Utils reorganization (#1392)

* Utils reorganization

* Add new utils files

* cleanup

* simplify

* reduce datasets.py

* remove evolve.sh

* loadWebcam cleanup

detect.py

@@ -10,14 +10,15 @@ from numpy import random
 from models.experimental import attempt_load
 from utils.datasets import LoadStreams, LoadImages
 from utils.general import check_img_size, non_max_suppression, apply_classifier, scale_coords, xyxy2xywh, \
-    plot_one_box, strip_optimizer, set_logging, increment_path
+    strip_optimizer, set_logging, increment_path
+from utils.plots import plot_one_box
 from utils.torch_utils import select_device, load_classifier, time_synchronized
 
 
 def detect(save_img=False):
     source, weights, view_img, save_txt, imgsz = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size
-    webcam = source.isnumeric() or source.endswith('.txt') or \
-             source.lower().startswith(('rtsp://', 'rtmp://', 'http://'))
+    webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(
+        ('rtsp://', 'rtmp://', 'http://'))
 
     # Directories
     save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok))  # increment run
@@ -38,8 +39,7 @@ def detect(save_img=False):
     classify = False
     if classify:
         modelc = load_classifier(name='resnet101', n=2)  # initialize
-        modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model'])  # load weights
-        modelc.to(device).eval()
+        modelc.load_state_dict(torch.load('weights/resnet101.pt', map_location=device)['model']).to(device).eval()
 
     # Set Dataloader
     vid_path, vid_writer = None, None
@@ -53,7 +53,7 @@ def detect(save_img=False):
 
     # Get names and colors
     names = model.module.names if hasattr(model, 'module') else model.names
-    colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(len(names))]
+    colors = [[random.randint(0, 255) for _ in range(3)] for _ in names]
 
     # Run inference
     t0 = time.time()

models/yolo.py

@@ -13,10 +13,16 @@ import torch.nn as nn
 
 from models.common import Conv, Bottleneck, SPP, DWConv, Focus, BottleneckCSP, Concat, NMS, autoShape
 from models.experimental import MixConv2d, CrossConv, C3
-from utils.general import check_anchor_order, make_divisible, check_file, set_logging
+from utils.autoanchor import check_anchor_order
+from utils.general import make_divisible, check_file, set_logging
 from utils.torch_utils import time_synchronized, fuse_conv_and_bn, model_info, scale_img, initialize_weights, \
     select_device, copy_attr
 
+try:
+    import thop  # for FLOPS computation
+except ImportError:
+    thop = None
+
 
 class Detect(nn.Module):
     stride = None  # strides computed during build
@@ -121,11 +127,7 @@ class Model(nn.Module):
                 x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f]  # from earlier layers
 
             if profile:
-                try:
-                    import thop
-                    o = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2  # FLOPS
-                except:
-                    o = 0
+                o = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 * 2 if thop else 0  # FLOPS
                 t = time_synchronized()
                 for _ in range(10):
                     _ = m(x)

test.py

@@ -11,9 +11,11 @@ from tqdm import tqdm
 
 from models.experimental import attempt_load
 from utils.datasets import create_dataloader
-from utils.general import coco80_to_coco91_class, check_dataset, check_file, check_img_size, compute_loss, \
-    non_max_suppression, scale_coords, xyxy2xywh, clip_coords, plot_images, xywh2xyxy, box_iou, output_to_target, \
-    ap_per_class, set_logging, increment_path
+from utils.general import coco80_to_coco91_class, check_dataset, check_file, check_img_size, box_iou, \
+    non_max_suppression, scale_coords, xyxy2xywh, xywh2xyxy, clip_coords, set_logging, increment_path
+from utils.loss import compute_loss
+from utils.metrics import ap_per_class
+from utils.plots import plot_images, output_to_target
 from utils.torch_utils import select_device, time_synchronized
 
 

train.py

@@ -3,7 +3,6 @@ import logging
 import math
 import os
 import random
-import shutil
 import time
 from pathlib import Path
 from warnings import warn
@@ -23,13 +22,15 @@ from tqdm import tqdm
 
 import test  # import test.py to get mAP after each epoch
 from models.yolo import Model
+from utils.autoanchor import check_anchors
 from utils.datasets import create_dataloader
-from utils.general import (
-    torch_distributed_zero_first, labels_to_class_weights, plot_labels, check_anchors, labels_to_image_weights,
-    compute_loss, plot_images, fitness, strip_optimizer, plot_results, get_latest_run, check_dataset, check_file,
-    check_git_status, check_img_size, increment_path, print_mutation, plot_evolution, set_logging, init_seeds)
+from utils.general import labels_to_class_weights, increment_path, labels_to_image_weights, init_seeds, \
+    fitness, strip_optimizer, get_latest_run, check_dataset, check_file, check_git_status, check_img_size, \
+    print_mutation, set_logging
 from utils.google_utils import attempt_download
-from utils.torch_utils import ModelEMA, select_device, intersect_dicts
+from utils.loss import compute_loss
+from utils.plots import plot_images, plot_labels, plot_results, plot_evolution
+from utils.torch_utils import ModelEMA, select_device, intersect_dicts, torch_distributed_zero_first
 
 logger = logging.getLogger(__name__)
 
@@ -209,7 +210,7 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
 
     # Start training
     t0 = time.time()
-    nw = max(round(hyp['warmup_epochs'] * nb), 1e3)  # number of warmup iterations, max(3 epochs, 1k iterations)
+    nw = max(round(hyp['warmup_epochs'] * nb), 1000)  # number of warmup iterations, max(3 epochs, 1k iterations)
     # nw = min(nw, (epochs - start_epoch) / 2 * nb)  # limit warmup to < 1/2 of training
     maps = np.zeros(nc)  # mAP per class
     results = (0, 0, 0, 0, 0, 0, 0)  # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls)
@@ -334,9 +335,9 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
                 os.system('gsutil cp %s gs://%s/results/results%s.txt' % (results_file, opt.bucket, opt.name))
 
             # Log
-            tags = ['train/giou_loss', 'train/obj_loss', 'train/cls_loss',  # train loss
+            tags = ['train/box_loss', 'train/obj_loss', 'train/cls_loss',  # train loss
                     'metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/mAP_0.5:0.95',
-                    'val/giou_loss', 'val/obj_loss', 'val/cls_loss',  # val loss
+                    'val/box_loss', 'val/obj_loss', 'val/cls_loss',  # val loss
                     'x/lr0', 'x/lr1', 'x/lr2']  # params
             for x, tag in zip(list(mloss[:-1]) + list(results) + lr, tags):
                 if tb_writer:

utils/activations.py

@@ -1,3 +1,5 @@
+# Activation functions
+
 import torch
 import torch.nn as nn
 import torch.nn.functional as F

utils/autoanchor.py

@@ -0,0 +1,152 @@
+# Auto-anchor utils
+
+import numpy as np
+import torch
+import yaml
+from scipy.cluster.vq import kmeans
+from tqdm import tqdm
+
+
+def check_anchor_order(m):
+    # Check anchor order against stride order for YOLOv5 Detect() module m, and correct if necessary
+    a = m.anchor_grid.prod(-1).view(-1)  # anchor area
+    da = a[-1] - a[0]  # delta a
+    ds = m.stride[-1] - m.stride[0]  # delta s
+    if da.sign() != ds.sign():  # same order
+        print('Reversing anchor order')
+        m.anchors[:] = m.anchors.flip(0)
+        m.anchor_grid[:] = m.anchor_grid.flip(0)
+
+
+def check_anchors(dataset, model, thr=4.0, imgsz=640):
+    # Check anchor fit to data, recompute if necessary
+    print('\nAnalyzing anchors... ', end='')
+    m = model.module.model[-1] if hasattr(model, 'module') else model.model[-1]  # Detect()
+    shapes = imgsz * dataset.shapes / dataset.shapes.max(1, keepdims=True)
+    scale = np.random.uniform(0.9, 1.1, size=(shapes.shape[0], 1))  # augment scale
+    wh = torch.tensor(np.concatenate([l[:, 3:5] * s for s, l in zip(shapes * scale, dataset.labels)])).float()  # wh
+
+    def metric(k):  # compute metric
+        r = wh[:, None] / k[None]
+        x = torch.min(r, 1. / r).min(2)[0]  # ratio metric
+        best = x.max(1)[0]  # best_x
+        aat = (x > 1. / thr).float().sum(1).mean()  # anchors above threshold
+        bpr = (best > 1. / thr).float().mean()  # best possible recall
+        return bpr, aat
+
+    bpr, aat = metric(m.anchor_grid.clone().cpu().view(-1, 2))
+    print('anchors/target = %.2f, Best Possible Recall (BPR) = %.4f' % (aat, bpr), end='')
+    if bpr < 0.98:  # threshold to recompute
+        print('. Attempting to improve anchors, please wait...')
+        na = m.anchor_grid.numel() // 2  # number of anchors
+        new_anchors = kmean_anchors(dataset, n=na, img_size=imgsz, thr=thr, gen=1000, verbose=False)
+        new_bpr = metric(new_anchors.reshape(-1, 2))[0]
+        if new_bpr > bpr:  # replace anchors
+            new_anchors = torch.tensor(new_anchors, device=m.anchors.device).type_as(m.anchors)
+            m.anchor_grid[:] = new_anchors.clone().view_as(m.anchor_grid)  # for inference
+            m.anchors[:] = new_anchors.clone().view_as(m.anchors) / m.stride.to(m.anchors.device).view(-1, 1, 1)  # loss
+            check_anchor_order(m)
+            print('New anchors saved to model. Update model *.yaml to use these anchors in the future.')
+        else:
+            print('Original anchors better than new anchors. Proceeding with original anchors.')
+    print('')  # newline
+
+
+def kmean_anchors(path='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen=1000, verbose=True):
+    """ Creates kmeans-evolved anchors from training dataset
+
+        Arguments:
+            path: path to dataset *.yaml, or a loaded dataset
+            n: number of anchors
+            img_size: image size used for training
+            thr: anchor-label wh ratio threshold hyperparameter hyp['anchor_t'] used for training, default=4.0
+            gen: generations to evolve anchors using genetic algorithm
+            verbose: print all results
+
+        Return:
+            k: kmeans evolved anchors
+
+        Usage:
+            from utils.general import *; _ = kmean_anchors()
+    """
+    thr = 1. / thr
+
+    def metric(k, wh):  # compute metrics
+        r = wh[:, None] / k[None]
+        x = torch.min(r, 1. / r).min(2)[0]  # ratio metric
+        # x = wh_iou(wh, torch.tensor(k))  # iou metric
+        return x, x.max(1)[0]  # x, best_x
+
+    def anchor_fitness(k):  # mutation fitness
+        _, best = metric(torch.tensor(k, dtype=torch.float32), wh)
+        return (best * (best > thr).float()).mean()  # fitness
+
+    def print_results(k):
+        k = k[np.argsort(k.prod(1))]  # sort small to large
+        x, best = metric(k, wh0)
+        bpr, aat = (best > thr).float().mean(), (x > thr).float().mean() * n  # best possible recall, anch > thr
+        print('thr=%.2f: %.4f best possible recall, %.2f anchors past thr' % (thr, bpr, aat))
+        print('n=%g, img_size=%s, metric_all=%.3f/%.3f-mean/best, past_thr=%.3f-mean: ' %
+              (n, img_size, x.mean(), best.mean(), x[x > thr].mean()), end='')
+        for i, x in enumerate(k):
+            print('%i,%i' % (round(x[0]), round(x[1])), end=',  ' if i < len(k) - 1 else '\n')  # use in *.cfg
+        return k
+
+    if isinstance(path, str):  # *.yaml file
+        with open(path) as f:
+            data_dict = yaml.load(f, Loader=yaml.FullLoader)  # model dict
+        from utils.datasets import LoadImagesAndLabels
+        dataset = LoadImagesAndLabels(data_dict['train'], augment=True, rect=True)
+    else:
+        dataset = path  # dataset
+
+    # Get label wh
+    shapes = img_size * dataset.shapes / dataset.shapes.max(1, keepdims=True)
+    wh0 = np.concatenate([l[:, 3:5] * s for s, l in zip(shapes, dataset.labels)])  # wh
+
+    # Filter
+    i = (wh0 < 3.0).any(1).sum()
+    if i:
+        print('WARNING: Extremely small objects found. '
+              '%g of %g labels are < 3 pixels in width or height.' % (i, len(wh0)))
+    wh = wh0[(wh0 >= 2.0).any(1)]  # filter > 2 pixels
+
+    # Kmeans calculation
+    print('Running kmeans for %g anchors on %g points...' % (n, len(wh)))
+    s = wh.std(0)  # sigmas for whitening
+    k, dist = kmeans(wh / s, n, iter=30)  # points, mean distance
+    k *= s
+    wh = torch.tensor(wh, dtype=torch.float32)  # filtered
+    wh0 = torch.tensor(wh0, dtype=torch.float32)  # unfiltered
+    k = print_results(k)
+
+    # Plot
+    # k, d = [None] * 20, [None] * 20
+    # for i in tqdm(range(1, 21)):
+    #     k[i-1], d[i-1] = kmeans(wh / s, i)  # points, mean distance
+    # fig, ax = plt.subplots(1, 2, figsize=(14, 7))
+    # ax = ax.ravel()
+    # ax[0].plot(np.arange(1, 21), np.array(d) ** 2, marker='.')
+    # fig, ax = plt.subplots(1, 2, figsize=(14, 7))  # plot wh
+    # ax[0].hist(wh[wh[:, 0]<100, 0],400)
+    # ax[1].hist(wh[wh[:, 1]<100, 1],400)
+    # fig.tight_layout()
+    # fig.savefig('wh.png', dpi=200)
+
+    # Evolve
+    npr = np.random
+    f, sh, mp, s = anchor_fitness(k), k.shape, 0.9, 0.1  # fitness, generations, mutation prob, sigma
+    pbar = tqdm(range(gen), desc='Evolving anchors with Genetic Algorithm')  # progress bar
+    for _ in pbar:
+        v = np.ones(sh)
+        while (v == 1).all():  # mutate until a change occurs (prevent duplicates)
+            v = ((npr.random(sh) < mp) * npr.random() * npr.randn(*sh) * s + 1).clip(0.3, 3.0)
+        kg = (k.copy() * v).clip(min=2.0)
+        fg = anchor_fitness(kg)
+        if fg > f:
+            f, k = fg, kg.copy()
+            pbar.desc = 'Evolving anchors with Genetic Algorithm: fitness = %.4f' % f
+            if verbose:
+                print_results(k)
+
+    return print_results(k)

utils/datasets.py

@@ -1,3 +1,5 @@
+# Dataset utils and dataloaders
+
 import glob
 import math
 import os
@@ -16,8 +18,10 @@ from PIL import Image, ExifTags
 from torch.utils.data import Dataset
 from tqdm import tqdm
 
-from utils.general import xyxy2xywh, xywh2xyxy, torch_distributed_zero_first
+from utils.general import xyxy2xywh, xywh2xyxy
+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']
 vid_formats = ['.mov', '.avi', '.mp4', '.mpg', '.mpeg', '.m4v', '.wmv', '.mkv']
@@ -50,7 +54,7 @@ def exif_size(img):
 
 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):
-    # Make sure only the first process in DDP process the dataset first, and the following others can use the cache.
+    # 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
@@ -75,9 +79,9 @@ def create_dataloader(path, imgsz, batch_size, stride, opt, hyp=None, augment=Fa
 
 
 class InfiniteDataLoader(torch.utils.data.dataloader.DataLoader):
-    """ Dataloader that reuses workers.
+    """ Dataloader that reuses workers
 
-    Uses same syntax as vanilla DataLoader.
+    Uses same syntax as vanilla DataLoader
     """
 
     def __init__(self, *args, **kwargs):
@@ -94,7 +98,7 @@ class InfiniteDataLoader(torch.utils.data.dataloader.DataLoader):
 
 
 class _RepeatSampler(object):
-    """ Sampler that repeats forever.
+    """ Sampler that repeats forever
 
     Args:
         sampler (Sampler)
@@ -177,7 +181,6 @@ class LoadImages:  # for inference
         img = img[:, :, ::-1].transpose(2, 0, 1)  # BGR to RGB, to 3x416x416
         img = np.ascontiguousarray(img)
 
-        # cv2.imwrite(path + '.letterbox.jpg', 255 * img.transpose((1, 2, 0))[:, :, ::-1])  # save letterbox image
         return path, img, img0, self.cap
 
     def new_video(self, path):
@@ -190,23 +193,15 @@ class LoadImages:  # for inference
 
 
 class LoadWebcam:  # for inference
-    def __init__(self, pipe=0, img_size=640):
+    def __init__(self, pipe='0', img_size=640):
         self.img_size = img_size
 
-        if pipe == '0':
-            pipe = 0  # local camera
+        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 = 'rtsp://170.93.143.139/rtplive/470011e600ef003a004ee33696235daa'  # IP traffic camera
         # pipe = 'http://wmccpinetop.axiscam.net/mjpg/video.mjpg'  # IP golf camera
 
-        # https://answers.opencv.org/question/215996/changing-gstreamer-pipeline-to-opencv-in-pythonsolved/
-        # pipe = '"rtspsrc location="rtsp://username:password@192.168.1.64/1" latency=10 ! appsink'  # GStreamer
-
-        # https://answers.opencv.org/question/200787/video-acceleration-gstremer-pipeline-in-videocapture/
-        # https://stackoverflow.com/questions/54095699/install-gstreamer-support-for-opencv-python-package  # install help
-        # pipe = "rtspsrc location=rtsp://root:root@192.168.0.91:554/axis-media/media.amp?videocodec=h264&resolution=3840x2160 protocols=GST_RTSP_LOWER_TRANS_TCP ! rtph264depay ! queue ! vaapih264dec ! videoconvert ! appsink"  # GStreamer
-
         self.pipe = pipe
         self.cap = cv2.VideoCapture(pipe)  # video capture object
         self.cap.set(cv2.CAP_PROP_BUFFERSIZE, 3)  # set buffer size
@@ -895,52 +890,6 @@ def cutout(image, labels):
     return labels
 
 
-def reduce_img_size(path='path/images', img_size=1024):  # from utils.datasets import *; reduce_img_size()
-    # creates a new ./images_reduced folder with reduced size images of maximum size img_size
-    path_new = path + '_reduced'  # reduced images path
-    create_folder(path_new)
-    for f in tqdm(glob.glob('%s/*.*' % path)):
-        try:
-            img = cv2.imread(f)
-            h, w = img.shape[:2]
-            r = img_size / max(h, w)  # size ratio
-            if r < 1.0:
-                img = cv2.resize(img, (int(w * r), int(h * r)), interpolation=cv2.INTER_AREA)  # _LINEAR fastest
-            fnew = f.replace(path, path_new)  # .replace(Path(f).suffix, '.jpg')
-            cv2.imwrite(fnew, img)
-        except:
-            print('WARNING: image failure %s' % f)
-
-
-def recursive_dataset2bmp(dataset='path/dataset_bmp'):  # from utils.datasets import *; recursive_dataset2bmp()
-    # Converts dataset to bmp (for faster training)
-    formats = [x.lower() for x in img_formats] + [x.upper() for x in img_formats]
-    for a, b, files in os.walk(dataset):
-        for file in tqdm(files, desc=a):
-            p = a + '/' + file
-            s = Path(file).suffix
-            if s == '.txt':  # replace text
-                with open(p, 'r') as f:
-                    lines = f.read()
-                for f in formats:
-                    lines = lines.replace(f, '.bmp')
-                with open(p, 'w') as f:
-                    f.write(lines)
-            elif s in formats:  # replace image
-                cv2.imwrite(p.replace(s, '.bmp'), cv2.imread(p))
-                if s != '.bmp':
-                    os.system("rm '%s'" % p)
-
-
-def imagelist2folder(path='path/images.txt'):  # from utils.datasets import *; imagelist2folder()
-    # Copies all the images in a text file (list of images) into a folder
-    create_folder(path[:-4])
-    with open(path, 'r') as f:
-        for line in f.read().splitlines():
-            os.system('cp "%s" %s' % (line, path[:-4]))
-            print(line)
-
-
 def create_folder(path='./new'):
     # Create folder
     if os.path.exists(path):

utils/evolve.sh

@@ -1,15 +0,0 @@
-#!/bin/bash
-# Hyperparameter evolution commands (avoids CUDA memory leakage issues)
-# Replaces train.py python generations 'for' loop with a bash 'for' loop
-
-# Start on 4-GPU machine
-#for i in 0 1 2 3; do
-#  t=ultralytics/yolov5:evolve && sudo docker pull $t && sudo docker run -d --ipc=host --gpus all -v "$(pwd)"/VOC:/usr/src/VOC $t bash utils/evolve.sh $i
-#  sleep 60 # avoid simultaneous evolve.txt read/write
-#done
-
-# Hyperparameter evolution commands
-while true; do
-  # python train.py --batch 64 --weights yolov5m.pt --data voc.yaml --img 512 --epochs 50 --evolve --bucket ult/evolve/voc --device $1
-  python train.py --batch 40 --weights yolov5m.pt --data coco.yaml --img 640 --epochs 30 --evolve --bucket ult/evolve/coco --device $1
-done

utils/general.py

@@ -1,3 +1,5 @@
+# General utils
+
 import glob
 import logging
 import math
@@ -5,27 +7,19 @@ import os
 import platform
 import random
 import re
-import shutil
 import subprocess
 import time
-from contextlib import contextmanager
-from copy import copy
 from pathlib import Path
 
 import cv2
 import matplotlib
-import matplotlib.pyplot as plt
 import numpy as np
 import torch
-import torch.nn as nn
 import yaml
-from PIL import Image
-from scipy.cluster.vq import kmeans
-from scipy.signal import butter, filtfilt
-from tqdm import tqdm
 
 from utils.google_utils import gsutil_getsize
-from utils.torch_utils import is_parallel, init_torch_seeds
+from utils.metrics import fitness
+from utils.torch_utils import init_torch_seeds
 
 # Set printoptions
 torch.set_printoptions(linewidth=320, precision=5, profile='long')
@@ -36,18 +30,6 @@ matplotlib.rc('font', **{'size': 11})
 cv2.setNumThreads(0)
 
 
-@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 set_logging(rank=-1):
     logging.basicConfig(
         format="%(message)s",
@@ -82,51 +64,6 @@ def check_img_size(img_size, s=32):
     return new_size
 
 
-def check_anchors(dataset, model, thr=4.0, imgsz=640):
-    # Check anchor fit to data, recompute if necessary
-    print('\nAnalyzing anchors... ', end='')
-    m = model.module.model[-1] if hasattr(model, 'module') else model.model[-1]  # Detect()
-    shapes = imgsz * dataset.shapes / dataset.shapes.max(1, keepdims=True)
-    scale = np.random.uniform(0.9, 1.1, size=(shapes.shape[0], 1))  # augment scale
-    wh = torch.tensor(np.concatenate([l[:, 3:5] * s for s, l in zip(shapes * scale, dataset.labels)])).float()  # wh
-
-    def metric(k):  # compute metric
-        r = wh[:, None] / k[None]
-        x = torch.min(r, 1. / r).min(2)[0]  # ratio metric
-        best = x.max(1)[0]  # best_x
-        aat = (x > 1. / thr).float().sum(1).mean()  # anchors above threshold
-        bpr = (best > 1. / thr).float().mean()  # best possible recall
-        return bpr, aat
-
-    bpr, aat = metric(m.anchor_grid.clone().cpu().view(-1, 2))
-    print('anchors/target = %.2f, Best Possible Recall (BPR) = %.4f' % (aat, bpr), end='')
-    if bpr < 0.98:  # threshold to recompute
-        print('. Attempting to generate improved anchors, please wait...' % bpr)
-        na = m.anchor_grid.numel() // 2  # number of anchors
-        new_anchors = kmean_anchors(dataset, n=na, img_size=imgsz, thr=thr, gen=1000, verbose=False)
-        new_bpr = metric(new_anchors.reshape(-1, 2))[0]
-        if new_bpr > bpr:  # replace anchors
-            new_anchors = torch.tensor(new_anchors, device=m.anchors.device).type_as(m.anchors)
-            m.anchor_grid[:] = new_anchors.clone().view_as(m.anchor_grid)  # for inference
-            m.anchors[:] = new_anchors.clone().view_as(m.anchors) / m.stride.to(m.anchors.device).view(-1, 1, 1)  # loss
-            check_anchor_order(m)
-            print('New anchors saved to model. Update model *.yaml to use these anchors in the future.')
-        else:
-            print('Original anchors better than new anchors. Proceeding with original anchors.')
-    print('')  # newline
-
-
-def check_anchor_order(m):
-    # Check anchor order against stride order for YOLOv5 Detect() module m, and correct if necessary
-    a = m.anchor_grid.prod(-1).view(-1)  # anchor area
-    da = a[-1] - a[0]  # delta a
-    ds = m.stride[-1] - m.stride[0]  # delta s
-    if da.sign() != ds.sign():  # same order
-        print('Reversing anchor order')
-        m.anchors[:] = m.anchors.flip(0)
-        m.anchor_grid[:] = m.anchor_grid.flip(0)
-
-
 def check_file(file):
     # Search for file if not found
     if os.path.isfile(file) or file == '':
@@ -139,7 +76,7 @@ def check_file(file):
 
 
 def check_dataset(dict):
-    # Download dataset if not found
+    # Download dataset if not found locally
     val, s = dict.get('val'), dict.get('download')
     if val and len(val):
         val = [os.path.abspath(x) for x in (val if isinstance(val, list) else [val])]  # val path
@@ -247,106 +184,6 @@ def clip_coords(boxes, img_shape):
     boxes[:, 3].clamp_(0, img_shape[0])  # y2
 
 
-def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, fname='precision-recall_curve.png'):
-    """ Compute the average precision, given the recall and precision curves.
-    Source: https://github.com/rafaelpadilla/Object-Detection-Metrics.
-    # Arguments
-        tp:  True positives (nparray, nx1 or nx10).
-        conf:  Objectness value from 0-1 (nparray).
-        pred_cls:  Predicted object classes (nparray).
-        target_cls:  True object classes (nparray).
-        plot:  Plot precision-recall curve at mAP@0.5
-        fname:  Plot filename
-    # Returns
-        The average precision as computed in py-faster-rcnn.
-    """
-
-    # Sort by objectness
-    i = np.argsort(-conf)
-    tp, conf, pred_cls = tp[i], conf[i], pred_cls[i]
-
-    # Find unique classes
-    unique_classes = np.unique(target_cls)
-
-    # Create Precision-Recall curve and compute AP for each class
-    px, py = np.linspace(0, 1, 1000), []  # for plotting
-    pr_score = 0.1  # score to evaluate P and R https://github.com/ultralytics/yolov3/issues/898
-    s = [unique_classes.shape[0], tp.shape[1]]  # number class, number iou thresholds (i.e. 10 for mAP0.5...0.95)
-    ap, p, r = np.zeros(s), np.zeros(s), np.zeros(s)
-    for ci, c in enumerate(unique_classes):
-        i = pred_cls == c
-        n_l = (target_cls == c).sum()  # number of labels
-        n_p = i.sum()  # number of predictions
-
-        if n_p == 0 or n_l == 0:
-            continue
-        else:
-            # Accumulate FPs and TPs
-            fpc = (1 - tp[i]).cumsum(0)
-            tpc = tp[i].cumsum(0)
-
-            # Recall
-            recall = tpc / (n_l + 1e-16)  # recall curve
-            r[ci] = np.interp(-pr_score, -conf[i], recall[:, 0])  # r at pr_score, negative x, xp because xp decreases
-
-            # Precision
-            precision = tpc / (tpc + fpc)  # precision curve
-            p[ci] = np.interp(-pr_score, -conf[i], precision[:, 0])  # p at pr_score
-
-            # AP from recall-precision curve
-            for j in range(tp.shape[1]):
-                ap[ci, j], mpre, mrec = compute_ap(recall[:, j], precision[:, j])
-                if j == 0:
-                    py.append(np.interp(px, mrec, mpre))  # precision at mAP@0.5
-
-    # Compute F1 score (harmonic mean of precision and recall)
-    f1 = 2 * p * r / (p + r + 1e-16)
-
-    if plot:
-        py = np.stack(py, axis=1)
-        fig, ax = plt.subplots(1, 1, figsize=(5, 5))
-        ax.plot(px, py, linewidth=0.5, color='grey')  # plot(recall, precision)
-        ax.plot(px, py.mean(1), linewidth=2, color='blue', label='all classes %.3f mAP@0.5' % ap[:, 0].mean())
-        ax.set_xlabel('Recall')
-        ax.set_ylabel('Precision')
-        ax.set_xlim(0, 1)
-        ax.set_ylim(0, 1)
-        plt.legend()
-        fig.tight_layout()
-        fig.savefig(fname, dpi=200)
-
-    return p, r, ap, f1, unique_classes.astype('int32')
-
-
-def compute_ap(recall, precision):
-    """ Compute the average precision, given the recall and precision curves.
-    Source: https://github.com/rbgirshick/py-faster-rcnn.
-    # Arguments
-        recall:    The recall curve (list).
-        precision: The precision curve (list).
-    # Returns
-        The average precision as computed in py-faster-rcnn.
-    """
-
-    # Append sentinel values to beginning and end
-    mrec = recall  # np.concatenate(([0.], recall, [recall[-1] + 1E-3]))
-    mpre = precision  # np.concatenate(([0.], precision, [0.]))
-
-    # Compute the precision envelope
-    mpre = np.flip(np.maximum.accumulate(np.flip(mpre)))
-
-    # Integrate area under curve
-    method = 'interp'  # methods: 'continuous', 'interp'
-    if method == 'interp':
-        x = np.linspace(0, 1, 101)  # 101-point interp (COCO)
-        ap = np.trapz(np.interp(x, mrec, mpre), x)  # integrate
-    else:  # 'continuous'
-        i = np.where(mrec[1:] != mrec[:-1])[0]  # points where x axis (recall) changes
-        ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])  # area under curve
-
-    return ap, mpre, mrec
-
-
 def bbox_iou(box1, box2, x1y1x2y2=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-9):
     # Returns the IoU of box1 to box2. box1 is 4, box2 is nx4
     box2 = box2.T
@@ -425,178 +262,6 @@ def wh_iou(wh1, wh2):
     return inter / (wh1.prod(2) + wh2.prod(2) - inter)  # iou = inter / (area1 + area2 - inter)
 
 
-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
-
-
-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()
-
-
-def compute_loss(p, targets, model):  # 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 = build_targets(p, targets, model)  # targets
-    h = model.hyp  # hyperparameters
-
-    # Define criteria
-    BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.Tensor([h['cls_pw']])).to(device)
-    BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.Tensor([h['obj_pw']])).to(device)
-
-    # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3
-    cp, cn = smooth_BCE(eps=0.0)
-
-    # Focal loss
-    g = h['fl_gamma']  # focal loss gamma
-    if g > 0:
-        BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g)
-
-    # Losses
-    nt = 0  # number of targets
-    np = len(p)  # number of outputs
-    balance = [4.0, 1.0, 0.4] if np == 3 else [4.0, 1.0, 0.4, 0.1]  # P3-5 or P3-6
-    for i, pi in enumerate(p):  # layer index, layer predictions
-        b, a, gj, gi = indices[i]  # image, anchor, gridy, gridx
-        tobj = torch.zeros_like(pi[..., 0], device=device)  # target obj
-
-        n = b.shape[0]  # number of targets
-        if n:
-            nt += n  # cumulative targets
-            ps = pi[b, a, gj, gi]  # prediction subset corresponding to targets
-
-            # Regression
-            pxy = ps[:, :2].sigmoid() * 2. - 0.5
-            pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i]
-            pbox = torch.cat((pxy, pwh), 1).to(device)  # 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 - model.gr) + model.gr * iou.detach().clamp(0).type(tobj.dtype)  # iou ratio
-
-            # Classification
-            if model.nc > 1:  # cls loss (only if multiple classes)
-                t = torch.full_like(ps[:, 5:], cn, device=device)  # targets
-                t[range(n), tcls[i]] = cp
-                lcls += BCEcls(ps[:, 5:], t)  # BCE
-
-            # Append targets to text file
-            # with open('targets.txt', 'a') as file:
-            #     [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)]
-
-        lobj += BCEobj(pi[..., 4], tobj) * balance[i]  # obj loss
-
-    s = 3 / np  # output count scaling
-    lbox *= h['box'] * s
-    lobj *= h['obj'] * s * (1.4 if np == 4 else 1.)
-    lcls *= h['cls'] * s
-    bs = tobj.shape[0]  # batch size
-
-    loss = lbox + lobj + lcls
-    return loss * bs, torch.cat((lbox, lobj, lcls, loss)).detach()
-
-
-def build_targets(p, targets, model):
-    # Build targets for compute_loss(), input targets(image,class,x,y,w,h)
-    det = model.module.model[-1] if is_parallel(model) else model.model[-1]  # Detect() module
-    na, nt = det.na, targets.shape[0]  # number of anchors, targets
-    tcls, tbox, indices, anch = [], [], [], []
-    gain = torch.ones(7, device=targets.device)  # 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(det.nl):
-        anchors = det.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] < model.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
-
-
 def non_max_suppression(prediction, conf_thres=0.1, iou_thres=0.6, merge=False, classes=None, agnostic=False):
     """Performs Non-Maximum Suppression (NMS) on inference results
 
@@ -662,15 +327,12 @@ def non_max_suppression(prediction, conf_thres=0.1, iou_thres=0.6, merge=False,
         if i.shape[0] > max_det:  # limit detections
             i = i[:max_det]
         if merge and (1 < n < 3E3):  # Merge NMS (boxes merged using weighted mean)
-            try:  # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)
-                iou = box_iou(boxes[i], boxes) > iou_thres  # iou matrix
-                weights = iou * scores[None]  # box weights
-                x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True)  # merged boxes
-                if redundant:
-                    i = i[iou.sum(1) > 1]  # require redundancy
-            except:  # possible CUDA error https://github.com/ultralytics/yolov3/issues/1139
-                print(x, i, x.shape, i.shape)
-                pass
+            # 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:
@@ -693,170 +355,6 @@ def strip_optimizer(f='weights/best.pt', s=''):  # from utils.general import *;
     print('Optimizer stripped from %s,%s %.1fMB' % (f, (' saved as %s,' % s) if s else '', mb))
 
 
-def coco_class_count(path='../coco/labels/train2014/'):
-    # Histogram of occurrences per class
-    nc = 80  # number classes
-    x = np.zeros(nc, dtype='int32')
-    files = sorted(glob.glob('%s/*.*' % path))
-    for i, file in enumerate(files):
-        labels = np.loadtxt(file, dtype=np.float32).reshape(-1, 5)
-        x += np.bincount(labels[:, 0].astype('int32'), minlength=nc)
-        print(i, len(files))
-
-
-def coco_only_people(path='../coco/labels/train2017/'):  # from utils.general import *; coco_only_people()
-    # Find images with only people
-    files = sorted(glob.glob('%s/*.*' % path))
-    for i, file in enumerate(files):
-        labels = np.loadtxt(file, dtype=np.float32).reshape(-1, 5)
-        if all(labels[:, 0] == 0):
-            print(labels.shape[0], file)
-
-
-def crop_images_random(path='../images/', scale=0.50):  # from utils.general import *; crop_images_random()
-    # crops images into random squares up to scale fraction
-    # WARNING: overwrites images!
-    for file in tqdm(sorted(glob.glob('%s/*.*' % path))):
-        img = cv2.imread(file)  # BGR
-        if img is not None:
-            h, w = img.shape[:2]
-
-            # create random mask
-            a = 30  # minimum size (pixels)
-            mask_h = random.randint(a, int(max(a, h * scale)))  # mask height
-            mask_w = mask_h  # mask width
-
-            # 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
-            cv2.imwrite(file, img[ymin:ymax, xmin:xmax])
-
-
-def coco_single_class_labels(path='../coco/labels/train2014/', label_class=43):
-    # Makes single-class coco datasets. from utils.general import *; coco_single_class_labels()
-    if os.path.exists('new/'):
-        shutil.rmtree('new/')  # delete output folder
-    os.makedirs('new/')  # make new output folder
-    os.makedirs('new/labels/')
-    os.makedirs('new/images/')
-    for file in tqdm(sorted(glob.glob('%s/*.*' % path))):
-        with open(file, 'r') as f:
-            labels = np.array([x.split() for x in f.read().splitlines()], dtype=np.float32)
-        i = labels[:, 0] == label_class
-        if any(i):
-            img_file = file.replace('labels', 'images').replace('txt', 'jpg')
-            labels[:, 0] = 0  # reset class to 0
-            with open('new/images.txt', 'a') as f:  # add image to dataset list
-                f.write(img_file + '\n')
-            with open('new/labels/' + Path(file).name, 'a') as f:  # write label
-                for l in labels[i]:
-                    f.write('%g %.6f %.6f %.6f %.6f\n' % tuple(l))
-            shutil.copyfile(src=img_file, dst='new/images/' + Path(file).name.replace('txt', 'jpg'))  # copy images
-
-
-def kmean_anchors(path='./data/coco128.yaml', n=9, img_size=640, thr=4.0, gen=1000, verbose=True):
-    """ Creates kmeans-evolved anchors from training dataset
-
-        Arguments:
-            path: path to dataset *.yaml, or a loaded dataset
-            n: number of anchors
-            img_size: image size used for training
-            thr: anchor-label wh ratio threshold hyperparameter hyp['anchor_t'] used for training, default=4.0
-            gen: generations to evolve anchors using genetic algorithm
-
-        Return:
-            k: kmeans evolved anchors
-
-        Usage:
-            from utils.general import *; _ = kmean_anchors()
-    """
-    thr = 1. / thr
-
-    def metric(k, wh):  # compute metrics
-        r = wh[:, None] / k[None]
-        x = torch.min(r, 1. / r).min(2)[0]  # ratio metric
-        # x = wh_iou(wh, torch.tensor(k))  # iou metric
-        return x, x.max(1)[0]  # x, best_x
-
-    def fitness(k):  # mutation fitness
-        _, best = metric(torch.tensor(k, dtype=torch.float32), wh)
-        return (best * (best > thr).float()).mean()  # fitness
-
-    def print_results(k):
-        k = k[np.argsort(k.prod(1))]  # sort small to large
-        x, best = metric(k, wh0)
-        bpr, aat = (best > thr).float().mean(), (x > thr).float().mean() * n  # best possible recall, anch > thr
-        print('thr=%.2f: %.4f best possible recall, %.2f anchors past thr' % (thr, bpr, aat))
-        print('n=%g, img_size=%s, metric_all=%.3f/%.3f-mean/best, past_thr=%.3f-mean: ' %
-              (n, img_size, x.mean(), best.mean(), x[x > thr].mean()), end='')
-        for i, x in enumerate(k):
-            print('%i,%i' % (round(x[0]), round(x[1])), end=',  ' if i < len(k) - 1 else '\n')  # use in *.cfg
-        return k
-
-    if isinstance(path, str):  # *.yaml file
-        with open(path) as f:
-            data_dict = yaml.load(f, Loader=yaml.FullLoader)  # model dict
-        from utils.datasets import LoadImagesAndLabels
-        dataset = LoadImagesAndLabels(data_dict['train'], augment=True, rect=True)
-    else:
-        dataset = path  # dataset
-
-    # Get label wh
-    shapes = img_size * dataset.shapes / dataset.shapes.max(1, keepdims=True)
-    wh0 = np.concatenate([l[:, 3:5] * s for s, l in zip(shapes, dataset.labels)])  # wh
-
-    # Filter
-    i = (wh0 < 3.0).any(1).sum()
-    if i:
-        print('WARNING: Extremely small objects found. '
-              '%g of %g labels are < 3 pixels in width or height.' % (i, len(wh0)))
-    wh = wh0[(wh0 >= 2.0).any(1)]  # filter > 2 pixels
-
-    # Kmeans calculation
-    print('Running kmeans for %g anchors on %g points...' % (n, len(wh)))
-    s = wh.std(0)  # sigmas for whitening
-    k, dist = kmeans(wh / s, n, iter=30)  # points, mean distance
-    k *= s
-    wh = torch.tensor(wh, dtype=torch.float32)  # filtered
-    wh0 = torch.tensor(wh0, dtype=torch.float32)  # unfiltered
-    k = print_results(k)
-
-    # Plot
-    # k, d = [None] * 20, [None] * 20
-    # for i in tqdm(range(1, 21)):
-    #     k[i-1], d[i-1] = kmeans(wh / s, i)  # points, mean distance
-    # fig, ax = plt.subplots(1, 2, figsize=(14, 7))
-    # ax = ax.ravel()
-    # ax[0].plot(np.arange(1, 21), np.array(d) ** 2, marker='.')
-    # fig, ax = plt.subplots(1, 2, figsize=(14, 7))  # plot wh
-    # ax[0].hist(wh[wh[:, 0]<100, 0],400)
-    # ax[1].hist(wh[wh[:, 1]<100, 1],400)
-    # fig.tight_layout()
-    # fig.savefig('wh.png', dpi=200)
-
-    # Evolve
-    npr = np.random
-    f, sh, mp, s = fitness(k), k.shape, 0.9, 0.1  # fitness, generations, mutation prob, sigma
-    pbar = tqdm(range(gen), desc='Evolving anchors with Genetic Algorithm')  # progress bar
-    for _ in pbar:
-        v = np.ones(sh)
-        while (v == 1).all():  # mutate until a change occurs (prevent duplicates)
-            v = ((npr.random(sh) < mp) * npr.random() * npr.randn(*sh) * s + 1).clip(0.3, 3.0)
-        kg = (k.copy() * v).clip(min=2.0)
-        fg = fitness(kg)
-        if fg > f:
-            f, k = fg, kg.copy()
-            pbar.desc = 'Evolving anchors with Genetic Algorithm: fitness = %.4f' % f
-            if verbose:
-                print_results(k)
-
-    return print_results(k)
-
-
 def 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
@@ -923,34 +421,6 @@ def apply_classifier(x, model, img, im0):
     return x
 
 
-def fitness(x):
-    # Returns fitness (for use with results.txt or evolve.txt)
-    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 output_to_target(output, width, height):
-    # Convert model output to target format [batch_id, class_id, x, y, w, h, conf]
-    if isinstance(output, torch.Tensor):
-        output = output.cpu().numpy()
-
-    targets = []
-    for i, o in enumerate(output):
-        if o is not None:
-            for pred in o:
-                box = pred[:4]
-                w = (box[2] - box[0]) / width
-                h = (box[3] - box[1]) / height
-                x = box[0] / width + w / 2
-                y = box[1] / height + h / 2
-                conf = pred[4]
-                cls = int(pred[5])
-
-                targets.append([i, cls, x, y, w, h, conf])
-
-    return np.array(targets)
-
-
 def 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
@@ -962,339 +432,3 @@ def increment_path(path, exist_ok=True, sep=''):
         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
-
-
-# Plotting functions ---------------------------------------------------------------------------------------------------
-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
-        b, a = butter(order, normal_cutoff, btype='low', analog=False)
-        return b, a
-
-    b, a = butter_lowpass(cutoff, fs, order=order)
-    return filtfilt(b, a, data)  # forward-backward filter
-
-
-def plot_one_box(x, img, color=None, label=None, line_thickness=None):
-    # Plots one bounding box on image img
-    tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1  # line/font thickness
-    color = color or [random.randint(0, 255) for _ in range(3)]
-    c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))
-    cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA)
-    if label:
-        tf = max(tl - 1, 1)  # font thickness
-        t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]
-        c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3
-        cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA)  # filled
-        cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA)
-
-
-def plot_wh_methods():  # from utils.general import *; plot_wh_methods()
-    # Compares the two methods for width-height anchor multiplication
-    # https://github.com/ultralytics/yolov3/issues/168
-    x = np.arange(-4.0, 4.0, .1)
-    ya = np.exp(x)
-    yb = torch.sigmoid(torch.from_numpy(x)).numpy() * 2
-
-    fig = plt.figure(figsize=(6, 3), dpi=150)
-    plt.plot(x, ya, '.-', label='YOLOv3')
-    plt.plot(x, yb ** 2, '.-', label='YOLOv5 ^2')
-    plt.plot(x, yb ** 1.6, '.-', label='YOLOv5 ^1.6')
-    plt.xlim(left=-4, right=4)
-    plt.ylim(bottom=0, top=6)
-    plt.xlabel('input')
-    plt.ylabel('output')
-    plt.grid()
-    plt.legend()
-    fig.tight_layout()
-    fig.savefig('comparison.png', dpi=200)
-
-
-def plot_images(images, targets, paths=None, fname='images.jpg', names=None, max_size=640, max_subplots=16):
-    tl = 3  # line thickness
-    tf = max(tl - 1, 1)  # font thickness
-
-    if 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
-
-    bs, _, h, w = images.shape  # batch size, _, height, width
-    bs = min(bs, max_subplots)  # limit plot images
-    ns = np.ceil(bs ** 0.5)  # number of subplots (square)
-
-    # Check if we should resize
-    scale_factor = max_size / max(h, w)
-    if scale_factor < 1:
-        h = math.ceil(scale_factor * h)
-        w = math.ceil(scale_factor * w)
-
-    # Empty array for output
-    mosaic = np.full((int(ns * h), int(ns * w), 3), 255, dtype=np.uint8)
-
-    # Fix class - colour map
-    prop_cycle = plt.rcParams['axes.prop_cycle']
-    # https://stackoverflow.com/questions/51350872/python-from-color-name-to-rgb
-    hex2rgb = lambda h: tuple(int(h[1 + i:1 + i + 2], 16) for i in (0, 2, 4))
-    color_lut = [hex2rgb(h) for h in prop_cycle.by_key()['color']]
-
-    for i, img in enumerate(images):
-        if i == max_subplots:  # if last batch has fewer images than we expect
-            break
-
-        block_x = int(w * (i // ns))
-        block_y = int(h * (i % ns))
-
-        img = img.transpose(1, 2, 0)
-        if scale_factor < 1:
-            img = cv2.resize(img, (w, h))
-
-        mosaic[block_y:block_y + h, block_x:block_x + w, :] = img
-        if len(targets) > 0:
-            image_targets = targets[targets[:, 0] == i]
-            boxes = xywh2xyxy(image_targets[:, 2:6]).T
-            classes = image_targets[:, 1].astype('int')
-            labels = image_targets.shape[1] == 6  # labels if no conf column
-            conf = None if labels else image_targets[:, 6]  # check for confidence presence (label vs pred)
-
-            boxes[[0, 2]] *= w
-            boxes[[0, 2]] += block_x
-            boxes[[1, 3]] *= h
-            boxes[[1, 3]] += block_y
-            for j, box in enumerate(boxes.T):
-                cls = int(classes[j])
-                color = color_lut[cls % len(color_lut)]
-                cls = names[cls] if names else cls
-                if labels or conf[j] > 0.3:  # 0.3 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 is not None:
-            label = os.path.basename(paths[i])[:40]  # trim to 40 char
-            t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]
-            cv2.putText(mosaic, label, (block_x + 5, block_y + t_size[1] + 5), 0, tl / 3, [220, 220, 220], thickness=tf,
-                        lineType=cv2.LINE_AA)
-
-        # Image border
-        cv2.rectangle(mosaic, (block_x, block_y), (block_x + w, block_y + h), (255, 255, 255), thickness=3)
-
-    if fname is not None:
-        r = min(1280. / max(h, w) / ns, 1.0)  # ratio to limit image size
-        mosaic = cv2.resize(mosaic, (int(ns * w * r), int(ns * h * r)), interpolation=cv2.INTER_AREA)
-        # cv2.imwrite(fname, cv2.cvtColor(mosaic, cv2.COLOR_BGR2RGB))  # cv2 save
-        Image.fromarray(mosaic).save(fname)  # PIL save
-    return mosaic
-
-
-def plot_lr_scheduler(optimizer, scheduler, epochs=300, save_dir=''):
-    # Plot LR simulating training for full epochs
-    optimizer, scheduler = copy(optimizer), copy(scheduler)  # do not modify originals
-    y = []
-    for _ in range(epochs):
-        scheduler.step()
-        y.append(optimizer.param_groups[0]['lr'])
-    plt.plot(y, '.-', label='LR')
-    plt.xlabel('epoch')
-    plt.ylabel('LR')
-    plt.grid()
-    plt.xlim(0, epochs)
-    plt.ylim(0)
-    plt.tight_layout()
-    plt.savefig(Path(save_dir) / 'LR.png', dpi=200)
-
-
-def plot_test_txt():  # from utils.general import *; plot_test()
-    # Plot test.txt histograms
-    x = np.loadtxt('test.txt', dtype=np.float32)
-    box = xyxy2xywh(x[:, :4])
-    cx, cy = box[:, 0], box[:, 1]
-
-    fig, ax = plt.subplots(1, 1, figsize=(6, 6), tight_layout=True)
-    ax.hist2d(cx, cy, bins=600, cmax=10, cmin=0)
-    ax.set_aspect('equal')
-    plt.savefig('hist2d.png', dpi=300)
-
-    fig, ax = plt.subplots(1, 2, figsize=(12, 6), tight_layout=True)
-    ax[0].hist(cx, bins=600)
-    ax[1].hist(cy, bins=600)
-    plt.savefig('hist1d.png', dpi=200)
-
-
-def plot_targets_txt():  # from utils.general import *; plot_targets_txt()
-    # Plot targets.txt histograms
-    x = np.loadtxt('targets.txt', dtype=np.float32).T
-    s = ['x targets', 'y targets', 'width targets', 'height targets']
-    fig, ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)
-    ax = ax.ravel()
-    for i in range(4):
-        ax[i].hist(x[i], bins=100, label='%.3g +/- %.3g' % (x[i].mean(), x[i].std()))
-        ax[i].legend()
-        ax[i].set_title(s[i])
-    plt.savefig('targets.jpg', dpi=200)
-
-
-def plot_study_txt(f='study.txt', x=None):  # from utils.general import *; plot_study_txt()
-    # Plot study.txt generated by test.py
-    fig, ax = plt.subplots(2, 4, figsize=(10, 6), tight_layout=True)
-    ax = ax.ravel()
-
-    fig2, ax2 = plt.subplots(1, 1, figsize=(8, 4), tight_layout=True)
-    for f in ['study/study_coco_yolov5%s.txt' % x for x in ['s', 'm', 'l', 'x']]:
-        y = np.loadtxt(f, dtype=np.float32, usecols=[0, 1, 2, 3, 7, 8, 9], ndmin=2).T
-        x = np.arange(y.shape[1]) if x is None else np.array(x)
-        s = ['P', 'R', 'mAP@.5', 'mAP@.5:.95', 't_inference (ms/img)', 't_NMS (ms/img)', 't_total (ms/img)']
-        for i in range(7):
-            ax[i].plot(x, y[i], '.-', linewidth=2, markersize=8)
-            ax[i].set_title(s[i])
-
-        j = y[3].argmax() + 1
-        ax2.plot(y[6, :j], y[3, :j] * 1E2, '.-', linewidth=2, markersize=8,
-                 label=Path(f).stem.replace('study_coco_', '').replace('yolo', 'YOLO'))
-
-    ax2.plot(1E3 / np.array([209, 140, 97, 58, 35, 18]), [34.6, 40.5, 43.0, 47.5, 49.7, 51.5],
-             'k.-', linewidth=2, markersize=8, alpha=.25, label='EfficientDet')
-
-    ax2.grid()
-    ax2.set_xlim(0, 30)
-    ax2.set_ylim(28, 50)
-    ax2.set_yticks(np.arange(30, 55, 5))
-    ax2.set_xlabel('GPU Speed (ms/img)')
-    ax2.set_ylabel('COCO AP val')
-    ax2.legend(loc='lower right')
-    plt.savefig('study_mAP_latency.png', dpi=300)
-    plt.savefig(f.replace('.txt', '.png'), dpi=300)
-
-
-def plot_labels(labels, save_dir=''):
-    # plot dataset labels
-    c, b = labels[:, 0], labels[:, 1:].transpose()  # classes, boxes
-    nc = int(c.max() + 1)  # number of classes
-
-    fig, ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)
-    ax = ax.ravel()
-    ax[0].hist(c, bins=np.linspace(0, nc, nc + 1) - 0.5, rwidth=0.8)
-    ax[0].set_xlabel('classes')
-    ax[1].scatter(b[0], b[1], c=hist2d(b[0], b[1], 90), cmap='jet')
-    ax[1].set_xlabel('x')
-    ax[1].set_ylabel('y')
-    ax[2].scatter(b[2], b[3], c=hist2d(b[2], b[3], 90), cmap='jet')
-    ax[2].set_xlabel('width')
-    ax[2].set_ylabel('height')
-    plt.savefig(Path(save_dir) / 'labels.png', dpi=200)
-    plt.close()
-
-    # seaborn correlogram
-    try:
-        import seaborn as sns
-        import pandas as pd
-        x = pd.DataFrame(b.transpose(), columns=['x', 'y', 'width', 'height'])
-        sns.pairplot(x, corner=True, diag_kind='hist', kind='scatter', markers='o',
-                     plot_kws=dict(s=3, edgecolor=None, linewidth=1, alpha=0.02),
-                     diag_kws=dict(bins=50))
-        plt.savefig(Path(save_dir) / 'labels_correlogram.png', dpi=200)
-        plt.close()
-    except Exception as e:
-        pass
-
-
-def plot_evolution(yaml_file='data/hyp.finetune.yaml'):  # from utils.general import *; plot_evolution()
-    # Plot hyperparameter evolution results in evolve.txt
-    with open(yaml_file) as f:
-        hyp = yaml.load(f, Loader=yaml.FullLoader)
-    x = np.loadtxt('evolve.txt', ndmin=2)
-    f = fitness(x)
-    # weights = (f - f.min()) ** 2  # for weighted results
-    plt.figure(figsize=(10, 12), tight_layout=True)
-    matplotlib.rc('font', **{'size': 8})
-    for i, (k, v) in enumerate(hyp.items()):
-        y = x[:, i + 7]
-        # mu = (y * weights).sum() / weights.sum()  # best weighted result
-        mu = y[f.argmax()]  # best single result
-        plt.subplot(6, 5, i + 1)
-        plt.scatter(y, f, c=hist2d(y, f, 20), cmap='viridis', alpha=.8, edgecolors='none')
-        plt.plot(mu, f.max(), 'k+', markersize=15)
-        plt.title('%s = %.3g' % (k, mu), fontdict={'size': 9})  # limit to 40 characters
-        if i % 5 != 0:
-            plt.yticks([])
-        print('%15s: %.3g' % (k, mu))
-    plt.savefig('evolve.png', dpi=200)
-    print('\nPlot saved as evolve.png')
-
-
-def plot_results_overlay(start=0, stop=0):  # from utils.general import *; plot_results_overlay()
-    # Plot training 'results*.txt', overlaying train and val losses
-    s = ['train', 'train', 'train', 'Precision', 'mAP@0.5', 'val', 'val', 'val', 'Recall', 'mAP@0.5:0.95']  # legends
-    t = ['Box', 'Objectness', 'Classification', 'P-R', 'mAP-F1']  # titles
-    for f in sorted(glob.glob('results*.txt') + glob.glob('../../Downloads/results*.txt')):
-        results = np.loadtxt(f, usecols=[2, 3, 4, 8, 9, 12, 13, 14, 10, 11], ndmin=2).T
-        n = results.shape[1]  # number of rows
-        x = range(start, min(stop, n) if stop else n)
-        fig, ax = plt.subplots(1, 5, figsize=(14, 3.5), tight_layout=True)
-        ax = ax.ravel()
-        for i in range(5):
-            for j in [i, i + 5]:
-                y = results[j, x]
-                ax[i].plot(x, y, marker='.', label=s[j])
-                # y_smooth = butter_lowpass_filtfilt(y)
-                # ax[i].plot(x, np.gradient(y_smooth), marker='.', label=s[j])
-
-            ax[i].set_title(t[i])
-            ax[i].legend()
-            ax[i].set_ylabel(f) if i == 0 else None  # add filename
-        fig.savefig(f.replace('.txt', '.png'), dpi=200)
-
-
-def plot_results(start=0, stop=0, bucket='', id=(), labels=(), save_dir=''):
-    # from utils.general import *; plot_results(save_dir='runs/train/exp0')
-    # Plot training 'results*.txt' as seen in https://github.com/ultralytics/yolov5#reproduce-our-training
-    fig, ax = plt.subplots(2, 5, figsize=(12, 6))
-    ax = ax.ravel()
-    s = ['Box', 'Objectness', 'Classification', 'Precision', 'Recall',
-         'val Box', 'val Objectness', 'val Classification', 'mAP@0.5', 'mAP@0.5:0.95']
-    if bucket:
-        # os.system('rm -rf storage.googleapis.com')
-        # files = ['https://storage.googleapis.com/%s/results%g.txt' % (bucket, x) for x in id]
-        files = ['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 = glob.glob(str(Path(save_dir) / 'results*.txt')) + glob.glob('../../Downloads/results*.txt')
-    assert len(files), 'No results.txt files found in %s, nothing to plot.' % os.path.abspath(save_dir)
-    for fi, f in enumerate(files):
-        try:
-            results = np.loadtxt(f, usecols=[2, 3, 4, 8, 9, 12, 13, 14, 10, 11], ndmin=2).T
-            n = results.shape[1]  # number of rows
-            x = range(start, min(stop, n) if stop else n)
-            for i in range(10):
-                y = results[i, x]
-                if i in [0, 1, 2, 5, 6, 7]:
-                    y[y == 0] = np.nan  # don't show zero loss values
-                    # y /= y[0]  # normalize
-                label = labels[fi] if len(labels) else Path(f).stem
-                ax[i].plot(x, y, marker='.', label=label, linewidth=1, markersize=6)
-                ax[i].set_title(s[i])
-                # if i in [5, 6, 7]:  # share train and val loss y axes
-                #     ax[i].get_shared_y_axes().join(ax[i], ax[i - 5])
-        except Exception as e:
-            print('Warning: Plotting error for %s; %s' % (f, e))
-
-    fig.tight_layout()
-    ax[1].legend()
-    fig.savefig(Path(save_dir) / 'results.png', dpi=200)

utils/google_utils.py

@@ -1,6 +1,4 @@
-# This file contains google utils: https://cloud.google.com/storage/docs/reference/libraries
-# pip install --upgrade google-cloud-storage
-# from google.cloud import storage
+# Google utils: https://cloud.google.com/storage/docs/reference/libraries
 
 import os
 import platform

utils/loss.py

@@ -0,0 +1,179 @@
+# Loss functions
+
+import torch
+import torch.nn as nn
+
+from utils.general import bbox_iou
+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 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
+
+
+def compute_loss(p, targets, model):  # 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 = build_targets(p, targets, model)  # targets
+    h = model.hyp  # hyperparameters
+
+    # Define criteria
+    BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.Tensor([h['cls_pw']])).to(device)
+    BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.Tensor([h['obj_pw']])).to(device)
+
+    # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3
+    cp, cn = smooth_BCE(eps=0.0)
+
+    # Focal loss
+    g = h['fl_gamma']  # focal loss gamma
+    if g > 0:
+        BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g)
+
+    # Losses
+    nt = 0  # number of targets
+    no = len(p)  # number of outputs
+    balance = [4.0, 1.0, 0.4] if no == 3 else [4.0, 1.0, 0.4, 0.1]  # P3-5 or P3-6
+    for i, pi in enumerate(p):  # layer index, layer predictions
+        b, a, gj, gi = indices[i]  # image, anchor, gridy, gridx
+        tobj = torch.zeros_like(pi[..., 0], device=device)  # target obj
+
+        n = b.shape[0]  # number of targets
+        if n:
+            nt += n  # cumulative targets
+            ps = pi[b, a, gj, gi]  # prediction subset corresponding to targets
+
+            # Regression
+            pxy = ps[:, :2].sigmoid() * 2. - 0.5
+            pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i]
+            pbox = torch.cat((pxy, pwh), 1).to(device)  # 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 - model.gr) + model.gr * iou.detach().clamp(0).type(tobj.dtype)  # iou ratio
+
+            # Classification
+            if model.nc > 1:  # cls loss (only if multiple classes)
+                t = torch.full_like(ps[:, 5:], cn, device=device)  # targets
+                t[range(n), tcls[i]] = cp
+                lcls += BCEcls(ps[:, 5:], t)  # BCE
+
+            # Append targets to text file
+            # with open('targets.txt', 'a') as file:
+            #     [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)]
+
+        lobj += BCEobj(pi[..., 4], tobj) * balance[i]  # obj loss
+
+    s = 3 / no  # output count scaling
+    lbox *= h['box'] * s
+    lobj *= h['obj'] * s * (1.4 if no == 4 else 1.)
+    lcls *= h['cls'] * s
+    bs = tobj.shape[0]  # batch size
+
+    loss = lbox + lobj + lcls
+    return loss * bs, torch.cat((lbox, lobj, lcls, loss)).detach()
+
+
+def build_targets(p, targets, model):
+    # Build targets for compute_loss(), input targets(image,class,x,y,w,h)
+    det = model.module.model[-1] if is_parallel(model) else model.model[-1]  # Detect() module
+    na, nt = det.na, targets.shape[0]  # number of anchors, targets
+    tcls, tbox, indices, anch = [], [], [], []
+    gain = torch.ones(7, device=targets.device)  # 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(det.nl):
+        anchors = det.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] < model.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

utils/metrics.py

@@ -0,0 +1,110 @@
+# Model validation metrics
+
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+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, plot=False, fname='precision-recall_curve.png'):
+    """ Compute the average precision, given the recall and precision curves.
+    Source: https://github.com/rafaelpadilla/Object-Detection-Metrics.
+    # Arguments
+        tp:  True positives (nparray, nx1 or nx10).
+        conf:  Objectness value from 0-1 (nparray).
+        pred_cls:  Predicted object classes (nparray).
+        target_cls:  True object classes (nparray).
+        plot:  Plot precision-recall curve at mAP@0.5
+        fname:  Plot filename
+    # Returns
+        The average precision as computed in py-faster-rcnn.
+    """
+
+    # Sort by objectness
+    i = np.argsort(-conf)
+    tp, conf, pred_cls = tp[i], conf[i], pred_cls[i]
+
+    # Find unique classes
+    unique_classes = np.unique(target_cls)
+
+    # Create Precision-Recall curve and compute AP for each class
+    px, py = np.linspace(0, 1, 1000), []  # for plotting
+    pr_score = 0.1  # score to evaluate P and R https://github.com/ultralytics/yolov3/issues/898
+    s = [unique_classes.shape[0], tp.shape[1]]  # number class, number iou thresholds (i.e. 10 for mAP0.5...0.95)
+    ap, p, r = np.zeros(s), np.zeros(s), np.zeros(s)
+    for ci, c in enumerate(unique_classes):
+        i = pred_cls == c
+        n_l = (target_cls == c).sum()  # number of labels
+        n_p = i.sum()  # number of predictions
+
+        if n_p == 0 or n_l == 0:
+            continue
+        else:
+            # Accumulate FPs and TPs
+            fpc = (1 - tp[i]).cumsum(0)
+            tpc = tp[i].cumsum(0)
+
+            # Recall
+            recall = tpc / (n_l + 1e-16)  # recall curve
+            r[ci] = np.interp(-pr_score, -conf[i], recall[:, 0])  # r at pr_score, negative x, xp because xp decreases
+
+            # Precision
+            precision = tpc / (tpc + fpc)  # precision curve
+            p[ci] = np.interp(-pr_score, -conf[i], precision[:, 0])  # p at pr_score
+
+            # AP from recall-precision curve
+            for j in range(tp.shape[1]):
+                ap[ci, j], mpre, mrec = compute_ap(recall[:, j], precision[:, j])
+                if j == 0:
+                    py.append(np.interp(px, mrec, mpre))  # precision at mAP@0.5
+
+    # Compute F1 score (harmonic mean of precision and recall)
+    f1 = 2 * p * r / (p + r + 1e-16)
+
+    if plot:
+        py = np.stack(py, axis=1)
+        fig, ax = plt.subplots(1, 1, figsize=(5, 5))
+        ax.plot(px, py, linewidth=0.5, color='grey')  # plot(recall, precision)
+        ax.plot(px, py.mean(1), linewidth=2, color='blue', label='all classes %.3f mAP@0.5' % ap[:, 0].mean())
+        ax.set_xlabel('Recall')
+        ax.set_ylabel('Precision')
+        ax.set_xlim(0, 1)
+        ax.set_ylim(0, 1)
+        plt.legend()
+        fig.tight_layout()
+        fig.savefig(fname, dpi=200)
+
+    return p, r, ap, f1, unique_classes.astype('int32')
+
+
+def compute_ap(recall, precision):
+    """ Compute the average precision, given the recall and precision curves.
+    Source: https://github.com/rbgirshick/py-faster-rcnn.
+    # Arguments
+        recall:    The recall curve (list).
+        precision: The precision curve (list).
+    # Returns
+        The average precision as computed in py-faster-rcnn.
+    """
+
+    # Append sentinel values to beginning and end
+    mrec = recall  # np.concatenate(([0.], recall, [recall[-1] + 1E-3]))
+    mpre = precision  # np.concatenate(([0.], precision, [0.]))
+
+    # Compute the precision envelope
+    mpre = np.flip(np.maximum.accumulate(np.flip(mpre)))
+
+    # Integrate area under curve
+    method = 'interp'  # methods: 'continuous', 'interp'
+    if method == 'interp':
+        x = np.linspace(0, 1, 101)  # 101-point interp (COCO)
+        ap = np.trapz(np.interp(x, mrec, mpre), x)  # integrate
+    else:  # 'continuous'
+        i = np.where(mrec[1:] != mrec[:-1])[0]  # points where x axis (recall) changes
+        ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])  # area under curve
+
+    return ap, mpre, mrec

utils/plots.py

@@ -0,0 +1,377 @@
+# 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 torch
+import yaml
+from PIL import Image
+from scipy.signal import butter, filtfilt
+
+from utils.general import xywh2xyxy, xyxy2xywh
+from utils.metrics import fitness
+
+
+def color_list():
+    # Return first 10 plt colors as (r,g,b) https://stackoverflow.com/questions/51350872/python-from-color-name-to-rgb
+    def hex2rgb(h):
+        return tuple(int(h[1 + i:1 + i + 2], 16) for i in (0, 2, 4))
+
+    return [hex2rgb(h) for h in plt.rcParams['axes.prop_cycle'].by_key()['color']]
+
+
+def hist2d(x, y, n=100):
+    # 2d histogram used in labels.png and evolve.png
+    xedges, yedges = np.linspace(x.min(), x.max(), n), np.linspace(y.min(), y.max(), n)
+    hist, xedges, yedges = np.histogram2d(x, y, (xedges, yedges))
+    xidx = np.clip(np.digitize(x, xedges) - 1, 0, hist.shape[0] - 1)
+    yidx = np.clip(np.digitize(y, yedges) - 1, 0, hist.shape[1] - 1)
+    return np.log(hist[xidx, yidx])
+
+
+def butter_lowpass_filtfilt(data, cutoff=1500, fs=50000, order=5):
+    # https://stackoverflow.com/questions/28536191/how-to-filter-smooth-with-scipy-numpy
+    def butter_lowpass(cutoff, fs, order):
+        nyq = 0.5 * fs
+        normal_cutoff = cutoff / nyq
+        return butter(order, normal_cutoff, btype='low', analog=False)
+
+    b, a = butter_lowpass(cutoff, fs, order=order)
+    return filtfilt(b, a, data)  # forward-backward filter
+
+
+def plot_one_box(x, img, color=None, label=None, line_thickness=None):
+    # Plots one bounding box on image img
+    tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1  # line/font thickness
+    color = color or [random.randint(0, 255) for _ in range(3)]
+    c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))
+    cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA)
+    if label:
+        tf = max(tl - 1, 1)  # font thickness
+        t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]
+        c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3
+        cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA)  # filled
+        cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA)
+
+
+def plot_wh_methods():  # from utils.general import *; plot_wh_methods()
+    # Compares the two methods for width-height anchor multiplication
+    # https://github.com/ultralytics/yolov3/issues/168
+    x = np.arange(-4.0, 4.0, .1)
+    ya = np.exp(x)
+    yb = torch.sigmoid(torch.from_numpy(x)).numpy() * 2
+
+    fig = plt.figure(figsize=(6, 3), dpi=150)
+    plt.plot(x, ya, '.-', label='YOLOv3')
+    plt.plot(x, yb ** 2, '.-', label='YOLOv5 ^2')
+    plt.plot(x, yb ** 1.6, '.-', label='YOLOv5 ^1.6')
+    plt.xlim(left=-4, right=4)
+    plt.ylim(bottom=0, top=6)
+    plt.xlabel('input')
+    plt.ylabel('output')
+    plt.grid()
+    plt.legend()
+    fig.tight_layout()
+    fig.savefig('comparison.png', dpi=200)
+
+
+def output_to_target(output, width, height):
+    # Convert model output to target format [batch_id, class_id, x, y, w, h, conf]
+    if isinstance(output, torch.Tensor):
+        output = output.cpu().numpy()
+
+    targets = []
+    for i, o in enumerate(output):
+        if o is not None:
+            for pred in o:
+                box = pred[:4]
+                w = (box[2] - box[0]) / width
+                h = (box[3] - box[1]) / height
+                x = box[0] / width + w / 2
+                y = box[1] / height + h / 2
+                conf = pred[4]
+                cls = int(pred[5])
+
+                targets.append([i, cls, x, y, w, h, conf])
+
+    return np.array(targets)
+
+
+def plot_images(images, targets, paths=None, fname='images.jpg', names=None, max_size=640, max_subplots=16):
+    # Plot image grid with labels
+
+    if isinstance(images, torch.Tensor):
+        images = images.cpu().float().numpy()
+    if isinstance(targets, torch.Tensor):
+        targets = targets.cpu().numpy()
+
+    # un-normalise
+    if np.max(images[0]) <= 1:
+        images *= 255
+
+    tl = 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)
+
+            boxes[[0, 2]] *= w
+            boxes[[0, 2]] += block_x
+            boxes[[1, 3]] *= h
+            boxes[[1, 3]] += block_y
+            for j, box in enumerate(boxes.T):
+                cls = int(classes[j])
+                color = colors[cls % len(colors)]
+                cls = names[cls] if names else cls
+                if labels or conf[j] > 0.3:  # 0.3 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 is not None:
+            label = os.path.basename(paths[i])[:40]  # trim to 40 char
+            t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]
+            cv2.putText(mosaic, label, (block_x + 5, block_y + t_size[1] + 5), 0, tl / 3, [220, 220, 220], thickness=tf,
+                        lineType=cv2.LINE_AA)
+
+        # Image border
+        cv2.rectangle(mosaic, (block_x, block_y), (block_x + w, block_y + h), (255, 255, 255), thickness=3)
+
+    if fname is not None:
+        r = min(1280. / max(h, w) / ns, 1.0)  # ratio to limit image size
+        mosaic = cv2.resize(mosaic, (int(ns * w * r), int(ns * h * r)), interpolation=cv2.INTER_AREA)
+        # cv2.imwrite(fname, cv2.cvtColor(mosaic, cv2.COLOR_BGR2RGB))  # cv2 save
+        Image.fromarray(mosaic).save(fname)  # PIL save
+    return mosaic
+
+
+def plot_lr_scheduler(optimizer, scheduler, epochs=300, save_dir=''):
+    # Plot LR simulating training for full epochs
+    optimizer, scheduler = copy(optimizer), copy(scheduler)  # do not modify originals
+    y = []
+    for _ in range(epochs):
+        scheduler.step()
+        y.append(optimizer.param_groups[0]['lr'])
+    plt.plot(y, '.-', label='LR')
+    plt.xlabel('epoch')
+    plt.ylabel('LR')
+    plt.grid()
+    plt.xlim(0, epochs)
+    plt.ylim(0)
+    plt.tight_layout()
+    plt.savefig(Path(save_dir) / 'LR.png', dpi=200)
+
+
+def plot_test_txt():  # from utils.general import *; plot_test()
+    # Plot test.txt histograms
+    x = np.loadtxt('test.txt', dtype=np.float32)
+    box = xyxy2xywh(x[:, :4])
+    cx, cy = box[:, 0], box[:, 1]
+
+    fig, ax = plt.subplots(1, 1, figsize=(6, 6), tight_layout=True)
+    ax.hist2d(cx, cy, bins=600, cmax=10, cmin=0)
+    ax.set_aspect('equal')
+    plt.savefig('hist2d.png', dpi=300)
+
+    fig, ax = plt.subplots(1, 2, figsize=(12, 6), tight_layout=True)
+    ax[0].hist(cx, bins=600)
+    ax[1].hist(cy, bins=600)
+    plt.savefig('hist1d.png', dpi=200)
+
+
+def plot_targets_txt():  # from utils.general import *; plot_targets_txt()
+    # Plot targets.txt histograms
+    x = np.loadtxt('targets.txt', dtype=np.float32).T
+    s = ['x targets', 'y targets', 'width targets', 'height targets']
+    fig, ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)
+    ax = ax.ravel()
+    for i in range(4):
+        ax[i].hist(x[i], bins=100, label='%.3g +/- %.3g' % (x[i].mean(), x[i].std()))
+        ax[i].legend()
+        ax[i].set_title(s[i])
+    plt.savefig('targets.jpg', dpi=200)
+
+
+def plot_study_txt(f='study.txt', x=None):  # from utils.general import *; plot_study_txt()
+    # Plot study.txt generated by test.py
+    fig, ax = plt.subplots(2, 4, figsize=(10, 6), tight_layout=True)
+    ax = ax.ravel()
+
+    fig2, ax2 = plt.subplots(1, 1, figsize=(8, 4), tight_layout=True)
+    for f in ['study/study_coco_yolov5%s.txt' % x for x in ['s', 'm', 'l', 'x']]:
+        y = np.loadtxt(f, dtype=np.float32, usecols=[0, 1, 2, 3, 7, 8, 9], ndmin=2).T
+        x = np.arange(y.shape[1]) if x is None else np.array(x)
+        s = ['P', 'R', 'mAP@.5', 'mAP@.5:.95', 't_inference (ms/img)', 't_NMS (ms/img)', 't_total (ms/img)']
+        for i in range(7):
+            ax[i].plot(x, y[i], '.-', linewidth=2, markersize=8)
+            ax[i].set_title(s[i])
+
+        j = y[3].argmax() + 1
+        ax2.plot(y[6, :j], y[3, :j] * 1E2, '.-', linewidth=2, markersize=8,
+                 label=Path(f).stem.replace('study_coco_', '').replace('yolo', 'YOLO'))
+
+    ax2.plot(1E3 / np.array([209, 140, 97, 58, 35, 18]), [34.6, 40.5, 43.0, 47.5, 49.7, 51.5],
+             'k.-', linewidth=2, markersize=8, alpha=.25, label='EfficientDet')
+
+    ax2.grid()
+    ax2.set_xlim(0, 30)
+    ax2.set_ylim(28, 50)
+    ax2.set_yticks(np.arange(30, 55, 5))
+    ax2.set_xlabel('GPU Speed (ms/img)')
+    ax2.set_ylabel('COCO AP val')
+    ax2.legend(loc='lower right')
+    plt.savefig('study_mAP_latency.png', dpi=300)
+    plt.savefig(f.replace('.txt', '.png'), dpi=300)
+
+
+def plot_labels(labels, save_dir=''):
+    # plot dataset labels
+    c, b = labels[:, 0], labels[:, 1:].transpose()  # classes, boxes
+    nc = int(c.max() + 1)  # number of classes
+
+    fig, ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)
+    ax = ax.ravel()
+    ax[0].hist(c, bins=np.linspace(0, nc, nc + 1) - 0.5, rwidth=0.8)
+    ax[0].set_xlabel('classes')
+    ax[1].scatter(b[0], b[1], c=hist2d(b[0], b[1], 90), cmap='jet')
+    ax[1].set_xlabel('x')
+    ax[1].set_ylabel('y')
+    ax[2].scatter(b[2], b[3], c=hist2d(b[2], b[3], 90), cmap='jet')
+    ax[2].set_xlabel('width')
+    ax[2].set_ylabel('height')
+    plt.savefig(Path(save_dir) / 'labels.png', dpi=200)
+    plt.close()
+
+    # seaborn correlogram
+    try:
+        import seaborn as sns
+        import pandas as pd
+        x = pd.DataFrame(b.transpose(), columns=['x', 'y', 'width', 'height'])
+        sns.pairplot(x, corner=True, diag_kind='hist', kind='scatter', markers='o',
+                     plot_kws=dict(s=3, edgecolor=None, linewidth=1, alpha=0.02),
+                     diag_kws=dict(bins=50))
+        plt.savefig(Path(save_dir) / 'labels_correlogram.png', dpi=200)
+        plt.close()
+    except Exception as e:
+        pass
+
+
+def plot_evolution(yaml_file='data/hyp.finetune.yaml'):  # from utils.general import *; plot_evolution()
+    # Plot hyperparameter evolution results in evolve.txt
+    with open(yaml_file) as f:
+        hyp = yaml.load(f, Loader=yaml.FullLoader)
+    x = np.loadtxt('evolve.txt', ndmin=2)
+    f = fitness(x)
+    # weights = (f - f.min()) ** 2  # for weighted results
+    plt.figure(figsize=(10, 12), tight_layout=True)
+    matplotlib.rc('font', **{'size': 8})
+    for i, (k, v) in enumerate(hyp.items()):
+        y = x[:, i + 7]
+        # mu = (y * weights).sum() / weights.sum()  # best weighted result
+        mu = y[f.argmax()]  # best single result
+        plt.subplot(6, 5, i + 1)
+        plt.scatter(y, f, c=hist2d(y, f, 20), cmap='viridis', alpha=.8, edgecolors='none')
+        plt.plot(mu, f.max(), 'k+', markersize=15)
+        plt.title('%s = %.3g' % (k, mu), fontdict={'size': 9})  # limit to 40 characters
+        if i % 5 != 0:
+            plt.yticks([])
+        print('%15s: %.3g' % (k, mu))
+    plt.savefig('evolve.png', dpi=200)
+    print('\nPlot saved as evolve.png')
+
+
+def plot_results_overlay(start=0, stop=0):  # from utils.general import *; plot_results_overlay()
+    # Plot training 'results*.txt', overlaying train and val losses
+    s = ['train', 'train', 'train', 'Precision', 'mAP@0.5', 'val', 'val', 'val', 'Recall', 'mAP@0.5:0.95']  # legends
+    t = ['Box', 'Objectness', 'Classification', 'P-R', 'mAP-F1']  # titles
+    for f in sorted(glob.glob('results*.txt') + glob.glob('../../Downloads/results*.txt')):
+        results = np.loadtxt(f, usecols=[2, 3, 4, 8, 9, 12, 13, 14, 10, 11], ndmin=2).T
+        n = results.shape[1]  # number of rows
+        x = range(start, min(stop, n) if stop else n)
+        fig, ax = plt.subplots(1, 5, figsize=(14, 3.5), tight_layout=True)
+        ax = ax.ravel()
+        for i in range(5):
+            for j in [i, i + 5]:
+                y = results[j, x]
+                ax[i].plot(x, y, marker='.', label=s[j])
+                # y_smooth = butter_lowpass_filtfilt(y)
+                # ax[i].plot(x, np.gradient(y_smooth), marker='.', label=s[j])
+
+            ax[i].set_title(t[i])
+            ax[i].legend()
+            ax[i].set_ylabel(f) if i == 0 else None  # add filename
+        fig.savefig(f.replace('.txt', '.png'), dpi=200)
+
+
+def plot_results(start=0, stop=0, bucket='', id=(), labels=(), save_dir=''):
+    # from utils.general import *; plot_results(save_dir='runs/train/exp0')
+    # Plot training 'results*.txt' as seen in https://github.com/ultralytics/yolov5#reproduce-our-training
+    fig, ax = plt.subplots(2, 5, figsize=(12, 6))
+    ax = ax.ravel()
+    s = ['Box', 'Objectness', 'Classification', 'Precision', 'Recall',
+         'val Box', 'val Objectness', 'val Classification', 'mAP@0.5', 'mAP@0.5:0.95']
+    if bucket:
+        # os.system('rm -rf storage.googleapis.com')
+        # files = ['https://storage.googleapis.com/%s/results%g.txt' % (bucket, x) for x in id]
+        files = ['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 = glob.glob(str(Path(save_dir) / 'results*.txt')) + glob.glob('../../Downloads/results*.txt')
+    assert len(files), 'No results.txt files found in %s, nothing to plot.' % os.path.abspath(save_dir)
+    for fi, f in enumerate(files):
+        try:
+            results = np.loadtxt(f, usecols=[2, 3, 4, 8, 9, 12, 13, 14, 10, 11], ndmin=2).T
+            n = results.shape[1]  # number of rows
+            x = range(start, min(stop, n) if stop else n)
+            for i in range(10):
+                y = results[i, x]
+                if i in [0, 1, 2, 5, 6, 7]:
+                    y[y == 0] = np.nan  # don't show zero loss values
+                    # y /= y[0]  # normalize
+                label = labels[fi] if len(labels) else Path(f).stem
+                ax[i].plot(x, y, marker='.', label=label, linewidth=1, markersize=6)
+                ax[i].set_title(s[i])
+                # if i in [5, 6, 7]:  # share train and val loss y axes
+                #     ax[i].get_shared_y_axes().join(ax[i], ax[i - 5])
+        except Exception as e:
+            print('Warning: Plotting error for %s; %s' % (f, e))
+
+    fig.tight_layout()
+    ax[1].legend()
+    fig.savefig(Path(save_dir) / 'results.png', dpi=200)

utils/torch_utils.py

@@ -1,7 +1,10 @@
+# PyTorch utils
+
 import logging
 import math
 import os
 import time
+from contextlib import contextmanager
 from copy import deepcopy
 
 import torch
@@ -13,10 +16,21 @@ import torchvision
 logger = logging.getLogger(__name__)
 
 
-def init_torch_seeds(seed=0):
-    torch.manual_seed(seed)
+@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.deterministic = True
         cudnn.benchmark = False
@@ -104,8 +118,6 @@ def prune(model, amount=0.3):
 
 def fuse_conv_and_bn(conv, bn):
     # Fuse convolution and batchnorm layers https://tehnokv.com/posts/fusing-batchnorm-and-conv/
-
-    # init
     fusedconv = nn.Conv2d(conv.in_channels,
                           conv.out_channels,
                           kernel_size=conv.kernel_size,
@@ -145,8 +157,7 @@ def model_info(model, verbose=False):
     except ImportError:
         fs = ''
 
-    logger.info(
-        'Model Summary: %g layers, %g parameters, %g gradients%s' % (len(list(model.parameters())), n_p, n_g, 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):