392 lines
13 KiB
Python
392 lines
13 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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# Modified from https://github.com/zejiangh/MILAN
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from collections import OrderedDict
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from typing import Optional, Tuple, Union
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import numpy as np
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import torch
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from mmengine.logging import MMLogger
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from torch import nn
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class LayerNorm(nn.LayerNorm):
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"""Subclass torch's LayerNorm to handle fp16."""
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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"""Forward function."""
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orig_type = x.dtype
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ret = super().forward(x.type(torch.float32))
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return ret.type(orig_type)
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class QuickGELU(nn.Module):
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"""A faster version of GELU."""
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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"""Forward function."""
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return x * torch.sigmoid(1.702 * x)
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class ResidualAttentionBlock(nn.Module):
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"""Residual Attention Block (RAB).
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This module implements the same function as the MultiheadAttention,
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but with a different interface, which is mainly used
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in CLIP.
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Args:
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d_model (int): The feature dimension.
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n_head (int): The number of attention heads.
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attn_mask (torch.Tensor, optional): The attention mask.
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Defaults to None.
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"""
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def __init__(self,
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d_model: int,
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n_head: int,
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attn_mask: Optional[torch.Tensor] = None,
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return_attention: bool = False) -> None:
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super().__init__()
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self.attn = nn.MultiheadAttention(d_model, n_head)
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self.ln_1 = LayerNorm(d_model)
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self.mlp = nn.Sequential(
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OrderedDict([('c_fc', nn.Linear(d_model, d_model * 4)),
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('gelu', QuickGELU()),
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('c_proj', nn.Linear(d_model * 4, d_model))]))
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self.ln_2 = LayerNorm(d_model)
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self.attn_mask = attn_mask
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self.return_attention = return_attention
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def attention(self, x: torch.Tensor) -> torch.Tensor:
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"""Attention function."""
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self.attn_mask = self.attn_mask.to(
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dtype=x.dtype,
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device=x.device) if self.attn_mask is not None else None
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if self.return_attention:
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return self.attn(
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x,
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x,
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x,
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need_weights=self.return_attention,
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attn_mask=self.attn_mask)
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else:
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return self.attn(
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x,
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x,
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x,
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need_weights=self.return_attention,
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attn_mask=self.attn_mask)[0]
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def forward(
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self, x: torch.Tensor
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) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
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"""Forward function."""
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if self.return_attention:
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x_, attention = self.attention(self.ln_1(x))
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x = x + x_
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x = x + self.mlp(self.ln_2(x))
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return x, attention
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else:
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x = x + self.attention(self.ln_1(x))
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x = x + self.mlp(self.ln_2(x))
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return x
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class Transformer(nn.Module):
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"""Transformer.
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Both visual and text branches use this transformer.
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Args:
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width (int): The feature dimension.
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layers (int): The number of layers.
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heads (int): The number of attention heads.
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attn_mask (torch.Tensor, optional): The attention mask.
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"""
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def __init__(self,
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width: int,
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layers: int,
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heads: int,
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attn_mask: Optional[torch.Tensor] = None) -> None:
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super().__init__()
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self.width = width
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self.layers = layers
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self.resblocks = nn.ModuleList()
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for _ in range(layers - 1):
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self.resblocks.append(
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ResidualAttentionBlock(width, heads, attn_mask))
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self.resblocks.append(
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ResidualAttentionBlock(
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width, heads, attn_mask, return_attention=True))
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def forward(
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self, x: torch.Tensor
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) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
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"""Forward function."""
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z = []
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for idx, blk in enumerate(self.resblocks):
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if idx < self.layers - 1:
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x = blk(x)
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z.append(x.permute(1, 0, 2))
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else:
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x, attention = blk(x)
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z.append(x.permute(1, 0, 2))
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return x, attention, z
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class VisionTransformer(nn.Module):
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"""Vision Transformer for CLIP.
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Args:
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input_resolution (int): The image size.
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patch_size (int): The patch size.
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width (int): The feature dimension.
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layers (int): The number of layers.
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heads (int): The number of attention heads.
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out_dim (int): The output dimension.
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fineturn (bool): Whether to fineturn the model.
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average_target (bool): Whether to average the target.
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"""
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def __init__(self,
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input_resolution: int,
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patch_size: int,
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width: int,
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layers: int,
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heads: int,
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output_dim: int,
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finetune=False,
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average_targets: int = 1) -> None:
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super().__init__()
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self.input_resolution = input_resolution
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self.output_dim = output_dim
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self.conv1 = nn.Conv2d(
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in_channels=3,
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out_channels=width,
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kernel_size=patch_size,
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stride=patch_size,
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bias=False)
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scale = width**-0.5
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self.class_embedding = nn.Parameter(scale * torch.randn(width))
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self.positional_embedding = nn.Parameter(scale * torch.randn(
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(input_resolution // patch_size)**2 + 1, width))
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self.ln_pre = LayerNorm(width)
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self.transformer = Transformer(width, layers, heads)
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self.finetune = finetune
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if finetune is False:
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self.ln_post = LayerNorm(width)
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self.proj = nn.Parameter(scale * torch.randn(width, output_dim))
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self.average_targets = average_targets
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def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
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"""Forward function."""
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x = self.conv1(x) # shape = [*, width, grid, grid]
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x = x.reshape(x.shape[0], x.shape[1],
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-1) # shape = [*, width, grid ** 2]
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x = x.permute(0, 2, 1) # shape = [*, grid ** 2, width]
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x = torch.cat([
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self.class_embedding.to(x.dtype) + torch.zeros(
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x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), x
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],
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dim=1) # shape = [*, grid ** 2 + 1, width]
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x = x + self.positional_embedding.to(x.dtype)
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x = self.ln_pre(x)
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x = x.permute(1, 0, 2) # NLD -> LND
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x, attention, z = self.transformer(x)
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x = x.permute(1, 0, 2) # LND -> NLD
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x = self.ln_post(x)
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if self.proj is not None:
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x = x @ self.proj
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return x, attention
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class CLIP(nn.Module):
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"""CLIP.
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Args:
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embed_dim (int): The embedding dimension.
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image_resolution (int): The image size.
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vision_layers (int): The number of layers in the vision transformer.
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vision_width (int): The feature dimension in the vision transformer.
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vision_patch_size (int): The patch size in the vision transformer.
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context_length (int): The context length.
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vocab_size (int): The vocabulary size.
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transformer_width (int): The feature dimension in the text transformer.
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transformer_heads (int): The number of attention heads in the
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text transformer.
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transformer_layers (int): The number of layers in the text transformer.
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fineturn (bool): Whether to fineturn the model.
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average_target (bool): Whether to average the target.
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"""
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def __init__(
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self,
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embed_dim: int,
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image_resolution: int,
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vision_layers: Union[Tuple[int, int, int, int], int],
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vision_width: int,
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vision_patch_size: int,
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context_length: int,
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vocab_size: int,
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transformer_width: int,
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transformer_heads: int,
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transformer_layers: int,
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finetune: bool = False,
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average_targets: int = 1,
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) -> None:
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super().__init__()
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self.context_length = context_length
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vision_heads = vision_width // 64
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self.visual = VisionTransformer(
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input_resolution=image_resolution,
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patch_size=vision_patch_size,
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width=vision_width,
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layers=vision_layers,
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heads=vision_heads,
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output_dim=embed_dim,
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finetune=finetune,
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average_targets=average_targets,
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)
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self.transformer = Transformer(
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width=transformer_width,
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layers=transformer_layers,
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heads=transformer_heads,
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attn_mask=self.build_attention_mask())
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self.vocab_size = vocab_size
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self.token_embedding = nn.Embedding(vocab_size, transformer_width)
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self.positional_embedding = nn.Parameter(
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torch.empty(self.context_length, transformer_width))
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self.ln_final = LayerNorm(transformer_width)
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self.text_projection = nn.Parameter(
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torch.empty(transformer_width, embed_dim))
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self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))
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self.initialize_parameters()
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def initialize_parameters(self) -> None:
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"""Initialize the parameters.
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The pretrained weight will override the initialized parameters by this
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function.
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"""
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nn.init.normal_(self.token_embedding.weight, std=0.02)
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nn.init.normal_(self.positional_embedding, std=0.01)
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proj_std = (self.transformer.width**-0.5) * (
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(2 * self.transformer.layers)**-0.5)
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attn_std = self.transformer.width**-0.5
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fc_std = (2 * self.transformer.width)**-0.5
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for block in self.transformer.resblocks:
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nn.init.normal_(block.attn.in_proj_weight, std=attn_std)
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nn.init.normal_(block.attn.out_proj.weight, std=proj_std)
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nn.init.normal_(block.mlp.c_fc.weight, std=fc_std)
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nn.init.normal_(block.mlp.c_proj.weight, std=proj_std)
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if self.text_projection is not None:
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nn.init.normal_(
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self.text_projection, std=self.transformer.width**-0.5)
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def build_attention_mask(self) -> torch.Tensor:
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"""Build the attention mask."""
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# lazily create causal attention mask, with full attention between the
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# vision tokens pytorch uses additive attention mask; fill with -inf
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mask = torch.empty(self.context_length, self.context_length)
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mask.fill_(float('-inf'))
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mask.triu_(1) # zero out the lower diagonal
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return mask
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@property
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def dtype(self) -> torch.dtype:
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"""Get the dtype."""
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return self.visual.conv1.weight.dtype
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def encode_image(self,
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image: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
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"""Encode the image.
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Get the feature and attention mask from the last layer of the visual
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branch of CLIP.
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Args:
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image (torch.Tensor): The image tensor with shape NCHW.
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Returns:
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Tuple[torch.Tensor, torch.Tensor]: The feature and attention mask.
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"""
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return self.visual(image.type(self.dtype))
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def build_clip_model(state_dict: dict,
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finetune: bool = False,
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average_targets: int = 1) -> nn.Module:
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"""Build the CLIP model.
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Args:
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state_dict (dict): The pretrained state dict.
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finetune (bool): Whether to fineturn the model.
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average_targets (bool): Whether to average the target.
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Returns:
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nn.Module: The CLIP model.
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"""
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vit = 'visual.proj' in state_dict
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if vit:
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vision_width = state_dict['visual.conv1.weight'].shape[0]
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vision_layers = len([
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k for k in state_dict.keys()
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if k.startswith('visual.') and k.endswith('.attn.in_proj_weight')
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])
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vision_patch_size = state_dict['visual.conv1.weight'].shape[-1]
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grid_size = round(
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(state_dict['visual.positional_embedding'].shape[0] - 1)**0.5)
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image_resolution = vision_patch_size * grid_size
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embed_dim = state_dict['text_projection'].shape[1]
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context_length = state_dict['positional_embedding'].shape[0]
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vocab_size = state_dict['token_embedding.weight'].shape[0]
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transformer_width = state_dict['ln_final.weight'].shape[0]
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transformer_heads = transformer_width // 64
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transformer_layers = len(
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set(
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k.split('.')[2] for k in state_dict
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if k.startswith('transformer.resblocks')))
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model = CLIP(
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embed_dim,
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image_resolution,
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vision_layers,
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vision_width,
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vision_patch_size,
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context_length,
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vocab_size,
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transformer_width,
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transformer_heads,
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transformer_layers,
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finetune,
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average_targets,
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)
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for key in ['input_resolution', 'context_length', 'vocab_size']:
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if key in state_dict:
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del state_dict[key]
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msg = model.load_state_dict(state_dict, strict=False)
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MMLogger.get_current_instance().info(f'Load CLIP model: {msg}')
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return model.eval()
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