Minor fix

utils/downloads.py

@@ -0,0 +1,103 @@
+import logging
+import os
+import subprocess
+import urllib
+from pathlib import Path
+
+import requests
+import torch
+
+
+def is_url(url, check=True):
+    # Check if string is URL and check if URL exists
+    try:
+        url = str(url)
+        result = urllib.parse.urlparse(url)
+        assert all([result.scheme, result.netloc])  # check if is url
+        return (urllib.request.urlopen(url).getcode() == 200) if check else True  # check if exists online
+    except (AssertionError, urllib.request.HTTPError):
+        return False
+
+
+def gsutil_getsize(url=''):
+    # gs://bucket/file size https://cloud.google.com/storage/docs/gsutil/commands/du
+    s = subprocess.check_output(f'gsutil du {url}', shell=True).decode('utf-8')
+    return eval(s.split(' ')[0]) if len(s) else 0  # bytes
+
+
+def url_getsize(url='https://ultralytics.com/images/bus.jpg'):
+    # Return downloadable file size in bytes
+    response = requests.head(url, allow_redirects=True)
+    return int(response.headers.get('content-length', -1))
+
+
+def safe_download(file, url, url2=None, min_bytes=1E0, error_msg=''):
+    # Attempts to download file from url or url2, checks and removes incomplete downloads < min_bytes
+    from utils.general import LOGGER
+
+    file = Path(file)
+    assert_msg = f"Downloaded file '{file}' does not exist or size is < min_bytes={min_bytes}"
+    try:  # url1
+        LOGGER.info(f'Downloading {url} to {file}...')
+        torch.hub.download_url_to_file(url, str(file), progress=LOGGER.level <= logging.INFO)
+        assert file.exists() and file.stat().st_size > min_bytes, assert_msg  # check
+    except Exception as e:  # url2
+        if file.exists():
+            file.unlink()  # remove partial downloads
+        LOGGER.info(f'ERROR: {e}\nRe-attempting {url2 or url} to {file}...')
+        os.system(f"curl -# -L '{url2 or url}' -o '{file}' --retry 3 -C -")  # curl download, retry and resume on fail
+    finally:
+        if not file.exists() or file.stat().st_size < min_bytes:  # check
+            if file.exists():
+                file.unlink()  # remove partial downloads
+            LOGGER.info(f"ERROR: {assert_msg}\n{error_msg}")
+        LOGGER.info('')
+
+
+def attempt_download(file, repo='ultralytics/yolov5', release='v7.0'):
+    # Attempt file download from GitHub release assets if not found locally. release = 'latest', 'v7.0', etc.
+    from utils.general import LOGGER
+
+    def github_assets(repository, version='latest'):
+        # Return GitHub repo tag (i.e. 'v7.0') and assets (i.e. ['yolov5s.pt', 'yolov5m.pt', ...])
+        if version != 'latest':
+            version = f'tags/{version}'  # i.e. tags/v7.0
+        response = requests.get(f'https://api.github.com/repos/{repository}/releases/{version}').json()  # github api
+        return response['tag_name'], [x['name'] for x in response['assets']]  # tag, assets
+
+    file = Path(str(file).strip().replace("'", ''))
+    if not file.exists():
+        # URL specified
+        name = Path(urllib.parse.unquote(str(file))).name  # decode '%2F' to '/' etc.
+        if str(file).startswith(('http:/', 'https:/')):  # download
+            url = str(file).replace(':/', '://')  # Pathlib turns :// -> :/
+            file = name.split('?')[0]  # parse authentication https://url.com/file.txt?auth...
+            if Path(file).is_file():
+                LOGGER.info(f'Found {url} locally at {file}')  # file already exists
+            else:
+                safe_download(file=file, url=url, min_bytes=1E5)
+            return file
+
+        # GitHub assets
+        assets = [f'yolov5{size}{suffix}.pt' for size in 'nsmlx' for suffix in ('', '6', '-cls', '-seg')]  # default
+        try:
+            tag, assets = github_assets(repo, release)
+        except Exception:
+            try:
+                tag, assets = github_assets(repo)  # latest release
+            except Exception:
+                try:
+                    tag = subprocess.check_output('git tag', shell=True, stderr=subprocess.STDOUT).decode().split()[-1]
+                except Exception:
+                    tag = release
+
+        file.parent.mkdir(parents=True, exist_ok=True)  # make parent dir (if required)
+        if name in assets:
+            url3 = 'https://drive.google.com/drive/folders/1EFQTEUeXWSFww0luse2jB9M1QNZQGwNl'  # backup gdrive mirror
+            safe_download(
+                file,
+                url=f'https://github.com/{repo}/releases/download/{tag}/{name}',
+                min_bytes=1E5,
+                error_msg=f'{file} missing, try downloading from https://github.com/{repo}/releases/{tag} or {url3}')
+
+    return str(file)

utils/metrics.py

@@ -0,0 +1,397 @@
+import math
+import warnings
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+
+from utils import TryExcept, threaded
+
+
+def fitness(x):
+    # Model fitness as a weighted combination of metrics
+    w = [0.0, 0.0, 0.1, 0.9]  # weights for [P, R, mAP@0.5, mAP@0.5:0.95]
+    return (x[:, :4] * w).sum(1)
+
+
+def smooth(y, f=0.05):
+    # Box filter of fraction f
+    nf = round(len(y) * f * 2) // 2 + 1  # number of filter elements (must be odd)
+    p = np.ones(nf // 2)  # ones padding
+    yp = np.concatenate((p * y[0], y, p * y[-1]), 0)  # y padded
+    return np.convolve(yp, np.ones(nf) / nf, mode='valid')  # y-smoothed
+
+
+def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir='.', names=(), eps=1e-16, prefix=""):
+    """ Compute the average precision, given the recall and precision curves.
+    Source: https://github.com/rafaelpadilla/Object-Detection-Metrics.
+    # Arguments
+        tp:  True positives (nparray, nx1 or nx10).
+        conf:  Objectness value from 0-1 (nparray).
+        pred_cls:  Predicted object classes (nparray).
+        target_cls:  True object classes (nparray).
+        plot:  Plot precision-recall curve at mAP@0.5
+        save_dir:  Plot save directory
+    # Returns
+        The average precision as computed in py-faster-rcnn.
+    """
+
+    # Sort by objectness
+    i = np.argsort(-conf)
+    tp, conf, pred_cls = tp[i], conf[i], pred_cls[i]
+
+    # Find unique classes
+    unique_classes, nt = np.unique(target_cls, return_counts=True)
+    nc = unique_classes.shape[0]  # number of classes, number of detections
+
+    # Create Precision-Recall curve and compute AP for each class
+    px, py = np.linspace(0, 1, 1000), []  # for plotting
+    ap, p, r = np.zeros((nc, tp.shape[1])), np.zeros((nc, 1000)), np.zeros((nc, 1000))
+    for ci, c in enumerate(unique_classes):
+        i = pred_cls == c
+        n_l = nt[ci]  # number of labels
+        n_p = i.sum()  # number of predictions
+        if n_p == 0 or n_l == 0:
+            continue
+
+        # Accumulate FPs and TPs
+        fpc = (1 - tp[i]).cumsum(0)
+        tpc = tp[i].cumsum(0)
+
+        # Recall
+        recall = tpc / (n_l + eps)  # recall curve
+        r[ci] = np.interp(-px, -conf[i], recall[:, 0], left=0)  # negative x, xp because xp decreases
+
+        # Precision
+        precision = tpc / (tpc + fpc)  # precision curve
+        p[ci] = np.interp(-px, -conf[i], precision[:, 0], left=1)  # p at pr_score
+
+        # AP from recall-precision curve
+        for j in range(tp.shape[1]):
+            ap[ci, j], mpre, mrec = compute_ap(recall[:, j], precision[:, j])
+            if plot and j == 0:
+                py.append(np.interp(px, mrec, mpre))  # precision at mAP@0.5
+
+    # Compute F1 (harmonic mean of precision and recall)
+    f1 = 2 * p * r / (p + r + eps)
+    names = [v for k, v in names.items() if k in unique_classes]  # list: only classes that have data
+    names = dict(enumerate(names))  # to dict
+    if plot:
+        plot_pr_curve(px, py, ap, Path(save_dir) / f'{prefix}PR_curve.png', names)
+        plot_mc_curve(px, f1, Path(save_dir) / f'{prefix}F1_curve.png', names, ylabel='F1')
+        plot_mc_curve(px, p, Path(save_dir) / f'{prefix}P_curve.png', names, ylabel='Precision')
+        plot_mc_curve(px, r, Path(save_dir) / f'{prefix}R_curve.png', names, ylabel='Recall')
+
+    i = smooth(f1.mean(0), 0.1).argmax()  # max F1 index
+    p, r, f1 = p[:, i], r[:, i], f1[:, i]
+    tp = (r * nt).round()  # true positives
+    fp = (tp / (p + eps) - tp).round()  # false positives
+    return tp, fp, p, r, f1, ap, unique_classes.astype(int)
+
+
+def compute_ap(recall, precision):
+    """ Compute the average precision, given the recall and precision curves
+    # Arguments
+        recall:    The recall curve (list)
+        precision: The precision curve (list)
+    # Returns
+        Average precision, precision curve, recall curve
+    """
+
+    # Append sentinel values to beginning and end
+    mrec = np.concatenate(([0.0], recall, [1.0]))
+    mpre = np.concatenate(([1.0], precision, [0.0]))
+
+    # Compute the precision envelope
+    mpre = np.flip(np.maximum.accumulate(np.flip(mpre)))
+
+    # Integrate area under curve
+    method = 'interp'  # methods: 'continuous', 'interp'
+    if method == 'interp':
+        x = np.linspace(0, 1, 101)  # 101-point interp (COCO)
+        ap = np.trapz(np.interp(x, mrec, mpre), x)  # integrate
+    else:  # 'continuous'
+        i = np.where(mrec[1:] != mrec[:-1])[0]  # points where x axis (recall) changes
+        ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])  # area under curve
+
+    return ap, mpre, mrec
+
+
+class ConfusionMatrix:
+    # Updated version of https://github.com/kaanakan/object_detection_confusion_matrix
+    def __init__(self, nc, conf=0.25, iou_thres=0.45):
+        self.matrix = np.zeros((nc + 1, nc + 1))
+        self.nc = nc  # number of classes
+        self.conf = conf
+        self.iou_thres = iou_thres
+
+    def process_batch(self, detections, labels):
+        """
+        Return intersection-over-union (Jaccard index) of boxes.
+        Both sets of boxes are expected to be in (x1, y1, x2, y2) format.
+        Arguments:
+            detections (Array[N, 6]), x1, y1, x2, y2, conf, class
+            labels (Array[M, 5]), class, x1, y1, x2, y2
+        Returns:
+            None, updates confusion matrix accordingly
+        """
+        if detections is None:
+            gt_classes = labels.int()
+            for gc in gt_classes:
+                self.matrix[self.nc, gc] += 1  # background FN
+            return
+
+        detections = detections[detections[:, 4] > self.conf]
+        gt_classes = labels[:, 0].int()
+        detection_classes = detections[:, 5].int()
+        iou = box_iou(labels[:, 1:], detections[:, :4])
+
+        x = torch.where(iou > self.iou_thres)
+        if x[0].shape[0]:
+            matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy()
+            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]]
+        else:
+            matches = np.zeros((0, 3))
+
+        n = matches.shape[0] > 0
+        m0, m1, _ = matches.transpose().astype(int)
+        for i, gc in enumerate(gt_classes):
+            j = m0 == i
+            if n and sum(j) == 1:
+                self.matrix[detection_classes[m1[j]], gc] += 1  # correct
+            else:
+                self.matrix[self.nc, gc] += 1  # true background
+
+        if n:
+            for i, dc in enumerate(detection_classes):
+                if not any(m1 == i):
+                    self.matrix[dc, self.nc] += 1  # predicted background
+
+    def matrix(self):
+        return self.matrix
+
+    def tp_fp(self):
+        tp = self.matrix.diagonal()  # true positives
+        fp = self.matrix.sum(1) - tp  # false positives
+        # fn = self.matrix.sum(0) - tp  # false negatives (missed detections)
+        return tp[:-1], fp[:-1]  # remove background class
+
+    @TryExcept('WARNING ⚠️ ConfusionMatrix plot failure')
+    def plot(self, normalize=True, save_dir='', names=()):
+        import seaborn as sn
+
+        array = self.matrix / ((self.matrix.sum(0).reshape(1, -1) + 1E-9) if normalize else 1)  # normalize columns
+        array[array < 0.005] = np.nan  # don't annotate (would appear as 0.00)
+
+        fig, ax = plt.subplots(1, 1, figsize=(12, 9), tight_layout=True)
+        nc, nn = self.nc, len(names)  # number of classes, names
+        sn.set(font_scale=1.0 if nc < 50 else 0.8)  # for label size
+        labels = (0 < nn < 99) and (nn == nc)  # apply names to ticklabels
+        ticklabels = (names + ['background']) if labels else "auto"
+        with warnings.catch_warnings():
+            warnings.simplefilter('ignore')  # suppress empty matrix RuntimeWarning: All-NaN slice encountered
+            sn.heatmap(array,
+                       ax=ax,
+                       annot=nc < 30,
+                       annot_kws={
+                           "size": 8},
+                       cmap='Blues',
+                       fmt='.2f',
+                       square=True,
+                       vmin=0.0,
+                       xticklabels=ticklabels,
+                       yticklabels=ticklabels).set_facecolor((1, 1, 1))
+        ax.set_ylabel('True')
+        ax.set_ylabel('Predicted')
+        ax.set_title('Confusion Matrix')
+        fig.savefig(Path(save_dir) / 'confusion_matrix.png', dpi=250)
+        plt.close(fig)
+
+    def print(self):
+        for i in range(self.nc + 1):
+            print(' '.join(map(str, self.matrix[i])))
+            
+
+class WIoU_Scale:
+    ''' monotonous: {
+            None: origin v1
+            True: monotonic FM v2
+            False: non-monotonic FM v3
+        }
+        momentum: The momentum of running mean'''
+    
+    iou_mean = 1.
+    monotonous = False
+    _momentum = 1 - 0.5 ** (1 / 7000)
+    _is_train = True
+
+    def __init__(self, iou):
+        self.iou = iou
+        self._update(self)
+    
+    @classmethod
+    def _update(cls, self):
+        if cls._is_train: cls.iou_mean = (1 - cls._momentum) * cls.iou_mean + \
+                                         cls._momentum * self.iou.detach().mean().item()
+    
+    @classmethod
+    def _scaled_loss(cls, self, gamma=1.9, delta=3):
+        if isinstance(self.monotonous, bool):
+            if self.monotonous:
+                return (self.iou.detach() / self.iou_mean).sqrt()
+            else:
+                beta = self.iou.detach() / self.iou_mean
+                alpha = delta * torch.pow(gamma, beta - delta)
+                return beta / alpha
+        return 1
+
+
+def bbox_iou(box1, box2, xywh=True, GIoU=False, DIoU=False, CIoU=False, MDPIoU=False, feat_h=640, feat_w=640, eps=1e-7):
+    # Returns Intersection over Union (IoU) of box1(1,4) to box2(n,4)
+
+    # Get the coordinates of bounding boxes
+    if xywh:  # transform from xywh to xyxy
+        (x1, y1, w1, h1), (x2, y2, w2, h2) = box1.chunk(4, -1), box2.chunk(4, -1)
+        w1_, h1_, w2_, h2_ = w1 / 2, h1 / 2, w2 / 2, h2 / 2
+        b1_x1, b1_x2, b1_y1, b1_y2 = x1 - w1_, x1 + w1_, y1 - h1_, y1 + h1_
+        b2_x1, b2_x2, b2_y1, b2_y2 = x2 - w2_, x2 + w2_, y2 - h2_, y2 + h2_
+    else:  # x1, y1, x2, y2 = box1
+        b1_x1, b1_y1, b1_x2, b1_y2 = box1.chunk(4, -1)
+        b2_x1, b2_y1, b2_x2, b2_y2 = box2.chunk(4, -1)
+        w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + eps
+        w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps
+
+    # Intersection area
+    inter = (torch.min(b1_x2, b2_x2) - torch.max(b1_x1, b2_x1)).clamp(0) * \
+            (torch.min(b1_y2, b2_y2) - torch.max(b1_y1, b2_y1)).clamp(0)
+
+    # Union Area
+    union = w1 * h1 + w2 * h2 - inter + eps
+
+    # IoU
+    iou = inter / union
+    if CIoU or DIoU or GIoU:
+        cw = torch.max(b1_x2, b2_x2) - torch.min(b1_x1, b2_x1)  # convex (smallest enclosing box) width
+        ch = torch.max(b1_y2, b2_y2) - torch.min(b1_y1, b2_y1)  # convex height
+        if CIoU or DIoU:  # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1
+            c2 = cw ** 2 + ch ** 2 + eps  # convex diagonal squared
+            rho2 = ((b2_x1 + b2_x2 - b1_x1 - b1_x2) ** 2 + (b2_y1 + b2_y2 - b1_y1 - b1_y2) ** 2) / 4  # center dist ** 2
+            if CIoU:  # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47
+                v = (4 / math.pi ** 2) * torch.pow(torch.atan(w2 / h2) - torch.atan(w1 / h1), 2)
+                with torch.no_grad():
+                    alpha = v / (v - iou + (1 + eps))
+                return iou - (rho2 / c2 + v * alpha)  # CIoU
+            return iou - rho2 / c2  # DIoU
+        c_area = cw * ch + eps  # convex area
+        return iou - (c_area - union) / c_area  # GIoU https://arxiv.org/pdf/1902.09630.pdf
+    elif MDPIoU:
+        d1 = (b2_x1 - b1_x1) ** 2 + (b2_y1 - b1_y1) ** 2
+        d2 = (b2_x2 - b1_x2) ** 2 + (b2_y2 - b1_y2) ** 2
+        mpdiou_hw_pow = feat_h ** 2 + feat_w ** 2
+        return iou - d1 / mpdiou_hw_pow - d2 / mpdiou_hw_pow  # MPDIoU
+    return iou  # IoU
+
+
+def box_iou(box1, box2, eps=1e-7):
+    # https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py
+    """
+    Return intersection-over-union (Jaccard index) of boxes.
+    Both sets of boxes are expected to be in (x1, y1, x2, y2) format.
+    Arguments:
+        box1 (Tensor[N, 4])
+        box2 (Tensor[M, 4])
+    Returns:
+        iou (Tensor[N, M]): the NxM matrix containing the pairwise
+            IoU values for every element in boxes1 and boxes2
+    """
+
+    # inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2)
+    (a1, a2), (b1, b2) = box1.unsqueeze(1).chunk(2, 2), box2.unsqueeze(0).chunk(2, 2)
+    inter = (torch.min(a2, b2) - torch.max(a1, b1)).clamp(0).prod(2)
+
+    # IoU = inter / (area1 + area2 - inter)
+    return inter / ((a2 - a1).prod(2) + (b2 - b1).prod(2) - inter + eps)
+
+
+def bbox_ioa(box1, box2, eps=1e-7):
+    """Returns the intersection over box2 area given box1, box2. Boxes are x1y1x2y2
+    box1:       np.array of shape(nx4)
+    box2:       np.array of shape(mx4)
+    returns:    np.array of shape(nxm)
+    """
+
+    # Get the coordinates of bounding boxes
+    b1_x1, b1_y1, b1_x2, b1_y2 = box1.T
+    b2_x1, b2_y1, b2_x2, b2_y2 = box2.T
+
+    # Intersection area
+    inter_area = (np.minimum(b1_x2[:, None], b2_x2) - np.maximum(b1_x1[:, None], b2_x1)).clip(0) * \
+                 (np.minimum(b1_y2[:, None], b2_y2) - np.maximum(b1_y1[:, None], b2_y1)).clip(0)
+
+    # box2 area
+    box2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1) + eps
+
+    # Intersection over box2 area
+    return inter_area / box2_area
+
+
+def wh_iou(wh1, wh2, eps=1e-7):
+    # Returns the nxm IoU matrix. wh1 is nx2, wh2 is mx2
+    wh1 = wh1[:, None]  # [N,1,2]
+    wh2 = wh2[None]  # [1,M,2]
+    inter = torch.min(wh1, wh2).prod(2)  # [N,M]
+    return inter / (wh1.prod(2) + wh2.prod(2) - inter + eps)  # iou = inter / (area1 + area2 - inter)
+
+
+# Plots ----------------------------------------------------------------------------------------------------------------
+
+
+@threaded
+def plot_pr_curve(px, py, ap, save_dir=Path('pr_curve.png'), names=()):
+    # Precision-recall curve
+    fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True)
+    py = np.stack(py, axis=1)
+
+    if 0 < len(names) < 21:  # display per-class legend if < 21 classes
+        for i, y in enumerate(py.T):
+            ax.plot(px, y, linewidth=1, label=f'{names[i]} {ap[i, 0]:.3f}')  # plot(recall, precision)
+    else:
+        ax.plot(px, py, linewidth=1, color='grey')  # plot(recall, precision)
+
+    ax.plot(px, py.mean(1), linewidth=3, color='blue', label='all classes %.3f mAP@0.5' % ap[:, 0].mean())
+    ax.set_xlabel('Recall')
+    ax.set_ylabel('Precision')
+    ax.set_xlim(0, 1)
+    ax.set_ylim(0, 1)
+    ax.legend(bbox_to_anchor=(1.04, 1), loc="upper left")
+    ax.set_title('Precision-Recall Curve')
+    fig.savefig(save_dir, dpi=250)
+    plt.close(fig)
+
+
+@threaded
+def plot_mc_curve(px, py, save_dir=Path('mc_curve.png'), names=(), xlabel='Confidence', ylabel='Metric'):
+    # Metric-confidence curve
+    fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True)
+
+    if 0 < len(names) < 21:  # display per-class legend if < 21 classes
+        for i, y in enumerate(py):
+            ax.plot(px, y, linewidth=1, label=f'{names[i]}')  # plot(confidence, metric)
+    else:
+        ax.plot(px, py.T, linewidth=1, color='grey')  # plot(confidence, metric)
+
+    y = smooth(py.mean(0), 0.05)
+    ax.plot(px, y, linewidth=3, color='blue', label=f'all classes {y.max():.2f} at {px[y.argmax()]:.3f}')
+    ax.set_xlabel(xlabel)
+    ax.set_ylabel(ylabel)
+    ax.set_xlim(0, 1)
+    ax.set_ylim(0, 1)
+    ax.legend(bbox_to_anchor=(1.04, 1), loc="upper left")
+    ax.set_title(f'{ylabel}-Confidence Curve')
+    fig.savefig(save_dir, dpi=250)
+    plt.close(fig)