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我使用这个（链接）pytorch 教程并希望在其中添加网格搜索功能，sklearn.model_selection.GridSearchCV（链接），以优化超参数。我很难理解 gs.fit(x,y) 中的 X 和 Y 应该是什么；根据文档（链接） x 和 y 应该具有以下结构，但我无法弄清楚如何从代码中获取这些。PennFudanDataset 类的输出以不符合 X、YI 需要的形式返回 img 和 target。n_samples、n_features 是否在以下代码块中或在教程的关于模型的块中？

fit(X, y=None, *, groups=None, **fit_params)[source]

Run fit with all sets of parameters.

参数

Xarray-like of shape (n_samples, n_features)
Training vector, where n_samples is the number of samples and n_features is the number of features.

yarray-like of shape (n_samples, n_output) or (n_samples,), default=None
Target relative to X for classification or regression; None for unsupervised learning.

有没有其他我们可以使用的东西来代替这个特定的教程更容易实现？我读过有关 ray tune( link )、optuna( link ) 等的信息，但它们似乎比这更复杂。我目前也在研究 scipy.optimize.brute( link )，这似乎更简单。

PennFundanDataset 类：

import os
import numpy as np
import torch
from PIL import Image


class PennFudanDataset(object):
def __init__(self, root, transforms):
    self.root = root
    self.transforms = transforms
    # load all image files, sorting them to
    # ensure that they are aligned
    self.imgs = list(sorted(os.listdir(os.path.join(root, "PNGImages"))))
    self.masks = list(sorted(os.listdir(os.path.join(root, "PedMasks"))))

def __getitem__(self, idx):
    # load images ad masks
    img_path = os.path.join(self.root, "PNGImages", self.imgs[idx])
    mask_path = os.path.join(self.root, "PedMasks", self.masks[idx])
    img = Image.open(img_path).convert("RGB")
    # note that we haven't converted the mask to RGB,
    # because each color corresponds to a different instance
    # with 0 being background
    mask = Image.open(mask_path)
    # convert the PIL Image into a numpy array
    mask = np.array(mask)
    # instances are encoded as different colors
    obj_ids = np.unique(mask)
    # first id is the background, so remove it
    obj_ids = obj_ids[1:]

    # split the color-encoded mask into a set
    # of binary masks
    masks = mask == obj_ids[:, None, None]

    # get bounding box coordinates for each mask
    num_objs = len(obj_ids)
    boxes = []
    for i in range(num_objs):
        pos = np.where(masks[i])
        xmin = np.min(pos[1])
        xmax = np.max(pos[1])
        ymin = np.min(pos[0])
        ymax = np.max(pos[0])
        boxes.append([xmin, ymin, xmax, ymax])

    # convert everything into a torch.Tensor
    boxes = torch.as_tensor(boxes, dtype=torch.float32)
    # there is only one class
    labels = torch.ones((num_objs,), dtype=torch.int64)
    masks = torch.as_tensor(masks, dtype=torch.uint8)

    image_id = torch.tensor([idx])
    area = (boxes[:, 3] - boxes[:, 1]) * (boxes[:, 2] - boxes[:, 0])
    # suppose all instances are not crowd
    iscrowd = torch.zeros((num_objs,), dtype=torch.int64)

    target = {}
    target["boxes"] = boxes
    target["labels"] = labels
    target["masks"] = masks
    target["image_id"] = image_id
    target["area"] = area
    target["iscrowd"] = iscrowd

    if self.transforms is not None:
        img, target = self.transforms(img, target)

    return img, target

def __len__(self):
    return len(self.imgs)
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