Import YOLOv5 dataloader (#94)
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
This commit is contained in:
Ayush Chaurasia
GitHub
10
.github/workflows/ci.yaml
vendored
10
.github/workflows/ci.yaml
vendored
@ -20,21 +20,21 @@ jobs:
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matrix:
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os: [ ubuntu-latest ]
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python-version: [ '3.10' ]
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model: [ yolov5n ]
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model: [ yolov8n ]
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torch: [ latest ]
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# include:
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# - os: ubuntu-latest
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# python-version: '3.7' # '3.6.8' min
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# model: yolov5n
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# model: yolov8n
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# - os: ubuntu-latest
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# python-version: '3.8'
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# model: yolov5n
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# model: yolov8n
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# - os: ubuntu-latest
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# python-version: '3.9'
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# model: yolov5n
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# model: yolov8n
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# - os: ubuntu-latest
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# python-version: '3.8' # torch 1.7.0 requires python >=3.6, <=3.8
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# model: yolov5n
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# model: yolov8n
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# torch: '1.7.0' # min torch version CI https://pypi.org/project/torchvision/
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steps:
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- uses: actions/checkout@v3
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402
ultralytics/yolo/data/dataloaders/v5augmentations.py
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402
ultralytics/yolo/data/dataloaders/v5augmentations.py
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@ -0,0 +1,402 @@
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# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
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"""
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Image augmentation functions
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"""
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import math
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import random
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import cv2
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import numpy as np
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import torch
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import torchvision.transforms as T
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import torchvision.transforms.functional as TF
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from ultralytics.yolo.utils import LOGGER, colorstr
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from ultralytics.yolo.utils.checks import check_version
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from ultralytics.yolo.utils.metrics import bbox_ioa
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from ultralytics.yolo.utils.ops import resample_segments, segment2box, xywhn2xyxy
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IMAGENET_MEAN = 0.485, 0.456, 0.406 # RGB mean
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IMAGENET_STD = 0.229, 0.224, 0.225 # RGB standard deviation
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class Albumentations:
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# YOLOv5 Albumentations class (optional, only used if package is installed)
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def __init__(self, size=640):
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self.transform = None
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prefix = colorstr('albumentations: ')
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try:
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import albumentations as A
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check_version(A.__version__, '1.0.3', hard=True) # version requirement
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T = [
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A.RandomResizedCrop(height=size, width=size, scale=(0.8, 1.0), ratio=(0.9, 1.11), p=0.0),
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A.Blur(p=0.01),
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A.MedianBlur(p=0.01),
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A.ToGray(p=0.01),
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A.CLAHE(p=0.01),
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A.RandomBrightnessContrast(p=0.0),
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A.RandomGamma(p=0.0),
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A.ImageCompression(quality_lower=75, p=0.0)] # transforms
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self.transform = A.Compose(T, bbox_params=A.BboxParams(format='yolo', label_fields=['class_labels']))
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LOGGER.info(prefix + ', '.join(f'{x}'.replace('always_apply=False, ', '') for x in T if x.p))
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except ImportError: # package not installed, skip
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pass
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except Exception as e:
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LOGGER.info(f'{prefix}{e}')
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def __call__(self, im, labels, p=1.0):
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if self.transform and random.random() < p:
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new = self.transform(image=im, bboxes=labels[:, 1:], class_labels=labels[:, 0]) # transformed
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im, labels = new['image'], np.array([[c, *b] for c, b in zip(new['class_labels'], new['bboxes'])])
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return im, labels
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def normalize(x, mean=IMAGENET_MEAN, std=IMAGENET_STD, inplace=False):
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# Denormalize RGB images x per ImageNet stats in BCHW format, i.e. = (x - mean) / std
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return TF.normalize(x, mean, std, inplace=inplace)
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def denormalize(x, mean=IMAGENET_MEAN, std=IMAGENET_STD):
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# Denormalize RGB images x per ImageNet stats in BCHW format, i.e. = x * std + mean
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for i in range(3):
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x[:, i] = x[:, i] * std[i] + mean[i]
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return x
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def augment_hsv(im, hgain=0.5, sgain=0.5, vgain=0.5):
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# HSV color-space augmentation
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if hgain or sgain or vgain:
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r = np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] + 1 # random gains
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hue, sat, val = cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV))
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dtype = im.dtype # uint8
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x = np.arange(0, 256, dtype=r.dtype)
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lut_hue = ((x * r[0]) % 180).astype(dtype)
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lut_sat = np.clip(x * r[1], 0, 255).astype(dtype)
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lut_val = np.clip(x * r[2], 0, 255).astype(dtype)
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im_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val)))
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cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR, dst=im) # no return needed
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def hist_equalize(im, clahe=True, bgr=False):
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# Equalize histogram on BGR image 'im' with im.shape(n,m,3) and range 0-255
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yuv = cv2.cvtColor(im, cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV)
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if clahe:
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c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
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yuv[:, :, 0] = c.apply(yuv[:, :, 0])
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else:
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yuv[:, :, 0] = cv2.equalizeHist(yuv[:, :, 0]) # equalize Y channel histogram
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return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB) # convert YUV image to RGB
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def replicate(im, labels):
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# Replicate labels
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h, w = im.shape[:2]
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boxes = labels[:, 1:].astype(int)
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x1, y1, x2, y2 = boxes.T
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s = ((x2 - x1) + (y2 - y1)) / 2 # side length (pixels)
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for i in s.argsort()[:round(s.size * 0.5)]: # smallest indices
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x1b, y1b, x2b, y2b = boxes[i]
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bh, bw = y2b - y1b, x2b - x1b
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yc, xc = int(random.uniform(0, h - bh)), int(random.uniform(0, w - bw)) # offset x, y
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x1a, y1a, x2a, y2a = [xc, yc, xc + bw, yc + bh]
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im[y1a:y2a, x1a:x2a] = im[y1b:y2b, x1b:x2b] # im4[ymin:ymax, xmin:xmax]
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labels = np.append(labels, [[labels[i, 0], x1a, y1a, x2a, y2a]], axis=0)
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return im, labels
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def letterbox(im, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True, stride=32):
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# Resize and pad image while meeting stride-multiple constraints
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shape = im.shape[:2] # current shape [height, width]
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if isinstance(new_shape, int):
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new_shape = (new_shape, new_shape)
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# Scale ratio (new / old)
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r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
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if not scaleup: # only scale down, do not scale up (for better val mAP)
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r = min(r, 1.0)
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# Compute padding
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ratio = r, r # width, height ratios
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new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
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dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding
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if auto: # minimum rectangle
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dw, dh = np.mod(dw, stride), np.mod(dh, stride) # wh padding
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elif scaleFill: # stretch
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dw, dh = 0.0, 0.0
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new_unpad = (new_shape[1], new_shape[0])
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ratio = new_shape[1] / shape[1], new_shape[0] / shape[0] # width, height ratios
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dw /= 2 # divide padding into 2 sides
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dh /= 2
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if shape[::-1] != new_unpad: # resize
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im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)
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top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
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left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
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im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border
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return im, ratio, (dw, dh)
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def random_perspective(im,
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targets=(),
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segments=(),
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degrees=10,
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translate=.1,
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scale=.1,
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shear=10,
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perspective=0.0,
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border=(0, 0)):
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# torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(0.1, 0.1), scale=(0.9, 1.1), shear=(-10, 10))
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# targets = [cls, xyxy]
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height = im.shape[0] + border[0] * 2 # shape(h,w,c)
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width = im.shape[1] + border[1] * 2
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# Center
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C = np.eye(3)
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C[0, 2] = -im.shape[1] / 2 # x translation (pixels)
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C[1, 2] = -im.shape[0] / 2 # y translation (pixels)
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# Perspective
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P = np.eye(3)
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P[2, 0] = random.uniform(-perspective, perspective) # x perspective (about y)
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P[2, 1] = random.uniform(-perspective, perspective) # y perspective (about x)
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# Rotation and Scale
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R = np.eye(3)
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a = random.uniform(-degrees, degrees)
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# a += random.choice([-180, -90, 0, 90]) # add 90deg rotations to small rotations
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s = random.uniform(1 - scale, 1 + scale)
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# s = 2 ** random.uniform(-scale, scale)
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R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s)
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# Shear
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S = np.eye(3)
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S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # x shear (deg)
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S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # y shear (deg)
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# Translation
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T = np.eye(3)
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T[0, 2] = random.uniform(0.5 - translate, 0.5 + translate) * width # x translation (pixels)
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T[1, 2] = random.uniform(0.5 - translate, 0.5 + translate) * height # y translation (pixels)
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# Combined rotation matrix
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M = T @ S @ R @ P @ C # order of operations (right to left) is IMPORTANT
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if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any(): # image changed
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if perspective:
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im = cv2.warpPerspective(im, M, dsize=(width, height), borderValue=(114, 114, 114))
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else: # affine
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im = cv2.warpAffine(im, M[:2], dsize=(width, height), borderValue=(114, 114, 114))
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# Visualize
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# import matplotlib.pyplot as plt
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# ax = plt.subplots(1, 2, figsize=(12, 6))[1].ravel()
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# ax[0].imshow(im[:, :, ::-1]) # base
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# ax[1].imshow(im2[:, :, ::-1]) # warped
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# Transform label coordinates
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n = len(targets)
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if n:
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use_segments = any(x.any() for x in segments)
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new = np.zeros((n, 4))
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if use_segments: # warp segments
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segments = resample_segments(segments) # upsample
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for i, segment in enumerate(segments):
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xy = np.ones((len(segment), 3))
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xy[:, :2] = segment
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xy = xy @ M.T # transform
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xy = xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2] # perspective rescale or affine
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# clip
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new[i] = segment2box(xy, width, height)
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else: # warp boxes
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xy = np.ones((n * 4, 3))
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xy[:, :2] = targets[:, [1, 2, 3, 4, 1, 4, 3, 2]].reshape(n * 4, 2) # x1y1, x2y2, x1y2, x2y1
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xy = xy @ M.T # transform
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xy = (xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2]).reshape(n, 8) # perspective rescale or affine
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# create new boxes
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x = xy[:, [0, 2, 4, 6]]
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y = xy[:, [1, 3, 5, 7]]
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new = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T
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# clip
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new[:, [0, 2]] = new[:, [0, 2]].clip(0, width)
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new[:, [1, 3]] = new[:, [1, 3]].clip(0, height)
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# filter candidates
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i = box_candidates(box1=targets[:, 1:5].T * s, box2=new.T, area_thr=0.01 if use_segments else 0.10)
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targets = targets[i]
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targets[:, 1:5] = new[i]
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return im, targets
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def copy_paste(im, labels, segments, p=0.5):
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# Implement Copy-Paste augmentation https://arxiv.org/abs/2012.07177, labels as nx5 np.array(cls, xyxy)
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n = len(segments)
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if p and n:
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h, w, c = im.shape # height, width, channels
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im_new = np.zeros(im.shape, np.uint8)
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# calculate ioa first then select indexes randomly
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boxes = np.stack([w - labels[:, 3], labels[:, 2], w - labels[:, 1], labels[:, 4]], axis=-1) # (n, 4)
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ioa = bbox_ioa(boxes, labels[:, 1:5]) # intersection over area
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indexes = np.nonzero((ioa < 0.30).all(1))[0] # (N, )
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n = len(indexes)
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for j in random.sample(list(indexes), k=round(p * n)):
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l, box, s = labels[j], boxes[j], segments[j]
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labels = np.concatenate((labels, [[l[0], *box]]), 0)
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segments.append(np.concatenate((w - s[:, 0:1], s[:, 1:2]), 1))
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cv2.drawContours(im_new, [segments[j].astype(np.int32)], -1, (1, 1, 1), cv2.FILLED)
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result = cv2.flip(im, 1) # augment segments (flip left-right)
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i = cv2.flip(im_new, 1).astype(bool)
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im[i] = result[i] # cv2.imwrite('debug.jpg', im) # debug
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return im, labels, segments
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def cutout(im, labels, p=0.5):
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# Applies image cutout augmentation https://arxiv.org/abs/1708.04552
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if random.random() < p:
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h, w = im.shape[:2]
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scales = [0.5] * 1 + [0.25] * 2 + [0.125] * 4 + [0.0625] * 8 + [0.03125] * 16 # image size fraction
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for s in scales:
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mask_h = random.randint(1, int(h * s)) # create random masks
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mask_w = random.randint(1, int(w * s))
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# box
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xmin = max(0, random.randint(0, w) - mask_w // 2)
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ymin = max(0, random.randint(0, h) - mask_h // 2)
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xmax = min(w, xmin + mask_w)
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ymax = min(h, ymin + mask_h)
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# apply random color mask
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im[ymin:ymax, xmin:xmax] = [random.randint(64, 191) for _ in range(3)]
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# return unobscured labels
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if len(labels) and s > 0.03:
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box = np.array([[xmin, ymin, xmax, ymax]], dtype=np.float32)
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ioa = bbox_ioa(box, xywhn2xyxy(labels[:, 1:5], w, h))[0] # intersection over area
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labels = labels[ioa < 0.60] # remove >60% obscured labels
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return labels
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def mixup(im, labels, im2, labels2):
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# Applies MixUp augmentation https://arxiv.org/pdf/1710.09412.pdf
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r = np.random.beta(32.0, 32.0) # mixup ratio, alpha=beta=32.0
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im = (im * r + im2 * (1 - r)).astype(np.uint8)
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labels = np.concatenate((labels, labels2), 0)
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return im, labels
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def box_candidates(box1, box2, wh_thr=2, ar_thr=100, area_thr=0.1, eps=1e-16): # box1(4,n), box2(4,n)
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# Compute candidate boxes: box1 before augment, box2 after augment, wh_thr (pixels), aspect_ratio_thr, area_ratio
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w1, h1 = box1[2] - box1[0], box1[3] - box1[1]
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w2, h2 = box2[2] - box2[0], box2[3] - box2[1]
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ar = np.maximum(w2 / (h2 + eps), h2 / (w2 + eps)) # aspect ratio
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return (w2 > wh_thr) & (h2 > wh_thr) & (w2 * h2 / (w1 * h1 + eps) > area_thr) & (ar < ar_thr) # candidates
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def classify_albumentations(
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augment=True,
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size=224,
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scale=(0.08, 1.0),
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ratio=(0.75, 1.0 / 0.75), # 0.75, 1.33
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hflip=0.5,
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vflip=0.0,
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jitter=0.4,
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mean=IMAGENET_MEAN,
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std=IMAGENET_STD,
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auto_aug=False):
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# YOLOv5 classification Albumentations (optional, only used if package is installed)
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prefix = colorstr('albumentations: ')
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try:
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import albumentations as A
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from albumentations.pytorch import ToTensorV2
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check_version(A.__version__, '1.0.3', hard=True) # version requirement
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if augment: # Resize and crop
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T = [A.RandomResizedCrop(height=size, width=size, scale=scale, ratio=ratio)]
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if auto_aug:
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# TODO: implement AugMix, AutoAug & RandAug in albumentation
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LOGGER.info(f'{prefix}auto augmentations are currently not supported')
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else:
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if hflip > 0:
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T += [A.HorizontalFlip(p=hflip)]
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if vflip > 0:
|
||||
T += [A.VerticalFlip(p=vflip)]
|
||||
if jitter > 0:
|
||||
color_jitter = (float(jitter),) * 3 # repeat value for brightness, contrast, satuaration, 0 hue
|
||||
T += [A.ColorJitter(*color_jitter, 0)]
|
||||
else: # Use fixed crop for eval set (reproducibility)
|
||||
T = [A.SmallestMaxSize(max_size=size), A.CenterCrop(height=size, width=size)]
|
||||
T += [A.Normalize(mean=mean, std=std), ToTensorV2()] # Normalize and convert to Tensor
|
||||
LOGGER.info(prefix + ', '.join(f'{x}'.replace('always_apply=False, ', '') for x in T if x.p))
|
||||
return A.Compose(T)
|
||||
|
||||
except ImportError: # package not installed, skip
|
||||
LOGGER.warning(f'{prefix}⚠️ not found, install with `pip install albumentations` (recommended)')
|
||||
except Exception as e:
|
||||
LOGGER.info(f'{prefix}{e}')
|
||||
|
||||
|
||||
def classify_transforms(size=224):
|
||||
# Transforms to apply if albumentations not installed
|
||||
assert isinstance(size, int), f'ERROR: classify_transforms size {size} must be integer, not (list, tuple)'
|
||||
# T.Compose([T.ToTensor(), T.Resize(size), T.CenterCrop(size), T.Normalize(IMAGENET_MEAN, IMAGENET_STD)])
|
||||
return T.Compose([CenterCrop(size), ToTensor(), T.Normalize(IMAGENET_MEAN, IMAGENET_STD)])
|
||||
|
||||
|
||||
class LetterBox:
|
||||
# YOLOv5 LetterBox class for image preprocessing, i.e. T.Compose([LetterBox(size), ToTensor()])
|
||||
def __init__(self, size=(640, 640), auto=False, stride=32):
|
||||
super().__init__()
|
||||
self.h, self.w = (size, size) if isinstance(size, int) else size
|
||||
self.auto = auto # pass max size integer, automatically solve for short side using stride
|
||||
self.stride = stride # used with auto
|
||||
|
||||
def __call__(self, im): # im = np.array HWC
|
||||
imh, imw = im.shape[:2]
|
||||
r = min(self.h / imh, self.w / imw) # ratio of new/old
|
||||
h, w = round(imh * r), round(imw * r) # resized image
|
||||
hs, ws = (math.ceil(x / self.stride) * self.stride for x in (h, w)) if self.auto else self.h, self.w
|
||||
top, left = round((hs - h) / 2 - 0.1), round((ws - w) / 2 - 0.1)
|
||||
im_out = np.full((self.h, self.w, 3), 114, dtype=im.dtype)
|
||||
im_out[top:top + h, left:left + w] = cv2.resize(im, (w, h), interpolation=cv2.INTER_LINEAR)
|
||||
return im_out
|
||||
|
||||
|
||||
class CenterCrop:
|
||||
# YOLOv5 CenterCrop class for image preprocessing, i.e. T.Compose([CenterCrop(size), ToTensor()])
|
||||
def __init__(self, size=640):
|
||||
super().__init__()
|
||||
self.h, self.w = (size, size) if isinstance(size, int) else size
|
||||
|
||||
def __call__(self, im): # im = np.array HWC
|
||||
imh, imw = im.shape[:2]
|
||||
m = min(imh, imw) # min dimension
|
||||
top, left = (imh - m) // 2, (imw - m) // 2
|
||||
return cv2.resize(im[top:top + m, left:left + m], (self.w, self.h), interpolation=cv2.INTER_LINEAR)
|
||||
|
||||
|
||||
class ToTensor:
|
||||
# YOLOv5 ToTensor class for image preprocessing, i.e. T.Compose([LetterBox(size), ToTensor()])
|
||||
def __init__(self, half=False):
|
||||
super().__init__()
|
||||
self.half = half
|
||||
|
||||
def __call__(self, im): # im = np.array HWC in BGR order
|
||||
im = np.ascontiguousarray(im.transpose((2, 0, 1))[::-1]) # HWC to CHW -> BGR to RGB -> contiguous
|
||||
im = torch.from_numpy(im) # to torch
|
||||
im = im.half() if self.half else im.float() # uint8 to fp16/32
|
||||
im /= 255.0 # 0-255 to 0.0-1.0
|
||||
return im
|
||||
1216
ultralytics/yolo/data/dataloaders/v5loader.py
Normal file
1216
ultralytics/yolo/data/dataloaders/v5loader.py
Normal file
File diff suppressed because it is too large
Load Diff
@ -1,7 +1,7 @@
|
||||
import torch
|
||||
import yaml
|
||||
|
||||
# from ultralytics import yolo
|
||||
from ultralytics import yolo # (required for python usage)
|
||||
# from ultralytics.yolo.data.utils import check_dataset, check_dataset_yaml
|
||||
from ultralytics.yolo.engine.trainer import DEFAULT_CONFIG
|
||||
from ultralytics.yolo.utils import LOGGER
|
||||
|
||||
@ -110,8 +110,9 @@ class BaseTrainer:
|
||||
world_size = torch.cuda.device_count()
|
||||
if world_size > 1 and "LOCAL_RANK" not in os.environ:
|
||||
command = generate_ddp_command(world_size, self)
|
||||
print('DDP command: ', command)
|
||||
subprocess.Popen(command)
|
||||
ddp_cleanup(command, self)
|
||||
# ddp_cleanup(command, self) # TODO: uncomment and fix
|
||||
else:
|
||||
self._do_train(int(os.getenv("RANK", -1)), world_size)
|
||||
|
||||
@ -121,7 +122,7 @@ class BaseTrainer:
|
||||
torch.cuda.set_device(rank)
|
||||
self.device = torch.device('cuda', rank)
|
||||
self.console.info(f"RANK - WORLD_SIZE - DEVICE: {rank} - {world_size} - {self.device} ")
|
||||
mp.use_start_method('spawn', force=True)
|
||||
mp.set_start_method('spawn', force=True)
|
||||
dist.init_process_group("nccl" if dist.is_nccl_available() else "gloo", rank=rank, world_size=world_size)
|
||||
|
||||
def _setup_train(self, rank, world_size):
|
||||
@ -195,6 +196,11 @@ class BaseTrainer:
|
||||
for i, batch in pbar:
|
||||
self.trigger_callbacks("on_train_batch_start")
|
||||
|
||||
# update dataloader attributes (optional)
|
||||
if epoch == (self.epochs - self.args.close_mosaic) and hasattr(self.train_loader.dataset, 'mosaic'):
|
||||
LOGGER.info("Closing dataloader mosaic")
|
||||
self.train_loader.dataset.mosaic = False
|
||||
|
||||
# warmup
|
||||
ni = i + nb * epoch
|
||||
if ni <= nw:
|
||||
@ -207,7 +213,7 @@ class BaseTrainer:
|
||||
if 'momentum' in x:
|
||||
x['momentum'] = np.interp(ni, xi, [self.args.warmup_momentum, self.args.momentum])
|
||||
|
||||
# Forward
|
||||
# forward
|
||||
with torch.cuda.amp.autocast(self.amp):
|
||||
batch = self.preprocess_batch(batch)
|
||||
preds = self.model(batch["img"])
|
||||
@ -217,10 +223,10 @@ class BaseTrainer:
|
||||
self.tloss = (self.tloss * i + self.loss_items) / (i + 1) if self.tloss is not None \
|
||||
else self.loss_items
|
||||
|
||||
# Backward
|
||||
# backward
|
||||
self.scaler.scale(self.loss).backward()
|
||||
|
||||
# Optimize - https://pytorch.org/docs/master/notes/amp_examples.html
|
||||
# optimize - https://pytorch.org/docs/master/notes/amp_examples.html
|
||||
if ni - last_opt_step >= self.accumulate:
|
||||
self.optimizer_step()
|
||||
last_opt_step = ni
|
||||
|
||||
@ -6,13 +6,15 @@ import sys
|
||||
import threading
|
||||
from pathlib import Path
|
||||
|
||||
import cv2
|
||||
import IPython
|
||||
import pandas as pd
|
||||
|
||||
# Constants
|
||||
FILE = Path(__file__).resolve()
|
||||
ROOT = FILE.parents[2] # YOLO
|
||||
RANK = int(os.getenv('RANK', -1))
|
||||
DATASETS_DIR = ROOT.parent / 'datasets' # YOLOv5 datasets directory
|
||||
DATASETS_DIR = Path(os.getenv('YOLOv5_DATASETS_DIR', ROOT.parent / 'datasets')) # global datasets directory
|
||||
NUM_THREADS = min(8, max(1, os.cpu_count() - 1)) # number of YOLOv5 multiprocessing threads
|
||||
AUTOINSTALL = str(os.getenv('YOLOv5_AUTOINSTALL', True)).lower() == 'true' # global auto-install mode
|
||||
FONT = 'Arial.ttf' # https://ultralytics.com/assets/Arial.ttf
|
||||
@ -20,6 +22,14 @@ VERBOSE = str(os.getenv('YOLOv5_VERBOSE', True)).lower() == 'true' # global ver
|
||||
TQDM_BAR_FORMAT = '{l_bar}{bar:10}{r_bar}' # tqdm bar format
|
||||
LOGGING_NAME = 'yolov5'
|
||||
|
||||
# Settings
|
||||
# torch.set_printoptions(linewidth=320, precision=5, profile='long')
|
||||
# np.set_printoptions(linewidth=320, formatter={'float_kind': '{:11.5g}'.format}) # format short g, %precision=5
|
||||
pd.options.display.max_columns = 10
|
||||
cv2.setNumThreads(0) # prevent OpenCV from multithreading (incompatible with PyTorch DataLoader)
|
||||
os.environ['NUMEXPR_MAX_THREADS'] = str(NUM_THREADS) # NumExpr max threads
|
||||
os.environ['OMP_NUM_THREADS'] = '1' if platform.system() == 'darwin' else str(NUM_THREADS) # OpenMP (PyTorch and SciPy)
|
||||
|
||||
|
||||
def is_colab():
|
||||
# Is environment a Google Colab instance?
|
||||
|
||||
@ -2,13 +2,13 @@
|
||||
# Default training settings and hyperparameters for medium-augmentation COCO training
|
||||
|
||||
# Task and Mode
|
||||
task: "classify" # choices=['detect', 'segment', 'classify', 'init'] # init is a special case
|
||||
mode: "train" # choice=['train', 'val', 'infer']
|
||||
task: "classify" # choices=['detect', 'segment', 'classify', 'init'] # init is a special case
|
||||
mode: "train" # choice=['train', 'val', 'infer']
|
||||
|
||||
# Train settings -------------------------------------------------------------------------------------------------------
|
||||
model: null # i.e. yolov5s.pt, yolo.yaml
|
||||
data: null # i.e. coco128.yaml
|
||||
epochs: 300
|
||||
epochs: 100
|
||||
batch_size: 16
|
||||
imgsz: 640
|
||||
nosave: False
|
||||
@ -42,10 +42,10 @@ noval: False
|
||||
save_json: False
|
||||
save_hybrid: False
|
||||
conf_thres: 0.001
|
||||
iou_thres: 0.6
|
||||
iou_thres: 0.7
|
||||
max_det: 300
|
||||
half: True
|
||||
dnn: False # use OpenCV DNN for ONNX inference
|
||||
dnn: False # use OpenCV DNN for ONNX inference
|
||||
plots: True
|
||||
|
||||
# Prediction settings:
|
||||
@ -56,9 +56,9 @@ save_conf: False
|
||||
save_crop: False
|
||||
hide_labels: False # hide labels
|
||||
hide_conf: False
|
||||
vid_stride: 1 # video frame-rate stride
|
||||
vid_stride: 1 # video frame-rate stride
|
||||
line_thickness: 3 # bounding box thickness (pixels)
|
||||
update: False # Update all models
|
||||
update: False # Update all models
|
||||
visualize: False
|
||||
augment: False
|
||||
agnostic_nms: False # class-agnostic NMS
|
||||
@ -77,7 +77,7 @@ cls: 0.5 # cls loss gain (scale with pixels)
|
||||
dfl: 1.5 # dfl loss gain
|
||||
fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
|
||||
label_smoothing: 0.0
|
||||
nbs: 64 # nominal batch size
|
||||
nbs: 64 # nominal batch size
|
||||
hsv_h: 0.015 # image HSV-Hue augmentation (fraction)
|
||||
hsv_s: 0.7 # image HSV-Saturation augmentation (fraction)
|
||||
hsv_v: 0.4 # image HSV-Value augmentation (fraction)
|
||||
@ -92,6 +92,9 @@ mosaic: 1.0 # image mosaic (probability)
|
||||
mixup: 0.0 # image mixup (probability)
|
||||
copy_paste: 0.0 # segment copy-paste (probability)
|
||||
|
||||
# For debugging. Don't change
|
||||
v5loader: True
|
||||
|
||||
# Hydra configs --------------------------------------------------------------------------------------------------------
|
||||
hydra:
|
||||
output_subdir: null # disable hydra directory creation
|
||||
|
||||
@ -47,7 +47,7 @@ def generate_ddp_command(world_size, trainer):
|
||||
if using_cli:
|
||||
file_name = generate_ddp_file(trainer)
|
||||
return [
|
||||
sys.executable, "-m", "torch.distributed.launch", "--nproc_per_node", f"{world_size}", "--master_port",
|
||||
sys.executable, "-m", "torch.distributed.run", "--nproc_per_node", f"{world_size}", "--master_port",
|
||||
f"{find_free_network_port()}", file_name] + sys.argv[1:]
|
||||
|
||||
|
||||
@ -55,7 +55,7 @@ def ddp_cleanup(command, trainer):
|
||||
# delete temp file if created
|
||||
# TODO: this is a temp solution in case the file is deleted before DDP launching
|
||||
time.sleep(5)
|
||||
tempfile_suffix = str(id(trainer)) + ".py"
|
||||
tempfile_suffix = f"{id(trainer)}.py"
|
||||
if tempfile_suffix in "".join(command):
|
||||
for chunk in command:
|
||||
if tempfile_suffix in chunk:
|
||||
|
||||
@ -4,7 +4,9 @@ import torch.nn as nn
|
||||
|
||||
from ultralytics.yolo import v8
|
||||
from ultralytics.yolo.data import build_dataloader
|
||||
from ultralytics.yolo.data.dataloaders.v5loader import create_dataloader
|
||||
from ultralytics.yolo.engine.trainer import DEFAULT_CONFIG, BaseTrainer
|
||||
from ultralytics.yolo.utils import colorstr
|
||||
from ultralytics.yolo.utils.loss import BboxLoss
|
||||
from ultralytics.yolo.utils.metrics import smooth_BCE
|
||||
from ultralytics.yolo.utils.modeling.tasks import DetectionModel
|
||||
@ -21,7 +23,22 @@ class DetectionTrainer(BaseTrainer):
|
||||
# TODO: manage splits differently
|
||||
# calculate stride - check if model is initialized
|
||||
gs = max(int(de_parallel(self.model).stride.max() if self.model else 0), 32)
|
||||
return build_dataloader(self.args, batch_size, img_path=dataset_path, stride=gs, rank=rank, mode=mode)[0]
|
||||
return create_dataloader(path=dataset_path,
|
||||
imgsz=self.args.imgsz,
|
||||
batch_size=batch_size,
|
||||
stride=gs,
|
||||
hyp=dict(self.args),
|
||||
augment=mode == "train",
|
||||
cache=self.args.cache,
|
||||
pad=0 if mode == "train" else 0.5,
|
||||
rect=self.args.rect,
|
||||
rank=rank,
|
||||
workers=self.args.workers,
|
||||
close_mosaic=self.args.close_mosaic != 0,
|
||||
prefix=colorstr(f'{mode}: '),
|
||||
shuffle=mode == "train",
|
||||
seed=self.args.seed)[0] if self.args.v5loader else \
|
||||
build_dataloader(self.args, batch_size, img_path=dataset_path, stride=gs, rank=rank, mode=mode)[0]
|
||||
|
||||
def preprocess_batch(self, batch):
|
||||
batch["img"] = batch["img"].to(self.device, non_blocking=True).float() / 255
|
||||
|
||||
42
ultralytics/yolo/v8/models/yolov8m.yaml
Normal file
42
ultralytics/yolo/v8/models/yolov8m.yaml
Normal file
@ -0,0 +1,42 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 0.67 # model depth multiple
|
||||
width_multiple: 0.75 # layer channel multiple
|
||||
|
||||
# YOLOv8.0m backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[[-1, 1, Conv, [64, 3, 2]], # 0-P1/2
|
||||
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
|
||||
[-1, 3, C2f, [128, True]],
|
||||
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
|
||||
[-1, 6, C2f, [256, True]],
|
||||
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
|
||||
[-1, 6, C2f, [512, True]],
|
||||
[-1, 1, Conv, [768, 3, 2]], # 7-P5/32
|
||||
[-1, 3, C2f, [768, True]],
|
||||
[-1, 1, SPPF, [768, 5]], # 9
|
||||
]
|
||||
|
||||
# YOLOv8.0m head
|
||||
head:
|
||||
[[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 6], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 3, C2f, [512]], # 13
|
||||
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 4], 1, Concat, [1]], # cat backbone P3
|
||||
[-1, 3, C2f, [256]], # 17 (P3/8-small)
|
||||
|
||||
[-1, 1, Conv, [256, 3, 2]],
|
||||
[[-1, 12], 1, Concat, [1]], # cat head P4
|
||||
[-1, 3, C2f, [512]], # 20 (P4/16-medium)
|
||||
|
||||
[-1, 1, Conv, [512, 3, 2]],
|
||||
[[-1, 9], 1, Concat, [1]], # cat head P5
|
||||
[-1, 3, C2f, [768]], # 23 (P5/32-large)
|
||||
|
||||
[[15, 18, 21], 1, Detect, [nc]], # Detect(P3, P4, P5)
|
||||
]
|
||||
42
ultralytics/yolo/v8/models/yolov8s.yaml
Normal file
42
ultralytics/yolo/v8/models/yolov8s.yaml
Normal file
@ -0,0 +1,42 @@
|
||||
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||||
|
||||
# Parameters
|
||||
nc: 80 # number of classes
|
||||
depth_multiple: 0.33 # model depth multiple
|
||||
width_multiple: 0.50 # layer channel multiple
|
||||
|
||||
# YOLOv8.0s backbone
|
||||
backbone:
|
||||
# [from, number, module, args]
|
||||
[[-1, 1, Conv, [64, 3, 2]], # 0-P1/2
|
||||
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
|
||||
[-1, 3, C2f, [128, True]],
|
||||
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
|
||||
[-1, 6, C2f, [256, True]],
|
||||
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
|
||||
[-1, 6, C2f, [512, True]],
|
||||
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
|
||||
[-1, 3, C2f, [1024, True]],
|
||||
[-1, 1, SPPF, [1024, 5]], # 9
|
||||
]
|
||||
|
||||
# YOLOv8.0s head
|
||||
head:
|
||||
[[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 6], 1, Concat, [1]], # cat backbone P4
|
||||
[-1, 3, C2f, [512]], # 13
|
||||
|
||||
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
|
||||
[[-1, 4], 1, Concat, [1]], # cat backbone P3
|
||||
[-1, 3, C2f, [256]], # 17 (P3/8-small)
|
||||
|
||||
[-1, 1, Conv, [256, 3, 2]],
|
||||
[[-1, 12], 1, Concat, [1]], # cat head P4
|
||||
[-1, 3, C2f, [512]], # 20 (P4/16-medium)
|
||||
|
||||
[-1, 1, Conv, [512, 3, 2]],
|
||||
[[-1, 9], 1, Concat, [1]], # cat head P5
|
||||
[-1, 3, C2f, [1024]], # 23 (P5/32-large)
|
||||
|
||||
[[15, 18, 21], 1, Detect, [nc]], # Detect(P3, P4, P5)
|
||||
]
|
||||
Reference in New Issue
Block a user