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from collections import OrderedDict
import logging
import os
import torch
from torch import nn
import torch.nn.functional as F
import torch.utils.checkpoint as checkpoint
from maskrcnn_benchmark.config import try_to_find
from timm.models.layers import DropPath, trunc_normal_
logger = logging.getLogger(__name__)
class LayerNorm(nn.Module):
def __init__(self, hidden_size, eps=1e-12):
"""Construct a layernorm module in the TF style (epsilon inside the square root).
"""
super(LayerNorm, self).__init__()
self.weight = nn.Parameter(torch.ones(hidden_size))
self.bias = nn.Parameter(torch.zeros(hidden_size))
self.variance_epsilon = eps
def forward(self, x):
pdtype = x.dtype
x = x.float()
u = x.mean(-1, keepdim=True)
s = (x - u).pow(2).mean(-1, keepdim=True)
x = (x - u) / torch.sqrt(s + self.variance_epsilon)
return self.weight * x.to(pdtype) + self.bias
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,
drop_path: float = 0.0):
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.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
def attention(self, x: torch.Tensor, key_padding_mask: torch.Tensor = None):
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, key_padding_mask=key_padding_mask)[0]
def forward(self, x: torch.Tensor, key_padding_mask: torch.Tensor = None):
x = x + self.drop_path(self.attention(self.ln_1(x), key_padding_mask=key_padding_mask))
x = x + self.drop_path(self.mlp(self.ln_2(x)))
return x
class CLIPTransformer(nn.Module):
def __init__(self, cfg):
super().__init__()
self.cfg = cfg
self.use_checkpoint = cfg.MODEL.LANGUAGE_BACKBONE.USE_CHECKPOINT
print("LANGUAGE BACKBONE USE GRADIENT CHECKPOINTING: ", self.cfg.MODEL.LANGUAGE_BACKBONE.USE_CHECKPOINT)
self.context_length = self.cfg.MODEL.CLIP.CONTEXT_LENGTH
self.width = self.cfg.MODEL.CLIP.WIDTH
self.layers = self.cfg.MODEL.CLIP.LAYERS
self.heads = self.cfg.MODEL.CLIP.HEADS
self.drop_path = self.cfg.MODEL.CLIP.DROP_PATH
self.vocab_size = self.cfg.MODEL.CLIP.VOCAB_SIZE
self.token_embedding = nn.Embedding(self.vocab_size, self.width)
self.positional_embedding = nn.Parameter(
torch.empty(self.context_length, self.width)
)
# attn_mask = self.build_attention_mask()
attn_mask = None
dpr = [x.item() for x in torch.linspace(0, self.drop_path, self.layers)] # stochastic depth decay rule
self.resblocks = nn.ModuleList(
[
ResidualAttentionBlock(self.width, self.heads, attn_mask, dpr[i])
for i in range(self.layers)
]
)
self.ln_final = LayerNorm(self.width)
trunc_normal_(self.positional_embedding, std=.02)
# nn.init.normal_(self.token_embedding, std=.02)
trunc_normal_(self.token_embedding.weight, std=.02)
self.apply(self._init_weights)
# loading pre-trained weight from our CLIP models
if len(self.cfg.MODEL.LANGUAGE_BACKBONE.WEIGHT) > 0:
self.init_weights(pretrained=try_to_find(self.cfg.MODEL.LANGUAGE_BACKBONE.WEIGHT),
pretrained_layers=['*'])
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
def _init_weights(self, m):
if isinstance(m, (nn.Linear, nn.Conv2d)):
trunc_normal_(m.weight, std=0.02)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, (nn.LayerNorm, nn.BatchNorm2d)):
nn.init.constant_(m.bias, 0)
def resize_pos_embed_1d(self, posemb, shape_new):
# rescale the grid of position embeddings when loading from state_dict
ntok_old = posemb.shape[0]
if ntok_old > 1:
ntok_new = shape_new[0]
posemb_grid = posemb.unsqueeze(dim=0).permute(0, 2, 1).unsqueeze(dim=-1)
posemb_grid = F.interpolate(posemb_grid, size=[ntok_new, 1], mode='bilinear')
posemb_grid = posemb_grid.squeeze(dim=-1).permute(0, 2, 1).squeeze(dim=0)
posemb = posemb_grid
return posemb
def init_weights(self, pretrained="", pretrained_layers=[], verbose=False):
if os.path.isfile(pretrained):
pretrained_dict = torch.load(pretrained, map_location="cpu")
logger.info(f'=> loading pretrained clip text model {pretrained}')
model_dict = self.state_dict()
need_init_state_dict = {}
for k, v in pretrained_dict.items():
need_init = (
k.split('.')[0] in pretrained_layers
or pretrained_layers[0] is '*'
)
if need_init:
if k.startswith('text.') and k[5:] in model_dict.keys():
need_init_state_dict[k[5:]] = v
# notice the context length now changes from 77 to 256, so we need to resize the positional embedding
if "positional_embedding" in need_init_state_dict.keys():
old_pos_embed = need_init_state_dict["positional_embedding"].float()
new_pos_embed = self.resize_pos_embed_1d(old_pos_embed,
(self.cfg.MODEL.CLIP.CONTEXT_LENGTH, old_pos_embed.shape[1]))
need_init_state_dict["positional_embedding"] = new_pos_embed
self.load_state_dict(need_init_state_dict, strict=True)
@torch.jit.ignore
def no_weight_decay(self):
return {
'positional_embedding',
'token_embedding',
}
def forward(self, text):
input = text["input_ids"]
mask = text["attention_mask"]
# get extended attention mask for nn.MultiHeadAttention
key_padding_mask = (1.0 - mask).to(torch.bool)
x = self.token_embedding(input) # [batch_size, n_ctx, d_model]
x = x + self.positional_embedding
x = x.permute(1, 0, 2) # NLD -> LND
for resblock in self.resblocks:
if self.use_checkpoint:
x = checkpoint.checkpoint(resblock, x, key_padding_mask)
else:
x = resblock(x, key_padding_mask)
x = x.permute(1, 0, 2) # LND -> NLD
x = self.ln_final(x)
# x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)]
ret = {
"aggregate": x,
"embedded": x,
"masks": mask,
"hidden": x
}
return ret |