Swin Transformer PyTorch
时间: 2023-11-15 22:48:59 浏览: 43
Swin Transformer PyTorch 是一个基于Transformer架构的深度学习模型,用于图像分类、目标检测和语义分割等任务。它是由香港科技大学和微软亚洲研究院的研究人员开发的,采用了分层的Transformer结构和局部注意力机制,能够处理大尺度的图像数据。Swin Transformer PyTorch已经在COCO2017目标检测挑战赛中获得了最佳单模型性能,并且在ImageNet数据集上也取得了很好的结果。
相关问题
swin transformer pytorch代码实现
Swin Transformer 是一种新型的 Transformer 模型,它在计算效率和模型性能之间取得了很好的平衡。以下是使用 PyTorch 实现 Swin Transformer 的代码示例:
```python
import torch
import torch.nn as nn
from einops.layers.torch import Rearrange
class SwinBlock(nn.Module):
def __init__(self, in_channels, out_channels, window_size=7, shift_size=2):
super().__init__()
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=1)
self.norm1 = nn.BatchNorm2d(out_channels)
self.window_size = window_size
self.shift_size = shift_size
self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=window_size, stride=shift_size, padding=window_size//2, groups=out_channels)
self.norm2 = nn.BatchNorm2d(out_channels)
self.conv3 = nn.Conv2d(out_channels, out_channels, kernel_size=1)
self.norm3 = nn.BatchNorm2d(out_channels)
self.downsample = nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size=1),
nn.BatchNorm2d(out_channels)
)
def forward(self, x):
identity = x
x = self.conv1(x)
x = self.norm1(x)
x = nn.functional.relu(x)
x = Rearrange(x, 'b c h w -> b (h w) c')
x = Rearrange(x, 'b (h w) c -> b h w c', h=int(x.shape[1]**0.5))
x = self.conv2(x)
x = self.norm2(x)
x = nn.functional.relu(x)
x = self.conv3(x)
x = self.norm3(x)
if identity.shape != x.shape:
identity = self.downsample(identity)
x += identity
return x
class SwinTransformer(nn.Module):
def __init__(self, in_channels=3, num_classes=1000, hidden_dim=96, layers=[2, 2, 6, 2], window_sizes=[7, 3, 3, 3], shift_sizes=[4, 2, 2, 2]):
super().__init__()
self.stem = nn.Sequential(
nn.Conv2d(in_channels, hidden_dim, kernel_size=4, stride=4),
nn.BatchNorm2d(hidden_dim),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2)
)
self.layers = nn.ModuleList()
for i in range(len(layers)):
layer = nn.ModuleList()
for j in range(layers[i]):
if j == 0:
block = SwinBlock(hidden_dim*(2**i), hidden_dim*(2**i), window_sizes[i], shift_sizes[i])
else:
block = SwinBlock(hidden_dim*(2**i), hidden_dim*(2**i+1), window_sizes[i], shift_sizes[i])
layer.append(block)
self.layers.append(layer)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(hidden_dim*(2**len(layers)), num_classes)
def forward(self, x):
x = self.stem(x)
for i in range(len(self.layers)):
for j in range(len(self.layers[i])):
x = self.layers[i][j](x)
x = self.avgpool(x)
x = torch.flatten(x, 1)
x = self.fc(x)
return x
```
这是一个简单的 Swin Transformer 实现,包含了 SwinBlock 和 SwinTransformer 两个类。其中 SwinBlock 是 Swin Transformer 的基本模块,SwinTransformer 是整个模型的实现。你可以根据自己的需求进行修改和扩展。
swin transformer pytorch上的代码
以下是Swin Transformer在PyTorch上的代码示例:
```python
import torch
import torch.nn as nn
from swin_transformer import SwinTransformer
# 定义模型
class CustomModel(nn.Module):
def __init__(self):
super().__init__()
self.swin = SwinTransformer(
img_size=224,
patch_size=4,
in_chans=3,
num_classes=10,
embed_dim=96,
depths=[2, 2, 6, 2],
num_heads=[3, 6, 12, 24],
window_size=7,
mlp_ratio=4.0,
qkv_bias=True,
qk_scale=None,
drop_rate=0.0,
attn_drop_rate=0.0,
drop_path_rate=0.2,
norm_layer=nn.LayerNorm,
ape=False,
patch_norm=True,
use_checkpoint=False
)
def forward(self, x):
x = self.swin(x)
return x
# 初始化模型并进行前向传播
model = CustomModel()
x = torch.randn(1, 3, 224, 224)
output = model(x)
```