第一次提交Yolo项目

2025-12-27 02:14:11 +08:00
commit 604951f9c2
33 changed files with 5891 additions and 0 deletions
--- a/mobileclip/modules/common/transformer.py
+++ b/mobileclip/modules/common/transformer.py
@@ -0,0 +1,410 @@
+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+#
+# For licensing see accompanying LICENSE file.
+# Copyright (C) 2024 Apple Inc. All Rights Reserved.
+#
+"""
+Implementation of the following modules is borrowed from ml-cvnets repo:
+https://github.com/apple/ml-cvnets/blob/main/cvnets/layers/multi_head_attention.py
+https://github.com/apple/ml-cvnets/blob/main/cvnets/text_encoders/transformer.py.
+
+Please see ACKNOWLEDGMENTS for license details.
+"""
+
+from __future__ import annotations
+
+import torch
+from torch import Size, Tensor, nn
+from torch.nn import functional as F
+from torchvision.ops import StochasticDepth
+
+from mobileclip import logger
+
+
+class LayerNormFP32(nn.LayerNorm):
+    """Applies `Layer Normalization <https://arxiv.org/abs/1607.06450>`_ over a input tensor with FP32 precision."""
+
+    def __init__(
+        self,
+        normalized_shape: int | list[int] | Size,
+        eps: float | None = 1e-5,
+        elementwise_affine: bool | None = True,
+        *args,
+        **kwargs,
+    ):
+        super().__init__(
+            normalized_shape=normalized_shape,
+            eps=eps,
+            elementwise_affine=elementwise_affine,
+            *args,
+            **kwargs,
+        )
+
+    def forward(self, x: Tensor) -> Tensor:
+        # Convert input from dtype X to FP32 and perform normalization operation.
+        # This may help with underflow/overflow issues that we typically see with normalization layers
+        inp_dtype = x.dtype
+        return super().forward(x.to(torch.float32)).to(inp_dtype)
+
+
+def get_normalization_layer(norm_type, num_features):
+    if norm_type == "layer_norm":
+        return nn.LayerNorm(num_features)
+    elif norm_type == "layer_norm_fp32":
+        return LayerNormFP32(num_features)
+    else:
+        raise NotImplementedError(f"Option: {norm_type} not supported.")
+
+
+class PositionalEmbedding(nn.Module):
+    def __init__(
+        self,
+        num_embeddings: int,
+        embedding_dim: int,
+        padding_idx: int | None = None,
+        is_learnable: bool | None = False,
+        interpolation_mode: str | None = "bilinear",
+        *args,
+        **kwargs,
+    ):
+        super().__init__()
+        # Add other pos embedding here and logic to choose between them
+        module = LearnablePositionalEmbedding
+
+        self.pos_embed = module(
+            num_embeddings=num_embeddings,
+            embedding_dim=embedding_dim,
+            padding_idx=padding_idx,
+            interpolation_mode=interpolation_mode,
+            *args,
+            **kwargs,
+        )
+
+    def forward(self, seq_len: int, *args, **kwargs) -> Tensor:
+        return self.pos_embed(seq_len, *args, **kwargs)
+
+    def __repr__(self):
+        return self.pos_embed.__repr__()
+
+
+class LearnablePositionalEmbedding(nn.Module):
+    """Learnable Positional embedding."""
+
+    def __init__(
+        self,
+        num_embeddings: int,
+        embedding_dim: int,
+        padding_idx: int | None = None,
+        interpolation_mode: str | None = "bilinear",
+        *args,
+        **kwargs,
+    ):
+        super().__init__()
+        self.pos_embed = nn.Parameter(torch.empty(1, 1, num_embeddings, embedding_dim))
+        self.embedding_dim = embedding_dim
+        self.num_embeddings = num_embeddings
+        self.padding_idx = padding_idx
+        self.interpolation_mode = interpolation_mode
+
+        self.reset_parameters()
+
+    def reset_parameters(self) -> None:
+        nn.init.trunc_normal_(self.pos_embed, mean=0, std=self.embedding_dim**-0.5)
+        if self.padding_idx is not None:
+            with torch.no_grad():
+                self.pos_embed[:, :, self.padding_idx, ...] = 0.0
+
+    def forward(self, seq_len: int, *args, **kwargs) -> Tensor:
+        # scale pos embedding
+        pos_embed = self.pos_embed
+        if self.padding_idx is not None:
+            with torch.no_grad():
+                pos_embed[:, :, self.padding_idx, ...] = 0.0
+
+        if seq_len != self.num_embeddings:
+            pos_embed = F.interpolate(
+                pos_embed,
+                size=(seq_len, self.embedding_dim),
+                mode=self.interpolation_mode,
+            )
+
+        # Input is of the form [Batch, Seq_len, Embedding_dim]
+        return pos_embed.reshape(1, seq_len, self.embedding_dim)
+
+    def __repr__(self):
+        return f"{self.__class__.__name__}(num_embeddings={self.num_embeddings}, embedding_dim={self.embedding_dim}, padding_idx={self.padding_idx})"
+
+
+class MultiHeadAttention(nn.Module):
+    """This layer applies a multi-head self- or cross-attention as described in `Attention is all you need
+    <https://arxiv.org/abs/1706.03762>`_ paper.
+
+    Args:
+        embed_dim (int): :math:`C_{in}` from an expected input of size :math:`(N, S, C_{in})`
+        num_heads (int): Number of heads in multi-head attention
+        attn_dropout (Optional[float]): Attention dropout. Default: 0.0
+        bias (Optional[bool]): Use bias or not. Default: ``True``
+
+    Notes:
+        - Input:
+           - Query tensor (x_q) :math:`(N, S, C_{in})` where :math:`N` is batch size, :math:`S` is number of source tokens,
+        and: math:`C_{in}` is input embedding dim
+           - Optional Key-Value tensor (x_kv) :math:`(N, T, C_{in})` where :math:`T` is number of target tokens
+        - Output: same shape as the input
+    """
+
+    def __init__(
+        self,
+        embed_dim: int,
+        num_heads: int,
+        attn_dropout: float | None = 0.0,
+        bias: bool | None = True,
+        output_dim: int | None = None,
+        *args,
+        **kwargs,
+    ) -> None:
+        if output_dim is None:
+            output_dim = embed_dim
+        super().__init__()
+        if embed_dim % num_heads != 0:
+            logger.error(
+                f"Embedding dim must be divisible by number of heads in {self.__class__.__name__}. Got: embed_dim={embed_dim} and num_heads={num_heads}"
+            )
+
+        self.qkv_proj = nn.Linear(in_features=embed_dim, out_features=3 * embed_dim, bias=bias)
+
+        self.attn_dropout = nn.Dropout(p=attn_dropout)
+        self.out_proj = nn.Linear(in_features=embed_dim, out_features=output_dim, bias=bias)
+
+        self.head_dim = embed_dim // num_heads
+        self.scaling = self.head_dim**-0.5
+        self.softmax = nn.Softmax(dim=-1)
+        self.num_heads = num_heads
+        self.embed_dim = embed_dim
+        self.use_separate_proj_weight = embed_dim != output_dim
+
+    def __repr__(self):
+        return f"{self.__class__.__name__}(head_dim={self.head_dim}, num_heads={self.num_heads}, attn_dropout={self.attn_dropout.p})"
+
+    def _forward_impl(
+        self,
+        x_q: Tensor,
+        x_kv: Tensor | None = None,
+        key_padding_mask: Tensor | None = None,
+        attn_mask: Tensor | None = None,
+    ) -> Tensor:
+        # [N, S, C]
+        b_sz, S_len, _in_channels = x_q.shape
+
+        if x_kv is None:
+            # self-attention
+            # [N, S, C] --> [N, S, 3C] --> [N, S, 3, h, c] where C = hc
+            qkv = self.qkv_proj(x_q).reshape(b_sz, S_len, 3, self.num_heads, -1)
+            # [N, S, 3, h, c] --> [N, h, 3, S, C]
+            qkv = qkv.transpose(1, 3).contiguous()
+
+            # [N, h, 3, S, C] --> [N, h, S, C] x 3
+            query, key, value = qkv[:, :, 0], qkv[:, :, 1], qkv[:, :, 2]
+        else:
+            T_len = x_kv.shape[1]
+
+            # cross-attention
+            # [N, S, C]
+            query = F.linear(
+                x_q,
+                weight=self.qkv_proj.weight[: self.embed_dim, ...],
+                bias=self.qkv_proj.bias[: self.embed_dim] if self.qkv_proj.bias is not None else None,
+            )
+            # [N, S, C] --> [N, S, h, c] --> [N, h, S, c]
+            query = query.reshape(b_sz, S_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous()
+
+            # [N, T, C] --> [N, T, 2C]
+            kv = F.linear(
+                x_kv,
+                weight=self.qkv_proj.weight[self.embed_dim :, ...],
+                bias=self.qkv_proj.bias[self.embed_dim :] if self.qkv_proj.bias is not None else None,
+            )
+            # [N, T, 2C] --> [N, T, 2, h, c]
+            kv = kv.reshape(b_sz, T_len, 2, self.num_heads, self.head_dim)
+            # [N, T, 2, h, c] --> [N, h, 2, T, c]
+            kv = kv.transpose(1, 3).contiguous()
+            key, value = kv[:, :, 0], kv[:, :, 1]
+
+        query = query * self.scaling
+
+        # [N h, T, c] --> [N, h, c, T]
+        key = key.transpose(-1, -2)
+
+        # QK^T
+        # [N, h, S, c] x [N, h, c, T] --> [N, h, S, T]
+        attn = torch.matmul(query, key)
+
+        batch_size, _num_heads, num_src_tokens, num_tgt_tokens = attn.shape
+        if attn_mask is not None:
+            # attn_mask shape should be the same as attn
+            assert list(attn_mask.shape) == [
+                batch_size,
+                num_src_tokens,
+                num_tgt_tokens,
+            ], (
+                f"Shape of attention mask should be [{batch_size}, {num_src_tokens}, {num_tgt_tokens}]. Got: {attn_mask.shape}"
+            )
+            # [N, S, T] --> [N, 1, S, T]
+            attn_mask = attn_mask.unsqueeze(1)
+            attn = attn + attn_mask
+
+        if key_padding_mask is not None:
+            # Do not attend to padding positions
+            # key padding mask size is [N, T]
+            assert key_padding_mask.dim() == 2 and list(key_padding_mask.shape) == [
+                batch_size,
+                num_tgt_tokens,
+            ], (
+                f"Key_padding_mask should be 2-dimension with shape [{batch_size}, {num_tgt_tokens}]. Got: {key_padding_mask.shape}"
+            )
+            attn = attn.masked_fill(
+                key_padding_mask.unsqueeze(1).unsqueeze(2).to(torch.bool),  # [N, T] --> [N, 1, 1, T]
+                float("-inf"),
+            )
+
+        attn_dtype = attn.dtype
+        attn_as_float = self.softmax(attn.float())
+        attn = attn_as_float.to(attn_dtype)
+        attn = self.attn_dropout(attn)
+
+        # weighted sum
+        # [N, h, S, T] x [N, h, T, c] --> [N, h, S, c]
+        out = torch.matmul(attn, value)
+
+        # [N, h, S, c] --> [N, S, h, c] --> [N, S, C]
+        out = out.transpose(1, 2).reshape(b_sz, S_len, -1)
+        out = self.out_proj(out)
+
+        return out
+
+    def forward(
+        self,
+        x_q: Tensor,
+        x_kv: Tensor | None = None,
+        key_padding_mask: Tensor | None = None,
+        attn_mask: Tensor | None = None,
+        *args,
+        **kwargs,
+    ) -> Tensor:
+        # [Batch , Sequence, Hidden_dim]
+        return self._forward_impl(
+            x_q=x_q,
+            x_kv=x_kv,
+            key_padding_mask=key_padding_mask,
+            attn_mask=attn_mask,
+        )
+
+
+class TransformerEncoder(nn.Module):
+    """This class defines the pre-norm `Transformer encoder <https://arxiv.org/abs/1706.03762>`_.
+
+    Args:
+        embed_dim: :math:`C_{in}` from an expected input of size :math:`(N, P, C_{in})`.
+        ffn_latent_dim: Inner dimension of the FFN.
+        num_heads: Number of heads in multi-head attention. Default: 8.
+        attn_dropout: Dropout rate for attention in multi-head attention. Default: 0.0
+        dropout: Dropout rate. Default: 0.0.
+        ffn_dropout: Dropout between FFN layers. Default: 0.0.
+        transformer_norm_layer: Normalization layer. Default: layer_norm.
+        stochastic_dropout: Stochastic dropout setting. Default: 0.0.
+
+    Notes:
+        - Input: :math:`(N, P, C_{in})` where :math:`N` is batch size, :math:`P` is number of patches,
+        and: math:`C_{in}` is input embedding dim
+        - Output: same shape as the input
+    """
+
+    def __init__(
+        self,
+        embed_dim: int,
+        ffn_latent_dim: int,
+        num_heads: int | None = 8,
+        attn_dropout: float | None = 0.0,
+        dropout: float | None = 0.0,
+        ffn_dropout: float | None = 0.0,
+        transformer_norm_layer: str | None = "layer_norm",
+        stochastic_dropout: float | None = 0.0,
+        *args,
+        **kwargs,
+    ) -> None:
+        super().__init__()
+
+        # Build attention layer
+        attn_unit = MultiHeadAttention(
+            embed_dim,
+            num_heads,
+            attn_dropout=attn_dropout,
+            bias=True,
+        )
+
+        self.pre_norm_mha = nn.Sequential(
+            get_normalization_layer(norm_type=transformer_norm_layer, num_features=embed_dim),
+            attn_unit,
+            nn.Dropout(p=dropout),
+        )
+
+        act_name = nn.GELU()
+        self.pre_norm_ffn = nn.Sequential(
+            get_normalization_layer(norm_type=transformer_norm_layer, num_features=embed_dim),
+            nn.Linear(in_features=embed_dim, out_features=ffn_latent_dim, bias=True),
+            act_name,
+            nn.Dropout(p=ffn_dropout),
+            nn.Linear(in_features=ffn_latent_dim, out_features=embed_dim, bias=True),
+            nn.Dropout(p=dropout),
+        )
+
+        self.drop_path = nn.Identity()
+        if stochastic_dropout > 0.0:
+            if dropout > 0.0:
+                logger.error(
+                    "Stochastic dropout and dropout are mutually exclusive. "
+                    "Use either of them, but not both."
+                    f"Got: {stochastic_dropout} and {dropout}"
+                )
+            self.drop_path = StochasticDepth(p=stochastic_dropout, mode="row")
+
+        self.embed_dim = embed_dim
+        self.ffn_dim = ffn_latent_dim
+        self.ffn_dropout = ffn_dropout
+        self.stochastic_dropout = stochastic_dropout
+        self.std_dropout = dropout
+        self.attn_fn_name = attn_unit.__class__.__name__
+        self.act_fn_name = act_name.__class__.__name__
+        self.norm_type = transformer_norm_layer
+
+    def __repr__(self) -> str:
+        return f"{self.__class__.__name__}(embed_dim={self.embed_dim}, ffn_dim={self.ffn_dim}, dropout={self.std_dropout}, ffn_dropout={self.ffn_dropout}, stochastic_dropout={self.stochastic_dropout}, attn_fn={self.attn_fn_name}, act_fn={self.act_fn_name}, norm_fn={self.norm_type})"
+
+    def forward(
+        self,
+        x: Tensor,
+        x_prev: Tensor | None = None,
+        key_padding_mask: Tensor | None = None,
+        attn_mask: Tensor | None = None,
+        *args,
+        **kwargs,
+    ) -> Tensor:
+        # Multi-head attention
+        res = x
+        x = self.pre_norm_mha[0](x)  # norm
+        x = self.pre_norm_mha[1](
+            x_q=x,
+            x_kv=x_prev,
+            key_padding_mask=key_padding_mask,
+            attn_mask=attn_mask,
+            *args,
+            **kwargs,
+        )  # mha
+
+        x = self.drop_path(self.pre_norm_mha[2](x))  # applying stochastic depth
+        x = x + res
+
+        # Feed forward network
+        x = x + self.drop_path(self.pre_norm_ffn(x))
+        return x