onnx_test.py

import argparse
import json
import os
from pathlib import Path
from tqdm import tqdm
import glob
import math
from PIL import ExifTags, Image
import shutil
from torch.utils.data import DataLoader
from torch.utils.data import Dataset
from utils import *
import onnxruntime
import matplotlib.pyplot as plt

for orientation in ExifTags.TAGS.keys():
    if ExifTags.TAGS[orientation] == 'Orientation':
        break


def create_folder(path='./new_folder'):
    # Create folder
    if os.path.exists(path):
        shutil.rmtree(path)  # delete output folder
    os.makedirs(path)  # make new output folder


def exif_size(img):
    # Returns exif-corrected PIL size
    s = img.size  # (width, height)
    try:
        rotation = dict(img._getexif().items())[orientation]
        if rotation == 6:  # rotation 270
            s = (s[1], s[0])
        elif rotation == 8:  # rotation 90
            s = (s[1], s[0])
    except BaseException:
        pass

    return s


def ap_per_class(tp, conf, pred_cls, target_cls):
    """ 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).
    # 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 = np.unique(target_cls)

    # Create Precision-Recall curve and compute AP for each class
    pr_score = 0.1
    # score to evaluate P and R
    # https://github.com/ultralytics/yolov3/issues/898
    # number class, number iou thresholds (i.e. 10 for mAP0.5...0.95)
    s = [unique_classes.shape[0], tp.shape[1]]
    ap, p, r = np.zeros(s), np.zeros(s), np.zeros(s)
    for ci, c in enumerate(unique_classes):
        i = pred_cls == c
        n_gt = (target_cls == c).sum()  # Number of ground truth objects
        n_p = i.sum()  # Number of predicted objects

        if n_p == 0 or n_gt == 0:
            continue
        else:
            # Accumulate FPs and TPs
            fpc = (1 - tp[i]).cumsum(0)
            tpc = tp[i].cumsum(0)

            # Recall
            recall = tpc / (n_gt + 1e-16)  # recall curve
            # r at pr_score, negative x, xp because xp decreases
            r[ci] = np.interp(-pr_score, -conf[i], recall[:, 0])

            # Precision
            precision = tpc / (tpc + fpc)  # precision curve
            p[ci] = np.interp(-pr_score, -conf[i],
                              precision[:, 0])  # p at pr_score

            # AP from recall-precision curve
            for j in range(tp.shape[1]):
                ap[ci, j] = compute_ap(recall[:, j], precision[:, j])

            # Plot
            # fig, ax = plt.subplots(1, 1, figsize=(5, 5))
            # ax.plot(recall, precision)
            # ax.set_xlabel('Recall')
            # ax.set_ylabel('Precision')
            # ax.set_xlim(0, 1.01)
            # ax.set_ylim(0, 1.01)
            # fig.tight_layout()
            # fig.savefig('PR_curve.png', dpi=300)

    # Compute F1 score (harmonic mean of precision and recall)
    f1 = 2 * p * r / (p + r + 1e-16)

    return p, r, ap, f1, unique_classes.astype('int32')


def time_synchronized():
    torch.cuda.synchronize() if torch.cuda.is_available() else None
    return time.time()


def plot_images(
        images,
        targets,
        paths=None,
        fname='images.jpg',
        names=None,
        max_size=640,
        max_subplots=16):
    tl = 3  # line thickness
    tf = max(tl - 1, 1)  # font thickness
    if os.path.isfile(fname):  # do not overwrite
        return None

    if isinstance(images, torch.Tensor):
        images = images.cpu().numpy()

    if isinstance(targets, torch.Tensor):
        targets = targets.cpu().numpy()

    # un-normalise
    if np.max(images[0]) <= 1:
        images *= 255

    bs, _, h, w = images.shape  # batch size, _, height, width
    bs = min(bs, max_subplots)  # limit plot images
    ns = np.ceil(bs ** 0.5)  # number of subplots (square)

    # Check if we should resize
    scale_factor = max_size / max(h, w)
    if scale_factor < 1:
        h = math.ceil(scale_factor * h)
        w = math.ceil(scale_factor * w)

    # Empty array for output
    mosaic = np.full((int(ns * h), int(ns * w), 3), 255, dtype=np.uint8)

    # Fix class - colour map
    prop_cycle = plt.rcParams['axes.prop_cycle']

    # https://stackoverflow.com/questions/51350872/python-from-color-name-to-rgb
    def hex2rgb(h):
        return tuple(
            int(h[1 + i:1 + i + 2], 16) for i in (0, 2, 4))

    color_lut = [hex2rgb(h) for h in prop_cycle.by_key()['color']]

    for i, img in enumerate(images):
        if i == max_subplots:  # if last batch has fewer images than we expect
            break

        block_x = int(w * (i // ns))
        block_y = int(h * (i % ns))

        img = img.transpose(1, 2, 0)
        if scale_factor < 1:
            img = cv2.resize(img, (w, h))

        mosaic[block_y:block_y + h, block_x:block_x + w, :] = img
        if len(targets) > 0:
            image_targets = targets[targets[:, 0] == i]
            boxes = xywh2xyxy(image_targets[:, 2:6]).T
            classes = image_targets[:, 1].astype('int')
            gt = image_targets.shape[1] == 6
            # ground truth if no conf column
            # check for confidence presence (gt vs pred)
            conf = None if gt else image_targets[:, 6]

            boxes[[0, 2]] *= w
            boxes[[0, 2]] += block_x
            boxes[[1, 3]] *= h
            boxes[[1, 3]] += block_y
            for j, box in enumerate(boxes.T):
                cls = int(classes[j])
                color = color_lut[cls % len(color_lut)]
                cls = names[cls] if names else cls
                if gt or conf[j] > 0.3:  # 0.3 conf thresh
                    label = '%s' % cls if gt else '%s %.1f' % (cls, conf[j])
                    plot_one_box(box, mosaic, label=label,
                                 color=color, line_thickness=tl)

        # Draw image filename labels
        if paths is not None:
            label = os.path.basename(paths[i])[:40]  # trim to 40 char
            t_size = cv2.getTextSize(
                label, 0, fontScale=tl / 3, thickness=tf)[0]
            cv2.putText(mosaic, label, (block_x +
                                        5, block_y +
                                        t_size[1] +
                                        5), 0, tl /
                        3, [220, 220, 220], thickness=tf, lineType=cv2.LINE_AA)

        # Image border
        cv2.rectangle(mosaic, (block_x, block_y), (block_x + w,
                                                   block_y + h), (255, 255, 255), thickness=3)

    if fname is not None:
        mosaic = cv2.resize(mosaic,
                            (int(ns * w * 0.5),
                             int(ns * h * 0.5)),
                            interpolation=cv2.INTER_AREA)
        cv2.imwrite(fname, cv2.cvtColor(mosaic, cv2.COLOR_BGR2RGB))

    return mosaic


def random_affine(img, targets=(), degrees=10, translate=.1,
                  scale=.1, shear=10, border=0):
    # targets = [cls, xyxy]

    height = img.shape[0] + border * 2
    width = img.shape[1] + border * 2

    # Rotation and Scale
    R = np.eye(3)
    a = random.uniform(-degrees, degrees)
    # a += random.choice([-180, -90, 0, 90])
    # add 90deg rotations to small rotations
    s = random.uniform(1 - scale, 1 + scale)
    # s = 2 ** random.uniform(-scale, scale)
    R[:2] = cv2.getRotationMatrix2D(angle=a,
                                    center=(img.shape[1] / 2,
                                            img.shape[0] / 2),
                                    scale=s)

    # Translation
    T = np.eye(3)
    T[0, 2] = (random.uniform(-translate, translate) *
               img.shape[0] + border)  # x translation (pixels)
    T[1, 2] = (random.uniform(-translate, translate) *
               img.shape[1] + border)  # y translation (pixels)

    # Shear
    S = np.eye(3)
    S[0, 1] = math.tan(random.uniform(-shear, shear) *
                       math.pi / 180)  # x shear (deg)
    S[1, 0] = math.tan(random.uniform(-shear, shear) *
                       math.pi / 180)  # y shear (deg)

    # Combined rotation matrix
    M = S @ T @ R  # ORDER IS IMPORTANT HERE!!
    if (border != 0) or (M != np.eye(3)).any():  # image changed
        img = cv2.warpAffine(img, M[:2], dsize=(width, height),
                             flags=cv2.INTER_LINEAR,
                             borderValue=(114, 114, 114))

    # Transform label coordinates
    n = len(targets)
    if n:
        # warp points
        xy = np.ones((n * 4, 3))
        xy[:, :2] = (targets[:, [1, 2, 3, 4, 1, 4, 3, 2]].
                     reshape(n * 4, 2))
        # x1y1, x2y2, x1y2, x2y1
        xy = (xy @ M.T)[:, :2].reshape(n, 8)

        # create new boxes
        x = xy[:, [0, 2, 4, 6]]
        y = xy[:, [1, 3, 5, 7]]
        xy = np.concatenate((x.min(1), y.min(1), x.max(1),
                             y.max(1))).reshape(4, n).T

        # reject warped points outside of image
        xy[:, [0, 2]] = xy[:, [0, 2]].clip(0, width)
        xy[:, [1, 3]] = xy[:, [1, 3]].clip(0, height)
        w = xy[:, 2] - xy[:, 0]
        h = xy[:, 3] - xy[:, 1]
        area = w * h
        area0 = ((targets[:, 3] - targets[:, 1]) *
                 (targets[:, 4] - targets[:, 2]))
        ar = np.maximum(w / (h + 1e-16), h / (w + 1e-16))
        # aspect ratio
        i = (w > 4) & (h > 4) & (area / (area0 * s + 1e-16)
                                 > 0.2) & (ar < 10)

        targets = targets[i]
        targets[:, 1:5] = xy[i]

    return img, targets


def output_to_target(output, width, height):
    """
    Convert a YOLO model output to target format
    [batch_id, class_id, x, y, w, h, conf]
    """
    if isinstance(output, torch.Tensor):
        output = output.cpu().numpy()

    targets = []
    for i, o in enumerate(output):
        if o is not None:
            for pred in o:
                box = pred[:4]
                w = (box[2] - box[0]) / width
                h = (box[3] - box[1]) / height
                x = box[0] / width + w / 2
                y = box[1] / height + h / 2
                conf = pred[4]
                cls = int(pred[5])

                targets.append([i, cls, x, y, w, h, conf])

    return np.array(targets)


def xyxy2xywh(x):
    # Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] where
    # xy1=top-left, xy2=bottom-right
    y = torch.zeros_like(x) if isinstance(
        x, torch.Tensor) else np.zeros_like(x)
    y[:, 0] = (x[:, 0] + x[:, 2]) / 2  # x center
    y[:, 1] = (x[:, 1] + x[:, 3]) / 2  # y center
    y[:, 2] = x[:, 2] - x[:, 0]  # width
    y[:, 3] = x[:, 3] - x[:, 1]  # height
    return y


def coco80_to_coco91_class():  # converts 80-index (val2014) to 91-index (paper)
    # https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/
    x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20,
         21, 22, 23, 24, 25, 27, 28, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,
         41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58,
         59, 60, 61, 62, 63, 64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79,
         80, 81, 82, 84, 85, 86, 87, 88, 89, 90]
    return x


def check_file(file):
    # Searches for file if not found locally
    if os.path.isfile(file):
        return file
    else:
        files = glob.glob('./**/' + file, recursive=True)  # find file
        assert len(files), 'File Not Found: %s' % file  # assert file was found
        return files[0]  # return first file if multiple found


def load_classes(path):
    # Loads *.names file at 'path'
    with open(path, 'r') as f:
        names = f.read().split('\n')
    # filter removes empty strings (such as last line)
    return list(filter(None, names))


def load_image(self, index):
    # loads 1 image from dataset, returns img, original hw, resized hw
    img = self.imgs[index]
    if img is None:  # not cached
        path = self.img_files[index]
        img = cv2.imread(path)  # BGR
        assert img is not None, 'Image Not Found ' + path
        h0, w0 = img.shape[:2]  # orig hw
        r = self.img_size / max(h0, w0)  # resize image to img_size
        if r != 1:
            # always resize down, only resize up if training with augmentation
            interp = cv2.INTER_AREA if r < 1 and not self.augment \
                else cv2.INTER_LINEAR
            img = cv2.resize(img, (int(w0 * r), int(h0 * r)),
                             interpolation=interp)
        return img, (h0, w0), img.shape[:2]  # img, hw_original, hw_resized
    else:
        # img, hw_original, hw_resized
        return self.imgs[index], self.img_hw0[index], self.img_hw[index]


def load_mosaic(self, index):
    # loads images in a mosaic

    labels4 = []
    s = self.img_size
    xc, yc = [int(random.uniform(s * 0.5, s * 1.5))
              for _ in range(2)]  # mosaic center x, y
    indices = [index] + [random.randint(0, len(self.labels) - 1)
                         for _ in range(3)]  # 3 additional image indices
    for i, index in enumerate(indices):
        # Load image
        img, _, (h, w) = load_image(self, 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,
                                  max(xc, 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
        x = self.labels[index]
        labels = x.copy()
        if x.size > 0:  # Normalized xywh to pixel xyxy format
            labels[:, 1] = w * (x[:, 1] - x[:, 3] / 2) + padw
            labels[:, 2] = h * (x[:, 2] - x[:, 4] / 2) + padh
            labels[:, 3] = w * (x[:, 1] + x[:, 3] / 2) + padw
            labels[:, 4] = h * (x[:, 2] + x[:, 4] / 2) + padh
        labels4.append(labels)

    # Concat/clip labels
    if len(labels4):
        labels4 = np.concatenate(labels4, 0)
        # np.clip(labels4[:, 1:] - s / 2, 0, s, out=labels4[:, 1:])
        # use with center crop
        np.clip(labels4[:, 1:], 0, 2 * s, out=labels4[:, 1:])
        # use with random_affine

    # Augment
    # img4 = img4[s // 2: int(s * 1.5), s // 2:int(s * 1.5)]
    # center crop (WARNING, requires box pruning)
    img4, labels4 = random_affine(img4, labels4,
                                  degrees=self.hyp['degrees'],
                                  translate=self.hyp['translate'],
                                  scale=self.hyp['scale'],
                                  shear=self.hyp['shear'],
                                  border=-s // 2)  # border to remove

    return img4, labels4


def compute_ap(recall, precision):
    """ Compute the average precision, given the recall and precision curves.
    Source: https://github.com/rbgirshick/py-faster-rcnn.
    # Arguments
        recall:    The recall curve (list).
        precision: The precision curve (list).
    # Returns
        The average precision as computed in py-faster-rcnn.
    """

    # Append sentinel values to beginning and end
    mrec = np.concatenate(([0.], recall, [min(recall[-1] + 1E-3, 1.)]))
    mpre = np.concatenate(([0.], precision, [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'
        # points where x axis (recall) changes
        i = np.where(mrec[1:] != mrec[:-1])[0]
        ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])  # area under curve

    return ap


def augment_hsv(img, hgain=0.5, sgain=0.5, vgain=0.5):
    r = (np.random.uniform(-1, 1, 3) *
         [hgain, sgain, vgain] + 1)  # random gains
    hue, sat, val = cv2.split(cv2.cvtColor(img, cv2.COLOR_BGR2HSV))
    dtype = img.dtype  # uint8

    x = np.arange(0, 256, dtype=np.int16)
    lut_hue = ((x * r[0]) % 180).astype(dtype)
    lut_sat = np.clip(x * r[1], 0, 255).astype(dtype)
    lut_val = np.clip(x * r[2], 0, 255).astype(dtype)

    img_hsv = cv2.merge((cv2.LUT(hue, lut_hue),
                         cv2.LUT(sat, lut_sat),
                         cv2.LUT(val, lut_val))).astype(dtype)
    cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR, dst=img)
    # no return needed


class LoadImagesAndLabels(Dataset):  # for training/testing
    def __init__(
            self,
            path,
            img_size=416,
            batch_size=16,
            augment=False,
            hyp=None,
            rect=False,
            image_weights=False,
            cache_images=False,
            single_cls=False,
            pad=0.0):
        try:
            path = str(Path(path))  # os-agnostic
            parent = str(Path(path).parent) + os.sep
            if os.path.isfile(path):  # file
                with open(path, 'r') as f:
                    f = f.read().splitlines()
                    # local to global path
                    f = [
                        x.replace(
                            './',
                            parent) if x.startswith('./') else x for x in f]
            elif os.path.isdir(path):  # folder
                f = glob.iglob(path + os.sep + '*.*')
            else:
                raise Exception('%s does not exist' % path)
            self.img_files = [x.replace(
                '/', os.sep) for x in f if
                os.path.splitext(x)[-1].lower() in img_formats]

        except BaseException:
            raise Exception(
                'Error loading data from %s. See %s' %
                (path, help_url))

        n = len(self.img_files)
        assert n > 0, 'No images found in %s. See %s' % (path, help_url)
        bi = np.floor(np.arange(n) / batch_size).astype(int)  # batch index
        nb = bi[-1] + 1  # number of batches

        self.n = n  # number of images
        self.batch = bi  # batch index of image
        self.img_size = img_size
        self.augment = augment
        self.hyp = hyp
        self.image_weights = image_weights
        self.rect = False if image_weights else rect
        # load 4 images at a time into a mosaic (only during training)
        self.mosaic = self.augment and not self.rect

        # Define labels
        self.label_files = [x.replace('images', 'labels').replace(
            os.path.splitext(x)[-1], '.txt') for x in self.img_files]

        # Read image shapes (wh)
        sp = path.replace('.txt', '') + '.shapes'  # shapefile path
        try:
            with open(sp, 'r') as f:  # read existing shapefile
                s = [x.split() for x in f.read().splitlines()]
                assert len(s) == n, 'Shapefile out of sync'
        except BaseException:
            s = [exif_size(Image.open(f)) for f in tqdm(
                self.img_files,
                desc='Reading image shapes')]
            np.savetxt(sp, s, fmt='%g')  # overwrites existing (if any)

        self.shapes = np.array(s, dtype=np.float64)

        # Rectangular Training
        # https://github.com/ultralytics/yolov3/issues/232
        if self.rect:
            # Sort by aspect ratio
            s = self.shapes  # wh
            ar = s[:, 1] / s[:, 0]  # aspect ratio
            irect = ar.argsort()
            self.img_files = [self.img_files[i] for i in irect]
            self.label_files = [self.label_files[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 / 32. + pad).astype(int) * 32

        # Cache labels
        self.imgs = [None] * n
        self.labels = [np.zeros((0, 5), dtype=np.float32)] * n
        create_datasubset, extract_bounding_boxes, labels_loaded = \
            False, False, False
        # number missing, found, empty, datasubset, duplicate
        nm, nf, ne, ns, nd = 0, 0, 0, 0, 0
        # saved labels in *.npy file
        np_labels_path = str(Path(self.label_files[0]).parent) + '.npy'
        if os.path.isfile(np_labels_path):
            s = np_labels_path  # print string

            print(np_labels_path)

            x = np.load(np_labels_path, allow_pickle=True)
            if len(x) == n:
                self.labels = x
                labels_loaded = True
        else:
            s = path.replace('images', 'labels')

        pbar = tqdm(self.label_files)
        for i, file in enumerate(pbar):
            if labels_loaded:
                l = self.labels[i]
                # np.savetxt(file, l, '%g')  # save *.txt from *.npy file
            else:
                try:
                    with open(file, 'r') as f:
                        l = np.array(
                            [x.split() for x in f.read().splitlines()],
                            dtype=np.float32)
                except BaseException:
                    # print('missing labels for image %s' % self.img_files[i])
                    # # file missing
                    nm += 1
                    continue

            if l.shape[0]:
                assert l.shape[1] == 5, '> 5 label columns: %s' % file
                assert (l >= 0).all(), 'negative labels: %s' % file
                assert (l[:, 1:] <= 1).all(
                ), 'non-normalized or out of bounds coordinate labels: %s' % file
                if np.unique(
                        l, axis=0).shape[0] < l.shape[0]:  # duplicate rows
                    # print('WARNING: duplicate rows in %s' %
                    # self.label_files[i])  # duplicate rows
                    nd += 1
                if single_cls:
                    l[:, 0] = 0  # force dataset into single-class mode
                self.labels[i] = l
                nf += 1  # file found

                # Create subdataset (a smaller dataset)
                if create_datasubset and ns < 1E4:
                    if ns == 0:
                        create_folder(path='./datasubset')
                        os.makedirs('./datasubset/images')
                    exclude_classes = 43
                    if exclude_classes not in l[:, 0]:
                        ns += 1
                        # shutil.copy(src=self.img_files[i],
                        # dst='./datasubset/images/')  # copy image
                        with open('./datasubset/images.txt', 'a') as f:
                            f.write(self.img_files[i] + '\n')

                # Extract object detection boxes for a second stage classifier
                if extract_bounding_boxes:
                    p = Path(self.img_files[i])
                    img = cv2.imread(str(p))
                    h, w = img.shape[:2]
                    for j, x in enumerate(l):
                        f = '%s%sclassifier%s%g_%g_%s' % (
                            p.parent.parent, os.sep, os.sep, x[0], j, p.name)
                        if not os.path.exists(Path(f).parent):
                            # make new output folder
                            os.makedirs(Path(f).parent)

                        b = x[1:] * [w, h, w, h]  # box
                        b[2:] = b[2:].max()  # rectangle to square
                        b[2:] = b[2:] * 1.3 + 30  # pad
                        b = xywh2xyxy(b.reshape(-1, 4)).ravel().astype(int)

                        # clip boxes outside of image
                        b[[0, 2]] = np.clip(b[[0, 2]], 0, w)
                        b[[1, 3]] = np.clip(b[[1, 3]], 0, h)
                        assert cv2.imwrite(
                            f, img[b[1]:b[3], b[0]:b[2]]), \
                            'Failure extracting classifier boxes'
            else:
                # print('empty labels for image %s' % self.img_files[i])  #
                # file empty
                ne += 1
                # os.system("rm '%s' '%s'" % (self.img_files[i],
                # self.label_files[i]))  # remove

            pbar.desc = 'Caching labels %s (%g found, %g missing, %g empty,\
             %g duplicate, for %g images)' % (
                s, nf, nm, ne, nd, n)
        assert nf > 0 or n == 20288, 'No labels found in %s. See %s' % (
            os.path.dirname(file) + os.sep, help_url)
        if not labels_loaded and n > 1000:
            print(
                'Saving labels to %s for faster future loading' %
                np_labels_path)
            # np.save(np_labels_path, self.labels)  # save for next time

        # Cache images into memory for faster training (WARNING: large datasets
        # may exceed system RAM)
        if cache_images:  # if training
            gb = 0  # Gigabytes of cached images
            pbar = tqdm(range(len(self.img_files)), desc='Caching images')
            self.img_hw0, self.img_hw = [None] * n, [None] * n
            for i in pbar:  # max 10k images
                self.imgs[i], self.img_hw0[i], self.img_hw[i] = load_image(
                    self, i)  # img, hw_original, hw_resized
                gb += self.imgs[i].nbytes
                pbar.desc = 'Caching images (%.1fGB)' % (gb / 1E9)

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

    def __getitem__(self, index):
        if self.image_weights:
            index = self.indices[index]

        hyp = self.hyp
        if self.mosaic:
            # Load mosaic
            img, labels = load_mosaic(self, index)
            shapes = None

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

            # Load labels
            labels = []
            x = self.labels[index]
            if x.size > 0:
                # Normalized xywh to pixel xyxy format
                labels = x.copy()
                labels[:, 1] = ratio[0] * w * (x[:, 1] - x[:, 3] / 2) + pad[0]  # pad width
                labels[:, 2] = ratio[1] * h * (x[:, 2] - x[:, 4] / 2) + pad[1]  # pad height
                labels[:, 3] = ratio[0] * w * (x[:, 1] + x[:, 3] / 2) + pad[0]
                labels[:, 4] = ratio[1] * h * (x[:, 2] + x[:, 4] / 2) + pad[1]

        if self.augment:
            # Augment imagespace
            if not self.mosaic:
                img, labels = random_affine(img, labels,
                                            degrees=hyp['degrees'],
                                            translate=hyp['translate'],
                                            scale=hyp['scale'],
                                            shear=hyp['shear'])

            # Augment colorspace
            augment_hsv(img, hgain=hyp['hsv_h'], sgain=hyp['hsv_s'], vgain=hyp['hsv_v'])

            # Apply cutouts
            # if random.random() < 0.9:
            #     labels = cutout(img, labels)

        nL = len(labels)  # number of labels
        if nL:
            # convert xyxy to xywh
            labels[:, 1:5] = xyxy2xywh(labels[:, 1:5])

            # Normalize coordinates 0 - 1
            labels[:, [2, 4]] /= img.shape[0]  # height
            labels[:, [1, 3]] /= img.shape[1]  # width

        if self.augment:
            # random left-right flip
            lr_flip = True
            if lr_flip and random.random() < 0.5:
                img = np.fliplr(img)
                if nL:
                    labels[:, 1] = 1 - labels[:, 1]

            # random up-down flip
            ud_flip = False
            if ud_flip and random.random() < 0.5:
                img = np.flipud(img)
                if nL:
                    labels[:, 2] = 1 - labels[:, 2]

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

        # Convert
        img = img[:, :, ::-1].transpose(2, 0, 1)  # BGR to RGB, to 3x416x416
        img = np.ascontiguousarray(img)

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

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


def parse_data_cfg(path):
    # Parses the data configuration file
    if not os.path.exists(path) and os.path.exists(
            'data' + os.sep + path):  # add data/ prefix if omitted
        path = 'data' + os.sep + path

    with open(path, 'r') as f:
        lines = f.readlines()

    options = dict()
    for line in lines:
        line = line.strip()
        if line == '' or line.startswith('#'):
            continue
        key, val = line.split('=')
        options[key.strip()] = val.strip()

    return options


def create_grids(ng=(13, 13), device='cpu'):
    nx, ny = ng  # x and y grid size
    ng = torch.tensor(ng, dtype=torch.float)

    # build xy offsets
    yv, xv = torch.meshgrid(
        [torch.arange(ny, device=device), torch.arange(nx, device=device)])
    grid = torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float()

    return grid


def post_process(x):
    stride = [32, 16, 8]
    anchors = [[10, 13, 16, 30, 33, 23],
               [30, 61, 62, 45, 59, 119],
               [116, 90, 156, 198, 373, 326]]
    temp = [13, 26, 52]

    res = []
    for i in range(3):
        out = torch.from_numpy(x[i]) if not torch.is_tensor(x[i]) else x[i]

        bs, _, ny, nx = out.shape  # bs, 255, 13, 13

        anchor = torch.Tensor(anchors[2 - i]).reshape(3, 2)
        anchor_vec = anchor / stride[i]
        anchor_wh = anchor_vec.view(1, 3, 1, 1, 2)

        grid = create_grids((nx, ny))

        # p.view(bs, 255, 13, 13) -- > (bs, 3, 13, 13, 85)  # (bs, anchors,
        # grid, grid, classes + xywh)
        out = out.view(
            bs, 3, 85, temp[i], temp[i]).permute(
            0, 1, 3, 4, 2).contiguous()  # prediction

        io = out.clone()  # inference output

        io[..., :2] = torch.sigmoid(io[..., :2]) + grid  # xy
        io[..., 2:4] = torch.exp(io[..., 2:4]) * anchor_wh  # wh yolo method
        io[..., :4] *= stride[i]
        torch.sigmoid_(io[..., 4:])

        res.append(io.view(bs, -1, 85))
    return torch.cat(res, 1), x


def test(data,
         batch_size=32,
         imgsz=416,
         conf_thres=0.001,
         iou_thres=0.6,  # for nms
         save_json=False,
         single_cls=False,
         augment=False,
         model=None,
         dataloader=None,
         multi_label=True,
         names='data/coco.names',
         onnx_runtime=True,
         onnx_weights="yolov3-8",
         ipu=False,
         provider_config='vaip_config.json'):
    """
    COCO average precision (AP) Evaluation. Iterate inference on the test dataset
    and the results are evaluated by COCO API.
    """

    device = torch.device('cpu')
    verbose = False
    if isinstance(onnx_weights, list):
        onnx_weights = onnx_weights[0]

    if ipu:
        providers = ["VitisAIExecutionProvider"]
        provider_options = [{"config_file": provider_config}]
    else:
        providers = ['CUDAExecutionProvider', 'CPUExecutionProvider']
        provider_options = None

    onnx_model = onnxruntime.InferenceSession(
        onnx_weights,
        providers=providers,
        provider_options=provider_options)

    # Configure run
    data = parse_data_cfg(data)
    nc = 1 if single_cls else int(data['classes'])  # number of classes
    path = data['valid']  # path to test images
    names = load_classes(data['names'])  # class names
    iouv = torch.linspace(0.5, 0.95, 10).to(
        device)  # iou vector for mAP@0.5:0.95
    iouv = iouv[0].view(1)  # comment for mAP@0.5:0.95
    niou = iouv.numel()

    # Dataloader
    if dataloader is None:
        dataset = LoadImagesAndLabels(
            path,
            imgsz,
            batch_size,
            rect=False,
            single_cls=opt.single_cls,
            pad=0.5)
        batch_size = min(batch_size, len(dataset))
        dataloader = DataLoader(dataset,
                                batch_size=batch_size,
                                num_workers=min([os.cpu_count(),
                                                 batch_size if
                                                 batch_size > 1 else 0,
                                                 8]),
                                pin_memory=True,
                                collate_fn=dataset.collate_fn)

    seen = 0

    coco91class = coco80_to_coco91_class()
    s = ('%20s' + '%10s' * 6) % ('Class', 'Images', 'Targets', 'P', 'R',
                                 'mAP@0.5', 'F1')
    p, r, f1, mp, mr, map, mf1, t0, t1 = 0., 0., 0., 0., 0., 0., 0., 0., 0.
    loss = torch.zeros(3, device=device)
    jdict, stats, ap, ap_class = [], [], [], []

    for batch_i, (imgs, targets, paths, shapes) in enumerate(
            tqdm(dataloader, desc=s)):
        # uint8 to float32, 0 - 255 to 0.0 - 1.0
        imgs = imgs.to(device).float() / 255.0
        targets = targets.to(device)
        nb, _, height, width = imgs.shape
        # batch size, channels, height, width
        whwh = torch.Tensor([width, height, width, height]).to(device)

        if onnx_runtime:
            outputs = onnx_model.run(
                None, {onnx_model.get_inputs()[0].name: imgs.cpu().numpy()})
            outputs = [torch.tensor(item).to(device) for item in outputs]
            inf_out, train_out = post_process(outputs)

        else:

            # Disable gradients
            with torch.no_grad():
                # Run model
                t = time_synchronized()

                # inference and training outputs
                inf_out, train_out = model(imgs, augment=augment)
                t0 += time_synchronized() - t

        # Compute loss
        # if is_training:  # if model has loss hyperparameters
        # loss += compute_loss(train_out, targets, model)[1][:3]  # GIoU,
        # obj, cls

        # Run NMS
        t = time_synchronized()
        output = non_max_suppression(
            inf_out,
            conf_thres=conf_thres,
            iou_thres=iou_thres,
            multi_label=multi_label)
        t1 += time_synchronized() - t

        # Statistics per image
        for si, pred in enumerate(output):
            labels = targets[targets[:, 0] == si, 1:]
            nl = len(labels)
            tcls = labels[:, 0].tolist() if nl else []  # target class
            seen += 1

            if pred is None:
                if nl:
                    stats.append(
                        (torch.zeros(
                            0,
                            niou,
                            dtype=torch.bool),
                         torch.Tensor(),
                         torch.Tensor(),
                         tcls))
                continue

            # Append to text file
            # with open('test.txt', 'a') as file:
            #    [file.write('%11.5g' * 7 % tuple(x) + '\n') for x in pred]

            # Clip boxes to image bounds
            clip_coords(pred, (height, width))

            # Append to pycocotools JSON dictionary
            if save_json:
                image_id = int(Path(paths[si]).stem.split('_')[-1])
                box = pred[:, :4].clone()  # xyxy
                scale_coords(imgs[si].shape[1:], box, shapes[si]
                [0], shapes[si][1])  # to original shape
                box = xyxy2xywh(box)  # xywh
                box[:, :2] -= box[:, 2:] / 2  # xy center to top-left corner
                for p, b in zip(pred.tolist(), box.tolist()):
                    jdict.append({'image_id': image_id,
                                  'category_id': coco91class[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 nl:
                detected = []  # target indices
                tcls_tensor = labels[:, 0]

                # target boxes
                tbox = xywh2xyxy(labels[:, 1:5]) * whwh

                # Per target class
                for cls in torch.unique(tcls_tensor):
                    ti = (cls == tcls_tensor).nonzero(
                    ).view(-1)  # target indices
                    pi = (cls == pred[:, 5]).nonzero(
                    ).view(-1)  # prediction indices

                    # Search for detections
                    if pi.shape[0]:
                        # Prediction to target ious
                        ious, i = box_iou(pred[pi, :4], tbox[ti].cpu()).max(
                            1)  # best ious, indices

                        # Append detections
                        for j in (ious > iouv[0].cpu()).nonzero():
                            d = ti[i[j]]  # detected target
                            if d not in detected:
                                detected.append(d)
                                # iou_thres is 1xn
                                correct[pi[j]] = ious[j] > iouv.cpu()
                                if len(
                                        detected) == nl:
                                    # all targets already located in image
                                    break

            # Append statistics (correct, conf, pcls, tcls)
            stats.append(
                (correct.cpu(), pred[:, 4].cpu(), pred[:, 5].cpu(), tcls))

        # Plot images
        if batch_i < 1:
            f = 'test_batch%g_gt.jpg' % batch_i  # filename
            plot_images(imgs, targets, paths=paths, names=names,
                        fname=f)  # ground truth
            f = 'test_batch%g_pred.jpg' % batch_i
            plot_images(imgs, output_to_target(output, width, height),
                        paths=paths, names=names, fname=f)  # predictions

    # test end

    # Compute statistics
    stats = [np.concatenate(x, 0) for x in zip(*stats)]  # to numpy
    if len(stats):
        p, r, ap, f1, ap_class = ap_per_class(*stats)
        if niou > 1:
            p, r, ap, f1 = p[:, 0], r[:, 0], ap.mean(
                1), ap[:, 0]  # [P, R, AP@0.5:0.95, AP@0.5]
        mp, mr, map, mf1 = p.mean(), r.mean(), ap.mean(), f1.mean()
        nt = np.bincount(stats[3].astype(np.int64),
                         minlength=nc)  # number of targets per class
    else:
        nt = torch.zeros(1)

    # Print results
    pf = '%20s' + '%10.3g' * 6  # print format
    print(pf % ('all', seen, nt.sum(), mp, mr, map, mf1))

    # Print results per class
    if verbose and nc > 1 and len(stats):
        for i, c in enumerate(ap_class):
            print(pf % (names[c], seen, nt[c], p[i], r[i], ap[i], f1[i]))

    # Print speeds
    if verbose or save_json:
        t = tuple(x / seen * 1E3 for x in (t0, t1, t0 + t1)) + \
            (imgsz, imgsz, batch_size)  # tuple
        print(
            'Speed: %.1f/%.1f/%.1f ms \
            inference/NMS/total per %gx%g image at batch-size %g' % t)

    # Save JSON
    if save_json and map and len(jdict):
        print('\nCOCO mAP with pycocotools...')
        imgIds = [int(Path(x).stem.split('_')[-1])
                  for x in dataloader.dataset.img_files]
        with open('results.json', 'w') as file:
            json.dump(jdict, file)

        try:
            from pycocotools.coco import COCO
            from pycocotools.cocoeval import COCOeval

            # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb
            # initialize COCO ground truth api
            cocoGt = COCO(
                glob.glob('coco/annotations/instances_val*.json')[0])
            cocoDt = cocoGt.loadRes('results.json')  # initialize COCO pred api
            cocoEval = COCOeval(cocoGt, cocoDt, 'bbox')
            # [:32]  # only evaluate these images
            cocoEval.params.imgIds = imgIds
            cocoEval.evaluate()
            cocoEval.accumulate()
            cocoEval.summarize()
            # mf1, map = cocoEval.stats[:2]  # update to pycocotools results
            # (mAP@0.5:0.95, mAP@0.5)
        except BaseException:
            print(
                'WARNING: pycocotools must be installed with \
                numpy==1.17 to run correctly. '
                'See https://github.com/cocodataset/cocoapi/issues/356')

    # Return results
    maps = np.zeros(nc) + map
    for i, c in enumerate(ap_class):
        maps[c] = ap[i]
    return (mp, mr, map, mf1, *(loss.cpu() / len(dataloader)).tolist()), maps


if __name__ == '__main__':
    parser = argparse.ArgumentParser(prog='Test onnx model performance on COCO dataset')
    parser.add_argument(
        '--data',
        type=str,
        default='coco2017.data',
        help='Path of *.data')
    parser.add_argument(
        '--batch-size',
        type=int,
        default=1,
        help='Size of each image batch')
    parser.add_argument(
        '--img-size',
        type=int,
        default=416,
        help='Inference size (pixels)')
    parser.add_argument(
        '--conf-thres',
        type=float,
        default=0.001,
        help='Object confidence threshold')
    parser.add_argument(
        '--iou-thres',
        type=float,
        default=0.5,
        help='IOU threshold for NMS')
    parser.add_argument(
        '--save-json',
        action='store_true',
        help='Save a COCOapi-compatible JSON results file')
    parser.add_argument(
        '--device',
        default='',
        help='Device id (i.e. 0 or 0,1) or cpu')
    parser.add_argument(
        '--augment',
        action='store_true',
        help='Augmented inference')
    parser.add_argument('--sync_bn', action='store_true')
    parser.add_argument('--print_model', action='store_true')
    parser.add_argument('--test_rect', action='store_true')

    parser.add_argument(
        '--onnx_runtime',
        action='store_true',
        help='Use onnx runtime')
    parser.add_argument(
        '--onnx_weights',
        default='yolov3-8.onnx',
        nargs='+',
        type=str,
        help='Path of onnx weights')
    parser.add_argument(
        '--single-cls',
        action='store_true',
        help='Run as single-class dataset')
    parser.add_argument(
        "--ipu",
        action="store_true",
        help="Use IPU for inference")
    parser.add_argument(
        "--provider_config",
        type=str,
        default="vaip_config.json",
        help="Path of the config file for seting provider_options")

    opt = parser.parse_args()
    opt.save_json = opt.save_json or any(
        [x in opt.data for x in ['coco.data',
                                 'coco2014.data', 'coco2017.data']])
    opt.data = check_file(opt.data)  # check file
    print(opt)

    help_url = 'https://github.com/ultralytics/yolov3/wiki/Train-Custom-Data'
    img_formats = ['.bmp', '.jpg', '.jpeg', '.png', '.tif', '.tiff', '.dng']
    vid_formats = ['.mov', '.avi', '.mp4', '.mpg', '.mpeg', '.m4v', '.wmv',
                   '.mkv']

    test(opt.data,
         opt.batch_size,
         opt.img_size,
         opt.conf_thres,
         opt.iou_thres,
         opt.save_json,
         opt.single_cls,
         opt.augment,
         names='data/coco.names',
         onnx_weights=opt.onnx_weights,
         ipu=opt.ipu,
         provider_config=opt.provider_config
         )