# Copyright 2021 Huawei Technologies Co., Ltd

#

# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

#

#     http://www.apache.org/licenses/LICENSE-2.0

#

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.



# Auto-anchor utils



import numpy as np

import torch

import yaml

from scipy.cluster.vq import kmeans

from tqdm import tqdm





def check_anchor_order(m):

    # Check anchor order against stride order for YOLOv5 Detect() module m, and correct if necessary

    a = m.anchor_grid.prod(-1).view(-1)  # anchor area

    da = a[-1] - a[0]  # delta a

    ds = m.stride[-1] - m.stride[0]  # delta s

    if da.sign() != ds.sign():  # same order

        print('Reversing anchor order')

        m.anchors[:] = m.anchors.flip(0)

        m.anchor_grid[:] = m.anchor_grid.flip(0)





def check_anchors(dataset, model, thr=4.0, imgsz=640):

    # Check anchor fit to data, recompute if necessary

    print('\nAnalyzing anchors... ', end='')

    m = model.module.model[-1] if hasattr(model, 'module') else model.model[-1]  # Detect()

    shapes = imgsz * dataset.shapes / dataset.shapes.max(1, keepdims=True)

    scale = np.random.uniform(0.9, 1.1, size=(shapes.shape[0], 1))  # augment scale

    wh = torch.tensor(np.concatenate([l[:, 3:5] * s for s, l in zip(shapes * scale, dataset.labels)])).float()  # wh



    def metric(k):  # compute metric

        r = wh[:, None] / k[None]

        x = torch.min(r, 1. / r).min(2)[0]  # ratio metric

        best = x.max(1)[0]  # best_x

        aat = (x > 1. / thr).float().sum(1).mean()  # anchors above threshold

        bpr = (best > 1. / thr).float().mean()  # best possible recall

        return bpr, aat



    bpr, aat = metric(m.anchor_grid.clone().cpu().view(-1, 2))

    print('anchors/target = %.2f, Best Possible Recall (BPR) = %.4f' % (aat, bpr), end='')

    if bpr < 0.98:  # threshold to recompute

        print('. Attempting to improve anchors, please wait...')

        na = m.anchor_grid.numel() // 2  # number of anchors

        new_anchors = kmean_anchors(dataset, n=na, img_size=imgsz, thr=thr, gen=1000, verbose=False)

        new_bpr = metric(new_anchors.reshape(-1, 2))[0]

        if new_bpr > bpr:  # replace anchors

            new_anchors = torch.tensor(new_anchors, device=m.anchors.device).type_as(m.anchors)

            m.anchor_grid[:] = new_anchors.clone().view_as(m.anchor_grid)  # for inference

            m.anchors[:] = new_anchors.clone().view_as(m.anchors) / m.stride.to(m.anchors.device).view(-1, 1, 1)  # loss

            check_anchor_order(m)

            print('New anchors saved to model. Update model *.yaml to use these anchors in the future.')

        else:

            print('Original anchors better than new anchors. Proceeding with original anchors.')

    print('')  # newline





def kmean_anchors(path='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen=1000, verbose=True):

    """ 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

            verbose: print all results



        Return:

            k: kmeans evolved anchors



        Usage:

            from utils.general 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 anchor_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



    if isinstance(path, str):  # *.yaml file

        with open(path) as f:

            data_dict = yaml.load(f, Loader=yaml.FullLoader)  # model dict

        from utils.datasets import LoadImagesAndLabels

        dataset = LoadImagesAndLabels(data_dict['train'], augment=True, rect=True)

    else:

        dataset = path  # dataset



    # Get label wh

    shapes = img_size * dataset.shapes / dataset.shapes.max(1, keepdims=True)

    wh0 = np.concatenate([l[:, 3:5] * s for s, l in zip(shapes, dataset.labels)])  # 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)  # unfiltered

    k = print_results(k)



    # Plot

    # k, d = [None] * 20, [None] * 20

    # for i in tqdm(range(1, 21)):

    #     k[i-1], d[i-1] = kmeans(wh / s, i)  # points, mean distance

    # fig, ax = plt.subplots(1, 2, figsize=(14, 7))

    # ax = ax.ravel()

    # ax[0].plot(np.arange(1, 21), np.array(d) ** 2, marker='.')

    # fig, ax = plt.subplots(1, 2, figsize=(14, 7))  # plot wh

    # ax[0].hist(wh[wh[:, 0]<100, 0],400)

    # ax[1].hist(wh[wh[:, 1]<100, 1],400)

    # fig.tight_layout()

    # fig.savefig('wh.png', dpi=200)



    # Evolve

    npr = np.random

    f, sh, mp, s = anchor_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 = anchor_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)