Paddle.Hub 详解：通过 Paddle.Hub API 分享自己的项目

本文介绍如何通过Paddle.Hub在GitHub分享模型。先搭建MLP-Mixer模型，含MlpBlock、MixerBlock等组件及mixer_b、mixer_l预置模型。接着构建项目，含模型代码文件和hubconf.py配置文件。经本地测试后，上传至GitHub，他人可方便加载使用，接入过程简单。

引入

搭建模型

# 导入必要的包import paddleimport paddle.nn as nn# MLP Blockclass MlpBlock(nn.Layer):    def __init__(self, features_dim, mlp_dim):        super().__init__()        self.fc_0 = nn.Linear(features_dim, mlp_dim)        self.fc_1 = nn.Linear(mlp_dim, features_dim)    def forward(self, x):        y = self.fc_0(x)        y = nn.functional.gelu(y)        y = self.fc_1(y)        return y# Mixer Blockclass MixerBlock(nn.Layer):    def __init__(self, token_dim, channels_dim,                 tokens_mlp_dim, channels_mlp_dim,                 norm_layer=nn.LayerNorm, epsilon=1e-6):        super().__init__()        self.norm_0 = norm_layer(channels_dim, epsilon=epsilon)        self.token_mixing = MlpBlock(token_dim, tokens_mlp_dim)        self.norm_1 = norm_layer(channels_dim, epsilon=epsilon)        self.channel_mixing = MlpBlock(channels_dim, channels_mlp_dim)    def forward(self, x):        y = self.norm_0(x)        y = y.transpose((0, 2, 1))        y = self.token_mixing(y)        y = y.transpose((0, 2, 1))        x = x + y        y = self.norm_1(x)        y = self.channel_mixing(y)        x = x + y        return x# MLP-Mixerclass MlpMixer(nn.Layer):    def __init__(self, img_size=(224, 224), patch_size=(16, 16),                 num_blocks=12, hidden_dim=768,                 tokens_mlp_dim=384, channels_mlp_dim=3072,                 norm_layer=nn.LayerNorm, epsilon=1e-6,                 class_dim=1000):        super().__init__()        self.class_dim = class_dim        self.stem = nn.Conv2D(            3, hidden_dim, kernel_size=patch_size, stride=patch_size)        blocks = [            MixerBlock(                (img_size[0] // patch_size[0]) ** 2,                hidden_dim,                tokens_mlp_dim,                channels_mlp_dim,                norm_layer,                epsilon            ) for _ in range(num_blocks)        ]        self.blocks = nn.Sequential(*blocks)        self.pre_head_layer_norm = norm_layer(hidden_dim, epsilon=epsilon)        if class_dim > 0:            self.head = nn.Linear(hidden_dim, class_dim)    def forward(self, inputs):        x = self.stem(inputs)        x = x.transpose((0, 2, 3, 1))        x = x.flatten(1, 2)        x = self.blocks(x)        x = self.pre_head_layer_norm(x)        if self.class_dim > 0:            x = x.mean(axis=1)            x = self.head(x)        return x# 预置模型 mixer_b# 如果需要给出帮助信息# 需要在下方添加类似的注释def mixer_b(pretrained=False, **kwargs):    '''     Model: MLP-mixer-base    Params:        pretrained: load the pretrained model        img_size: input image size        patch_size: patch size        num_classes: number of classes        num_blocks: number of MixerBlock        hidden_dim: dim of hidden        tokens_mlp_dim: dim of tokens_mlp        channels_mlp_dim: dim of channels_mlp    '''    model = MlpMixer(        hidden_dim=768,         num_blocks=12,         tokens_mlp_dim=384,         channels_mlp_dim=3072,         **kwargs    )    # 一般分享模型都是包含预训练模型的    # 可通过网络加载    # 或者通过存放于项目目录中直接加载（GitHub 托管时不推荐，会极大的增加项目大小，拉取时间会非常漫长）    if pretrained:        path = paddle.utils.download.get_weights_path_from_url('https://bj.bcebos.com/v1/ai-studio-online/8fcd0b6ba98042d68763bbcbfe96375cbfd97ffed8334ac09787ef73ecf9989f?responseContentDisposition=attachment%3B%20filename%3Dimagenet1k_Mixer-B_16.pdparams')        model.set_dict(paddle.load(path))    return model# 预置模型 mixer_l# 如果需要给出帮助信息# 需要在下方添加类似的注释def mixer_l(pretrained=False, **kwargs):    '''     Model: MLP-mixer-large    Params:        pretrained: load the pretrained model        img_size: input image size        patch_size: patch size        num_classes: number of classes        num_blocks: number of MixerBlock        hidden_dim: dim of hidden        tokens_mlp_dim: dim of tokens_mlp        channels_mlp_dim: dim of channels_mlp    '''    model = MlpMixer(        hidden_dim=1024,         num_blocks=24,         tokens_mlp_dim=512,         channels_mlp_dim=4096,         **kwargs    )    # 一般分享模型都是包含预训练模型的    # 可通过网络加载    # 或者通过存放于项目目录中直接加载（GitHub 托管时不推荐，会极大的增加项目大小，拉取时间会非常漫长）    if pretrained:        path = paddle.utils.download.get_weights_path_from_url('https://bj.bcebos.com/v1/ai-studio-online/ca74ababd4834e34b089c1485989738de4fdf6a97be645ed81b6e39449c5815c?responseContentDisposition=attachment%3B%20filename%3Dimagenet1k_Mixer-L_16.pdparams')        model.set_dict(paddle.load(path))    return model

测试模型

model = mixer_b(pretrained=True)out = model(paddle.randn((1, 3, 224, 224)))print(out.shape)

[1, 1000]

项目搭建

# hubconf.py# 依赖包dependencies = ['paddle', 'numpy']# 导入要分享的模型from model import mixer_b, mixer_l

项目测试

import paddle# 获取模型列表model_list = paddle.hub.list('MLP-Mixer-Paddle', source='local', force_reload=False)print("模型列表如下：\n", model_list)# 获取 mixer_b 的帮助文档model_help = paddle.hub.help('MLP-Mixer-Paddle', 'mixer_b', source='local', force_reload=False)print("mixer_b 的帮助文档如下：\n", model_help)# 加载 mixer_b 模型并测试model = paddle.hub.load('MLP-Mixer-Paddle', 'mixer_b', source='local', force_reload=False, pretrained=True)out = model(paddle.randn((1, 3, 224, 224)))print("输出的形状为：", out.shape)

模型列表如下： ['mixer_b', 'mixer_l']mixer_b 的帮助文档如下：      Model: MLP-mixer-base    Params:        pretrained: load the pretrained model        img_size: input image size        patch_size: patch size        num_classes: number of classes        num_blocks: number of MixerBlock        hidden_dim: dim of hidden        tokens_mlp_dim: dim of tokens_mlp        channels_mlp_dim: dim of channels_mlp    输出的形状为： [1, 1000]

上传代码

import paddle# 获取模型列表model_list = paddle.hub.list('jm12138/MLP-Mixer-Paddle:main', source='github', force_reload=False)print("模型列表如下：\n", model_list)# 获取 mixer_b 的帮助文档model_help = paddle.hub.help('jm12138/MLP-Mixer-Paddle:main', 'mixer_b', source='github', force_reload=False)print("mixer_b 的帮助文档如下：\n", model_help)# 加载 mixer_b 模型并测试model = paddle.hub.load('jm12138/MLP-Mixer-Paddle:main', 'mixer_b', source='github', force_reload=False, pretrained=True)out = model(paddle.randn((1, 3, 224, 224)))print("输出的形状为：", out.shape)

模型列表如下： ['mixer_b', 'mixer_l']mixer_b 的帮助文档如下：      Model: MLP-mixer-base    Params:        pretrained: load the pretrained model        img_size: input image size        patch_size: patch size        num_classes: number of classes        num_blocks: number of MixerBlock        hidden_dim: dim of hidden        tokens_mlp_dim: dim of tokens_mlp        channels_mlp_dim: dim of channels_mlp

Using cache found in /home/aistudio/.cache/paddle/hub/jm12138_MLP-Mixer-Paddle_mainUsing cache found in /home/aistudio/.cache/paddle/hub/jm12138_MLP-Mixer-Paddle_main

输出的形状为： [1, 1000]

总结

你是一名 AI 行业编辑，请围绕下面这条热点输出一份资讯解读：
热点：Paddle.Hub 详解：通过 Paddle.Hub API 分享自己的项目要求：
1. 先用一句话解释这条热点在讲什么
2. 再总结它为什么重要
3. 说明会影响哪些 AI 产品或内容方向
4. 最后给出 3 个适合资讯站使用的标题