| from torch import nn, Tensor |
| from torch.nn import ModuleList, LayerNorm |
|
|
| from .modules import PatchEmbedding3d, Block |
| from .positional_embedding import SinCosPositionalEmbedding |
|
|
|
|
| class MarlinEncoder(nn.Module): |
|
|
| def __init__(self, img_size=224, patch_size=16, n_frames=16, embed_dim=768, depth=12, |
| num_heads=12, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop_rate=0., attn_drop_rate=0., |
| norm_layer="LayerNorm", init_values=0., tubelet_size=2 |
| ): |
| super().__init__() |
|
|
| self.embed_dim = embed_dim |
| self.patch_embedding = PatchEmbedding3d( |
| input_size=(3, n_frames, img_size, img_size), |
| patch_size=(tubelet_size, patch_size, patch_size), |
| embedding=embed_dim |
| ) |
| num_patches = (img_size // patch_size) * (img_size // patch_size) * (n_frames // tubelet_size) |
|
|
| |
| self.pos_embedding = SinCosPositionalEmbedding((num_patches, embed_dim), dropout_rate=0.) |
|
|
| if norm_layer == "LayerNorm": |
| self.norm_layer = LayerNorm |
| self.norm = self.norm_layer(embed_dim) |
| else: |
| raise NotImplementedError("Only LayerNorm is supported") |
|
|
| self.blocks = ModuleList([ |
| Block( |
| dim=embed_dim, num_heads=num_heads, mlp_ratio=mlp_ratio, qkv_bias=qkv_bias, qk_scale=qk_scale, |
| drop=drop_rate, attn_drop=attn_drop_rate, norm_layer=self.norm_layer, |
| init_values=init_values) |
| for _ in range(depth) |
| ]) |
|
|
| self.apply(self._init_weights) |
|
|
| @staticmethod |
| def _init_weights(m): |
| if isinstance(m, nn.Linear): |
| nn.init.xavier_uniform_(m.weight) |
| if isinstance(m, nn.Linear) and m.bias is not None: |
| nn.init.constant_(m.bias, 0) |
| elif isinstance(m, nn.LayerNorm): |
| nn.init.constant_(m.bias, 0) |
| nn.init.constant_(m.weight, 1.0) |
|
|
| def forward_features(self, x): |
| for block in self.blocks: |
| x = block(x) |
| x = self.norm(x) |
| return x |
|
|
| def forward(self, x: Tensor, mask: Tensor) -> Tensor: |
| |
| assert len(x.shape) == 5, "x must be 5D" |
| emb = self.patch_embedding(x) |
| emb = self.pos_embedding(emb) |
| b, _, c = emb.shape |
| emb = emb[mask].view(b, -1, c) |
| emb = self.forward_features(emb) |
| return emb |
|
|
| def extract_features(self, x: Tensor, seq_mean_pool: bool) -> Tensor: |
| x = self.patch_embedding(x) |
| x = self.pos_embedding(x) |
| for block in self.blocks: |
| x = block(x) |
|
|
| if seq_mean_pool: |
| x = x.mean(dim=1) |
| x = self.norm(x) |
| return x |
|
|
