Delete utils

utils/__init__.py

@@ -1 +0,0 @@
-# init

utils/activations.py

@@ -1,72 +0,0 @@
-# Activation functions
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-# SiLU https://arxiv.org/pdf/1606.08415.pdf ----------------------------------------------------------------------------
-class SiLU(nn.Module):  # export-friendly version of nn.SiLU()
-    @staticmethod
-    def forward(x):
-        return x * torch.sigmoid(x)
-
-
-class Hardswish(nn.Module):  # export-friendly version of nn.Hardswish()
-    @staticmethod
-    def forward(x):
-        # return x * F.hardsigmoid(x)  # for torchscript and CoreML
-        return x * F.hardtanh(x + 3, 0., 6.) / 6.  # for torchscript, CoreML and ONNX
-
-
-class MemoryEfficientSwish(nn.Module):
-    class F(torch.autograd.Function):
-        @staticmethod
-        def forward(ctx, x):
-            ctx.save_for_backward(x)
-            return x * torch.sigmoid(x)
-
-        @staticmethod
-        def backward(ctx, grad_output):
-            x = ctx.saved_tensors[0]
-            sx = torch.sigmoid(x)
-            return grad_output * (sx * (1 + x * (1 - sx)))
-
-    def forward(self, x):
-        return self.F.apply(x)
-
-
-# Mish https://github.com/digantamisra98/Mish --------------------------------------------------------------------------
-class Mish(nn.Module):
-    @staticmethod
-    def forward(x):
-        return x * F.softplus(x).tanh()
-
-
-class MemoryEfficientMish(nn.Module):
-    class F(torch.autograd.Function):
-        @staticmethod
-        def forward(ctx, x):
-            ctx.save_for_backward(x)
-            return x.mul(torch.tanh(F.softplus(x)))  # x * tanh(ln(1 + exp(x)))
-
-        @staticmethod
-        def backward(ctx, grad_output):
-            x = ctx.saved_tensors[0]
-            sx = torch.sigmoid(x)
-            fx = F.softplus(x).tanh()
-            return grad_output * (fx + x * sx * (1 - fx * fx))
-
-    def forward(self, x):
-        return self.F.apply(x)
-
-
-# FReLU https://arxiv.org/abs/2007.11824 -------------------------------------------------------------------------------
-class FReLU(nn.Module):
-    def __init__(self, c1, k=3):  # ch_in, kernel
-        super().__init__()
-        self.conv = nn.Conv2d(c1, c1, k, 1, 1, groups=c1, bias=False)
-        self.bn = nn.BatchNorm2d(c1)
-
-    def forward(self, x):
-        return torch.max(x, self.bn(self.conv(x)))

utils/add_nms.py

@@ -1,155 +0,0 @@
-import numpy as np
-import onnx
-from onnx import shape_inference
-try:
-    import onnx_graphsurgeon as gs
-except Exception as e:
-    print('Import onnx_graphsurgeon failure: %s' % e)
-
-import logging
-
-LOGGER = logging.getLogger(__name__)
-
-class RegisterNMS(object):
-    def __init__(
-        self,
-        onnx_model_path: str,
-        precision: str = "fp32",
-    ):
-
-        self.graph = gs.import_onnx(onnx.load(onnx_model_path))
-        assert self.graph
-        LOGGER.info("ONNX graph created successfully")
-        # Fold constants via ONNX-GS that PyTorch2ONNX may have missed
-        self.graph.fold_constants()
-        self.precision = precision
-        self.batch_size = 1
-    def infer(self):
-        """
-        Sanitize the graph by cleaning any unconnected nodes, do a topological resort,
-        and fold constant inputs values. When possible, run shape inference on the
-        ONNX graph to determine tensor shapes.
-        """
-        for _ in range(3):
-            count_before = len(self.graph.nodes)
-
-            self.graph.cleanup().toposort()
-            try:
-                for node in self.graph.nodes:
-                    for o in node.outputs:
-                        o.shape = None
-                model = gs.export_onnx(self.graph)
-                model = shape_inference.infer_shapes(model)
-                self.graph = gs.import_onnx(model)
-            except Exception as e:
-                LOGGER.info(f"Shape inference could not be performed at this time:\n{e}")
-            try:
-                self.graph.fold_constants(fold_shapes=True)
-            except TypeError as e:
-                LOGGER.error(
-                    "This version of ONNX GraphSurgeon does not support folding shapes, "
-                    f"please upgrade your onnx_graphsurgeon module. Error:\n{e}"
-                )
-                raise
-
-            count_after = len(self.graph.nodes)
-            if count_before == count_after:
-                # No new folding occurred in this iteration, so we can stop for now.
-                break
-
-    def save(self, output_path):
-        """
-        Save the ONNX model to the given location.
-        Args:
-            output_path: Path pointing to the location where to write
-                out the updated ONNX model.
-        """
-        self.graph.cleanup().toposort()
-        model = gs.export_onnx(self.graph)
-        onnx.save(model, output_path)
-        LOGGER.info(f"Saved ONNX model to {output_path}")
-
-    def register_nms(
-        self,
-        *,
-        score_thresh: float = 0.25,
-        nms_thresh: float = 0.45,
-        detections_per_img: int = 100,
-    ):
-        """
-        Register the ``EfficientNMS_TRT`` plugin node.
-        NMS expects these shapes for its input tensors:
-            - box_net: [batch_size, number_boxes, 4]
-            - class_net: [batch_size, number_boxes, number_labels]
-        Args:
-            score_thresh (float): The scalar threshold for score (low scoring boxes are removed).
-            nms_thresh (float): The scalar threshold for IOU (new boxes that have high IOU
-                overlap with previously selected boxes are removed).
-            detections_per_img (int): Number of best detections to keep after NMS.
-        """
-
-        self.infer()
-        # Find the concat node at the end of the network
-        op_inputs = self.graph.outputs
-        op = "EfficientNMS_TRT"
-        attrs = {
-            "plugin_version": "1",
-            "background_class": -1,  # no background class
-            "max_output_boxes": detections_per_img,
-            "score_threshold": score_thresh,
-            "iou_threshold": nms_thresh,
-            "score_activation": False,
-            "box_coding": 0,
-        }
-
-        if self.precision == "fp32":
-            dtype_output = np.float32
-        elif self.precision == "fp16":
-            dtype_output = np.float16
-        else:
-            raise NotImplementedError(f"Currently not supports precision: {self.precision}")
-
-        # NMS Outputs
-        output_num_detections = gs.Variable(
-            name="num_dets",
-            dtype=np.int32,
-            shape=[self.batch_size, 1],
-        )  # A scalar indicating the number of valid detections per batch image.
-        output_boxes = gs.Variable(
-            name="det_boxes",
-            dtype=dtype_output,
-            shape=[self.batch_size, detections_per_img, 4],
-        )
-        output_scores = gs.Variable(
-            name="det_scores",
-            dtype=dtype_output,
-            shape=[self.batch_size, detections_per_img],
-        )
-        output_labels = gs.Variable(
-            name="det_classes",
-            dtype=np.int32,
-            shape=[self.batch_size, detections_per_img],
-        )
-
-        op_outputs = [output_num_detections, output_boxes, output_scores, output_labels]
-
-        # Create the NMS Plugin node with the selected inputs. The outputs of the node will also
-        # become the final outputs of the graph.
-        self.graph.layer(op=op, name="batched_nms", inputs=op_inputs, outputs=op_outputs, attrs=attrs)
-        LOGGER.info(f"Created NMS plugin '{op}' with attributes: {attrs}")
-
-        self.graph.outputs = op_outputs
-
-        self.infer()
-
-    def save(self, output_path):
-        """
-        Save the ONNX model to the given location.
-        Args:
-            output_path: Path pointing to the location where to write
-                out the updated ONNX model.
-        """
-        self.graph.cleanup().toposort()
-        model = gs.export_onnx(self.graph)
-        onnx.save(model, output_path)
-        LOGGER.info(f"Saved ONNX model to {output_path}")

utils/autoanchor.py

@@ -1,160 +0,0 @@
-# Auto-anchor utils
-
-import numpy as np
-import torch
-import yaml
-from scipy.cluster.vq import kmeans
-from tqdm import tqdm
-
-from utils.general import colorstr
-
-
-def check_anchor_order(m):
-    # Check anchor order against stride order for YOLO 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
-    prefix = colorstr('autoanchor: ')
-    print(f'\n{prefix}Analyzing 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
-
-    anchors = m.anchor_grid.clone().cpu().view(-1, 2)  # current anchors
-    bpr, aat = metric(anchors)
-    print(f'anchors/target = {aat:.2f}, Best Possible Recall (BPR) = {bpr:.4f}', end='')
-    if bpr < 0.98:  # threshold to recompute
-        print('. Attempting to improve anchors, please wait...')
-        na = m.anchor_grid.numel() // 2  # number of anchors
-        try:
-            anchors = kmean_anchors(dataset, n=na, img_size=imgsz, thr=thr, gen=1000, verbose=False)
-        except Exception as e:
-            print(f'{prefix}ERROR: {e}')
-        new_bpr = metric(anchors)[0]
-        if new_bpr > bpr:  # replace anchors
-            anchors = torch.tensor(anchors, device=m.anchors.device).type_as(m.anchors)
-            m.anchor_grid[:] = anchors.clone().view_as(m.anchor_grid)  # for inference
-            check_anchor_order(m)
-            m.anchors[:] = anchors.clone().view_as(m.anchors) / m.stride.to(m.anchors.device).view(-1, 1, 1)  # loss
-            print(f'{prefix}New anchors saved to model. Update model *.yaml to use these anchors in the future.')
-        else:
-            print(f'{prefix}Original anchors better than new anchors. Proceeding with original anchors.')
-    print('')  # newline
-
-
-def kmean_anchors(path='./data/coco.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.autoanchor import *; _ = kmean_anchors()
-    """
-    thr = 1. / thr
-    prefix = colorstr('autoanchor: ')
-
-    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(f'{prefix}thr={thr:.2f}: {bpr:.4f} best possible recall, {aat:.2f} anchors past thr')
-        print(f'{prefix}n={n}, img_size={img_size}, metric_all={x.mean():.3f}/{best.mean():.3f}-mean/best, '
-              f'past_thr={x[x > thr].mean():.3f}-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.SafeLoader)  # 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(f'{prefix}WARNING: Extremely small objects found. {i} of {len(wh0)} labels are < 3 pixels in size.')
-    wh = wh0[(wh0 >= 2.0).any(1)]  # filter > 2 pixels
-    # wh = wh * (np.random.rand(wh.shape[0], 1) * 0.9 + 0.1)  # multiply by random scale 0-1
-
-    # Kmeans calculation
-    print(f'{prefix}Running kmeans for {n} anchors on {len(wh)} points...')
-    s = wh.std(0)  # sigmas for whitening
-    k, dist = kmeans(wh / s, n, iter=30)  # points, mean distance
-    assert len(k) == n, print(f'{prefix}ERROR: scipy.cluster.vq.kmeans requested {n} points but returned only {len(k)}')
-    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), tight_layout=True)
-    # 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.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=f'{prefix}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 = f'{prefix}Evolving anchors with Genetic Algorithm: fitness = {f:.4f}'
-            if verbose:
-                print_results(k)
-
-    return print_results(k)

utils/aws/__init__.py

@@ -1 +0,0 @@
-#init

utils/aws/mime.sh

@@ -1,26 +0,0 @@
-# AWS EC2 instance startup 'MIME' script https://aws.amazon.com/premiumsupport/knowledge-center/execute-user-data-ec2/
-# This script will run on every instance restart, not only on first start
-# --- DO NOT COPY ABOVE COMMENTS WHEN PASTING INTO USERDATA ---
-
-Content-Type: multipart/mixed; boundary="//"
-MIME-Version: 1.0
-
---//
-Content-Type: text/cloud-config; charset="us-ascii"
-MIME-Version: 1.0
-Content-Transfer-Encoding: 7bit
-Content-Disposition: attachment; filename="cloud-config.txt"
-
-#cloud-config
-cloud_final_modules:
-- [scripts-user, always]
-
---//
-Content-Type: text/x-shellscript; charset="us-ascii"
-MIME-Version: 1.0
-Content-Transfer-Encoding: 7bit
-Content-Disposition: attachment; filename="userdata.txt"
-
-#!/bin/bash
-# --- paste contents of userdata.sh here ---
---//

utils/aws/resume.py

@@ -1,37 +0,0 @@
-# Resume all interrupted trainings in yolor/ dir including DDP trainings
-# Usage: $ python utils/aws/resume.py
-
-import os
-import sys
-from pathlib import Path
-
-import torch
-import yaml
-
-sys.path.append('./')  # to run '$ python *.py' files in subdirectories
-
-port = 0  # --master_port
-path = Path('').resolve()
-for last in path.rglob('*/**/last.pt'):
-    ckpt = torch.load(last)
-    if ckpt['optimizer'] is None:
-        continue
-
-    # Load opt.yaml
-    with open(last.parent.parent / 'opt.yaml') as f:
-        opt = yaml.load(f, Loader=yaml.SafeLoader)
-
-    # Get device count
-    d = opt['device'].split(',')  # devices
-    nd = len(d)  # number of devices
-    ddp = nd > 1 or (nd == 0 and torch.cuda.device_count() > 1)  # distributed data parallel
-
-    if ddp:  # multi-GPU
-        port += 1
-        cmd = f'python -m torch.distributed.launch --nproc_per_node {nd} --master_port {port} train.py --resume {last}'
-    else:  # single-GPU
-        cmd = f'python train.py --resume {last}'
-
-    cmd += ' > /dev/null 2>&1 &'  # redirect output to dev/null and run in daemon thread
-    print(cmd)
-    os.system(cmd)

utils/aws/userdata.sh

@@ -1,27 +0,0 @@
-#!/bin/bash
-# AWS EC2 instance startup script https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/user-data.html
-# This script will run only once on first instance start (for a re-start script see mime.sh)
-# /home/ubuntu (ubuntu) or /home/ec2-user (amazon-linux) is working dir
-# Use >300 GB SSD
-
-cd home/ubuntu
-if [ ! -d yolor ]; then
-  echo "Running first-time script." # install dependencies, download COCO, pull Docker
-  git clone -b main https://github.com/WongKinYiu/yolov7 && sudo chmod -R 777 yolov7
-  cd yolov7
-  bash data/scripts/get_coco.sh && echo "Data done." &
-  sudo docker pull nvcr.io/nvidia/pytorch:21.08-py3 && echo "Docker done." &
-  python -m pip install --upgrade pip && pip install -r requirements.txt && python detect.py && echo "Requirements done." &
-  wait && echo "All tasks done." # finish background tasks
-else
-  echo "Running re-start script." # resume interrupted runs
-  i=0
-  list=$(sudo docker ps -qa) # container list i.e. $'one\ntwo\nthree\nfour'
-  while IFS= read -r id; do
-    ((i++))
-    echo "restarting container $i: $id"
-    sudo docker start $id
-    # sudo docker exec -it $id python train.py --resume # single-GPU
-    sudo docker exec -d $id python utils/aws/resume.py # multi-scenario
-  done <<<"$list"
-fi

utils/datasets.py

@@ -1,1320 +0,0 @@
-# Dataset utils and dataloaders
-
-import glob
-import logging
-import math
-import os
-import random
-import shutil
-import time
-from itertools import repeat
-from multiprocessing.pool import ThreadPool
-from pathlib import Path
-from threading import Thread
-
-import cv2
-import numpy as np
-import torch
-import torch.nn.functional as F
-from PIL import Image, ExifTags
-from torch.utils.data import Dataset
-from tqdm import tqdm
-
-import pickle
-from copy import deepcopy
-#from pycocotools import mask as maskUtils
-from torchvision.utils import save_image
-from torchvision.ops import roi_pool, roi_align, ps_roi_pool, ps_roi_align
-
-from utils.general import check_requirements, xyxy2xywh, xywh2xyxy, xywhn2xyxy, xyn2xy, segment2box, segments2boxes, \
-    resample_segments, clean_str
-from utils.torch_utils import torch_distributed_zero_first
-
-# Parameters
-help_url = 'https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data'
-img_formats = ['bmp', 'jpg', 'jpeg', 'png', 'tif', 'tiff', 'dng', 'webp', 'mpo']  # acceptable image suffixes
-vid_formats = ['mov', 'avi', 'mp4', 'mpg', 'mpeg', 'm4v', 'wmv', 'mkv']  # acceptable video suffixes
-logger = logging.getLogger(__name__)
-
-# Get orientation exif tag
-for orientation in ExifTags.TAGS.keys():
-    if ExifTags.TAGS[orientation] == 'Orientation':
-        break
-
-
-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 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 create_dataloader(path, imgsz, batch_size, stride, opt, hyp=None, augment=False, cache=False, pad=0.0, rect=False,
-                      rank=-1, world_size=1, workers=8, image_weights=False, quad=False, prefix=''):
-    # 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
-                                      rect=rect,  # rectangular training
-                                      cache_images=cache,
-                                      single_cls=opt.single_cls,
-                                      stride=int(stride),
-                                      pad=pad,
-                                      image_weights=image_weights,
-                                      prefix=prefix)
-
-    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
-    loader = torch.utils.data.DataLoader if image_weights else InfiniteDataLoader
-    # Use torch.utils.data.DataLoader() if dataset.properties will update during training else InfiniteDataLoader()
-    dataloader = loader(dataset,
-                        batch_size=batch_size,
-                        num_workers=nw,
-                        sampler=sampler,
-                        pin_memory=True,
-                        collate_fn=LoadImagesAndLabels.collate_fn4 if quad else LoadImagesAndLabels.collate_fn)
-    return dataloader, dataset
-
-
-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)
-
-
-class LoadImages:  # for inference
-    def __init__(self, path, img_size=640, stride=32):
-        p = str(Path(path).absolute())  # os-agnostic absolute path
-        if '*' in p:
-            files = sorted(glob.glob(p, recursive=True))  # glob
-        elif os.path.isdir(p):
-            files = sorted(glob.glob(os.path.join(p, '*.*')))  # dir
-        elif os.path.isfile(p):
-            files = [p]  # files
-        else:
-            raise Exception(f'ERROR: {p} does not exist')
-
-        images = [x for x in files if x.split('.')[-1].lower() in img_formats]
-        videos = [x for x in files if x.split('.')[-1].lower() in vid_formats]
-        ni, nv = len(images), len(videos)
-
-        self.img_size = img_size
-        self.stride = stride
-        self.files = images + videos
-        self.nf = ni + nv  # number of files
-        self.video_flag = [False] * ni + [True] * nv
-        self.mode = 'image'
-        if any(videos):
-            self.new_video(videos[0])  # new video
-        else:
-            self.cap = None
-        assert self.nf > 0, f'No images or videos found in {p}. ' \
-                            f'Supported formats are:\nimages: {img_formats}\nvideos: {vid_formats}'
-
-    def __iter__(self):
-        self.count = 0
-        return self
-
-    def __next__(self):
-        if self.count == self.nf:
-            raise StopIteration
-        path = self.files[self.count]
-
-        if self.video_flag[self.count]:
-            # Read video
-            self.mode = 'video'
-            ret_val, img0 = self.cap.read()
-            if not ret_val:
-                self.count += 1
-                self.cap.release()
-                if self.count == self.nf:  # last video
-                    raise StopIteration
-                else:
-                    path = self.files[self.count]
-                    self.new_video(path)
-                    ret_val, img0 = self.cap.read()
-
-            self.frame += 1
-            print(f'video {self.count + 1}/{self.nf} ({self.frame}/{self.nframes}) {path}: ', end='')
-
-        else:
-            # Read image
-            self.count += 1
-            img0 = cv2.imread(path)  # BGR
-            assert img0 is not None, 'Image Not Found ' + path
-            #print(f'image {self.count}/{self.nf} {path}: ', end='')
-
-        # Padded resize
-        img = letterbox(img0, self.img_size, stride=self.stride)[0]
-
-        # Convert
-        img = img[:, :, ::-1].transpose(2, 0, 1)  # BGR to RGB, to 3x416x416
-        img = np.ascontiguousarray(img)
-
-        return path, img, img0, self.cap
-
-    def new_video(self, path):
-        self.frame = 0
-        self.cap = cv2.VideoCapture(path)
-        self.nframes = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT))
-
-    def __len__(self):
-        return self.nf  # number of files
-
-
-class LoadWebcam:  # for inference
-    def __init__(self, pipe='0', img_size=640, stride=32):
-        self.img_size = img_size
-        self.stride = stride
-
-        if pipe.isnumeric():
-            pipe = eval(pipe)  # local camera
-        # pipe = 'rtsp://192.168.1.64/1'  # IP camera
-        # pipe = 'rtsp://username:password@192.168.1.64/1'  # IP camera with login
-        # pipe = 'http://wmccpinetop.axiscam.net/mjpg/video.mjpg'  # IP golf camera
-
-        self.pipe = pipe
-        self.cap = cv2.VideoCapture(pipe)  # video capture object
-        self.cap.set(cv2.CAP_PROP_BUFFERSIZE, 3)  # set buffer size
-
-    def __iter__(self):
-        self.count = -1
-        return self
-
-    def __next__(self):
-        self.count += 1
-        if cv2.waitKey(1) == ord('q'):  # q to quit
-            self.cap.release()
-            cv2.destroyAllWindows()
-            raise StopIteration
-
-        # Read frame
-        if self.pipe == 0:  # local camera
-            ret_val, img0 = self.cap.read()
-            img0 = cv2.flip(img0, 1)  # flip left-right
-        else:  # IP camera
-            n = 0
-            while True:
-                n += 1
-                self.cap.grab()
-                if n % 30 == 0:  # skip frames
-                    ret_val, img0 = self.cap.retrieve()
-                    if ret_val:
-                        break
-
-        # Print
-        assert ret_val, f'Camera Error {self.pipe}'
-        img_path = 'webcam.jpg'
-        print(f'webcam {self.count}: ', end='')
-
-        # Padded resize
-        img = letterbox(img0, self.img_size, stride=self.stride)[0]
-
-        # Convert
-        img = img[:, :, ::-1].transpose(2, 0, 1)  # BGR to RGB, to 3x416x416
-        img = np.ascontiguousarray(img)
-
-        return img_path, img, img0, None
-
-    def __len__(self):
-        return 0
-
-
-class LoadStreams:  # multiple IP or RTSP cameras
-    def __init__(self, sources='streams.txt', img_size=640, stride=32):
-        self.mode = 'stream'
-        self.img_size = img_size
-        self.stride = stride
-
-        if os.path.isfile(sources):
-            with open(sources, 'r') as f:
-                sources = [x.strip() for x in f.read().strip().splitlines() if len(x.strip())]
-        else:
-            sources = [sources]
-
-        n = len(sources)
-        self.imgs = [None] * n
-        self.sources = [clean_str(x) for x in sources]  # clean source names for later
-        for i, s in enumerate(sources):
-            # Start the thread to read frames from the video stream
-            print(f'{i + 1}/{n}: {s}... ', end='')
-            url = eval(s) if s.isnumeric() else s
-            if 'youtube.com/' in str(url) or 'youtu.be/' in str(url):  # if source is YouTube video
-                check_requirements(('pafy', 'youtube_dl'))
-                import pafy
-                url = pafy.new(url).getbest(preftype="mp4").url
-            cap = cv2.VideoCapture(url)
-            assert cap.isOpened(), f'Failed to open {s}'
-            w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
-            h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
-            self.fps = cap.get(cv2.CAP_PROP_FPS) % 100
-
-            _, self.imgs[i] = cap.read()  # guarantee first frame
-            thread = Thread(target=self.update, args=([i, cap]), daemon=True)
-            print(f' success ({w}x{h} at {self.fps:.2f} FPS).')
-            thread.start()
-        print('')  # newline
-
-        # check for common shapes
-        s = np.stack([letterbox(x, self.img_size, stride=self.stride)[0].shape for x in self.imgs], 0)  # shapes
-        self.rect = np.unique(s, axis=0).shape[0] == 1  # rect inference if all shapes equal
-        if not self.rect:
-            print('WARNING: Different stream shapes detected. For optimal performance supply similarly-shaped streams.')
-
-    def update(self, index, cap):
-        # Read next stream frame in a daemon thread
-        n = 0
-        while cap.isOpened():
-            n += 1
-            # _, self.imgs[index] = cap.read()
-            cap.grab()
-            if n == 4:  # read every 4th frame
-                success, im = cap.retrieve()
-                self.imgs[index] = im if success else self.imgs[index] * 0
-                n = 0
-            time.sleep(1 / self.fps)  # wait time
-
-    def __iter__(self):
-        self.count = -1
-        return self
-
-    def __next__(self):
-        self.count += 1
-        img0 = self.imgs.copy()
-        if cv2.waitKey(1) == ord('q'):  # q to quit
-            cv2.destroyAllWindows()
-            raise StopIteration
-
-        # Letterbox
-        img = [letterbox(x, self.img_size, auto=self.rect, stride=self.stride)[0] for x in img0]
-
-        # Stack
-        img = np.stack(img, 0)
-
-        # Convert
-        img = img[:, :, :, ::-1].transpose(0, 3, 1, 2)  # BGR to RGB, to bsx3x416x416
-        img = np.ascontiguousarray(img)
-
-        return self.sources, img, img0, None
-
-    def __len__(self):
-        return 0  # 1E12 frames = 32 streams at 30 FPS for 30 years
-
-
-def img2label_paths(img_paths):
-    # Define label paths as a function of image paths
-    sa, sb = os.sep + 'images' + os.sep, os.sep + 'labels' + os.sep  # /images/, /labels/ substrings
-    return ['txt'.join(x.replace(sa, sb, 1).rsplit(x.split('.')[-1], 1)) for x in img_paths]
-
-
-class LoadImagesAndLabels(Dataset):  # for training/testing
-    def __init__(self, path, img_size=640, batch_size=16, augment=False, hyp=None, rect=False, image_weights=False,
-                 cache_images=False, single_cls=False, stride=32, pad=0.0, prefix=''):
-        self.img_size = img_size
-        self.augment = augment
-        self.hyp = hyp
-        self.image_weights = image_weights
-        self.rect = False if image_weights else rect
-        self.mosaic = self.augment and not self.rect  # load 4 images at a time into a mosaic (only during training)
-        self.mosaic_border = [-img_size // 2, -img_size // 2]
-        self.stride = stride
-        self.path = path        
-        #self.albumentations = Albumentations() if augment else None
-
-        try:
-            f = []  # image files
-            for p in path if isinstance(path, list) else [path]:
-                p = Path(p)  # os-agnostic
-                if p.is_dir():  # dir
-                    f += glob.glob(str(p / '**' / '*.*'), recursive=True)
-                    # f = list(p.rglob('**/*.*'))  # pathlib
-                elif p.is_file():  # file
-                    with open(p, 'r') as t:
-                        t = t.read().strip().splitlines()
-                        parent = str(p.parent) + os.sep
-                        f += [x.replace('./', parent) if x.startswith('./') else x for x in t]  # local to global path
-                        # f += [p.parent / x.lstrip(os.sep) for x in t]  # local to global path (pathlib)
-                else:
-                    raise Exception(f'{prefix}{p} does not exist')
-            self.img_files = sorted([x.replace('/', os.sep) for x in f if x.split('.')[-1].lower() in img_formats])
-            # self.img_files = sorted([x for x in f if x.suffix[1:].lower() in img_formats])  # pathlib
-            assert self.img_files, f'{prefix}No images found'
-        except Exception as e:
-            raise Exception(f'{prefix}Error loading data from {path}: {e}\nSee {help_url}')
-
-        # Check cache
-        self.label_files = img2label_paths(self.img_files)  # labels
-        cache_path = (p if p.is_file() else Path(self.label_files[0]).parent).with_suffix('.cache')  # cached labels
-        if cache_path.is_file():
-            cache, exists = torch.load(cache_path), True  # load
-            #if cache['hash'] != get_hash(self.label_files + self.img_files) or 'version' not in cache:  # changed
-            #    cache, exists = self.cache_labels(cache_path, prefix), False  # re-cache
-        else:
-            cache, exists = self.cache_labels(cache_path, prefix), False  # cache
-
-        # Display cache
-        nf, nm, ne, nc, n = cache.pop('results')  # found, missing, empty, corrupted, total
-        if exists:
-            d = f"Scanning '{cache_path}' images and labels... {nf} found, {nm} missing, {ne} empty, {nc} corrupted"
-            tqdm(None, desc=prefix + d, total=n, initial=n)  # display cache results
-        assert nf > 0 or not augment, f'{prefix}No labels in {cache_path}. Can not train without labels. See {help_url}'
-
-        # Read cache
-        cache.pop('hash')  # remove hash
-        cache.pop('version')  # remove version
-        labels, shapes, self.segments = 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
-        if single_cls:
-            for x in self.labels:
-                x[:, 0] = 0
-
-        n = len(shapes)  # number of images
-        bi = np.floor(np.arange(n) / batch_size).astype(int)  # batch index
-        nb = bi[-1] + 1  # number of batches
-        self.batch = bi  # batch index of image
-        self.n = n
-        self.indices = range(n)
-
-        # 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(int) * stride
-
-        # Cache images into memory for faster training (WARNING: large datasets may exceed system RAM)
-        self.imgs = [None] * n
-        if cache_images:
-            if cache_images == 'disk':
-                self.im_cache_dir = Path(Path(self.img_files[0]).parent.as_posix() + '_npy')
-                self.img_npy = [self.im_cache_dir / Path(f).with_suffix('.npy').name for f in self.img_files]
-                self.im_cache_dir.mkdir(parents=True, exist_ok=True)
-            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)))
-            pbar = tqdm(enumerate(results), total=n)
-            for i, x in pbar:
-                if cache_images == 'disk':
-                    if not self.img_npy[i].exists():
-                        np.save(self.img_npy[i].as_posix(), x[0])
-                    gb += self.img_npy[i].stat().st_size
-                else:
-                    self.imgs[i], self.img_hw0[i], self.img_hw[i] = x
-                    gb += self.imgs[i].nbytes
-                pbar.desc = f'{prefix}Caching images ({gb / 1E9:.1f}GB)'
-            pbar.close()
-
-    def cache_labels(self, path=Path('./labels.cache'), prefix=''):
-        # Cache dataset labels, check images and read shapes
-        x = {}  # dict
-        nm, nf, ne, nc = 0, 0, 0, 0  # number missing, found, empty, duplicate
-        pbar = tqdm(zip(self.img_files, self.label_files), desc='Scanning images', total=len(self.img_files))
-        for i, (im_file, lb_file) in enumerate(pbar):
-            try:
-                # verify images
-                im = Image.open(im_file)
-                im.verify()  # PIL verify
-                shape = exif_size(im)  # image size
-                segments = []  # instance segments
-                assert (shape[0] > 9) & (shape[1] > 9), f'image size {shape} <10 pixels'
-                assert im.format.lower() in img_formats, f'invalid image format {im.format}'
-
-                # verify labels
-                if os.path.isfile(lb_file):
-                    nf += 1  # label found
-                    with open(lb_file, 'r') as f:
-                        l = [x.split() for x in f.read().strip().splitlines()]
-                        if any([len(x) > 8 for x in l]):  # is segment
-                            classes = np.array([x[0] for x in l], dtype=np.float32)
-                            segments = [np.array(x[1:], dtype=np.float32).reshape(-1, 2) for x in l]  # (cls, xy1...)
-                            l = np.concatenate((classes.reshape(-1, 1), segments2boxes(segments)), 1)  # (cls, xywh)
-                        l = np.array(l, dtype=np.float32)
-                    if len(l):
-                        assert l.shape[1] == 5, 'labels require 5 columns each'
-                        assert (l >= 0).all(), 'negative labels'
-                        assert (l[:, 1:] <= 1).all(), 'non-normalized or out of bounds coordinate labels'
-                        assert np.unique(l, axis=0).shape[0] == l.shape[0], 'duplicate labels'
-                    else:
-                        ne += 1  # label empty
-                        l = np.zeros((0, 5), dtype=np.float32)
-                else:
-                    nm += 1  # label missing
-                    l = np.zeros((0, 5), dtype=np.float32)
-                x[im_file] = [l, shape, segments]
-            except Exception as e:
-                nc += 1
-                print(f'{prefix}WARNING: Ignoring corrupted image and/or label {im_file}: {e}')
-
-            pbar.desc = f"{prefix}Scanning '{path.parent / path.stem}' images and labels... " \
-                        f"{nf} found, {nm} missing, {ne} empty, {nc} corrupted"
-        pbar.close()
-
-        if nf == 0:
-            print(f'{prefix}WARNING: No labels found in {path}. See {help_url}')
-
-        x['hash'] = get_hash(self.label_files + self.img_files)
-        x['results'] = nf, nm, ne, nc, i + 1
-        x['version'] = 0.1  # cache version
-        torch.save(x, path)  # save for next time
-        logging.info(f'{prefix}New cache created: {path}')
-        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):
-        index = self.indices[index]  # linear, shuffled, or image_weights
-
-        hyp = self.hyp
-        mosaic = self.mosaic and random.random() < hyp['mosaic']
-        if mosaic:
-            # Load mosaic
-            if random.random() < 0.8:
-                img, labels = load_mosaic(self, index)
-            else:
-                img, labels = load_mosaic9(self, index)
-            shapes = None
-
-            # MixUp https://arxiv.org/pdf/1710.09412.pdf
-            if random.random() < hyp['mixup']:
-                if random.random() < 0.8:
-                    img2, labels2 = load_mosaic(self, random.randint(0, len(self.labels) - 1))
-                else:
-                    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
-
-            labels = self.labels[index].copy()
-            if labels.size:  # normalized xywh to pixel xyxy format
-                labels[:, 1:] = xywhn2xyxy(labels[:, 1:], ratio[0] * w, ratio[1] * h, padw=pad[0], padh=pad[1])
-
-        if self.augment:
-            # 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'])
-            
-            
-            #img, labels = self.albumentations(img, labels)
-
-            # Augment colorspace
-            augment_hsv(img, hgain=hyp['hsv_h'], sgain=hyp['hsv_s'], vgain=hyp['hsv_v'])
-
-            # Apply cutouts
-            # if random.random() < 0.9:
-            #     labels = cutout(img, labels)
-            
-            if random.random() < hyp['paste_in']:
-                sample_labels, sample_images, sample_masks = [], [], [] 
-                while len(sample_labels) < 30:
-                    sample_labels_, sample_images_, sample_masks_ = load_samples(self, random.randint(0, len(self.labels) - 1))
-                    sample_labels += sample_labels_
-                    sample_images += sample_images_
-                    sample_masks += sample_masks_
-                    #print(len(sample_labels))
-                    if len(sample_labels) == 0:
-                        break
-                labels = pastein(img, labels, sample_labels, sample_images, sample_masks)
-
-        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]
-
-        labels_out = torch.zeros((nL, 6))
-        if nL:
-            labels_out[:, 1:] = torch.from_numpy(labels)
-
-        # Convert
-        img = img[:, :, ::-1].transpose(2, 0, 1)  # BGR to RGB, to 3x416x416
-        img = np.ascontiguousarray(img)
-
-        return torch.from_numpy(img), labels_out, self.img_files[index], shapes
-
-    @staticmethod
-    def collate_fn(batch):
-        img, label, path, shapes = zip(*batch)  # transposed
-        for i, l in enumerate(label):
-            l[:, 0] = i  # add target image index for build_targets()
-        return torch.stack(img, 0), torch.cat(label, 0), path, shapes
-
-    @staticmethod
-    def collate_fn4(batch):
-        img, label, path, shapes = zip(*batch)  # transposed
-        n = len(shapes) // 4
-        img4, label4, path4, shapes4 = [], [], path[:n], shapes[:n]
-
-        ho = torch.tensor([[0., 0, 0, 1, 0, 0]])
-        wo = torch.tensor([[0., 0, 1, 0, 0, 0]])
-        s = torch.tensor([[1, 1, .5, .5, .5, .5]])  # scale
-        for i in range(n):  # zidane torch.zeros(16,3,720,1280)  # BCHW
-            i *= 4
-            if random.random() < 0.5:
-                im = F.interpolate(img[i].unsqueeze(0).float(), scale_factor=2., mode='bilinear', align_corners=False)[
-                    0].type(img[i].type())
-                l = label[i]
-            else:
-                im = torch.cat((torch.cat((img[i], img[i + 1]), 1), torch.cat((img[i + 2], img[i + 3]), 1)), 2)
-                l = torch.cat((label[i], label[i + 1] + ho, label[i + 2] + wo, label[i + 3] + ho + wo), 0) * s
-            img4.append(im)
-            label4.append(l)
-
-        for i, l in enumerate(label4):
-            l[:, 0] = i  # add target image index for build_targets()
-
-        return torch.stack(img4, 0), torch.cat(label4, 0), path4, shapes4
-
-
-# Ancillary functions --------------------------------------------------------------------------------------------------
-def load_image(self, index):
-    # loads 1 image from dataset, returns img, original hw, resized hw
-    img = self.imgs[index]
-    if img is None:  # not cached
-        path = self.img_files[index]
-        img = cv2.imread(path)  # BGR
-        assert img is not None, 'Image Not Found ' + path
-        h0, w0 = img.shape[:2]  # orig hw
-        r = self.img_size / max(h0, w0)  # resize image to img_size
-        if r != 1:  # always resize down, only resize up if training with augmentation
-            interp = cv2.INTER_AREA if r < 1 and not self.augment else cv2.INTER_LINEAR
-            img = cv2.resize(img, (int(w0 * r), int(h0 * r)), interpolation=interp)
-        return img, (h0, w0), img.shape[:2]  # img, hw_original, hw_resized
-    else:
-        return self.imgs[index], self.img_hw0[index], self.img_hw[index]  # img, hw_original, hw_resized
-
-
-def augment_hsv(img, hgain=0.5, sgain=0.5, vgain=0.5):
-    r = np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] + 1  # random gains
-    hue, sat, val = cv2.split(cv2.cvtColor(img, cv2.COLOR_BGR2HSV))
-    dtype = img.dtype  # uint8
-
-    x = np.arange(0, 256, dtype=np.int16)
-    lut_hue = ((x * r[0]) % 180).astype(dtype)
-    lut_sat = np.clip(x * r[1], 0, 255).astype(dtype)
-    lut_val = np.clip(x * r[2], 0, 255).astype(dtype)
-
-    img_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))).astype(dtype)
-    cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR, dst=img)  # no return needed
-
-
-def hist_equalize(img, clahe=True, bgr=False):
-    # Equalize histogram on BGR image 'img' with img.shape(n,m,3) and range 0-255
-    yuv = cv2.cvtColor(img, cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV)
-    if clahe:
-        c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
-        yuv[:, :, 0] = c.apply(yuv[:, :, 0])
-    else:
-        yuv[:, :, 0] = cv2.equalizeHist(yuv[:, :, 0])  # equalize Y channel histogram
-    return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB)  # convert YUV image to RGB
-
-
-def load_mosaic(self, index):
-    # loads images in a 4-mosaic
-
-    labels4, segments4 = [], []
-    s = self.img_size
-    yc, xc = [int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border]  # mosaic center x, y
-    indices = [index] + random.choices(self.indices, k=3)  # 3 additional image indices
-    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
-        labels, segments = self.labels[index].copy(), self.segments[index].copy()
-        if labels.size:
-            labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padw, padh)  # normalized xywh to pixel xyxy format
-            segments = [xyn2xy(x, w, h, padw, padh) for x in segments]
-        labels4.append(labels)
-        segments4.extend(segments)
-
-    # Concat/clip labels
-    labels4 = np.concatenate(labels4, 0)
-    for x in (labels4[:, 1:], *segments4):
-        np.clip(x, 0, 2 * s, out=x)  # clip when using random_perspective()
-    # img4, labels4 = replicate(img4, labels4)  # replicate
-
-    # Augment
-    #img4, labels4, segments4 = remove_background(img4, labels4, segments4)
-    #sample_segments(img4, labels4, segments4, probability=self.hyp['copy_paste'])
-    img4, labels4, segments4 = copy_paste(img4, labels4, segments4, probability=self.hyp['copy_paste'])
-    img4, labels4 = random_perspective(img4, labels4, segments4,
-                                       degrees=self.hyp['degrees'],
-                                       translate=self.hyp['translate'],
-                                       scale=self.hyp['scale'],
-                                       shear=self.hyp['shear'],
-                                       perspective=self.hyp['perspective'],
-                                       border=self.mosaic_border)  # border to remove
-
-    return img4, labels4
-
-
-def load_mosaic9(self, index):
-    # loads images in a 9-mosaic
-
-    labels9, segments9 = [], []
-    s = self.img_size
-    indices = [index] + random.choices(self.indices, k=8)  # 8 additional image indices
-    for i, index in enumerate(indices):
-        # Load image
-        img, _, (h, w) = load_image(self, index)
-
-        # place img in img9
-        if i == 0:  # center
-            img9 = np.full((s * 3, s * 3, img.shape[2]), 114, dtype=np.uint8)  # base image with 4 tiles
-            h0, w0 = h, w
-            c = s, s, s + w, s + h  # xmin, ymin, xmax, ymax (base) coordinates
-        elif i == 1:  # top
-            c = s, s - h, s + w, s
-        elif i == 2:  # top right
-            c = s + wp, s - h, s + wp + w, s
-        elif i == 3:  # right
-            c = s + w0, s, s + w0 + w, s + h
-        elif i == 4:  # bottom right
-            c = s + w0, s + hp, s + w0 + w, s + hp + h
-        elif i == 5:  # bottom
-            c = s + w0 - w, s + h0, s + w0, s + h0 + h
-        elif i == 6:  # bottom left
-            c = s + w0 - wp - w, s + h0, s + w0 - wp, s + h0 + h
-        elif i == 7:  # left
-            c = s - w, s + h0 - h, s, s + h0
-        elif i == 8:  # top left
-            c = s - w, s + h0 - hp - h, s, s + h0 - hp
-
-        padx, pady = c[:2]
-        x1, y1, x2, y2 = [max(x, 0) for x in c]  # allocate coords
-
-        # Labels
-        labels, segments = self.labels[index].copy(), self.segments[index].copy()
-        if labels.size:
-            labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padx, pady)  # normalized xywh to pixel xyxy format
-            segments = [xyn2xy(x, w, h, padx, pady) for x in segments]
-        labels9.append(labels)
-        segments9.extend(segments)
-
-        # Image
-        img9[y1:y2, x1:x2] = img[y1 - pady:, x1 - padx:]  # img9[ymin:ymax, xmin:xmax]
-        hp, wp = h, w  # height, width previous
-
-    # Offset
-    yc, xc = [int(random.uniform(0, s)) for _ in self.mosaic_border]  # mosaic center x, y
-    img9 = img9[yc:yc + 2 * s, xc:xc + 2 * s]
-
-    # Concat/clip labels
-    labels9 = np.concatenate(labels9, 0)
-    labels9[:, [1, 3]] -= xc
-    labels9[:, [2, 4]] -= yc
-    c = np.array([xc, yc])  # centers
-    segments9 = [x - c for x in segments9]
-
-    for x in (labels9[:, 1:], *segments9):
-        np.clip(x, 0, 2 * s, out=x)  # clip when using random_perspective()
-    # img9, labels9 = replicate(img9, labels9)  # replicate
-
-    # Augment
-    #img9, labels9, segments9 = remove_background(img9, labels9, segments9)
-    img9, labels9, segments9 = copy_paste(img9, labels9, segments9, probability=self.hyp['copy_paste'])
-    img9, labels9 = random_perspective(img9, labels9, segments9,
-                                       degrees=self.hyp['degrees'],
-                                       translate=self.hyp['translate'],
-                                       scale=self.hyp['scale'],
-                                       shear=self.hyp['shear'],
-                                       perspective=self.hyp['perspective'],
-                                       border=self.mosaic_border)  # border to remove
-
-    return img9, labels9
-
-
-def load_samples(self, index):
-    # loads images in a 4-mosaic
-
-    labels4, segments4 = [], []
-    s = self.img_size
-    yc, xc = [int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border]  # mosaic center x, y
-    indices = [index] + random.choices(self.indices, k=3)  # 3 additional image indices
-    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
-        labels, segments = self.labels[index].copy(), self.segments[index].copy()
-        if labels.size:
-            labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padw, padh)  # normalized xywh to pixel xyxy format
-            segments = [xyn2xy(x, w, h, padw, padh) for x in segments]
-        labels4.append(labels)
-        segments4.extend(segments)
-
-    # Concat/clip labels
-    labels4 = np.concatenate(labels4, 0)
-    for x in (labels4[:, 1:], *segments4):
-        np.clip(x, 0, 2 * s, out=x)  # clip when using random_perspective()
-    # img4, labels4 = replicate(img4, labels4)  # replicate
-
-    # Augment
-    #img4, labels4, segments4 = remove_background(img4, labels4, segments4)
-    sample_labels, sample_images, sample_masks = sample_segments(img4, labels4, segments4, probability=0.5)
-
-    return sample_labels, sample_images, sample_masks
-
-
-def copy_paste(img, labels, segments, probability=0.5):
-    # Implement Copy-Paste augmentation https://arxiv.org/abs/2012.07177, labels as nx5 np.array(cls, xyxy)
-    n = len(segments)
-    if probability and n:
-        h, w, c = img.shape  # height, width, channels
-        im_new = np.zeros(img.shape, np.uint8)
-        for j in random.sample(range(n), k=round(probability * n)):
-            l, s = labels[j], segments[j]
-            box = w - l[3], l[2], w - l[1], l[4]
-            ioa = bbox_ioa(box, labels[:, 1:5])  # intersection over area
-            if (ioa < 0.30).all():  # allow 30% obscuration of existing labels
-                labels = np.concatenate((labels, [[l[0], *box]]), 0)
-                segments.append(np.concatenate((w - s[:, 0:1], s[:, 1:2]), 1))
-                cv2.drawContours(im_new, [segments[j].astype(np.int32)], -1, (255, 255, 255), cv2.FILLED)
-
-        result = cv2.bitwise_and(src1=img, src2=im_new)
-        result = cv2.flip(result, 1)  # augment segments (flip left-right)
-        i = result > 0  # pixels to replace
-        # i[:, :] = result.max(2).reshape(h, w, 1)  # act over ch
-        img[i] = result[i]  # cv2.imwrite('debug.jpg', img)  # debug
-
-    return img, labels, segments
-
-
-def remove_background(img, labels, segments):
-    # Implement Copy-Paste augmentation https://arxiv.org/abs/2012.07177, labels as nx5 np.array(cls, xyxy)
-    n = len(segments)
-    h, w, c = img.shape  # height, width, channels
-    im_new = np.zeros(img.shape, np.uint8)
-    img_new = np.ones(img.shape, np.uint8) * 114
-    for j in range(n):
-        cv2.drawContours(im_new, [segments[j].astype(np.int32)], -1, (255, 255, 255), cv2.FILLED)
-
-        result = cv2.bitwise_and(src1=img, src2=im_new)
-        
-        i = result > 0  # pixels to replace
-        img_new[i] = result[i]  # cv2.imwrite('debug.jpg', img)  # debug
-
-    return img_new, labels, segments
-
-
-def sample_segments(img, labels, segments, probability=0.5):
-    # Implement Copy-Paste augmentation https://arxiv.org/abs/2012.07177, labels as nx5 np.array(cls, xyxy)
-    n = len(segments)
-    sample_labels = []
-    sample_images = []
-    sample_masks = []
-    if probability and n:
-        h, w, c = img.shape  # height, width, channels
-        for j in random.sample(range(n), k=round(probability * n)):
-            l, s = labels[j], segments[j]
-            box = l[1].astype(int).clip(0,w-1), l[2].astype(int).clip(0,h-1), l[3].astype(int).clip(0,w-1), l[4].astype(int).clip(0,h-1) 
-            
-            #print(box)
-            if (box[2] <= box[0]) or (box[3] <= box[1]):
-                continue
-            
-            sample_labels.append(l[0])
-            
-            mask = np.zeros(img.shape, np.uint8)
-            
-            cv2.drawContours(mask, [segments[j].astype(np.int32)], -1, (255, 255, 255), cv2.FILLED)
-            sample_masks.append(mask[box[1]:box[3],box[0]:box[2],:])
-            
-            result = cv2.bitwise_and(src1=img, src2=mask)
-            i = result > 0  # pixels to replace
-            mask[i] = result[i]  # cv2.imwrite('debug.jpg', img)  # debug
-            #print(box)
-            sample_images.append(mask[box[1]:box[3],box[0]:box[2],:])
-
-    return sample_labels, sample_images, sample_masks
-
-
-def replicate(img, labels):
-    # Replicate labels
-    h, w = img.shape[:2]
-    boxes = labels[:, 1:].astype(int)
-    x1, y1, x2, y2 = boxes.T
-    s = ((x2 - x1) + (y2 - y1)) / 2  # side length (pixels)
-    for i in s.argsort()[:round(s.size * 0.5)]:  # smallest indices
-        x1b, y1b, x2b, y2b = boxes[i]
-        bh, bw = y2b - y1b, x2b - x1b
-        yc, xc = int(random.uniform(0, h - bh)), int(random.uniform(0, w - bw))  # offset x, y
-        x1a, y1a, x2a, y2a = [xc, yc, xc + bw, yc + bh]
-        img[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b]  # img4[ymin:ymax, xmin:xmax]
-        labels = np.append(labels, [[labels[i, 0], x1a, y1a, x2a, y2a]], axis=0)
-
-    return img, labels
-
-
-def letterbox(img, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True, stride=32):
-    # Resize and pad image while meeting stride-multiple constraints
-    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, stride), np.mod(dh, stride)  # 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))
-    img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)  # add border
-    return img, ratio, (dw, dh)
-
-
-def random_perspective(img, targets=(), segments=(), 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, 1.1 + 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:
-        use_segments = any(x.any() for x in segments)
-        new = np.zeros((n, 4))
-        if use_segments:  # warp segments
-            segments = resample_segments(segments)  # upsample
-            for i, segment in enumerate(segments):
-                xy = np.ones((len(segment), 3))
-                xy[:, :2] = segment
-                xy = xy @ M.T  # transform
-                xy = xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2]  # perspective rescale or affine
-
-                # clip
-                new[i] = segment2box(xy, width, height)
-
-        else:  # warp boxes
-            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
-            xy = (xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2]).reshape(n, 8)  # perspective rescale or affine
-
-            # create new boxes
-            x = xy[:, [0, 2, 4, 6]]
-            y = xy[:, [1, 3, 5, 7]]
-            new = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T
-
-            # clip
-            new[:, [0, 2]] = new[:, [0, 2]].clip(0, width)
-            new[:, [1, 3]] = new[:, [1, 3]].clip(0, height)
-
-        # filter candidates
-        i = box_candidates(box1=targets[:, 1:5].T * s, box2=new.T, area_thr=0.01 if use_segments else 0.10)
-        targets = targets[i]
-        targets[:, 1:5] = new[i]
-
-    return img, targets
-
-
-def box_candidates(box1, box2, wh_thr=2, ar_thr=20, area_thr=0.1, eps=1e-16):  # 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 + eps), h2 / (w2 + eps))  # aspect ratio
-    return (w2 > wh_thr) & (h2 > wh_thr) & (w2 * h2 / (w1 * h1 + eps) > area_thr) & (ar < ar_thr)  # candidates
-
-
-def bbox_ioa(box1, box2):
-    # Returns the intersection over box2 area given box1, box2. box1 is 4, box2 is nx4. boxes are x1y1x2y2
-    box2 = box2.transpose()
-
-    # Get the coordinates of bounding boxes
-    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]
-
-    # Intersection area
-    inter_area = (np.minimum(b1_x2, b2_x2) - np.maximum(b1_x1, b2_x1)).clip(0) * \
-                 (np.minimum(b1_y2, b2_y2) - np.maximum(b1_y1, b2_y1)).clip(0)
-
-    # box2 area
-    box2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1) + 1e-16
-
-    # Intersection over box2 area
-    return inter_area / box2_area
-    
-
-def cutout(image, labels):
-    # Applies image cutout augmentation https://arxiv.org/abs/1708.04552
-    h, w = image.shape[:2]
-
-    # create random masks
-    scales = [0.5] * 1 + [0.25] * 2 + [0.125] * 4 + [0.0625] * 8 + [0.03125] * 16  # image size fraction
-    for s in scales:
-        mask_h = random.randint(1, int(h * s))
-        mask_w = random.randint(1, int(w * s))
-
-        # box
-        xmin = max(0, random.randint(0, w) - mask_w // 2)
-        ymin = max(0, random.randint(0, h) - mask_h // 2)
-        xmax = min(w, xmin + mask_w)
-        ymax = min(h, ymin + mask_h)
-
-        # apply random color mask
-        image[ymin:ymax, xmin:xmax] = [random.randint(64, 191) for _ in range(3)]
-
-        # return unobscured labels
-        if len(labels) and s > 0.03:
-            box = np.array([xmin, ymin, xmax, ymax], dtype=np.float32)
-            ioa = bbox_ioa(box, labels[:, 1:5])  # intersection over area
-            labels = labels[ioa < 0.60]  # remove >60% obscured labels
-
-    return labels
-    
-
-def pastein(image, labels, sample_labels, sample_images, sample_masks):
-    # Applies image cutout augmentation https://arxiv.org/abs/1708.04552
-    h, w = image.shape[:2]
-
-    # create random masks
-    scales = [0.75] * 2 + [0.5] * 4 + [0.25] * 4 + [0.125] * 4 + [0.0625] * 6  # image size fraction
-    for s in scales:
-        if random.random() < 0.2:
-            continue
-        mask_h = random.randint(1, int(h * s))
-        mask_w = random.randint(1, int(w * s))
-
-        # box
-        xmin = max(0, random.randint(0, w) - mask_w // 2)
-        ymin = max(0, random.randint(0, h) - mask_h // 2)
-        xmax = min(w, xmin + mask_w)
-        ymax = min(h, ymin + mask_h)   
-        
-        box = np.array([xmin, ymin, xmax, ymax], dtype=np.float32)
-        if len(labels):
-            ioa = bbox_ioa(box, labels[:, 1:5])  # intersection over area     
-        else:
-            ioa = np.zeros(1)
-        
-        if (ioa < 0.30).all() and len(sample_labels) and (xmax > xmin+20) and (ymax > ymin+20):  # allow 30% obscuration of existing labels
-            sel_ind = random.randint(0, len(sample_labels)-1)
-            #print(len(sample_labels))
-            #print(sel_ind)
-            #print((xmax-xmin, ymax-ymin))
-            #print(image[ymin:ymax, xmin:xmax].shape)
-            #print([[sample_labels[sel_ind], *box]])
-            #print(labels.shape)
-            hs, ws, cs = sample_images[sel_ind].shape
-            r_scale = min((ymax-ymin)/hs, (xmax-xmin)/ws)
-            r_w = int(ws*r_scale)
-            r_h = int(hs*r_scale)
-            
-            if (r_w > 10) and (r_h > 10):
-                r_mask = cv2.resize(sample_masks[sel_ind], (r_w, r_h))
-                r_image = cv2.resize(sample_images[sel_ind], (r_w, r_h))
-                temp_crop = image[ymin:ymin+r_h, xmin:xmin+r_w]
-                m_ind = r_mask > 0
-                if m_ind.astype(np.int32).sum() > 60:
-                    temp_crop[m_ind] = r_image[m_ind]
-                    #print(sample_labels[sel_ind])
-                    #print(sample_images[sel_ind].shape)
-                    #print(temp_crop.shape)
-                    box = np.array([xmin, ymin, xmin+r_w, ymin+r_h], dtype=np.float32)
-                    if len(labels):
-                        labels = np.concatenate((labels, [[sample_labels[sel_ind], *box]]), 0)
-                    else:
-                        labels = np.array([[sample_labels[sel_ind], *box]])
-                              
-                    image[ymin:ymin+r_h, xmin:xmin+r_w] = temp_crop
-
-    return labels
-
-class Albumentations:
-    # YOLOv5 Albumentations class (optional, only used if package is installed)
-    def __init__(self):
-        self.transform = None
-        import albumentations as A
-
-        self.transform = A.Compose([
-            A.CLAHE(p=0.01),
-            A.RandomBrightnessContrast(brightness_limit=0.2, contrast_limit=0.2, p=0.01),
-            A.RandomGamma(gamma_limit=[80, 120], p=0.01),
-            A.Blur(p=0.01),
-            A.MedianBlur(p=0.01),
-            A.ToGray(p=0.01),
-            A.ImageCompression(quality_lower=75, p=0.01),],
-            bbox_params=A.BboxParams(format='pascal_voc', label_fields=['class_labels']))
-
-            #logging.info(colorstr('albumentations: ') + ', '.join(f'{x}' for x in self.transform.transforms if x.p))
-
-    def __call__(self, im, labels, p=1.0):
-        if self.transform and random.random() < p:
-            new = self.transform(image=im, bboxes=labels[:, 1:], class_labels=labels[:, 0])  # transformed
-            im, labels = new['image'], np.array([[c, *b] for c, b in zip(new['class_labels'], new['bboxes'])])
-        return im, labels
-
-
-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
-
-
-def flatten_recursive(path='../coco'):
-    # Flatten a recursive directory by bringing all files to top level
-    new_path = Path(path + '_flat')
-    create_folder(new_path)
-    for file in tqdm(glob.glob(str(Path(path)) + '/**/*.*', recursive=True)):
-        shutil.copyfile(file, new_path / Path(file).name)
-
-
-def extract_boxes(path='../coco/'):  # from utils.datasets import *; extract_boxes('../coco128')
-    # Convert detection dataset into classification dataset, with one directory per class
-
-    path = Path(path)  # images dir
-    shutil.rmtree(path / 'classifier') if (path / 'classifier').is_dir() else None  # remove existing
-    files = list(path.rglob('*.*'))
-    n = len(files)  # number of files
-    for im_file in tqdm(files, total=n):
-        if im_file.suffix[1:] in img_formats:
-            # image
-            im = cv2.imread(str(im_file))[..., ::-1]  # BGR to RGB
-            h, w = im.shape[:2]
-
-            # labels
-            lb_file = Path(img2label_paths([str(im_file)])[0])
-            if Path(lb_file).exists():
-                with open(lb_file, 'r') as f:
-                    lb = np.array([x.split() for x in f.read().strip().splitlines()], dtype=np.float32)  # labels
-
-                for j, x in enumerate(lb):
-                    c = int(x[0])  # class
-                    f = (path / 'classifier') / f'{c}' / f'{path.stem}_{im_file.stem}_{j}.jpg'  # new filename
-                    if not f.parent.is_dir():
-                        f.parent.mkdir(parents=True)
-
-                    b = x[1:] * [w, h, w, h]  # box
-                    # b[2:] = b[2:].max()  # rectangle to square
-                    b[2:] = b[2:] * 1.2 + 3  # 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(str(f), im[b[1]:b[3], b[0]:b[2]]), f'box failure in {f}'
-
-
-def autosplit(path='../coco', weights=(0.9, 0.1, 0.0), annotated_only=False):
-    """ Autosplit a dataset into train/val/test splits and save path/autosplit_*.txt files
-    Usage: from utils.datasets import *; autosplit('../coco')
-    Arguments
-        path:           Path to images directory
-        weights:        Train, val, test weights (list)
-        annotated_only: Only use images with an annotated txt file
-    """
-    path = Path(path)  # images dir
-    files = sum([list(path.rglob(f"*.{img_ext}")) for img_ext in img_formats], [])  # image files only
-    n = len(files)  # number of files
-    indices = random.choices([0, 1, 2], weights=weights, k=n)  # assign each image to a split
-
-    txt = ['autosplit_train.txt', 'autosplit_val.txt', 'autosplit_test.txt']  # 3 txt files
-    [(path / x).unlink() for x in txt if (path / x).exists()]  # remove existing
-
-    print(f'Autosplitting images from {path}' + ', using *.txt labeled images only' * annotated_only)
-    for i, img in tqdm(zip(indices, files), total=n):
-        if not annotated_only or Path(img2label_paths([str(img)])[0]).exists():  # check label
-            with open(path / txt[i], 'a') as f:
-                f.write(str(img) + '\n')  # add image to txt file
-    
-    
-def load_segmentations(self, index):
-    key = '/work/handsomejw66/coco17/' + self.img_files[index]
-    #print(key)
-    # /work/handsomejw66/coco17/
-    return self.segs[key]

utils/general.py

@@ -1,892 +0,0 @@
-# YOLOR general utils
-
-import glob
-import logging
-import math
-import os
-import platform
-import random
-import re
-import subprocess
-import time
-from pathlib import Path
-
-import cv2
-import numpy as np
-import pandas as pd
-import torch
-import torchvision
-import yaml
-
-from utils.google_utils import gsutil_getsize
-from utils.metrics import fitness
-from utils.torch_utils import init_torch_seeds
-
-# Settings
-torch.set_printoptions(linewidth=320, precision=5, profile='long')
-np.set_printoptions(linewidth=320, formatter={'float_kind': '{:11.5g}'.format})  # format short g, %precision=5
-pd.options.display.max_columns = 10
-cv2.setNumThreads(0)  # prevent OpenCV from multithreading (incompatible with PyTorch DataLoader)
-os.environ['NUMEXPR_MAX_THREADS'] = str(min(os.cpu_count(), 8))  # NumExpr max threads
-
-
-def set_logging(rank=-1):
-    logging.basicConfig(
-        format="%(message)s",
-        level=logging.INFO if rank in [-1, 0] else logging.WARN)
-
-
-def init_seeds(seed=0):
-    # Initialize random number generator (RNG) seeds
-    random.seed(seed)
-    np.random.seed(seed)
-    init_torch_seeds(seed)
-
-
-def get_latest_run(search_dir='.'):
-    # Return path to most recent 'last.pt' in /runs (i.e. to --resume from)
-    last_list = glob.glob(f'{search_dir}/**/last*.pt', recursive=True)
-    return max(last_list, key=os.path.getctime) if last_list else ''
-
-
-def isdocker():
-    # Is environment a Docker container
-    return Path('/workspace').exists()  # or Path('/.dockerenv').exists()
-
-
-def emojis(str=''):
-    # Return platform-dependent emoji-safe version of string
-    return str.encode().decode('ascii', 'ignore') if platform.system() == 'Windows' else str
-
-
-def check_online():
-    # Check internet connectivity
-    import socket
-    try:
-        socket.create_connection(("1.1.1.1", 443), 5)  # check host accesability
-        return True
-    except OSError:
-        return False
-
-
-def check_git_status():
-    # Recommend 'git pull' if code is out of date
-    print(colorstr('github: '), end='')
-    try:
-        assert Path('.git').exists(), 'skipping check (not a git repository)'
-        assert not isdocker(), 'skipping check (Docker image)'
-        assert check_online(), 'skipping check (offline)'
-
-        cmd = 'git fetch && git config --get remote.origin.url'
-        url = subprocess.check_output(cmd, shell=True).decode().strip().rstrip('.git')  # github repo url
-        branch = subprocess.check_output('git rev-parse --abbrev-ref HEAD', shell=True).decode().strip()  # checked out
-        n = int(subprocess.check_output(f'git rev-list {branch}..origin/master --count', shell=True))  # commits behind
-        if n > 0:
-            s = f"⚠️ WARNING: code is out of date by {n} commit{'s' * (n > 1)}. " \
-                f"Use 'git pull' to update or 'git clone {url}' to download latest."
-        else:
-            s = f'up to date with {url} ✅'
-        print(emojis(s))  # emoji-safe
-    except Exception as e:
-        print(e)
-
-
-def check_requirements(requirements='requirements.txt', exclude=()):
-    # Check installed dependencies meet requirements (pass *.txt file or list of packages)
-    import pkg_resources as pkg
-    prefix = colorstr('red', 'bold', 'requirements:')
-    if isinstance(requirements, (str, Path)):  # requirements.txt file
-        file = Path(requirements)
-        if not file.exists():
-            print(f"{prefix} {file.resolve()} not found, check failed.")
-            return
-        requirements = [f'{x.name}{x.specifier}' for x in pkg.parse_requirements(file.open()) if x.name not in exclude]
-    else:  # list or tuple of packages
-        requirements = [x for x in requirements if x not in exclude]
-
-    n = 0  # number of packages updates
-    for r in requirements:
-        try:
-            pkg.require(r)
-        except Exception as e:  # DistributionNotFound or VersionConflict if requirements not met
-            n += 1
-            print(f"{prefix} {e.req} not found and is required by YOLOR, attempting auto-update...")
-            print(subprocess.check_output(f"pip install '{e.req}'", shell=True).decode())
-
-    if n:  # if packages updated
-        source = file.resolve() if 'file' in locals() else requirements
-        s = f"{prefix} {n} package{'s' * (n > 1)} updated per {source}\n" \
-            f"{prefix} ⚠️ {colorstr('bold', 'Restart runtime or rerun command for updates to take effect')}\n"
-        print(emojis(s))  # emoji-safe
-
-
-def check_img_size(img_size, s=32):
-    # Verify img_size is a multiple of stride s
-    new_size = make_divisible(img_size, int(s))  # ceil gs-multiple
-    if new_size != img_size:
-        print('WARNING: --img-size %g must be multiple of max stride %g, updating to %g' % (img_size, s, new_size))
-    return new_size
-
-
-def check_imshow():
-    # Check if environment supports image displays
-    try:
-        assert not isdocker(), 'cv2.imshow() is disabled in Docker environments'
-        cv2.imshow('test', np.zeros((1, 1, 3)))
-        cv2.waitKey(1)
-        cv2.destroyAllWindows()
-        cv2.waitKey(1)
-        return True
-    except Exception as e:
-        print(f'WARNING: Environment does not support cv2.imshow() or PIL Image.show() image displays\n{e}')
-        return False
-
-
-def check_file(file):
-    # Search for file if not found
-    if Path(file).is_file() or file == '':
-        return file
-    else:
-        files = glob.glob('./**/' + file, recursive=True)  # find file
-        assert len(files), f'File Not Found: {file}'  # assert file was found
-        assert len(files) == 1, f"Multiple files match '{file}', specify exact path: {files}"  # assert unique
-        return files[0]  # return file
-
-
-def check_dataset(dict):
-    # Download dataset if not found locally
-    val, s = dict.get('val'), dict.get('download')
-    if val and len(val):
-        val = [Path(x).resolve() for x in (val if isinstance(val, list) else [val])]  # val path
-        if not all(x.exists() for x in val):
-            print('\nWARNING: Dataset not found, nonexistent paths: %s' % [str(x) for x in val if not x.exists()])
-            if s and len(s):  # download script
-                print('Downloading %s ...' % s)
-                if s.startswith('http') and s.endswith('.zip'):  # URL
-                    f = Path(s).name  # filename
-                    torch.hub.download_url_to_file(s, f)
-                    r = os.system('unzip -q %s -d ../ && rm %s' % (f, f))  # unzip
-                else:  # bash script
-                    r = os.system(s)
-                print('Dataset autodownload %s\n' % ('success' if r == 0 else 'failure'))  # analyze return value
-            else:
-                raise Exception('Dataset not found.')
-
-
-def make_divisible(x, divisor):
-    # Returns x evenly divisible by divisor
-    return math.ceil(x / divisor) * divisor
-
-
-def clean_str(s):
-    # Cleans a string by replacing special characters with underscore _
-    return re.sub(pattern="[|@#!¡·$€%&()=?¿^*;:,¨´><+]", repl="_", string=s)
-
-
-def one_cycle(y1=0.0, y2=1.0, steps=100):
-    # lambda function for sinusoidal ramp from y1 to y2
-    return lambda x: ((1 - math.cos(x * math.pi / steps)) / 2) * (y2 - y1) + y1
-
-
-def colorstr(*input):
-    # Colors a string https://en.wikipedia.org/wiki/ANSI_escape_code, i.e.  colorstr('blue', 'hello world')
-    *args, string = input if len(input) > 1 else ('blue', 'bold', input[0])  # color arguments, string
-    colors = {'black': '\033[30m',  # basic colors
-              'red': '\033[31m',
-              'green': '\033[32m',
-              'yellow': '\033[33m',
-              'blue': '\033[34m',
-              'magenta': '\033[35m',
-              'cyan': '\033[36m',
-              'white': '\033[37m',
-              'bright_black': '\033[90m',  # bright colors
-              'bright_red': '\033[91m',
-              'bright_green': '\033[92m',
-              'bright_yellow': '\033[93m',
-              'bright_blue': '\033[94m',
-              'bright_magenta': '\033[95m',
-              'bright_cyan': '\033[96m',
-              'bright_white': '\033[97m',
-              'end': '\033[0m',  # misc
-              'bold': '\033[1m',
-              'underline': '\033[4m'}
-    return ''.join(colors[x] for x in args) + f'{string}' + colors['end']
-
-
-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.int32)  # 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 labels_to_image_weights(labels, nc=80, class_weights=np.ones(80)):
-    # Produces image weights based on class_weights and image contents
-    class_counts = np.array([np.bincount(x[:, 0].astype(np.int32), minlength=nc) for x in labels])
-    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 coco80_to_coco91_class():  # converts 80-index (val2014) to 91-index (paper)
-    # https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/
-    # a = np.loadtxt('data/coco.names', dtype='str', delimiter='\n')
-    # b = np.loadtxt('data/coco_paper.names', dtype='str', delimiter='\n')
-    # x1 = [list(a[i] == b).index(True) + 1 for i in range(80)]  # darknet to coco
-    # x2 = [list(b[i] == a).index(True) if any(b[i] == a) else None for i in range(91)]  # coco to darknet
-    x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 27, 28, 31, 32, 33, 34,
-         35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63,
-         64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90]
-    return x
-
-
-def xyxy2xywh(x):
-    # Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] where xy1=top-left, xy2=bottom-right
-    y = x.clone() if isinstance(x, torch.Tensor) else np.copy(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 = x.clone() if isinstance(x, torch.Tensor) else np.copy(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 xywhn2xyxy(x, w=640, h=640, padw=0, padh=0):
-    # Convert nx4 boxes from [x, y, w, h] normalized to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
-    y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
-    y[:, 0] = w * (x[:, 0] - x[:, 2] / 2) + padw  # top left x
-    y[:, 1] = h * (x[:, 1] - x[:, 3] / 2) + padh  # top left y
-    y[:, 2] = w * (x[:, 0] + x[:, 2] / 2) + padw  # bottom right x
-    y[:, 3] = h * (x[:, 1] + x[:, 3] / 2) + padh  # bottom right y
-    return y
-
-
-def xyn2xy(x, w=640, h=640, padw=0, padh=0):
-    # Convert normalized segments into pixel segments, shape (n,2)
-    y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
-    y[:, 0] = w * x[:, 0] + padw  # top left x
-    y[:, 1] = h * x[:, 1] + padh  # top left y
-    return y
-
-
-def segment2box(segment, width=640, height=640):
-    # Convert 1 segment label to 1 box label, applying inside-image constraint, i.e. (xy1, xy2, ...) to (xyxy)
-    x, y = segment.T  # segment xy
-    inside = (x >= 0) & (y >= 0) & (x <= width) & (y <= height)
-    x, y, = x[inside], y[inside]
-    return np.array([x.min(), y.min(), x.max(), y.max()]) if any(x) else np.zeros((1, 4))  # xyxy
-
-
-def segments2boxes(segments):
-    # Convert segment labels to box labels, i.e. (cls, xy1, xy2, ...) to (cls, xywh)
-    boxes = []
-    for s in segments:
-        x, y = s.T  # segment xy
-        boxes.append([x.min(), y.min(), x.max(), y.max()])  # cls, xyxy
-    return xyxy2xywh(np.array(boxes))  # cls, xywh
-
-
-def resample_segments(segments, n=1000):
-    # Up-sample an (n,2) segment
-    for i, s in enumerate(segments):
-        s = np.concatenate((s, s[0:1, :]), axis=0)
-        x = np.linspace(0, len(s) - 1, n)
-        xp = np.arange(len(s))
-        segments[i] = np.concatenate([np.interp(x, xp, s[:, i]) for i in range(2)]).reshape(2, -1).T  # segment xy
-    return segments
-
-
-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 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 bbox_iou(box1, box2, x1y1x2y2=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7):
-    # 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 + eps
-    w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps
-    union = w1 * h1 + w2 * h2 - inter + eps
-
-    iou = inter / union
-
-    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 CIoU or DIoU:  # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1
-            c2 = cw ** 2 + ch ** 2 + eps  # convex diagonal squared
-            rho2 = ((b2_x1 + b2_x2 - b1_x1 - b1_x2) ** 2 +
-                    (b2_y1 + b2_y2 - b1_y1 - b1_y2) ** 2) / 4  # center distance squared
-            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 + eps)) - torch.atan(w1 / (h1 + eps)), 2)
-                with torch.no_grad():
-                    alpha = v / (v - iou + (1 + eps))
-                return iou - (rho2 / c2 + v * alpha)  # CIoU
-        else:  # GIoU https://arxiv.org/pdf/1902.09630.pdf
-            c_area = cw * ch + eps  # convex area
-            return iou - (c_area - union) / c_area  # GIoU
-    else:
-        return iou  # IoU
-
-
-
-
-def bbox_alpha_iou(box1, box2, x1y1x2y2=False, GIoU=False, DIoU=False, CIoU=False, alpha=2, eps=1e-9):
-    # Returns tsqrt_he 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 + eps
-    w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps
-    union = w1 * h1 + w2 * h2 - inter + eps
-
-    # change iou into pow(iou+eps)
-    # iou = inter / union
-    iou = torch.pow(inter/union + eps, alpha)
-    # beta = 2 * alpha
-    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 CIoU or DIoU:  # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1
-            c2 = (cw ** 2 + ch ** 2) ** alpha + eps  # convex diagonal
-            rho_x = torch.abs(b2_x1 + b2_x2 - b1_x1 - b1_x2)
-            rho_y = torch.abs(b2_y1 + b2_y2 - b1_y1 - b1_y2)
-            rho2 = ((rho_x ** 2 + rho_y ** 2) / 4) ** alpha  # center distance
-            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_ciou = v / ((1 + eps) - inter / union + v)
-                # return iou - (rho2 / c2 + v * alpha_ciou)  # CIoU
-                return iou - (rho2 / c2 + torch.pow(v * alpha_ciou + eps, alpha))  # CIoU
-        else:  # GIoU https://arxiv.org/pdf/1902.09630.pdf
-            # c_area = cw * ch + eps  # convex area
-            # return iou - (c_area - union) / c_area  # GIoU
-            c_area = torch.max(cw * ch + eps, union) # convex area
-            return iou - torch.pow((c_area - union) / c_area + eps, alpha)  # GIoU
-    else:
-        return iou # torch.log(iou+eps) or 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 wh_iou(wh1, wh2):
-    # Returns the nxm IoU matrix. wh1 is nx2, wh2 is mx2
-    wh1 = wh1[:, None]  # [N,1,2]
-    wh2 = wh2[None]  # [1,M,2]
-    inter = torch.min(wh1, wh2).prod(2)  # [N,M]
-    return inter / (wh1.prod(2) + wh2.prod(2) - inter)  # iou = inter / (area1 + area2 - inter)
-
-
-def box_giou(box1, box2):
-    """
-    Return generalized intersection-over-union (Jaccard index) between two sets of boxes.
-    Both sets of boxes are expected to be in ``(x1, y1, x2, y2)`` format with
-    ``0 <= x1 < x2`` and ``0 <= y1 < y2``.
-    Args:
-        boxes1 (Tensor[N, 4]): first set of boxes
-        boxes2 (Tensor[M, 4]): second set of boxes
-    Returns:
-        Tensor[N, M]: the NxM matrix containing the pairwise generalized 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 = (torch.min(box1[:, None, 2:], box2[:, 2:]) - torch.max(box1[:, None, :2], box2[:, :2])).clamp(0).prod(2)
-    union = (area1[:, None] + area2 - inter)
-
-    iou = inter / union
-
-    lti = torch.min(box1[:, None, :2], box2[:, :2])
-    rbi = torch.max(box1[:, None, 2:], box2[:, 2:])
-
-    whi = (rbi - lti).clamp(min=0)  # [N,M,2]
-    areai = whi[:, :, 0] * whi[:, :, 1]
-
-    return iou - (areai - union) / areai
-
-
-def box_ciou(box1, box2, eps: float = 1e-7):
-    """
-    Return complete intersection-over-union (Jaccard index) between two sets of boxes.
-    Both sets of boxes are expected to be in ``(x1, y1, x2, y2)`` format with
-    ``0 <= x1 < x2`` and ``0 <= y1 < y2``.
-    Args:
-        boxes1 (Tensor[N, 4]): first set of boxes
-        boxes2 (Tensor[M, 4]): second set of boxes
-        eps (float, optional): small number to prevent division by zero. Default: 1e-7
-    Returns:
-        Tensor[N, M]: the NxM matrix containing the pairwise complete 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 = (torch.min(box1[:, None, 2:], box2[:, 2:]) - torch.max(box1[:, None, :2], box2[:, :2])).clamp(0).prod(2)
-    union = (area1[:, None] + area2 - inter)
-
-    iou = inter / union
-
-    lti = torch.min(box1[:, None, :2], box2[:, :2])
-    rbi = torch.max(box1[:, None, 2:], box2[:, 2:])
-
-    whi = (rbi - lti).clamp(min=0)  # [N,M,2]
-    diagonal_distance_squared = (whi[:, :, 0] ** 2) + (whi[:, :, 1] ** 2) + eps
-
-    # centers of boxes
-    x_p = (box1[:, None, 0] + box1[:, None, 2]) / 2
-    y_p = (box1[:, None, 1] + box1[:, None, 3]) / 2
-    x_g = (box2[:, 0] + box2[:, 2]) / 2
-    y_g = (box2[:, 1] + box2[:, 3]) / 2
-    # The distance between boxes' centers squared.
-    centers_distance_squared = (x_p - x_g) ** 2 + (y_p - y_g) ** 2
-
-    w_pred = box1[:, None, 2] - box1[:, None, 0]
-    h_pred = box1[:, None, 3] - box1[:, None, 1]
-
-    w_gt = box2[:, 2] - box2[:, 0]
-    h_gt = box2[:, 3] - box2[:, 1]
-
-    v = (4 / (torch.pi ** 2)) * torch.pow((torch.atan(w_gt / h_gt) - torch.atan(w_pred / h_pred)), 2)
-    with torch.no_grad():
-        alpha = v / (1 - iou + v + eps)
-    return iou - (centers_distance_squared / diagonal_distance_squared) - alpha * v
-
-
-def box_diou(box1, box2, eps: float = 1e-7):
-    """
-    Return distance intersection-over-union (Jaccard index) between two sets of boxes.
-    Both sets of boxes are expected to be in ``(x1, y1, x2, y2)`` format with
-    ``0 <= x1 < x2`` and ``0 <= y1 < y2``.
-    Args:
-        boxes1 (Tensor[N, 4]): first set of boxes
-        boxes2 (Tensor[M, 4]): second set of boxes
-        eps (float, optional): small number to prevent division by zero. Default: 1e-7
-    Returns:
-        Tensor[N, M]: the NxM matrix containing the pairwise distance 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 = (torch.min(box1[:, None, 2:], box2[:, 2:]) - torch.max(box1[:, None, :2], box2[:, :2])).clamp(0).prod(2)
-    union = (area1[:, None] + area2 - inter)
-
-    iou = inter / union
-
-    lti = torch.min(box1[:, None, :2], box2[:, :2])
-    rbi = torch.max(box1[:, None, 2:], box2[:, 2:])
-
-    whi = (rbi - lti).clamp(min=0)  # [N,M,2]
-    diagonal_distance_squared = (whi[:, :, 0] ** 2) + (whi[:, :, 1] ** 2) + eps
-
-    # centers of boxes
-    x_p = (box1[:, None, 0] + box1[:, None, 2]) / 2
-    y_p = (box1[:, None, 1] + box1[:, None, 3]) / 2
-    x_g = (box2[:, 0] + box2[:, 2]) / 2
-    y_g = (box2[:, 1] + box2[:, 3]) / 2
-    # The distance between boxes' centers squared.
-    centers_distance_squared = (x_p - x_g) ** 2 + (y_p - y_g) ** 2
-
-    # The distance IoU is the IoU penalized by a normalized
-    # distance between boxes' centers squared.
-    return iou - (centers_distance_squared / diagonal_distance_squared)
-
-
-def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, multi_label=False,
-                        labels=()):
-    """Runs Non-Maximum Suppression (NMS) on inference results
-
-    Returns:
-         list of detections, on (n,6) tensor per image [xyxy, conf, cls]
-    """
-
-    nc = prediction.shape[2] - 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
-    max_nms = 30000  # maximum number of boxes into torchvision.ops.nms()
-    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)
-    merge = False  # use merge-NMS
-
-    t = time.time()
-    output = [torch.zeros((0, 6), device=prediction.device)] * 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
-
-        # Cat apriori labels if autolabelling
-        if labels and len(labels[xi]):
-            l = labels[xi]
-            v = torch.zeros((len(l), nc + 5), device=x.device)
-            v[:, :4] = l[:, 1:5]  # box
-            v[:, 4] = 1.0  # conf
-            v[range(len(l)), l[:, 0].long() + 5] = 1.0  # cls
-            x = torch.cat((x, v), 0)
-
-        # If none remain process next image
-        if not x.shape[0]:
-            continue
-
-        # Compute conf
-        if nc == 1:
-            x[:, 5:] = x[:, 4:5] # for models with one class, cls_loss is 0 and cls_conf is always 0.5,
-                                 # so there is no need to multiplicate.
-        else:
-            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 is not None:
-            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)]
-
-        # Check shape
-        n = x.shape[0]  # number of boxes
-        if not n:  # no boxes
-            continue
-        elif n > max_nms:  # excess boxes
-            x = x[x[:, 4].argsort(descending=True)[:max_nms]]  # sort by confidence
-
-        # 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 = torchvision.ops.nms(boxes, scores, iou_thres)  # NMS
-        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)
-            # 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
-
-        output[xi] = x[i]
-        if (time.time() - t) > time_limit:
-            print(f'WARNING: NMS time limit {time_limit}s exceeded')
-            break  # time limit exceeded
-
-    return output
-
-
-def non_max_suppression_kpt(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, multi_label=False,
-                        labels=(), kpt_label=False, nc=None, nkpt=None):
-    """Runs Non-Maximum Suppression (NMS) on inference results
-
-    Returns:
-         list of detections, on (n,6) tensor per image [xyxy, conf, cls]
-    """
-    if nc is None:
-        nc = prediction.shape[2] - 5  if not kpt_label else prediction.shape[2] - 56 # 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
-    max_nms = 30000  # maximum number of boxes into torchvision.ops.nms()
-    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)
-    merge = False  # use merge-NMS
-
-    t = time.time()
-    output = [torch.zeros((0,6), device=prediction.device)] * 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
-
-        # Cat apriori labels if autolabelling
-        if labels and len(labels[xi]):
-            l = labels[xi]
-            v = torch.zeros((len(l), nc + 5), device=x.device)
-            v[:, :4] = l[:, 1:5]  # box
-            v[:, 4] = 1.0  # conf
-            v[range(len(l)), l[:, 0].long() + 5] = 1.0  # cls
-            x = torch.cat((x, v), 0)
-
-        # If none remain process next image
-        if not x.shape[0]:
-            continue
-
-        # Compute conf
-        x[:, 5:5+nc] *= 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
-            if not kpt_label:
-                conf, j = x[:, 5:].max(1, keepdim=True)
-                x = torch.cat((box, conf, j.float()), 1)[conf.view(-1) > conf_thres]
-            else:
-                kpts = x[:, 6:]
-                conf, j = x[:, 5:6].max(1, keepdim=True)
-                x = torch.cat((box, conf, j.float(), kpts), 1)[conf.view(-1) > conf_thres]
-
-
-        # Filter by class
-        if classes is not None:
-            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)]
-
-        # Check shape
-        n = x.shape[0]  # number of boxes
-        if not n:  # no boxes
-            continue
-        elif n > max_nms:  # excess boxes
-            x = x[x[:, 4].argsort(descending=True)[:max_nms]]  # sort by confidence
-
-        # 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 = torchvision.ops.nms(boxes, scores, iou_thres)  # NMS
-        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)
-            # 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
-
-        output[xi] = x[i]
-        if (time.time() - t) > time_limit:
-            print(f'WARNING: NMS time limit {time_limit}s exceeded')
-            break  # time limit exceeded
-
-    return output
-
-
-def strip_optimizer(f='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'))
-    if x.get('ema'):
-        x['model'] = x['ema']  # replace model with ema
-    for k in 'optimizer', 'training_results', 'wandb_id', 'ema', 'updates':  # keys
-        x[k] = 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(f"Optimizer stripped from {f},{(' saved as %s,' % s) if s else ''} {mb:.1f}MB")
-
-
-def print_mutation(hyp, results, yaml_file='hyp_evolved.yaml', bucket=''):
-    # Print mutation results to evolve.txt (for use with train.py --evolve)
-    a = '%10s' * len(hyp) % tuple(hyp.keys())  # hyperparam keys
-    b = '%10.3g' * len(hyp) % tuple(hyp.values())  # hyperparam values
-    c = '%10.4g' * len(results) % results  # results (P, R, mAP@0.5, mAP@0.5:0.95, val_losses x 3)
-    print('\n%s\n%s\nEvolved fitness: %s\n' % (a, b, c))
-
-    if bucket:
-        url = 'gs://%s/evolve.txt' % bucket
-        if gsutil_getsize(url) > (os.path.getsize('evolve.txt') if os.path.exists('evolve.txt') else 0):
-            os.system('gsutil cp %s .' % url)  # download evolve.txt if larger than local
-
-    with open('evolve.txt', 'a') as f:  # append result
-        f.write(c + b + '\n')
-    x = np.unique(np.loadtxt('evolve.txt', ndmin=2), axis=0)  # load unique rows
-    x = x[np.argsort(-fitness(x))]  # sort
-    np.savetxt('evolve.txt', x, '%10.3g')  # save sort by fitness
-
-    # Save yaml
-    for i, k in enumerate(hyp.keys()):
-        hyp[k] = float(x[0, i + 7])
-    with open(yaml_file, 'w') as f:
-        results = tuple(x[0, :7])
-        c = '%10.4g' * len(results) % results  # results (P, R, mAP@0.5, mAP@0.5:0.95, val_losses x 3)
-        f.write('# Hyperparameter Evolution Results\n# Generations: %g\n# Metrics: ' % len(x) + c + '\n\n')
-        yaml.dump(hyp, f, sort_keys=False)
-
-    if bucket:
-        os.system('gsutil cp evolve.txt %s gs://%s' % (yaml_file, bucket))  # upload
-
-
-def apply_classifier(x, model, img, im0):
-    # applies a second stage classifier to yolo outputs
-    im0 = [im0] if isinstance(im0, np.ndarray) else im0
-    for i, d in enumerate(x):  # per image
-        if d is not None and len(d):
-            d = d.clone()
-
-            # Reshape and pad cutouts
-            b = xyxy2xywh(d[:, :4])  # boxes
-            b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1)  # rectangle to square
-            b[:, 2:] = b[:, 2:] * 1.3 + 30  # pad
-            d[:, :4] = xywh2xyxy(b).long()
-
-            # Rescale boxes from img_size to im0 size
-            scale_coords(img.shape[2:], d[:, :4], im0[i].shape)
-
-            # Classes
-            pred_cls1 = d[:, 5].long()
-            ims = []
-            for j, a in enumerate(d):  # per item
-                cutout = im0[i][int(a[1]):int(a[3]), int(a[0]):int(a[2])]
-                im = cv2.resize(cutout, (224, 224))  # BGR
-                # cv2.imwrite('test%i.jpg' % j, cutout)
-
-                im = im[:, :, ::-1].transpose(2, 0, 1)  # BGR to RGB, to 3x416x416
-                im = np.ascontiguousarray(im, dtype=np.float32)  # uint8 to float32
-                im /= 255.0  # 0 - 255 to 0.0 - 1.0
-                ims.append(im)
-
-            pred_cls2 = model(torch.Tensor(ims).to(d.device)).argmax(1)  # classifier prediction
-            x[i] = x[i][pred_cls1 == pred_cls2]  # retain matching class detections
-
-    return x
-
-
-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

utils/google_app_engine/Dockerfile

@@ -1,25 +0,0 @@
-FROM gcr.io/google-appengine/python
-
-# Create a virtualenv for dependencies. This isolates these packages from
-# system-level packages.
-# Use -p python3 or -p python3.7 to select python version. Default is version 2.
-RUN virtualenv /env -p python3
-
-# Setting these environment variables are the same as running
-# source /env/bin/activate.
-ENV VIRTUAL_ENV /env
-ENV PATH /env/bin:$PATH
-
-RUN apt-get update && apt-get install -y python-opencv
-
-# Copy the application's requirements.txt and run pip to install all
-# dependencies into the virtualenv.
-ADD requirements.txt /app/requirements.txt
-RUN pip install -r /app/requirements.txt
-
-# Add the application source code.
-ADD . /app
-
-# Run a WSGI server to serve the application. gunicorn must be declared as
-# a dependency in requirements.txt.
-CMD gunicorn -b :$PORT main:app

utils/google_app_engine/additional_requirements.txt

@@ -1,4 +0,0 @@
-# add these requirements in your app on top of the existing ones
-pip==18.1
-Flask==1.0.2
-gunicorn==19.9.0

utils/google_app_engine/app.yaml

@@ -1,14 +0,0 @@
-runtime: custom
-env: flex
-
-service: yolorapp
-
-liveness_check:
-  initial_delay_sec: 600
-
-manual_scaling:
-  instances: 1
-resources:
-  cpu: 1
-  memory_gb: 4
-  disk_size_gb: 20

utils/google_utils.py

@@ -1,123 +0,0 @@
-# Google utils: https://cloud.google.com/storage/docs/reference/libraries
-
-import os
-import platform
-import subprocess
-import time
-from pathlib import Path
-
-import requests
-import torch
-
-
-def gsutil_getsize(url=''):
-    # gs://bucket/file size https://cloud.google.com/storage/docs/gsutil/commands/du
-    s = subprocess.check_output(f'gsutil du {url}', shell=True).decode('utf-8')
-    return eval(s.split(' ')[0]) if len(s) else 0  # bytes
-
-
-def attempt_download(file, repo='WongKinYiu/yolov7'):
-    # Attempt file download if does not exist
-    file = Path(str(file).strip().replace("'", '').lower())
-
-    if not file.exists():
-        try:
-            response = requests.get(f'https://api.github.com/repos/{repo}/releases/latest').json()  # github api
-            assets = [x['name'] for x in response['assets']]  # release assets
-            tag = response['tag_name']  # i.e. 'v1.0'
-        except:  # fallback plan
-            assets = ['yolov7.pt', 'yolov7-tiny.pt', 'yolov7x.pt', 'yolov7-d6.pt', 'yolov7-e6.pt', 
-                      'yolov7-e6e.pt', 'yolov7-w6.pt']
-            tag = subprocess.check_output('git tag', shell=True).decode().split()[-1]
-
-        name = file.name
-        if name in assets:
-            msg = f'{file} missing, try downloading from https://github.com/{repo}/releases/'
-            redundant = False  # second download option
-            try:  # GitHub
-                url = f'https://github.com/{repo}/releases/download/{tag}/{name}'
-                print(f'Downloading {url} to {file}...')
-                torch.hub.download_url_to_file(url, file)
-                assert file.exists() and file.stat().st_size > 1E6  # check
-            except Exception as e:  # GCP
-                print(f'Download error: {e}')
-                assert redundant, 'No secondary mirror'
-                url = f'https://storage.googleapis.com/{repo}/ckpt/{name}'
-                print(f'Downloading {url} to {file}...')
-                os.system(f'curl -L {url} -o {file}')  # torch.hub.download_url_to_file(url, weights)
-            finally:
-                if not file.exists() or file.stat().st_size < 1E6:  # check
-                    file.unlink(missing_ok=True)  # remove partial downloads
-                    print(f'ERROR: Download failure: {msg}')
-                print('')
-                return
-
-
-def gdrive_download(id='', file='tmp.zip'):
-    # Downloads a file from Google Drive. from yolov7.utils.google_utils import *; gdrive_download()
-    t = time.time()
-    file = Path(file)
-    cookie = Path('cookie')  # gdrive cookie
-    print(f'Downloading https://drive.google.com/uc?export=download&id={id} as {file}... ', end='')
-    file.unlink(missing_ok=True)  # remove existing file
-    cookie.unlink(missing_ok=True)  # remove existing cookie
-
-    # Attempt file download
-    out = "NUL" if platform.system() == "Windows" else "/dev/null"
-    os.system(f'curl -c ./cookie -s -L "drive.google.com/uc?export=download&id={id}" > {out}')
-    if os.path.exists('cookie'):  # large file
-        s = f'curl -Lb ./cookie "drive.google.com/uc?export=download&confirm={get_token()}&id={id}" -o {file}'
-    else:  # small file
-        s = f'curl -s -L -o {file} "drive.google.com/uc?export=download&id={id}"'
-    r = os.system(s)  # execute, capture return
-    cookie.unlink(missing_ok=True)  # remove existing cookie
-
-    # Error check
-    if r != 0:
-        file.unlink(missing_ok=True)  # remove partial
-        print('Download error ')  # raise Exception('Download error')
-        return r
-
-    # Unzip if archive
-    if file.suffix == '.zip':
-        print('unzipping... ', end='')
-        os.system(f'unzip -q {file}')  # unzip
-        file.unlink()  # remove zip to free space
-
-    print(f'Done ({time.time() - t:.1f}s)')
-    return r
-
-
-def get_token(cookie="./cookie"):
-    with open(cookie) as f:
-        for line in f:
-            if "download" in line:
-                return line.split()[-1]
-    return ""
-
-# def upload_blob(bucket_name, source_file_name, destination_blob_name):
-#     # Uploads a file to a bucket
-#     # https://cloud.google.com/storage/docs/uploading-objects#storage-upload-object-python
-#
-#     storage_client = storage.Client()
-#     bucket = storage_client.get_bucket(bucket_name)
-#     blob = bucket.blob(destination_blob_name)
-#
-#     blob.upload_from_filename(source_file_name)
-#
-#     print('File {} uploaded to {}.'.format(
-#         source_file_name,
-#         destination_blob_name))
-#
-#
-# def download_blob(bucket_name, source_blob_name, destination_file_name):
-#     # Uploads a blob from a bucket
-#     storage_client = storage.Client()
-#     bucket = storage_client.get_bucket(bucket_name)
-#     blob = bucket.blob(source_blob_name)
-#
-#     blob.download_to_filename(destination_file_name)
-#
-#     print('Blob {} downloaded to {}.'.format(
-#         source_blob_name,
-#         destination_file_name))

utils/loss.py

@@ -1,1697 +0,0 @@
-# Loss functions
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from utils.general import bbox_iou, bbox_alpha_iou, box_iou, box_giou, box_diou, box_ciou, xywh2xyxy
-from utils.torch_utils import is_parallel
-
-
-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
-
-
-class BCEBlurWithLogitsLoss(nn.Module):
-    # BCEwithLogitLoss() with reduced missing label effects.
-    def __init__(self, alpha=0.05):
-        super(BCEBlurWithLogitsLoss, self).__init__()
-        self.loss_fcn = nn.BCEWithLogitsLoss(reduction='none')  # must be nn.BCEWithLogitsLoss()
-        self.alpha = alpha
-
-    def forward(self, pred, true):
-        loss = self.loss_fcn(pred, true)
-        pred = torch.sigmoid(pred)  # prob from logits
-        dx = pred - true  # reduce only missing label effects
-        # dx = (pred - true).abs()  # reduce missing label and false label effects
-        alpha_factor = 1 - torch.exp((dx - 1) / (self.alpha + 1e-4))
-        loss *= alpha_factor
-        return loss.mean()
-
-
-class SigmoidBin(nn.Module):
-    stride = None  # strides computed during build
-    export = False  # onnx export
-
-    def __init__(self, bin_count=10, min=0.0, max=1.0, reg_scale = 2.0, use_loss_regression=True, use_fw_regression=True, BCE_weight=1.0, smooth_eps=0.0):
-        super(SigmoidBin, self).__init__()
-        
-        self.bin_count = bin_count
-        self.length = bin_count + 1
-        self.min = min
-        self.max = max
-        self.scale = float(max - min)
-        self.shift = self.scale / 2.0
-
-        self.use_loss_regression = use_loss_regression
-        self.use_fw_regression = use_fw_regression
-        self.reg_scale = reg_scale
-        self.BCE_weight = BCE_weight
-
-        start = min + (self.scale/2.0) / self.bin_count
-        end = max - (self.scale/2.0) / self.bin_count
-        step = self.scale / self.bin_count
-        self.step = step
-        #print(f" start = {start}, end = {end}, step = {step} ")
-
-        bins = torch.range(start, end + 0.0001, step).float() 
-        self.register_buffer('bins', bins) 
-               
-
-        self.cp = 1.0 - 0.5 * smooth_eps
-        self.cn = 0.5 * smooth_eps
-
-        self.BCEbins = nn.BCEWithLogitsLoss(pos_weight=torch.Tensor([BCE_weight]))
-        self.MSELoss = nn.MSELoss()
-
-    def get_length(self):
-        return self.length
-
-    def forward(self, pred):
-        assert pred.shape[-1] == self.length, 'pred.shape[-1]=%d is not equal to self.length=%d' % (pred.shape[-1], self.length)
-
-        pred_reg = (pred[..., 0] * self.reg_scale - self.reg_scale/2.0) * self.step
-        pred_bin = pred[..., 1:(1+self.bin_count)]
-
-        _, bin_idx = torch.max(pred_bin, dim=-1)
-        bin_bias = self.bins[bin_idx]
-
-        if self.use_fw_regression:
-            result = pred_reg + bin_bias
-        else:
-            result = bin_bias
-        result = result.clamp(min=self.min, max=self.max)
-
-        return result
-
-
-    def training_loss(self, pred, target):
-        assert pred.shape[-1] == self.length, 'pred.shape[-1]=%d is not equal to self.length=%d' % (pred.shape[-1], self.length)
-        assert pred.shape[0] == target.shape[0], 'pred.shape=%d is not equal to the target.shape=%d' % (pred.shape[0], target.shape[0])
-        device = pred.device
-
-        pred_reg = (pred[..., 0].sigmoid() * self.reg_scale - self.reg_scale/2.0) * self.step
-        pred_bin = pred[..., 1:(1+self.bin_count)]
-
-        diff_bin_target = torch.abs(target[..., None] - self.bins)
-        _, bin_idx = torch.min(diff_bin_target, dim=-1)
-    
-        bin_bias = self.bins[bin_idx]
-        bin_bias.requires_grad = False
-        result = pred_reg + bin_bias
-
-        target_bins = torch.full_like(pred_bin, self.cn, device=device)  # targets
-        n = pred.shape[0] 
-        target_bins[range(n), bin_idx] = self.cp
-
-        loss_bin = self.BCEbins(pred_bin, target_bins) # BCE
-
-        if self.use_loss_regression:
-            loss_regression = self.MSELoss(result, target)  # MSE        
-            loss = loss_bin + loss_regression
-        else:
-            loss = loss_bin
-
-        out_result = result.clamp(min=self.min, max=self.max)
-
-        return loss, out_result
-
-
-class FocalLoss(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
-
-
-class QFocalLoss(nn.Module):
-    # Wraps Quality 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(QFocalLoss, 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)
-
-        pred_prob = torch.sigmoid(pred)  # prob from logits
-        alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha)
-        modulating_factor = torch.abs(true - pred_prob) ** 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
-
-class RankSort(torch.autograd.Function):
-    @staticmethod
-    def forward(ctx, logits, targets, delta_RS=0.50, eps=1e-10): 
-
-        classification_grads=torch.zeros(logits.shape).cuda()
-        
-        #Filter fg logits
-        fg_labels = (targets > 0.)
-        fg_logits = logits[fg_labels]
-        fg_targets = targets[fg_labels]
-        fg_num = len(fg_logits)
-
-        #Do not use bg with scores less than minimum fg logit
-        #since changing its score does not have an effect on precision
-        threshold_logit = torch.min(fg_logits)-delta_RS
-        relevant_bg_labels=((targets==0) & (logits>=threshold_logit))
-        
-        relevant_bg_logits = logits[relevant_bg_labels] 
-        relevant_bg_grad=torch.zeros(len(relevant_bg_logits)).cuda()
-        sorting_error=torch.zeros(fg_num).cuda()
-        ranking_error=torch.zeros(fg_num).cuda()
-        fg_grad=torch.zeros(fg_num).cuda()
-        
-        #sort the fg logits
-        order=torch.argsort(fg_logits)
-        #Loops over each positive following the order
-        for ii in order:
-            # Difference Transforms (x_ij)
-            fg_relations=fg_logits-fg_logits[ii] 
-            bg_relations=relevant_bg_logits-fg_logits[ii]
-
-            if delta_RS > 0:
-                fg_relations=torch.clamp(fg_relations/(2*delta_RS)+0.5,min=0,max=1)
-                bg_relations=torch.clamp(bg_relations/(2*delta_RS)+0.5,min=0,max=1)
-            else:
-                fg_relations = (fg_relations >= 0).float()
-                bg_relations = (bg_relations >= 0).float()
-
-            # Rank of ii among pos and false positive number (bg with larger scores)
-            rank_pos=torch.sum(fg_relations)
-            FP_num=torch.sum(bg_relations)
-
-            # Rank of ii among all examples
-            rank=rank_pos+FP_num
-                            
-            # Ranking error of example ii. target_ranking_error is always 0. (Eq. 7)
-            ranking_error[ii]=FP_num/rank      
-
-            # Current sorting error of example ii. (Eq. 7)
-            current_sorting_error = torch.sum(fg_relations*(1-fg_targets))/rank_pos
-
-            #Find examples in the target sorted order for example ii         
-            iou_relations = (fg_targets >= fg_targets[ii])
-            target_sorted_order = iou_relations * fg_relations
-
-            #The rank of ii among positives in sorted order
-            rank_pos_target = torch.sum(target_sorted_order)
-
-            #Compute target sorting error. (Eq. 8)
-            #Since target ranking error is 0, this is also total target error 
-            target_sorting_error= torch.sum(target_sorted_order*(1-fg_targets))/rank_pos_target
-
-            #Compute sorting error on example ii
-            sorting_error[ii] = current_sorting_error - target_sorting_error
-  
-            #Identity Update for Ranking Error 
-            if FP_num > eps:
-                #For ii the update is the ranking error
-                fg_grad[ii] -= ranking_error[ii]
-                #For negatives, distribute error via ranking pmf (i.e. bg_relations/FP_num)
-                relevant_bg_grad += (bg_relations*(ranking_error[ii]/FP_num))
-
-            #Find the positives that are misranked (the cause of the error)
-            #These are the ones with smaller IoU but larger logits
-            missorted_examples = (~ iou_relations) * fg_relations
-
-            #Denominotor of sorting pmf 
-            sorting_pmf_denom = torch.sum(missorted_examples)
-
-            #Identity Update for Sorting Error 
-            if sorting_pmf_denom > eps:
-                #For ii the update is the sorting error
-                fg_grad[ii] -= sorting_error[ii]
-                #For positives, distribute error via sorting pmf (i.e. missorted_examples/sorting_pmf_denom)
-                fg_grad += (missorted_examples*(sorting_error[ii]/sorting_pmf_denom))
-
-        #Normalize gradients by number of positives 
-        classification_grads[fg_labels]= (fg_grad/fg_num)
-        classification_grads[relevant_bg_labels]= (relevant_bg_grad/fg_num)
-
-        ctx.save_for_backward(classification_grads)
-
-        return ranking_error.mean(), sorting_error.mean()
-
-    @staticmethod
-    def backward(ctx, out_grad1, out_grad2):
-        g1, =ctx.saved_tensors
-        return g1*out_grad1, None, None, None
-
-class aLRPLoss(torch.autograd.Function):
-    @staticmethod
-    def forward(ctx, logits, targets, regression_losses, delta=1., eps=1e-5): 
-        classification_grads=torch.zeros(logits.shape).cuda()
-        
-        #Filter fg logits
-        fg_labels = (targets == 1)
-        fg_logits = logits[fg_labels]
-        fg_num = len(fg_logits)
-
-        #Do not use bg with scores less than minimum fg logit
-        #since changing its score does not have an effect on precision
-        threshold_logit = torch.min(fg_logits)-delta
-
-        #Get valid bg logits
-        relevant_bg_labels=((targets==0)&(logits>=threshold_logit))
-        relevant_bg_logits=logits[relevant_bg_labels] 
-        relevant_bg_grad=torch.zeros(len(relevant_bg_logits)).cuda()
-        rank=torch.zeros(fg_num).cuda()
-        prec=torch.zeros(fg_num).cuda()
-        fg_grad=torch.zeros(fg_num).cuda()
-        
-        max_prec=0                                           
-        #sort the fg logits
-        order=torch.argsort(fg_logits)
-        #Loops over each positive following the order
-        for ii in order:
-            #x_ij s as score differences with fgs
-            fg_relations=fg_logits-fg_logits[ii] 
-            #Apply piecewise linear function and determine relations with fgs
-            fg_relations=torch.clamp(fg_relations/(2*delta)+0.5,min=0,max=1)
-            #Discard i=j in the summation in rank_pos
-            fg_relations[ii]=0
-
-            #x_ij s as score differences with bgs
-            bg_relations=relevant_bg_logits-fg_logits[ii]
-            #Apply piecewise linear function and determine relations with bgs
-            bg_relations=torch.clamp(bg_relations/(2*delta)+0.5,min=0,max=1)
-
-            #Compute the rank of the example within fgs and number of bgs with larger scores
-            rank_pos=1+torch.sum(fg_relations)
-            FP_num=torch.sum(bg_relations)
-            #Store the total since it is normalizer also for aLRP Regression error
-            rank[ii]=rank_pos+FP_num
-                            
-            #Compute precision for this example to compute classification loss 
-            prec[ii]=rank_pos/rank[ii]                
-            #For stability, set eps to a infinitesmall value (e.g. 1e-6), then compute grads
-            if FP_num > eps:   
-                fg_grad[ii] = -(torch.sum(fg_relations*regression_losses)+FP_num)/rank[ii]
-                relevant_bg_grad += (bg_relations*(-fg_grad[ii]/FP_num))   
-                    
-        #aLRP with grad formulation fg gradient
-        classification_grads[fg_labels]= fg_grad
-        #aLRP with grad formulation bg gradient
-        classification_grads[relevant_bg_labels]= relevant_bg_grad 
- 
-        classification_grads /= (fg_num)
-    
-        cls_loss=1-prec.mean()
-        ctx.save_for_backward(classification_grads)
-
-        return cls_loss, rank, order
-
-    @staticmethod
-    def backward(ctx, out_grad1, out_grad2, out_grad3):
-        g1, =ctx.saved_tensors
-        return g1*out_grad1, None, None, None, None
-    
-    
-class APLoss(torch.autograd.Function):
-    @staticmethod
-    def forward(ctx, logits, targets, delta=1.): 
-        classification_grads=torch.zeros(logits.shape).cuda()
-        
-        #Filter fg logits
-        fg_labels = (targets == 1)
-        fg_logits = logits[fg_labels]
-        fg_num = len(fg_logits)
-
-        #Do not use bg with scores less than minimum fg logit
-        #since changing its score does not have an effect on precision
-        threshold_logit = torch.min(fg_logits)-delta
-
-        #Get valid bg logits
-        relevant_bg_labels=((targets==0)&(logits>=threshold_logit))
-        relevant_bg_logits=logits[relevant_bg_labels] 
-        relevant_bg_grad=torch.zeros(len(relevant_bg_logits)).cuda()
-        rank=torch.zeros(fg_num).cuda()
-        prec=torch.zeros(fg_num).cuda()
-        fg_grad=torch.zeros(fg_num).cuda()
-        
-        max_prec=0                                           
-        #sort the fg logits
-        order=torch.argsort(fg_logits)
-        #Loops over each positive following the order
-        for ii in order:
-            #x_ij s as score differences with fgs
-            fg_relations=fg_logits-fg_logits[ii] 
-            #Apply piecewise linear function and determine relations with fgs
-            fg_relations=torch.clamp(fg_relations/(2*delta)+0.5,min=0,max=1)
-            #Discard i=j in the summation in rank_pos
-            fg_relations[ii]=0
-
-            #x_ij s as score differences with bgs
-            bg_relations=relevant_bg_logits-fg_logits[ii]
-            #Apply piecewise linear function and determine relations with bgs
-            bg_relations=torch.clamp(bg_relations/(2*delta)+0.5,min=0,max=1)
-
-            #Compute the rank of the example within fgs and number of bgs with larger scores
-            rank_pos=1+torch.sum(fg_relations)
-            FP_num=torch.sum(bg_relations)
-            #Store the total since it is normalizer also for aLRP Regression error
-            rank[ii]=rank_pos+FP_num
-                            
-            #Compute precision for this example 
-            current_prec=rank_pos/rank[ii]
-            
-            #Compute interpolated AP and store gradients for relevant bg examples
-            if (max_prec<=current_prec):
-                max_prec=current_prec
-                relevant_bg_grad += (bg_relations/rank[ii])
-            else:
-                relevant_bg_grad += (bg_relations/rank[ii])*(((1-max_prec)/(1-current_prec)))
-            
-            #Store fg gradients
-            fg_grad[ii]=-(1-max_prec)
-            prec[ii]=max_prec 
-
-        #aLRP with grad formulation fg gradient
-        classification_grads[fg_labels]= fg_grad
-        #aLRP with grad formulation bg gradient
-        classification_grads[relevant_bg_labels]= relevant_bg_grad 
- 
-        classification_grads /= fg_num
-    
-        cls_loss=1-prec.mean()
-        ctx.save_for_backward(classification_grads)
-
-        return cls_loss
-
-    @staticmethod
-    def backward(ctx, out_grad1):
-        g1, =ctx.saved_tensors
-        return g1*out_grad1, None, None
-
-
-class ComputeLoss:
-    # Compute losses
-    def __init__(self, model, autobalance=False):
-        super(ComputeLoss, self).__init__()
-        device = next(model.parameters()).device  # get model device
-        h = model.hyp  # hyperparameters
-
-        # Define criteria
-        BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device))
-        BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device))
-
-        # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3
-        self.cp, self.cn = smooth_BCE(eps=h.get('label_smoothing', 0.0))  # positive, negative BCE targets
-
-        # Focal loss
-        g = h['fl_gamma']  # focal loss gamma
-        if g > 0:
-            BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g)
-
-        det = model.module.model[-1] if is_parallel(model) else model.model[-1]  # Detect() module
-        self.balance = {3: [4.0, 1.0, 0.4]}.get(det.nl, [4.0, 1.0, 0.25, 0.06, .02])  # P3-P7
-        #self.balance = {3: [4.0, 1.0, 0.4]}.get(det.nl, [4.0, 1.0, 0.25, 0.1, .05])  # P3-P7
-        #self.balance = {3: [4.0, 1.0, 0.4]}.get(det.nl, [4.0, 1.0, 0.5, 0.4, .1])  # P3-P7
-        self.ssi = list(det.stride).index(16) if autobalance else 0  # stride 16 index
-        self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, model.gr, h, autobalance
-        for k in 'na', 'nc', 'nl', 'anchors':
-            setattr(self, k, getattr(det, k))
-
-    def __call__(self, p, targets):  # predictions, targets, model
-        device = targets.device
-        lcls, lbox, lobj = torch.zeros(1, device=device), torch.zeros(1, device=device), torch.zeros(1, device=device)
-        tcls, tbox, indices, anchors = self.build_targets(p, targets)  # targets
-
-        # Losses
-        for i, pi in enumerate(p):  # layer index, layer predictions
-            b, a, gj, gi = indices[i]  # image, anchor, gridy, gridx
-            tobj = torch.zeros_like(pi[..., 0], device=device)  # target obj
-
-            n = b.shape[0]  # number of targets
-            if n:
-                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]
-                pbox = torch.cat((pxy, pwh), 1)  # predicted box
-                iou = bbox_iou(pbox.T, tbox[i], x1y1x2y2=False, CIoU=True)  # iou(prediction, target)
-                lbox += (1.0 - iou).mean()  # iou loss
-
-                # Objectness
-                tobj[b, a, gj, gi] = (1.0 - self.gr) + self.gr * iou.detach().clamp(0).type(tobj.dtype)  # iou ratio
-
-                # Classification
-                if self.nc > 1:  # cls loss (only if multiple classes)
-                    t = torch.full_like(ps[:, 5:], self.cn, device=device)  # targets
-                    t[range(n), tcls[i]] = self.cp
-                    #t[t==self.cp] = iou.detach().clamp(0).type(t.dtype)
-                    lcls += self.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)]
-
-            obji = self.BCEobj(pi[..., 4], tobj)
-            lobj += obji * self.balance[i]  # obj loss
-            if self.autobalance:
-                self.balance[i] = self.balance[i] * 0.9999 + 0.0001 / obji.detach().item()
-
-        if self.autobalance:
-            self.balance = [x / self.balance[self.ssi] for x in self.balance]
-        lbox *= self.hyp['box']
-        lobj *= self.hyp['obj']
-        lcls *= self.hyp['cls']
-        bs = tobj.shape[0]  # batch size
-
-        loss = lbox + lobj + lcls
-        return loss * bs, torch.cat((lbox, lobj, lcls, loss)).detach()
-
-    def build_targets(self, p, targets):
-        # Build targets for compute_loss(), input targets(image,class,x,y,w,h)
-        na, nt = self.na, targets.shape[0]  # number of anchors, targets
-        tcls, tbox, indices, anch = [], [], [], []
-        gain = torch.ones(7, device=targets.device).long()  # normalized to gridspace gain
-        ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt)  # same as .repeat_interleave(nt)
-        targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, None]), 2)  # append anchor indices
-
-        g = 0.5  # bias
-        off = torch.tensor([[0, 0],
-                            [1, 0], [0, 1], [-1, 0], [0, -1],  # j,k,l,m
-                            # [1, 1], [1, -1], [-1, 1], [-1, -1],  # jk,jm,lk,lm
-                            ], device=targets.device).float() * g  # offsets
-
-        for i in range(self.nl):
-            anchors = self.anchors[i]
-            gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]]  # xyxy gain
-
-            # Match targets to anchors
-            t = targets * gain
-            if nt:
-                # Matches
-                r = t[:, :, 4:6] / anchors[:, None]  # wh ratio
-                j = torch.max(r, 1. / r).max(2)[0] < self.hyp['anchor_t']  # compare
-                # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t']  # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2))
-                t = t[j]  # filter
-
-                # Offsets
-                gxy = t[:, 2:4]  # grid xy
-                gxi = gain[[2, 3]] - gxy  # inverse
-                j, k = ((gxy % 1. < g) & (gxy > 1.)).T
-                l, m = ((gxi % 1. < g) & (gxi > 1.)).T
-                j = torch.stack((torch.ones_like(j), j, k, l, m))
-                t = t.repeat((5, 1, 1))[j]
-                offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j]
-            else:
-                t = targets[0]
-                offsets = 0
-
-            # Define
-            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
-            a = t[:, 6].long()  # anchor 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(anchors[a])  # anchors
-            tcls.append(c)  # class
-
-        return tcls, tbox, indices, anch
-
-
-class ComputeLossOTA:
-    # Compute losses
-    def __init__(self, model, autobalance=False):
-        super(ComputeLossOTA, self).__init__()
-        device = next(model.parameters()).device  # get model device
-        h = model.hyp  # hyperparameters
-
-        # Define criteria
-        BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device))
-        BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device))
-
-        # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3
-        self.cp, self.cn = smooth_BCE(eps=h.get('label_smoothing', 0.0))  # positive, negative BCE targets
-
-        # Focal loss
-        g = h['fl_gamma']  # focal loss gamma
-        if g > 0:
-            BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g)
-
-        det = model.module.model[-1] if is_parallel(model) else model.model[-1]  # Detect() module
-        self.balance = {3: [4.0, 1.0, 0.4]}.get(det.nl, [4.0, 1.0, 0.25, 0.06, .02])  # P3-P7
-        self.ssi = list(det.stride).index(16) if autobalance else 0  # stride 16 index
-        self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, model.gr, h, autobalance
-        for k in 'na', 'nc', 'nl', 'anchors', 'stride':
-            setattr(self, k, getattr(det, k))
-
-    def __call__(self, p, targets, imgs):  # predictions, targets, model   
-        device = targets.device
-        lcls, lbox, lobj = torch.zeros(1, device=device), torch.zeros(1, device=device), torch.zeros(1, device=device)
-        bs, as_, gjs, gis, targets, anchors = self.build_targets(p, targets, imgs)
-        pre_gen_gains = [torch.tensor(pp.shape, device=device)[[3, 2, 3, 2]] for pp in p] 
-    
-
-        # Losses
-        for i, pi in enumerate(p):  # layer index, layer predictions
-            b, a, gj, gi = bs[i], as_[i], gjs[i], gis[i]  # image, anchor, gridy, gridx
-            tobj = torch.zeros_like(pi[..., 0], device=device)  # target obj
-
-            n = b.shape[0]  # number of targets
-            if n:
-                ps = pi[b, a, gj, gi]  # prediction subset corresponding to targets
-
-                # Regression
-                grid = torch.stack([gi, gj], dim=1)
-                pxy = ps[:, :2].sigmoid() * 2. - 0.5
-                #pxy = ps[:, :2].sigmoid() * 3. - 1.
-                pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i]
-                pbox = torch.cat((pxy, pwh), 1)  # predicted box
-                selected_tbox = targets[i][:, 2:6] * pre_gen_gains[i]
-                selected_tbox[:, :2] -= grid
-                iou = bbox_iou(pbox.T, selected_tbox, x1y1x2y2=False, CIoU=True)  # iou(prediction, target)
-                lbox += (1.0 - iou).mean()  # iou loss
-
-                # Objectness
-                tobj[b, a, gj, gi] = (1.0 - self.gr) + self.gr * iou.detach().clamp(0).type(tobj.dtype)  # iou ratio
-
-                # Classification
-                selected_tcls = targets[i][:, 1].long()
-                if self.nc > 1:  # cls loss (only if multiple classes)
-                    t = torch.full_like(ps[:, 5:], self.cn, device=device)  # targets
-                    t[range(n), selected_tcls] = self.cp
-                    lcls += self.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)]
-
-            obji = self.BCEobj(pi[..., 4], tobj)
-            lobj += obji * self.balance[i]  # obj loss
-            if self.autobalance:
-                self.balance[i] = self.balance[i] * 0.9999 + 0.0001 / obji.detach().item()
-
-        if self.autobalance:
-            self.balance = [x / self.balance[self.ssi] for x in self.balance]
-        lbox *= self.hyp['box']
-        lobj *= self.hyp['obj']
-        lcls *= self.hyp['cls']
-        bs = tobj.shape[0]  # batch size
-
-        loss = lbox + lobj + lcls
-        return loss * bs, torch.cat((lbox, lobj, lcls, loss)).detach()
-
-    def build_targets(self, p, targets, imgs):
-        
-        #indices, anch = self.find_positive(p, targets)
-        indices, anch = self.find_3_positive(p, targets)
-        #indices, anch = self.find_4_positive(p, targets)
-        #indices, anch = self.find_5_positive(p, targets)
-        #indices, anch = self.find_9_positive(p, targets)
-        device = torch.device(targets.device)
-        matching_bs = [[] for pp in p]
-        matching_as = [[] for pp in p]
-        matching_gjs = [[] for pp in p]
-        matching_gis = [[] for pp in p]
-        matching_targets = [[] for pp in p]
-        matching_anchs = [[] for pp in p]
-        
-        nl = len(p)    
-    
-        for batch_idx in range(p[0].shape[0]):
-        
-            b_idx = targets[:, 0]==batch_idx
-            this_target = targets[b_idx]
-            if this_target.shape[0] == 0:
-                continue
-                
-            txywh = this_target[:, 2:6] * imgs[batch_idx].shape[1]
-            txyxy = xywh2xyxy(txywh)
-
-            pxyxys = []
-            p_cls = []
-            p_obj = []
-            from_which_layer = []
-            all_b = []
-            all_a = []
-            all_gj = []
-            all_gi = []
-            all_anch = []
-            
-            for i, pi in enumerate(p):
-                
-                b, a, gj, gi = indices[i]
-                idx = (b == batch_idx)
-                b, a, gj, gi = b[idx], a[idx], gj[idx], gi[idx]                
-                all_b.append(b)
-                all_a.append(a)
-                all_gj.append(gj)
-                all_gi.append(gi)
-                all_anch.append(anch[i][idx])
-                from_which_layer.append((torch.ones(size=(len(b),)) * i).to(device))
-                
-                fg_pred = pi[b, a, gj, gi]                
-                p_obj.append(fg_pred[:, 4:5])
-                p_cls.append(fg_pred[:, 5:])
-                
-                grid = torch.stack([gi, gj], dim=1)
-                pxy = (fg_pred[:, :2].sigmoid() * 2. - 0.5 + grid) * self.stride[i] #/ 8.
-                #pxy = (fg_pred[:, :2].sigmoid() * 3. - 1. + grid) * self.stride[i]
-                pwh = (fg_pred[:, 2:4].sigmoid() * 2) ** 2 * anch[i][idx] * self.stride[i] #/ 8.
-                pxywh = torch.cat([pxy, pwh], dim=-1)
-                pxyxy = xywh2xyxy(pxywh)
-                pxyxys.append(pxyxy)
-            
-            pxyxys = torch.cat(pxyxys, dim=0)
-            if pxyxys.shape[0] == 0:
-                continue
-            p_obj = torch.cat(p_obj, dim=0)
-            p_cls = torch.cat(p_cls, dim=0)
-            from_which_layer = torch.cat(from_which_layer, dim=0)
-            all_b = torch.cat(all_b, dim=0)
-            all_a = torch.cat(all_a, dim=0)
-            all_gj = torch.cat(all_gj, dim=0)
-            all_gi = torch.cat(all_gi, dim=0)
-            all_anch = torch.cat(all_anch, dim=0)
-        
-            pair_wise_iou = box_iou(txyxy, pxyxys)
-
-            pair_wise_iou_loss = -torch.log(pair_wise_iou + 1e-8)
-
-            top_k, _ = torch.topk(pair_wise_iou, min(10, pair_wise_iou.shape[1]), dim=1)
-            dynamic_ks = torch.clamp(top_k.sum(1).int(), min=1)
-
-            gt_cls_per_image = (
-                F.one_hot(this_target[:, 1].to(torch.int64), self.nc)
-                .float()
-                .unsqueeze(1)
-                .repeat(1, pxyxys.shape[0], 1)
-            )
-
-            num_gt = this_target.shape[0]
-            cls_preds_ = (
-                p_cls.float().unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_()
-                * p_obj.unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_()
-            )
-
-            y = cls_preds_.sqrt_()
-            pair_wise_cls_loss = F.binary_cross_entropy_with_logits(
-               torch.log(y/(1-y)) , gt_cls_per_image, reduction="none"
-            ).sum(-1)
-            del cls_preds_
-        
-            cost = (
-                pair_wise_cls_loss
-                + 3.0 * pair_wise_iou_loss
-            )
-
-            matching_matrix = torch.zeros_like(cost, device=device)
-
-            for gt_idx in range(num_gt):
-                _, pos_idx = torch.topk(
-                    cost[gt_idx], k=dynamic_ks[gt_idx].item(), largest=False
-                )
-                matching_matrix[gt_idx][pos_idx] = 1.0
-
-            del top_k, dynamic_ks
-            anchor_matching_gt = matching_matrix.sum(0)
-            if (anchor_matching_gt > 1).sum() > 0:
-                _, cost_argmin = torch.min(cost[:, anchor_matching_gt > 1], dim=0)
-                matching_matrix[:, anchor_matching_gt > 1] *= 0.0
-                matching_matrix[cost_argmin, anchor_matching_gt > 1] = 1.0
-            fg_mask_inboxes = (matching_matrix.sum(0) > 0.0).to(device)
-            matched_gt_inds = matching_matrix[:, fg_mask_inboxes].argmax(0)
-        
-            from_which_layer = from_which_layer[fg_mask_inboxes]
-            all_b = all_b[fg_mask_inboxes]
-            all_a = all_a[fg_mask_inboxes]
-            all_gj = all_gj[fg_mask_inboxes]
-            all_gi = all_gi[fg_mask_inboxes]
-            all_anch = all_anch[fg_mask_inboxes]
-        
-            this_target = this_target[matched_gt_inds]
-        
-            for i in range(nl):
-                layer_idx = from_which_layer == i
-                matching_bs[i].append(all_b[layer_idx])
-                matching_as[i].append(all_a[layer_idx])
-                matching_gjs[i].append(all_gj[layer_idx])
-                matching_gis[i].append(all_gi[layer_idx])
-                matching_targets[i].append(this_target[layer_idx])
-                matching_anchs[i].append(all_anch[layer_idx])
-
-        for i in range(nl):
-            if matching_targets[i] != []:
-                matching_bs[i] = torch.cat(matching_bs[i], dim=0)
-                matching_as[i] = torch.cat(matching_as[i], dim=0)
-                matching_gjs[i] = torch.cat(matching_gjs[i], dim=0)
-                matching_gis[i] = torch.cat(matching_gis[i], dim=0)
-                matching_targets[i] = torch.cat(matching_targets[i], dim=0)
-                matching_anchs[i] = torch.cat(matching_anchs[i], dim=0)
-            else:
-                matching_bs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
-                matching_as[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
-                matching_gjs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
-                matching_gis[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
-                matching_targets[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
-                matching_anchs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
-
-        return matching_bs, matching_as, matching_gjs, matching_gis, matching_targets, matching_anchs           
-
-    def find_3_positive(self, p, targets):
-        # Build targets for compute_loss(), input targets(image,class,x,y,w,h)
-        na, nt = self.na, targets.shape[0]  # number of anchors, targets
-        indices, anch = [], []
-        gain = torch.ones(7, device=targets.device).long()  # normalized to gridspace gain
-        ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt)  # same as .repeat_interleave(nt)
-        targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, None]), 2)  # append anchor indices
-
-        g = 0.5  # bias
-        off = torch.tensor([[0, 0],
-                            [1, 0], [0, 1], [-1, 0], [0, -1],  # j,k,l,m
-                            # [1, 1], [1, -1], [-1, 1], [-1, -1],  # jk,jm,lk,lm
-                            ], device=targets.device).float() * g  # offsets
-
-        for i in range(self.nl):
-            anchors = self.anchors[i]
-            gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]]  # xyxy gain
-
-            # Match targets to anchors
-            t = targets * gain
-            if nt:
-                # Matches
-                r = t[:, :, 4:6] / anchors[:, None]  # wh ratio
-                j = torch.max(r, 1. / r).max(2)[0] < self.hyp['anchor_t']  # compare
-                # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t']  # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2))
-                t = t[j]  # filter
-
-                # Offsets
-                gxy = t[:, 2:4]  # grid xy
-                gxi = gain[[2, 3]] - gxy  # inverse
-                j, k = ((gxy % 1. < g) & (gxy > 1.)).T
-                l, m = ((gxi % 1. < g) & (gxi > 1.)).T
-                j = torch.stack((torch.ones_like(j), j, k, l, m))
-                t = t.repeat((5, 1, 1))[j]
-                offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j]
-            else:
-                t = targets[0]
-                offsets = 0
-
-            # Define
-            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
-            a = t[:, 6].long()  # anchor indices
-            indices.append((b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1)))  # image, anchor, grid indices
-            anch.append(anchors[a])  # anchors
-
-        return indices, anch
-    
-
-class ComputeLossBinOTA:
-    # Compute losses
-    def __init__(self, model, autobalance=False):
-        super(ComputeLossBinOTA, self).__init__()
-        device = next(model.parameters()).device  # get model device
-        h = model.hyp  # hyperparameters
-
-        # Define criteria
-        BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device))
-        BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device))
-        #MSEangle = nn.MSELoss().to(device)
-
-        # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3
-        self.cp, self.cn = smooth_BCE(eps=h.get('label_smoothing', 0.0))  # positive, negative BCE targets
-
-        # Focal loss
-        g = h['fl_gamma']  # focal loss gamma
-        if g > 0:
-            BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g)
-
-        det = model.module.model[-1] if is_parallel(model) else model.model[-1]  # Detect() module
-        self.balance = {3: [4.0, 1.0, 0.4]}.get(det.nl, [4.0, 1.0, 0.25, 0.06, .02])  # P3-P7
-        self.ssi = list(det.stride).index(16) if autobalance else 0  # stride 16 index
-        self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, model.gr, h, autobalance
-        for k in 'na', 'nc', 'nl', 'anchors', 'stride', 'bin_count':
-            setattr(self, k, getattr(det, k))
-
-        #xy_bin_sigmoid = SigmoidBin(bin_count=11, min=-0.5, max=1.5, use_loss_regression=False).to(device)
-        wh_bin_sigmoid = SigmoidBin(bin_count=self.bin_count, min=0.0, max=4.0, use_loss_regression=False).to(device)
-        #angle_bin_sigmoid = SigmoidBin(bin_count=31, min=-1.1, max=1.1, use_loss_regression=False).to(device)
-        self.wh_bin_sigmoid = wh_bin_sigmoid
-
-    def __call__(self, p, targets, imgs):  # predictions, targets, model   
-        device = targets.device
-        lcls, lbox, lobj = torch.zeros(1, device=device), torch.zeros(1, device=device), torch.zeros(1, device=device)
-        bs, as_, gjs, gis, targets, anchors = self.build_targets(p, targets, imgs)
-        pre_gen_gains = [torch.tensor(pp.shape, device=device)[[3, 2, 3, 2]] for pp in p] 
-    
-
-        # Losses
-        for i, pi in enumerate(p):  # layer index, layer predictions
-            b, a, gj, gi = bs[i], as_[i], gjs[i], gis[i]  # image, anchor, gridy, gridx
-            tobj = torch.zeros_like(pi[..., 0], device=device)  # target obj
-
-            obj_idx = self.wh_bin_sigmoid.get_length()*2 + 2     # x,y, w-bce, h-bce     # xy_bin_sigmoid.get_length()*2
-
-            n = b.shape[0]  # number of targets
-            if n:
-                ps = pi[b, a, gj, gi]  # prediction subset corresponding to targets
-
-                # Regression
-                grid = torch.stack([gi, gj], dim=1)
-                selected_tbox = targets[i][:, 2:6] * pre_gen_gains[i]
-                selected_tbox[:, :2] -= grid
-                
-                #pxy = ps[:, :2].sigmoid() * 2. - 0.5
-                ##pxy = ps[:, :2].sigmoid() * 3. - 1.
-                #pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i]
-                #pbox = torch.cat((pxy, pwh), 1)  # predicted box
-
-                #x_loss, px = xy_bin_sigmoid.training_loss(ps[..., 0:12], tbox[i][..., 0])
-                #y_loss, py = xy_bin_sigmoid.training_loss(ps[..., 12:24], tbox[i][..., 1])
-                w_loss, pw = self.wh_bin_sigmoid.training_loss(ps[..., 2:(3+self.bin_count)], selected_tbox[..., 2] / anchors[i][..., 0])
-                h_loss, ph = self.wh_bin_sigmoid.training_loss(ps[..., (3+self.bin_count):obj_idx], selected_tbox[..., 3] / anchors[i][..., 1])
-
-                pw *= anchors[i][..., 0]
-                ph *= anchors[i][..., 1]
-
-                px = ps[:, 0].sigmoid() * 2. - 0.5
-                py = ps[:, 1].sigmoid() * 2. - 0.5
-
-                lbox += w_loss + h_loss # + x_loss + y_loss
-
-                #print(f"\n px = {px.shape}, py = {py.shape}, pw = {pw.shape}, ph = {ph.shape} \n")
-
-                pbox = torch.cat((px.unsqueeze(1), py.unsqueeze(1), pw.unsqueeze(1), ph.unsqueeze(1)), 1).to(device)  # predicted box
-
-                
-                
-                
-                iou = bbox_iou(pbox.T, selected_tbox, x1y1x2y2=False, CIoU=True)  # iou(prediction, target)
-                lbox += (1.0 - iou).mean()  # iou loss
-
-                # Objectness
-                tobj[b, a, gj, gi] = (1.0 - self.gr) + self.gr * iou.detach().clamp(0).type(tobj.dtype)  # iou ratio
-
-                # Classification
-                selected_tcls = targets[i][:, 1].long()
-                if self.nc > 1:  # cls loss (only if multiple classes)
-                    t = torch.full_like(ps[:, (1+obj_idx):], self.cn, device=device)  # targets
-                    t[range(n), selected_tcls] = self.cp
-                    lcls += self.BCEcls(ps[:, (1+obj_idx):], t)  # BCE
-
-                # Append targets to text file
-                # with open('targets.txt', 'a') as file:
-                #     [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)]
-
-            obji = self.BCEobj(pi[..., obj_idx], tobj)
-            lobj += obji * self.balance[i]  # obj loss
-            if self.autobalance:
-                self.balance[i] = self.balance[i] * 0.9999 + 0.0001 / obji.detach().item()
-
-        if self.autobalance:
-            self.balance = [x / self.balance[self.ssi] for x in self.balance]
-        lbox *= self.hyp['box']
-        lobj *= self.hyp['obj']
-        lcls *= self.hyp['cls']
-        bs = tobj.shape[0]  # batch size
-
-        loss = lbox + lobj + lcls
-        return loss * bs, torch.cat((lbox, lobj, lcls, loss)).detach()
-
-    def build_targets(self, p, targets, imgs):
-        
-        #indices, anch = self.find_positive(p, targets)
-        indices, anch = self.find_3_positive(p, targets)
-        #indices, anch = self.find_4_positive(p, targets)
-        #indices, anch = self.find_5_positive(p, targets)
-        #indices, anch = self.find_9_positive(p, targets)
-
-        matching_bs = [[] for pp in p]
-        matching_as = [[] for pp in p]
-        matching_gjs = [[] for pp in p]
-        matching_gis = [[] for pp in p]
-        matching_targets = [[] for pp in p]
-        matching_anchs = [[] for pp in p]
-        
-        nl = len(p)    
-    
-        for batch_idx in range(p[0].shape[0]):
-        
-            b_idx = targets[:, 0]==batch_idx
-            this_target = targets[b_idx]
-            if this_target.shape[0] == 0:
-                continue
-                
-            txywh = this_target[:, 2:6] * imgs[batch_idx].shape[1]
-            txyxy = xywh2xyxy(txywh)
-
-            pxyxys = []
-            p_cls = []
-            p_obj = []
-            from_which_layer = []
-            all_b = []
-            all_a = []
-            all_gj = []
-            all_gi = []
-            all_anch = []
-            
-            for i, pi in enumerate(p):
-                
-                obj_idx = self.wh_bin_sigmoid.get_length()*2 + 2
-                
-                b, a, gj, gi = indices[i]
-                idx = (b == batch_idx)
-                b, a, gj, gi = b[idx], a[idx], gj[idx], gi[idx]                
-                all_b.append(b)
-                all_a.append(a)
-                all_gj.append(gj)
-                all_gi.append(gi)
-                all_anch.append(anch[i][idx])
-                from_which_layer.append(torch.ones(size=(len(b),)) * i)
-                
-                fg_pred = pi[b, a, gj, gi]                
-                p_obj.append(fg_pred[:, obj_idx:(obj_idx+1)])
-                p_cls.append(fg_pred[:, (obj_idx+1):])
-                
-                grid = torch.stack([gi, gj], dim=1)
-                pxy = (fg_pred[:, :2].sigmoid() * 2. - 0.5 + grid) * self.stride[i] #/ 8.
-                #pwh = (fg_pred[:, 2:4].sigmoid() * 2) ** 2 * anch[i][idx] * self.stride[i] #/ 8.
-                pw = self.wh_bin_sigmoid.forward(fg_pred[..., 2:(3+self.bin_count)].sigmoid()) * anch[i][idx][:, 0] * self.stride[i]
-                ph = self.wh_bin_sigmoid.forward(fg_pred[..., (3+self.bin_count):obj_idx].sigmoid()) * anch[i][idx][:, 1] * self.stride[i]
-                
-                pxywh = torch.cat([pxy, pw.unsqueeze(1), ph.unsqueeze(1)], dim=-1)
-                pxyxy = xywh2xyxy(pxywh)
-                pxyxys.append(pxyxy)
-            
-            pxyxys = torch.cat(pxyxys, dim=0)
-            if pxyxys.shape[0] == 0:
-                continue
-            p_obj = torch.cat(p_obj, dim=0)
-            p_cls = torch.cat(p_cls, dim=0)
-            from_which_layer = torch.cat(from_which_layer, dim=0)
-            all_b = torch.cat(all_b, dim=0)
-            all_a = torch.cat(all_a, dim=0)
-            all_gj = torch.cat(all_gj, dim=0)
-            all_gi = torch.cat(all_gi, dim=0)
-            all_anch = torch.cat(all_anch, dim=0)
-        
-            pair_wise_iou = box_iou(txyxy, pxyxys)
-
-            pair_wise_iou_loss = -torch.log(pair_wise_iou + 1e-8)
-
-            top_k, _ = torch.topk(pair_wise_iou, min(10, pair_wise_iou.shape[1]), dim=1)
-            dynamic_ks = torch.clamp(top_k.sum(1).int(), min=1)
-
-            gt_cls_per_image = (
-                F.one_hot(this_target[:, 1].to(torch.int64), self.nc)
-                .float()
-                .unsqueeze(1)
-                .repeat(1, pxyxys.shape[0], 1)
-            )
-
-            num_gt = this_target.shape[0]            
-            cls_preds_ = (
-                p_cls.float().unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_()
-                * p_obj.unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_()
-            )
-
-            y = cls_preds_.sqrt_()
-            pair_wise_cls_loss = F.binary_cross_entropy_with_logits(
-               torch.log(y/(1-y)) , gt_cls_per_image, reduction="none"
-            ).sum(-1)
-            del cls_preds_
-        
-            cost = (
-                pair_wise_cls_loss
-                + 3.0 * pair_wise_iou_loss
-            )
-
-            matching_matrix = torch.zeros_like(cost)
-
-            for gt_idx in range(num_gt):
-                _, pos_idx = torch.topk(
-                    cost[gt_idx], k=dynamic_ks[gt_idx].item(), largest=False
-                )
-                matching_matrix[gt_idx][pos_idx] = 1.0
-
-            del top_k, dynamic_ks
-            anchor_matching_gt = matching_matrix.sum(0)
-            if (anchor_matching_gt > 1).sum() > 0:
-                _, cost_argmin = torch.min(cost[:, anchor_matching_gt > 1], dim=0)
-                matching_matrix[:, anchor_matching_gt > 1] *= 0.0
-                matching_matrix[cost_argmin, anchor_matching_gt > 1] = 1.0
-            fg_mask_inboxes = matching_matrix.sum(0) > 0.0
-            matched_gt_inds = matching_matrix[:, fg_mask_inboxes].argmax(0)
-        
-            from_which_layer = from_which_layer[fg_mask_inboxes]
-            all_b = all_b[fg_mask_inboxes]
-            all_a = all_a[fg_mask_inboxes]
-            all_gj = all_gj[fg_mask_inboxes]
-            all_gi = all_gi[fg_mask_inboxes]
-            all_anch = all_anch[fg_mask_inboxes]
-        
-            this_target = this_target[matched_gt_inds]
-        
-            for i in range(nl):
-                layer_idx = from_which_layer == i
-                matching_bs[i].append(all_b[layer_idx])
-                matching_as[i].append(all_a[layer_idx])
-                matching_gjs[i].append(all_gj[layer_idx])
-                matching_gis[i].append(all_gi[layer_idx])
-                matching_targets[i].append(this_target[layer_idx])
-                matching_anchs[i].append(all_anch[layer_idx])
-
-        for i in range(nl):
-            if matching_targets[i] != []:
-                matching_bs[i] = torch.cat(matching_bs[i], dim=0)
-                matching_as[i] = torch.cat(matching_as[i], dim=0)
-                matching_gjs[i] = torch.cat(matching_gjs[i], dim=0)
-                matching_gis[i] = torch.cat(matching_gis[i], dim=0)
-                matching_targets[i] = torch.cat(matching_targets[i], dim=0)
-                matching_anchs[i] = torch.cat(matching_anchs[i], dim=0)
-            else:
-                matching_bs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
-                matching_as[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
-                matching_gjs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
-                matching_gis[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
-                matching_targets[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
-                matching_anchs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
-
-        return matching_bs, matching_as, matching_gjs, matching_gis, matching_targets, matching_anchs       
-
-    def find_3_positive(self, p, targets):
-        # Build targets for compute_loss(), input targets(image,class,x,y,w,h)
-        na, nt = self.na, targets.shape[0]  # number of anchors, targets
-        indices, anch = [], []
-        gain = torch.ones(7, device=targets.device).long()  # normalized to gridspace gain
-        ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt)  # same as .repeat_interleave(nt)
-        targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, None]), 2)  # append anchor indices
-
-        g = 0.5  # bias
-        off = torch.tensor([[0, 0],
-                            [1, 0], [0, 1], [-1, 0], [0, -1],  # j,k,l,m
-                            # [1, 1], [1, -1], [-1, 1], [-1, -1],  # jk,jm,lk,lm
-                            ], device=targets.device).float() * g  # offsets
-
-        for i in range(self.nl):
-            anchors = self.anchors[i]
-            gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]]  # xyxy gain
-
-            # Match targets to anchors
-            t = targets * gain
-            if nt:
-                # Matches
-                r = t[:, :, 4:6] / anchors[:, None]  # wh ratio
-                j = torch.max(r, 1. / r).max(2)[0] < self.hyp['anchor_t']  # compare
-                # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t']  # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2))
-                t = t[j]  # filter
-
-                # Offsets
-                gxy = t[:, 2:4]  # grid xy
-                gxi = gain[[2, 3]] - gxy  # inverse
-                j, k = ((gxy % 1. < g) & (gxy > 1.)).T
-                l, m = ((gxi % 1. < g) & (gxi > 1.)).T
-                j = torch.stack((torch.ones_like(j), j, k, l, m))
-                t = t.repeat((5, 1, 1))[j]
-                offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j]
-            else:
-                t = targets[0]
-                offsets = 0
-
-            # Define
-            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
-            a = t[:, 6].long()  # anchor indices
-            indices.append((b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1)))  # image, anchor, grid indices
-            anch.append(anchors[a])  # anchors
-
-        return indices, anch
-
-
-class ComputeLossAuxOTA:
-    # Compute losses
-    def __init__(self, model, autobalance=False):
-        super(ComputeLossAuxOTA, self).__init__()
-        device = next(model.parameters()).device  # get model device
-        h = model.hyp  # hyperparameters
-
-        # Define criteria
-        BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device))
-        BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device))
-
-        # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3
-        self.cp, self.cn = smooth_BCE(eps=h.get('label_smoothing', 0.0))  # positive, negative BCE targets
-
-        # Focal loss
-        g = h['fl_gamma']  # focal loss gamma
-        if g > 0:
-            BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g)
-
-        det = model.module.model[-1] if is_parallel(model) else model.model[-1]  # Detect() module
-        self.balance = {3: [4.0, 1.0, 0.4]}.get(det.nl, [4.0, 1.0, 0.25, 0.06, .02])  # P3-P7
-        self.ssi = list(det.stride).index(16) if autobalance else 0  # stride 16 index
-        self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, model.gr, h, autobalance
-        for k in 'na', 'nc', 'nl', 'anchors', 'stride':
-            setattr(self, k, getattr(det, k))
-
-    def __call__(self, p, targets, imgs):  # predictions, targets, model   
-        device = targets.device
-        lcls, lbox, lobj = torch.zeros(1, device=device), torch.zeros(1, device=device), torch.zeros(1, device=device)
-        bs_aux, as_aux_, gjs_aux, gis_aux, targets_aux, anchors_aux = self.build_targets2(p[:self.nl], targets, imgs)
-        bs, as_, gjs, gis, targets, anchors = self.build_targets(p[:self.nl], targets, imgs)
-        pre_gen_gains_aux = [torch.tensor(pp.shape, device=device)[[3, 2, 3, 2]] for pp in p[:self.nl]] 
-        pre_gen_gains = [torch.tensor(pp.shape, device=device)[[3, 2, 3, 2]] for pp in p[:self.nl]] 
-    
-
-        # Losses
-        for i in range(self.nl):  # layer index, layer predictions
-            pi = p[i]
-            pi_aux = p[i+self.nl]
-            b, a, gj, gi = bs[i], as_[i], gjs[i], gis[i]  # image, anchor, gridy, gridx
-            b_aux, a_aux, gj_aux, gi_aux = bs_aux[i], as_aux_[i], gjs_aux[i], gis_aux[i]  # image, anchor, gridy, gridx
-            tobj = torch.zeros_like(pi[..., 0], device=device)  # target obj
-            tobj_aux = torch.zeros_like(pi_aux[..., 0], device=device)  # target obj
-
-            n = b.shape[0]  # number of targets
-            if n:
-                ps = pi[b, a, gj, gi]  # prediction subset corresponding to targets
-
-                # Regression
-                grid = torch.stack([gi, gj], dim=1)
-                pxy = ps[:, :2].sigmoid() * 2. - 0.5
-                pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i]
-                pbox = torch.cat((pxy, pwh), 1)  # predicted box
-                selected_tbox = targets[i][:, 2:6] * pre_gen_gains[i]
-                selected_tbox[:, :2] -= grid
-                iou = bbox_iou(pbox.T, selected_tbox, x1y1x2y2=False, CIoU=True)  # iou(prediction, target)
-                lbox += (1.0 - iou).mean()  # iou loss
-
-                # Objectness
-                tobj[b, a, gj, gi] = (1.0 - self.gr) + self.gr * iou.detach().clamp(0).type(tobj.dtype)  # iou ratio
-
-                # Classification
-                selected_tcls = targets[i][:, 1].long()
-                if self.nc > 1:  # cls loss (only if multiple classes)
-                    t = torch.full_like(ps[:, 5:], self.cn, device=device)  # targets
-                    t[range(n), selected_tcls] = self.cp
-                    lcls += self.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)]
-            
-            n_aux = b_aux.shape[0]  # number of targets
-            if n_aux:
-                ps_aux = pi_aux[b_aux, a_aux, gj_aux, gi_aux]  # prediction subset corresponding to targets
-                grid_aux = torch.stack([gi_aux, gj_aux], dim=1)
-                pxy_aux = ps_aux[:, :2].sigmoid() * 2. - 0.5
-                #pxy_aux = ps_aux[:, :2].sigmoid() * 3. - 1.
-                pwh_aux = (ps_aux[:, 2:4].sigmoid() * 2) ** 2 * anchors_aux[i]
-                pbox_aux = torch.cat((pxy_aux, pwh_aux), 1)  # predicted box
-                selected_tbox_aux = targets_aux[i][:, 2:6] * pre_gen_gains_aux[i]
-                selected_tbox_aux[:, :2] -= grid_aux
-                iou_aux = bbox_iou(pbox_aux.T, selected_tbox_aux, x1y1x2y2=False, CIoU=True)  # iou(prediction, target)
-                lbox += 0.25 * (1.0 - iou_aux).mean()  # iou loss
-
-                # Objectness
-                tobj_aux[b_aux, a_aux, gj_aux, gi_aux] = (1.0 - self.gr) + self.gr * iou_aux.detach().clamp(0).type(tobj_aux.dtype)  # iou ratio
-
-                # Classification
-                selected_tcls_aux = targets_aux[i][:, 1].long()
-                if self.nc > 1:  # cls loss (only if multiple classes)
-                    t_aux = torch.full_like(ps_aux[:, 5:], self.cn, device=device)  # targets
-                    t_aux[range(n_aux), selected_tcls_aux] = self.cp
-                    lcls += 0.25 * self.BCEcls(ps_aux[:, 5:], t_aux)  # BCE
-
-            obji = self.BCEobj(pi[..., 4], tobj)
-            obji_aux = self.BCEobj(pi_aux[..., 4], tobj_aux)
-            lobj += obji * self.balance[i] + 0.25 * obji_aux * self.balance[i] # obj loss
-            if self.autobalance:
-                self.balance[i] = self.balance[i] * 0.9999 + 0.0001 / obji.detach().item()
-
-        if self.autobalance:
-            self.balance = [x / self.balance[self.ssi] for x in self.balance]
-        lbox *= self.hyp['box']
-        lobj *= self.hyp['obj']
-        lcls *= self.hyp['cls']
-        bs = tobj.shape[0]  # batch size
-
-        loss = lbox + lobj + lcls
-        return loss * bs, torch.cat((lbox, lobj, lcls, loss)).detach()
-
-    def build_targets(self, p, targets, imgs):
-        
-        indices, anch = self.find_3_positive(p, targets)
-
-        matching_bs = [[] for pp in p]
-        matching_as = [[] for pp in p]
-        matching_gjs = [[] for pp in p]
-        matching_gis = [[] for pp in p]
-        matching_targets = [[] for pp in p]
-        matching_anchs = [[] for pp in p]
-        
-        nl = len(p)    
-    
-        for batch_idx in range(p[0].shape[0]):
-        
-            b_idx = targets[:, 0]==batch_idx
-            this_target = targets[b_idx]
-            if this_target.shape[0] == 0:
-                continue
-                
-            txywh = this_target[:, 2:6] * imgs[batch_idx].shape[1]
-            txyxy = xywh2xyxy(txywh)
-
-            pxyxys = []
-            p_cls = []
-            p_obj = []
-            from_which_layer = []
-            all_b = []
-            all_a = []
-            all_gj = []
-            all_gi = []
-            all_anch = []
-            
-            for i, pi in enumerate(p):
-                
-                b, a, gj, gi = indices[i]
-                idx = (b == batch_idx)
-                b, a, gj, gi = b[idx], a[idx], gj[idx], gi[idx]                
-                all_b.append(b)
-                all_a.append(a)
-                all_gj.append(gj)
-                all_gi.append(gi)
-                all_anch.append(anch[i][idx])
-                from_which_layer.append(torch.ones(size=(len(b),)) * i)
-                
-                fg_pred = pi[b, a, gj, gi]                
-                p_obj.append(fg_pred[:, 4:5])
-                p_cls.append(fg_pred[:, 5:])
-                
-                grid = torch.stack([gi, gj], dim=1)
-                pxy = (fg_pred[:, :2].sigmoid() * 2. - 0.5 + grid) * self.stride[i] #/ 8.
-                #pxy = (fg_pred[:, :2].sigmoid() * 3. - 1. + grid) * self.stride[i]
-                pwh = (fg_pred[:, 2:4].sigmoid() * 2) ** 2 * anch[i][idx] * self.stride[i] #/ 8.
-                pxywh = torch.cat([pxy, pwh], dim=-1)
-                pxyxy = xywh2xyxy(pxywh)
-                pxyxys.append(pxyxy)
-            
-            pxyxys = torch.cat(pxyxys, dim=0)
-            if pxyxys.shape[0] == 0:
-                continue
-            p_obj = torch.cat(p_obj, dim=0)
-            p_cls = torch.cat(p_cls, dim=0)
-            from_which_layer = torch.cat(from_which_layer, dim=0)
-            all_b = torch.cat(all_b, dim=0)
-            all_a = torch.cat(all_a, dim=0)
-            all_gj = torch.cat(all_gj, dim=0)
-            all_gi = torch.cat(all_gi, dim=0)
-            all_anch = torch.cat(all_anch, dim=0)
-        
-            pair_wise_iou = box_iou(txyxy, pxyxys)
-
-            pair_wise_iou_loss = -torch.log(pair_wise_iou + 1e-8)
-
-            top_k, _ = torch.topk(pair_wise_iou, min(20, pair_wise_iou.shape[1]), dim=1)
-            dynamic_ks = torch.clamp(top_k.sum(1).int(), min=1)
-
-            gt_cls_per_image = (
-                F.one_hot(this_target[:, 1].to(torch.int64), self.nc)
-                .float()
-                .unsqueeze(1)
-                .repeat(1, pxyxys.shape[0], 1)
-            )
-
-            num_gt = this_target.shape[0]
-            cls_preds_ = (
-                p_cls.float().unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_()
-                * p_obj.unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_()
-            )
-
-            y = cls_preds_.sqrt_()
-            pair_wise_cls_loss = F.binary_cross_entropy_with_logits(
-               torch.log(y/(1-y)) , gt_cls_per_image, reduction="none"
-            ).sum(-1)
-            del cls_preds_
-        
-            cost = (
-                pair_wise_cls_loss
-                + 3.0 * pair_wise_iou_loss
-            )
-
-            matching_matrix = torch.zeros_like(cost)
-
-            for gt_idx in range(num_gt):
-                _, pos_idx = torch.topk(
-                    cost[gt_idx], k=dynamic_ks[gt_idx].item(), largest=False
-                )
-                matching_matrix[gt_idx][pos_idx] = 1.0
-
-            del top_k, dynamic_ks
-            anchor_matching_gt = matching_matrix.sum(0)
-            if (anchor_matching_gt > 1).sum() > 0:
-                _, cost_argmin = torch.min(cost[:, anchor_matching_gt > 1], dim=0)
-                matching_matrix[:, anchor_matching_gt > 1] *= 0.0
-                matching_matrix[cost_argmin, anchor_matching_gt > 1] = 1.0
-            fg_mask_inboxes = matching_matrix.sum(0) > 0.0
-            matched_gt_inds = matching_matrix[:, fg_mask_inboxes].argmax(0)
-        
-            from_which_layer = from_which_layer[fg_mask_inboxes]
-            all_b = all_b[fg_mask_inboxes]
-            all_a = all_a[fg_mask_inboxes]
-            all_gj = all_gj[fg_mask_inboxes]
-            all_gi = all_gi[fg_mask_inboxes]
-            all_anch = all_anch[fg_mask_inboxes]
-        
-            this_target = this_target[matched_gt_inds]
-        
-            for i in range(nl):
-                layer_idx = from_which_layer == i
-                matching_bs[i].append(all_b[layer_idx])
-                matching_as[i].append(all_a[layer_idx])
-                matching_gjs[i].append(all_gj[layer_idx])
-                matching_gis[i].append(all_gi[layer_idx])
-                matching_targets[i].append(this_target[layer_idx])
-                matching_anchs[i].append(all_anch[layer_idx])
-
-        for i in range(nl):
-            if matching_targets[i] != []:
-                matching_bs[i] = torch.cat(matching_bs[i], dim=0)
-                matching_as[i] = torch.cat(matching_as[i], dim=0)
-                matching_gjs[i] = torch.cat(matching_gjs[i], dim=0)
-                matching_gis[i] = torch.cat(matching_gis[i], dim=0)
-                matching_targets[i] = torch.cat(matching_targets[i], dim=0)
-                matching_anchs[i] = torch.cat(matching_anchs[i], dim=0)
-            else:
-                matching_bs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
-                matching_as[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
-                matching_gjs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
-                matching_gis[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
-                matching_targets[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
-                matching_anchs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
-
-        return matching_bs, matching_as, matching_gjs, matching_gis, matching_targets, matching_anchs
-
-    def build_targets2(self, p, targets, imgs):
-        
-        indices, anch = self.find_5_positive(p, targets)
-
-        matching_bs = [[] for pp in p]
-        matching_as = [[] for pp in p]
-        matching_gjs = [[] for pp in p]
-        matching_gis = [[] for pp in p]
-        matching_targets = [[] for pp in p]
-        matching_anchs = [[] for pp in p]
-        
-        nl = len(p)    
-    
-        for batch_idx in range(p[0].shape[0]):
-        
-            b_idx = targets[:, 0]==batch_idx
-            this_target = targets[b_idx]
-            if this_target.shape[0] == 0:
-                continue
-                
-            txywh = this_target[:, 2:6] * imgs[batch_idx].shape[1]
-            txyxy = xywh2xyxy(txywh)
-
-            pxyxys = []
-            p_cls = []
-            p_obj = []
-            from_which_layer = []
-            all_b = []
-            all_a = []
-            all_gj = []
-            all_gi = []
-            all_anch = []
-            
-            for i, pi in enumerate(p):
-                
-                b, a, gj, gi = indices[i]
-                idx = (b == batch_idx)
-                b, a, gj, gi = b[idx], a[idx], gj[idx], gi[idx]                
-                all_b.append(b)
-                all_a.append(a)
-                all_gj.append(gj)
-                all_gi.append(gi)
-                all_anch.append(anch[i][idx])
-                from_which_layer.append(torch.ones(size=(len(b),)) * i)
-                
-                fg_pred = pi[b, a, gj, gi]                
-                p_obj.append(fg_pred[:, 4:5])
-                p_cls.append(fg_pred[:, 5:])
-                
-                grid = torch.stack([gi, gj], dim=1)
-                pxy = (fg_pred[:, :2].sigmoid() * 2. - 0.5 + grid) * self.stride[i] #/ 8.
-                #pxy = (fg_pred[:, :2].sigmoid() * 3. - 1. + grid) * self.stride[i]
-                pwh = (fg_pred[:, 2:4].sigmoid() * 2) ** 2 * anch[i][idx] * self.stride[i] #/ 8.
-                pxywh = torch.cat([pxy, pwh], dim=-1)
-                pxyxy = xywh2xyxy(pxywh)
-                pxyxys.append(pxyxy)
-            
-            pxyxys = torch.cat(pxyxys, dim=0)
-            if pxyxys.shape[0] == 0:
-                continue
-            p_obj = torch.cat(p_obj, dim=0)
-            p_cls = torch.cat(p_cls, dim=0)
-            from_which_layer = torch.cat(from_which_layer, dim=0)
-            all_b = torch.cat(all_b, dim=0)
-            all_a = torch.cat(all_a, dim=0)
-            all_gj = torch.cat(all_gj, dim=0)
-            all_gi = torch.cat(all_gi, dim=0)
-            all_anch = torch.cat(all_anch, dim=0)
-        
-            pair_wise_iou = box_iou(txyxy, pxyxys)
-
-            pair_wise_iou_loss = -torch.log(pair_wise_iou + 1e-8)
-
-            top_k, _ = torch.topk(pair_wise_iou, min(20, pair_wise_iou.shape[1]), dim=1)
-            dynamic_ks = torch.clamp(top_k.sum(1).int(), min=1)
-
-            gt_cls_per_image = (
-                F.one_hot(this_target[:, 1].to(torch.int64), self.nc)
-                .float()
-                .unsqueeze(1)
-                .repeat(1, pxyxys.shape[0], 1)
-            )
-
-            num_gt = this_target.shape[0]
-            cls_preds_ = (
-                p_cls.float().unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_()
-                * p_obj.unsqueeze(0).repeat(num_gt, 1, 1).sigmoid_()
-            )
-
-            y = cls_preds_.sqrt_()
-            pair_wise_cls_loss = F.binary_cross_entropy_with_logits(
-               torch.log(y/(1-y)) , gt_cls_per_image, reduction="none"
-            ).sum(-1)
-            del cls_preds_
-        
-            cost = (
-                pair_wise_cls_loss
-                + 3.0 * pair_wise_iou_loss
-            )
-
-            matching_matrix = torch.zeros_like(cost)
-
-            for gt_idx in range(num_gt):
-                _, pos_idx = torch.topk(
-                    cost[gt_idx], k=dynamic_ks[gt_idx].item(), largest=False
-                )
-                matching_matrix[gt_idx][pos_idx] = 1.0
-
-            del top_k, dynamic_ks
-            anchor_matching_gt = matching_matrix.sum(0)
-            if (anchor_matching_gt > 1).sum() > 0:
-                _, cost_argmin = torch.min(cost[:, anchor_matching_gt > 1], dim=0)
-                matching_matrix[:, anchor_matching_gt > 1] *= 0.0
-                matching_matrix[cost_argmin, anchor_matching_gt > 1] = 1.0
-            fg_mask_inboxes = matching_matrix.sum(0) > 0.0
-            matched_gt_inds = matching_matrix[:, fg_mask_inboxes].argmax(0)
-        
-            from_which_layer = from_which_layer[fg_mask_inboxes]
-            all_b = all_b[fg_mask_inboxes]
-            all_a = all_a[fg_mask_inboxes]
-            all_gj = all_gj[fg_mask_inboxes]
-            all_gi = all_gi[fg_mask_inboxes]
-            all_anch = all_anch[fg_mask_inboxes]
-        
-            this_target = this_target[matched_gt_inds]
-        
-            for i in range(nl):
-                layer_idx = from_which_layer == i
-                matching_bs[i].append(all_b[layer_idx])
-                matching_as[i].append(all_a[layer_idx])
-                matching_gjs[i].append(all_gj[layer_idx])
-                matching_gis[i].append(all_gi[layer_idx])
-                matching_targets[i].append(this_target[layer_idx])
-                matching_anchs[i].append(all_anch[layer_idx])
-
-        for i in range(nl):
-            if matching_targets[i] != []:
-                matching_bs[i] = torch.cat(matching_bs[i], dim=0)
-                matching_as[i] = torch.cat(matching_as[i], dim=0)
-                matching_gjs[i] = torch.cat(matching_gjs[i], dim=0)
-                matching_gis[i] = torch.cat(matching_gis[i], dim=0)
-                matching_targets[i] = torch.cat(matching_targets[i], dim=0)
-                matching_anchs[i] = torch.cat(matching_anchs[i], dim=0)
-            else:
-                matching_bs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
-                matching_as[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
-                matching_gjs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
-                matching_gis[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
-                matching_targets[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
-                matching_anchs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
-
-        return matching_bs, matching_as, matching_gjs, matching_gis, matching_targets, matching_anchs              
-
-    def find_5_positive(self, p, targets):
-        # Build targets for compute_loss(), input targets(image,class,x,y,w,h)
-        na, nt = self.na, targets.shape[0]  # number of anchors, targets
-        indices, anch = [], []
-        gain = torch.ones(7, device=targets.device).long()  # normalized to gridspace gain
-        ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt)  # same as .repeat_interleave(nt)
-        targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, None]), 2)  # append anchor indices
-
-        g = 1.0  # bias
-        off = torch.tensor([[0, 0],
-                            [1, 0], [0, 1], [-1, 0], [0, -1],  # j,k,l,m
-                            # [1, 1], [1, -1], [-1, 1], [-1, -1],  # jk,jm,lk,lm
-                            ], device=targets.device).float() * g  # offsets
-
-        for i in range(self.nl):
-            anchors = self.anchors[i]
-            gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]]  # xyxy gain
-
-            # Match targets to anchors
-            t = targets * gain
-            if nt:
-                # Matches
-                r = t[:, :, 4:6] / anchors[:, None]  # wh ratio
-                j = torch.max(r, 1. / r).max(2)[0] < self.hyp['anchor_t']  # compare
-                # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t']  # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2))
-                t = t[j]  # filter
-
-                # Offsets
-                gxy = t[:, 2:4]  # grid xy
-                gxi = gain[[2, 3]] - gxy  # inverse
-                j, k = ((gxy % 1. < g) & (gxy > 1.)).T
-                l, m = ((gxi % 1. < g) & (gxi > 1.)).T
-                j = torch.stack((torch.ones_like(j), j, k, l, m))
-                t = t.repeat((5, 1, 1))[j]
-                offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j]
-            else:
-                t = targets[0]
-                offsets = 0
-
-            # Define
-            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
-            a = t[:, 6].long()  # anchor indices
-            indices.append((b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1)))  # image, anchor, grid indices
-            anch.append(anchors[a])  # anchors
-
-        return indices, anch                 
-
-    def find_3_positive(self, p, targets):
-        # Build targets for compute_loss(), input targets(image,class,x,y,w,h)
-        na, nt = self.na, targets.shape[0]  # number of anchors, targets
-        indices, anch = [], []
-        gain = torch.ones(7, device=targets.device).long()  # normalized to gridspace gain
-        ai = torch.arange(na, device=targets.device).float().view(na, 1).repeat(1, nt)  # same as .repeat_interleave(nt)
-        targets = torch.cat((targets.repeat(na, 1, 1), ai[:, :, None]), 2)  # append anchor indices
-
-        g = 0.5  # bias
-        off = torch.tensor([[0, 0],
-                            [1, 0], [0, 1], [-1, 0], [0, -1],  # j,k,l,m
-                            # [1, 1], [1, -1], [-1, 1], [-1, -1],  # jk,jm,lk,lm
-                            ], device=targets.device).float() * g  # offsets
-
-        for i in range(self.nl):
-            anchors = self.anchors[i]
-            gain[2:6] = torch.tensor(p[i].shape)[[3, 2, 3, 2]]  # xyxy gain
-
-            # Match targets to anchors
-            t = targets * gain
-            if nt:
-                # Matches
-                r = t[:, :, 4:6] / anchors[:, None]  # wh ratio
-                j = torch.max(r, 1. / r).max(2)[0] < self.hyp['anchor_t']  # compare
-                # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t']  # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2))
-                t = t[j]  # filter
-
-                # Offsets
-                gxy = t[:, 2:4]  # grid xy
-                gxi = gain[[2, 3]] - gxy  # inverse
-                j, k = ((gxy % 1. < g) & (gxy > 1.)).T
-                l, m = ((gxi % 1. < g) & (gxi > 1.)).T
-                j = torch.stack((torch.ones_like(j), j, k, l, m))
-                t = t.repeat((5, 1, 1))[j]
-                offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j]
-            else:
-                t = targets[0]
-                offsets = 0
-
-            # Define
-            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
-            a = t[:, 6].long()  # anchor indices
-            indices.append((b, a, gj.clamp_(0, gain[3] - 1), gi.clamp_(0, gain[2] - 1)))  # image, anchor, grid indices
-            anch.append(anchors[a])  # anchors
-
-        return indices, anch

utils/metrics.py

@@ -1,227 +0,0 @@
-# Model validation metrics
-
-from pathlib import Path
-
-import matplotlib.pyplot as plt
-import numpy as np
-import torch
-
-from . import general
-
-
-def fitness(x):
-    # Model fitness as a weighted combination of metrics
-    w = [0.0, 0.0, 0.1, 0.9]  # weights for [P, R, mAP@0.5, mAP@0.5:0.95]
-    return (x[:, :4] * w).sum(1)
-
-
-def ap_per_class(tp, conf, pred_cls, target_cls, v5_metric=False, plot=False, save_dir='.', names=()):
-    """ Compute the average precision, given the recall and precision curves.
-    Source: https://github.com/rafaelpadilla/Object-Detection-Metrics.
-    # Arguments
-        tp:  True positives (nparray, nx1 or nx10).
-        conf:  Objectness value from 0-1 (nparray).
-        pred_cls:  Predicted object classes (nparray).
-        target_cls:  True object classes (nparray).
-        plot:  Plot precision-recall curve at mAP@0.5
-        save_dir:  Plot save directory
-    # Returns
-        The average precision as computed in py-faster-rcnn.
-    """
-
-    # Sort by objectness
-    i = np.argsort(-conf)
-    tp, conf, pred_cls = tp[i], conf[i], pred_cls[i]
-
-    # Find unique classes
-    unique_classes = np.unique(target_cls)
-    nc = unique_classes.shape[0]  # number of classes, number of detections
-
-    # Create Precision-Recall curve and compute AP for each class
-    px, py = np.linspace(0, 1, 1000), []  # for plotting
-    ap, p, r = np.zeros((nc, tp.shape[1])), np.zeros((nc, 1000)), np.zeros((nc, 1000))
-    for ci, c in enumerate(unique_classes):
-        i = pred_cls == c
-        n_l = (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(-px, -conf[i], recall[:, 0], left=0)  # negative x, xp because xp decreases
-
-            # Precision
-            precision = tpc / (tpc + fpc)  # precision curve
-            p[ci] = np.interp(-px, -conf[i], precision[:, 0], left=1)  # p at pr_score
-
-            # AP from recall-precision curve
-            for j in range(tp.shape[1]):
-                ap[ci, j], mpre, mrec = compute_ap(recall[:, j], precision[:, j], v5_metric=v5_metric)
-                if plot and j == 0:
-                    py.append(np.interp(px, mrec, mpre))  # precision at mAP@0.5
-
-    # Compute F1 (harmonic mean of precision and recall)
-    f1 = 2 * p * r / (p + r + 1e-16)
-    if plot:
-        plot_pr_curve(px, py, ap, Path(save_dir) / 'PR_curve.png', names)
-        plot_mc_curve(px, f1, Path(save_dir) / 'F1_curve.png', names, ylabel='F1')
-        plot_mc_curve(px, p, Path(save_dir) / 'P_curve.png', names, ylabel='Precision')
-        plot_mc_curve(px, r, Path(save_dir) / 'R_curve.png', names, ylabel='Recall')
-
-    i = f1.mean(0).argmax()  # max F1 index
-    return p[:, i], r[:, i], ap, f1[:, i], unique_classes.astype('int32')
-
-
-def compute_ap(recall, precision, v5_metric=False):
-    """ Compute the average precision, given the recall and precision curves
-    # Arguments
-        recall:    The recall curve (list)
-        precision: The precision curve (list)
-        v5_metric: Assume maximum recall to be 1.0, as in YOLOv5, MMDetetion etc.
-    # Returns
-        Average precision, precision curve, recall curve
-    """
-
-    # Append sentinel values to beginning and end
-    if v5_metric:  # New YOLOv5 metric, same as MMDetection and Detectron2 repositories
-        mrec = np.concatenate(([0.], recall, [1.0]))
-    else:  # Old YOLOv5 metric, i.e. default YOLOv7 metric
-        mrec = np.concatenate(([0.], recall, [recall[-1] + 0.01]))
-    mpre = np.concatenate(([1.], 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
-
-
-class ConfusionMatrix:
-    # Updated version of https://github.com/kaanakan/object_detection_confusion_matrix
-    def __init__(self, nc, conf=0.25, iou_thres=0.45):
-        self.matrix = np.zeros((nc + 1, nc + 1))
-        self.nc = nc  # number of classes
-        self.conf = conf
-        self.iou_thres = iou_thres
-
-    def process_batch(self, detections, labels):
-        """
-        Return intersection-over-union (Jaccard index) of boxes.
-        Both sets of boxes are expected to be in (x1, y1, x2, y2) format.
-        Arguments:
-            detections (Array[N, 6]), x1, y1, x2, y2, conf, class
-            labels (Array[M, 5]), class, x1, y1, x2, y2
-        Returns:
-            None, updates confusion matrix accordingly
-        """
-        detections = detections[detections[:, 4] > self.conf]
-        gt_classes = labels[:, 0].int()
-        detection_classes = detections[:, 5].int()
-        iou = general.box_iou(labels[:, 1:], detections[:, :4])
-
-        x = torch.where(iou > self.iou_thres)
-        if x[0].shape[0]:
-            matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy()
-            if x[0].shape[0] > 1:
-                matches = matches[matches[:, 2].argsort()[::-1]]
-                matches = matches[np.unique(matches[:, 1], return_index=True)[1]]
-                matches = matches[matches[:, 2].argsort()[::-1]]
-                matches = matches[np.unique(matches[:, 0], return_index=True)[1]]
-        else:
-            matches = np.zeros((0, 3))
-
-        n = matches.shape[0] > 0
-        m0, m1, _ = matches.transpose().astype(np.int16)
-        for i, gc in enumerate(gt_classes):
-            j = m0 == i
-            if n and sum(j) == 1:
-                self.matrix[gc, detection_classes[m1[j]]] += 1  # correct
-            else:
-                self.matrix[self.nc, gc] += 1  # background FP
-
-        if n:
-            for i, dc in enumerate(detection_classes):
-                if not any(m1 == i):
-                    self.matrix[dc, self.nc] += 1  # background FN
-
-    def matrix(self):
-        return self.matrix
-
-    def plot(self, save_dir='', names=()):
-        try:
-            import seaborn as sn
-
-            array = self.matrix / (self.matrix.sum(0).reshape(1, self.nc + 1) + 1E-6)  # normalize
-            array[array < 0.005] = np.nan  # don't annotate (would appear as 0.00)
-
-            fig = plt.figure(figsize=(12, 9), tight_layout=True)
-            sn.set(font_scale=1.0 if self.nc < 50 else 0.8)  # for label size
-            labels = (0 < len(names) < 99) and len(names) == self.nc  # apply names to ticklabels
-            sn.heatmap(array, annot=self.nc < 30, annot_kws={"size": 8}, cmap='Blues', fmt='.2f', square=True,
-                       xticklabels=names + ['background FP'] if labels else "auto",
-                       yticklabels=names + ['background FN'] if labels else "auto").set_facecolor((1, 1, 1))
-            fig.axes[0].set_xlabel('True')
-            fig.axes[0].set_ylabel('Predicted')
-            fig.savefig(Path(save_dir) / 'confusion_matrix.png', dpi=250)
-        except Exception as e:
-            pass
-
-    def print(self):
-        for i in range(self.nc + 1):
-            print(' '.join(map(str, self.matrix[i])))
-
-
-# Plots ----------------------------------------------------------------------------------------------------------------
-
-def plot_pr_curve(px, py, ap, save_dir='pr_curve.png', names=()):
-    # Precision-recall curve
-    fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True)
-    py = np.stack(py, axis=1)
-
-    if 0 < len(names) < 21:  # display per-class legend if < 21 classes
-        for i, y in enumerate(py.T):
-            ax.plot(px, y, linewidth=1, label=f'{names[i]} {ap[i, 0]:.3f}')  # plot(recall, precision)
-    else:
-        ax.plot(px, py, linewidth=1, color='grey')  # plot(recall, precision)
-
-    ax.plot(px, py.mean(1), linewidth=3, color='blue', label='all classes %.3f mAP@0.5' % ap[:, 0].mean())
-    ax.set_xlabel('Recall')
-    ax.set_ylabel('Precision')
-    ax.set_xlim(0, 1)
-    ax.set_ylim(0, 1)
-    plt.legend(bbox_to_anchor=(1.04, 1), loc="upper left")
-    fig.savefig(Path(save_dir), dpi=250)
-
-
-def plot_mc_curve(px, py, save_dir='mc_curve.png', names=(), xlabel='Confidence', ylabel='Metric'):
-    # Metric-confidence curve
-    fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True)
-
-    if 0 < len(names) < 21:  # display per-class legend if < 21 classes
-        for i, y in enumerate(py):
-            ax.plot(px, y, linewidth=1, label=f'{names[i]}')  # plot(confidence, metric)
-    else:
-        ax.plot(px, py.T, linewidth=1, color='grey')  # plot(confidence, metric)
-
-    y = py.mean(0)
-    ax.plot(px, y, linewidth=3, color='blue', label=f'all classes {y.max():.2f} at {px[y.argmax()]:.3f}')
-    ax.set_xlabel(xlabel)
-    ax.set_ylabel(ylabel)
-    ax.set_xlim(0, 1)
-    ax.set_ylim(0, 1)
-    plt.legend(bbox_to_anchor=(1.04, 1), loc="upper left")
-    fig.savefig(Path(save_dir), dpi=250)

utils/plots.py

@@ -1,489 +0,0 @@
-# Plotting utils
-
-import glob
-import math
-import os
-import random
-from copy import copy
-from pathlib import Path
-
-import cv2
-import matplotlib
-import matplotlib.pyplot as plt
-import numpy as np
-import pandas as pd
-import seaborn as sns
-import torch
-import yaml
-from PIL import Image, ImageDraw, ImageFont
-from scipy.signal import butter, filtfilt
-
-from utils.general import xywh2xyxy, xyxy2xywh
-from utils.metrics import fitness
-
-# Settings
-matplotlib.rc('font', **{'size': 11})
-matplotlib.use('Agg')  # for writing to files only
-
-
-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 matplotlib.colors.TABLEAU_COLORS.values()]  # or BASE_ (8), CSS4_ (148), XKCD_ (949)
-
-
-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=3):
-    # 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_one_box_PIL(box, img, color=None, label=None, line_thickness=None):
-    img = Image.fromarray(img)
-    draw = ImageDraw.Draw(img)
-    line_thickness = line_thickness or max(int(min(img.size) / 200), 2)
-    draw.rectangle(box, width=line_thickness, outline=tuple(color))  # plot
-    if label:
-        fontsize = max(round(max(img.size) / 40), 12)
-        font = ImageFont.truetype("Arial.ttf", fontsize)
-        txt_width, txt_height = font.getsize(label)
-        draw.rectangle([box[0], box[1] - txt_height + 4, box[0] + txt_width, box[1]], fill=tuple(color))
-        draw.text((box[0], box[1] - txt_height + 1), label, fill=(255, 255, 255), font=font)
-    return np.asarray(img)
-
-
-def plot_wh_methods():  # from utils.plots 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), tight_layout=True)
-    plt.plot(x, ya, '.-', label='YOLOv3')
-    plt.plot(x, yb ** 2, '.-', label='YOLOR ^2')
-    plt.plot(x, yb ** 1.6, '.-', label='YOLOR ^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.savefig('comparison.png', dpi=200)
-
-
-def output_to_target(output):
-    # Convert model output to target format [batch_id, class_id, x, y, w, h, conf]
-    targets = []
-    for i, o in enumerate(output):
-        for *box, conf, cls in o.cpu().numpy():
-            targets.append([i, cls, *list(*xyxy2xywh(np.array(box)[None])), 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 = 3  # 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)
-
-            if boxes.shape[1]:
-                if boxes.max() <= 1.01:  # if normalized with tolerance 0.01
-                    boxes[[0, 2]] *= w  # scale to pixels
-                    boxes[[1, 3]] *= h
-                elif scale_factor < 1:  # absolute coords need scale if image scales
-                    boxes *= scale_factor
-            boxes[[0, 2]] += block_x
-            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.savefig(Path(save_dir) / 'LR.png', dpi=200)
-    plt.close()
-
-
-def plot_test_txt():  # from utils.plots 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.plots 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(path='', x=None):  # from utils.plots 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 [Path(path) / f'study_coco_{x}.txt' for x in ['yolor-p6', 'yolor-w6', 'yolor-e6', 'yolor-d6']]:
-    for f in sorted(Path(path).glob('study*.txt')):
-        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, 1:j], y[3, 1:j] * 1E2, '.-', linewidth=2, markersize=8,
-                 label=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(alpha=0.2)
-    ax2.set_yticks(np.arange(20, 60, 5))
-    ax2.set_xlim(0, 57)
-    ax2.set_ylim(30, 55)
-    ax2.set_xlabel('GPU Speed (ms/img)')
-    ax2.set_ylabel('COCO AP val')
-    ax2.legend(loc='lower right')
-    plt.savefig(str(Path(path).name) + '.png', dpi=300)
-
-
-def plot_labels(labels, names=(), save_dir=Path(''), loggers=None):
-    # plot dataset labels
-    print('Plotting labels... ')
-    c, b = labels[:, 0], labels[:, 1:].transpose()  # classes, boxes
-    nc = int(c.max() + 1)  # number of classes
-    colors = color_list()
-    x = pd.DataFrame(b.transpose(), columns=['x', 'y', 'width', 'height'])
-
-    # seaborn correlogram
-    sns.pairplot(x, corner=True, diag_kind='auto', kind='hist', diag_kws=dict(bins=50), plot_kws=dict(pmax=0.9))
-    plt.savefig(save_dir / 'labels_correlogram.jpg', dpi=200)
-    plt.close()
-
-    # matplotlib labels
-    matplotlib.use('svg')  # faster
-    ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)[1].ravel()
-    ax[0].hist(c, bins=np.linspace(0, nc, nc + 1) - 0.5, rwidth=0.8)
-    ax[0].set_ylabel('instances')
-    if 0 < len(names) < 30:
-        ax[0].set_xticks(range(len(names)))
-        ax[0].set_xticklabels(names, rotation=90, fontsize=10)
-    else:
-        ax[0].set_xlabel('classes')
-    sns.histplot(x, x='x', y='y', ax=ax[2], bins=50, pmax=0.9)
-    sns.histplot(x, x='width', y='height', ax=ax[3], bins=50, pmax=0.9)
-
-    # rectangles
-    labels[:, 1:3] = 0.5  # center
-    labels[:, 1:] = xywh2xyxy(labels[:, 1:]) * 2000
-    img = Image.fromarray(np.ones((2000, 2000, 3), dtype=np.uint8) * 255)
-    for cls, *box in labels[:1000]:
-        ImageDraw.Draw(img).rectangle(box, width=1, outline=colors[int(cls) % 10])  # plot
-    ax[1].imshow(img)
-    ax[1].axis('off')
-
-    for a in [0, 1, 2, 3]:
-        for s in ['top', 'right', 'left', 'bottom']:
-            ax[a].spines[s].set_visible(False)
-
-    plt.savefig(save_dir / 'labels.jpg', dpi=200)
-    matplotlib.use('Agg')
-    plt.close()
-
-    # loggers
-    for k, v in loggers.items() or {}:
-        if k == 'wandb' and v:
-            v.log({"Labels": [v.Image(str(x), caption=x.name) for x in save_dir.glob('*labels*.jpg')]}, commit=False)
-
-
-def plot_evolution(yaml_file='data/hyp.finetune.yaml'):  # from utils.plots import *; plot_evolution()
-    # Plot hyperparameter evolution results in evolve.txt
-    with open(yaml_file) as f:
-        hyp = yaml.load(f, Loader=yaml.SafeLoader)
-    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 profile_idetection(start=0, stop=0, labels=(), save_dir=''):
-    # Plot iDetection '*.txt' per-image logs. from utils.plots import *; profile_idetection()
-    ax = plt.subplots(2, 4, figsize=(12, 6), tight_layout=True)[1].ravel()
-    s = ['Images', 'Free Storage (GB)', 'RAM Usage (GB)', 'Battery', 'dt_raw (ms)', 'dt_smooth (ms)', 'real-world FPS']
-    files = list(Path(save_dir).glob('frames*.txt'))
-    for fi, f in enumerate(files):
-        try:
-            results = np.loadtxt(f, ndmin=2).T[:, 90:-30]  # clip first and last rows
-            n = results.shape[1]  # number of rows
-            x = np.arange(start, min(stop, n) if stop else n)
-            results = results[:, x]
-            t = (results[0] - results[0].min())  # set t0=0s
-            results[0] = x
-            for i, a in enumerate(ax):
-                if i < len(results):
-                    label = labels[fi] if len(labels) else f.stem.replace('frames_', '')
-                    a.plot(t, results[i], marker='.', label=label, linewidth=1, markersize=5)
-                    a.set_title(s[i])
-                    a.set_xlabel('time (s)')
-                    # if fi == len(files) - 1:
-                    #     a.set_ylim(bottom=0)
-                    for side in ['top', 'right']:
-                        a.spines[side].set_visible(False)
-                else:
-                    a.remove()
-        except Exception as e:
-            print('Warning: Plotting error for %s; %s' % (f, e))
-
-    ax[1].legend()
-    plt.savefig(Path(save_dir) / 'idetection_profile.png', dpi=200)
-
-
-def plot_results_overlay(start=0, stop=0):  # from utils.plots 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=''):
-    # Plot training 'results*.txt'. from utils.plots import *; plot_results(save_dir='runs/train/exp')
-    fig, ax = plt.subplots(2, 5, figsize=(12, 6), tight_layout=True)
-    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:
-        # files = ['https://storage.googleapis.com/%s/results%g.txt' % (bucket, x) for x in id]
-        files = ['results%g.txt' % x for x in id]
-        c = ('gsutil cp ' + '%s ' * len(files) + '.') % tuple('gs://%s/results%g.txt' % (bucket, x) for x in id)
-        os.system(c)
-    else:
-        files = list(Path(save_dir).glob('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 f.stem
-                ax[i].plot(x, y, marker='.', label=label, linewidth=2, markersize=8)
-                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))
-
-    ax[1].legend()
-    fig.savefig(Path(save_dir) / 'results.png', dpi=200)
-    
-    
-def output_to_keypoint(output):
-    # Convert model output to target format [batch_id, class_id, x, y, w, h, conf]
-    targets = []
-    for i, o in enumerate(output):
-        kpts = o[:,6:]
-        o = o[:,:6]
-        for index, (*box, conf, cls) in enumerate(o.detach().cpu().numpy()):
-            targets.append([i, cls, *list(*xyxy2xywh(np.array(box)[None])), conf, *list(kpts.detach().cpu().numpy()[index])])
-    return np.array(targets)
-
-
-def plot_skeleton_kpts(im, kpts, steps, orig_shape=None):
-    #Plot the skeleton and keypointsfor coco datatset
-    palette = np.array([[255, 128, 0], [255, 153, 51], [255, 178, 102],
-                        [230, 230, 0], [255, 153, 255], [153, 204, 255],
-                        [255, 102, 255], [255, 51, 255], [102, 178, 255],
-                        [51, 153, 255], [255, 153, 153], [255, 102, 102],
-                        [255, 51, 51], [153, 255, 153], [102, 255, 102],
-                        [51, 255, 51], [0, 255, 0], [0, 0, 255], [255, 0, 0],
-                        [255, 255, 255]])
-
-    skeleton = [[16, 14], [14, 12], [17, 15], [15, 13], [12, 13], [6, 12],
-                [7, 13], [6, 7], [6, 8], [7, 9], [8, 10], [9, 11], [2, 3],
-                [1, 2], [1, 3], [2, 4], [3, 5], [4, 6], [5, 7]]
-
-    pose_limb_color = palette[[9, 9, 9, 9, 7, 7, 7, 0, 0, 0, 0, 0, 16, 16, 16, 16, 16, 16, 16]]
-    pose_kpt_color = palette[[16, 16, 16, 16, 16, 0, 0, 0, 0, 0, 0, 9, 9, 9, 9, 9, 9]]
-    radius = 5
-    num_kpts = len(kpts) // steps
-
-    for kid in range(num_kpts):
-        r, g, b = pose_kpt_color[kid]
-        x_coord, y_coord = kpts[steps * kid], kpts[steps * kid + 1]
-        if not (x_coord % 640 == 0 or y_coord % 640 == 0):
-            if steps == 3:
-                conf = kpts[steps * kid + 2]
-                if conf < 0.5:
-                    continue
-            cv2.circle(im, (int(x_coord), int(y_coord)), radius, (int(r), int(g), int(b)), -1)
-
-    for sk_id, sk in enumerate(skeleton):
-        r, g, b = pose_limb_color[sk_id]
-        pos1 = (int(kpts[(sk[0]-1)*steps]), int(kpts[(sk[0]-1)*steps+1]))
-        pos2 = (int(kpts[(sk[1]-1)*steps]), int(kpts[(sk[1]-1)*steps+1]))
-        if steps == 3:
-            conf1 = kpts[(sk[0]-1)*steps+2]
-            conf2 = kpts[(sk[1]-1)*steps+2]
-            if conf1<0.5 or conf2<0.5:
-                continue
-        if pos1[0]%640 == 0 or pos1[1]%640==0 or pos1[0]<0 or pos1[1]<0:
-            continue
-        if pos2[0] % 640 == 0 or pos2[1] % 640 == 0 or pos2[0]<0 or pos2[1]<0:
-            continue
-        cv2.line(im, pos1, pos2, (int(r), int(g), int(b)), thickness=2)

utils/torch_utils.py

@@ -1,374 +0,0 @@
-# YOLOR PyTorch utils
-
-import datetime
-import logging
-import math
-import os
-import platform
-import subprocess
-import time
-from contextlib import contextmanager
-from copy import deepcopy
-from pathlib import Path
-
-import torch
-import torch.backends.cudnn as cudnn
-import torch.nn as nn
-import torch.nn.functional as F
-import torchvision
-
-try:
-    import thop  # for FLOPS computation
-except ImportError:
-    thop = None
-logger = logging.getLogger(__name__)
-
-
-@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 init_torch_seeds(seed=0):
-    # Speed-reproducibility tradeoff https://pytorch.org/docs/stable/notes/randomness.html
-    torch.manual_seed(seed)
-    if seed == 0:  # slower, more reproducible
-        cudnn.benchmark, cudnn.deterministic = False, True
-    else:  # faster, less reproducible
-        cudnn.benchmark, cudnn.deterministic = True, False
-
-
-def date_modified(path=__file__):
-    # return human-readable file modification date, i.e. '2021-3-26'
-    t = datetime.datetime.fromtimestamp(Path(path).stat().st_mtime)
-    return f'{t.year}-{t.month}-{t.day}'
-
-
-def git_describe(path=Path(__file__).parent):  # path must be a directory
-    # return human-readable git description, i.e. v5.0-5-g3e25f1e https://git-scm.com/docs/git-describe
-    s = f'git -C {path} describe --tags --long --always'
-    try:
-        return subprocess.check_output(s, shell=True, stderr=subprocess.STDOUT).decode()[:-1]
-    except subprocess.CalledProcessError as e:
-        return ''  # not a git repository
-
-
-def select_device(device='', batch_size=None):
-    # device = 'cpu' or '0' or '0,1,2,3'
-    s = f'YOLOR 🚀 {git_describe() or date_modified()} torch {torch.__version__} '  # string
-    cpu = device.lower() == 'cpu'
-    if cpu:
-        os.environ['CUDA_VISIBLE_DEVICES'] = '-1'  # force torch.cuda.is_available() = False
-    elif device:  # non-cpu device requested
-        os.environ['CUDA_VISIBLE_DEVICES'] = device  # set environment variable
-        assert torch.cuda.is_available(), f'CUDA unavailable, invalid device {device} requested'  # check availability
-
-    cuda = not cpu and torch.cuda.is_available()
-    if cuda:
-        n = torch.cuda.device_count()
-        if n > 1 and batch_size:  # check that batch_size is compatible with device_count
-            assert batch_size % n == 0, f'batch-size {batch_size} not multiple of GPU count {n}'
-        space = ' ' * len(s)
-        for i, d in enumerate(device.split(',') if device else range(n)):
-            p = torch.cuda.get_device_properties(i)
-            s += f"{'' if i == 0 else space}CUDA:{d} ({p.name}, {p.total_memory / 1024 ** 2}MB)\n"  # bytes to MB
-    else:
-        s += 'CPU\n'
-
-    logger.info(s.encode().decode('ascii', 'ignore') if platform.system() == 'Windows' else s)  # emoji-safe
-    return torch.device('cuda:0' if cuda else 'cpu')
-
-
-def time_synchronized():
-    # pytorch-accurate time
-    if torch.cuda.is_available():
-        torch.cuda.synchronize()
-    return time.time()
-
-
-def profile(x, ops, n=100, device=None):
-    # profile a pytorch module or list of modules. Example usage:
-    #     x = torch.randn(16, 3, 640, 640)  # input
-    #     m1 = lambda x: x * torch.sigmoid(x)
-    #     m2 = nn.SiLU()
-    #     profile(x, [m1, m2], n=100)  # profile speed over 100 iterations
-
-    device = device or torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
-    x = x.to(device)
-    x.requires_grad = True
-    print(torch.__version__, device.type, torch.cuda.get_device_properties(0) if device.type == 'cuda' else '')
-    print(f"\n{'Params':>12s}{'GFLOPS':>12s}{'forward (ms)':>16s}{'backward (ms)':>16s}{'input':>24s}{'output':>24s}")
-    for m in ops if isinstance(ops, list) else [ops]:
-        m = m.to(device) if hasattr(m, 'to') else m  # device
-        m = m.half() if hasattr(m, 'half') and isinstance(x, torch.Tensor) and x.dtype is torch.float16 else m  # type
-        dtf, dtb, t = 0., 0., [0., 0., 0.]  # dt forward, backward
-        try:
-            flops = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2  # GFLOPS
-        except:
-            flops = 0
-
-        for _ in range(n):
-            t[0] = time_synchronized()
-            y = m(x)
-            t[1] = time_synchronized()
-            try:
-                _ = y.sum().backward()
-                t[2] = time_synchronized()
-            except:  # no backward method
-                t[2] = float('nan')
-            dtf += (t[1] - t[0]) * 1000 / n  # ms per op forward
-            dtb += (t[2] - t[1]) * 1000 / n  # ms per op backward
-
-        s_in = tuple(x.shape) if isinstance(x, torch.Tensor) else 'list'
-        s_out = tuple(y.shape) if isinstance(y, torch.Tensor) else 'list'
-        p = sum(list(x.numel() for x in m.parameters())) if isinstance(m, nn.Module) else 0  # parameters
-        print(f'{p:12}{flops:12.4g}{dtf:16.4g}{dtb:16.4g}{str(s_in):>24s}{str(s_out):>24s}')
-
-
-def is_parallel(model):
-    return type(model) in (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel)
-
-
-def intersect_dicts(da, db, exclude=()):
-    # Dictionary intersection of matching keys and shapes, omitting 'exclude' keys, using da values
-    return {k: v for k, v in da.items() if k in db and not any(x in k for x in exclude) and v.shape == db[k].shape}
-
-
-def initialize_weights(model):
-    for m in model.modules():
-        t = type(m)
-        if t is nn.Conv2d:
-            pass  # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
-        elif t is nn.BatchNorm2d:
-            m.eps = 1e-3
-            m.momentum = 0.03
-        elif t in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6]:
-            m.inplace = True
-
-
-def find_modules(model, mclass=nn.Conv2d):
-    # Finds layer indices matching module class 'mclass'
-    return [i for i, m in enumerate(model.module_list) if isinstance(m, mclass)]
-
-
-def sparsity(model):
-    # Return global model sparsity
-    a, b = 0., 0.
-    for p in model.parameters():
-        a += p.numel()
-        b += (p == 0).sum()
-    return b / a
-
-
-def prune(model, amount=0.3):
-    # Prune model to requested global sparsity
-    import torch.nn.utils.prune as prune
-    print('Pruning model... ', end='')
-    for name, m in model.named_modules():
-        if isinstance(m, nn.Conv2d):
-            prune.l1_unstructured(m, name='weight', amount=amount)  # prune
-            prune.remove(m, 'weight')  # make permanent
-    print(' %.3g global sparsity' % sparsity(model))
-
-
-def fuse_conv_and_bn(conv, bn):
-    # Fuse convolution and batchnorm layers https://tehnokv.com/posts/fusing-batchnorm-and-conv/
-    fusedconv = nn.Conv2d(conv.in_channels,
-                          conv.out_channels,
-                          kernel_size=conv.kernel_size,
-                          stride=conv.stride,
-                          padding=conv.padding,
-                          groups=conv.groups,
-                          bias=True).requires_grad_(False).to(conv.weight.device)
-
-    # prepare filters
-    w_conv = conv.weight.clone().view(conv.out_channels, -1)
-    w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var)))
-    fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.shape))
-
-    # prepare spatial bias
-    b_conv = torch.zeros(conv.weight.size(0), device=conv.weight.device) if conv.bias is None else conv.bias
-    b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps))
-    fusedconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn)
-
-    return fusedconv
-
-
-def model_info(model, verbose=False, img_size=640):
-    # Model information. img_size may be int or list, i.e. img_size=640 or img_size=[640, 320]
-    n_p = sum(x.numel() for x in model.parameters())  # number parameters
-    n_g = sum(x.numel() for x in model.parameters() if x.requires_grad)  # number gradients
-    if verbose:
-        print('%5s %40s %9s %12s %20s %10s %10s' % ('layer', 'name', 'gradient', 'parameters', 'shape', 'mu', 'sigma'))
-        for i, (name, p) in enumerate(model.named_parameters()):
-            name = name.replace('module_list.', '')
-            print('%5g %40s %9s %12g %20s %10.3g %10.3g' %
-                  (i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std()))
-
-    try:  # FLOPS
-        from thop import profile
-        stride = max(int(model.stride.max()), 32) if hasattr(model, 'stride') else 32
-        img = torch.zeros((1, model.yaml.get('ch', 3), stride, stride), device=next(model.parameters()).device)  # input
-        flops = profile(deepcopy(model), inputs=(img,), verbose=False)[0] / 1E9 * 2  # stride GFLOPS
-        img_size = img_size if isinstance(img_size, list) else [img_size, img_size]  # expand if int/float
-        fs = ', %.1f GFLOPS' % (flops * img_size[0] / stride * img_size[1] / stride)  # 640x640 GFLOPS
-    except (ImportError, Exception):
-        fs = ''
-
-    logger.info(f"Model Summary: {len(list(model.modules()))} layers, {n_p} parameters, {n_g} gradients{fs}")
-
-
-def load_classifier(name='resnet101', n=2):
-    # Loads a pretrained model reshaped to n-class output
-    model = torchvision.models.__dict__[name](pretrained=True)
-
-    # ResNet model properties
-    # input_size = [3, 224, 224]
-    # input_space = 'RGB'
-    # input_range = [0, 1]
-    # mean = [0.485, 0.456, 0.406]
-    # std = [0.229, 0.224, 0.225]
-
-    # Reshape output to n classes
-    filters = model.fc.weight.shape[1]
-    model.fc.bias = nn.Parameter(torch.zeros(n), requires_grad=True)
-    model.fc.weight = nn.Parameter(torch.zeros(n, filters), requires_grad=True)
-    model.fc.out_features = n
-    return model
-
-
-def scale_img(img, ratio=1.0, same_shape=False, gs=32):  # img(16,3,256,416)
-    # scales img(bs,3,y,x) by ratio constrained to gs-multiple
-    if ratio == 1.0:
-        return img
-    else:
-        h, w = img.shape[2:]
-        s = (int(h * ratio), int(w * ratio))  # new size
-        img = F.interpolate(img, size=s, mode='bilinear', align_corners=False)  # resize
-        if not same_shape:  # pad/crop img
-            h, w = [math.ceil(x * ratio / gs) * gs for x in (h, w)]
-        return F.pad(img, [0, w - s[1], 0, h - s[0]], value=0.447)  # value = imagenet mean
-
-
-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.module.state_dict() if is_parallel(model) else 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)
-
-
-class BatchNormXd(torch.nn.modules.batchnorm._BatchNorm):
-    def _check_input_dim(self, input):
-        # The only difference between BatchNorm1d, BatchNorm2d, BatchNorm3d, etc
-        # is this method that is overwritten by the sub-class
-        # This original goal of this method was for tensor sanity checks
-        # If you're ok bypassing those sanity checks (eg. if you trust your inference
-        # to provide the right dimensional inputs), then you can just use this method
-        # for easy conversion from SyncBatchNorm
-        # (unfortunately, SyncBatchNorm does not store the original class - if it did
-        #  we could return the one that was originally created)
-        return
-
-def revert_sync_batchnorm(module):
-    # this is very similar to the function that it is trying to revert:
-    # https://github.com/pytorch/pytorch/blob/c8b3686a3e4ba63dc59e5dcfe5db3430df256833/torch/nn/modules/batchnorm.py#L679
-    module_output = module
-    if isinstance(module, torch.nn.modules.batchnorm.SyncBatchNorm):
-        new_cls = BatchNormXd
-        module_output = BatchNormXd(module.num_features,
-                                               module.eps, module.momentum,
-                                               module.affine,
-                                               module.track_running_stats)
-        if module.affine:
-            with torch.no_grad():
-                module_output.weight = module.weight
-                module_output.bias = module.bias
-        module_output.running_mean = module.running_mean
-        module_output.running_var = module.running_var
-        module_output.num_batches_tracked = module.num_batches_tracked
-        if hasattr(module, "qconfig"):
-            module_output.qconfig = module.qconfig
-    for name, child in module.named_children():
-        module_output.add_module(name, revert_sync_batchnorm(child))
-    del module
-    return module_output
-
-
-class TracedModel(nn.Module):
-
-    def __init__(self, model=None, device=None, img_size=(640,640)): 
-        super(TracedModel, self).__init__()
-        
-        print(" Convert model to Traced-model... ") 
-        self.stride = model.stride
-        self.names = model.names
-        self.model = model
-
-        self.model = revert_sync_batchnorm(self.model)
-        self.model.to('cpu')
-        self.model.eval()
-
-        self.detect_layer = self.model.model[-1]
-        self.model.traced = True
-        
-        rand_example = torch.rand(1, 3, img_size, img_size)
-        
-        traced_script_module = torch.jit.trace(self.model, rand_example, strict=False)
-        #traced_script_module = torch.jit.script(self.model)
-        traced_script_module.save("traced_model.pt")
-        print(" traced_script_module saved! ")
-        self.model = traced_script_module
-        self.model.to(device)
-        self.detect_layer.to(device)
-        print(" model is traced! \n") 
-
-    def forward(self, x, augment=False, profile=False):
-        out = self.model(x)
-        out = self.detect_layer(out)
-        return out

utils/wandb_logging/__init__.py

@@ -1 +0,0 @@
-# init

utils/wandb_logging/log_dataset.py

@@ -1,24 +0,0 @@
-import argparse
-
-import yaml
-
-from wandb_utils import WandbLogger
-
-WANDB_ARTIFACT_PREFIX = 'wandb-artifact://'
-
-
-def create_dataset_artifact(opt):
-    with open(opt.data) as f:
-        data = yaml.load(f, Loader=yaml.SafeLoader)  # data dict
-    logger = WandbLogger(opt, '', None, data, job_type='Dataset Creation')
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--data', type=str, default='data/coco.yaml', help='data.yaml path')
-    parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset')
-    parser.add_argument('--project', type=str, default='YOLOR', help='name of W&B Project')
-    opt = parser.parse_args()
-    opt.resume = False  # Explicitly disallow resume check for dataset upload job
-
-    create_dataset_artifact(opt)

utils/wandb_logging/wandb_utils.py

@@ -1,306 +0,0 @@
-import json
-import sys
-from pathlib import Path
-
-import torch
-import yaml
-from tqdm import tqdm
-
-sys.path.append(str(Path(__file__).parent.parent.parent))  # add utils/ to path
-from utils.datasets import LoadImagesAndLabels
-from utils.datasets import img2label_paths
-from utils.general import colorstr, xywh2xyxy, check_dataset
-
-try:
-    import wandb
-    from wandb import init, finish
-except ImportError:
-    wandb = None
-
-WANDB_ARTIFACT_PREFIX = 'wandb-artifact://'
-
-
-def remove_prefix(from_string, prefix=WANDB_ARTIFACT_PREFIX):
-    return from_string[len(prefix):]
-
-
-def check_wandb_config_file(data_config_file):
-    wandb_config = '_wandb.'.join(data_config_file.rsplit('.', 1))  # updated data.yaml path
-    if Path(wandb_config).is_file():
-        return wandb_config
-    return data_config_file
-
-
-def get_run_info(run_path):
-    run_path = Path(remove_prefix(run_path, WANDB_ARTIFACT_PREFIX))
-    run_id = run_path.stem
-    project = run_path.parent.stem
-    model_artifact_name = 'run_' + run_id + '_model'
-    return run_id, project, model_artifact_name
-
-
-def check_wandb_resume(opt):
-    process_wandb_config_ddp_mode(opt) if opt.global_rank not in [-1, 0] else None
-    if isinstance(opt.resume, str):
-        if opt.resume.startswith(WANDB_ARTIFACT_PREFIX):
-            if opt.global_rank not in [-1, 0]:  # For resuming DDP runs
-                run_id, project, model_artifact_name = get_run_info(opt.resume)
-                api = wandb.Api()
-                artifact = api.artifact(project + '/' + model_artifact_name + ':latest')
-                modeldir = artifact.download()
-                opt.weights = str(Path(modeldir) / "last.pt")
-            return True
-    return None
-
-
-def process_wandb_config_ddp_mode(opt):
-    with open(opt.data) as f:
-        data_dict = yaml.load(f, Loader=yaml.SafeLoader)  # data dict
-    train_dir, val_dir = None, None
-    if isinstance(data_dict['train'], str) and data_dict['train'].startswith(WANDB_ARTIFACT_PREFIX):
-        api = wandb.Api()
-        train_artifact = api.artifact(remove_prefix(data_dict['train']) + ':' + opt.artifact_alias)
-        train_dir = train_artifact.download()
-        train_path = Path(train_dir) / 'data/images/'
-        data_dict['train'] = str(train_path)
-
-    if isinstance(data_dict['val'], str) and data_dict['val'].startswith(WANDB_ARTIFACT_PREFIX):
-        api = wandb.Api()
-        val_artifact = api.artifact(remove_prefix(data_dict['val']) + ':' + opt.artifact_alias)
-        val_dir = val_artifact.download()
-        val_path = Path(val_dir) / 'data/images/'
-        data_dict['val'] = str(val_path)
-    if train_dir or val_dir:
-        ddp_data_path = str(Path(val_dir) / 'wandb_local_data.yaml')
-        with open(ddp_data_path, 'w') as f:
-            yaml.dump(data_dict, f)
-        opt.data = ddp_data_path
-
-
-class WandbLogger():
-    def __init__(self, opt, name, run_id, data_dict, job_type='Training'):
-        # Pre-training routine --
-        self.job_type = job_type
-        self.wandb, self.wandb_run, self.data_dict = wandb, None if not wandb else wandb.run, data_dict
-        # It's more elegant to stick to 1 wandb.init call, but useful config data is overwritten in the WandbLogger's wandb.init call
-        if isinstance(opt.resume, str):  # checks resume from artifact
-            if opt.resume.startswith(WANDB_ARTIFACT_PREFIX):
-                run_id, project, model_artifact_name = get_run_info(opt.resume)
-                model_artifact_name = WANDB_ARTIFACT_PREFIX + model_artifact_name
-                assert wandb, 'install wandb to resume wandb runs'
-                # Resume wandb-artifact:// runs here| workaround for not overwriting wandb.config
-                self.wandb_run = wandb.init(id=run_id, project=project, resume='allow')
-                opt.resume = model_artifact_name
-        elif self.wandb:
-            self.wandb_run = wandb.init(config=opt,
-                                        resume="allow",
-                                        project='YOLOR' if opt.project == 'runs/train' else Path(opt.project).stem,
-                                        name=name,
-                                        job_type=job_type,
-                                        id=run_id) if not wandb.run else wandb.run
-        if self.wandb_run:
-            if self.job_type == 'Training':
-                if not opt.resume:
-                    wandb_data_dict = self.check_and_upload_dataset(opt) if opt.upload_dataset else data_dict
-                    # Info useful for resuming from artifacts
-                    self.wandb_run.config.opt = vars(opt)
-                    self.wandb_run.config.data_dict = wandb_data_dict
-                self.data_dict = self.setup_training(opt, data_dict)
-            if self.job_type == 'Dataset Creation':
-                self.data_dict = self.check_and_upload_dataset(opt)
-        else:
-            prefix = colorstr('wandb: ')
-            print(f"{prefix}Install Weights & Biases for YOLOR logging with 'pip install wandb' (recommended)")
-
-    def check_and_upload_dataset(self, opt):
-        assert wandb, 'Install wandb to upload dataset'
-        check_dataset(self.data_dict)
-        config_path = self.log_dataset_artifact(opt.data,
-                                                opt.single_cls,
-                                                'YOLOR' if opt.project == 'runs/train' else Path(opt.project).stem)
-        print("Created dataset config file ", config_path)
-        with open(config_path) as f:
-            wandb_data_dict = yaml.load(f, Loader=yaml.SafeLoader)
-        return wandb_data_dict
-
-    def setup_training(self, opt, data_dict):
-        self.log_dict, self.current_epoch, self.log_imgs = {}, 0, 16  # Logging Constants
-        self.bbox_interval = opt.bbox_interval
-        if isinstance(opt.resume, str):
-            modeldir, _ = self.download_model_artifact(opt)
-            if modeldir:
-                self.weights = Path(modeldir) / "last.pt"
-                config = self.wandb_run.config
-                opt.weights, opt.save_period, opt.batch_size, opt.bbox_interval, opt.epochs, opt.hyp = str(
-                    self.weights), config.save_period, config.total_batch_size, config.bbox_interval, config.epochs, \
-                                                                                                       config.opt['hyp']
-            data_dict = dict(self.wandb_run.config.data_dict)  # eliminates the need for config file to resume
-        if 'val_artifact' not in self.__dict__:  # If --upload_dataset is set, use the existing artifact, don't download
-            self.train_artifact_path, self.train_artifact = self.download_dataset_artifact(data_dict.get('train'),
-                                                                                           opt.artifact_alias)
-            self.val_artifact_path, self.val_artifact = self.download_dataset_artifact(data_dict.get('val'),
-                                                                                       opt.artifact_alias)
-            self.result_artifact, self.result_table, self.val_table, self.weights = None, None, None, None
-            if self.train_artifact_path is not None:
-                train_path = Path(self.train_artifact_path) / 'data/images/'
-                data_dict['train'] = str(train_path)
-            if self.val_artifact_path is not None:
-                val_path = Path(self.val_artifact_path) / 'data/images/'
-                data_dict['val'] = str(val_path)
-                self.val_table = self.val_artifact.get("val")
-                self.map_val_table_path()
-        if self.val_artifact is not None:
-            self.result_artifact = wandb.Artifact("run_" + wandb.run.id + "_progress", "evaluation")
-            self.result_table = wandb.Table(["epoch", "id", "prediction", "avg_confidence"])
-        if opt.bbox_interval == -1:
-            self.bbox_interval = opt.bbox_interval = (opt.epochs // 10) if opt.epochs > 10 else 1
-        return data_dict
-
-    def download_dataset_artifact(self, path, alias):
-        if isinstance(path, str) and path.startswith(WANDB_ARTIFACT_PREFIX):
-            dataset_artifact = wandb.use_artifact(remove_prefix(path, WANDB_ARTIFACT_PREFIX) + ":" + alias)
-            assert dataset_artifact is not None, "'Error: W&B dataset artifact doesn\'t exist'"
-            datadir = dataset_artifact.download()
-            return datadir, dataset_artifact
-        return None, None
-
-    def download_model_artifact(self, opt):
-        if opt.resume.startswith(WANDB_ARTIFACT_PREFIX):
-            model_artifact = wandb.use_artifact(remove_prefix(opt.resume, WANDB_ARTIFACT_PREFIX) + ":latest")
-            assert model_artifact is not None, 'Error: W&B model artifact doesn\'t exist'
-            modeldir = model_artifact.download()
-            epochs_trained = model_artifact.metadata.get('epochs_trained')
-            total_epochs = model_artifact.metadata.get('total_epochs')
-            assert epochs_trained < total_epochs, 'training to %g epochs is finished, nothing to resume.' % (
-                total_epochs)
-            return modeldir, model_artifact
-        return None, None
-
-    def log_model(self, path, opt, epoch, fitness_score, best_model=False):
-        model_artifact = wandb.Artifact('run_' + wandb.run.id + '_model', type='model', metadata={
-            'original_url': str(path),
-            'epochs_trained': epoch + 1,
-            'save period': opt.save_period,
-            'project': opt.project,
-            'total_epochs': opt.epochs,
-            'fitness_score': fitness_score
-        })
-        model_artifact.add_file(str(path / 'last.pt'), name='last.pt')
-        wandb.log_artifact(model_artifact,
-                           aliases=['latest', 'epoch ' + str(self.current_epoch), 'best' if best_model else ''])
-        print("Saving model artifact on epoch ", epoch + 1)
-
-    def log_dataset_artifact(self, data_file, single_cls, project, overwrite_config=False):
-        with open(data_file) as f:
-            data = yaml.load(f, Loader=yaml.SafeLoader)  # data dict
-        nc, names = (1, ['item']) if single_cls else (int(data['nc']), data['names'])
-        names = {k: v for k, v in enumerate(names)}  # to index dictionary
-        self.train_artifact = self.create_dataset_table(LoadImagesAndLabels(
-            data['train']), names, name='train') if data.get('train') else None
-        self.val_artifact = self.create_dataset_table(LoadImagesAndLabels(
-            data['val']), names, name='val') if data.get('val') else None
-        if data.get('train'):
-            data['train'] = WANDB_ARTIFACT_PREFIX + str(Path(project) / 'train')
-        if data.get('val'):
-            data['val'] = WANDB_ARTIFACT_PREFIX + str(Path(project) / 'val')
-        path = data_file if overwrite_config else '_wandb.'.join(data_file.rsplit('.', 1))  # updated data.yaml path
-        data.pop('download', None)
-        with open(path, 'w') as f:
-            yaml.dump(data, f)
-
-        if self.job_type == 'Training':  # builds correct artifact pipeline graph
-            self.wandb_run.use_artifact(self.val_artifact)
-            self.wandb_run.use_artifact(self.train_artifact)
-            self.val_artifact.wait()
-            self.val_table = self.val_artifact.get('val')
-            self.map_val_table_path()
-        else:
-            self.wandb_run.log_artifact(self.train_artifact)
-            self.wandb_run.log_artifact(self.val_artifact)
-        return path
-
-    def map_val_table_path(self):
-        self.val_table_map = {}
-        print("Mapping dataset")
-        for i, data in enumerate(tqdm(self.val_table.data)):
-            self.val_table_map[data[3]] = data[0]
-
-    def create_dataset_table(self, dataset, class_to_id, name='dataset'):
-        # TODO: Explore multiprocessing to slpit this loop parallely| This is essential for speeding up the the logging
-        artifact = wandb.Artifact(name=name, type="dataset")
-        img_files = tqdm([dataset.path]) if isinstance(dataset.path, str) and Path(dataset.path).is_dir() else None
-        img_files = tqdm(dataset.img_files) if not img_files else img_files
-        for img_file in img_files:
-            if Path(img_file).is_dir():
-                artifact.add_dir(img_file, name='data/images')
-                labels_path = 'labels'.join(dataset.path.rsplit('images', 1))
-                artifact.add_dir(labels_path, name='data/labels')
-            else:
-                artifact.add_file(img_file, name='data/images/' + Path(img_file).name)
-                label_file = Path(img2label_paths([img_file])[0])
-                artifact.add_file(str(label_file),
-                                  name='data/labels/' + label_file.name) if label_file.exists() else None
-        table = wandb.Table(columns=["id", "train_image", "Classes", "name"])
-        class_set = wandb.Classes([{'id': id, 'name': name} for id, name in class_to_id.items()])
-        for si, (img, labels, paths, shapes) in enumerate(tqdm(dataset)):
-            height, width = shapes[0]
-            labels[:, 2:] = (xywh2xyxy(labels[:, 2:].view(-1, 4))) * torch.Tensor([width, height, width, height])
-            box_data, img_classes = [], {}
-            for cls, *xyxy in labels[:, 1:].tolist():
-                cls = int(cls)
-                box_data.append({"position": {"minX": xyxy[0], "minY": xyxy[1], "maxX": xyxy[2], "maxY": xyxy[3]},
-                                 "class_id": cls,
-                                 "box_caption": "%s" % (class_to_id[cls]),
-                                 "scores": {"acc": 1},
-                                 "domain": "pixel"})
-                img_classes[cls] = class_to_id[cls]
-            boxes = {"ground_truth": {"box_data": box_data, "class_labels": class_to_id}}  # inference-space
-            table.add_data(si, wandb.Image(paths, classes=class_set, boxes=boxes), json.dumps(img_classes),
-                           Path(paths).name)
-        artifact.add(table, name)
-        return artifact
-
-    def log_training_progress(self, predn, path, names):
-        if self.val_table and self.result_table:
-            class_set = wandb.Classes([{'id': id, 'name': name} for id, name in names.items()])
-            box_data = []
-            total_conf = 0
-            for *xyxy, conf, cls in predn.tolist():
-                if conf >= 0.25:
-                    box_data.append(
-                        {"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"})
-                    total_conf = total_conf + conf
-            boxes = {"predictions": {"box_data": box_data, "class_labels": names}}  # inference-space
-            id = self.val_table_map[Path(path).name]
-            self.result_table.add_data(self.current_epoch,
-                                       id,
-                                       wandb.Image(self.val_table.data[id][1], boxes=boxes, classes=class_set),
-                                       total_conf / max(1, len(box_data))
-                                       )
-
-    def log(self, log_dict):
-        if self.wandb_run:
-            for key, value in log_dict.items():
-                self.log_dict[key] = value
-
-    def end_epoch(self, best_result=False):
-        if self.wandb_run:
-            wandb.log(self.log_dict)
-            self.log_dict = {}
-            if self.result_artifact:
-                train_results = wandb.JoinedTable(self.val_table, self.result_table, "id")
-                self.result_artifact.add(train_results, 'result')
-                wandb.log_artifact(self.result_artifact, aliases=['latest', 'epoch ' + str(self.current_epoch),
-                                                                  ('best' if best_result else '')])
-                self.result_table = wandb.Table(["epoch", "id", "prediction", "avg_confidence"])
-                self.result_artifact = wandb.Artifact("run_" + wandb.run.id + "_progress", "evaluation")
-
-    def finish_run(self):
-        if self.wandb_run:
-            if self.log_dict:
-                wandb.log(self.log_dict)
-            wandb.run.finish()