| import torch |
| from torch import nn, einsum |
| import torch.nn.functional as F |
|
|
| from einops import rearrange, repeat |
| from einops.layers.torch import Rearrange |
|
|
| random_select = True |
|
|
| class Residual(nn.Module): |
| def __init__(self, fn): |
| super().__init__() |
| self.fn = fn |
| def forward(self, x, **kwargs): |
| return self.fn(x, **kwargs) + x |
|
|
| 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): |
| return self.net(x) |
|
|
| class Attention(nn.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.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, mask = None): |
| b, n, _, h = *x.shape, self.heads |
| 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 = h), qkv) |
|
|
| dots = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale |
| mask_value = -torch.finfo(dots.dtype).max |
|
|
| if mask is not None: |
| mask = F.pad(mask.flatten(1), (1, 0), value = True) |
| assert mask.shape[-1] == dots.shape[-1], 'mask has incorrect dimensions' |
| mask = rearrange(mask, 'b i -> b () i ()') * rearrange(mask, 'b j -> b () () j') |
| dots.masked_fill_(~mask, mask_value) |
| del mask |
|
|
| attn = dots.softmax(dim=-1) |
|
|
| out = einsum('b h i j, b h j d -> b h i d', attn, v) |
| out = rearrange(out, 'b h n d -> b n (h d)') |
| out = self.to_out(out) |
| return out |
|
|
| 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([ |
| Residual(PreNorm(dim, Attention(dim, heads = heads, dim_head = dim_head, dropout = dropout))), |
| Residual(PreNorm(dim, FeedForward(dim, mlp_dim, dropout = dropout))) |
| ])) |
| def forward(self, x, mask = None): |
| for attn, ff in self.layers: |
| x = attn(x, mask = mask) |
| x = ff(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__() |
| assert image_size % patch_size == 0, 'Image dimensions must be divisible by the patch size.' |
| num_patches = (image_size // patch_size) ** 2 |
| patch_dim = channels * patch_size ** 2 |
| 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_size, p2 = patch_size), |
| nn.Linear(patch_dim, 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, mask = None): |
| x = self.to_patch_embedding(img) |
| b, n, _ = x.shape |
|
|
| cls_tokens = repeat(self.cls_token, '() n d -> b n d', b = b) |
| x = torch.cat((cls_tokens, x), dim=1) |
| x += self.pos_embedding[:, :(n + 1)] |
| x = self.dropout(x) |
|
|
| x = self.transformer(x, mask) |
|
|
| x = x.mean(dim = 1) if self.pool == 'mean' else x[:, 0] |
|
|
| x = self.to_latent(x) |
| return self.mlp_head(x) |
|
|
|
|
|
|
| def valid_idx(idx, h): |
| i = idx // h |
| j = idx % h |
| pad = h // 7 |
| if j < pad or i >= h - pad or j >= h - pad: |
| return False |
| else: |
| return True |
|
|
| import random |
| from math import sqrt |
| class RandomSelect(nn.Module): |
| def __init__(self): |
| super().__init__() |
|
|
| def forward(self, x): |
| |
| size=x.shape[1] |
| h=int(sqrt(size)) |
| candidates = list(range(size)) |
| candidates = [idx for idx in candidates if valid_idx(idx, h)] |
| max_k = len(candidates) |
| if self.training and random_select: |
| k = 8 |
| if k==-1: |
| k=max_k |
| else: |
| k = max_k |
| candidates = random.sample(candidates, k) |
| x = x[:,candidates] |
| return x |
|
|
| class VideoiT(nn.Module): |
| def __init__(self, *, image_size, patch_size, num_patches, num_classes, dim, depth, heads, mlp_dim, pool = 'cls', channels = 3, dim_head = 64, dropout = 0., emb_dropout = 0.): |
| super().__init__() |
| assert image_size % patch_size == 0, 'Image dimensions must be divisible by the patch size.' |
| patch_dim = channels * patch_size ** 2 |
| assert pool in {'cls', 'mean'}, 'pool type must be either cls (cls token) or mean (mean pooling)' |
|
|
| self.to_patch = Rearrange('b c t (h p1) (w p2) -> b (h w) t (p1 p2 c)', p1 = patch_size, p2 = patch_size) |
| self.patch_to_embedding=nn.Linear(patch_dim, dim) |
| self.num_patches=num_patches |
| |
|
|
| 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.random_select=RandomSelect() |
| self.to_latent = nn.Identity() |
|
|
| self.mlp_head = nn.Sequential( |
| nn.LayerNorm(dim), |
| nn.Linear(dim, num_classes) |
| ) |
|
|
| def forward(self, img, mask = None): |
| real_b=img.shape[0] |
| x = self.to_patch(img) |
| x = self.random_select(x) |
| n=x.shape[1] |
| x=x.reshape(real_b*n,self.num_patches,-1) |
| x = self.patch_to_embedding(x) |
| b, n, _ = x.shape |
|
|
| cls_tokens = repeat(self.cls_token, '() n d -> b n d', b = b) |
| x = torch.cat((cls_tokens, x), dim=1) |
| x += self.pos_embedding[:, :(n + 1)] |
| x = self.dropout(x) |
|
|
| x = self.transformer(x, mask) |
|
|
| x = x.mean(dim = 1) if self.pool == 'mean' else x[:, 0] |
|
|
| x = self.to_latent(x) |
| x = self.mlp_head(x) |
| x = x.reshape(real_b,-1) |
| return x |
|
|
|
|
| class TimeTransformer(nn.Module): |
| def __init__(self,num_patches, num_classes, dim, depth, heads, mlp_dim, pool = 'cls', dim_head = 64, dropout = 0., emb_dropout = 0.): |
| super().__init__() |
| assert pool in {'cls', 'mean'}, 'pool type must be either cls (cls token) or mean (mean pooling)' |
|
|
| self.num_patches=num_patches |
| 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, x): |
| b, n, _ = x.shape |
|
|
| cls_tokens = repeat(self.cls_token, '() n d -> b n d', b = b) |
| x = torch.cat((cls_tokens, x), dim=1) |
| x += self.pos_embedding[:, :(n + 1)] |
| x = self.dropout(x) |
|
|
| x = self.transformer(x, mask=None) |
|
|
| x = x.mean(dim = 1) if self.pool == 'mean' else x[:, 0] |
|
|
| x = self.to_latent(x) |
| return self.mlp_head(x) |
