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b41dbe3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 | import torch
from torch import nn
from torch.nn import Module, ModuleList
from einops import rearrange, repeat
from einops.layers.torch import Rearrange
# helpers
def pair(t):
return t if isinstance(t, tuple) else (t, t)
# classes
class FeedForward(Module):
def __init__(self, dim, hidden_dim, dropout = 0.):
super().__init__()
self.net = nn.Sequential(
nn.LayerNorm(dim),
nn.Linear(dim, hidden_dim),
nn.GELU(),
nn.Dropout(dropout),
nn.Linear(hidden_dim, dim),
nn.Dropout(dropout)
)
def forward(self, x):
return self.net(x)
class Attention(Module):
def __init__(self, dim, heads = 8, dim_head = 64, dropout = 0.):
super().__init__()
inner_dim = dim_head * heads
project_out = not (heads == 1 and dim_head == dim)
self.heads = heads
self.scale = dim_head ** -0.5
self.norm = nn.LayerNorm(dim)
self.attend = nn.Softmax(dim = -1)
self.dropout = nn.Dropout(dropout)
self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False)
self.to_out = nn.Sequential(
nn.Linear(inner_dim, dim),
nn.Dropout(dropout)
) if project_out else nn.Identity()
def forward(self, x):
x = self.norm(x)
qkv = self.to_qkv(x).chunk(3, dim = -1)
q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = self.heads), qkv)
dots = torch.matmul(q, k.transpose(-1, -2)) * self.scale
attn = self.attend(dots)
attn = self.dropout(attn)
out = torch.matmul(attn, v)
out = rearrange(out, 'b h n d -> b n (h d)')
return self.to_out(out)
class Transformer(Module):
def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout = 0.):
super().__init__()
self.norm = nn.LayerNorm(dim)
self.layers = ModuleList([])
for _ in range(depth):
self.layers.append(ModuleList([
Attention(dim, heads = heads, dim_head = dim_head, dropout = dropout),
FeedForward(dim, mlp_dim, dropout = dropout)
]))
def forward(self, x):
for attn, ff in self.layers:
x = attn(x) + x
x = ff(x) + x
return self.norm(x)
class ViT(Module):
def __init__(self, img_size=(128, 1024), patch_size=16, in_chans=2, n_classes=2, embed_dim=128, depth=6, num_heads=8, mlp_ratio=4., emb_dropout=0.2, dropout=0.2):
super().__init__()
image_height, image_width = pair(img_size)
patch_height, patch_width = pair(patch_size)
self.n_classes = n_classes
assert image_height % patch_height == 0 and image_width % patch_width == 0, 'Image dimensions must be divisible by the patch size.'
self.grid_h = image_height // patch_height # 128//16=8
self.grid_w = image_width // patch_width # 1024//16=64
num_patches = self.grid_h * self.grid_w
patch_dim = in_chans * patch_height * patch_width
self.to_patch_embedding = nn.Sequential(
Rearrange('b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1 = patch_height, p2 = patch_width),
nn.LayerNorm(patch_dim),
nn.Linear(patch_dim, embed_dim),
nn.LayerNorm(embed_dim),
)
self.cls_token = nn.Parameter(torch.randn(1, 1, embed_dim)) # num_cls_tokens = 1
self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, embed_dim))
self.dropout = nn.Dropout(emb_dropout)
dim_head = embed_dim // num_heads
mlp_dim = int(embed_dim * mlp_ratio)
self.transformer = Transformer(embed_dim, depth, num_heads, dim_head, mlp_dim, dropout)
self.seg_head = nn.Sequential(
Rearrange('b (h w) d -> b d h w', h=8, w=64, d=128),
# 8×64 → 16×128
nn.Upsample(scale_factor=(2,2), mode='bilinear', align_corners=False),
nn.Conv2d(128, 64, 3, padding=1, bias=False),
nn.BatchNorm2d(64),
nn.GELU(),
# 16×128 → 32×256
nn.Upsample(scale_factor=(2,2), mode='bilinear', align_corners=False),
nn.Conv2d(64, 32, 3, padding=1, bias=False),
nn.BatchNorm2d(32),
nn.GELU(),
# 32×256 → 128×1024（
nn.Upsample(scale_factor=(4,4), mode='bilinear', align_corners=False),
nn.Conv2d(32, self.n_classes, 1, padding=0),
)
def forward(self, img):
batch = img.shape[0]
x = self.to_patch_embedding(img)
cls_tokens = repeat(self.cls_token, '1 1 d -> b 1 d', b=batch)
x = torch.cat((cls_tokens, x), dim = 1)
seq = x.shape[1]
x = x + self.pos_embedding[:, :seq, :]
x = self.dropout(x)
x = self.transformer(x)
# # classification task
# x = x[:, 0]
# x = self.to_latent(x)
# x = self.mlp_head(x)
# segmentation task
x = x[:, 1:, :] # drop CLS, shape (batch, num_patches, embed_dim)
x = self.seg_head(x)
return x
if __name__ == "__main__":
model = ViT()
input_img = torch.randn(2, 2, 128, 1024)
print("input shape:", input_img.shape) # output: torch.Size([2, 2, 128, 1024])
output = model(input_img)
print("output shape:", output.shape) # output: torch.Size([2, 2, 128, 1024])
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