MultiModalMamba下载 - MultiModalMamba源代码下载

MultiModalMamba

Ai源码

1.0.0

下载

多模态

多模态曼巴 - [MMM]

多模态 Mamba（ MultiModalMamba ）是一种全新的 AI 模型，集成了 Vision Transformer（ViT）和 Mamba，创建了高性能的多模态模型。 MultiModalMamba基于 Zeta 构建，Zeta 是一个简约而强大的 AI 框架，旨在简化和增强机器学习模型管理。

同时处理和解释多种数据类型的能力至关重要，世界不是一维的。 MultiModalMamba通过利用 Vision Transformer 和 Mamba 的功能来满足这一需求，从而实现文本和图像数据的高效处理。这使得MultiModalMamba成为适用于各种人工智能任务的多功能解决方案。

安装

pip3 install mmm-zeta

用法

`MultiModalMamba Block`

 # Import the necessary libraries
import torch 
from torch import nn
from mm_mamba import MultiModalMamba Block

# Create some random input tensors
x = torch . randn ( 1 , 16 , 64 )  # Tensor with shape (batch_size, sequence_length, feature_dim)
y = torch . randn ( 1 , 3 , 64 , 64 )  # Tensor with shape (batch_size, num_channels, image_height, image_width)

# Create an instance of the MultiModalMamba Block model
model = MultiModalMamba Block (
    dim = 64 ,  # Dimension of the token embeddings
    depth = 5 ,  # Number of Mamba layers
    dropout = 0.1 ,  # Dropout probability
    heads = 4 ,  # Number of attention heads
    d_state = 16 ,  # Dimension of the state embeddings
    image_size = 64 ,  # Size of the input image
    patch_size = 16 ,  # Size of each image patch
    encoder_dim = 64 ,  # Dimension of the encoder token embeddings
    encoder_depth = 5 ,  # Number of encoder transformer layers
    encoder_heads = 4 ,  # Number of encoder attention heads
    fusion_method = "mlp" ,
)

# Pass the input tensors through the model
out = model ( x , y )

# Print the shape of the output tensor
print ( out . shape )

`MultiModalMamba` , 准备训练模型

数据类型的灵活性： MultiModalMamba模型可以同时处理文本和图像数据。这使得它能够接受更广泛的数据集和任务的训练，包括那些需要理解文本和图像数据的数据集和任务。
可定制的架构： MultiModalMamba模型具有许多参数，例如深度、dropout、heads、d_state、image_size、patch_size、encoder_dim、encoder_depth、encoder_heads 和 fusion_method。这些参数可以根据当前任务的具体要求进行调整，从而允许模型架构的高度定制。
返回嵌入的选项： MultiModalMamba模型有一个 return_embeddings 选项。当设置为 True 时，模型将返回嵌入而不是最终输出。这对于需要访问模型学习的中间表示的任务非常有用，例如迁移学习或特征提取任务。

 import torch  # Import the torch library

# Import the MultiModalMamba model from the mm_mamba module
from mm_mamba import MultiModalMamba

# Generate a random tensor 'x' of size (1, 224) with random elements between 0 and 10000
x = torch . randint ( 0 , 10000 , ( 1 , 196 ))

# Generate a random image tensor 'img' of size (1, 3, 224, 224)
img = torch . randn ( 1 , 3 , 224 , 224 )

# Audio tensor 'aud' of size 2d
aud = torch . randn ( 1 , 224 )

# Video tensor 'vid' of size 5d - (batch_size, channels, frames, height, width)
vid = torch . randn ( 1 , 3 , 16 , 224 , 224 )

# Create a MultiModalMamba model object with the following parameters:
model = MultiModalMamba (
    vocab_size = 10000 ,
    dim = 512 ,
    depth = 6 ,
    dropout = 0.1 ,
    heads = 8 ,
    d_state = 512 ,
    image_size = 224 ,
    patch_size = 16 ,
    encoder_dim = 512 ,
    encoder_depth = 6 ,
    encoder_heads = 8 ,
    fusion_method = "mlp" ,
    return_embeddings = False ,
    post_fuse_norm = True ,
)

# Pass the tensor 'x' and 'img' through the model and store the output in 'out'
out = model ( x , img , aud , vid )

# Print the shape of the output tensor 'out'
print ( out . shape )