第一次提交Yolo项目

mobileclip/text_encoder.py

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+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# For licensing see accompanying LICENSE file.
+# Copyright (C) 2024 Apple Inc. All Rights Reserved.
+
+from __future__ import annotations
+
+import math
+from collections.abc import Sequence
+
+import torch
+from torch import Tensor, nn
+
+from mobileclip import logger
+from mobileclip.modules.common.transformer import (
+    PositionalEmbedding,
+    TransformerEncoder,
+    get_normalization_layer,
+)
+from mobileclip.modules.text.repmixer import RepMixerBlock
+
+
+class TextTransformer(nn.Module):
+    def __init__(self, cfg: dict, projection_dim: int, *args, **kwargs) -> None:
+        super().__init__()
+
+        model_dim = cfg["dim"]
+        no_scale_embedding = cfg.get("no_scale_embedding", False)
+        no_pos_embedding = cfg.get("no_pos_embedding", False)
+        embed_dropout = cfg.get("embed_dropout", 0.0)
+        norm_layer = cfg["norm_layer"]
+        variant = cfg["model_name"]
+        self.vocab_size = cfg["vocab_size"]
+        self.projection_dim = projection_dim
+
+        # Token embedding layer
+        self.embedding_layer = nn.Embedding(embedding_dim=model_dim, num_embeddings=self.vocab_size)
+        self.embed_scale = 1.0 if no_scale_embedding else model_dim**-0.5
+
+        # Context length
+        context_length = cfg["context_length"]
+        assert context_length is not None, "Context length can't be None. Please set value accordingly."
+
+        self.positional_embedding = (
+            None if no_pos_embedding else PositionalEmbedding(num_embeddings=context_length, embedding_dim=model_dim)
+        )
+
+        self.embedding_dropout = nn.Dropout(p=embed_dropout)
+
+        # Transformer layer
+        n_transformer_layers = cfg["n_transformer_layers"]
+
+        # FFN multipliers for transformer layer
+        ffn_multipliers = cfg["ffn_multiplier_per_layer"]
+        if isinstance(ffn_multipliers, (float, int)):
+            ffn_multipliers = [ffn_multipliers] * n_transformer_layers
+
+        if not isinstance(ffn_multipliers, Sequence):
+            logger.error(
+                f"{self.__class__.__name__} expects FFN multipliers as a list, whose length is the same as"
+                f" number of transformer layers. Got: {type(ffn_multipliers)}"
+            )
+        elif isinstance(ffn_multipliers, Sequence) and len(ffn_multipliers) != n_transformer_layers:
+            logger.error(
+                f"We need FFN multiplier for each transformer layer. Got {len(ffn_multipliers)} ffn"
+                f" multipliers while number of transformer layers = {n_transformer_layers}"
+            )
+        ffn_dims = [int(math.ceil(model_dim * ffn_mult / 16.0) * 16.0) for ffn_mult in ffn_multipliers]
+
+        # Heads for transformer layers
+        mha_heads = cfg["n_heads_per_layer"]
+        if isinstance(mha_heads, int):
+            mha_heads = [mha_heads] * n_transformer_layers
+
+        if not isinstance(mha_heads, Sequence):
+            logger.error(
+                f"{self.__class__.__name__} expects MHA heads as a list, whose length is the same as number of "
+                f"transformer layers. Got: {type(mha_heads)}"
+            )
+        elif isinstance(mha_heads, Sequence) and len(mha_heads) != n_transformer_layers:
+            logger.error(
+                f"{self.__class__.__name__} needs MHA heads for each transformer layer. Got {len(mha_heads)} mha heads while"
+                f" number of transformer layers = {n_transformer_layers}"
+            )
+
+        if variant == "base":
+            self.transformer = nn.ModuleList(
+                [
+                    TransformerEncoder(
+                        embed_dim=model_dim,
+                        num_heads=mha_heads[layer_idx],
+                        ffn_latent_dim=ffn_dims[layer_idx],
+                        transformer_norm_layer=norm_layer,
+                    )
+                    for layer_idx in range(n_transformer_layers)
+                ]
+            )
+        elif variant == "mct":
+            self.transformer = nn.ModuleList([RepMixerBlock(dim=model_dim)])
+            self.transformer.extend(
+                [
+                    TransformerEncoder(
+                        embed_dim=model_dim,
+                        num_heads=mha_heads[layer_idx],
+                        ffn_latent_dim=ffn_dims[layer_idx],
+                        transformer_norm_layer=norm_layer,
+                    )
+                    for layer_idx in range(n_transformer_layers)
+                ]
+            )
+            self.transformer.extend([RepMixerBlock(dim=model_dim)])
+        else:
+            raise ValueError(f"Unrecognized text encoder variant {variant}")
+
+        self.final_layer_norm = get_normalization_layer(num_features=model_dim, norm_type=norm_layer)
+
+        self.projection_layer = nn.Parameter(torch.empty(model_dim, self.projection_dim))
+        self.model_dim = model_dim
+        self.causal_masking = cfg["causal_masking"]
+
+    def forward_embedding(self, text_tokens: Tensor) -> Tensor:
+        """Return text embedding for all tokens.
+
+        Args:
+            text_tokens: a tensor of token indices. Shape: [batch_size, context_length]
+
+        Returns:
+            A tensor of [batch_size, context_length, hidden_dim].
+        """
+        # [batch_size, context_length] --> [batch_size, context_length, hidden_dim]
+        token_emb = self.embedding_layer(text_tokens)
+        seq_len = token_emb.shape[1]
+        if self.positional_embedding is not None:
+            token_emb = token_emb + self.positional_embedding(seq_len).to(token_emb.dtype)
+        token_emb = self.embedding_dropout(token_emb)
+        return token_emb
+
+    @staticmethod
+    @torch.jit.script  # use scripting to avoid device constant
+    def build_attention_mask(text_tokens: torch.Tensor) -> Tensor:
+        """Build causal attention mask [batch_size, context_length, context_length]."""
+        # Build mask with full attention between the tokens
+        # pytorch uses additive attention mask; fill with -inf
+        batch_size, context_length = text_tokens.shape
+        mask = torch.empty(context_length, context_length, device=text_tokens.device, dtype=torch.float32)
+        mask.fill_(float("-inf"))
+        mask.triu_(1)  # zero out the lower diagonal
+        mask = mask.unsqueeze(0)  # add dummy batch dimension
+        mask = mask.expand(batch_size, -1, -1)
+        return mask
+
+    def encode_text(
+        self,
+        text_tokens: Tensor,
+        key_padding_mask: Tensor | None = None,
+        return_all_tokens: bool = False,
+        *args,
+        **kwargs,
+    ) -> Tensor:
+        """Return text token embeddings.
+
+        Args:
+            text_tokens: a tensor of token indices. Shape: [batch_size, context_length]
+            key_padding_mask: a tensor of boolean values as the padding mask of shape [batch_size, context_length]
+            return_all_tokens: a boolean flag to return all tokens, defaults to False to return only EOT token
+                embedding.
+
+        Returns:
+            A tensor of [batch_size, context_length, hidden_dim] if return_all_tokens is
+            True, otherwise a tensor of [batch_size, hidden_dim].
+        """
+        # Discrete tokens to continuous embeddings
+        # [batch_size, context_length] --> [batch_size, context_length, hidden_dim]
+        token_emb = self.forward_embedding(text_tokens)
+
+        # [1, context_length, context_length]
+        attn_mask = None
+        if self.causal_masking:
+            attn_mask = self.build_attention_mask(text_tokens=text_tokens)
+            key_padding_mask = None
+
+        for layer in self.transformer:
+            token_emb = layer(
+                token_emb,
+                key_padding_mask=key_padding_mask,
+                attn_mask=attn_mask,
+            )
+
+        # Apply layer norm
+        token_emb = self.final_layer_norm(token_emb)
+
+        if return_all_tokens:
+            return token_emb
+
+        # Take features from the eot embedding (eot_token is the highest number in each sequence)
+        token_emb = token_emb[torch.arange(text_tokens.shape[0]), text_tokens.argmax(dim=-1)]
+
+        token_emb = token_emb @ self.projection_layer
+        return token_emb
+
+    def forward(
+        self,
+        text_tokens: Tensor,
+        key_padding_mask: Tensor | None = None,
+        return_all_tokens: bool = False,
+        *args,
+        **kwargs,
+    ) -> Tensor:
+        # Image-text pair data with single caption
+        # [B, CL] --> [B, d]
+        text_tokens = self.encode_text(
+            text_tokens=text_tokens,
+            key_padding_mask=key_padding_mask,
+            return_all_tokens=return_all_tokens,
+            *args,
+            **kwargs,
+        )
+        return text_tokens