YOLOv5代码阅读笔记

YOLOv5代码阅读笔记 1.train.py1.1 设置日志文件输出位置、名称等1.2 载入图片、标签1.3 确定图片训练的尺寸1.4 判断参数的形式为weight或bias，定义模型不同部分，并设置相应的学习率和参数。1.5 设置学习率的变化方式，这里采用cosine 学习率趋势1.6 定义训练集和验证集的数据读取方式1.7 统计样本分布1.8 获取anchor 2. dataset.py 中的数据增强2.1 mosaic数据增强 def load_mosaic(self, index)2.2 色彩空间HSV增强 3. 模型文件 YOLO.py主要讲解class Model 4. utils.py中的损失函数文章引用

1.train.py

该部分主要讲解train.py中的train(hyp)函数。

1.1 设置日志文件输出位置、名称等

log_dir = tb_writer.log_dir if tb_writer else 'runs-yolov5x/evolution' # run directory wdir = str(Path(log_dir) / 'weights') + os.sep # weights directory os.makedirs(wdir, exist_ok=True) last = wdir + 'last.pt' best = wdir + 'best.pt' results_file = log_dir + os.sep + 'results.txt' # Save run settings with open(Path(log_dir) / 'hyp.yaml', 'w') as f: yaml.dump(hyp, f, sort_keys=False) with open(Path(log_dir) / 'opt.yaml', 'w') as f: yaml.dump(vars(opt), f, sort_keys=False) 12345678910111213

1.2 载入图片、标签

# Configure init_seeds(1) with open(opt.data) as f: data_dict = yaml.load(f, Loader=yaml.FullLoader) # model dict train_path = data_dict['train'] test_path = data_dict['val'] nc, names = (1, ['item']) if opt.single_cls else (int(data_dict['nc']), data_dict['names']) # number classes, names assert len(names) == nc, '%g names found for nc=%g dataset in %s' % (len(names), nc, opt.data) # check 12345678

1.3 确定图片训练的尺寸

# Image sizes gs = int(max(model.stride)) # grid size (max stride) imgsz, imgsz_test = [check_img_size(x, gs) for x in opt.img_size] # verify imgsz are gs-multiples 123

1.4 判断参数的形式为weight或bias，定义模型不同部分，并设置相应的学习率和参数。

# Optimizer nbs = 64 # nominal batch size accumulate = max(round(nbs / batch_size), 1) # accumulate loss before optimizing hyp['weight_decay'] *= batch_size * accumulate / nbs # scale weight_decay pg0, pg1, pg2 = [], [], [] # optimizer parameter groups for k, v in model.named_parameters(): if v.requires_grad: if '.bias' in k: pg2.append(v) # biases elif '.weight' in k and '.bn' not in k: pg1.append(v) # apply weight decay else: pg0.append(v) # all else if hyp['optimizer'] == 'adam': # https://pytorch.org/docs/stable/_modules/torch/optim/lr_scheduler.html#OneCycleLR optimizer = optim.Adam(pg0, lr=hyp['lr0'], betas=(hyp['momentum'], 0.999)) # adjust beta1 to momentum else: optimizer = optim.SGD(pg0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True) optimizer.add_param_group({'params': pg1, 'weight_decay': hyp['weight_decay']}) # add pg1 with weight_decay optimizer.add_param_group({'params': pg2}) # add pg2 (biases) print('Optimizer groups: %g .bias, %g conv.weight, %g other' % (len(pg2), len(pg1), len(pg0))) del pg0, pg1, pg2

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1.5 设置学习率的变化方式，这里采用cosine 学习率趋势

因为学习率周期性变化可以解决陷入鞍点的问题。

# Scheduler https://arxiv.org/pdf/1812.01187.pdf lf = lambda x: (((1 + math.cos(x * math.pi / epochs)) / 2) ** 1.0) * 0.9 + 0.1 # cosine scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf) 123

1.6 定义训练集和验证集的数据读取方式

有博主说区别在于：testloader使用的是测试图片，保留了原来的宽长比（矩形输入），没有数据增强和mosaic过程。
但是 此代码中好像并未体现？

# Trainloader dataloader, dataset = create_dataloader(train_path, imgsz, batch_size, gs, opt, hyp=hyp, augment=True, cache=opt.cache_images, rect=opt.rect) mlc = np.concatenate(dataset.labels, 0)[:, 0].max() # max label class nb = len(dataloader) # number of batches assert mlc < nc, 'Label class %g exceeds nc=%g in %s. Correct your labels or your model.' % (mlc, nc, opt.cfg) # Testloader testloader = create_dataloader(test_path, imgsz_test, batch_size, gs, opt, hyp=hyp, augment=False, cache=opt.cache_images, rect=True)[0] 12345678910

1.7 统计样本分布

# Class frequency labels = np.concatenate(dataset.labels, 0) c = torch.tensor(labels[:, 0]) # classes # cf = torch.bincount(c.long(), minlength=nc) + 1. # model._initialize_biases(cf.to(device)) plot_labels(labels, save_dir=log_dir) if tb_writer: # tb_writer.add_hparams(hyp, {}) # causes duplicate https://github.com/ultralytics/yolov5/pull/384 tb_writer.add_histogram('classes', c, 0) 123456789

1.8 获取anchor

# Check anchors if not opt.noautoanchor: check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz) 123

2. dataset.py 中的数据增强

2.1 mosaic数据增强 def load_mosaic(self, index)

step1.
随机挑选4张图构成一个大图

self.mosaic = self.augment and not self.rect # load 4 images at a time into a mosaic (only during training) 1

step2.
获取该大图的中心位置 yc, xc。

labels4 = [] s = self.img_size yc, xc = [int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border] # mosaic center x, y indices = [index] + [random.randint(0, len(self.labels) - 1) for _ in range(3)] # 3 additional image indices 1234

此处的mosaic_border 定义如下：（其中，该代码中的img_size设置为640）

self.mosaic_border = [-img_size // 2, -img_size // 2] 1

step3.
再额外随机挑选3张图片，索引号为indices

indices = [index] + [random.randint(0, len(self.labels) - 1) for _ in range(3)] # 3 additional image indices 1

step4.
将4张图片按照 左上、左下、右上、右下放置
问题： step3.中挑选了3张图，此处的另一张小图来自于哪里？

# place img in img4 if i == 0: # top left img4 = np.full((s * 2, s * 2, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc # xmin, ymin, xmax, ymax (large image) x1b, y1b, x2b, y2b = w - (x2a - x1a), h - (y2a - y1a), w, h # xmin, ymin, xmax, ymax (small image) elif i == 1: # top right x1a, y1a, x2a, y2a = xc, max(yc - h, 0), min(xc + w, s * 2), yc x1b, y1b, x2b, y2b = 0, h - (y2a - y1a), min(w, x2a - x1a), h elif i == 2: # bottom left x1a, y1a, x2a, y2a = max(xc - w, 0), yc, xc, min(s * 2, yc + h) x1b, y1b, x2b, y2b = w - (x2a - x1a), 0, max(xc, w), min(y2a - y1a, h) elif i == 3: # bottom right x1a, y1a, x2a, y2a = xc, yc, min(xc + w, s * 2), min(s * 2, yc + h) x1b, y1b, x2b, y2b = 0, 0, min(w, x2a - x1a), min(y2a - y1a, h) 1234567891011121314

x1a, y1a, x2a, y2a代表小图在大图中的相对位置；
x1b, y1b, x2b, y2b代表小图本身的绝对位置；

step4.
小图中的坐标变换：

img4[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b] # img4[ymin:ymax, xmin:xmax] padw = x1a - x1b padh = y1a - y1b # Labels x = self.labels[index] labels = x.copy() if x.size > 0: # Normalized xywh to pixel xyxy format labels[:, 1] = w * (x[:, 1] - x[:, 3] / 2) + padw labels[:, 2] = h * (x[:, 2] - x[:, 4] / 2) + padh labels[:, 3] = w * (x[:, 1] + x[:, 3] / 2) + padw labels[:, 4] = h * (x[:, 2] + x[:, 4] / 2) + padh labels4.append(labels) 12345678910111213

step5.
对生成的坐标进行clip

# Concat/clip labels if len(labels4): labels4 = np.concatenate(labels4, 0) # np.clip(labels4[:, 1:] - s / 2, 0, s, out=labels4[:, 1:]) # use with center crop np.clip(labels4[:, 1:], 0, 2 * s, out=labels4[:, 1:]) # use with random_affine 12345

step6.
对mosaic之后的大图进行旋转、缩放等操作。
假设图片尺寸为6406403，经过mosaic操作后的尺寸为128012803，经过旋转，缩放后尺寸又变为6406403，也就是输入网络的图片尺寸。

# Augment # img4 = img4[s // 2: int(s * 1.5), s // 2:int(s * 1.5)] # center crop (WARNING, requires box pruning) img4, labels4 = random_affine(img4, labels4, degrees=self.hyp['degrees'], translate=self.hyp['translate'], scale=self.hyp['scale'], shear=self.hyp['shear'], border=self.mosaic_border) # border to remove return img4, labels4 12345678910

2.2 色彩空间HSV增强

def augment_hsv(img, hgain=0.5, sgain=0.5, vgain=0.5): r = np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] + 1 # random gains hue, sat, val = cv2.split(cv2.cvtColor(img, cv2.COLOR_BGR2HSV)) dtype = img.dtype # uint8 x = np.arange(0, 256, dtype=np.int16) lut_hue = ((x * r[0]) % 180).astype(dtype) lut_sat = np.clip(x * r[1], 0, 255).astype(dtype) lut_val = np.clip(x * r[2], 0, 255).astype(dtype) img_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))).astype(dtype) cv2.cvtColor(img_hsv, cv2.COLOR_HSV2BGR, dst=img) # no return needed 12345678910111213

3. 模型文件 YOLO.py

主要讲解class Model

其中比较重要的是计算输入和输出特征图之间的尺度比值 stride

# Build strides, anchors m = self.model[-1] # Detect() if isinstance(m, Detect): s = 128 # 2x min stride m.stride = torch.tensor([s / x.shape[-2] for x in self.forward(torch.zeros(1, ch, s, s))]) # forward m.anchors /= m.stride.view(-1, 1, 1) check_anchor_order(m) self.stride = m.stride self._initialize_biases() # only run once # print('Strides: %s' % m.stride.tolist()) 1234567891011

4. utils.py中的损失函数

详细见代码

文章引用

[1]. https://blog.csdn.net/weixin_41153216/article/details/106924348

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