| import torch |
| from torch import nn |
| from torch.nn import Module, ModuleList |
|
|
| from einops import rearrange, repeat |
| from einops.layers.torch import Rearrange |
|
|
| |
|
|
| def pair(t): |
| return t if isinstance(t, tuple) else (t, t) |
|
|
| |
|
|
| 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 |
| self.grid_w = image_width // patch_width |
|
|
| 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)) |
| 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), |
| |
| 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(), |
| |
| 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(), |
| |
| 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) |
|
|
| |
| |
| |
| |
|
|
| |
| x = x[:, 1:, :] |
| 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 = model(input_img) |
| print("output shape:", output.shape) |
|
|
