YOLO-v5_yolo5n

YOLO-v5

• Yolo-V5

https://github.com/ultralytics/yolov5

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自定义数据集

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因特尔加速

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TensorRT加速

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一 分类

二 检测

训练

python train.py --data coco.yaml --cfg yolov5s.yaml --weights '模型路径' --batch-size 16
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预测

python detect.py  --weights  '模型路径' --source '图像路径、图像文件夹路径、视频路径、视频网址、图像网址'  
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导出ONNX

三 分割加检测

1.训练

python segment/train.py --data coco128-seg.yaml --weights yolov5s-seg.pt --img 640
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2.预测

python segment/predict.py --weights yolov5m-seg.pt --data data/images/bus.jpg

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3.验证

python segment/val.py --weights yolov5s-seg.pt --data coco.yaml --img 640  # validate

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== 引入工具包 ==

== step 0 参数配置 ==

== step 1 数据处理 ==

class LoadImagesAndLabels(Dataset):
    # YOLOv5 train_loader/val_loader, loads images and labels for training and validation
    cache_version = 0.6  # dataset labels *.cache version
    rand_interp_methods = [cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4]

    def __init__(self,
                 path,
                 img_size=640,
                 batch_size=16,
                 augment=False,
                 hyp=None,
                 rect=False,
                 image_weights=False,
                 cache_images=False,
                 single_cls=False,
                 stride=32,
                 pad=0.0,
                 min_items=0,
                 prefix=''):
        self.img_size = img_size
        self.augment = augment
        self.hyp = hyp
        self.image_weights = image_weights
        self.rect = False if image_weights else rect
        self.mosaic = self.augment and not self.rect  # load 4 images at a time into a mosaic (only during training)
        self.mosaic_border = [-img_size // 2, -img_size // 2]
        self.stride = stride
        self.path = path
        self.albumentations = Albumentations(size=img_size) if augment else None

        try:
            f = []  # image files
            for p in path if isinstance(path, list) else [path]:
                p = Path(p)  # os-agnostic
                if p.is_dir():  # dir
                    f += glob.glob(str(p / '**' / '*.*'), recursive=True)
                    # f = list(p.rglob('*.*'))  # pathlib
                elif p.is_file():  # file
                    with open(p) as t:
                        t = t.read().strip().splitlines()
                        parent = str(p.parent) + os.sep
                        f += [x.replace('./', parent, 1) if x.startswith('./') else x for x in t]  # to global path
                        # f += [p.parent / x.lstrip(os.sep) for x in t]  # to global path (pathlib)
                else:
                    raise FileNotFoundError(f'{prefix}{p} does not exist')
            self.im_files = sorted(x.replace('/', os.sep) for x in f if x.split('.')[-1].lower() in IMG_FORMATS)
            # self.img_files = sorted([x for x in f if x.suffix[1:].lower() in IMG_FORMATS])  # pathlib
            assert self.im_files, f'{prefix}No images found'
        except Exception as e:
            raise Exception(f'{prefix}Error loading data from {path}: {e}\n{HELP_URL}') from e

        # Check cache
        self.label_files = img2label_paths(self.im_files)  # labels
        cache_path = (p if p.is_file() else Path(self.label_files[0]).parent).with_suffix('.cache')
        try:
            cache, exists = np.load(cache_path, allow_pickle=True).item(), True  # load dict
            assert cache['version'] == self.cache_version  # matches current version
            assert cache['hash'] == get_hash(self.label_files + self.im_files)  # identical hash
        except Exception:
            cache, exists = self.cache_labels(cache_path, prefix), False  # run cache ops

        # Display cache
        nf, nm, ne, nc, n = cache.pop('results')  # found, missing, empty, corrupt, total
        if exists and LOCAL_RANK in {-1, 0}:
            d = f'Scanning {cache_path}... {nf} images, {nm + ne} backgrounds, {nc} corrupt'
            tqdm(None, desc=prefix + d, total=n, initial=n, bar_format=TQDM_BAR_FORMAT)  # display cache results
            if cache['msgs']:
                LOGGER.info('\n'.join(cache['msgs']))  # display warnings
        assert nf > 0 or not augment, f'{prefix}No labels found in {cache_path}, can not start training. {HELP_URL}'

        # Read cache
        [cache.pop(k) for k in ('hash', 'version', 'msgs')]  # remove items
        labels, shapes, self.segments = zip(*cache.values())
        nl = len(np.concatenate(labels, 0))  # number of labels
        assert nl > 0 or not augment, f'{prefix}All labels empty in {cache_path}, can not start training. {HELP_URL}'
        self.labels = list(labels)
        self.shapes = np.array(shapes)
        self.im_files = list(cache.keys())  # update
        self.label_files = img2label_paths(cache.keys())  # update

        # Filter images
        if min_items:
            include = np.array([len(x) >= min_items for x in self.labels]).nonzero()[0].astype(int)
            LOGGER.info(f'{prefix}{n - len(include)}/{n} images filtered from dataset')
            self.im_files = [self.im_files[i] for i in include]
            self.label_files = [self.label_files[i] for i in include]
            self.labels = [self.labels[i] for i in include]
            self.segments = [self.segments[i] for i in include]
            self.shapes = self.shapes[include]  # wh

        # Create indices
        n = len(self.shapes)  # number of images
        bi = np.floor(np.arange(n) / batch_size).astype(int)  # batch index
        nb = bi[-1] + 1  # number of batches
        self.batch = bi  # batch index of image
        self.n = n
        self.indices = range(n)

        # Update labels
        include_class = []  # filter labels to include only these classes (optional)
        include_class_array = np.array(include_class).reshape(1, -1)
        for i, (label, segment) in enumerate(zip(self.labels, self.segments)):
            if include_class:
                j = (label[:, 0:1] == include_class_array).any(1)
                self.labels[i] = label[j]
                if segment:
                    self.segments[i] = segment[j]
            if single_cls:  # single-class training, merge all classes into 0
                self.labels[i][:, 0] = 0

        # Rectangular Training
        if self.rect:
            # Sort by aspect ratio
            s = self.shapes  # wh
            ar = s[:, 1] / s[:, 0]  # aspect ratio
            irect = ar.argsort()
            self.im_files = [self.im_files[i] for i in irect]
            self.label_files = [self.label_files[i] for i in irect]
            self.labels = [self.labels[i] for i in irect]
            self.segments = [self.segments[i] for i in irect]
            self.shapes = s[irect]  # wh
            ar = ar[irect]

            # Set training image shapes
            shapes = [[1, 1]] * nb
            for i in range(nb):
                ari = ar[bi == i]
                mini, maxi = ari.min(), ari.max()
                if maxi < 1:
                    shapes[i] = [maxi, 1]
                elif mini > 1:
                    shapes[i] = [1, 1 / mini]

            self.batch_shapes = np.ceil(np.array(shapes) * img_size / stride + pad).astype(int) * stride

        # Cache images into RAM/disk for faster training
        if cache_images == 'ram' and not self.check_cache_ram(prefix=prefix):
            cache_images = False
        self.ims = [None] * n
        self.npy_files = [Path(f).with_suffix('.npy') for f in self.im_files]
        if cache_images:
            b, gb = 0, 1 << 30  # bytes of cached images, bytes per gigabytes
            self.im_hw0, self.im_hw = [None] * n, [None] * n
            fcn = self.cache_images_to_disk if cache_images == 'disk' else self.load_image
            results = ThreadPool(NUM_THREADS).imap(fcn, range(n))
            pbar = tqdm(enumerate(results), total=n, bar_format=TQDM_BAR_FORMAT, disable=LOCAL_RANK > 0)
            for i, x in pbar:
                if cache_images == 'disk':
                    b += self.npy_files[i].stat().st_size
                else:  # 'ram'
                    self.ims[i], self.im_hw0[i], self.im_hw[i] = x  # im, hw_orig, hw_resized = load_image(self, i)
                    b += self.ims[i].nbytes
                pbar.desc = f'{prefix}Caching images ({b / gb:.1f}GB {cache_images})'
            pbar.close()

    def check_cache_ram(self, safety_margin=0.1, prefix=''):
        # Check image caching requirements vs available memory
        b, gb = 0, 1 << 30  # bytes of cached images, bytes per gigabytes
        n = min(self.n, 30)  # extrapolate from 30 random images
        for _ in range(n):
            im = cv2.imread(random.choice(self.im_files))  # sample image
            ratio = self.img_size / max(im.shape[0], im.shape[1])  # max(h, w)  # ratio
            b += im.nbytes * ratio ** 2
        mem_required = b * self.n / n  # GB required to cache dataset into RAM
        mem = psutil.virtual_memory()
        cache = mem_required * (1 + safety_margin) < mem.available  # to cache or not to cache, that is the question
        if not cache:
            LOGGER.info(f'{prefix}{mem_required / gb:.1f}GB RAM required, '
                        f'{mem.available / gb:.1f}/{mem.total / gb:.1f}GB available, '
                        f"{'caching images ✅' if cache else 'not caching images ⚠️'}")
        return cache

    def cache_labels(self, path=Path('./labels.cache'), prefix=''):
        # Cache dataset labels, check images and read shapes
        x = {}  # dict
        nm, nf, ne, nc, msgs = 0, 0, 0, 0, []  # number missing, found, empty, corrupt, messages
        desc = f'{prefix}Scanning {path.parent / path.stem}...'
        with Pool(NUM_THREADS) as pool:
            pbar = tqdm(pool.imap(verify_image_label, zip(self.im_files, self.label_files, repeat(prefix))),
                        desc=desc,
                        total=len(self.im_files),
                        bar_format=TQDM_BAR_FORMAT)
            for im_file, lb, shape, segments, nm_f, nf_f, ne_f, nc_f, msg in pbar:
                nm += nm_f
                nf += nf_f
                ne += ne_f
                nc += nc_f
                if im_file:
                    x[im_file] = [lb, shape, segments]
                if msg:
                    msgs.append(msg)
                pbar.desc = f'{desc} {nf} images, {nm + ne} backgrounds, {nc} corrupt'

        pbar.close()
        if msgs:
            LOGGER.info('\n'.join(msgs))
        if nf == 0:
            LOGGER.warning(f'{prefix}WARNING ⚠️ No labels found in {path}. {HELP_URL}')
        x['hash'] = get_hash(self.label_files + self.im_files)
        x['results'] = nf, nm, ne, nc, len(self.im_files)
        x['msgs'] = msgs  # warnings
        x['version'] = self.cache_version  # cache version
        try:
            np.save(path, x)  # save cache for next time
            path.with_suffix('.cache.npy').rename(path)  # remove .npy suffix
            LOGGER.info(f'{prefix}New cache created: {path}')
        except Exception as e:
            LOGGER.warning(f'{prefix}WARNING ⚠️ Cache directory {path.parent} is not writeable: {e}')  # not writeable
        return x

    def __len__(self):
        return len(self.im_files)

    # def __iter__(self):
    #     self.count = -1
    #     print('ran dataset iter')
    #     #self.shuffled_vector = np.random.permutation(self.nF) if self.augment else np.arange(self.nF)
    #     return self

    def __getitem__(self, index):
        index = self.indices[index]  # linear, shuffled, or image_weights

        hyp = self.hyp
        mosaic = self.mosaic and random.random() < hyp['mosaic']
        if mosaic:
            # Load mosaic
            img, labels = self.load_mosaic(index)
            shapes = None

            # MixUp augmentation
            if random.random() < hyp['mixup']:
                img, labels = mixup(img, labels, *self.load_mosaic(random.randint(0, self.n - 1)))

        else:
            # Load image
            img, (h0, w0), (h, w) = self.load_image(index)

            # Letterbox
            shape = self.batch_shapes[self.batch[index]] if self.rect else self.img_size  # final letterboxed shape
            img, ratio, pad = letterbox(img, shape, auto=False, scaleup=self.augment)
            shapes = (h0, w0), ((h / h0, w / w0), pad)  # for COCO mAP rescaling

            labels = self.labels[index].copy()
            if labels.size:  # normalized xywh to pixel xyxy format
                labels[:, 1:] = xywhn2xyxy(labels[:, 1:], ratio[0] * w, ratio[1] * h, padw=pad[0], padh=pad[1])

            if self.augment:
                img, labels = random_perspective(img,
                                                 labels,
                                                 degrees=hyp['degrees'],
                                                 translate=hyp['translate'],
                                                 scale=hyp['scale'],
                                                 shear=hyp['shear'],
                                                 perspective=hyp['perspective'])

        nl = len(labels)  # number of labels
        if nl:
            labels[:, 1:5] = xyxy2xywhn(labels[:, 1:5], w=img.shape[1], h=img.shape[0], clip=True, eps=1E-3)

        if self.augment:
            # Albumentations
            img, labels = self.albumentations(img, labels)
            nl = len(labels)  # update after albumentations

            # HSV color-space
            augment_hsv(img, hgain=hyp['hsv_h'], sgain=hyp['hsv_s'], vgain=hyp['hsv_v'])

            # Flip up-down
            if random.random() < hyp['flipud']:
                img = np.flipud(img)
                if nl:
                    labels[:, 2] = 1 - labels[:, 2]

            # Flip left-right
            if random.random() < hyp['fliplr']:
                img = np.fliplr(img)
                if nl:
                    labels[:, 1] = 1 - labels[:, 1]

            # Cutouts
            # labels = cutout(img, labels, p=0.5)
            # nl = len(labels)  # update after cutout

        labels_out = torch.zeros((nl, 6))
        if nl:
            labels_out[:, 1:] = torch.from_numpy(labels)

        # Convert
        img = img.transpose((2, 0, 1))[::-1]  # HWC to CHW, BGR to RGB
        img = np.ascontiguousarray(img)

        return torch.from_numpy(img), labels_out, self.im_files[index], shapes

    def load_image(self, i):
        # Loads 1 image from dataset index 'i', returns (im, original hw, resized hw)
        im, f, fn = self.ims[i], self.im_files[i], self.npy_files[i],
        if im is None:  # not cached in RAM
            if fn.exists():  # load npy
                im = np.load(fn)
            else:  # read image
                im = cv2.imread(f)  # BGR
                assert im is not None, f'Image Not Found {f}'
            h0, w0 = im.shape[:2]  # orig hw
            r = self.img_size / max(h0, w0)  # ratio
            if r != 1:  # if sizes are not equal
                interp = cv2.INTER_LINEAR if (self.augment or r > 1) else cv2.INTER_AREA
                im = cv2.resize(im, (math.ceil(w0 * r), math.ceil(h0 * r)), interpolation=interp)
            return im, (h0, w0), im.shape[:2]  # im, hw_original, hw_resized
        return self.ims[i], self.im_hw0[i], self.im_hw[i]  # im, hw_original, hw_resized

    def cache_images_to_disk(self, i):
        # Saves an image as an *.npy file for faster loading
        f = self.npy_files[i]
        if not f.exists():
            np.save(f.as_posix(), cv2.imread(self.im_files[i]))

    def load_mosaic(self, index):
        # YOLOv5 4-mosaic loader. Loads 1 image + 3 random images into a 4-image mosaic
        labels4, segments4 = [], []
        s = self.img_size
        yc, xc = (int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border)  # mosaic center x, y
        indices = [index] + random.choices(self.indices, k=3)  # 3 additional image indices
        random.shuffle(indices)
        for i, index in enumerate(indices):
            # Load image
            img, _, (h, w) = self.load_image(index)

            # place img in img4
            if i == 0:  # top left
                img4 = np.full((s * 2, s * 2, img.shape[2]), 114, dtype=np.uint8)  # base image with 4 tiles
                x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc  # xmin, ymin, xmax, ymax (large image)
                x1b, y1b, x2b, y2b = w - (x2a - x1a), h - (y2a - y1a), w, h  # xmin, ymin, xmax, ymax (small image)
            elif i == 1:  # top right
                x1a, y1a, x2a, y2a = xc, max(yc - h, 0), min(xc + w, s * 2), yc
                x1b, y1b, x2b, y2b = 0, h - (y2a - y1a), min(w, x2a - x1a), h
            elif i == 2:  # bottom left
                x1a, y1a, x2a, y2a = max(xc - w, 0), yc, xc, min(s * 2, yc + h)
                x1b, y1b, x2b, y2b = w - (x2a - x1a), 0, w, min(y2a - y1a, h)
            elif i == 3:  # bottom right
                x1a, y1a, x2a, y2a = xc, yc, min(xc + w, s * 2), min(s * 2, yc + h)
                x1b, y1b, x2b, y2b = 0, 0, min(w, x2a - x1a), min(y2a - y1a, h)

            img4[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b]  # img4[ymin:ymax, xmin:xmax]
            padw = x1a - x1b
            padh = y1a - y1b

            # Labels
            labels, segments = self.labels[index].copy(), self.segments[index].copy()
            if labels.size:
                labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padw, padh)  # normalized xywh to pixel xyxy format
                segments = [xyn2xy(x, w, h, padw, padh) for x in segments]
            labels4.append(labels)
            segments4.extend(segments)

        # Concat/clip labels
        labels4 = np.concatenate(labels4, 0)
        for x in (labels4[:, 1:], *segments4):
            np.clip(x, 0, 2 * s, out=x)  # clip when using random_perspective()
        # img4, labels4 = replicate(img4, labels4)  # replicate

        # Augment
        img4, labels4, segments4 = copy_paste(img4, labels4, segments4, p=self.hyp['copy_paste'])
        img4, labels4 = random_perspective(img4,
                                           labels4,
                                           segments4,
                                           degrees=self.hyp['degrees'],
                                           translate=self.hyp['translate'],
                                           scale=self.hyp['scale'],
                                           shear=self.hyp['shear'],
                                           perspective=self.hyp['perspective'],
                                           border=self.mosaic_border)  # border to remove

        return img4, labels4

    def load_mosaic9(self, index):
        # YOLOv5 9-mosaic loader. Loads 1 image + 8 random images into a 9-image mosaic
        labels9, segments9 = [], []
        s = self.img_size
        indices = [index] + random.choices(self.indices, k=8)  # 8 additional image indices
        random.shuffle(indices)
        hp, wp = -1, -1  # height, width previous
        for i, index in enumerate(indices):
            # Load image
            img, _, (h, w) = self.load_image(index)

            # place img in img9
            if i == 0:  # center
                img9 = np.full((s * 3, s * 3, img.shape[2]), 114, dtype=np.uint8)  # base image with 4 tiles
                h0, w0 = h, w
                c = s, s, s + w, s + h  # xmin, ymin, xmax, ymax (base) coordinates
            elif i == 1:  # top
                c = s, s - h, s + w, s
            elif i == 2:  # top right
                c = s + wp, s - h, s + wp + w, s
            elif i == 3:  # right
                c = s + w0, s, s + w0 + w, s + h
            elif i == 4:  # bottom right
                c = s + w0, s + hp, s + w0 + w, s + hp + h
            elif i == 5:  # bottom
                c = s + w0 - w, s + h0, s + w0, s + h0 + h
            elif i == 6:  # bottom left
                c = s + w0 - wp - w, s + h0, s + w0 - wp, s + h0 + h
            elif i == 7:  # left
                c = s - w, s + h0 - h, s, s + h0
            elif i == 8:  # top left
                c = s - w, s + h0 - hp - h, s, s + h0 - hp

            padx, pady = c[:2]
            x1, y1, x2, y2 = (max(x, 0) for x in c)  # allocate coords

            # Labels
            labels, segments = self.labels[index].copy(), self.segments[index].copy()
            if labels.size:
                labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padx, pady)  # normalized xywh to pixel xyxy format
                segments = [xyn2xy(x, w, h, padx, pady) for x in segments]
            labels9.append(labels)
            segments9.extend(segments)

            # Image
            img9[y1:y2, x1:x2] = img[y1 - pady:, x1 - padx:]  # img9[ymin:ymax, xmin:xmax]
            hp, wp = h, w  # height, width previous

        # Offset
        yc, xc = (int(random.uniform(0, s)) for _ in self.mosaic_border)  # mosaic center x, y
        img9 = img9[yc:yc + 2 * s, xc:xc + 2 * s]

        # Concat/clip labels
        labels9 = np.concatenate(labels9, 0)
        labels9[:, [1, 3]] -= xc
        labels9[:, [2, 4]] -= yc
        c = np.array([xc, yc])  # centers
        segments9 = [x - c for x in segments9]

        for x in (labels9[:, 1:], *segments9):
            np.clip(x, 0, 2 * s, out=x)  # clip when using random_perspective()
        # img9, labels9 = replicate(img9, labels9)  # replicate

        # Augment
        img9, labels9, segments9 = copy_paste(img9, labels9, segments9, p=self.hyp['copy_paste'])
        img9, labels9 = random_perspective(img9,
                                           labels9,
                                           segments9,
                                           degrees=self.hyp['degrees'],
                                           translate=self.hyp['translate'],
                                           scale=self.hyp['scale'],
                                           shear=self.hyp['shear'],
                                           perspective=self.hyp['perspective'],
                                           border=self.mosaic_border)  # border to remove

        return img9, labels9

    @staticmethod
    def collate_fn(batch):
        im, label, path, shapes = zip(*batch)  # transposed
        for i, lb in enumerate(label):
            lb[:, 0] = i  # add target image index for build_targets()
        return torch.stack(im, 0), torch.cat(label, 0), path, shapes

    @staticmethod
    def collate_fn4(batch):
        im, label, path, shapes = zip(*batch)  # transposed
        n = len(shapes) // 4
        im4, label4, path4, shapes4 = [], [], path[:n], shapes[:n]

        ho = torch.tensor([[0.0, 0, 0, 1, 0, 0]])
        wo = torch.tensor([[0.0, 0, 1, 0, 0, 0]])
        s = torch.tensor([[1, 1, 0.5, 0.5, 0.5, 0.5]])  # scale
        for i in range(n):  # zidane torch.zeros(16,3,720,1280)  # BCHW
            i *= 4
            if random.random() < 0.5:
                im1 = F.interpolate(im[i].unsqueeze(0).float(), scale_factor=2.0, mode='bilinear',
                                    align_corners=False)[0].type(im[i].type())
                lb = label[i]
            else:
                im1 = torch.cat((torch.cat((im[i], im[i + 1]), 1), torch.cat((im[i + 2], im[i + 3]), 1)), 2)
                lb = torch.cat((label[i], label[i + 1] + ho, label[i + 2] + wo, label[i + 3] + ho + wo), 0) * s
            im4.append(im1)
            label4.append(lb)

        for i, lb in enumerate(label4):
            lb[:, 0] = i  # add target image index for build_targets()

        return torch.stack(im4, 0), torch.cat(label4, 0), path4, shapes4

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== step 2 模型 ==

class BaseModel(nn.Module):
    # YOLOv5 base model
    def forward(self, x, profile=False, visualize=False):
        return self._forward_once(x, profile, visualize)  # single-scale inference, train

    def _forward_once(self, x, profile=False, visualize=False):
        y, dt = [], []  # outputs
        for m in self.model:
            if m.f != -1:  # if not from previous layer
                x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f]  # from earlier layers
            if profile:
                self._profile_one_layer(m, x, dt)
            x = m(x)  # run
            y.append(x if m.i in self.save else None)  # save output
            if visualize:
                feature_visualization(x, m.type, m.i, save_dir=visualize)
        return x

    def _profile_one_layer(self, m, x, dt):
        c = m == self.model[-1]  # is final layer, copy input as inplace fix
        o = thop.profile(m, inputs=(x.copy() if c else x,), verbose=False)[0] / 1E9 * 2 if thop else 0  # FLOPs
        t = time_sync()
        for _ in range(10):
            m(x.copy() if c else x)
        dt.append((time_sync() - t) * 100)
        if m == self.model[0]:
            LOGGER.info(f"{'time (ms)':>10s} {'GFLOPs':>10s} {'params':>10s}  module")
        LOGGER.info(f'{dt[-1]:10.2f} {o:10.2f} {m.np:10.0f}  {m.type}')
        if c:
            LOGGER.info(f"{sum(dt):10.2f} {'-':>10s} {'-':>10s}  Total")

    def fuse(self):  # fuse model Conv2d() + BatchNorm2d() layers
        LOGGER.info('Fusing layers... ')
        for m in self.model.modules():
            if isinstance(m, (Conv, DWConv)) and hasattr(m, 'bn'):
                m.conv = fuse_conv_and_bn(m.conv, m.bn)  # update conv
                delattr(m, 'bn')  # remove batchnorm
                m.forward = m.forward_fuse  # update forward
        self.info()
        return self

    def info(self, verbose=False, img_size=640):  # print model information
        model_info(self, verbose, img_size)

    def _apply(self, fn):
        # Apply to(), cpu(), cuda(), half() to model tensors that are not parameters or registered buffers
        self = super()._apply(fn)
        m = self.model[-1]  # Detect()
        if isinstance(m, (Detect, Segment)):
            m.stride = fn(m.stride)
            m.grid = list(map(fn, m.grid))
            if isinstance(m.anchor_grid, list):
                m.anchor_grid = list(map(fn, m.anchor_grid))
        return self


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检测模型

class DetectionModel(BaseModel):
    # YOLOv5 detection model
    def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None, anchors=None):  # model, input channels, number of classes
        super().__init__()
        if isinstance(cfg, dict):
            self.yaml = cfg  # model dict
        else:  # is *.yaml
            import yaml  # for torch hub
            self.yaml_file = Path(cfg).name
            with open(cfg, encoding='ascii', errors='ignore') as f:
                self.yaml = yaml.safe_load(f)  # model dict

        # Define model
        ch = self.yaml['ch'] = self.yaml.get('ch', ch)  # input channels
        if nc and nc != self.yaml['nc']:
            LOGGER.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}")
            self.yaml['nc'] = nc  # override yaml value
        if anchors:
            LOGGER.info(f'Overriding model.yaml anchors with anchors={anchors}')
            self.yaml['anchors'] = round(anchors)  # override yaml value
        self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch])  # model, savelist
        self.names = [str(i) for i in range(self.yaml['nc'])]  # default names
        self.inplace = self.yaml.get('inplace', True)

        # Build strides, anchors
        m = self.model[-1]  # Detect()
        if isinstance(m, (Detect, Segment)):
            s = 256  # 2x min stride
            m.inplace = self.inplace
            forward = lambda x: self.forward(x)[0] if isinstance(m, Segment) else self.forward(x)
            m.stride = torch.tensor([s / x.shape[-2] for x in forward(torch.zeros(1, ch, s, s))])  # forward
            check_anchor_order(m)
            m.anchors /= m.stride.view(-1, 1, 1)
            self.stride = m.stride
            self._initialize_biases()  # only run once

        # Init weights, biases
        initialize_weights(self)
        self.info()
        LOGGER.info('')

    def forward(self, x, augment=False, profile=False, visualize=False):
        if augment:
            return self._forward_augment(x)  # augmented inference, None
        return self._forward_once(x, profile, visualize)  # single-scale inference, train

    def _forward_augment(self, x):
        img_size = x.shape[-2:]  # height, width
        s = [1, 0.83, 0.67]  # scales
        f = [None, 3, None]  # flips (2-ud, 3-lr)
        y = []  # outputs
        for si, fi in zip(s, f):
            xi = scale_img(x.flip(fi) if fi else x, si, gs=int(self.stride.max()))
            yi = self._forward_once(xi)[0]  # forward
            # cv2.imwrite(f'img_{si}.jpg', 255 * xi[0].cpu().numpy().transpose((1, 2, 0))[:, :, ::-1])  # save
            yi = self._descale_pred(yi, fi, si, img_size)
            y.append(yi)
        y = self._clip_augmented(y)  # clip augmented tails
        return torch.cat(y, 1), None  # augmented inference, train

    def _descale_pred(self, p, flips, scale, img_size):
        # de-scale predictions following augmented inference (inverse operation)
        if self.inplace:
            p[..., :4] /= scale  # de-scale
            if flips == 2:
                p[..., 1] = img_size[0] - p[..., 1]  # de-flip ud
            elif flips == 3:
                p[..., 0] = img_size[1] - p[..., 0]  # de-flip lr
        else:
            x, y, wh = p[..., 0:1] / scale, p[..., 1:2] / scale, p[..., 2:4] / scale  # de-scale
            if flips == 2:
                y = img_size[0] - y  # de-flip ud
            elif flips == 3:
                x = img_size[1] - x  # de-flip lr
            p = torch.cat((x, y, wh, p[..., 4:]), -1)
        return p

    def _clip_augmented(self, y):
        # Clip YOLOv5 augmented inference tails
        nl = self.model[-1].nl  # number of detection layers (P3-P5)
        g = sum(4 ** x for x in range(nl))  # grid points
        e = 1  # exclude layer count
        i = (y[0].shape[1] // g) * sum(4 ** x for x in range(e))  # indices
        y[0] = y[0][:, :-i]  # large
        i = (y[-1].shape[1] // g) * sum(4 ** (nl - 1 - x) for x in range(e))  # indices
        y[-1] = y[-1][:, i:]  # small
        return y

    def _initialize_biases(self, cf=None):  # initialize biases into Detect(), cf is class frequency
        # https://arxiv.org/abs/1708.02002 section 3.3
        # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1.
        m = self.model[-1]  # Detect() module
        for mi, s in zip(m.m, m.stride):  # from
            b = mi.bias.view(m.na, -1)  # conv.bias(255) to (3,85)
            b.data[:, 4] += math.log(8 / (640 / s) ** 2)  # obj (8 objects per 640 image)
            b.data[:, 5:5 + m.nc] += math.log(0.6 / (m.nc - 0.99999)) if cf is None else torch.log(cf / cf.sum())  # cls
            mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)


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分割模型

class SegmentationModel(DetectionModel):
    # YOLOv5 segmentation model
    def __init__(self, cfg='yolov5s-seg.yaml', ch=3, nc=None, anchors=None):
        super().__init__(cfg, ch, nc, anchors)

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分类模型

class ClassificationModel(BaseModel):
    # YOLOv5 classification model
    def __init__(self, cfg=None, model=None, nc=1000, cutoff=10):  # yaml, model, number of classes, cutoff index
        super().__init__()
        self._from_detection_model(model, nc, cutoff) if model is not None else self._from_yaml(cfg)

    def _from_detection_model(self, model, nc=1000, cutoff=10):
        # Create a YOLOv5 classification model from a YOLOv5 detection model
        if isinstance(model, DetectMultiBackend):
            model = model.model  # unwrap DetectMultiBackend
        model.model = model.model[:cutoff]  # backbone
        m = model.model[-1]  # last layer
        ch = m.conv.in_channels if hasattr(m, 'conv') else m.cv1.conv.in_channels  # ch into module
        c = Classify(ch, nc)  # Classify()
        c.i, c.f, c.type = m.i, m.f, 'models.common.Classify'  # index, from, type
        model.model[-1] = c  # replace
        self.model = model.model
        self.stride = model.stride
        self.save = []
        self.nc = nc

    def _from_yaml(self, cfg):
        # Create a YOLOv5 classification model from a *.yaml file
        self.model = None

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== step 3 损失函数 ==

class ComputeLoss:
    sort_obj_iou = False

    # Compute losses
    def __init__(self, model, autobalance=False):
        device = next(model.parameters()).device  # get model device
        h = model.hyp  # hyperparameters

        # Define criteria
        BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device))
        BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device))

        # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3
        self.cp, self.cn = smooth_BCE(eps=h.get('label_smoothing', 0.0))  # positive, negative BCE targets

        # Focal loss
        g = h['fl_gamma']  # focal loss gamma
        if g > 0:
            BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g)

        m = de_parallel(model).model[-1]  # Detect() module
        self.balance = {3: [4.0, 1.0, 0.4]}.get(m.nl, [4.0, 1.0, 0.25, 0.06, 0.02])  # P3-P7
        self.ssi = list(m.stride).index(16) if autobalance else 0  # stride 16 index
        self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, 1.0, h, autobalance
        self.na = m.na  # number of anchors
        self.nc = m.nc  # number of classes
        self.nl = m.nl  # number of layers
        self.anchors = m.anchors
        self.device = device

    def __call__(self, p, targets):  # predictions, targets
        lcls = torch.zeros(1, device=self.device)  # class loss
        lbox = torch.zeros(1, device=self.device)  # box loss
        lobj = torch.zeros(1, device=self.device)  # object loss
        tcls, tbox, indices, anchors = self.build_targets(p, targets)  # targets

        # Losses
        for i, pi in enumerate(p):  # layer index, layer predictions
            b, a, gj, gi = indices[i]  # image, anchor, gridy, gridx
            tobj = torch.zeros(pi.shape[:4], dtype=pi.dtype, device=self.device)  # target obj

            n = b.shape[0]  # number of targets
            if n:
                # pxy, pwh, _, pcls = pi[b, a, gj, gi].tensor_split((2, 4, 5), dim=1)  # faster, requires torch 1.8.0
                pxy, pwh, _, pcls = pi[b, a, gj, gi].split((2, 2, 1, self.nc), 1)  # target-subset of predictions

                # Regression
                pxy = pxy.sigmoid() * 2 - 0.5
                pwh = (pwh.sigmoid() * 2) ** 2 * anchors[i]
                pbox = torch.cat((pxy, pwh), 1)  # predicted box
                iou = bbox_iou(pbox, tbox[i], CIoU=True).squeeze()  # iou(prediction, target)
                lbox += (1.0 - iou).mean()  # iou loss

                # Objectness
                iou = iou.detach().clamp(0).type(tobj.dtype)
                if self.sort_obj_iou:
                    j = iou.argsort()
                    b, a, gj, gi, iou = b[j], a[j], gj[j], gi[j], iou[j]
                if self.gr < 1:
                    iou = (1.0 - self.gr) + self.gr * iou
                tobj[b, a, gj, gi] = iou  # iou ratio

                # Classification
                if self.nc > 1:  # cls loss (only if multiple classes)
                    t = torch.full_like(pcls, self.cn, device=self.device)  # targets
                    t[range(n), tcls[i]] = self.cp
                    lcls += self.BCEcls(pcls, t)  # BCE

                # Append targets to text file
                # with open('targets.txt', 'a') as file:
                #     [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)]

            obji = self.BCEobj(pi[..., 4], tobj)
            lobj += obji * self.balance[i]  # obj loss
            if self.autobalance:
                self.balance[i] = self.balance[i] * 0.9999 + 0.0001 / obji.detach().item()

        if self.autobalance:
            self.balance = [x / self.balance[self.ssi] for x in self.balance]
        lbox *= self.hyp['box']
        lobj *= self.hyp['obj']
        lcls *= self.hyp['cls']
        bs = tobj.shape[0]  # batch size

        return (lbox + lobj + lcls) * bs, torch.cat((lbox, lobj, lcls)).detach()

    def build_targets(self, p, targets):
        # Build targets for compute_loss(), input targets(image,class,x,y,w,h)
        na, nt = self.na, targets.shape[0]  # number of anchors, targets
        tcls, tbox, indices, anch = [], [], [], []
        gain = torch.ones(7, device=self.device)  # normalized to gridspace gain
        ai = torch.arange(na, device=self.device).float().view(na, 1).repeat(1, nt)  # same as .repeat_interleave(nt)
        targets = torch.cat((targets.repeat(na, 1, 1), ai[..., None]), 2)  # append anchor indices

        g = 0.5  # bias
        off = torch.tensor(
            [
                [0, 0],
                [1, 0],
                [0, 1],
                [-1, 0],
                [0, -1],  # j,k,l,m
                # [1, 1], [1, -1], [-1, 1], [-1, -1],  # jk,jm,lk,lm
            ],
            device=self.device).float() * g  # offsets

        for i in range(self.nl):
            anchors, shape = self.anchors[i], p[i].shape
            gain[2:6] = torch.tensor(shape)[[3, 2, 3, 2]]  # xyxy gain

            # Match targets to anchors
            t = targets * gain  # shape(3,n,7)
            if nt:
                # Matches
                r = t[..., 4:6] / anchors[:, None]  # wh ratio
                j = torch.max(r, 1 / r).max(2)[0] < self.hyp['anchor_t']  # compare
                # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t']  # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2))
                t = t[j]  # filter

                # Offsets
                gxy = t[:, 2:4]  # grid xy
                gxi = gain[[2, 3]] - gxy  # inverse
                j, k = ((gxy % 1 < g) & (gxy > 1)).T
                l, m = ((gxi % 1 < g) & (gxi > 1)).T
                j = torch.stack((torch.ones_like(j), j, k, l, m))
                t = t.repeat((5, 1, 1))[j]
                offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j]
            else:
                t = targets[0]
                offsets = 0

            # Define
            bc, gxy, gwh, a = t.chunk(4, 1)  # (image, class), grid xy, grid wh, anchors
            a, (b, c) = a.long().view(-1), bc.long().T  # anchors, image, class
            gij = (gxy - offsets).long()
            gi, gj = gij.T  # grid indices

            # Append
            indices.append((b, a, gj.clamp_(0, shape[2] - 1), gi.clamp_(0, shape[3] - 1)))  # image, anchor, grid
            tbox.append(torch.cat((gxy - gij, gwh), 1))  # box
            anch.append(anchors[a])  # anchors
            tcls.append(c)  # class

        return tcls, tbox, indices, anch

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== step 4 优化器 ==

== step 5 评测函数==

== step 6 训练 ==

== step 7 训练可视化 ==

== inference ==

---

---

0.数据增强

• 1.一 基于深层神经网络的目标检测
• 2.二 YoloV5网络结构
• 3.三 代码实现
• 4.四 问题思索
• 5.五 实验参数设置
• 6.六 额外补充

《YOLOv5: The friendliest Al architecture you’ll ever use》
—YOLOv5
作者：Glenn Jocher
单位：
发表会议及时间：2020
Submission history
2021.10.12：发布v6.0版本

---

• Abstract

---

核心模块

Conv

C3

SPPF

提升了精度，增加了由浅入深的特征提取过程

一 基于深层神经网络的目标检测

基于深层神经网络的目标检测

双阶段(two-stage):
第一段网络用于候选区域提取;
第二段网络对提取的候选区域进行分类和精确坐标回归，例如RCNN系列。

单阶段(one-stage):
放弃了候选区域提取，只用一段网络来完成了分类和回归两个任务，例如YOLO、SSD等。

Anchor Free:
取消anchor生成机制，加快了速度，代表算法:CenterNet、 CornerNet、 Fcos等

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模型由小到大顺序

1. 最小的：yolo5n（Nano）：适用于移动端或者CPU的部署环境
2. yolo5s(small)
3. yolo5m(middle)
4. yolo5l(large)
5. 最大的：yolo5x

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[-1, 1, Conv, [256, 3, 2]]

-1:表示输入通道数，由上一层的输出通道数决定（传入对应于common.py中的类模块中）
1: 重复构造1次
Conv:网络模块（名字对应于common.py中的类模块名）
[256, 3, 2]:网络模块参数（传入对应于common.py中的类模块中）
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yolo5n

# YOLOv5 
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