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