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| import torch | |
| from torch import nn | |
| 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 PreNorm(nn.Module): | |
| def __init__(self, dim, fn): | |
| super().__init__() | |
| self.norm = nn.LayerNorm(dim) | |
| self.fn = fn | |
| def forward(self, x, **kwargs): | |
| return self.fn(self.norm(x), **kwargs) | |
| class FeedForward(nn.Module): | |
| def __init__(self, dim, hidden_dim, dropout = 0.): | |
| super().__init__() | |
| self.net = nn.Sequential( | |
| nn.Linear(dim, hidden_dim), | |
| nn.GELU(), | |
| nn.Dropout(dropout), | |
| nn.Linear(hidden_dim, dim), | |
| nn.Dropout(dropout) | |
| ) | |
| def forward(self, x): | |
| print(f'ff input size: {x.size()}') | |
| return self.net(x) | |
| class Attention(nn.Module): | |
| def __init__(self, dim, heads = 8, dim_head = 64, dropout = 0.): | |
| super().__init__() | |
| # dim_head: qkv output size of each head | |
| self.inner_dim = inner_dim = dim_head * heads | |
| project_out = not (heads == 1 and dim_head == dim) | |
| self.heads = heads | |
| self.scale = dim_head ** -0.5 | |
| self.attend = nn.Softmax(dim = -1) | |
| self.dropout = nn.Dropout(dropout) | |
| self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False) # to_q: (embed_dim, num_head, dim_head) | |
| self.to_out = nn.Sequential( | |
| nn.Linear(inner_dim, dim), | |
| nn.Dropout(dropout) | |
| ) if project_out else nn.Identity() | |
| def forward(self, x): | |
| print(f'attn input size: {x.size()}, to_qkv weight: {self.to_qkv}') | |
| print(self.inner_dim) | |
| qkv = self.to_qkv(x).chunk(3, dim = -1) | |
| print([i.size() for i in qkv]) | |
| q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = self.heads), qkv) | |
| print(q.size(), k.size(), v.size()) # (batch size 2, num_heads 12, num_patches + 1 65, d: dim_head) | |
| dots = torch.matmul(q, k.transpose(-1, -2)) * self.scale | |
| attn = self.attend(dots) | |
| attn = self.dropout(attn) | |
| out = torch.matmul(attn, v) | |
| # Attention(Q, K, V) = softmax(QK^T / sqrt(d_k)) * V | |
| out = rearrange(out, 'b h n d -> b n (h d)') | |
| print(f'out: {out.size()}') | |
| res = self.to_out(out) | |
| print(f'linear: {self.to_out}') | |
| print(f'result (out after linear): {res.size()}') | |
| return res | |
| class Transformer(nn.Module): | |
| def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout = 0.): | |
| super().__init__() | |
| self.layers = nn.ModuleList([]) | |
| for _ in range(depth): | |
| self.layers.append(nn.ModuleList([ | |
| PreNorm(dim, Attention(dim, heads = heads, dim_head = dim_head, dropout = dropout)), | |
| PreNorm(dim, 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 x | |
| class ViT(nn.Module): | |
| def __init__(self, *, image_size, patch_size, num_classes, dim, depth, heads, mlp_dim, pool = 'cls', channels = 3, dim_head = 64, dropout = 0., emb_dropout = 0.): | |
| super().__init__() | |
| image_height, image_width = pair(image_size) | |
| patch_height, patch_width = pair(patch_size) | |
| assert image_height % patch_height == 0 and image_width % patch_width == 0, 'Image dimensions must be divisible by the patch size.' | |
| num_patches = (image_height // patch_height) * (image_width // patch_width) | |
| self.patch_dim = patch_dim = channels * patch_height * patch_width | |
| assert pool in {'cls', 'mean'}, 'pool type must be either cls (cls token) or mean (mean pooling)' | |
| 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, dim), | |
| nn.LayerNorm(dim), | |
| ) | |
| self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim)) | |
| self.cls_token = nn.Parameter(torch.randn(1, 1, dim)) | |
| self.dropout = nn.Dropout(emb_dropout) | |
| self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim, dropout) | |
| self.pool = pool | |
| self.to_latent = nn.Identity() | |
| self.mlp_head = nn.Sequential( | |
| nn.LayerNorm(dim), | |
| nn.Linear(dim, num_classes) | |
| ) | |
| def forward(self, img): | |
| print(f'raw img: {img.size()}') # (B, c, h, w) | |
| x = self.to_patch_embedding(img) # (B, h*w/p^2, c*p^2) -> (B, h*w/p^2, d) | |
| print(f'raw patch dim: {self.patch_dim}') | |
| print(f'patch embeddings: {x.size()}') | |
| b, n, _ = x.shape # b: batch size, n: # patches | |
| cls_tokens = repeat(self.cls_token, '1 1 d -> b 1 d', b = b) | |
| print(f'class tokens: {cls_tokens.size()}') | |
| x = torch.cat((cls_tokens, x), dim=1) | |
| print(f'class tokens + patch embeddings: {x.size()}') | |
| # print(self.pos_embedding[:, :(n + 1)].size(), self.pos_embedding.size()) | |
| x += self.pos_embedding[:, :(n + 1)] | |
| x = self.dropout(x) | |
| x = self.transformer(x) | |
| x = x.mean(dim = 1) if self.pool == 'mean' else x[:, 0] | |
| x = self.to_latent(x) | |
| return self.mlp_head(x) | |
| if __name__ == '__main__': | |
| vit_b_32 = ViT( | |
| image_size = 256, | |
| patch_size = 32, | |
| num_classes = 1000, | |
| dim = 1024, # encoder layer/attention input/output size (Hidden Size D in the paper) | |
| depth = 12, | |
| heads = 12, # (Heads in the paper) | |
| dim_head = 64, # attention hidden size (seems be default, never change this) | |
| mlp_dim = 3072, # mlp layer hidden size (MLP size in the paper) | |
| dropout = 0., | |
| emb_dropout = 0. | |
| ) | |
| with torch.no_grad(): | |
| r = torch.rand((2, 3, 256, 256)) | |
| print(vit_b_32(r).size()) | |
| import os | |
| torch.save(vit_b_32, './vit_l.pt') | |
| print(os.path.getsize('./vit_l.pt') / 1024**2) | |
| os.remove('./vit_l.pt') |