MultiModalMamba下載 - MultiModalMamba原始碼下載

MultiModalMamba

Ai源碼

1.0.0

下載

多模態

多模態曼巴 - [MMM]

多模態 Mamba（ MultiModalMamba ）是一種全新的 AI 模型，整合了 Vision Transformer（ViT）和 Mamba，創建了高效能的多模態模型。 MultiModalMamba基於 Zeta 構建，Zeta 是一個簡約而強大的 AI 框架，旨在簡化和增強機器學習模型管理。

同時處理和解釋多種資料類型的能力至關重要，世界不是一維的。 MultiModalMamba透過利用 Vision Transformer 和 Mamba 的功能來滿足這項需求，從而實現文字和影像資料的高效處理。這使得MultiModalMamba成為適用於各種人工智慧任務的多功能解決方案。

安裝

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` , 準備訓練模型

資料類型的靈活性： MultiModalMamba模型可以同時處理文字和圖像資料。這使得它能夠接受更廣泛的資料集和任務的訓練，包括那些需要理解文字和圖像資料的資料集和任務。
可自訂的架構： MultiModalMamba模型具有許多參數，例如深度、dropout、heads、d_state、image_size、patch_size、encoder_dim、encoder_depth、encoder_heads 和 fusion_method。這些參數可以根據當前任務的特定要求進行調整，從而允許模型架構的高度客製化。
傳回嵌入的選項： MultiModalMamba模型有一個 return_embeddings 選項。當設定為 True 時，模型將返回嵌入而不是最終輸出。這對於需要存取模型學習的中間表示的任務非常有用，例如遷移學習或特徵提取任務。

 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 )