CoreML NMS and half fixes (#143)
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>single_channel
Glenn Jocher
2 years ago
committed by
GitHub
parent
55bdca6768
commit
172cef2d20
@ -1,15 +0,0 @@
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from ultralytics import YOLO
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from ultralytics.yolo.utils import ROOT
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if __name__ == "__main__":
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for m in list((ROOT / 'yolo/v8/models').rglob('*.yaml')):
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try:
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YOLO(m.name, verbose=True)
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except Exception as e:
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print(f'ERROR for {m}: {e}')
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# n vs n-seg: 8.9GFLOPs vs 12.8GFLOPs, 3.16M vs 3.6M. ch[0] // 4 (11.9GFLOPs, 3.39M)
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# s vs s-seg: 28.8GFLOPs vs 44.4GFLOPs, 11.1M vs 12.9M. ch[0] // 4 (39.5GFLOPs, 11.7M)
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# m vs m-seg: 79.3GFLOPs vs 113.8GFLOPs, 25.9M vs 29.5M. ch[0] // 4 (103.GFLOPs, 27.1M)
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# l vs l-seg: 165.7GFLOPs vs 226.3GFLOPs, 43.7M vs 49.6M. ch[0] // 4 (207GFLOPs, 45.7M)
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# x vs x-seg: 258.5GFLOPs vs 353.0GFLOPs, 68.3M vs 77.5M. ch[0] // 4 (324GFLOPs, 71.4M)
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@ -1,29 +0,0 @@
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lr0: 0.001 # initial learning rate (SGD=1E-2, Adam=1E-3)
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lrf: 0.01 # final OneCycleLR learning rate (lr0 * lrf)
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momentum: 0.937 # SGD momentum/Adam beta1
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weight_decay: 0.0005 # optimizer weight decay 5e-4
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warmup_epochs: 3.0 # warmup epochs (fractions ok)
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warmup_momentum: 0.8 # warmup initial momentum
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warmup_bias_lr: 0.1 # warmup initial bias lr
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box: 0.05 # box loss gain
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cls: 0.5 # cls loss gain
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cls_pw: 1.0 # cls BCELoss positive_weight
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obj: 1.0 # obj loss gain (scale with pixels)
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obj_pw: 1.0 # obj BCELoss positive_weight
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iou_t: 0.20 # IoU training threshold
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anchor_t: 4.0 # anchor-multiple threshold
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# anchors: 3 # anchors per output layer (0 to ignore)
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fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
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hsv_h: 0.015 # image HSV-Hue augmentation (fraction)
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hsv_s: 0.7 # image HSV-Saturation augmentation (fraction)
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hsv_v: 0.4 # image HSV-Value augmentation (fraction)
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degrees: 0.0 # image rotation (+/- deg)
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translate: 0.1 # image translation (+/- fraction)
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scale: 0.5 # image scale (+/- gain)
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shear: 0.0 # image shear (+/- deg)
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perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
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flipud: 0.0 # image flip up-down (probability)
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fliplr: 0.5 # image flip left-right (probability)
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mosaic: 1.0 # image mosaic (probability)
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mixup: 0.0 # image mixup (probability)
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copy_paste: 0.5 # segment copy-paste (probability)
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@ -1,85 +0,0 @@
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import cv2
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import hydra
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from ultralytics.yolo.data import build_dataloader
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from ultralytics.yolo.utils import DEFAULT_CONFIG
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from ultralytics.yolo.utils.plotting import plot_images
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class Colors:
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# Ultralytics color palette https://ultralytics.com/
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def __init__(self):
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# hex = matplotlib.colors.TABLEAU_COLORS.values()
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hexs = ('FF3838', 'FF9D97', 'FF701F', 'FFB21D', 'CFD231', '48F90A', '92CC17', '3DDB86', '1A9334', '00D4BB',
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'2C99A8', '00C2FF', '344593', '6473FF', '0018EC', '8438FF', '520085', 'CB38FF', 'FF95C8', 'FF37C7')
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self.palette = [self.hex2rgb(f'#{c}') for c in hexs]
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self.n = len(self.palette)
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def __call__(self, i, bgr=False):
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c = self.palette[int(i) % self.n]
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return (c[2], c[1], c[0]) if bgr else c
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@staticmethod
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def hex2rgb(h): # rgb order (PIL)
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return tuple(int(h[1 + i:1 + i + 2], 16) for i in (0, 2, 4))
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colors = Colors() # create instance for 'from utils.plots import colors'
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def plot_one_box(x, img, color=None, label=None, line_thickness=None):
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import random
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# Plots one bounding box on image img
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tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1 # line/font thickness
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color = color or [random.randint(0, 255) for _ in range(3)]
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c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))
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cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA)
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if label:
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tf = max(tl - 1, 1) # font thickness
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t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]
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c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3
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cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA) # filled
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cv2.putText(
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img,
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label,
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(c1[0], c1[1] - 2),
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0,
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tl / 3,
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[225, 255, 255],
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thickness=tf,
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lineType=cv2.LINE_AA,
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)
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@hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name)
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def test(cfg):
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cfg.task = "detect"
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cfg.mode = "train"
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dataloader, _ = build_dataloader(
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cfg=cfg,
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batch_size=4,
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img_path="/d/dataset/COCO/coco128-seg/images",
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stride=32,
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label_path=None,
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mode=cfg.mode,
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)
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for d in dataloader:
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images = d["img"]
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cls = d["cls"].squeeze(-1)
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bboxes = d["bboxes"]
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paths = d["im_file"]
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batch_idx = d["batch_idx"]
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result = plot_images(images, batch_idx, cls, bboxes, paths=paths)
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cv2.imshow("p", result)
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if cv2.waitKey(0) == ord("q"):
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break
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if __name__ == "__main__":
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test()
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# test(augment=True, rect=False)
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# test(augment=False, rect=True)
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# test(augment=False, rect=False)
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@ -1,125 +0,0 @@
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import cv2
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import numpy as np
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from omegaconf import OmegaConf
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from ultralytics.yolo.data import build_dataloader
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class Colors:
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# Ultralytics color palette https://ultralytics.com/
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def __init__(self):
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# hex = matplotlib.colors.TABLEAU_COLORS.values()
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hexs = ('FF3838', 'FF9D97', 'FF701F', 'FFB21D', 'CFD231', '48F90A', '92CC17', '3DDB86', '1A9334', '00D4BB',
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'2C99A8', '00C2FF', '344593', '6473FF', '0018EC', '8438FF', '520085', 'CB38FF', 'FF95C8', 'FF37C7')
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self.palette = [self.hex2rgb(f'#{c}') for c in hexs]
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self.n = len(self.palette)
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def __call__(self, i, bgr=False):
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c = self.palette[int(i) % self.n]
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return (c[2], c[1], c[0]) if bgr else c
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@staticmethod
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def hex2rgb(h): # rgb order (PIL)
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return tuple(int(h[1 + i:1 + i + 2], 16) for i in (0, 2, 4))
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colors = Colors() # create instance for 'from utils.plots import colors'
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def plot_one_box(x, img, keypoints=None, color=None, label=None, line_thickness=None):
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import random
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# Plots one bounding box on image img
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tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1 # line/font thickness
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color = color or [random.randint(0, 255) for _ in range(3)]
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c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))
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cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA)
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if label:
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tf = max(tl - 1, 1) # font thickness
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t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]
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c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3
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cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA) # filled
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cv2.putText(
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img,
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label,
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(c1[0], c1[1] - 2),
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0,
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tl / 3,
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[225, 255, 255],
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thickness=tf,
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lineType=cv2.LINE_AA,
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)
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if keypoints is not None:
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plot_keypoint(img, keypoints, color, tl)
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def plot_keypoint(img, keypoints, color, tl):
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num_l = len(keypoints)
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# clors = [(255, 0, 0),(0, 255, 0),(0, 0, 255),(255, 255, 0),(0, 255, 255)]
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# clors = [[random.randint(0, 255) for _ in range(3)] for _ in range(num_l)]
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for i in range(num_l):
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point_x = int(keypoints[i][0])
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point_y = int(keypoints[i][1])
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cv2.circle(img, (point_x, point_y), tl + 3, color, -1)
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with open("ultralytics/tests/data/dataloader/hyp_test.yaml") as f:
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hyp = OmegaConf.load(f)
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def test(augment, rect):
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dataloader, _ = build_dataloader(
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img_path="/d/dataset/COCO/images/val2017",
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imgsz=640,
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label_path=None,
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cache=False,
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hyp=hyp,
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augment=augment,
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prefix="",
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rect=rect,
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batch_size=4,
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stride=32,
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pad=0.5,
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use_segments=False,
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use_keypoints=True,
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)
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for d in dataloader:
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idx = 1 # show which image inside one batch
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img = d["img"][idx].numpy()
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img = np.ascontiguousarray(img.transpose(1, 2, 0))
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ih, iw = img.shape[:2]
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# print(img.shape)
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bidx = d["batch_idx"]
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cls = d["cls"][bidx == idx].numpy()
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bboxes = d["bboxes"][bidx == idx].numpy()
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bboxes[:, [0, 2]] *= iw
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bboxes[:, [1, 3]] *= ih
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keypoints = d["keypoints"][bidx == idx]
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keypoints[..., 0] *= iw
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keypoints[..., 1] *= ih
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# print(keypoints, keypoints.shape)
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# print(d["im_file"])
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for i, b in enumerate(bboxes):
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x, y, w, h = b
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x1 = x - w / 2
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x2 = x + w / 2
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y1 = y - h / 2
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y2 = y + h / 2
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c = int(cls[i][0])
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# print(x1, y1, x2, y2)
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plot_one_box([int(x1), int(y1), int(x2), int(y2)],
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img,
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keypoints=keypoints[i],
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label=f"{c}",
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color=colors(c))
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cv2.imshow("p", img)
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if cv2.waitKey(0) == ord("q"):
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break
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if __name__ == "__main__":
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test(augment=True, rect=False)
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test(augment=False, rect=True)
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test(augment=False, rect=False)
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@ -1,85 +0,0 @@
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import cv2
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import hydra
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from ultralytics.yolo.data import build_dataloader
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from ultralytics.yolo.utils import DEFAULT_CONFIG
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from ultralytics.yolo.utils.plotting import plot_images
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class Colors:
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# Ultralytics color palette https://ultralytics.com/
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def __init__(self):
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# hex = matplotlib.colors.TABLEAU_COLORS.values()
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hexs = ('FF3838', 'FF9D97', 'FF701F', 'FFB21D', 'CFD231', '48F90A', '92CC17', '3DDB86', '1A9334', '00D4BB',
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'2C99A8', '00C2FF', '344593', '6473FF', '0018EC', '8438FF', '520085', 'CB38FF', 'FF95C8', 'FF37C7')
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self.palette = [self.hex2rgb(f'#{c}') for c in hexs]
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self.n = len(self.palette)
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def __call__(self, i, bgr=False):
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c = self.palette[int(i) % self.n]
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return (c[2], c[1], c[0]) if bgr else c
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@staticmethod
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def hex2rgb(h): # rgb order (PIL)
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return tuple(int(h[1 + i:1 + i + 2], 16) for i in (0, 2, 4))
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colors = Colors() # create instance for 'from utils.plots import colors'
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def plot_one_box(x, img, color=None, label=None, line_thickness=None):
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import random
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# Plots one bounding box on image img
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tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1 # line/font thickness
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color = color or [random.randint(0, 255) for _ in range(3)]
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c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))
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cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA)
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if label:
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tf = max(tl - 1, 1) # font thickness
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t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]
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c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3
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cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA) # filled
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cv2.putText(
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img,
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label,
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(c1[0], c1[1] - 2),
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0,
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tl / 3,
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[225, 255, 255],
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thickness=tf,
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lineType=cv2.LINE_AA,
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)
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@hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name)
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def test(cfg):
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cfg.task = "segment"
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cfg.mode = "train"
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dataloader, _ = build_dataloader(
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cfg=cfg,
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batch_size=4,
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img_path="/d/dataset/COCO/coco128-seg/images",
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stride=32,
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label_path=None,
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mode=cfg.mode,
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)
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for d in dataloader:
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images = d["img"]
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masks = d["masks"]
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cls = d["cls"].squeeze(-1)
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bboxes = d["bboxes"]
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paths = d["im_file"]
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batch_idx = d["batch_idx"]
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result = plot_images(images, batch_idx, cls, bboxes, masks, paths=paths)
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cv2.imshow("p", result)
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if cv2.waitKey(0) == ord("q"):
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break
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if __name__ == "__main__":
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test()
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# test(augment=True, rect=False)
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# test(augment=False, rect=True)
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# test(augment=False, rect=False)
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@ -1,32 +0,0 @@
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from ultralytics.nn.tasks import DetectionModel
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from ultralytics.yolo.utils.checks import check_yaml
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def test_model_parser():
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cfg = check_yaml("yolov8n.yaml") # check YAML
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# Create model
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model = DetectionModel(cfg)
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model.info()
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'''
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# Options
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if opt.line_profile: # profile layer by layer
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model(im, profile=True)
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elif opt.profile: # profile forward-backward
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results = profile(input=im, ops=[model], n=3)
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elif opt.test: # test all models
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for cfg in Path(ROOT / 'models').rglob('yolo*.yaml'):
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try:
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_ = Model(cfg)
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except Exception as e:
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print(f'Error in {cfg}: {e}')
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else: # report fused model summary
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model.fuse()
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'''
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if __name__ == "__main__":
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test_model_parser()
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