getNUQ_f.py

# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
"""
Validate a trained YOLOv5 detection model on a detection dataset.

Usage:
    $ python val.py --weights yolov5s.pt --data coco128.yaml --img 640

Usage - formats:
    $ python val.py --weights yolov5s.pt                 # PyTorch
                              yolov5s.torchscript        # TorchScript
                              yolov5s.onnx               # ONNX Runtime or OpenCV DNN with --dnn
                              yolov5s_openvino_model     # OpenVINO
                              yolov5s.engine             # TensorRT
                              yolov5s.mlmodel            # CoreML (macOS-only)
                              yolov5s_saved_model        # TensorFlow SavedModel
                              yolov5s.pb                 # TensorFlow GraphDef
                              yolov5s.tflite             # TensorFlow Lite
                              yolov5s_edgetpu.tflite     # TensorFlow Edge TPU
                              yolov5s_paddle_model       # PaddlePaddle
"""

import argparse
import json
import os
import pickle
import subprocess
import sys
from pathlib import Path

import numpy as np
import torch
import torchvision.utils
from tqdm import tqdm

from LModel.SCI.network import Model
from data.unaligned_dataset import show_tensor

FILE = Path(__file__).resolve()
ROOT = FILE.parents[0]  # YOLOv5 root directory
if str(ROOT) not in sys.path:
    sys.path.append(str(ROOT))  # add ROOT to PATH
ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative

from HModel.YOLOV5.models.common import DetectMultiBackend
from HModel.YOLOV5.utils.callbacks import Callbacks
from HModel.YOLOV5.utils.dataloaders import create_dataloader
from HModel.YOLOV5.utils.general import (
    LOGGER,
    TQDM_BAR_FORMAT,
    Profile,
    check_dataset,
    check_img_size,
    check_requirements,
    check_yaml,
    coco80_to_coco91_class,
    colorstr,
    increment_path,
    non_max_suppression,
    print_args,
    scale_boxes,
    xywh2xyxy,
    xyxy2xywh,
)
from HModel.YOLOV5.utils.metrics import ConfusionMatrix, ap_per_class, box_iou
from HModel.YOLOV5.utils.plots import output_to_target, plot_images, plot_val_study
from HModel.YOLOV5.utils.torch_utils import select_device, smart_inference_mode
import torch.nn.functional as F


def save_one_txt(predn, save_conf, shape, file):
    """Saves one detection result to a txt file in normalized xywh format, optionally including confidence."""
    gn = torch.tensor(shape)[[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(file, "a") as f:
            f.write(("%g " * len(line)).rstrip() % line + "\n")


def save_one_json(predn, jdict, path, class_map):
    """
    Saves one JSON detection result with image ID, category ID, bounding box, and score.

    Example: {"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(predn.tolist(), box.tolist()):
        jdict.append(
            {
                "image_id": image_id,
                "category_id": class_map[int(p[5])],
                "bbox": [round(x, 3) for x in b],
                "score": round(p[4], 5),
            }
        )


def process_batch(detections, labels, iouv):
    """
    Return correct prediction matrix.

    Arguments:
        detections (array[N, 6]), x1, y1, x2, y2, conf, class
        labels (array[M, 5]), class, x1, y1, x2, y2
    Returns:
        correct (array[N, 10]), for 10 IoU levels
    """
    correct = np.zeros((detections.shape[0], iouv.shape[0])).astype(bool)
    iou = box_iou(labels[:, 1:], detections[:, :4])
    correct_class = labels[:, 0:1] == detections[:, 5]
    for i in range(len(iouv)):
        x = torch.where((iou >= iouv[i]) & correct_class)  # IoU > threshold and classes match
        if x[0].shape[0]:
            matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy()  # [label, detect, iou]
            if x[0].shape[0] > 1:
                matches = matches[matches[:, 2].argsort()[::-1]]
                matches = matches[np.unique(matches[:, 1], return_index=True)[1]]
                # matches = matches[matches[:, 2].argsort()[::-1]]
                matches = matches[np.unique(matches[:, 0], return_index=True)[1]]
            correct[matches[:, 1].astype(int), i] = True
    return torch.tensor(correct, dtype=torch.bool, device=iouv.device)


@smart_inference_mode()
def run(
        data,
        weights=None,  # model.pt path(s)
        batch_size=32,  # batch size
        imgsz=640,  # inference size (pixels)
        conf_thres=0.1,  # confidence threshold
        iou_thres=0.6,  # NMS IoU threshold
        max_det=300,  # maximum detections per image
        task="val",  # train, val, test, speed or study
        device="",  # cuda device, i.e. 0 or 0,1,2,3 or cpu
        workers=8,  # max dataloader workers (per RANK in DDP mode)
        single_cls=False,  # treat as single-class dataset
        augment=False,  # augmented inference
        verbose=False,  # verbose output
        save_txt=False,  # save results to *.txt
        save_hybrid=False,  # save label+prediction hybrid results to *.txt
        save_conf=False,  # save confidences in --save-txt labels
        save_json=False,  # save a COCO-JSON results file
        project=ROOT / "runs/val",  # save to project/name
        name="exp",  # save to project/name
        exist_ok=False,  # existing project/name ok, do not increment
        half=True,  # use FP16 half-precision inference
        dnn=False,  # use OpenCV DNN for ONNX inference
        model=None,
        dataloader=None,
        save_dir=Path(""),
        plots=True,
        callbacks=Callbacks(),
        compute_loss=None,
):
    # Initialize/load model and set device
    training = model is not None
    if training:  # called by train.py
        device, pt, jit, engine = next(model.parameters()).device, True, False, False  # get model device, PyTorch model
        half &= device.type != "cpu"  # half precision only supported on CUDA
        model.half() if half else model.float()
    else:  # called directly
        device = select_device(device, batch_size=batch_size)

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

        # # 加载图像恢复模型
        # LModel = Model().to(device)
        # LModel.eval()
        #
        # # 加载VaT
        # from TTrainer.network4 import Model as VaTModel
        # VaT = VaTModel(input_channel=6).to(device)
        # VaT.load_state_dict(
        #     torch.load("/data1/wjw/bridge/bridgeLH/checkSave/dedark_detection/checkpoint/30_net_detecTrainer_mix4.pth", map_location='cpu'))
        # VaT.eval()

        # Load model
        model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)
        stride, pt, jit, engine = model.stride, model.pt, model.jit, model.engine
        imgsz = check_img_size(imgsz, s=stride)  # check image size
        half = model.fp16  # FP16 supported on limited backends with CUDA
        if engine:
            batch_size = model.batch_size
        else:
            device = model.device
            if not (pt or jit):
                batch_size = 1  # export.py models default to batch-size 1
                LOGGER.info(f"Forcing --batch-size 1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models")

        # Data
        data = check_dataset(data)  # check

    # Configure
    model.eval()
    cuda = device.type != "cpu"
    is_coco = isinstance(data.get("val"), str) and data["val"].endswith(f"coco{os.sep}val2017.txt")  # COCO dataset
    nc = 1 if single_cls else int(data["nc"])  # number of classes
    iouv = torch.linspace(0.5, 0.95, 10, device=device)  # iou vector for mAP@0.5:0.95
    niou = iouv.numel()

    # Dataloader
    if not training:
        if pt and not single_cls:  # check --weights are trained on --data
            ncm = model.model.nc
            assert ncm == nc, (
                f"{weights} ({ncm} classes) trained on different --data than what you passed ({nc} "
                f"classes). Pass correct combination of --weights and --data that are trained together."
            )
        model.warmup(imgsz=(1 if pt else batch_size, 3, imgsz, imgsz))  # warmup
        pad, rect = (0.0, False) if task == "speed" else (0.5, pt)  # square inference for benchmarks
        task = task if task in ("train", "val", "test") else "val"  # path to train/val/test images
        dataloader = create_dataloader(
            data[task],
            imgsz,
            batch_size,
            stride,
            single_cls,
            pad=pad,
            rect=rect,
            workers=workers,
            prefix=colorstr(f"{task}: "),
        )[0]

    seen = 0
    confusion_matrix = ConfusionMatrix(nc=nc)
    names = model.names if hasattr(model, "names") else model.module.names  # get class names
    if isinstance(names, (list, tuple)):  # old format
        names = dict(enumerate(names))
    class_map = coco80_to_coco91_class() if is_coco else list(range(1000))
    s = ("%22s" + "%11s" * 6) % ("Class", "Images", "Instances", "P", "R", "mAP50", "mAP50-95")
    tp, fp, p, r, f1, mp, mr, map50, ap50, map = 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
    dt = Profile(device=device), Profile(device=device), Profile(device=device)  # profiling times
    loss = torch.zeros(3, device=device)
    jdict, stats, ap, ap_class = [], [], [], []
    callbacks.run("on_val_start")
    pbar = tqdm(dataloader, desc=s, bar_format=TQDM_BAR_FORMAT)  # progress bar

    # 记录NUQ所需训练数据
    NUQ_data_x = []
    NUQ_data_y = []
    NUQ_data_cls = []
    for batch_i, (im, targets, paths, shapes) in enumerate(pbar):
        callbacks.run("on_val_batch_start")
        with dt[0]:
            if cuda:
                im = im.to(device, non_blocking=True)
                targets = targets.to(device)
            im = im.half() if half else im.float()  # uint8 to fp16/32
            im /= 255  # 0 - 255 to 0.0 - 1.0
            nb, _, height, width = im.shape  # batch size, channels, height, width

        # Inference
        with dt[1]:
            # 低光增强模型+VaT模型进行预处理
            with torch.no_grad():
                # torchvision.utils.save_image(im[:1], 'temp0.jpg')
                im_s = im.clone()
                im = F.interpolate(im, (512, 512), mode='bilinear', align_corners=False)
                # torchvision.utils.save_image(im[:1], 'temp1.jpg')
                # im_l = LModel(im)
                # torchvision.utils.save_image(im_l[:1], 'temp2.jpg')
                # im_l = VaT(torch.cat([im_l, im], dim=1))[0]
                # torchvision.utils.save_image(im[:1], 'temp3.jpg')
                # im_res = im_l - im
                im_res = im
                im_res = F.interpolate(im_res, (height, width), mode='bilinear', align_corners=False)
                im = im_s + im_res
                # torchvision.utils.save_image(im[:1], 'temp4.jpg')
                preds, train_out = model(im) if compute_loss else (model(im, augment=augment), None)

        # Loss
        if compute_loss:
            loss += compute_loss(train_out, targets)[1]  # box, obj, cls

        # NMS
        targets[:, 2:] *= torch.tensor((width, height, width, height), device=device)  # to pixels
        lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_hybrid else []  # for autolabelling
        with dt[2]:
            preds, preds_ori, preds_f = non_max_suppression(
                preds, conf_thres, iou_thres, labels=lb, multi_label=False,
                agnostic=single_cls, max_det=max_det, return_ori=True, preds_f=preds[1]
            )

        # Metrics
        for si, pred in enumerate(preds):
            labels = targets[targets[:, 0] == si, 1:]
            nl, npr = labels.shape[0], pred.shape[0]  # number of labels, predictions
            path, shape = Path(paths[si]), shapes[si][0]
            correct = torch.zeros(npr, niou, dtype=torch.bool, device=device)  # init
            seen += 1

            if npr == 0:
                if nl:
                    stats.append((correct, *torch.zeros((2, 0), device=device), labels[:, 0]))
                    if plots:
                        confusion_matrix.process_batch(detections=None, labels=labels[:, 0])
                continue

            # Predictions
            if single_cls:
                pred[:, 5] = 0
            predn = pred.clone()
            scale_boxes(im[si].shape[1:], predn[:, :4], shape, shapes[si][1])  # native-space pred

            # Evaluate
            if nl:
                tbox = xywh2xyxy(labels[:, 1:5])  # target boxes
                scale_boxes(im[si].shape[1:], tbox, shape, shapes[si][1])  # native-space labels
                labelsn = torch.cat((labels[:, 0:1], tbox), 1)  # native-space labels
                correct = process_batch(predn, labelsn, iouv)
                if plots:
                    confusion_matrix.process_batch(predn, labelsn)
            stats.append((correct, pred[:, 4], pred[:, 5], labels[:, 0]))  # (correct, conf, pcls, tcls)

            # 记录NUQ所需训练数据
            # labels = torch.sum(correct, dim=1) / correct.shape[1]
            NUQ_data_x.append(preds_f[si])
            NUQ_data_y.append(preds_ori[si][:, 4])
            NUQ_data_cls.append(pred[:, 5])

            # Save/log
            if save_txt:
                (save_dir / "labels").mkdir(parents=True, exist_ok=True)
                save_one_txt(predn, save_conf, shape, file=save_dir / "labels" / f"{path.stem}.txt")
            if save_json:
                save_one_json(predn, jdict, path, class_map)  # append to COCO-JSON dictionary
            callbacks.run("on_val_image_end", pred, predn, path, names, im[si])

        # Plot images
        if plots and batch_i < 3:
            plot_images(im, targets, paths, save_dir / f"val_batch{batch_i}_labels.jpg", names)  # labels
            plot_images(im, output_to_target(preds), paths, save_dir / f"val_batch{batch_i}_pred.jpg", names)  # pred

        callbacks.run("on_val_batch_end", batch_i, im, targets, paths, shapes, preds)

    # NUQ 保存
    NUQ_data_x = torch.cat(NUQ_data_x, dim=0).detach().cpu().numpy()
    NUQ_data_y = torch.cat(NUQ_data_y, dim=0).detach().cpu().numpy()
    NUQ_data_cls = torch.cat(NUQ_data_cls, dim=0).detach().cpu().numpy()
    NUQ_data = {'x': NUQ_data_x, 'y': NUQ_data_y, 'cls': NUQ_data_cls}
    file = open('NUQ_train_data.pickle', 'wb')
    pickle.dump(NUQ_data, file)
    file.close()


def parse_opt():
    """Parses command-line options for YOLOv5 model inference configuration."""
    parser = argparse.ArgumentParser()
    parser.add_argument("--data", type=str, default=ROOT / "./HModel/YOLOV5/voch.yaml", help="dataset.yaml path")
    parser.add_argument("--weights", nargs="+", type=str,
                        default=ROOT / "E:/connection/yolov5-master/runs/train/exp4/weights/best.pt",
                        help="model path(s)")
    parser.add_argument("--batch-size", type=int, default=32, help="batch size")
    parser.add_argument("--imgsz", "--img", "--img-size", type=int, default=640, help="inference size (pixels)")
    parser.add_argument("--conf-thres", type=float, default=0.1, help="confidence threshold")
    parser.add_argument("--iou-thres", type=float, default=0.6, help="NMS IoU threshold")
    parser.add_argument("--max-det", type=int, default=300, help="maximum detections per image")
    parser.add_argument("--task", default="train", help="train, val, test, speed or study")
    parser.add_argument("--device", default="0", help="cuda device, i.e. 0 or 0,1,2,3 or cpu")
    parser.add_argument("--workers", type=int, default=0, help="max dataloader workers (per RANK in DDP mode)")
    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 COCO-JSON results file")
    parser.add_argument("--project", default=ROOT / "runs/val", 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("--half", action="store_true", help="use FP16 half-precision inference")
    parser.add_argument("--dnn", action="store_true", help="use OpenCV DNN for ONNX inference")
    opt = parser.parse_args()
    opt.data = check_yaml(opt.data)  # check YAML
    opt.save_json |= opt.data.endswith("coco.yaml")
    opt.save_txt |= opt.save_hybrid
    print_args(vars(opt))
    return opt


def main(opt):
    """Executes YOLOv5 tasks like training, validation, testing, speed, and study benchmarks based on provided
    options.
    """
    check_requirements(ROOT / "requirements.txt", exclude=("tensorboard", "thop"))

    if opt.task in ("train", "val", "test"):  # run normally
        if opt.conf_thres > 0.001:  # https://github.com/ultralytics/yolov5/issues/1466
            LOGGER.info(f"WARNING ⚠️ confidence threshold {opt.conf_thres} > 0.001 produces invalid results")
        if opt.save_hybrid:
            LOGGER.info("WARNING ⚠️ --save-hybrid will return high mAP from hybrid labels, not from predictions alone")
        run(**vars(opt))

    else:
        weights = opt.weights if isinstance(opt.weights, list) else [opt.weights]
        opt.half = torch.cuda.is_available() and opt.device != "cpu"  # FP16 for fastest results
        if opt.task == "speed":  # speed benchmarks
            # python val.py --task speed --data coco.yaml --batch 1 --weights yolov5n.pt yolov5s.pt...
            opt.conf_thres, opt.iou_thres, opt.save_json = 0.25, 0.45, False
            for opt.weights in weights:
                run(**vars(opt), plots=False)

        elif opt.task == "study":  # speed vs mAP benchmarks
            # python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n.pt yolov5s.pt...
            for opt.weights in weights:
                f = f"study_{Path(opt.data).stem}_{Path(opt.weights).stem}.txt"  # filename to save to
                x, y = list(range(256, 1536 + 128, 128)), []  # x axis (image sizes), y axis
                for opt.imgsz in x:  # img-size
                    LOGGER.info(f"\nRunning {f} --imgsz {opt.imgsz}...")
                    r, _, t = run(**vars(opt), plots=False)
                    y.append(r + t)  # results and times
                np.savetxt(f, y, fmt="%10.4g")  # save
            subprocess.run(["zip", "-r", "study.zip", "study_*.txt"])
            plot_val_study(x=x)  # plot
        else:
            raise NotImplementedError(f'--task {opt.task} not in ("train", "val", "test", "speed", "study")')


if __name__ == "__main__":
    opt = parse_opt()
    main(opt)