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

# 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

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

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

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)

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)