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])
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
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
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