# -*- coding: utf-8 -*-
"""
Created on Mon Mar 29 14:26:51 2021
@author: Gokulesh Danapal (GX6)
Confidentiality: Internal
"""

import torch
import torchvision as tv
import torch.nn.functional as F
import math
import numpy as np
from PIL import Image, ExifTags
import os
import random
import cv2
from copy import deepcopy
import time
import matplotlib.patches as patches
import matplotlib.pyplot as plt
from pathlib import Path
import torch.optim as optim
import torch.optim.lr_scheduler as lr_scheduler
from torch.nn.parallel import DistributedDataParallel as DDP
import torch.distributed as dist
from torch.cuda import amp
from tqdm import tqdm
import yaml
import torch.backends.cudnn as cudnn
import glob
from scipy.cluster.vq import kmeans
# from ray import tune
# from ray.tune.schedulers.pb2 import PB2
from torch.utils.tensorboard import SummaryWriter
import logging
import shutil
from multiprocessing.pool import ThreadPool
from itertools import repeat
import re
from contextlib import contextmanager
import json
import matplotlib
from scipy.signal import butter, filtfilt
from copy import copy
from torch.utils.data import Dataset
from models import Darknet
from utils.foggify import fogify
#logger = logging.getLogger(__name__)

def color_list():
    # Return first 10 plt colors as (r,g,b) 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))

    return [hex2rgb(h) for h in plt.rcParams['axes.prop_cycle'].by_key()['color']]


def hist2d(x, y, n=100):
    # 2d histogram used in labels.png and evolve.png
    xedges, yedges = np.linspace(x.min(), x.max(), n), np.linspace(y.min(), y.max(), n)
    hist, xedges, yedges = np.histogram2d(x, y, (xedges, yedges))
    xidx = np.clip(np.digitize(x, xedges) - 1, 0, hist.shape[0] - 1)
    yidx = np.clip(np.digitize(y, yedges) - 1, 0, hist.shape[1] - 1)
    return np.log(hist[xidx, yidx])


def butter_lowpass_filtfilt(data, cutoff=1500, fs=50000, order=5):
    # https://stackoverflow.com/questions/28536191/how-to-filter-smooth-with-scipy-numpy
    def butter_lowpass(cutoff, fs, order):
        nyq = 0.5 * fs
        normal_cutoff = cutoff / nyq
        return butter(order, normal_cutoff, btype='low', analog=False)

    b, a = butter_lowpass(cutoff, fs, order=order)
    return filtfilt(b, a, data)  # forward-backward filter


def plot_one_box(x, img, color=None, label=None, line_thickness=None):
    # Plots one bounding box on image img
    tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1  # line/font thickness
    color = color or [random.randint(0, 255) for _ in range(3)]
    c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))
    cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA)
    if label:
        tf = max(tl - 1, 1)  # font thickness
        t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]
        c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3
        cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA)  # filled
        cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA)


def plot_wh_methods():  # from utils.general import *; plot_wh_methods()
    # Compares the two methods for width-height anchor multiplication
    # https://github.com/ultralytics/yolov3/issues/168
    x = np.arange(-4.0, 4.0, .1)
    ya = np.exp(x)
    yb = torch.sigmoid(torch.from_numpy(x)).numpy() * 2

    fig = plt.figure(figsize=(6, 3), dpi=150)
    plt.plot(x, ya, '.-', label='YOLO')
    plt.plot(x, yb ** 2, '.-', label='YOLO ^2')
    plt.plot(x, yb ** 1.6, '.-', label='YOLO ^1.6')
    plt.xlim(left=-4, right=4)
    plt.ylim(bottom=0, top=6)
    plt.xlabel('input')
    plt.ylabel('output')
    plt.grid()
    plt.legend()
    fig.tight_layout()
    fig.savefig('comparison.png', dpi=200)


def output_to_target(output, width, height):
    # Convert 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.cpu():
                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 plot_images(images, targets, paths=None, fname='images.jpg', names=None, max_size=640, max_subplots=16):
    # Plot image grid with labels

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

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

    tl = 1  # line thickness
    tf = max(tl - 1, 1)  # font thickness
    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)

    colors = color_list()  # list of colors
    mosaic = np.full((int(ns * h), int(ns * w), 3), 255, dtype=np.uint8)  # init
    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')
            labels = image_targets.shape[1] == 6  # labels if no conf column
            conf = None if labels else image_targets[:, 6]  # check for confidence presence (label vs pred)

            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 = colors[cls % len(colors)]
                cls = names[cls] if names else cls
                if labels or conf[j] > 0.25:  # 0.25 conf thresh
                    label = '%s' % cls if labels else '%s %.1f' % (cls, conf[j])
                    plot_one_box(box, mosaic, label=label, color=color, line_thickness=tl)

        # Draw image filename labels
        if paths:
            label = Path(paths[i]).name[: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:
        r = min(1280. / max(h, w) / ns, 1.0)  # ratio to limit image size
        mosaic = cv2.resize(mosaic, (int(ns * w * r), int(ns * h * r)), interpolation=cv2.INTER_AREA)
        cv2.imwrite(fname, cv2.cvtColor(mosaic, cv2.COLOR_BGR2RGB))  # cv2 save
        Image.fromarray(mosaic).save(fname)  # PIL save
    return mosaic


def plot_lr_scheduler(optimizer, scheduler, epochs=300, save_dir=''):
    # Plot LR simulating training for full epochs
    optimizer, scheduler = copy(optimizer), copy(scheduler)  # do not modify originals
    y = []
    for _ in range(epochs):
        scheduler.step()
        y.append(optimizer.param_groups[0]['lr'])
    plt.plot(y, '.-', label='LR')
    plt.xlabel('epoch')
    plt.ylabel('LR')
    plt.grid()
    plt.xlim(0, epochs)
    plt.ylim(0)
    plt.tight_layout()
    plt.savefig(Path(save_dir) / 'LR.png', dpi=200)


def plot_test_txt():  # from utils.general import *; plot_test()
    # Plot test.txt histograms
    x = np.loadtxt('test.txt', dtype=np.float32)
    box = xyxy2xywh(x[:, :4])
    cx, cy = box[:, 0], box[:, 1]

    fig, ax = plt.subplots(1, 1, figsize=(6, 6), tight_layout=True)
    ax.hist2d(cx, cy, bins=600, cmax=10, cmin=0)
    ax.set_aspect('equal')
    plt.savefig('hist2d.png', dpi=300)

    fig, ax = plt.subplots(1, 2, figsize=(12, 6), tight_layout=True)
    ax[0].hist(cx, bins=600)
    ax[1].hist(cy, bins=600)
    plt.savefig('hist1d.png', dpi=200)


def plot_targets_txt():  # from utils.general import *; plot_targets_txt()
    # Plot targets.txt histograms
    x = np.loadtxt('targets.txt', dtype=np.float32).T
    s = ['x targets', 'y targets', 'width targets', 'height targets']
    fig, ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)
    ax = ax.ravel()
    for i in range(4):
        ax[i].hist(x[i], bins=100, label='%.3g +/- %.3g' % (x[i].mean(), x[i].std()))
        ax[i].legend()
        ax[i].set_title(s[i])
    plt.savefig('targets.jpg', dpi=200)


def plot_study_txt(f='study.txt', x=None):  # from utils.general import *; plot_study_txt()
    # Plot study.txt generated by test.py
    fig, ax = plt.subplots(2, 4, figsize=(10, 6), tight_layout=True)
    ax = ax.ravel()

    fig2, ax2 = plt.subplots(1, 1, figsize=(8, 4), tight_layout=True)
    for f in ['study/study_coco_yolo%s.txt' % x for x in ['s', 'm', 'l', 'x']]:
        y = np.loadtxt(f, dtype=np.float32, usecols=[0, 1, 2, 3, 7, 8, 9], ndmin=2).T
        x = np.arange(y.shape[1]) if x is None else np.array(x)
        s = ['P', 'R', 'mAP@.5', 'mAP@.5:.95', 't_inference (ms/img)', 't_NMS (ms/img)', 't_total (ms/img)']
        for i in range(7):
            ax[i].plot(x, y[i], '.-', linewidth=2, markersize=8)
            ax[i].set_title(s[i])

        j = y[3].argmax() + 1
        ax2.plot(y[6, :j], y[3, :j] * 1E2, '.-', linewidth=2, markersize=8,
                 label=Path(f).stem.replace('study_coco_', '').replace('yolo', 'YOLO'))

    ax2.plot(1E3 / np.array([209, 140, 97, 58, 35, 18]), [34.6, 40.5, 43.0, 47.5, 49.7, 51.5],
             'k.-', linewidth=2, markersize=8, alpha=.25, label='EfficientDet')

    ax2.grid()
    ax2.set_xlim(0, 30)
    ax2.set_ylim(28, 50)
    ax2.set_yticks(np.arange(30, 55, 5))
    ax2.set_xlabel('GPU Speed (ms/img)')
    ax2.set_ylabel('COCO AP val')
    ax2.legend(loc='lower right')
    plt.savefig('study_mAP_latency.png', dpi=300)
    plt.savefig(f.replace('.txt', '.png'), dpi=300)


def plot_labels(labels, save_dir=''):
    # plot dataset labels
    c, b = labels[:, 0], labels[:, 1:].transpose()  # classes, boxes
    nc = int(c.max() + 1)  # number of classes

    fig, ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)
    ax = ax.ravel()
    ax[0].hist(c, bins=np.linspace(0, nc, nc + 1) - 0.5, rwidth=0.8)
    ax[0].set_xlabel('classes')
    ax[1].scatter(b[0], b[1], c=hist2d(b[0], b[1], 90), cmap='jet')
    ax[1].set_xlabel('x')
    ax[1].set_ylabel('y')
    ax[2].scatter(b[2], b[3], c=hist2d(b[2], b[3], 90), cmap='jet')
    ax[2].set_xlabel('width')
    ax[2].set_ylabel('height')
    plt.savefig(Path(save_dir) / 'labels.png', dpi=200)
    plt.close()

    # seaborn correlogram
    try:
        import seaborn as sns
        import pandas as pd
        x = pd.DataFrame(b.transpose(), columns=['x', 'y', 'width', 'height'])
        sns.pairplot(x, corner=True, diag_kind='hist', kind='scatter', markers='o',
                     plot_kws=dict(s=3, edgecolor=None, linewidth=1, alpha=0.02),
                     diag_kws=dict(bins=50))
        plt.savefig(Path(save_dir) / 'labels_correlogram.png', dpi=200)
        plt.close()
    except Exception as e:
        pass


def plot_evolution(yaml_file='data/hyp.finetune.yaml'):  # from utils.general import *; plot_evolution()
    # Plot hyperparameter evolution results in evolve.txt
    with open(yaml_file) as f:
        hyp = yaml.load(f, Loader=yaml.FullLoader)
    x = np.loadtxt('evolve.txt', ndmin=2)
    f = fitness(x)
    # weights = (f - f.min()) ** 2  # for weighted results
    plt.figure(figsize=(10, 12), tight_layout=True)
    matplotlib.rc('font', **{'size': 8})
    for i, (k, v) in enumerate(hyp.items()):
        y = x[:, i + 7]
        # mu = (y * weights).sum() / weights.sum()  # best weighted result
        mu = y[f.argmax()]  # best single result
        plt.subplot(6, 5, i + 1)
        plt.scatter(y, f, c=hist2d(y, f, 20), cmap='viridis', alpha=.8, edgecolors='none')
        plt.plot(mu, f.max(), 'k+', markersize=15)
        plt.title('%s = %.3g' % (k, mu), fontdict={'size': 9})  # limit to 40 characters
        if i % 5 != 0:
            plt.yticks([])
        print('%15s: %.3g' % (k, mu))
    plt.savefig('evolve.png', dpi=200)
    print('\nPlot saved as evolve.png')


def plot_results_overlay(start=0, stop=0):  # from utils.general import *; plot_results_overlay()
    # Plot training 'results*.txt', overlaying train and val losses
    s = ['train', 'train', 'train', 'Precision', 'mAP@0.5', 'val', 'val', 'val', 'Recall', 'mAP@0.5:0.95']  # legends
    t = ['Box', 'Objectness', 'Classification', 'P-R', 'mAP-F1']  # titles
    for f in sorted(glob.glob('results*.txt') + glob.glob('../../Downloads/results*.txt')):
        results = np.loadtxt(f, usecols=[2, 3, 4, 8, 9, 12, 13, 14, 10, 11], ndmin=2).T
        n = results.shape[1]  # number of rows
        x = range(start, min(stop, n) if stop else n)
        fig, ax = plt.subplots(1, 5, figsize=(14, 3.5), tight_layout=True)
        ax = ax.ravel()
        for i in range(5):
            for j in [i, i + 5]:
                y = results[j, x]
                ax[i].plot(x, y, marker='.', label=s[j])
                # y_smooth = butter_lowpass_filtfilt(y)
                # ax[i].plot(x, np.gradient(y_smooth), marker='.', label=s[j])

            ax[i].set_title(t[i])
            ax[i].legend()
            ax[i].set_ylabel(f) if i == 0 else None  # add filename
        fig.savefig(f.replace('.txt', '.png'), dpi=200)


def plot_results(start=0, stop=0, bucket='', id=(), labels=(), save_dir=''):
    # from utils.general import *; plot_results(save_dir='runs/train/exp0')
    # Plot training 'results*.txt'
    fig, ax = plt.subplots(2, 5, figsize=(12, 6))
    ax = ax.ravel()
    s = ['Box', 'Objectness', 'Classification', 'Precision', 'Recall',
         'val Box', 'val Objectness', 'val Classification', 'mAP@0.5', 'mAP@0.5:0.95']
    if bucket:
        # os.system('rm -rf storage.googleapis.com')
        # files = ['https://storage.googleapis.com/%s/results%g.txt' % (bucket, x) for x in id]
        files = ['%g.txt' % x for x in id]
        c = ('gsutil cp ' + '%s ' * len(files) + '.') % tuple('gs://%s/%g.txt' % (bucket, x) for x in id)
        os.system(c)
    else:
        files = glob.glob(str(Path(save_dir) / '*.txt')) + glob.glob('../../Downloads/results*.txt')
    assert len(files), 'No results.txt files found in %s, nothing to plot.' % os.path.abspath(save_dir)
    for fi, f in enumerate(files):
        try:
            results = np.loadtxt(f, usecols=[2, 3, 4, 8, 9, 12, 13, 14, 10, 11], ndmin=2).T
            n = results.shape[1]  # number of rows
            x = range(start, min(stop, n) if stop else n)
            for i in range(10):
                y = results[i, x]
                if i in [0, 1, 2, 5, 6, 7]:
                    y[y == 0] = np.nan  # don't show zero loss values
                    # y /= y[0]  # normalize
                label = labels[fi] if len(labels) else Path(f).stem
                ax[i].plot(x, y, marker='.', label=label, linewidth=1, markersize=6)
                ax[i].set_title(s[i])
                # if i in [5, 6, 7]:  # share train and val loss y axes
                #     ax[i].get_shared_y_axes().join(ax[i], ax[i - 5])
        except Exception as e:
            print('Warning: Plotting error for %s; %s' % (f, e))

    fig.tight_layout()
    ax[1].legend()
    fig.savefig(Path(save_dir) / 'results.png', dpi=200)

def select_device(device='1', batch_size=None, logger = None):
    # device = 'cpu' or '0' or '0,1,2,3'
    cpu_request = device.lower() == 'cpu'
    if device and not cpu_request:  # if device requested other than 'cpu'
        os.environ['CUDA_VISIBLE_DEVICES'] = device  # set environment variable
        assert torch.cuda.is_available(), 'CUDA unavailable, invalid device %s requested' % device  # check availablity

    cuda = False if cpu_request else torch.cuda.is_available()
    if cuda:
        c = 1024 ** 2  # bytes to MB
        ng = torch.cuda.device_count()
        if ng > 1 and batch_size:  # check that batch_size is compatible with device_count
            assert batch_size % ng == 0, 'batch-size %g not multiple of GPU count %g' % (batch_size, ng)
        x = [torch.cuda.get_device_properties(i) for i in range(ng)]
        s = f'Using torch {torch.__version__} '
        for i in range(0, ng):
            if i == 1:
                s = ' ' * len(s)
            logger.info("%sCUDA:%g (%s, %dMB)" % (s, i, x[i].name, x[i].total_memory / c))
    else:
        logger.info(f'Using torch {torch.__version__} CPU')

    logger.info('')  # skip a line
    return torch.device('cuda:0' if cuda else 'cpu')

def set_logging(rank=-1):
    logging.basicConfig(
        format="%(message)s",
        level=logging.INFO if rank in [-1, 0] else logging.WARN)
    
def strip_optimizer(f='weights/best.pt', s=''):  # from utils.general import *; strip_optimizer()
    # Strip optimizer from 'f' to finalize training, optionally save as 's'
    x = torch.load(f, map_location=torch.device('cpu'))
    x['optimizer'] = None
    x['training_results'] = None
    x['epoch'] = -1
    #x['model'].half()  # to FP16
    #for p in x['model'].parameters():
    #    p.requires_grad = False
    torch.save(x, s or f)
    mb = os.path.getsize(s or f) / 1E6  # filesize
    print('Optimizer stripped from %s,%s %.1fMB' % (f, (' saved as %s,' % s) if s else '', mb))
    
@contextmanager
def torch_distributed_zero_first(local_rank: int):
    """
    Decorator to make all processes in distributed training wait for each local_master to do something.
    """
    if local_rank not in [-1, 0]:
        torch.distributed.barrier()
    yield
    if local_rank == 0:
        torch.distributed.barrier()

def kmean_anchors(dataset, n=9, img_size=640, thr=4.0, gen=1000, verbose=False):
    """ Creates kmeans-evolved anchors from training dataset
        Arguments:
            path: path to dataset *.yaml, or a loaded dataset
            n: number of anchors
            img_size: image size used for training
            thr: anchor-label wh ratio threshold hyperparameter hyp['anchor_t'] used for training, default=4.0
            gen: generations to evolve anchors using genetic algorithm
        Return:
            k: kmeans evolved anchors
        Usage:
            from utils.utils import *; _ = kmean_anchors()
    """
    thr = 1. / thr
    
    def metric(k, wh):  # compute metrics
        r = wh[:, None] / k[None]
        x = torch.min(r, 1. / r).min(2)[0]  # ratio metric
        # x = wh_iou(wh, torch.tensor(k))  # iou metric
        return x, x.max(1)[0]  # x, best_x
    
    def fitness(k):  # mutation fitness
        _, best = metric(torch.tensor(k, dtype=torch.float32), wh)
        return (best * (best > thr).float()).mean()  # fitness
    
    def print_results(k):
        k = k[np.argsort(k.prod(1))]  # sort small to large
        x, best = metric(k, wh0)
        bpr, aat = (best > thr).float().mean(), (x > thr).float().mean() * n  # best possible recall, anch > thr
        print('thr=%.2f: %.4f best possible recall, %.2f anchors past thr' % (thr, bpr, aat))
        print('n=%g, img_size=%s, metric_all=%.3f/%.3f-mean/best, past_thr=%.3f-mean: ' %
              (n, img_size, x.mean(), best.mean(), x[x > thr].mean()), end='')
        for i, x in enumerate(k):
            print('%i,%i' % (round(x[0]), round(x[1])), end=',  ' if i < len(k) - 1 else '\n')  # use in *.cfg
        return k
    
    #Get label wh
    #shapes = img_size * dataset.img_hw0 / np.array(dataset.img_hw0)[:,1].max()
    label_list = []; shapes = [];
    for key in list(dataset.label_dict.keys()):
        label_list.append(dataset.label_dict[key])
        shapes.append(dataset.img_hw0[key])
    wh0 = np.concatenate([l[:, 3:5] * s for s, l in zip(shapes, label_list)])  # wh
    
    # Filter
    i = (wh0 < 3.0).any(1).sum()
    if i:
        print('WARNING: Extremely small objects found. '
              '%g of %g labels are < 3 pixels in width or height.' % (i, len(wh0)))
    wh = wh0[(wh0 >= 2.0).any(1)]  # filter > 2 pixels
    
    # Kmeans calculation
    print('Running kmeans for %g anchors on %g points...' % (n, len(wh)))
    s = wh.std(0)  # sigmas for whitening
    k, dist = kmeans(wh / s, n, iter=30)  # points, mean distance
    k *= s
    wh = torch.tensor(wh, dtype=torch.float32)  # filtered
    wh0 = torch.tensor(wh0, dtype=torch.float32)  # unflitered
    k = print_results(k)
    #return k
    # Evolve
    npr = np.random
    f, sh, mp, s = fitness(k), k.shape, 0.9, 0.1  # fitness, generations, mutation prob, sigma
    pbar = tqdm(range(gen), desc='Evolving anchors with Genetic Algorithm')  # progress bar
    for _ in pbar:
        v = np.ones(sh)
        while (v == 1).all():  # mutate until a change occurs (prevent duplicates)
            v = ((npr.random(sh) < mp) * npr.random() * npr.randn(*sh) * s + 1).clip(0.3, 3.0)
        kg = (k.copy() * v).clip(min=2.0)
        fg = fitness(kg)
        if fg > f:
            f, k = fg, kg.copy()
            pbar.desc = 'Evolving anchors with Genetic Algorithm: fitness = %.4f' % f
            if verbose:
                print_results(k)
    
    return print_results(k)

def increment_path(path, exist_ok=True, sep=''):
    # Increment path, i.e. runs/exp --> runs/exp{sep}0, runs/exp{sep}1 etc.
    path = Path(path)  # os-agnostic
    if (path.exists() and exist_ok) or (not path.exists()):
        return str(path)
    else:
        dirs = glob.glob(f"{path}{sep}*")  # similar paths
        matches = [re.search(rf"%s{sep}(\d+)" % path.stem, d) for d in dirs]
        i = [int(m.groups()[0]) for m in matches if m]  # indices
        n = max(i) + 1 if i else 2  # increment number
        return f"{path}{sep}{n}"  # update path
    
def labels_to_class_weights(labels, nc=80):
    # Get class weights (inverse frequency) from training labels
    if labels[0] is None:  # no labels loaded
        return torch.Tensor()

    labels = np.concatenate(labels, 0)  # labels.shape = (866643, 5) for COCO
    classes = labels[:, 0].astype(np.int)  # labels = [class xywh]
    weights = np.bincount(classes, minlength=nc)  # occurrences per class

    # Prepend gridpoint count (for uCE training)
    # gpi = ((320 / 32 * np.array([1, 2, 4])) ** 2 * 3).sum()  # gridpoints per image
    # weights = np.hstack([gpi * len(labels)  - weights.sum() * 9, weights * 9]) ** 0.5  # prepend gridpoints to start

    weights[weights == 0] = 1  # replace empty bins with 1
    weights = 1 / weights  # number of targets per class
    weights /= weights.sum()  # normalize
    return torch.from_numpy(weights)
   
def fitness(x):
    # Returns fitness (for use with results.txt or evolve.txt)
    w = [0.0, 0.0, 0.9, 0.1]  # weights for [P, R, mAP@0.5, mAP@0.5:0.95]
    return (x[:, :4] * w).sum(1)

def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, fname='precision-recall_curve.png'):
    """ 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
        fname:  Plot filename
    # 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
    px, py = np.linspace(0, 1, 1000), []  # for plotting
    pr_score = 0.1  # score to evaluate P and R https://github.com/ultralytics/yolov3/issues/898
    s = [unique_classes.shape[0], tp.shape[1]]  # number class, number iou thresholds (i.e. 10 for mAP0.5...0.95)
    ap, p, r = np.zeros(s), np.zeros(s), np.zeros(s)
    for ci, c in enumerate(unique_classes):
        i = pred_cls == c
        n_l = (target_cls == c).sum()  # number of labels
        n_p = i.sum()  # number of predictions

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

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

            # 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], mpre, mrec = compute_ap(recall[:, j], precision[:, j])
                if j == 0:
                    py.append(np.interp(px, mrec, mpre))  # precision at mAP@0.5

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

    if plot:
        py = np.stack(py, axis=1)
        fig, ax = plt.subplots(1, 1, figsize=(5, 5))
        ax.plot(px, py, linewidth=0.5, color='grey')  # plot(recall, precision)
        ax.plot(px, py.mean(1), linewidth=2, 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)
        plt.legend()
        fig.tight_layout()
        fig.savefig(fname, dpi=200)

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

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 = recall  # np.concatenate(([0.], recall, [recall[-1] + 1E-3]))
    mpre = precision  # 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'
        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

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

def init_seeds(seed=0):
    torch.manual_seed(seed)
    # Speed-reproducibility tradeoff https://pytorch.org/docs/stable/notes/randomness.html
    if seed == 0:  # slower, more reproducible
        cudnn.deterministic = True
        cudnn.benchmark = False
    else:  # faster, less reproducible
        cudnn.deterministic = False
        cudnn.benchmark = True
        
def init_seeds_master(seed=0):
    random.seed(seed)
    np.random.seed(seed)
    init_seeds(seed=seed)
    
class FocalLoss(torch.nn.Module):
    # Wraps focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5)
    def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):
        super(FocalLoss, self).__init__()
        self.loss_fcn = loss_fcn  # must be nn.BCEWithLogitsLoss()
        self.gamma = gamma
        self.alpha = alpha
        self.reduction = loss_fcn.reduction
        self.loss_fcn.reduction = 'none'  # required to apply FL to each element

    def forward(self, pred, true):
        loss = self.loss_fcn(pred, true)
        # p_t = torch.exp(-loss)
        # loss *= self.alpha * (1.000001 - p_t) ** self.gamma  # non-zero power for gradient stability

        # TF implementation https://github.com/tensorflow/addons/blob/v0.7.1/tensorflow_addons/losses/focal_loss.py
        pred_prob = torch.sigmoid(pred)  # prob from logits
        p_t = true * pred_prob + (1 - true) * (1 - pred_prob)
        alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha)
        modulating_factor = (1.0 - p_t) ** self.gamma
        loss *= alpha_factor * modulating_factor

        if self.reduction == 'mean':
            return loss.mean()
        elif self.reduction == 'sum':
            return loss.sum()
        else:  # 'none'
            return loss

def smooth_BCE(eps=0.1):  # https://github.com/ultralytics/yolov3/issues/238#issuecomment-598028441
    # return positive, negative label smoothing BCE targets
    return 1.0 - 0.5 * eps, 0.5 * eps


def compute_loss(p, targets, model):  # predictions, targets, model
    device = targets.device; hyp =  model.hyp
    lcls, lbox, lobj = torch.zeros(1, device=device), torch.zeros(1, device=device), torch.zeros(1, device=device)
    tcls, tbox, indices, anchors = build_targets(p, targets, model.strides, hyp)  # targets
    h = hyp  # hyperparameters
    
    # Define criteria
    BCEcls = torch.nn.BCEWithLogitsLoss(pos_weight=torch.Tensor([h['cls_pw']])).to(device)
    BCEobj = torch.nn.BCEWithLogitsLoss(pos_weight=torch.Tensor([h['obj_pw']])).to(device)
    
    # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3
    cp, cn = smooth_BCE(eps=0.0)
    
    # Focal loss
    g = h['fl_gamma']  # focal loss gamma
    if g > 0:
        BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g)
    
    # Losses
    nt = 0  # number of targets
    np = len(p)  # number of outputs
    balance = [4.0, 1.0, 0.4] if np == 3 else [4.0, 1.0, 0.4, 0.1]  # P3-5 or P3-6
    for i, pi in enumerate(p): # layer index, layer predictions
        #pi = pi.detach()
        b, a, gj, gi = indices[i]  # image, anchor, gridy, gridx
        tobj = torch.zeros_like(pi[..., 0], device=device)  # target obj
    
        n = b.shape[0]  # number of targets
        if n:
            nt += n  # cumulative targets
            ps = pi[b, a, gj, gi]  # prediction subset corresponding to targets
    
            # Regression
            pxy = ps[:, :2].sigmoid() * 2. - 0.5
            pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i]
            #pxy = torch.sigmoid(ps[:, 0:2])  # pxy = pxy * s - (s - 1) / 2,  s = 1.5  (scale_xy)
            #pwh = torch.exp(ps[:, 2:4]).clamp(max=1E3) * anchors[i]
            pbox = torch.cat((pxy, pwh), 1).to(device)  # predicted box
            giou = bbox_iou(pbox.T, tbox[i], x1y1x2y2=False, CIoU=True)  # giou(prediction, target)
            lbox += (1.0 - giou).mean()  # giou loss
    
            # Objectness
            tobj[b, a, gj, gi] = (1.0 - hyp['gr']) + hyp['gr'] * giou.detach().clamp(0).type(tobj.dtype)  # giou ratio
    
            # Classification
            if model.nc > 1:  # cls loss (only if multiple classes)
                t = torch.full_like(ps[:, 5:], cn, device=device)  # targets
                t[range(n), tcls[i]] = cp
                lcls += BCEcls(ps[:, 5:], 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)]
    
        lobj += BCEobj(pi[..., 4], tobj) * balance[i]  # obj loss
    
    s = 3 / np  # output count scaling
    lbox *= h['box'] * s
    lobj *= h['obj'] * s * (1.4 if np == 4 else 1.)
    lcls *= h['cls'] * s
    bs = tobj.shape[0]  # batch size
    
    loss = lbox + lobj + lcls
    return loss * bs, torch.cat((lbox, lobj, lcls, loss)).detach()

def build_targets(p, targets, strides,hyp):
    nt = targets.shape[0]  # number of anchors, targets
    tcls, tbox, indices, anch = [], [], [], []
    gain = torch.ones(6, device=targets.device)  # normalized to gridspace gain
    off = torch.tensor([[1, 0], [0, 1], [-1, 0], [0, -1]], device=targets.device).float()  # overlap offsets
    
    g = 0.5  # offset
    for i, ancs in enumerate(torch.from_numpy(np.array(hyp['anchors_g'])).reshape(3,3,2).to(targets.device)):#model.module.yolo_layers if multi_gpu else model.yolo_layers):
        # get number of grid points and anchor vec for this yolo layer
        #anchors = model.module.module_list[jj].anchor_vec if multi_gpu else model.module_list[jj].anchor_vec
        ancs = ancs // strides[i]
        gain[2:] = torch.tensor(p[i].shape)[[3, 2, 3, 2]]  # xyxy gain
    
        # Match targets to anchors
        a, t, offsets = [], targets * gain, 0
        if nt:
            na = ancs.shape[0]  # number of anchors
            at = torch.arange(na).view(na, 1).repeat(1, nt)  # anchor tensor, same as .repeat_interleave(nt)
            r = t[None, :, 4:6] / ancs[:, None]  # wh ratio
            j = torch.max(r, 1. / r).max(2)[0] < 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))
            a, t = at[j], t.repeat(na, 1, 1)[j]  # filter
    
            # overlaps
            gxy = t[:, 2:4]  # grid xy
            z = torch.zeros_like(gxy)
            j, k = ((gxy % 1. < g) & (gxy > 1.)).T
            l, m = ((gxy % 1. > (1 - g)) & (gxy < (gain[[2, 3]] - 1.))).T
            a, t = torch.cat((a, a[j], a[k], a[l], a[m]), 0), torch.cat((t, t[j], t[k], t[l], t[m]), 0)
            offsets = torch.cat((z, z[j] + off[0], z[k] + off[1], z[l] + off[2], z[m] + off[3]), 0) * g
    
        # Define
        b, c = t[:, :2].long().T  # image, class
        gxy = t[:, 2:4]  # grid xy
        gwh = t[:, 4:6]  # grid wh
        gij = (gxy - offsets).long()
        gi, gj = gij.T  # grid xy indices
    
        # Append
        #indices.append((b, a, gj, gi))  # image, anchor, grid indices
        indices.append((b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1)))  # image, anchor, grid indices
        tbox.append(torch.cat((gxy - gij, gwh), 1))  # box
        anch.append(ancs[a])  # anchors
        tcls.append(c)  # class
        
    return tcls, tbox, indices, anch

def bbox_iou(box1, box2, x1y1x2y2=True, GIoU=False, DIoU=False, CIoU=False):
    # Returns the IoU of box1 to box2. box1 is 4, box2 is nx4
    box2 = box2.T

    # Get the coordinates of bounding boxes
    if x1y1x2y2:  # x1, y1, x2, y2 = box1
        b1_x1, b1_y1, b1_x2, b1_y2 = box1[0], box1[1], box1[2], box1[3]
        b2_x1, b2_y1, b2_x2, b2_y2 = box2[0], box2[1], box2[2], box2[3]
    else:  # transform from xywh to xyxy
        b1_x1, b1_x2 = box1[0] - box1[2] / 2, box1[0] + box1[2] / 2
        b1_y1, b1_y2 = box1[1] - box1[3] / 2, box1[1] + box1[3] / 2
        b2_x1, b2_x2 = box2[0] - box2[2] / 2, box2[0] + box2[2] / 2
        b2_y1, b2_y2 = box2[1] - box2[3] / 2, box2[1] + box2[3] / 2

    # 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
    w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1
    w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1
    union = (w1 * h1 + 1e-16) + w2 * h2 - inter

    iou = inter / union  # iou
    if GIoU or DIoU or CIoU:
        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 GIoU:  # Generalized IoU https://arxiv.org/pdf/1902.09630.pdf
            c_area = cw * ch + 1e-16  # convex area
            return iou - (c_area - union) / c_area  # GIoU
        if DIoU or CIoU:  # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1
            # convex diagonal squared
            c2 = cw ** 2 + ch ** 2 + 1e-16
            # centerpoint distance squared
            rho2 = ((b2_x1 + b2_x2) - (b1_x1 + b1_x2)) ** 2 / 4 + ((b2_y1 + b2_y2) - (b1_y1 + b1_y2)) ** 2 / 4
            if DIoU:
                return iou - rho2 / c2  # DIoU
            elif 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 / (1 - iou + v + 1e-16)
                return iou - (rho2 / c2 + v * alpha)  # CIoU

    return iou

def box_iou(box1, box2):
    # 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
    """

    def box_area(box):
        # box = 4xn
        return (box[2] - box[0]) * (box[3] - box[1])

    area1 = box_area(box1.T)
    area2 = box_area(box2.T)

    # inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2)
    inter = (torch.min(box1[:, None, 2:], box2[:, 2:]) - torch.max(box1[:, None, :2], box2[:, :2])).clamp(0).prod(2)
    return inter / (area1[:, None] + area2 - inter)  # iou = inter / (area1 + area2 - inter)

def non_max_suppression(prediction, conf_thres=0.1, iou_thres=0.6, merge=False, classes=None, agnostic=False):
    """Performs Non-Maximum Suppression (NMS) on inference results
    Returns:
         detections with shape: nx6 (x1, y1, x2, y2, conf, cls)
    """
    if prediction.dtype is torch.float16:
        prediction = prediction.float()  # to FP32

    nc = prediction[0].shape[1] - 5  # number of classes
    xc = prediction[..., 4] > conf_thres  # candidates
    # Settings
    min_wh, max_wh = 2, 4096  # (pixels) minimum and maximum box width and height
    max_det = 300  # maximum number of detections per image
    time_limit = 10.0  # seconds to quit after
    redundant = True  # require redundant detections
    multi_label = nc > 1  # multiple labels per box (adds 0.5ms/img)

    t = time.time()
    output = [None] * prediction.shape[0]
    for xi, x in enumerate(prediction):  # image index, image inference
        # Apply constraints
        # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0  # width-height
        x = x[xc[xi]]  # confidence

        # If none remain process next image
        if not x.shape[0]:
            continue

        # Compute conf
        x[:, 5:] *= x[:, 4:5]  # conf = obj_conf * cls_conf

        # Box (center x, center y, width, height) to (x1, y1, x2, y2)
        box = xywh2xyxy(x[:, :4])

        # Detections matrix nx6 (xyxy, conf, cls)
        if multi_label:
            i, j = (x[:, 5:] > conf_thres).nonzero(as_tuple=False).T
            x = torch.cat((box[i], x[i, j + 5, None], j[:, None].float()), 1)
        else:  # best class only
            conf, j = x[:, 5:].max(1, keepdim=True)
            x = torch.cat((box, conf, j.float()), 1)[conf.view(-1) > conf_thres]
        
        # Filter by class
        if classes:
            x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)]

        # Apply finite constraint
        # if not torch.isfinite(x).all():
        #     x = x[torch.isfinite(x).all(1)]

        # If none remain process next image
        n = x.shape[0]  # number of boxes
        if not n:
            continue

        # Sort by confidence
        # x = x[x[:, 4].argsort(descending=True)]

        # Batched NMS
        c = x[:, 5:6] * (0 if agnostic else max_wh)  # classes
        boxes, scores = x[:, :4] + c, x[:, 4]  # boxes (offset by class), scores
        i = tv.ops.boxes.nms(boxes, scores, iou_thres)
        if i.shape[0] > max_det:  # limit detections
            i = i[:max_det]
        if merge and (1 < n < 3E3):  # Merge NMS (boxes merged using weighted mean)
            try:  # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)
                iou = box_iou(boxes[i], boxes) > iou_thres  # iou matrix
                weights = iou * scores[None]  # box weights
                x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True)  # merged boxes
                if redundant:
                    i = i[iou.sum(1) > 1]  # require redundancy
            except:  # possible CUDA error https://github.com/ultralytics/yolov3/issues/1139
                print(x, i, x.shape, i.shape)
                pass

        output[xi] = x[i]
        if (time.time() - t) > time_limit:
            break  # time limit exceeded

    return output

def is_parallel(model):
    return type(model) in (torch.nn.parallel.DataParallel, torch.nn.parallel.DistributedDataParallel)

def copy_attr(a, b, include=(), exclude=()):
    # Copy attributes from b to a, options to only include [...] and to exclude [...]
    for k, v in b.__dict__.items():
        if (len(include) and k not in include) or k.startswith('_') or k in exclude:
            continue
        else:
            setattr(a, k, v)

class ModelEMA:
    """ Model Exponential Moving Average from https://github.com/rwightman/pytorch-image-models
    Keep a moving average of everything in the model state_dict (parameters and buffers).
    This is intended to allow functionality like
    https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage
    A smoothed version of the weights is necessary for some training schemes to perform well.
    This class is sensitive where it is initialized in the sequence of model init,
    GPU assignment and distributed training wrappers.
    """
    def __init__(self, model, decay=0.9999, updates=0):
        # Create EMA
        self.ema = deepcopy(model.module if is_parallel(model) else model).eval()  # FP32 EMA
        # if next(model.parameters()).device.type != 'cpu':
        #     self.ema.half()  # FP16 EMA
        self.updates = updates  # number of EMA updates
        self.decay = lambda x: decay * (1 - math.exp(-x / 2000))  # decay exponential ramp (to help early epochs)
        for p in self.ema.parameters():
            p.requires_grad_(False)

    def update(self, model):
        # Update EMA parameters
        with torch.no_grad():
            self.updates += 1
            d = self.decay(self.updates)

            msd = model.state_dict()  # model state_dict
            for k, v in self.ema.state_dict().items():
                if v.dtype.is_floating_point:
                    v *= d
                    v += (1. - d) * msd[k].detach()

    def update_attr(self, model, include=(), exclude=('process_group', 'reducer')):
        # Update EMA attributes
        copy_attr(self.ema, model, include, exclude)

def clip_coords(boxes, img_shape):
    # Clip bounding xyxy bounding boxes to image shape (height, width)
    boxes[:, 0].clamp_(0, img_shape[1])  # x1
    boxes[:, 1].clamp_(0, img_shape[0])  # y1
    boxes[:, 2].clamp_(0, img_shape[1])  # x2
    boxes[:, 3].clamp_(0, img_shape[0])  # y2
        
def scale_coords(img1_shape, coords, img0_shape, ratio_pad=None):
    # Rescale coords (xyxy) from img1_shape to img0_shape
    if ratio_pad is None:  # calculate from img0_shape
        gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1])  # gain  = old / new
        pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2  # wh padding
    else:
        gain = ratio_pad[0][0]
        pad = ratio_pad[1]

    coords[:, [0, 2]] -= pad[0]  # x padding
    coords[:, [1, 3]] -= pad[1]  # y padding
    coords[:, :4] /= gain
    clip_coords(coords, img0_shape)
    return coords

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 xywh2xyxy(x):
    # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] 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  # top left x
    y[:, 1] = x[:, 1] - x[:, 3] / 2  # top left y
    y[:, 2] = x[:, 0] + x[:, 2] / 2  # bottom right x
    y[:, 3] = x[:, 1] + x[:, 3] / 2  # bottom right y
    return y

def box_candidates(box1, box2, wh_thr=2, ar_thr=20, area_thr=0.2):  # box1(4,n), box2(4,n)
    # Compute candidate boxes: box1 before augment, box2 after augment, wh_thr (pixels), aspect_ratio_thr, area_ratio
    w1, h1 = box1[2] - box1[0], box1[3] - box1[1]
    w2, h2 = box2[2] - box2[0], box2[3] - box2[1]
    ar = np.maximum(w2 / (h2 + 1e-16), h2 / (w2 + 1e-16))  # aspect ratio
    return (w2 > wh_thr) & (h2 > wh_thr) & (w2 * h2 / (w1 * h1 + 1e-16) > area_thr) & (ar < ar_thr)  # candidates


def labels_to_image_weights(labels, nc=80, class_weights=np.ones(80)):
    # Produces image weights based on class mAPs
    n = len(labels)
    class_counts = np.array([np.bincount(labels[i][:, 0].astype(np.int), minlength=nc) for i in range(n)])
    image_weights = (class_weights.reshape(1, nc) * class_counts).sum(1)
    # index = random.choices(range(n), weights=image_weights, k=1)  # weight image sample
    return image_weights

def random_perspective(img, targets=(), degrees=10, translate=.1, scale=.1, shear=10, perspective=0.0, border=(0, 0)):
    # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10))
    # targets = [cls, xyxy]

    height = img.shape[0] + border[0] * 2  # shape(h,w,c)
    width = img.shape[1] + border[1] * 2

    # Center
    C = np.eye(3)
    C[0, 2] = -img.shape[1] / 2  # x translation (pixels)
    C[1, 2] = -img.shape[0] / 2  # y translation (pixels)

    # Perspective
    P = np.eye(3)
    P[2, 0] = random.uniform(-perspective, perspective)  # x perspective (about y)
    P[2, 1] = random.uniform(-perspective, perspective)  # y perspective (about x)

    # 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=(0, 0), scale=s)

    # 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)

    # Translation
    T = np.eye(3)
    T[0, 2] = random.uniform(0.5 - translate, 0.5 + translate) * width  # x translation (pixels)
    T[1, 2] = random.uniform(0.5 - translate, 0.5 + translate) * height  # y translation (pixels)

    # Combined rotation matrix
    M = T @ S @ R @ P @ C  # order of operations (right to left) is IMPORTANT
    if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any():  # image changed
        if perspective:
            img = cv2.warpPerspective(img, M, dsize=(width, height), borderValue=(114, 114, 114))
        else:  # affine
            img = cv2.warpAffine(img, M[:2], dsize=(width, height), borderValue=(114, 114, 114))

    # Visualize
    # import matplotlib.pyplot as plt
    # ax = plt.subplots(1, 2, figsize=(12, 6))[1].ravel()
    # ax[0].imshow(img[:, :, ::-1])  # base
    # ax[1].imshow(img2[:, :, ::-1])  # warped

    # 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  # transform
        if perspective:
            xy = (xy[:, :2] / xy[:, 2:3]).reshape(n, 8)  # rescale
        else:  # affine
            xy = xy[:, :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

        # # apply angle-based reduction of bounding boxes
        # radians = a * math.pi / 180
        # reduction = max(abs(math.sin(radians)), abs(math.cos(radians))) ** 0.5
        # x = (xy[:, 2] + xy[:, 0]) / 2
        # y = (xy[:, 3] + xy[:, 1]) / 2
        # w = (xy[:, 2] - xy[:, 0]) * reduction
        # h = (xy[:, 3] - xy[:, 1]) * reduction
        # xy = np.concatenate((x - w / 2, y - h / 2, x + w / 2, y + h / 2)).reshape(4, n).T

        # clip boxes
        xy[:, [0, 2]] = xy[:, [0, 2]].clip(0, width)
        xy[:, [1, 3]] = xy[:, [1, 3]].clip(0, height)

        # filter candidates
        i = box_candidates(box1=targets[:, 1:5].T * s, box2=xy.T)
        targets = targets[i]
        targets[:, 1:5] = xy[i]

    return img, targets

def letterbox(img, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True):
  # Resize image to a 32-pixel-multiple rectangle https://github.com/ultralytics/yolov3/issues/232
  shape = img.shape[:2]  # current shape [height, width]
  if isinstance(new_shape, int):
      new_shape = (new_shape, new_shape)

  # Scale ratio (new / old)
  r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
  if not scaleup:  # only scale down, do not scale up (for better test mAP)
      r = min(r, 1.0)

  # Compute padding
  ratio = r, r  # width, height ratios
  new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
  dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1]  # wh padding
  if auto:  # minimum rectangle
      dw, dh = np.mod(dw, 64), np.mod(dh, 64)  # wh padding
  elif scaleFill:  # stretch
      dw, dh = 0.0, 0.0
      new_unpad = (new_shape[1], new_shape[0])
      ratio = new_shape[1] / shape[1], new_shape[0] / shape[0]  # width, height ratios

  dw /= 2  # divide padding into 2 sides
  dh /= 2

  if shape[::-1] != new_unpad:  # resize
      img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)
  top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
  left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
  if  img.shape[-1] > 3:
    img1 = cv2.copyMakeBorder(img[:,:,:3], top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)  # add border
    img2 = cv2.copyMakeBorder(img[:,:,3:], top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)  # add border
    if len(img2.shape) < 3:
       img2 = np.expand_dims(img2,2)
    img =  np.concatenate((img1,img2),axis=2)
  else:
    img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)  # add border
  return img, ratio, (dw, dh)

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
    vimg = img[:,:,:3]
    hue, sat, val = cv2.split(cv2.cvtColor(vimg, cv2.COLOR_BGR2HSV))
    dtype = vimg.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)
    img[:,:,:3] = cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR)  # no return needed
    return img

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

    return s

def load_image(self, index):
    # loads 1 image from dataset, returns img, original hw, resized hw
    img = self.imgs[index]
    c = self.channels
    if img is None or img.shape[0] != self.img_size:  # not cached
        path = self.img_files[index]
        if c in [3,5,6]:
            img = cv2.imread(path)  # BGR
            # if random.random() < self.foggify:
            #     depth = cv2.imread(path.replace('images','depth').replace('.png','.jpg'))[:,:,0]
            #     img = fogify(img,depth,0.005)
                #img = fogify(img,depth,random.choice([0.02, 0.04, 0.08, 0.16]))
            img = img[:, :, ::-1]
            assert img is not None, 'Image Not Found ' + path
        #img = cv2.imread(path.replace('images','radar'+os.sep+'radard'),cv2.IMREAD_GRAYSCALE)
        if c in [2,6]:
            img1 = cv2.imread(path.replace(self.replace,'radar_bev_image'))
            #img1 = cv2.rotate(cv2.imread(path.replace('images','radar_bev_image')), cv2.cv2.ROTATE_90_CLOCKWISE)[140:500,:,:]
        if c in [1,5]:
            img1 = cv2.imread(path.replace(self.replace,'radar_cam_image'+os.sep+'radarv'))[140:500,:,:]
            img2 = np.expand_dims(cv2.imread(path.replace(self.replace,'radar_cam_image'+os.sep+'radard'),cv2.IMREAD_GRAYSCALE),2)[140:500,:,:]
            assert img1 is not None or img2 is not None, 'Image Not Found ' + path
        if c == 2:
            img = img1
        if c == 1:
            img = np.concatenate((img1,img2),axis = 2)
        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
            if c in [1,2,3,5,6]:
                img = cv2.resize(img, (int(w0 * r), int(h0 * r)), interpolation=interp)
            if c in [5,6]:
                img1 = cv2.resize(img1, (int(w0 * r), int(h0 * r)), interpolation=cv2.INTER_NEAREST_EXACT)
            if c in [5]:
                img2 = np.expand_dims(cv2.resize(img2, (int(w0 * r), int(h0 * r)), interpolation=cv2.INTER_NEAREST_EXACT),2)
        if c == 5:
            img = np.concatenate((img,img1,img2),axis = 2)
        if c == 6:
            img = np.concatenate((img,img1),axis = 2)
        return img, (h0, w0), img.shape[:2]  # img, hw_original, hw_resized
    else:
        return self.imgs[index], self.img_hw0[index], self.img_hw[index]  # img, hw_original, hw_resized

def load_mosaic(self, index):
    # loads images in a mosaic
    
    labels4 = []
    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.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, 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:], 0, 2 * s, out=labels4[:, 1:])  # use with random_perspective
        # img4, labels4 = replicate(img4, labels4)  # replicate

    # Augment
    img4, labels4 = random_perspective(img4, labels4,
                                       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

class InfiniteDataLoader(torch.utils.data.dataloader.DataLoader):
    """ Dataloader that reuses workers
    Uses same syntax as vanilla DataLoader
    """

    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        object.__setattr__(self, 'batch_sampler', _RepeatSampler(self.batch_sampler))
        self.iterator = super().__iter__()

    def __len__(self):
        return len(self.batch_sampler.sampler)

    def __iter__(self):
        for i in range(len(self)):
            yield next(self.iterator)


class _RepeatSampler(object):
    """ Sampler that repeats forever
    Args:
        sampler (Sampler)
    """

    def __init__(self, sampler):
        self.sampler = sampler

    def __iter__(self):
        while True:
            yield from iter(self.sampler)

def create_dataloader(path, imgsz, batch_size, stride, opt, split = 'train',hyp=None, augment=False, cache=False, pad=0.0, rect=False,
                      rank=-1, world_size=1, workers=8):
    # Make sure only the first process in DDP process the dataset first, and the following others can use the cache
    with torch_distributed_zero_first(rank):
        dataset = LoadImagesAndLabels(path, imgsz, batch_size,
                                      augment=augment,  # augment images
                                      hyp=hyp,  # augmentation hyperparameters
                                      opt = opt,
                                      split = split,
                                      rect=rect,  # rectangular training
                                      cache_images=cache,
                                      single_cls=opt.single_cls,
                                      stride=int(stride),
                                      pad=pad,
                                      rank=rank)

    batch_size = min(batch_size, len(dataset))
    nw = min([os.cpu_count() // world_size, batch_size if batch_size > 1 else 0, workers])  # number of workers
    sampler = torch.utils.data.distributed.DistributedSampler(dataset) if rank != -1 else None
    dataloader = InfiniteDataLoader(dataset,
                                    batch_size=batch_size,
                                    num_workers=nw,
                                    sampler=sampler,
                                    pin_memory=True,
                                    collate_fn=LoadImagesAndLabels.collate_fn)  # torch.utils.data.DataLoader()
    return dataloader, dataset

def get_hash(files):
    # Returns a single hash value of a list of files
    return sum(os.path.getsize(f) for f in files if os.path.isfile(f))


def create_folder(path='./new'):
    # Create folder
    if os.path.exists(path):
        shutil.rmtree(path)  # delete output folder
    os.makedirs(path)  # make new output folder
    
class LoadImagesAndLabels(Dataset):  # for training/testing
    def __init__(self, path ,img_size=640, batch_size=16, augment=False,opt = None, 
                 split = 'train' ,hyp=None, rect=False, image_weights=False,
                 cache_images=False, single_cls=False, stride=32, pad=0.0, rank=-1):
        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.splits = torch.load(opt.splits)[split]
        self.channels = opt.channels
        self.foggify = opt.foggify
        self.replace = path.split('/nuscenes/')[-1]
        
        def img2label_paths(img_paths):
            # Define label paths as a function of image paths
            if self.channels == 2:
                folder = 'labels'
            else:
                folder = 'labels'
            sa, sb = os.sep + self.replace+ os.sep, os.sep + folder + os.sep  # /images/, /labels/ substrings
            return [x.replace(sa, sb, 1).replace(x.split('.')[-1], 'txt') for x in img_paths]

        try:
            f = []  # image files
            for p in path if isinstance(path, list) else [path]:
                p = Path(path)  # os-agnostic
                if p.is_dir():  # dir
                    f += glob.glob(str(p / '**' / '*.*'), recursive=True)
                elif p.is_file():  # file
                    with open(p, 'r') as t:
                        t = t.read().splitlines()
                        parent = str(p.parent) + os.sep
                        f += [x.replace('./', parent) if x.startswith('./') else x for x in t]  # local to global path
                else:
                    raise Exception('%s does not exist' % p)
            self.img_files = sorted([x.replace('/', os.sep) for x in f if x.split('.')[-1].lower() in ['png']])
            files = []
            for img in self.img_files:
                if img.split(os.sep)[-1] in self.splits:
                    files.append(img)
            self.img_files = sorted(files)
            assert self.img_files, 'No images found'
        except Exception as e:
            raise Exception('Error loading data from %s: %s\nSee %s' % (path, e, 'https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data'))

        # Check cache
        self.label_files = img2label_paths(self.img_files)  # labels
        cache_path = str(Path(self.label_files[0]).parent) + '.cache3'  # cached labels
        if os.path.isfile(cache_path):
            cache = torch.load(cache_path)  # load
            if cache['hash'] != get_hash(self.label_files + self.img_files):  # dataset changed
                cache = self.cache_labels(cache_path)  # re-cache
        else:
            cache = self.cache_labels(cache_path)  # cache

        # Read cache
        cache.pop('hash')  # remove hash
        labels, shapes = zip(*cache.values())
        self.labels = list(labels)
        self.shapes = np.array(shapes, dtype=np.float64)
        self.img_files = list(cache.keys())  # update
        self.label_files = img2label_paths(cache.keys())  # update

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

        # Rectangular Training
        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.labels = [self.labels[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(np.int) * stride

        # Check labels
        create_datasubset, extract_bounding_boxes = False, False
        nm, nf, ne, ns, nd = 0, 0, 0, 0, 0  # number missing, found, empty, datasubset, duplicate
        pbar = enumerate(self.label_files)
        if rank in [-1, 0]:
            pbar = tqdm(pbar)
        for i, file in pbar:
            l = self.labels[i]  # label
            if l is not None and l.shape[0]:
                assert l.shape[1] == 5, '> 5 label columns: %s' % file
                assert (l[:,:5] >= 0).all(), 'negative labels: %s' % file
                assert (l[:, 1:5] <= 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
                    nd += 1  # print('WARNING: duplicate rows in %s' % self.label_files[i])  # duplicate rows
                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):
                            os.makedirs(Path(f).parent)  # make new output folder

                        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(np.int)

                        b[[0, 2]] = np.clip(b[[0, 2]], 0, w)  # clip boxes outside of image
                        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:
                ne += 1  # print('empty labels for image %s' % self.img_files[i])  # file empty
                # os.system("rm '%s' '%s'" % (self.img_files[i], self.label_files[i]))  # remove

            if rank in [-1, 0]:
                pbar.desc = 'Scanning labels %s (%g found, %g missing, %g empty, %g duplicate, for %g images)' % (
                    cache_path, nf, nm, ne, nd, n)
        if nf == 0:
            s = 'WARNING: No labels found in %s. See %s' % (os.path.dirname(file) + os.sep, 'https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data')
            print(s)
            assert not augment, '%s. Can not train without labels.' % s

        # Cache images into memory for faster training (WARNING: large datasets may exceed system RAM)
        self.imgs = [None] * n
        if cache_images:
            gb = 0  # Gigabytes of cached images
            self.img_hw0, self.img_hw = [None] * n, [None] * n
            results = ThreadPool(8).imap(lambda x: load_image(*x), zip(repeat(self), range(n)))  # 8 threads
            pbar = tqdm(enumerate(results), total=n)
            for i, x in pbar:
                self.imgs[i], self.img_hw0[i], self.img_hw[i] = x  # img, hw_original, hw_resized = load_image(self, i)
                gb += self.imgs[i].nbytes
                pbar.desc = 'Caching images (%.1fGB)' % (gb / 1E9)
                
        if self.channels == 7:
            self.rads = [None]*n
            pbar = tqdm(enumerate(self.img_files))
            for i, file in pbar:
                r = np.load(file.replace(self.replace,'radar').replace('.png','.npy'))
                rad = np.zeros((8,125), dtype=np.float32)
                rad[:,:r.shape[-1]] = r
                self.rads[i] = rad[[0,1,2,3,6,7],:] # 8*n (x, y, z, rcs, vx, vy, vx_comp, vy_comp)
                pbar.desc = 'Caching radar'

    def cache_labels(self, path='labels.cache3'):
        # Cache dataset labels, check images and read shapes
        x = {}  # dict
        pbar = tqdm(zip(self.img_files, self.label_files), desc='Scanning images', total=len(self.img_files))
        for (img, label) in pbar:
            try:
                l = []
                im = Image.open(img)
                im.verify()  # PIL verify
                shape = exif_size(im)  # image size
                assert (shape[0] > 9) & (shape[1] > 9), 'image size <10 pixels'
                if os.path.isfile(label):
                    with open(label, 'r') as f:
                        l = np.array([x.split() for x in f.read().splitlines()], dtype=np.float32)  # labels
                if len(l) == 0:
                    l = np.zeros((0, 5), dtype=np.float32)
                x[img] = [l, shape]
            except Exception as e:
                print('WARNING: Ignoring corrupted image and/or label %s: %s' % (img, e))

        x['hash'] = get_hash(self.label_files + self.img_files)
        torch.save(x, path)  # save for next time
        return x

    def __len__(self):
        return len(self.img_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):
        if self.image_weights:
            index = self.indices[index]

        hyp = self.hyp
        mosaic = self.mosaic and random.random() < hyp['mosaic']
        if mosaic:
            # Load mosaic
            img, labels = load_mosaic(self, index)
            #img, labels = load_mosaic9(self, index)
            shapes = None

            # MixUp https://arxiv.org/pdf/1710.09412.pdf
            if random.random() < hyp['mixup']:
                img2, labels2 = load_mosaic(self, random.randint(0, len(self.labels) - 1))
                #img2, labels2 = load_mosaic9(self, random.randint(0, len(self.labels) - 1))
                r = np.random.beta(8.0, 8.0)  # mixup ratio, alpha=beta=8.0
                img = (img * r + img2 * (1 - r)).astype(np.uint8)
                labels = np.concatenate((labels, labels2), 0)

        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 mosaic:
                img, labels = random_perspective(img, labels,
                                                 degrees=hyp['degrees'],
                                                 translate=hyp['translate'],
                                                 scale=hyp['scale'],
                                                 shear=hyp['shear'],
                                                 perspective=hyp['perspective'])

            # Augment colorspace
            if self.channels > 2:
                img = 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:
            labels[:, 1:5] = xyxy2xywh(labels[:, 1:5])  # convert xyxy to xywh
            labels[:, [2, 4]] /= img.shape[0]  # normalized height 0-1
            labels[:, [1, 3]] /= img.shape[1]  # normalized width 0-1

        if self.augment:
            # 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]
                if self.channels ==7:
                    self.rads[index][1,:]=self.rads[index][1,:]*-1

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

        # Convert
        if len(img.shape)<3:
            img = np.expand_dims(img,2)
        img = img.transpose(2, 0, 1)  # BGR to RGB, to 3x416x416
        img = np.ascontiguousarray(img)
        img = img.copy()
        if self.channels > 6:
            return torch.from_numpy(img), labels_out, self.img_files[index], shapes, torch.from_numpy(self.rads[index])
        else:
            return torch.from_numpy(img), labels_out, self.img_files[index], shapes, None

    @staticmethod
    def collate_fn(batch):
        if batch[0][-1] is not None:
            img, label, path, shapes, rads = 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, torch.stack(rads, 0)
        else:
            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, None

   
def train(config = None, budget = None, hyp = None, opt = None, wandb=None, train_case = 'train'):
    logger = logging.getLogger(__name__)
    
    if opt.evolve:
        logger = logging.getLogger(__name__)
        opt.epochs = budget
        
        for key in list(config.get_dictionary().keys()):
            hyp[key] = config.get_dictionary()[key]
        print(config.get_dictionary())
        
    opt.local_rank = -1
    opt.total_batch_size = opt.batch_size
    opt.world_size = int(os.environ['WORLD_SIZE']) if 'WORLD_SIZE' in os.environ else 1
    opt.global_rank = int(os.environ['RANK']) if 'RANK' in os.environ else -1
    set_logging(opt.global_rank)
    # Resume
    if opt.resume:  # resume an interrupted run
        ckpt = opt.weights
        logger.info('Resuming training from %s' % ckpt)
    else:
        # opt.hyp = opt.hyp or ('hyp.finetune.yaml' if opt.weights else 'hyp.scratch.yaml')
        # opt.img_size.extend([opt.img_size[-1]] * (2 - len(opt.img_size)))  # extend to 2 sizes (train, test)
        opt.name = 'evolve' if opt.evolve else opt.name
        opt.save_dir = increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok | opt.evolve)  # increment run

    # DDP mode
    device = select_device(opt.device_num, batch_size=opt.batch_size, logger = logger)
    if opt.local_rank != -1:
        assert torch.cuda.device_count() > opt.local_rank
        torch.cuda.set_device(opt.local_rank)
        device = torch.device('cuda', opt.local_rank)
        dist.init_process_group(backend='nccl', init_method='env://')  # distributed backend
        assert opt.batch_size % opt.world_size == 0, '--batch-size must be multiple of CUDA device count'
        opt.batch_size = opt.total_batch_size // opt.world_size

    # Train
    logger.info(opt)
    logger.info(f'Hyperparameters {hyp}')
    
    save_dir, epochs, batch_size, total_batch_size, weights, rank, imgsz, imgsz_test,nc = \
    Path(opt.save_dir), opt.epochs, opt.batch_size, opt.total_batch_size, opt.weights, opt.global_rank, opt.img_size, opt.img_size, opt.nc
    tb_writer = None
    if not opt.evolve:  # init loggers
        if opt.global_rank in [-1, 0]:
            logger.info(f'Start Tensorboard with "tensorboard --logdir {opt.project}", view at http://localhost:6006/')
            tb_writer = SummaryWriter(opt.save_dir)  # Tensorboard
    
    # Directories
    wdir = save_dir / 'weights'
    wdir.mkdir(parents=True, exist_ok=True)  # make dir
    last = wdir / 'last.pt'
    best = wdir / 'best.pt'
    results_file = save_dir / 'results.txt'

    # Save run settings
    with open(save_dir / 'hyp.yaml', 'w') as f:
        yaml.dump(hyp, f, sort_keys=False)
    with open(save_dir / 'dict.yaml', 'w') as f:
        yaml.dump(vars(opt), f, sort_keys=False)

    # Configure
    plots = not opt.evolve and opt.channels in [1,2,3,5,6] # create plots
    cuda = device.type != 'cpu'
    init_seeds(2 + rank)
    # with open(opt.data) as f:
    #     data_dict = yaml.load(f, Loader=yaml.FullLoader)  # data dict
    #train_path = data_dict['train']
    #test_path = data_dict['val']
    #nc, names = (1, ['item']) if opt.single_cls else (int(data_dict['nc']), data_dict['names'])  # number classes, names
    #assert len(names) == nc, '%g names found for nc=%g dataset in %s' % (len(names), nc, opt.data)  # check

    # Model
    pretrained = weights.endswith('.pt')
    if pretrained:
        ckpt = torch.load(weights, map_location=device)  # load checkpoint
        model = Darknet(nc,hyp['anchors_g'],opt.channels).to(device)  # create
        state_dict = {k: v for k, v in ckpt['model'].items() if model.state_dict()[k].numel() == v.numel()}
        model.load_state_dict(state_dict, strict=False)
        print('Transferred %g/%g items from %s' % (len(state_dict), len(model.state_dict()), weights))  # report
    else:
        model = Darknet(nc,hyp['anchors_g'],opt.channels).to(device) # create
    freeze = 0
    for name,param in model.named_parameters():
        for pattern in opt.freeze:
             if pattern in name:
                 param.requires_grad = False
                 freeze += 1
    if freeze:
       print('frozen',freeze,'layers')
    # Optimizer
    nbs = 64  # nominal batch size
    accumulate = max(round(nbs / total_batch_size), 1)  # accumulate loss before optimizing
    hyp['weight_decay'] *= total_batch_size * accumulate / nbs  # scale weight_decay

    pg0, pg1, pg2 = [], [], []  # optimizer parameter groups
    for k, v in dict(model.named_parameters()).items():
        if '.bias' in k:
            pg2.append(v)  # biases
        elif '.weight' in k and '.batchnorm' not in k: # or '1.weight'in k:
            pg1.append(v)  # apply weight_decay
        #elif k in ['head.final3.weight','head.final4.weight','head.final5.weight']:
            #pg1.append(v)
        else:
            pg0.append(v)  # all else
            
    if opt.adam:
        optimizer = optim.Adam(pg0, lr=hyp['lr0'], betas=(hyp['momentum'], 0.999))  # adjust beta1 to momentum
    else:
        optimizer = optim.SGD(pg0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True)

    optimizer.add_param_group({'params': pg1, 'weight_decay': hyp['weight_decay']})  # add pg1 with weight_decay
    optimizer.add_param_group({'params': pg2})  # add pg2 (biases)
    logger.info('Optimizer groups: %g .bias, %g conv.weight, %g other' % (len(pg2), len(pg1), len(pg0)))
    del pg0, pg1, pg2

    # Scheduler https://arxiv.org/pdf/1812.01187.pdf
    # https://pytorch.org/docs/stable/_modules/torch/optim/lr_scheduler.html#OneCycleLR
    lf = lambda x: ((1 + math.cos(x * math.pi / epochs)) / 2) * (1 - hyp['lrf']) + hyp['lrf']   # cosine
    scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf) #lf
    # plot_lr_scheduler(optimizer, scheduler, epochs)

    # Logging
    if wandb and wandb.run is None:
        opt.hyp = hyp  # add hyperparameters
        wandb_run = wandb.init(config=opt, resume="allow",
                               project='YOLOv4' if opt.project == 'runs/train' else Path(opt.project).stem,
                               name=save_dir.stem,
                               id=ckpt.get('wandb_id') if 'ckpt' in locals() else None)

    # Resume
    start_epoch, best_fitness = 0, 0.0
    best_fitness_p, best_fitness_r, best_fitness_ap50, best_fitness_ap, best_fitness_f = 0.0, 0.0, 0.0, 0.0, 0.0
    if pretrained:
        # Optimizer
        if ckpt['optimizer'] is not None:
            optimizer.load_state_dict(ckpt['optimizer'])
            best_fitness = ckpt['best_fitness']
            # best_fitness_p = ckpt['best_fitness_p']
            # best_fitness_r = ckpt['best_fitness_r']
            # best_fitness_ap50 = ckpt['best_fitness_ap50']
            # best_fitness_ap = ckpt['best_fitness_ap']
            # best_fitness_f = ckpt['best_fitness_f']

        # Results
        if ckpt.get('training_results') is not None:
            with open(results_file, 'w') as file:
                file.write(ckpt['training_results'])  # write results.txt

        # Epochs
        start_epoch = ckpt['epoch'] + 1
        if opt.resume:
            assert start_epoch > 0, '%s training to %g epochs is finished, nothing to resume.' % (weights, epochs)
        if epochs < start_epoch:
            logger.info('%s has been trained for %g epochs. Fine-tuning for %g additional epochs.' %
                        (weights, ckpt['epoch'], epochs))
            epochs += ckpt['epoch']  # finetune additional epochs

        del ckpt, state_dict
    
    opt.resume = start_epoch > 1
    # Image sizes
    gs = 64 #int(max(model.stride))  # grid size (max stride)
    #imgsz, imgsz_test = [check_img_size(x, gs) for x in opt.img_size]  # verify imgsz are gs-multiples

    # DP mode
    if cuda and rank == -1 and torch.cuda.device_count() > 1:
        model = torch.nn.DataParallel(model)
        print('model parallelised DP')

    # SyncBatchNorm
    if opt.sync_bn and cuda and rank != -1:
        model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device)
        logger.info('Using SyncBatchNorm()')

    # EMA
    ema = ModelEMA(model) if rank in [-1, 0] else None

    # DDP mode
    if cuda and rank != -1:
        model = DDP(model, device_ids=[opt.local_rank], output_device=opt.local_rank)
        print('model parallised DDP')

    # Trainloader
    dataloader, dataset = create_dataloader(opt.root, imgsz, batch_size, gs, opt, split = train_case,
                                            hyp=hyp, augment=opt.augment, cache=opt.cache_images, rect=opt.rect,
                                            rank=rank, world_size=opt.world_size, workers=opt.workers)
    
    #mlc = np.concatenate(dataset.labels, 0)[:, 0].max()  # max label class
    nb = len(dataloader)  # number of batches
    #assert mlc < nc, 'Label class %g exceeds nc=%g in %s. Possible class labels are 0-%g' % (mlc, nc, opt.data, nc - 1)

    # Process 0
    if rank in [-1, 0]:
        ema.updates = start_epoch * nb // accumulate  # set EMA updates
        testloader = create_dataloader(opt.root, imgsz_test, batch_size, gs, opt, split = 'val' if train_case =='train' else train_case, #if opt.evolve else 'test',
                                       hyp=hyp, cache=opt.cache_images and not opt.notest, rect=opt.rect_test,
                                       rank=-1, world_size=opt.world_size, workers=opt.workers)[0]  # testloader

        if not opt.resume:
            labels = np.concatenate(dataset.labels, 0)
            c = torch.tensor(labels[:, 0])  # classes
            #cf = torch.bincount(c.long(), minlength=nc) + 1.  # frequency
            #model._initialize_biases(cf.to(device))
            if plots:
                plot_labels(labels, save_dir=save_dir)
                if tb_writer:
                    tb_writer.add_histogram('classes', c, 0)
                if wandb:
                    wandb.log({"Labels": [wandb.Image(str(x), caption=x.name) for x in save_dir.glob('*labels*.png')]})

            # Anchors
            # if not opt.noautoanchor:
            #     check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz)

    # Model parameters
    hyp['cls'] *= nc / 80.  # scale coco-tuned hyp['cls'] to current dataset
    model.nc = nc  # attach number of classes to model
    model.hyp = hyp  # attach hyperparameters to model
    model.gr = 1.0  # iou loss ratio (obj_loss = 1.0 or iou)
    model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device)  # attach class weights
    model.names = opt.names
    model.strides = [8,16,32]

    # Start training
    t0 = time.time()
    nw = max(round(hyp['warmup_epochs'] * nb), 1000)  # number of warmup iterations, max(3 epochs, 1k iterations)
    # nw = min(nw, (epochs - start_epoch) / 2 * nb)  # limit warmup to < 1/2 of training
    maps = np.zeros(nc)  # mAP per class
    results = (0, 0, 0, 0, 0, 0, 0)  # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls)
    scheduler.last_epoch = start_epoch - 1  # do not move
    scaler = amp.GradScaler(enabled=opt.gradscale)
    logger.info('Image sizes %g train, %g test\n'
                'Using %g dataloader workers\nLogging results to %s\n'
                'Starting training for %g epochs...' % (imgsz, imgsz_test, dataloader.num_workers, save_dir, epochs))
    
    torch.save(model, wdir / 'init.pt')
    
    for epoch in range(start_epoch, epochs):  # epoch ------------------------------------------------------------------
        model.train()

        # Update image weights (optional)
        if opt.image_weights:
            # Generate indices
            if rank in [-1, 0]:
                cw = model.class_weights.cpu().numpy() * (1 - maps) ** 2  # class weights
                iw = labels_to_image_weights(dataset.labels, nc=nc, class_weights=cw)  # image weights
                dataset.indices = random.choices(range(dataset.n), weights=iw, k=dataset.n)  # rand weighted idx
            # Broadcast if DDP
            if rank != -1:
                indices = (torch.tensor(dataset.indices) if rank == 0 else torch.zeros(dataset.n)).int()
                dist.broadcast(indices, 0)
                if rank != 0:
                    dataset.indices = indices.cpu().numpy()

        # Update mosaic border
        # b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs)
        # dataset.mosaic_border = [b - imgsz, -b]  # height, width borders

        mloss = torch.zeros(4, device=device)  # mean losses
        if rank != -1:
            dataloader.sampler.set_epoch(epoch)
        pbar = enumerate(dataloader)
        logger.info(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'box', 'obj', 'cls', 'total', 'targets', 'img_size'))
        if rank in [-1, 0]:
            pbar = tqdm(pbar, total=nb)  # progress bar
        optimizer.zero_grad()
        for i, (imgs, targets, paths, _, rads) in pbar:  # batch -------------------------------------------------------------
            ni = i + nb * epoch  # number integrated batches (since train start)
            imgs = imgs.to(device, non_blocking=True).float() / 255.0  # uint8 to float32, 0-255 to 0.0-1.0

            # Warmup
            if not opt.resume:
                if ni <= nw:
                    xi = [0, nw]  # x interp
                    # model.gr = np.interp(ni, xi, [0.0, 1.0])  # iou loss ratio (obj_loss = 1.0 or iou)
                    accumulate = max(1, np.interp(ni, xi, [1, nbs / total_batch_size]).round())
                    for j, x in enumerate(optimizer.param_groups):
                        # bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0
                        x['lr'] = np.interp(ni, xi, [hyp['warmup_bias_lr'] if j == 2 else 0.0, x['initial_lr'] * lf(epoch)])
                        if 'momentum' in x:
                            x['momentum'] = np.interp(ni, xi, [hyp['warmup_momentum'], hyp['momentum']])

            # Multi-scale
            if opt.multi_scale:
                sz = random.randrange(imgsz * 0.5, imgsz * 1.5 + gs) // gs * gs  # size
                sf = sz / max(imgs.shape[2:])  # scale factor
                if sf != 1:
                    ns = [math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]]  # new shape (stretched to gs-multiple)
                    imgs = F.interpolate(imgs, size=ns, mode='bilinear', align_corners=False)

            # Forward
            with amp.autocast(enabled=opt.gradscale):
                if opt.channels == 7:
                    rads = rads.to(device)
                    pred = model((imgs,rads))  # forward
                else:
                    pred = model(imgs)
                loss, loss_items = compute_loss(pred, targets.to(device), model)  # loss scaled by batch_size
                if rank != -1:
                    loss *= opt.world_size  # gradient averaged between devices in DDP mode

            # Backward
            scaler.scale(loss).backward()

            # Optimize
            if ni % accumulate == 0:
                scaler.step(optimizer)  # optimizer.step
                scaler.update()
                optimizer.zero_grad()
                if ema:
                    ema.update(model)

            # Print
            if rank in [-1, 0]:
                mloss = (mloss * i + loss_items) / (i + 1)  # update mean losses
                mem = '%.3gG' % (torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0)  # (GB)
                s = ('%10s' * 2 + '%10.4g' * 6) % (
                    '%g/%g' % (epoch, epochs - 1), mem, *mloss, targets.shape[0], imgs.shape[-1])
                pbar.set_description(s)

                #Plot
                if plots and ni < 3:
                    f = str(save_dir / f'train_batch{ni}.jpg')  # filename
                    result = plot_images(images=imgs[:,:3,:,:], targets=targets, paths=paths, fname=f)
                    if tb_writer:
                        tb_writer.add_image(f'train_batch{ni}.jpg', result, dataformats='HWC', global_step=epoch)
                        tb_writer.add_graph(model, imgs)  # add model to tensorboard
                elif plots and ni == 3 and wandb:
                    wandb.log({"Mosaics": [wandb.Image(str(x), caption=x.name) for x in save_dir.glob('train*.jpg')]})

            # end batch ------------------------------------------------------------------------------------------------
        # end epoch ----------------------------------------------------------------------------------------------------

        # Scheduler100
        lr = [x['lr'] for x in optimizer.param_groups]  # for tensorboard
        scheduler.step()

        # DDP process 0 or single-GPU
        if rank in [-1, 0]:
            # mAP
            if ema:
                ema.update_attr(model)
            final_epoch = epoch + 1 == epochs
            if not opt.notest or final_epoch:  # Calculate mAP
                #if epoch >= 10:
                    results, maps, times = test(opt, hyp,
                                                 model=ema.ema.module if hasattr(ema.ema, 'module') else ema.ema,
                                                 dataloader=testloader,
                                                 save_dir=save_dir,
                                                 plots=plots and final_epoch)
            
            # Write
            with open(results_file, 'a') as f:
                f.write(s + '%10.4g' * 7 % results + '\n')  # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls)
            if len(opt.name) and opt.bucket:
                os.system('gsutil cp %s gs://%s/results/results%s.txt' % (results_file, opt.bucket, opt.name))

            # Log
            tags = ['train/box_loss', 'train/obj_loss', 'train/cls_loss',  # train loss
                    'metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/mAP_0.5:0.95',
                    'val/box_loss', 'val/obj_loss', 'val/cls_loss',  # val loss
                    'x/lr0', 'x/lr1', 'x/lr2',# params
                    'x_map/car','x_map/pedestrian','x_map/barrier','x_map/truck','x_map/traffic_cone','x_map/trailer','x_map/bus',
                    'x_map/construction_vehicle','x_map/motorcycle','x_map/bicycle']
            for x, tag in zip(list(mloss[:-1]) + list(results) + lr + list(maps), tags):
                if tb_writer:
                    tb_writer.add_scalar(tag, x, epoch)  # tensorboard
                if wandb:
                    wandb.log({tag: x})  # W&B

            # Update best mAP
            fi = fitness(np.array(results).reshape(1, -1))  # weighted combination of [P, R, mAP@.5, mAP@.5-.95]
            #fi_p = fitness_p(np.array(results).reshape(1, -1))  # weighted combination of [P, R, mAP@.5, mAP@.5-.95]
            #fi_r = fitness_r(np.array(results).reshape(1, -1))  # weighted combination of [P, R, mAP@.5, mAP@.5-.95]
            #fi_ap50 = fitness_ap50(np.array(results).reshape(1, -1))  # weighted combination of [P, R, mAP@.5, mAP@.5-.95]
            #fi_ap = fitness_ap(np.array(results).reshape(1, -1))  # weighted combination of [P, R, mAP@.5, mAP@.5-.95]
            #if (fi_p > 0.0) or (fi_r > 0.0):
                #fi_f = fitness_f(np.array(results).reshape(1, -1))  # weighted combination of [P, R, mAP@.5, mAP@.5-.95]
            #else:
                #fi_f = 0.0
            if fi > best_fitness:
                best_fitness = fi
            # if fi_p > best_fitness_p:
            #     best_fitness_p = fi_p
            # if fi_r > best_fitness_r:
            #     best_fitness_r = fi_r
            # if fi_ap50 > best_fitness_ap50:
            #     best_fitness_ap50 = fi_ap50
            # if fi_ap > best_fitness_ap:
            #     best_fitness_ap = fi_ap
            # if fi_f > best_fitness_f:
            #     best_fitness_f = fi_f

            # Save model
            save = (not opt.nosave) or (final_epoch and not opt.evolve)
            if save:
                with open(results_file, 'r') as f:  # create checkpoint
                    ckpt = {'epoch': epoch,
                            'best_fitness': best_fitness,
                            'training_results': f.read(),
                            'model': ema.ema.module.state_dict() if hasattr(ema, 'module') else ema.ema.state_dict(),
                            'optimizer': None if final_epoch else optimizer.state_dict(),
                            'wandb_id': wandb_run.id if wandb else None}

                # Save last, best and delete
                torch.save(ckpt, last)
                if best_fitness == fi:
                    torch.save(ckpt, best)
                # if (best_fitness == fi) and (epoch >= 200):
                #     torch.save(ckpt, wdir / 'best_{:03d}.pt'.format(epoch))
                # if best_fitness == fi:
                #     torch.save(ckpt, wdir / 'best_overall.pt')
                # if best_fitness_p == fi_p:
                #     torch.save(ckpt, wdir / 'best_p.pt')
                # if best_fitness_r == fi_r:
                #     torch.save(ckpt, wdir / 'best_r.pt')
                # if best_fitness_ap50 == fi_ap50:
                #     torch.save(ckpt, wdir / 'best_ap50.pt')
                # if best_fitness_ap == fi_ap:
                #     torch.save(ckpt, wdir / 'best_ap.pt')
                # if best_fitness_f == fi_f:
                #     torch.save(ckpt, wdir / 'best_f.pt')
                # if epoch == 0:
                #     torch.save(ckpt, wdir / 'epoch_{:03d}.pt'.format(epoch))
                # if ((epoch+1) % 25) == 0:
                #     torch.save(ckpt, wdir / 'epoch_{:03d}.pt'.format(epoch))
                # if epoch >= (epochs-5):
                #     torch.save(ckpt, wdir / 'last_{:03d}.pt'.format(epoch))
                # elif epoch >= 420: 
                #     torch.save(ckpt, wdir / 'last_{:03d}.pt'.format(epoch))
                del ckpt
        # end epoch ----------------------------------------------------------------------------------------------------
    # end training

    if rank in [-1, 0]:
        # Strip optimizers
        n = opt.name if opt.name.isnumeric() else ''
        fresults, flast, fbest = save_dir / f'results{n}.txt', wdir / f'last{n}.pt', wdir / f'best{n}.pt'
        for f1, f2 in zip([wdir / 'last.pt', wdir / 'best.pt', results_file], [flast, fbest, fresults]):
            if f1.exists():
                os.rename(f1, f2)  # rename
                if str(f2).endswith('.pt'):  # is *.pt
                    strip_optimizer(f2)  # strip optimizer
                    os.system('gsutil cp %s gs://%s/weights' % (f2, opt.bucket)) if opt.bucket else None  # upload
        # Finish
        if plots:
            plot_results(save_dir=save_dir)  # save as results.png
            if wandb:
                wandb.log({"Results": [wandb.Image(str(save_dir / x), caption=x) for x in
                                        ['results.png', 'precision-recall_curve.png']]})
        logger.info('%g epochs completed in %.3f hours.\n' % (epoch - start_epoch + 1, (time.time() - t0) / 3600))
    else:
        dist.destroy_process_group()

    wandb.run.finish() if wandb and wandb.run else None
    torch.cuda.empty_cache()
    if opt.evolve:
        return {"fitness":1/fi, "cost": time.time()-t0, "info":{"AP50":results[2], "budget":budget}}
    else:
        return results



def test(opt, hyp, test_case = 'val', model=None, dataloader=None,save_dir=Path(''), plots=True, verbose = True):

    batch_size, weights, imgsz = opt.batch_size, opt.weights, opt.img_size
    # Initialize/load model and set device
    training = model is not None
    if training:  # called by train.py
        device = next(model.parameters()).device  # get model device

    else:  # called directly
        if opt.channels == 7:
            plots = False
        logger = logging.getLogger(__name__)    
        set_logging()
        device = select_device(opt.device_num, batch_size=batch_size, logger =  logger)
        save_txt = opt.save_txt  # save *.txt labels

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

        # Load model
        model = Darknet(opt.nc,hyp['anchors_g'],opt.channels).to(device)

        # load model
        #try:
        ckpt = torch.load(weights, map_location=device)  # load checkpoint
        ckpt['model'] = {k: v for k, v in ckpt['model'].items() if model.state_dict()[k].numel() == v.numel()}
        model.load_state_dict(ckpt['model'], strict=False)
        #except:
            #load_darknet_weights(model, weights[0])
        #imgsz = check_img_size(imgsz, s=64)  # check img_size

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

    # Configure
    model.eval()
    # is_coco = data.endswith('coco.yaml')  # is COCO dataset
    # with open(data) as f:
    #     data = yaml.load(f, Loader=yaml.FullLoader)  # model dict
    #check_dataset(data)  # check
    #nc = 1 if single_cls else int(data['nc'])  # number of classes
    nc = opt.nc
    iouv = torch.linspace(0.5, 0.95, 10).to(device)  # iou vector for mAP@0.5:0.95
    niou = iouv.numel()

    # Logging
    log_imgs, wandb = min(opt.log_imgs, 100), None  # ceil
    try:
        import wandb  # Weights & Biases
    except ImportError:
        log_imgs = 0

    # Dataloader
    if not training:
        #img = torch.zeros((1, opt.channels, imgsz, imgsz), device=device)  # init img
        #_ = model(img.half() if half else img) if device.type != 'cpu' else None  # run once
        dataloader = create_dataloader(opt.root, imgsz, batch_size, 64, opt = opt, split = test_case, hyp = hyp , pad=0.5, rect=opt.rect_test)[0]

    seen = 0
    # try:
    #     names = model.names if hasattr(model, 'names') else model.module.names
    # except:
    #     names = load_classes(opt.names)
    names =  opt.names
    #coco91class = coco80_to_coco91_class()
    s = ('%20s' + '%12s' * 6) % ('Class', 'Images', 'Targets', 'P', 'R', 'mAP@.5', 'mAP@.5:.95')
    p, r, f1, mp, mr, map50, map, t0, t1 = 0., 0., 0., 0., 0., 0., 0., 0., 0.
    loss = torch.zeros(3, device=device)
    jdict, stats, ap, ap_class, wandb_images = [], [], [], [], []
    for batch_i, (img, targets, paths, shapes,rads) in enumerate(tqdm(dataloader, desc=s)):
        img = img.to(device, non_blocking=True)
        img = img.half() if half else img.float()  # uint8 to fp16/32
        img /= 255.0  # 0 - 255 to 0.0 - 1.0
        targets = targets.to(device)
        nb, _, height, width = img.shape  # batch size, channels, height, width
        whwh = torch.Tensor([width, height, width, height]).to(device)
        # Disable gradients
        with torch.no_grad():
            # Run model
            t = time_synchronized()
            if opt.channels == 7:
                rads = rads.to(device, non_blocking=True)
                #rads1 = torch.zeros_like(rads).to(device, non_blocking=True)
                rads =  rads.half() if half else rads.float()
                inf_out, train_out = model((img,rads))  # inference and training outputs
            else:
                 inf_out, train_out = model(img) 
            t0 += time_synchronized() - t

            # Compute loss
            if training:  # if model has loss hyperparameters
                loss += compute_loss([x.float() for x in train_out], targets, model)[1][:3]  # box, obj, cls

            # Run NMS
            t = time_synchronized()
            output = non_max_suppression(inf_out, conf_thres=hyp['conf_t'], iou_thres=hyp['iou_t'])
            t1 += time_synchronized() - t

        # Statistics per image
        for si, pred in enumerate(output):
            labels = targets[targets[:, 0] == si, 1:]
            nl = len(labels)
            #tcls = labels[labels[:,-1] < opt.distance, 0].tolist() if nl else []  # target class
            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
            path = Path(paths[si])
            if opt.save_txt:
                gn = torch.tensor(shapes[si][0])[[1, 0, 1, 0]]  # normalization gain whwh
                x = pred.clone()
                x[:, :4] = scale_coords(img[si].shape[1:], x[:, :4], shapes[si][0], shapes[si][1])  # to original
                for *xyxy, conf, cls in x:
                    xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
                    line = (cls, *xywh, conf) if opt.save_conf else (cls, *xywh)  # label format
                    with open(save_dir / 'labels' / (path.stem + '.txt'), 'a') as f:
                        f.write(('%g ' * len(line)).rstrip() % line + '\n')

            # W&B logging
            if plots and len(wandb_images) < log_imgs:
                box_data = [{"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]},
                             "class_id": int(cls),
                             "box_caption": "%s %.3f" % (names[cls], conf),
                             "scores": {"class_score": conf},
                             "domain": "pixel"} for *xyxy, conf, cls in pred.tolist()]
                boxes = {"predictions": {"box_data": box_data, "class_labels": names}}
                wandb_images.append(wandb.Image(img[si], boxes=boxes, caption=path.name))

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

            # Append to pycocotools JSON dictionary
            if opt.save_json:
                # [{"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}, ...
                image_id = int(path.stem) if path.stem.isnumeric() else path.stem
                box = pred[:, :4].clone()  # xyxy
                scale_coords(img[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': 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)
            #distance = torch.zeros(pred.shape[0],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(as_tuple=False).view(-1)  # prediction indices
                    pi = (cls == pred[:, 5]).nonzero(as_tuple=False).view(-1)  # target indices

                    # Search for detections
                    if pi.shape[0]:
                        # Prediction to target ious
                        ious, i = box_iou(pred[pi, :4], tbox[ti]).max(1)  # best ious, indices
                        # Append detections
                        detected_set = set()
                        for j in (ious > iouv[0]).nonzero(as_tuple=False):
                            d = ti[i[j]]  # detected target
                            if d.item() not in detected_set:
                                detected_set.add(d.item())
                                detected.append(d)
                                correct[pi[j]] = ious[j] > iouv  # iou_thres is 1xn
                                if len(detected) == nl:  # all targets already located in image
                                    break
                        #select distance
                        #distance[pi] = labels[i,-1] < opt.distance
                    
            # Append statistics (correct, conf, pcls, tcls)
            #stats.append((correct[distance,:].cpu(), pred[distance, 4].cpu(), pred[distance, 5].cpu(), tcls)) #distance append
            stats.append((correct.cpu(), pred[:, 4].cpu(), pred[:, 5].cpu(), tcls)) #distance append

        # Plot images
        if plots and batch_i < 3:
            f = str(save_dir / f'test_batch{batch_i}_labels.jpg')  # filename
            plot_images(img[:,:3,:,:], targets, paths, f, names)  # labels
            f = str(save_dir / f'test_batch{batch_i}_pred.jpg')
            plot_images(img[:,:3,:,:], output_to_target(output, width, height), paths, f, names)  # predictions
    ap50 =  torch.zeros((10))
    # Compute statistics
    stats = [np.concatenate(x, 0) for x in zip(*stats)]  # to numpy
    if len(stats) and stats[0].any():
        p, r, ap, f1, ap_class = ap_per_class(*stats, plot=plots, fname=save_dir / 'precision-recall_curve.png')
        p, r, ap50, ap = p[:, 0], r[:, 0], ap[:, 0], ap.mean(1)  # [P, R, AP@0.5, AP@0.5:0.95]
        mp, mr, map50, map = p.mean(), r.mean(), ap50.mean(), ap.mean()
        nt = np.bincount(stats[3].astype(np.int64), minlength=nc)  # number of targets per class
    else:
        nt = torch.zeros(1)

    # W&B logging
    if plots and wandb:
        wandb.log({"Images": wandb_images})
        wandb.log({"Validation": [wandb.Image(str(x), caption=x.name) for x in sorted(save_dir.glob('test*.jpg'))]})

    # Print results
    pf = '%20s' + '%12.3g' * 6  # print format
    print(pf % ('all', seen, nt.sum(), mp, mr, map50, map))

    # 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], ap50[i], ap[i]))

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

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

    # Return results
    if not training:
        print('Results saved to %s' % save_dir)
    model.float()  # for training
    maps = np.zeros(nc) + map
    for i, c in enumerate(ap_class):
        maps[c] = ap[i]
    return (mp, mr, map50, map, *(loss.cpu() / len(dataloader)).tolist()), ap50, t
