写一个基于pytorch生成一个transformer机械手手部姿势识别的代码
时间: 2023-05-23 12:02:49 浏览: 53
以下是一个基于 PyTorch 的 transformer 机械手手部姿势识别代码的示例:
``` python
import torch
import torch.nn as nn
class TransformerHandPoseEstimator(nn.Module):
def __init__(self, num_poses, d_model=512, nhead=8, num_layers=6, dropout=0.1):
super(TransformerHandPoseEstimator, self).__init__()
self.pos_encoder = PositionalEncoding(d_model, dropout)
self.transformer_encoder = nn.TransformerEncoder(nn.TransformerEncoderLayer(d_model, nhead, dropout), num_layers)
self.linear = nn.Linear(d_model, num_poses)
def forward(self, src):
src = self.pos_encoder(src)
output = self.transformer_encoder(src)
output = self.linear(output)
return output
class PositionalEncoding(nn.Module):
def __init__(self, d_model=512, dropout=0.1, max_len=5000):
super(PositionalEncoding, self).__init__()
self.dropout = nn.Dropout(p=dropout)
# Compute the positional encodings once in log space.
pe = torch.zeros(max_len, d_model)
position = torch.arange(0, max_len, dtype=torch.float).unsqueeze(1)
div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
pe[:, 0::2] = torch.sin(position * div_term)
pe[:, 1::2] = torch.cos(position * div_term)
pe = pe.unsqueeze(0).transpose(0, 1)
self.register_buffer('pe', pe)
def forward(self, x):
x = x + self.pe[:x.size(0), :]
return self.dropout(x)
```
其中,PositionalEncoding 类是一个用于对输入进行位置编码的类,TransformerHandPoseEstimator 类是一个基于 transformer 的手部姿势识别模型。您可以根据自己的需求进行修改和调整。