Spaces:
Paused
Paused
File size: 6,954 Bytes
6e14436 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 |
# This code is modified from https://github.com/openai/CLIP/blob/main/clip/clip.py
# Modified by Xingyi Zhou
# The original code is under MIT license
# Copyright (c) Facebook, Inc. and its affiliates.
from typing import Union, List
from collections import OrderedDict
import torch
from torch import nn
import torch
from clip.simple_tokenizer import SimpleTokenizer as _Tokenizer
__all__ = ["tokenize"]
count = 0
class LayerNorm(nn.LayerNorm):
"""Subclass torch's LayerNorm to handle fp16."""
def forward(self, x: torch.Tensor):
orig_type = x.dtype
ret = super().forward(x.type(torch.float32))
return ret.type(orig_type)
class QuickGELU(nn.Module):
def forward(self, x: torch.Tensor):
return x * torch.sigmoid(1.702 * x)
class ResidualAttentionBlock(nn.Module):
def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = 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
def attention(self, x: torch.Tensor):
self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None
return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0]
def forward(self, x: torch.Tensor):
x = x + self.attention(self.ln_1(x))
x = x + self.mlp(self.ln_2(x))
return x
class Transformer(nn.Module):
def __init__(self, width: int, layers: int, heads: int, attn_mask: torch.Tensor = None):
super().__init__()
self.width = width
self.layers = layers
self.resblocks = nn.Sequential(
*[ResidualAttentionBlock(width, heads, attn_mask) \
for _ in range(layers)])
def forward(self, x: torch.Tensor):
return self.resblocks(x)
class CLIPTEXT(nn.Module):
def __init__(self,
embed_dim=512,
# text
context_length=77,
vocab_size=49408,
transformer_width=512,
transformer_heads=8,
transformer_layers=12
):
super().__init__()
self._tokenizer = _Tokenizer()
self.context_length = context_length
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):
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):
# 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 device(self):
return self.text_projection.device
@property
def dtype(self):
return self.text_projection.dtype
def tokenize(self,
texts: Union[str, List[str]], \
context_length: int = 77) -> torch.LongTensor:
"""
"""
if isinstance(texts, str):
texts = [texts]
sot_token = self._tokenizer.encoder["<|startoftext|>"]
eot_token = self._tokenizer.encoder["<|endoftext|>"]
all_tokens = [[sot_token] + self._tokenizer.encode(text) + [eot_token] for text in texts]
result = torch.zeros(len(all_tokens), context_length, dtype=torch.long)
for i, tokens in enumerate(all_tokens):
if len(tokens) > context_length:
st = torch.randint(
len(tokens) - context_length + 1, (1,))[0].item()
tokens = tokens[st: st + context_length]
# raise RuntimeError(f"Input {texts[i]} is too long for context length {context_length}")
result[i, :len(tokens)] = torch.tensor(tokens)
return result
def encode_text(self, text):
x = self.token_embedding(text).type(self.dtype) # [batch_size, n_ctx, d_model]
x = x + self.positional_embedding.type(self.dtype)
x = x.permute(1, 0, 2) # NLD -> LND
x = self.transformer(x)
x = x.permute(1, 0, 2) # LND -> NLD
x = self.ln_final(x).type(self.dtype)
# take features from the eot embedding (eot_token is the highest number in each sequence)
x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection
return x
def forward(self, captions):
'''
captions: list of strings
'''
text = self.tokenize(captions).to(self.device) # B x L x D
features = self.encode_text(text) # B x D
return features
def build_text_encoder(pretrain=True):
text_encoder = CLIPTEXT()
if pretrain:
import clip
pretrained_model, _ = clip.load("ViT-B/32", device='cpu')
state_dict = pretrained_model.state_dict()
to_delete_keys = ["logit_scale", "input_resolution", \
"context_length", "vocab_size"] + \
[k for k in state_dict.keys() if k.startswith('visual.')]
for k in to_delete_keys:
if k in state_dict:
del state_dict[k]
print('Loading pretrained CLIP')
text_encoder.load_state_dict(state_dict)
# import pdb; pdb.set_trace()
return text_encoder |