Pyramid Vision Transformer Code
Contents
Pyramid Vision Transformer Code#
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
MLP#
class MLP(nn.Module):
def __init__(self, in_channel, hidden_features):
super().__init__()
hidden_features = hidden_features if hidden_features is not None else in_channel
self.fc1 = nn.Linear(in_channel, hidden_features)
self.act = nn.GELU()
self.fc2 = nn.Linear(hidden_features, in_channel)
def forward(self, x):
x = self.fc1(x)
x = self.act(x)
x = self.fc2(x)
return x
Attention#
class Attention(nn.Module):
def __init__(self, input_dim, num_heads=8, qk_scale=None, attn_drop=0, proj_drop=0, sr_scale=1):
super().__init__()
self.dim = input_dim
self.num_heads = num_heads
head_dim = input_dim // num_heads # dimension of each head
self.scale = qk_scale if qk_scale is not None else np.math.sqrt(head_dim)
self.q = nn.Linear(self.dim, self.dim)
self.kv = nn.Linear(self.dim, self.dim * 2)
self.proj = nn.Linear(self.dim, self.dim)
self.sr_scale = sr_scale
if sr_scale > 1:
self.sr = nn.Conv2d(self.dim, self.dim, kernel_size=sr_scale, stride=sr_scale)
self.norm = nn.LayerNorm(self.dim)
def forward(self, x, H, W):
B, N, C = x.shape
q = self.q(x).reshape(B, N, self.num_heads, C // self.num_heads).permute(0, 2, 1, 3)
if self.sr_scale > 1:
x_ = x.permute(0, 2, 1).reshape(B, C, H, W)
x_ = self.sr(x_).reshape(B, C, -1).permute(0, 2, 1)
x_ = self.norm(x_)
kv = self.kv(x_).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
else:
kv = self.kv(x).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
k, v = kv[0], kv[1]
attn = torch.matmul(q, k.transpose(-2, -1)) * self.scale
attn = attn.softmax(dim=-1)
x = torch.matmul(attn, v).reshape(B, N, C)
x = self.proj(x)
return x
class PatchEmbed(nn.Module):
def __init__(self, img_size=224, patch_size=16, in_channel=3, embed_dim=768):
super().__init__()
self.img_size = (img_size, img_size)
self.patch_size = (patch_size, patch_size)
self.H, self.W = self.img_size[0] // self.patch_size[0], self.img_size[1] // self.patch_size[1] # number of patch(width, height)
self.num_patches = self.H * self.W # number of patches
self.proj = nn.Conv2d(in_channel, embed_dim, kernel_size=self.patch_size, stride=self.patch_size)
self.norm = nn.LayerNorm(embed_dim)
def forward(self, x):
"""
input : x -> feature map of image
output : x -> output feature map, (H, W) -> number of patch(height, weight)
"""
B, C, H, W = x.shape
x = self.proj(x).flatten(2).transpose(1, 2)
x = self.norm(x)
H, W = H // self.patch_size[0], W // self.patch_size[1]
return x, (H, W)
Block#
class Block(nn.Module):
def __init__(self, dim, num_heads, mlp_ratio = 4, qk_scale = None, sr_scale=1):
super().__init__()
self.dim = dim
self.norm1 = nn.LayerNorm(dim)
self.attn = Attention(dim, num_heads = num_heads, qk_scale=qk_scale, sr_scale=sr_scale)
self.norm2 = nn.LayerNorm(dim)
mlp_hidden_dim = int(dim * mlp_ratio)
self.mlp = MLP(in_channel=dim, hidden_features=mlp_hidden_dim)
def forward(self, x, H, W):
x = self.norm1(x)
x = x + self.attn(x, H, W)
x = self.norm2(x)
x = x + self.mlp(x)
return x
PyramidVisionTransformer#
class PyramidVisionTransformer(nn.Module):
def __init__(self, img_size=224, patch_size=16, in_channel=3, num_classes=1000, embed_dims=[64, 128, 256, 512], num_heads=[1, 2, 4, 8], mlp_ratios=[4, 4, 4, 4], qk_scale=None, depths=[3, 4, 6, 3], sr_scales=[8, 4, 2, 1]):
super().__init__()
self.num_classes = num_classes
self.depths = depths
# patch embedding
self.patch_embed1 = PatchEmbed(img_size=img_size, patch_size=patch_size, in_channel=in_channel, embed_dim=embed_dims[0])
self.patch_embed2 = PatchEmbed(img_size=img_size // 4, patch_size=2, in_channel=embed_dims[0], embed_dim=embed_dims[1])
self.patch_embed3 = PatchEmbed(img_size=img_size // 8, patch_size=2, in_channel=embed_dims[1], embed_dim=embed_dims[2])
self.patch_embed4 = PatchEmbed(img_size=img_size // 16, patch_size=2, in_channel=embed_dims[2], embed_dim=embed_dims[3])
# position embedding
self.pos_embed1 = nn.Parameter(torch.randn(1, self.patch_embed1.num_patches, embed_dims[0]))
self.pos_embed2 = nn.Parameter(torch.randn(1, self.patch_embed2.num_patches, embed_dims[1]))
self.pos_embed3 = nn.Parameter(torch.randn(1, self.patch_embed3.num_patches, embed_dims[2]))
self.pos_embed4 = nn.Parameter(torch.randn(1, self.patch_embed4.num_patches + 1, embed_dims[3]))
self.block1 = nn.ModuleList([Block(
dim=embed_dims[0], num_heads=num_heads[0], mlp_ratio=mlp_ratios[0], qk_scale=qk_scale, sr_scale=sr_scales[0]
) for _ in range(depths[0])])
self.block2 = nn.ModuleList([Block(
dim=embed_dims[1], num_heads=num_heads[1], mlp_ratio=mlp_ratios[1], qk_scale=qk_scale, sr_scale=sr_scales[1]
) for _ in range(depths[1])])
self.block3 = nn.ModuleList([Block(
dim=embed_dims[2], num_heads=num_heads[2], mlp_ratio=mlp_ratios[2], qk_scale=qk_scale, sr_scale=sr_scales[2]
) for _ in range(depths[2])])
self.block4 = nn.ModuleList([Block(
dim=embed_dims[3], num_heads=num_heads[3], mlp_ratio=mlp_ratios[3], qk_scale=qk_scale, sr_scale=sr_scales[3]
) for _ in range(depths[3])])
self.norm = nn.LayerNorm(embed_dims[3])
self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dims[3]))
self.head = nn.Linear(embed_dims[3], num_classes) if num_classes > 0 else nn.Identity()
def forward(self, x):
# STEP1 - embedding
B = x.shape[0]
#stage 1
print('stage1 :', x.shape)
x, (H, W) = self.patch_embed1(x)
x = x + self.pos_embed1
for blk in self.block1:
x = blk(x, H, W)
x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
# stage 2
print('stage2 :', x.shape)
x, (H, W) = self.patch_embed2(x)
x = x + self.pos_embed2
for blk in self.block2:
x = blk(x, H, W)
x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
# stage 3
print('stage3 :', x.shape)
x, (H, W) = self.patch_embed3(x)
x = x + self.pos_embed3
for blk in self.block3:
x = blk(x, H, W)
x = x.reshape(B, H, W, -1).permute(0, 3, 1, 2).contiguous()
# stage 4
print('stage4 :', x.shape)
x, (H, W) = self.patch_embed4(x)
cls_tokens = self.cls_token.expand(B, -1, -1)
x = torch.cat((cls_tokens, x), dim=1)
x = x + self.pos_embed4
for blk in self.block4:
x = blk(x, H, W)
x = self.norm(x)
x = x[:, 0]
x = self.head(x)
return x
PvT-Tiny#
def PVT_Tiny():
model = PyramidVisionTransformer(
patch_size=4, embed_dims=[64, 128, 320, 512], num_heads=[1, 2, 5, 8], mlp_ratios=[8, 8, 4, 4], depths=[2, 2, 2, 2]
, sr_scales=[8, 4, 2, 1])
return model
Author by 지승환
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