孟村网站建设价格,网站开发的地图接口,wordpress密码字典,广州市番禺区住房和建设局网站YOLOv5 分类模型 数据集加载 3 自定义类别
flyfish
YOLOv5 分类模型 数据集加载 1 样本处理 YOLOv5 分类模型 数据集加载 2 切片处理 YOLOv5 分类模型的预处理#xff08;1#xff09; Resize 和 CenterCrop YOLOv5 分类模型的预处理#xff08;2#xff09;ToTensor 和 …YOLOv5 分类模型 数据集加载 3 自定义类别
flyfish
YOLOv5 分类模型 数据集加载 1 样本处理 YOLOv5 分类模型 数据集加载 2 切片处理 YOLOv5 分类模型的预处理1 Resize 和 CenterCrop YOLOv5 分类模型的预处理2ToTensor 和 Normalize YOLOv5 分类模型 Top 1和Top 5 指标说明 YOLOv5 分类模型 Top 1和Top 5 指标实现
之前的处理方式是类别名字是文件夹名字类别ID是按照文件夹名字的字母顺序 现在是类别名字是文件夹名字按照文件列表名字顺序 例如
classes_name[n02086240, n02087394, n02088364, n02089973, n02093754,
n02096294, n02099601, n02105641, n02111889, n02115641]n02086240 类别ID是0 n02087394 类别ID是1 代码处理是
if classes_name is None or not classes_name:classes, class_to_idx self.find_classes(self.root)print(not classes_name)else:classes classes_nameclass_to_idx {cls_name: i for i, cls_name in enumerate(classes)}print(is classes_name)完整
import time
from models.common import DetectMultiBackend
import os
import os.path
from typing import Any, Callable, cast, Dict, List, Optional, Tuple, Union
import cv2
import numpy as npimport torch
from PIL import Image
import torchvision.transforms as transformsimport sysclasses_name[n02086240, n02087394, n02088364, n02089973, n02093754, n02096294, n02099601, n02105641, n02111889, n02115641]class DatasetFolder:def __init__(self,root: str,) - None:self.root rootif classes_name is None or not classes_name:classes, class_to_idx self.find_classes(self.root)print(not classes_name)else:classes classes_nameclass_to_idx {cls_name: i for i, cls_name in enumerate(classes)}print(is classes_name)print(classes:,classes)print(class_to_idx:,class_to_idx)samples self.make_dataset(self.root, class_to_idx)self.classes classesself.class_to_idx class_to_idxself.samples samplesself.targets [s[1] for s in samples]staticmethoddef make_dataset(directory: str,class_to_idx: Optional[Dict[str, int]] None,) - List[Tuple[str, int]]:directory os.path.expanduser(directory)if class_to_idx is None:_, class_to_idx self.find_classes(directory)elif not class_to_idx:raise ValueError(class_to_index must have at least one entry to collect any samples.)instances []available_classes set()for target_class in sorted(class_to_idx.keys()):class_index class_to_idx[target_class]target_dir os.path.join(directory, target_class)if not os.path.isdir(target_dir):continuefor root, _, fnames in sorted(os.walk(target_dir, followlinksTrue)):for fname in sorted(fnames):path os.path.join(root, fname)if 1: # 验证:item path, class_indexinstances.append(item)if target_class not in available_classes:available_classes.add(target_class)empty_classes set(class_to_idx.keys()) - available_classesif empty_classes:msg fFound no valid file for the classes {, .join(sorted(empty_classes))}. return instancesdef find_classes(self, directory: str) - Tuple[List[str], Dict[str, int]]:classes sorted(entry.name for entry in os.scandir(directory) if entry.is_dir())if not classes:raise FileNotFoundError(fCouldnt find any class folder in {directory}.)class_to_idx {cls_name: i for i, cls_name in enumerate(classes)}return classes, class_to_idxdef __getitem__(self, index: int) - Tuple[Any, Any]:path, target self.samples[index]sample self.loader(path)return sample, targetdef __len__(self) - int:return len(self.samples)def loader(self, path):print(path:, path)#img cv2.imread(path) # BGR HWCimgImage.open(path).convert(RGB) # RGB HWCreturn imgdef time_sync():return time.time()#sys.exit()
dataset DatasetFolder(root/media/a/flyfish/source/yolov5/datasets/imagewoof/val)#image, labeldataset[7]#
weights /home/a/classes.pt
device cpu
model DetectMultiBackend(weights, devicedevice, dnnFalse, fp16False)
model.eval()
print(model.names)
print(type(model.names))mean[0.485, 0.456, 0.406]
std[0.229, 0.224, 0.225]
def preprocess(images):#实现 PyTorch Resizetarget_size 224img_w images.widthimg_h images.heightif(img_h img_w):# hwresize_img images.resize((target_size, int(target_size * img_h / img_w)), Image.BILINEAR)else:resize_img images.resize((int(target_size * img_w / img_h),target_size), Image.BILINEAR)#实现 PyTorch CenterCropwidth resize_img.widthheight resize_img.heightcenter_x,center_y width//2,height//2left center_x - (target_size//2)top center_y- (target_size//2)right center_x target_size//2bottom center_ytarget_size//2cropped_img resize_img.crop((left, top, right, bottom))#实现 PyTorch ToTensor Normalizeimages np.asarray(cropped_img)print(preprocess:,images.shape)images images.astype(float32)images (images/255-mean)/stdimages images.transpose((2, 0, 1))# HWC to CHWprint(preprocess:,images.shape)images np.ascontiguousarray(images)imagestorch.from_numpy(images)#images images.unsqueeze(dim0).float()return imagespred, targets, loss, dt [], [], 0, [0.0, 0.0, 0.0]
# current batch size 1
for i, (images, labels) in enumerate(dataset):print(i:, i)im preprocess(images)images im.unsqueeze(0).to(cpu).float()print(images.shape)t1 time_sync()images images.to(device, non_blockingTrue)t2 time_sync()# dt[0] t2 - t1y model(images)yy.numpy()#print(y:, y)t3 time_sync()# dt[1] t3 - t2#batch size 1 图像推理结果是二维的#y: [[ 4.0855 -1.1707 -1.4998 -0.935 -1.9979 -2.258 -1.4691 -1.0867 -1.9042 -0.99979]]tmp1y.argsort()[:,::-1][:, :5]#batch size 1 图像推理结果是一维的 就要处理下argsort的维度#y: [ 3.7441 -1.135 -1.1293 -0.9422 -1.6029 -2.0561 -1.025 -1.5842 -1.3952 -1.1824]#print(tmp1:, tmp1)pred.append(tmp1)#print(labels:, labels)targets.append(labels)#print(for pred:, pred) # list#print(for targets:, targets) # list# dt[2] time_sync() - t3pred, targets np.concatenate(pred), np.array(targets)
print(pred:, pred)
print(pred:, pred.shape)
print(targets:, targets)
print(targets:, targets.shape)
correct ((targets[:, None] pred)).astype(np.float32)
print(correct:, correct.shape)
print(correct:, correct)
acc np.stack((correct[:, 0], correct.max(1)), axis1) # (top1, top5) accuracy
print(acc:, acc.shape)
print(acc:, acc)
top acc.mean(0)
print(top1:, top[0])
print(top5:, top[1])
