channels, height, width

        if masks is None or (isinstance(masks, (tuple, list)) and all([el is None for el in masks])):
            # no masks
            max_n_targets = -1
        else:
            # masks
            max_n_targets = max_n_masks

        with torch.no_grad():
            # Run model
            t = time_synchronized()
            pred, train_out = model(img, augment=augment)  # inference and training outputs
            t0 += time_synchronized() - t

            # Compute loss
            if compute_loss:
                masks_ = masks.to(device) if isinstance(masks, torch.Tensor) else masks
                loss += compute_loss(train_out, targets, masks=masks_)[1][:-1]  # box, obj, cls
                # loss += compute_loss([x for x in train_out], targets, masks=masks)[1][:3]  # box, obj, cls  # FIXME: test with boxes

            # Run NMS (on xywh coordintate)
            targets[:, 2:6] *= torch.Tensor([width, height, width, height]).to(device)  # to pixels
            lb = [targets[targets[:, 0] == i, 1:] for i in range(batch_size)] if save_hybrid else []  # for autolabelling

            t = time_synchronized()
            out = non_max_suppression(
                pred,
                conf_thres=conf_thres,
                iou_thres=iou_thres,
                labels=lb,
                multi_label=True,
                n_classes=model.n_classes if hasattr(model, "n_classes") else model.nc
            )
            t1 += time_synchronized() - t

        # Statistics per image
        plot_masks = []  # masks for plotting
        for si, pred in enumerate(out):
            labels = targets[targets[:, 0] == si, 1:]
            n_labels = len(labels)
            tcls = labels[:, 0].tolist() if n_labels else []  # target class
            path = Path(paths[si])
            seen += 1

            if len(pred) == 0:
                if n_labels:
                    stats.append((torch.zeros(0, niou, dtype=torch.bool), torch.Tensor(), torch.Tensor(), tcls))
                continue

            # Masks
            is_segmentation = False
            if isinstance(pred, torch.Tensor):
                pass
            elif isinstance(pred, list) and len(pred) == 2:
                is_segmentation = True
            else:
                raise TypeError(
                    f"Unexpected type of model output. "
                    f"A tensor or a two-element-list of tensors was expected but was {type(pred)}."
                )

            if is_segmentation:
                # lg_masks = [si] if overlap else targets[:, 0] == si
                # gt_masks = masks[lg_masks]
                proto_out = train_out[1][si]
                pred_masks = process(proto_out, pred[:, 6:], pred[:, :4], shape=img[si].shape[1:])

                # add masks to plot
                pred_masks = torch.as_tensor(pred_masks, dtype=torch.uint8)
                if plots and i_batch < 3:
                    prd_msks = pred_masks[:max_n_targets] if max_n_targets > 0 else pred_masks
                    plot_masks.append(prd_msks.cpu())  # filter top 15 to plot

            # Predictions
            predn = pred.clone()
            scale_coords(img[si].shape[1:], predn[:, :4], shapes[si][0], shapes[si][1])  # native-space pred

            # Append to text file
            if save_txt:
                gn = torch.tensor(shapes[si][0])[[1, 0, 1, 0]]  # normalization gain whwh
                for *xyxy, conf, cls in predn.tolist():
                    xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
                    line = (cls, *xywh, conf) if save_conf else (cls, *xywh)  # label format
                    with open(save_dir / 'labels' / (path.stem + '.txt'), 'a') as f:
                        f.write(('%g ' * len(line)).rstrip() % line + '\n')

            # W&B logging - Media Panel Plots
            if len(wandb_images) < log_imgs and wandb_logger.current_epoch > 0:  # Check for test operation
                if wandb_logger.current_epoch % wandb_logger.bbox_interval == 0:
                    box_data = [
                        {
                            "position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]},
                            "class_id": int(cls),
                            "box_caption": "%s %.3f" % (names[cls], conf),
                            "scores": {"class_score": conf},
                            "domain": "pixel"
                        } for *xyxy, conf, cls in pred.tolist()
                    ]
                    boxes = {"predictions": {"box_data": box_data, "class_labels": names}}  # inference-space
                    wandb_images.append(wandb_logger.wandb.Image(img[si], boxes=boxes, caption=path.name))
            wandb_logger.log_training_progress(predn, path, names) if wandb_logger and wandb_logger.wandb_run else None

            # 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 = int(path.stem) if path.stem.isnumeric() else path.stem
                box = xyxy2xywh(predn[:, :4])  # xywh
                box[:, :2] -= box[:, 2:] / 2  # xy center to top-left corner
                for p, b in zip(pred.tolist(), box.tolist()):
                    jdict.append({
                        'image_id': image_id,
                        'category_id': coco91class[int(p[5])] if is_coco else int(p[5]),
                        'bbox': [round(x, 3) for x in b],
                        'score': round(p[4], 5)
                    })

            # Assign all predictions as incorrect
            correct = torch.zeros(pred.shape[0], niou, dtype=torch.bool, device=device)
            if n_labels:
                detected = []  # target indices
                tcls_tensor = labels[:, 0]

                # target boxes
                tbox = xywh2xyxy(labels[:, 1:5])
                scale_coords(img[si].shape[1:], tbox, shapes[si][0], shapes[si][1])  # native-space labels
                if plots:
                    confusion_matrix.process_batch(predn, torch.cat((labels[:, 0:1], tbox), 1))

                # 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(predn[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) == n_labels:  # 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 plots and i_batch < (opt.n_batches_to_plot if hasattr(opt, "n_batches_to_plot") else 3):
            # plot ground truth
            file = save_dir / f'test_batch{i_batch}_labels.jpg'  # labels
            Thread(
                target=plot_images,
                args=(img, targets, paths, file, names),
                kwargs={"masks": masks},
                daemon=True
            ).start()

            # plot prediction
            file = save_dir / f'test_batch{i_batch}_pred.jpg'  # predictions
            Thread(
                target=plot_images,
                args=(img, output_to_target(out, max_det=max_n_targets), paths, file, names),
                kwargs={
                    "masks": torch.cat(plot_masks, dim=0) if len(plot_masks) else None,
                    "max_subplots": batch_size
                },
                daemon=True
            ).start()
            # im = plot_images(
            #     img,
            #     output_to_target(out, max_det=max_n_targets),
            #     paths,
            #     file,
            #     names,
            #     masks=torch.cat(plot_masks, dim=0) if len(plot_masks) else None,
            #     th_conf=conf_thres,
            #     max_subplots=batch_size
            # )


    # 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, plot=plots, v5_metric=v5_metric, save_dir=save_dir, names=names)
        ap50, ap = ap[:, 0], ap.mean(1)  # 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=n_classes)  # number of targets per class
    else:
        nt = torch.zeros(1)

    # Print results
    pf = '%20s' + '%12i' * 2 + '%12.3g' * 4  # print format
    print(pf % ('all', seen, nt.sum(), mp, mr, map50, map))

    # Print results per class
    if (verbose or (n_classes < 50 and not training)) and n_classes > 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, batch_size)  # tuple
    if not training:
        print('Speed: %.1f/%.1f/%.1f ms inference/NMS/total per %gx%g image at batch-size %g' % t)

    # Plots
    if plots:
        confusion_matrix.plot(save_dir=save_dir, names=list(names.values()))
        if wandb_logger and wandb_logger.wandb:
            val_batches = [wandb_logger.wandb.Image(str(f), caption=f.name) for f in sorted(save_dir.glob('test*.jpg'))]
            wandb_logger.log({"Validation": val_batches})
    if wandb_images:
        wandb_logger.log({"Bounding Box Debugger/Images": wandb_images})

    # Save JSON
    if save_json and len(jdict):
        w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else ''  # weights
        anno_json = './coco/annotations/instances_val2017.json'  # annotations json
        pred_json = str(save_dir / f"{w}_predictions.json")  # predictions json
        print('\nEvaluating pycocotools mAP... saving %s...' % pred_json)
        with open(pred_json, 'w') as fid:
            json.dump(jdict, fid)

        try:  # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb
            from pycocotools.coco import COCO
            from pycocotools.cocoeval import COCOeval

            anno = COCO(anno_json)  # init annotations api
            pred = anno.loadRes(pred_json)  # init predictions api
            eval = COCOeval(anno, pred, 'bbox')
            if is_coco:
                eval.params.imgIds = [int(Path(x).stem) for x in dataloader.dataset.img_files]  # image IDs to evaluate
            eval.evaluate()
            eval.accumulate()
            eval.summarize()
            map, map50 = eval.stats[:2]  # update results (mAP@0.5:0.95, mAP@0.5)
        except Exception as e:
            print(f'pycocotools unable to run: {e}')

    # Return results
    model.float()  # for training
    if not training:
        s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
        print(f"Results saved to {save_dir}{s}")
    maps = np.zeros(n_classes) + 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='yolov7.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', nargs='+', type=int, default=[640, 640], help='Image size')
    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('--task', default='val', help='train, val, test, speed or 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('--verbose', action='store_true', help='report mAP by class')
    parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
    parser.add_argument('--save-hybrid', action='store_true', help='save label+prediction hybrid results to *.txt')
    parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
    parser.add_argument('--save-json', action='store_true', help='save a cocoapi-compatible JSON results file')
    parser.add_argument('--project', default='runs/test', help='save to project/name')
    parser.add_argument('--name', default='exp', help='save to project/name')
    parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
    parser.add_argument('--no-trace', action='store_true', help='don`t trace model')
    parser.add_argument('--v5-metric', action='store_true', help='assume maximum recall as 1.0 in AP calculation')
    parser.add_argument('--plot', action='store_true', help='Plot predictions on images')
    parser.add_argument("--n-batches-to-plot", type=int, default=3,
                        help="Number of batches to plot if --plot is set.")
    parser.add_argument('--process-title', type=str, default=None, help='Names the process')

    opt = parser.parse_args()

    print(opt)

    if opt.process_title:
        set_process_title(opt.process_title)

    #check_requirements()
    if opt.task in ('train', 'val', 'test'):  # run normally
        test(
            data=opt.data,
            weights=opt.weights,
            batch_size=opt.batch_size,
            imgsz=opt.img_size,
            conf_thres=opt.conf_thres,
            iou_thres=opt.iou_thres,
            save_json=opt.save_json,
            single_cls=opt.single_cls,
            augment=opt.augment,
            verbose=opt.verbose,
            opt=opt,
            save_txt=opt.save_txt | opt.save_hybrid,
            save_hybrid=opt.save_hybrid,
            save_conf=opt.save_conf,
            trace=not opt.no_trace,
            v5_metric=opt.v5_metric,
            plots=opt.plot
        )

    elif opt.task == 'speed':  # speed benchmarks
        for w in opt.weights:
            test(opt.data, w, opt.batch_size, opt.img_size, 0.25, 0.45, save_json=False, plots=False, v5_metric=opt.v5_metric)

    elif opt.task == 'study':  # run over a range of settings and save/plot
        # python test.py --task study --data coco.yaml --iou 0.65 --weights yolov7.pt
        x = list(range(256, 1536 + 128, 128))  # x axis (image sizes)
        for w in opt.weights:
            f = f'study_{Path(opt.data).stem}_{Path(w).stem}.txt'  # filename to save to
            y = []  # y axis
            for i in x:  # img-size
                print(f'\nRunning {f} point {i}...')
                r, _, t = test(opt.data, w, opt.batch_size, i, opt.conf_thres, opt.iou_thres, opt.save_json,
                               plots=False, v5_metric=opt.v5_metric)
                y.append(r + t)  # results and times
            np.savetxt(f, y, fmt='%10.4g')  # save
        os.system('zip -r study.zip study_*.txt')
        plot_study_txt(x=x)  # plot