YOLOv5 分类模型 Top 1和Top 5 指标实现

YOLOv5 分类模型 Top 1和Top 5 指标实现

flyfish

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 np

import torch
from utils.augmentations import classify_transforms


class DatasetFolder:

    def __init__(
        self,
        root: str,

    ) -> None:
        self.root = root
        classes, class_to_idx = self.find_classes(self.root)
        samples = self.make_dataset(self.root, class_to_idx)

        self.classes = classes
        self.class_to_idx = class_to_idx
        self.samples = samples
        self.targets = [s[1] for s in samples]

    @staticmethod
    def 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):
                continue
            for root, _, fnames in sorted(os.walk(target_dir, followlinks=True)):
                for fname in sorted(fnames):
                    path = os.path.join(root, fname)
                    if 1:  # 验证:
                        item = path, class_index
                        instances.append(item)

                        if target_class not in available_classes:
                            available_classes.add(target_class)

        empty_classes = set(class_to_idx.keys()) - available_classes
        if empty_classes:
            msg = f"Found no valid file for the classes {', '.join(sorted(empty_classes))}. "

        return instances

    def 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(f"Couldn't find any class folder in {directory}.")

        class_to_idx = {cls_name: i for i, cls_name in enumerate(classes)}
        return classes, class_to_idx

    def __getitem__(self, index: int) -> Tuple[Any, Any]:

        path, target = self.samples[index]
        sample = self.loader(path)

        return sample, target

    def __len__(self) -> int:
        return len(self.samples)

    def loader(self, path):
        print("path:", path)
        img = cv2.imread(path)  # BGR HWC
        return img


def time_sync():
    return time.time()


dataset = DatasetFolder(root="/media/flyfish/test/val")

# image, label=dataset[7]
# print(image.shape)
#
weights = "/media/flyfish/yolov5-6.2/classes10.pt"
device = "cpu"
model = DetectMultiBackend(weights, device=device, dnn=False, fp16=False)
model.eval()

transforms = classify_transforms(224)

pred, targets, loss, dt = [], [], 0, [0.0, 0.0, 0.0]
# current batch size =1
for i, (images, labels) in enumerate(dataset):
    print("i:", i)
    print(images.shape, labels)
    im = cv2.cvtColor(images, cv2.COLOR_BGR2RGB)
    im = transforms(im)
    images = im.unsqueeze(0).to("cpu")
 
    print(images.shape)


        
    t1 = time_sync()
    images = images.to(device, non_blocking=True)
    t2 = time_sync()
    # dt[0] += t2 - t1

    y = model(images)
    y=y.numpy()
   
    print("y:", y)
    t3 = time_sync()
    # dt[1] += t3 - t2

    tmp1=y.argsort()[:,::-1][:, :5]
   
    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() - t3


pred, 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)), axis=1)  # (top1, top5) accuracy
print("acc:", acc.shape)
print("acc:", acc)
top = acc.mean(0)
print("top1:", top[0])
print("top5:", top[1])
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输出

pred: [[7 4 0 5 9]
 [9 2 4 6 7]
 [8 9 6 2 1]
 [8 9 6 2 7]
 [9 2 4 6 3]
 [6 7 1 2 9]
 [4 2 1 8 9]
 [6 8 9 5 2]
 [8 7 4 2 6]
 [9 8 2 6 4]
 [2 9 8 0 6]
 [7 4 8 6 3]]
pred: (12, 5)
targets: [0 0 0 0 1 1 1 1 2 2 2 2]
targets: (12,)
correct: (12, 5)
correct: [[          0           0           1           0           0]
 [          0           0           0           0           0]
 [          0           0           0           0           0]
 [          0           0           0           0           0]
 [          0           0           0           0           0]
 [          0           0           1           0           0]
 [          0           0           1           0           0]
 [          0           0           0           0           0]
 [          0           0           0           1           0]
 [          0           0           1           0           0]
 [          1           0           0           0           0]
 [          0           0           0           0           0]]
acc: (12, 2)
acc: [[          0           1]
 [          0           0]
 [          0           0]
 [          0           0]
 [          0           0]
 [          0           1]
 [          0           1]
 [          0           0]
 [          0           1]
 [          0           1]
 [          1           1]
 [          0           0]]
top1: 0.083333336
top5: 0.5
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Yolov5 6.2 原版输出

pred: tensor([[6, 7, 1, 2, 9],
        [9, 2, 4, 6, 3],
        [7, 4, 0, 5, 9],
        [9, 8, 2, 6, 4],
        [6, 8, 9, 5, 2],
        [8, 7, 4, 2, 6],
        [9, 2, 4, 6, 7],
        [2, 9, 8, 0, 6],
        [8, 9, 6, 2, 7],
        [7, 4, 8, 6, 3],
        [4, 2, 1, 8, 9],
        [8, 9, 6, 2, 1]])
pred: torch.Size([12, 5])
targets: tensor([1, 1, 0, 2, 1, 2, 0, 2, 0, 2, 1, 0])
targets: torch.Size([12])
correct: torch.Size([12, 5])
acc: torch.Size([12, 2])
top1: 0.0833333358168602
top5: 0.5
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文本代码是按照标签，即文件夹名字排序的，pred和target都是一一对应的，与Yolov5 6.2 原版相同