如何使用transforms.normalize（）函数处理py张量数据

在PyTorch中，`torch.nn.functional.normalize()` 函数用于归一化张量数据，通常应用于深度学习模型中，例如神经网络的输入特征或权重矩阵。这个函数可以按照指定的方式（L1、L2范数或其他自定义）将张量元素缩放到一定的范围内，使其在整个批次或通道维度上具有单位范数。

以下是基本的用法：

import torch
from torch.nn import functional as F

# 假设你有一个 PyTorch 张量 tensor，可能是输入特征或者模型的权重
tensor = torch.tensor([[1., 2., 3.], [4., 5., 6.]])  # 归一化前的张量

# 对整个张量进行 L2 归一化
normalized_tensor = F.normalize(tensor, p=2, dim=1)  # 第二个参数dim指定了沿哪个维度进行归一化

# 或者只对最后一维进行归一化（如果张量是多维的话）
normed = F.normalize(tensor, p=2, dim=-1)

# 输出的 normalized_tensor 中，每个样本都会沿着指定维度标准化到长度为1的向量

相关问题

import os import json import torch import torch.nn as nn import torch.optim as optim from torchvision import transforms, datasets from tqdm import tqdm from model import resnet34 def main(): device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") print("using {} device.".format(device)) data_transform = { "train": transforms.Compose([transforms.RandomResizedCrop(224), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])]), "val": transforms.Compose([transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])])}

这段代码中，首先引入了需要使用的库，包括os、json、torch、torch.nn、torch.optim、transforms、datasets和tqdm。然后定义了一个名为main的函数，这个函数包含以下几个步骤：

1. 判断是否可以使用CUDA加速，如果可以则使用CUDA，否则使用CPU。

2. 定义数据预处理的方法，包括在训练时进行随机裁剪、随机水平翻转、转换为张量、将像素值标准化等操作；在验证时进行大小调整、中心裁剪、转换为张量、将像素值标准化等操作。

在这段代码中，还用到了之前建立的模型，即从model.py文件中导入了resnet34模型。

yolov7的transforms.py代码

以下是YOLOv7中transforms.py的代码：

import torch
import numpy as np
import cv2


class RandomSize:
    def __init__(self, mode='range', size=0.2):
        self.mode = mode
        self.size = size

    def __call__(self, img, targets=None):
        if self.mode == 'range':
            rand_size = np.random.uniform(-self.size, self.size)
        elif self.mode == 'value':
            rand_size = self.size
        else:
            raise AttributeError("mode should be 'range' or 'value', but got {}".format(self.mode))

        height, width, _ = img.shape
        scale = 1 + rand_size
        new_height, new_width = int(height * scale), int(width * scale)

        img = cv2.resize(img, (new_width, new_height))

        if targets is not None:
            targets[:, [1, 3]] *= (new_width / width)
            targets[:, [2, 4]] *= (new_height / height)

        return img, targets


class RandomHSV:
    def __init__(self, hgain=0.5, sgain=0.5, vgain=0.5):
        self.hgain = hgain
        self.sgain = sgain
        self.vgain = vgain

    def __call__(self, img, targets=None):
        hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
        h, s, v = cv2.split(hsv)

        h = cv2.add(h, np.uint8(255 * self.hgain * np.random.uniform(-1, 1)))
        s = cv2.add(s, np.uint8(255 * self.sgain * np.random.uniform(-1, 1)))
        v = cv2.add(v, np.uint8(255 * self.vgain * np.random.uniform(-1, 1)))

        hsv = cv2.merge((h, s, v))
        img = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)

        return img, targets


class RandomBlur:
    def __init__(self, kernel_size=3):
        self.kernel_size = kernel_size

    def __call__(self, img, targets=None):
        if np.random.random() < 0.5:
            img = cv2.blur(img, (self.kernel_size, self.kernel_size))

        return img, targets


class RandomNoise:
    def __init__(self, mean=0, var=0.1):
        self.mean = mean
        self.var = var

    def __call__(self, img, targets=None):
        noise = np.zeros_like(img)
        cv2.randn(noise, self.mean, self.var)
        img = cv2.add(img, noise)

        return img, targets


class Resize:
    def __init__(self, size):
        self.size = size

    def __call__(self, img, targets=None):
        height, width, _ = img.shape
        new_height, new_width = self.size, self.size

        img = cv2.resize(img, (new_width, new_height))

        if targets is not None:
            targets[:, [1, 3]] *= (new_width / width)
            targets[:, [2, 4]] *= (new_height / height)

        return img, targets


class Letterbox:
    def __init__(self, size, color=(114, 114, 114)):
        self.size = size
        self.color = color

    def __call__(self, img, targets=None):
        height, width, _ = img.shape
        new_height, new_width = self.size, self.size

        scale = min(new_height / height, new_width / width)
        h, w = int(scale * height), int(scale * width)

        img = cv2.resize(img, (w, h))

        top = (new_height - h) // 2
        bottom = new_height - h - top
        left = (new_width - w) // 2
        right = new_width - w - left

        img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=self.color)

        if targets is not None:
            targets[:, [1, 3]] *= (w / width)
            targets[:, [2, 4]] *= (h / height)

            targets[:, [1, 3]] += left
            targets[:, [2, 4]] += top

        return img, targets


class RandomFlip:
    def __init__(self, prob=0.5):
        self.prob = prob

    def __call__(self, img, targets=None):
        if np.random.random() < self.prob:
            img = cv2.flip(img, 1)
            if targets is not None:
                targets[:, 1] = 1 - targets[:, 1]
        return img, targets


class Normalize:
    def __init__(self, mean=[0, 0, 0], std=[1, 1, 1], to_rgb=True):
        self.mean = mean
        self.std = std
        self.to_rgb = to_rgb

    def __call__(self, img, targets=None):
        if self.to_rgb:
            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
        img = img.astype(np.float32) / 255.

        img -= np.array(self.mean)
        img /= np.array(self.std)

        return img, targets


class ToTensor:
    def __call__(self, img, targets=None):
        img = img.transpose(2, 0, 1)
        img = torch.from_numpy(img).float()

        if targets is not None:
            targets = torch.from_numpy(targets).float()

        return img, targets


class Compose:
    def __init__(self, transforms):
        self.transforms = transforms

    def __call__(self, img, targets):
        for t in self.transforms:
            img, targets = t(img, targets)

        return img, targets

这个文件包含了一系列的数据增强函数，用于对输入图像进行处理，从而提高模型的鲁棒性和泛化能力。其中包括随机缩放、随机HSV变换、随机模糊、随机噪声、尺寸变换、letterbox变换、随机翻转、归一化和转换为张量等操作。这些函数可以根据需要进行灵活组合和选择，以便达到最佳的效果。