pvt2代码

### PVT2 Code Implementation and Example The Pyramid Vision Transformer (PVT) series has been widely adopted due to its efficiency in processing images on mobile or edge devices. Specifically, the second version of PVT introduces improvements over earlier versions through hierarchical pyramid structures designed similarly to ResNet architectures[^3]. Below is an illustrative Python-based implementation inspired by these principles: #### A Simplified Version of PVTv2 Architecture Using PyTorch ```python import torch.nn as nn from einops.layers.torch import Rearrange class PatchEmbedding(nn.Module): """ Converts image patches into embeddings """ def __init__(self, patch_size=4, embed_dim=96): super().__init__() self.patch_embed = nn.Conv2d(3, embed_dim, kernel_size=patch_size, stride=patch_size) self.norm = nn.LayerNorm(embed_dim) def forward(self, x): x = self.patch_embed(x).permute(0, 2, 3, 1) x = self.norm(x) return x.permute(0, 3, 1, 2) class TransformerBlock(nn.Module): """ Basic transformer block with multi-head attention mechanism """ def __init__(self, dim, num_heads=8, mlp_ratio=4., drop_rate=0.): super().__init__() hidden_dim = int(dim * mlp_ratio) self.attn_norm = nn.LayerNorm(dim) self.mha = nn.MultiheadAttention(dim, num_heads=num_heads, dropout=drop_rate) self.ffn_norm = nn.LayerNorm(dim) self.ffn = nn.Sequential( nn.Linear(dim, hidden_dim), nn.GELU(), nn.Dropout(drop_rate), nn.Linear(hidden_dim, dim), nn.Dropout(drop_rate) ) def forward(self, x): attn_input = x.flatten(2).transpose(-1, -2) normed_attn_input = self.attn_norm(attn_input.transpose(-1, -2)) attn_output, _ = self.mha(normed_attn_input, normed_attn_input, normed_attn_input) ffn_input = attn_input + attn_output normed_ffn_input = self.ffn_norm(ffn_input.transpose(-1, -2)).transpose(-1, -2) output = ffn_input + self.ffn(normed_ffn_input) return output.reshape_as(x) class PVTv2Stage(nn.Module): """ Single stage within the PVT architecture """ def __init__(self, depth, dim, downsample=True): super().__init__() layers = [] for i in range(depth): layers.append(TransformerBlock(dim)) self.transformer_blocks = nn.Sequential(*layers) if downsample: self.downsample_layer = PatchEmbedding(patch_size=2, embed_dim=dim*2) def forward(self, x): x = self.transformer_blocks(x) if hasattr(self, 'downsample_layer'): x = self.downsample_layer(x) return x class PVTv2Model(nn.Module): """ Full model definition based on stages described above """ def __init__(self, depths=[2, 2, 6, 2], dims=[64, 128, 320, 512]): super().__init__() self.stages = nn.ModuleList([ PVTv2Stage(depths[i], dims[i], True if i != len(dims)-1 else False) for i in range(len(dims))] ) self.final_head = nn.Linear(dims[-1], 1000) # Assuming classification task def forward(self, x): for stage in self.stages: x = stage(x) x = x.mean([2, 3]) # Global average pooling out = self.final_head(x) return out ``` This simplified code demonstrates how one might implement a basic structure resembling PVTv2 using modern deep learning libraries.