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''' |
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Exploring Temporal Coherence for More General Video Face Forgery Detection @ ICCV'2021 |
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Copyright (c) Xiamen University and its affiliates. |
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Modified by Yinglin Zheng from https://github.com/yinglinzheng/FTCN |
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''' |
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import torch |
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from torch import nn, einsum |
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import torch.nn.functional as F |
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from einops import rearrange, repeat |
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from einops.layers.torch import Rearrange |
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class Residual(nn.Module): |
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def __init__(self, fn): |
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super().__init__() |
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self.fn = fn |
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def forward(self, x, **kwargs): |
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return self.fn(x, **kwargs) + x |
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class PreNorm(nn.Module): |
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def __init__(self, dim, fn): |
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super().__init__() |
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self.norm = nn.LayerNorm(dim) |
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self.fn = fn |
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def forward(self, x, **kwargs): |
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return self.fn(self.norm(x), **kwargs) |
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class FeedForward(nn.Module): |
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def __init__(self, dim, hidden_dim, dropout = 0.): |
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super().__init__() |
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self.net = nn.Sequential( |
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nn.Linear(dim, hidden_dim), |
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nn.GELU(), |
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nn.Dropout(dropout), |
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nn.Linear(hidden_dim, dim), |
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nn.Dropout(dropout) |
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) |
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def forward(self, x): |
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return self.net(x) |
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class Attention(nn.Module): |
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def __init__(self, dim, heads = 8, dim_head = 64, dropout = 0.): |
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super().__init__() |
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inner_dim = dim_head * heads |
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project_out = not (heads == 1 and dim_head == dim) |
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self.heads = heads |
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self.scale = dim_head ** -0.5 |
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self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False) |
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self.to_out = nn.Sequential( |
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nn.Linear(inner_dim, dim), |
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nn.Dropout(dropout) |
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) if project_out else nn.Identity() |
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def forward(self, x, mask = None): |
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b, n, _, h = *x.shape, self.heads |
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qkv = self.to_qkv(x).chunk(3, dim = -1) |
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q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), qkv) |
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dots = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale |
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mask_value = -torch.finfo(dots.dtype).max |
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if mask is not None: |
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mask = F.pad(mask.flatten(1), (1, 0), value = True) |
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assert mask.shape[-1] == dots.shape[-1], 'mask has incorrect dimensions' |
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mask = rearrange(mask, 'b i -> b () i ()') * rearrange(mask, 'b j -> b () () j') |
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dots.masked_fill_(~mask, mask_value) |
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del mask |
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attn = dots.softmax(dim=-1) |
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out = einsum('b h i j, b h j d -> b h i d', attn, v) |
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out = rearrange(out, 'b h n d -> b n (h d)') |
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out = self.to_out(out) |
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return out |
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class Transformer(nn.Module): |
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def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout = 0.): |
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super().__init__() |
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self.layers = nn.ModuleList([]) |
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for _ in range(depth): |
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self.layers.append(nn.ModuleList([ |
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Residual(PreNorm(dim, Attention(dim, heads = heads, dim_head = dim_head, dropout = dropout))), |
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Residual(PreNorm(dim, FeedForward(dim, mlp_dim, dropout = dropout))) |
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])) |
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def forward(self, x, mask = None): |
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for attn, ff in self.layers: |
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x = attn(x, mask = mask) |
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x = ff(x) |
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return x |
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class ViT(nn.Module): |
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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.): |
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super().__init__() |
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assert image_size % patch_size == 0, 'Image dimensions must be divisible by the patch size.' |
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num_patches = (image_size // patch_size) ** 2 |
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patch_dim = channels * patch_size ** 2 |
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assert pool in {'cls', 'mean'}, 'pool type must be either cls (cls token) or mean (mean pooling)' |
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self.to_patch_embedding = nn.Sequential( |
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Rearrange('b c (h p1) (w p2) -> b (h w) (p1 p2 c)', p1 = patch_size, p2 = patch_size), |
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nn.Linear(patch_dim, dim), |
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) |
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self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim)) |
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self.cls_token = nn.Parameter(torch.randn(1, 1, dim)) |
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self.dropout = nn.Dropout(emb_dropout) |
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self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim, dropout) |
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self.pool = pool |
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self.to_latent = nn.Identity() |
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self.mlp_head = nn.Sequential( |
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nn.LayerNorm(dim), |
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nn.Linear(dim, num_classes) |
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) |
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def forward(self, img, mask = None): |
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x = self.to_patch_embedding(img) |
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b, n, _ = x.shape |
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cls_tokens = repeat(self.cls_token, '() n d -> b n d', b = b) |
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x = torch.cat((cls_tokens, x), dim=1) |
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x += self.pos_embedding[:, :(n + 1)] |
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x = self.dropout(x) |
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x = self.transformer(x, mask) |
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x = x.mean(dim = 1) if self.pool == 'mean' else x[:, 0] |
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x = self.to_latent(x) |
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return self.mlp_head(x) |
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def valid_idx(idx, h): |
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i = idx // h |
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j = idx % h |
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pad = h // 7 |
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if j < pad or i >= h - pad or j >= h - pad: |
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return False |
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else: |
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return True |
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import random |
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from math import sqrt |
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class RandomSelect(nn.Module): |
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def __init__(self): |
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super().__init__() |
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def forward(self, x): |
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size=x.shape[1] |
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h=int(sqrt(size)) |
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candidates = list(range(size)) |
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candidates = [idx for idx in candidates if valid_idx(idx, h)] |
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max_k = len(candidates) |
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if self.training: |
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k = 8 |
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if k==-1: |
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k=max_k |
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else: |
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k = max_k |
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candidates = random.sample(candidates, k) |
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x = x[:,candidates] |
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return x |
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class VideoiT(nn.Module): |
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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.): |
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super().__init__() |
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assert image_size % patch_size == 0, 'Image dimensions must be divisible by the patch size.' |
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patch_dim = channels * patch_size ** 2 |
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assert pool in {'cls', 'mean'}, 'pool type must be either cls (cls token) or mean (mean pooling)' |
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self.to_patch = Rearrange('b c t (h p1) (w p2) -> b (h w) t (p1 p2 c)', p1 = patch_size, p2 = patch_size) |
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self.patch_to_embedding=nn.Linear(patch_dim, dim) |
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self.num_patches=num_patches |
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self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim)) |
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self.cls_token = nn.Parameter(torch.randn(1, 1, dim)) |
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self.dropout = nn.Dropout(emb_dropout) |
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self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim, dropout) |
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self.pool = pool |
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self.random_select=RandomSelect() |
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self.to_latent = nn.Identity() |
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self.mlp_head = nn.Sequential( |
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nn.LayerNorm(dim), |
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nn.Linear(dim, num_classes) |
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) |
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def forward(self, img, mask = None): |
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real_b=img.shape[0] |
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x = self.to_patch(img) |
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x = self.random_select(x) |
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n=x.shape[1] |
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x=x.reshape(real_b*n,self.num_patches,-1) |
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x = self.patch_to_embedding(x) |
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b, n, _ = x.shape |
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cls_tokens = repeat(self.cls_token, '() n d -> b n d', b = b) |
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x = torch.cat((cls_tokens, x), dim=1) |
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x += self.pos_embedding[:, :(n + 1)] |
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x = self.dropout(x) |
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x = self.transformer(x, mask) |
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x = x.mean(dim = 1) if self.pool == 'mean' else x[:, 0] |
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x = self.to_latent(x) |
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x = self.mlp_head(x) |
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x = x.reshape(real_b,-1) |
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return x |
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class TimeTransformer(nn.Module): |
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def __init__(self,num_patches, num_classes, dim, depth, heads, mlp_dim, pool = 'cls', dim_head = 64, dropout = 0., emb_dropout = 0.): |
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super().__init__() |
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assert pool in {'cls', 'mean'}, 'pool type must be either cls (cls token) or mean (mean pooling)' |
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self.num_patches=num_patches |
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self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim)) |
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self.cls_token = nn.Parameter(torch.randn(1, 1, dim)) |
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self.dropout = nn.Dropout(emb_dropout) |
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self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim, dropout) |
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self.pool = pool |
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self.to_latent = nn.Identity() |
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self.mlp_head = nn.Sequential( |
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nn.LayerNorm(dim), |
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nn.Linear(dim, num_classes) |
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) |
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def forward(self, x): |
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b, n, _ = x.shape |
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cls_tokens = repeat(self.cls_token, '() n d -> b n d', b = b) |
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x = torch.cat((cls_tokens, x), dim=1) |
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x += self.pos_embedding[:, :(n + 1)] |
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x = self.dropout(x) |
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x = self.transformer(x, mask=None) |
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x = x.mean(dim = 1) if self.pool == 'mean' else x[:, 0] |
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x = self.to_latent(x) |
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return self.mlp_head(x) |
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