1060 lines
37 KiB
Python
1060 lines
37 KiB
Python
# -*- coding: utf-8 -*-
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"""Concise re-implementation of ``https://github.com/openai/CLIP'' and
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``https://github.com/mlfoundations/open_clip''.
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"""
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import math
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from functools import partial
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from importlib import find_loader
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from scepter.modules.model.base_model import BaseModel
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from scepter.modules.model.embedder.xlm_roberta import \
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XLMRoberta # used in XLMRobertaCLIP (multilingual)
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from scepter.modules.model.registry import EMBEDDERS
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from scepter.modules.utils.config import dict_to_yaml
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from scepter.modules.utils.distribute import we
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from scepter.modules.utils.file_system import FS
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def map_dtype(m, dtype=torch.float16):
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if isinstance(m, (nn.Linear, nn.Conv2d)):
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_ = m.to(dtype)
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elif isinstance(m, LayerNorm):
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_ = m.float()
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elif hasattr(m, 'head') and isinstance(m.head, nn.Parameter):
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p = getattr(m, 'head')
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p.data = p.data.to(dtype)
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class QuickGELU(nn.Module):
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def forward(self, x):
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return x * torch.sigmoid(1.702 * x)
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class LayerNorm(nn.LayerNorm):
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def forward(self, x):
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return super().forward(x.float()).type_as(x)
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class SelfAttention(nn.Module):
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def __init__(self,
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dim,
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num_heads,
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causal=False,
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attn_dropout=0.0,
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proj_dropout=0.0,
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flash_dtype=torch.float16):
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assert dim % num_heads == 0
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assert flash_dtype in (None, torch.float16, torch.bfloat16)
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super().__init__()
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self.dim = dim
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self.num_heads = num_heads
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self.head_dim = dim // num_heads
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self.causal = causal
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self.attn_dropout = attn_dropout
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self.proj_dropout = proj_dropout
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self.scale = math.pow(self.head_dim, -0.25)
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self.flash_dtype = flash_dtype
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# layers
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self.to_qkv = nn.Linear(dim, dim * 3)
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self.proj = nn.Linear(dim, dim)
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def forward(self, x):
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"""x: [B, L, C].
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"""
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b, s, c, n, d = *x.size(), self.num_heads, self.head_dim
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# compute query, key, value
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qkv = self.to_qkv(x).view(b, s, 3, n, d)
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# compute attention
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if x.device.type != 'cpu' and find_loader('flash_attn') and \
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self.flash_dtype is not None:
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# flash implementation
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from flash_attn.flash_attn_interface import (
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flash_attn_unpadded_qkvpacked_func, )
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dtype = qkv.dtype
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if dtype != self.flash_dtype:
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qkv = qkv.type(self.flash_dtype)
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cu_seqlens = torch.arange(0,
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b * s + 1,
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s,
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dtype=torch.int32,
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device=x.device)
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x = flash_attn_unpadded_qkvpacked_func(
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qkv=qkv.reshape(-1, 3, n, d),
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cu_seqlens=cu_seqlens,
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max_seqlen=s,
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dropout_p=self.attn_dropout if self.training else 0.0,
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causal=self.causal,
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return_attn_probs=False).reshape(b, s, n, d).type(dtype)
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else:
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# torch implementation
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q, k, v = qkv.unbind(2)
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attn = torch.einsum('binc,bjnc->bnij', q * self.scale,
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k * self.scale)
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if self.causal:
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attn = attn.masked_fill(
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torch.tril(attn.new_ones(1, 1, s,
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s).float()).type_as(attn) == 0,
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float('-inf'))
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attn = F.softmax(attn.float(), dim=-1).type_as(attn)
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x = torch.einsum('bnij,bjnc->binc', attn, v)
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# output
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x = x.reshape(b, s, c)
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x = self.proj(x)
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x = F.dropout(x, self.proj_dropout, self.training)
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return x
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class AttentionBlock(nn.Module):
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def __init__(self,
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dim,
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mlp_ratio,
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num_heads,
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causal=False,
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activation='quick_gelu',
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attn_dropout=0.0,
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proj_dropout=0.0,
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flash_dtype=torch.float16):
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assert activation in ['quick_gelu', 'gelu']
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super().__init__()
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self.dim = dim
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self.mlp_ratio = mlp_ratio
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self.num_heads = num_heads
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self.causal = causal
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self.flash_dtype = flash_dtype
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# layers
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self.norm1 = LayerNorm(dim)
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self.attn = SelfAttention(dim, num_heads, causal, attn_dropout,
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proj_dropout, flash_dtype)
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self.norm2 = LayerNorm(dim)
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self.mlp = nn.Sequential(
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nn.Linear(dim, int(dim * mlp_ratio)),
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QuickGELU() if activation == 'quick_gelu' else nn.GELU(),
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nn.Linear(int(dim * mlp_ratio), dim), nn.Dropout(proj_dropout))
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def forward(self, x):
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x = x + self.attn(self.norm1(x))
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x = x + self.mlp(self.norm2(x))
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return x
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class VisionTransformer(nn.Module):
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def __init__(self,
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image_size=224,
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patch_size=16,
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dim=768,
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mlp_ratio=4,
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out_dim=512,
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num_heads=12,
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num_layers=12,
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activation='quick_gelu',
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attn_dropout=0.0,
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proj_dropout=0.0,
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embedding_dropout=0.0,
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flash_dtype=torch.float16):
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assert image_size % patch_size == 0
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super().__init__()
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self.image_size = image_size
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self.patch_size = patch_size
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self.num_patches = (image_size // patch_size)**2
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self.dim = dim
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self.mlp_ratio = mlp_ratio
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self.out_dim = out_dim
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self.num_heads = num_heads
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self.num_layers = num_layers
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self.flash_dtype = flash_dtype
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# embeddings
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gain = 1.0 / math.sqrt(dim)
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self.patch_embedding = nn.Conv2d(3,
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dim,
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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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self.cls_embedding = nn.Parameter(gain * torch.randn(1, 1, dim))
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self.pos_embedding = nn.Parameter(
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gain * torch.randn(1, self.num_patches + 1, dim))
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self.dropout = nn.Dropout(embedding_dropout)
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# transformer
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self.pre_norm = LayerNorm(dim)
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self.transformer = nn.Sequential(*[
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AttentionBlock(dim, mlp_ratio, num_heads, False, activation,
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attn_dropout, proj_dropout, flash_dtype)
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for _ in range(num_layers)
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])
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self.post_norm = LayerNorm(dim)
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# head
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self.head = nn.Parameter(gain * torch.randn(dim, out_dim))
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def forward(self, x):
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b, dtype = x.size(0), self.head.dtype
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x = x.type(dtype)
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# patch-embedding
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x = self.patch_embedding(x).flatten(2).permute(0, 2, 1)
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x = torch.cat([self.cls_embedding.repeat(b, 1, 1).type(dtype), x],
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dim=1)
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x = self.dropout(x + self.pos_embedding.type(dtype))
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x = self.pre_norm(x)
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# transformer
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x = self.transformer(x)
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# head
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x = self.post_norm(x)
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x = torch.mm(x[:, 0, :], self.head)
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return x
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def fp16(self, dtype=torch.float16):
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return self.apply(partial(map_dtype, dtype=dtype))
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class TextTransformer(nn.Module):
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def __init__(self,
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vocab_size,
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text_len,
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dim=512,
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mlp_ratio=4,
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out_dim=512,
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num_heads=8,
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num_layers=12,
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activation='quick_gelu',
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attn_dropout=0.0,
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proj_dropout=0.0,
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embedding_dropout=0.0,
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flash_dtype=torch.float16):
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super().__init__()
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self.vocab_size = vocab_size
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self.text_len = text_len
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self.dim = dim
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self.mlp_ratio = mlp_ratio
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self.out_dim = out_dim
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self.num_heads = num_heads
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self.num_layers = num_layers
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self.flash_dtype = flash_dtype
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# embeddings
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self.token_embedding = nn.Embedding(vocab_size, dim)
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self.pos_embedding = nn.Parameter(0.01 * torch.randn(1, text_len, dim))
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self.dropout = nn.Dropout(embedding_dropout)
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# transformer
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self.transformer = nn.Sequential(*[
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AttentionBlock(dim, mlp_ratio, num_heads, True, activation,
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attn_dropout, proj_dropout, flash_dtype)
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for _ in range(num_layers)
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])
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self.norm = LayerNorm(dim)
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# head
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gain = 1.0 / math.sqrt(dim)
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self.head = nn.Parameter(gain * torch.randn(dim, out_dim))
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def forward(self, x):
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eot, dtype = x.argmax(dim=-1), self.head.dtype
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# embeddings
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x = self.dropout(
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self.token_embedding(x).type(dtype) +
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self.pos_embedding.type(dtype))
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# transformer
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x = self.transformer(x)
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# head
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x = self.norm(x)
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x = torch.mm(x[torch.arange(x.size(0)), eot], self.head)
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return x
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def fp16(self, dtype=torch.float16):
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return self.apply(partial(map_dtype, dtype=dtype))
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class CLIP(nn.Module):
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def __init__(self,
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embed_dim=512,
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image_size=224,
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patch_size=16,
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vision_dim=768,
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vision_mlp_ratio=4,
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vision_heads=12,
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vision_layers=12,
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vocab_size=49408,
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text_len=77,
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text_dim=512,
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text_mlp_ratio=4,
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text_heads=8,
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text_layers=12,
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activation='quick_gelu',
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attn_dropout=0.0,
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proj_dropout=0.0,
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embedding_dropout=0.0,
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flash_dtype=torch.float16,
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use_module=['visual', 'textual']):
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assert flash_dtype in (None, torch.float16, torch.bfloat16)
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super().__init__()
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self.embed_dim = embed_dim
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self.image_size = image_size
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self.patch_size = patch_size
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self.vision_dim = vision_dim
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self.vision_mlp_ratio = vision_mlp_ratio
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self.vision_heads = vision_heads
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self.vision_layers = vision_layers
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self.vocab_size = vocab_size
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self.text_len = text_len
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self.text_dim = text_dim
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self.text_mlp_ratio = text_mlp_ratio
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self.text_heads = text_heads
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self.text_layers = text_layers
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self.flash_dtype = flash_dtype
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self.use_module = use_module
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# models
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if 'visual' in use_module:
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self.visual = VisionTransformer(
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image_size=image_size,
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patch_size=patch_size,
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dim=vision_dim,
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mlp_ratio=vision_mlp_ratio,
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out_dim=embed_dim,
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num_heads=vision_heads,
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num_layers=vision_layers,
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activation=activation,
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attn_dropout=attn_dropout,
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proj_dropout=proj_dropout,
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embedding_dropout=embedding_dropout,
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flash_dtype=flash_dtype)
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self.scale = math.sqrt(self.visual.out_dim)
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else:
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self.visual = nn.Identity()
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if 'textual' in use_module:
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self.textual = TextTransformer(vocab_size=vocab_size,
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text_len=text_len,
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dim=text_dim,
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mlp_ratio=text_mlp_ratio,
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out_dim=embed_dim,
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num_heads=text_heads,
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num_layers=text_layers,
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activation=activation,
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attn_dropout=attn_dropout,
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proj_dropout=proj_dropout,
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embedding_dropout=embedding_dropout,
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flash_dtype=flash_dtype)
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else:
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self.textual = nn.Identity()
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self.log_scale = nn.Parameter(math.log(1 / 0.07) * torch.ones([]))
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# initialize weights
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self.init_weights()
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def forward(self, imgs, txt_tokens):
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"""imgs: [B, 3, H, W] of torch.float32.
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mean: [0.48145466, 0.4578275, 0.40821073]
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std: [0.26862954, 0.26130258, 0.27577711]
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txt_tokens: [B, L] of torch.long.
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Encoded by data.CLIPTokenizer.
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"""
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xi = self.visual(imgs)
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xt = self.textual(txt_tokens)
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return xi, xt
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def encode_image(self, x, skip_layers=0):
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# clip inference
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b, dtype = x.size(0), self.visual.head.dtype
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x = x.type(dtype)
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# # patch-embedding
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x = self.visual.patch_embedding(x).flatten(2).permute(0, 2, 1)
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x = torch.cat(
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[self.visual.cls_embedding.repeat(b, 1, 1).type(dtype), x], dim=1)
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x = self.visual.dropout(x + self.visual.pos_embedding.type(dtype))
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x = self.visual.pre_norm(x)
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# # transformer
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# assert skip_layers < 12
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if skip_layers == 0:
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x = self.visual.transformer(x)
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else:
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for m in self.visual.transformer[:-skip_layers]:
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x = m(x)
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# # head
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x = self.visual.post_norm(x)
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x = torch.mm(x[:, 0, :], self.visual.head)
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x = self.scale * F.normalize(x, p=2, dim=1)
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return x
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def encode_text(self):
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pass
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def init_weights(self):
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# embeddings
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if 'textual' in self.use_module:
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nn.init.normal_(self.textual.token_embedding.weight, std=0.02)
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if 'visual' in self.use_module:
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nn.init.normal_(self.visual.patch_embedding.weight, std=0.1)
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# attentions
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for modality in self.use_module:
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dim = self.vision_dim if modality == 'visual' else self.text_dim
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transformer = getattr(self, modality).transformer
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proj_gain = (1.0 / math.sqrt(dim)) * (
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1.0 / math.sqrt(2 * len(transformer)))
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attn_gain = 1.0 / math.sqrt(dim)
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mlp_gain = 1.0 / math.sqrt(2.0 * dim)
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for block in transformer:
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nn.init.normal_(block.attn.to_qkv.weight, std=attn_gain)
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nn.init.normal_(block.attn.proj.weight, std=proj_gain)
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nn.init.normal_(block.mlp[0].weight, std=mlp_gain)
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nn.init.normal_(block.mlp[2].weight, std=proj_gain)
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def param_groups(self):
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groups = [{
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'params': [
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p for n, p in self.named_parameters()
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if 'norm' in n or n.endswith('bias')
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],
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'weight_decay':
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0.0
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}, {
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'params': [
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p for n, p in self.named_parameters()
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if not ('norm' in n or n.endswith('bias'))
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]
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}]
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return groups
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def fp16(self, dtype=torch.float16):
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return self.apply(partial(map_dtype, dtype=dtype))
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def load_from_open_clip(self, checkpoint_or_path, **kwargs):
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"""Load and remap state-dict from open-clip.
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"""
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# load state-dict
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device = next(self.parameters()).device
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state = checkpoint_or_path
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if isinstance(state, str):
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state = torch.load(state, map_location=device)
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# reorder
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prefix = [
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'logit_scale', 'visual.', 'position', 'text_proj', 'token',
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'transformer.', 'ln_final.'
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]
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state = type(state)([(k, v) for u in prefix for k, v in state.items()
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if k.startswith(u)])
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# convert to target keys
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target = self.state_dict()
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target = {
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k: v.view(target[k].shape)
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for k, v in zip(target.keys(), state.values())
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}
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return self.load_state_dict(target, **kwargs)
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class XLMRobertaWithHead(XLMRoberta):
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def __init__(self, **kwargs):
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self.out_dim = kwargs.pop('out_dim')
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super().__init__(**kwargs)
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# head
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mid_dim = (self.dim + self.out_dim) // 2
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self.head = nn.Sequential(nn.Linear(self.dim, mid_dim, bias=False),
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nn.GELU(),
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nn.Linear(mid_dim, self.out_dim, bias=False))
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def forward(self, tokens):
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# xlm-roberta
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x = super().forward(tokens)
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|
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# average pooling
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mask = tokens.ne(self.pad_token).unsqueeze(-1).to(x)
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x = (x * mask).sum(dim=1) / mask.sum(dim=1)
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# head
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x = self.head(x)
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return x
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class XLMRobertaCLIP(nn.Module):
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def __init__(self,
|
|
embed_dim=1024,
|
|
image_size=224,
|
|
patch_size=14,
|
|
vision_dim=1280,
|
|
vision_mlp_ratio=4,
|
|
vision_heads=16,
|
|
vision_layers=32,
|
|
activation='gelu',
|
|
vocab_size=250002,
|
|
max_text_len=514,
|
|
type_size=1,
|
|
pad_token=1,
|
|
text_dim=1024,
|
|
text_heads=16,
|
|
text_layers=24,
|
|
text_eps=1e-5,
|
|
text_dropout=0.1,
|
|
attn_dropout=0.0,
|
|
proj_dropout=0.0,
|
|
embedding_dropout=0.0,
|
|
flash_dtype=torch.float16,
|
|
use_module=['visual', 'textual']):
|
|
assert flash_dtype in (None, torch.float16, torch.bfloat16)
|
|
super().__init__()
|
|
self.embed_dim = embed_dim
|
|
self.image_size = image_size
|
|
self.patch_size = patch_size
|
|
self.vision_dim = vision_dim
|
|
self.vision_mlp_ratio = vision_mlp_ratio
|
|
self.vision_heads = vision_heads
|
|
self.vision_layers = vision_layers
|
|
self.activation = activation
|
|
self.vocab_size = vocab_size
|
|
self.max_text_len = max_text_len
|
|
self.type_size = type_size
|
|
self.pad_token = pad_token
|
|
self.text_dim = text_dim
|
|
self.text_heads = text_heads
|
|
self.text_layers = text_layers
|
|
self.text_eps = text_eps
|
|
self.flash_dtype = flash_dtype
|
|
|
|
# models
|
|
if 'visual' in use_module:
|
|
self.visual = VisionTransformer(
|
|
image_size=image_size,
|
|
patch_size=patch_size,
|
|
dim=vision_dim,
|
|
mlp_ratio=vision_mlp_ratio,
|
|
out_dim=embed_dim,
|
|
num_heads=vision_heads,
|
|
num_layers=vision_layers,
|
|
activation=activation,
|
|
attn_dropout=attn_dropout,
|
|
proj_dropout=proj_dropout,
|
|
embedding_dropout=embedding_dropout,
|
|
flash_dtype=flash_dtype)
|
|
else:
|
|
self.visual = nn.Identity()
|
|
if 'textual' in use_module:
|
|
self.textual = XLMRobertaWithHead(vocab_size=vocab_size,
|
|
max_seq_len=max_text_len,
|
|
type_size=type_size,
|
|
pad_token=pad_token,
|
|
dim=text_dim,
|
|
out_dim=embed_dim,
|
|
num_heads=text_heads,
|
|
num_layers=text_layers,
|
|
dropout=text_dropout,
|
|
eps=text_eps)
|
|
else:
|
|
self.textual = nn.Identity()
|
|
self.log_scale = nn.Parameter(math.log(1 / 0.07) * torch.ones([]))
|
|
|
|
def forward(self, imgs, txt_tokens):
|
|
"""imgs: [B, 3, H, W] of torch.float32.
|
|
mean: [0.48145466, 0.4578275, 0.40821073]
|
|
std: [0.26862954, 0.26130258, 0.27577711]
|
|
txt_tokens: [B, L] of torch.long.
|
|
Encoded by data.CLIPTokenizer.
|
|
"""
|
|
xi = self.visual(imgs)
|
|
xt = self.textual(txt_tokens)
|
|
return xi, xt
|
|
|
|
def param_groups(self):
|
|
groups = [{
|
|
'params': [
|
|
p for n, p in self.named_parameters()
|
|
if 'norm' in n or n.endswith('bias')
|
|
],
|
|
'weight_decay':
|
|
0.0
|
|
}, {
|
|
'params': [
|
|
p for n, p in self.named_parameters()
|
|
if not ('norm' in n or n.endswith('bias'))
|
|
]
|
|
}]
|
|
return groups
|
|
|
|
def fp16(self, dtype=torch.float16):
|
|
return self.apply(partial(map_dtype, dtype=dtype))
|
|
|
|
def load_from_open_clip(self, checkpoint_or_path, **kwargs):
|
|
"""Load and remap state-dict from open-clip.
|
|
"""
|
|
# load state-dict
|
|
device = next(self.parameters()).device
|
|
state = checkpoint_or_path
|
|
if isinstance(state, str):
|
|
state = torch.load(state, map_location=device)
|
|
if 'state_dict' in state:
|
|
state = state['state_dict']
|
|
|
|
# reorder
|
|
keys = [
|
|
'logit_scale', 'visual.', 'word_embeddings',
|
|
'token_type_embeddings', 'position_embeddings',
|
|
'embeddings.LayerNorm', 'encoder.layer.', 'text.proj'
|
|
]
|
|
state = type(state)([(k, v) for u in keys for k, v in state.items()
|
|
if u in k])
|
|
|
|
# target state-dict
|
|
target = self.state_dict()
|
|
target = {
|
|
k: v.view(target[k].shape)
|
|
for k, v in zip(target.keys(), state.values())
|
|
}
|
|
return self.load_state_dict(target, **kwargs)
|
|
|
|
|
|
def _clip(pretrained=False, pretrained_path=None, model_cls=CLIP, **kwargs):
|
|
model = model_cls(**kwargs)
|
|
if pretrained and pretrained_path:
|
|
pretrain_model = torch.load(pretrained_path, map_location='cpu')
|
|
key_str = ' '.join(list(pretrain_model.keys()))
|
|
have_load = False
|
|
if 'use_module' in kwargs and len(kwargs['use_module']) < 2:
|
|
for module_name in kwargs['use_module']:
|
|
if hasattr(model, module_name) and module_name not in key_str:
|
|
missing, unexpected = getattr(
|
|
model, module_name).load_state_dict(pretrain_model,
|
|
strict=False)
|
|
if we.rank == 0:
|
|
print(f'Restored from {pretrained_path} with'
|
|
'{len(missing)} missing and {len(unexpected)}'
|
|
'unexpected keys')
|
|
if len(missing) > 0:
|
|
print(f'Missing Keys:\n {missing}')
|
|
if len(unexpected) > 0:
|
|
print(f'\nUnexpected Keys:\n {unexpected}')
|
|
have_load = True
|
|
if not have_load:
|
|
missing, unexpected = model.load_state_dict(pretrain_model,
|
|
strict=False)
|
|
if we.rank == 0:
|
|
print(
|
|
f'Restored from {pretrained_path} with {len(missing)} missing and {len(unexpected)} unexpected keys'
|
|
)
|
|
if len(missing) > 0:
|
|
print(f'Missing Keys:\n {missing}')
|
|
if len(unexpected) > 0:
|
|
print(f'\nUnexpected Keys:\n {unexpected}')
|
|
return model
|
|
|
|
|
|
def clip_vit_b_32(**kwargs):
|
|
cfg = dict(embed_dim=512,
|
|
image_size=224,
|
|
patch_size=32,
|
|
vision_dim=768,
|
|
vision_heads=12,
|
|
vision_layers=12,
|
|
vocab_size=49408,
|
|
text_len=77,
|
|
text_dim=512,
|
|
text_heads=8,
|
|
text_layers=12,
|
|
activation='quick_gelu')
|
|
cfg.update(**kwargs)
|
|
return cfg, CLIP
|
|
|
|
|
|
def clip_vit_b_16(**kwargs):
|
|
cfg = dict(embed_dim=512,
|
|
image_size=224,
|
|
patch_size=16,
|
|
vision_dim=768,
|
|
vision_heads=12,
|
|
vision_layers=12,
|
|
vocab_size=49408,
|
|
text_len=77,
|
|
text_dim=512,
|
|
text_heads=8,
|
|
text_layers=12,
|
|
activation='quick_gelu')
|
|
cfg.update(**kwargs)
|
|
return cfg, CLIP
|
|
|
|
|
|
def clip_vit_l_14(**kwargs):
|
|
cfg = dict(embed_dim=768,
|
|
image_size=224,
|
|
patch_size=14,
|
|
vision_dim=1024,
|
|
vision_heads=16,
|
|
vision_layers=24,
|
|
vocab_size=49408,
|
|
text_len=77,
|
|
text_dim=768,
|
|
text_heads=12,
|
|
text_layers=12,
|
|
activation='quick_gelu')
|
|
cfg.update(**kwargs)
|
|
return cfg, CLIP
|
|
|
|
|
|
def clip_vit_l_14_336px(**kwargs):
|
|
cfg = dict(embed_dim=768,
|
|
image_size=336,
|
|
patch_size=14,
|
|
vision_dim=1024,
|
|
vision_heads=16,
|
|
vision_layers=24,
|
|
vocab_size=49408,
|
|
text_len=77,
|
|
text_dim=768,
|
|
text_heads=12,
|
|
text_layers=12,
|
|
activation='quick_gelu')
|
|
cfg.update(**kwargs)
|
|
return cfg, CLIP
|
|
|
|
|
|
def clip_vit_h_14(**kwargs):
|
|
cfg = dict(embed_dim=1024,
|
|
image_size=224,
|
|
patch_size=14,
|
|
vision_dim=1280,
|
|
vision_heads=16,
|
|
vision_layers=32,
|
|
vocab_size=49408,
|
|
text_len=77,
|
|
text_dim=1024,
|
|
text_heads=16,
|
|
text_layers=24,
|
|
activation='gelu')
|
|
cfg.update(**kwargs)
|
|
return cfg, CLIP
|
|
|
|
|
|
def clip_vit_g_14(**kwargs):
|
|
cfg = dict(embed_dim=1024,
|
|
image_size=224,
|
|
patch_size=14,
|
|
vision_dim=1408,
|
|
vision_mlp_ratio=4.3637,
|
|
vision_heads=16,
|
|
vision_layers=40,
|
|
vocab_size=49408,
|
|
text_len=77,
|
|
text_dim=1024,
|
|
text_heads=16,
|
|
text_layers=24,
|
|
activation='gelu')
|
|
cfg.update(**kwargs)
|
|
return cfg, CLIP
|
|
|
|
|
|
def clip_vit_bigG_14(**kwargs):
|
|
cfg = dict(embed_dim=1280,
|
|
image_size=224,
|
|
patch_size=14,
|
|
vision_dim=1664,
|
|
vision_mlp_ratio=4.9231,
|
|
vision_heads=16,
|
|
vision_layers=48,
|
|
vocab_size=49408,
|
|
text_len=77,
|
|
text_dim=1280,
|
|
text_heads=20,
|
|
text_layers=32,
|
|
activation='gelu')
|
|
cfg.update(**kwargs)
|
|
return cfg, CLIP
|
|
|
|
|
|
def clip_xlm_roberta_vit_h_14(**kwargs):
|
|
cfg = dict(embed_dim=1024,
|
|
image_size=224,
|
|
patch_size=14,
|
|
vision_dim=1280,
|
|
vision_mlp_ratio=4,
|
|
vision_heads=16,
|
|
vision_layers=32,
|
|
activation='gelu',
|
|
vocab_size=250002,
|
|
max_text_len=514,
|
|
type_size=1,
|
|
pad_token=1,
|
|
text_dim=1024,
|
|
text_heads=16,
|
|
text_layers=24,
|
|
text_eps=1e-5,
|
|
text_dropout=0.1,
|
|
attn_dropout=0.0,
|
|
proj_dropout=0.0,
|
|
embedding_dropout=0.0,
|
|
flash_dtype=torch.float16)
|
|
cfg.update(**kwargs)
|
|
return cfg, XLMRobertaCLIP
|
|
|
|
|
|
clip_functions = {
|
|
'clip_vit_b_32': clip_vit_b_32,
|
|
'clip_vit_b_16': clip_vit_b_16,
|
|
'clip_vit_l_14': clip_vit_l_14,
|
|
'clip_vit_l_14_336px': clip_vit_l_14_336px,
|
|
'clip_vit_h_14': clip_vit_h_14,
|
|
'clip_vit_g_14': clip_vit_g_14,
|
|
'clip_vit_bigG_14': clip_vit_bigG_14,
|
|
'clip_xlm_roberta_vit_h_14': clip_xlm_roberta_vit_h_14
|
|
}
|
|
|
|
|
|
@EMBEDDERS.register_class()
|
|
class ClipEncoder(BaseModel):
|
|
para_dict = {
|
|
'CLIP_FUNC': {
|
|
'value': 'clip_vit_b_32',
|
|
'description':
|
|
f'Select clip model from {list(clip_functions.keys())}'
|
|
},
|
|
'PRETRAINED': {
|
|
'value': False,
|
|
'description': 'Wether load from pretrained model or not.'
|
|
},
|
|
'USE_GRAD': {
|
|
'value': False,
|
|
'description': ''
|
|
},
|
|
'PRETRAINED_PATH': {
|
|
'value': None,
|
|
'description': 'Pretrained model load from.'
|
|
},
|
|
'USE_MODULE': {
|
|
'value': ['visual', 'textual'],
|
|
'description':
|
|
"Use module from visual or textual, default is ['visual', 'textual']."
|
|
},
|
|
'CLIP_SKIP': {
|
|
'value': 2,
|
|
'description': "Textuxl branch skip blocks' num. Default is 2."
|
|
},
|
|
'TOKEN_LENGTH': {
|
|
'value': 77,
|
|
'description': 'The input token length for text. Default is 77.'
|
|
},
|
|
'KWARGS': {}
|
|
}
|
|
|
|
def __init__(self, cfg, logger=None):
|
|
super().__init__(cfg, logger=logger)
|
|
clip_func = cfg.CLIP_FUNC
|
|
pretrained = cfg.get('PRETRAINED', False)
|
|
pretrained_path = cfg.get('PRETRAINED_PATH', None)
|
|
use_module = cfg.get('USE_MODULE', ['visual', 'textual'])
|
|
self.clip_skip = cfg.get('CLIP_SKIP', 2)
|
|
self.use_grad = cfg.get('USE_GRAD', False)
|
|
self.token_length = cfg.get('TOKEN_LENGTH', 77)
|
|
kwargs = {k.lower(): v for k, v in cfg.get('KWARGS', {}).items()}
|
|
if pretrained and pretrained_path:
|
|
local_path = FS.get_from(pretrained_path, wait_finish=True)
|
|
else:
|
|
local_path = None
|
|
assert clip_func in clip_functions
|
|
if clip_func in clip_functions:
|
|
conf, model_cls = clip_functions[clip_func](**kwargs)
|
|
conf['use_module'] = use_module
|
|
self.clip_model = _clip(pretrained, local_path, model_cls, **conf)
|
|
for module in use_module:
|
|
if hasattr(self.clip_model, module):
|
|
setattr(self, module, getattr(self.clip_model, module))
|
|
|
|
def encode_image(self, image):
|
|
if not self.use_grad:
|
|
with torch.no_grad():
|
|
m = self.clip_model.visual
|
|
return m(image)
|
|
else:
|
|
m = self.clip_model.visual
|
|
return m(image)
|
|
|
|
def encode_text(self,
|
|
tokens,
|
|
tokenizer=None,
|
|
append_sentence_embedding=True):
|
|
def fn():
|
|
m = self.clip_model.textual
|
|
b, s = tokens.shape
|
|
mask = tokens.ne(m.pad_token).long()
|
|
# embeddings
|
|
x = m.token_embedding(tokens) + \
|
|
m.type_embedding(torch.zeros_like(tokens)) + \
|
|
m.pos_embedding(m.pad_token + torch.cumsum(mask, dim=1) * mask)
|
|
x = m.norm(x)
|
|
x = m.dropout(x)
|
|
|
|
# blocks
|
|
for block in m.blocks[:-1]:
|
|
x = block(x, mask.view(b, 1, 1, s))
|
|
words = x
|
|
|
|
sentence = m.blocks[-1](x, mask.view(b, 1, 1, s))
|
|
mask = tokens.ne(m.pad_token).unsqueeze(-1).to(sentence)
|
|
sentence = (sentence * mask).sum(dim=1) / mask.sum(dim=1)
|
|
sentence = m.head(sentence)
|
|
|
|
return {'crossattn': words, 'y': sentence}
|
|
|
|
if not self.use_grad:
|
|
with torch.no_grad():
|
|
return fn()
|
|
else:
|
|
return fn()
|
|
|
|
def dynamic_encode_text(self,
|
|
all_tokens,
|
|
tokenizer=None,
|
|
append_sentence_embedding=True):
|
|
'''
|
|
m: clip model
|
|
t: tokenzer
|
|
tokens: tensor(1, N)
|
|
'''
|
|
if tokenizer is None:
|
|
tokenizer = self.tokenizer
|
|
|
|
def fn():
|
|
m = self.clip_model.textual
|
|
ret_data = {'crossattn': [], 'y': []}
|
|
for tokens_id in range(all_tokens.shape[0]):
|
|
tokens = all_tokens[tokens_id]
|
|
text_len = self.token_length
|
|
device = tokens.device
|
|
dtype = m.type_embedding.weight.dtype
|
|
# special tokens
|
|
sos_emb, eos_emb, pad_emb = m.token_embedding(
|
|
torch.LongTensor([
|
|
tokenizer.sos_token, tokenizer.eos_token,
|
|
tokenizer.pad_token
|
|
]).to(device)).type(dtype).chunk(3)
|
|
|
|
# get raw input tokens
|
|
tokens = list(tokens.cpu().numpy())
|
|
while tokens[-1] == tokenizer.pad_token:
|
|
tokens = tokens[:-1]
|
|
tokens = tokens[1:-1]
|
|
embeds = m.token_embedding(
|
|
torch.LongTensor(tokens).to(device)).type(dtype)
|
|
|
|
# split into chunks to support any-length text
|
|
chunk_embeds, chunk_tokens = [], []
|
|
max_words = text_len - 2
|
|
if len(tokens) == 0:
|
|
chunk = torch.cat([sos_emb, eos_emb])
|
|
chunk = torch.cat(
|
|
[chunk,
|
|
pad_emb.repeat(text_len - len(chunk), 1)])
|
|
chunk_embeds.append(chunk)
|
|
|
|
chunk = torch.LongTensor([tokenizer.sos_token] +
|
|
[tokenizer.eos_token])
|
|
chunk = torch.cat([
|
|
chunk,
|
|
torch.LongTensor([tokenizer.pad_token] *
|
|
(text_len - len(chunk)))
|
|
])
|
|
chunk_tokens.append(chunk)
|
|
else:
|
|
while len(tokens) > 0:
|
|
# find splitting position
|
|
if len(tokens) <= max_words:
|
|
pos = len(tokens)
|
|
else:
|
|
pos = [
|
|
i for i, u in enumerate(tokens[:max_words])
|
|
if u == tokenizer.comma_token
|
|
]
|
|
pos = max_words if len(pos) == 0 else pos[-1] + 1
|
|
|
|
# collect chunk
|
|
chunk = torch.cat([sos_emb, embeds[:pos], eos_emb])
|
|
chunk = torch.cat(
|
|
[chunk,
|
|
pad_emb.repeat(text_len - len(chunk), 1)])
|
|
chunk_embeds.append(chunk)
|
|
|
|
chunk = torch.LongTensor([tokenizer.sos_token] +
|
|
tokens[:pos] +
|
|
[tokenizer.eos_token])
|
|
chunk = torch.cat([
|
|
chunk,
|
|
torch.LongTensor([tokenizer.pad_token] *
|
|
(text_len - len(chunk)))
|
|
])
|
|
chunk_tokens.append(chunk)
|
|
|
|
# update
|
|
tokens = tokens[pos:]
|
|
embeds = embeds[pos:]
|
|
# loop over chunks
|
|
words = []
|
|
sentences = []
|
|
for i, (chunk_token, chunk_embed) in enumerate(
|
|
zip(chunk_tokens, chunk_embeds)):
|
|
chunk_token = chunk_token.unsqueeze(0).to(device)
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|
chunk_embed = chunk_embed.unsqueeze(0).to(device)
|
|
# embeddings
|
|
mask = chunk_token.ne(tokenizer.pad_token).long()
|
|
x = chunk_embed.type(dtype) + m.type_embedding(
|
|
torch.zeros_like(chunk_token)) + m.pos_embedding(
|
|
m.pad_token + torch.cumsum(mask, dim=1) * mask)
|
|
|
|
x = m.norm(x)
|
|
x = m.dropout(x)
|
|
blocks = m.blocks[:-(self.clip_skip - 1)]
|
|
for block in blocks:
|
|
x = block(x, mask.view(1, 1, 1, -1))
|
|
print('word', torch.sum(x))
|
|
words.append(x.clone())
|
|
|
|
# if append_sentence_embedding:
|
|
# last layers
|
|
blocks = m.blocks[-(self.clip_skip - 1):]
|
|
for block in blocks:
|
|
x = block(x, mask.view(1, 1, 1, -1))
|
|
|
|
# get global embedding
|
|
x = (x * mask.unsqueeze(2)).sum(dim=1) / mask.sum(dim=1)
|
|
x = m.head(x)
|
|
print('sentence', torch.sum(x))
|
|
# output
|
|
sentences.append(x.unsqueeze(0))
|
|
sentence = torch.cat(sentences, dim=0).mean(dim=0)
|
|
words = torch.cat(words, dim=1)
|
|
ret_data['crossattn'] = words
|
|
ret_data['y'] = sentence
|
|
# ret_data['y'].append(sentence)
|
|
# ret_data.append(torch.cat([sentence] + words, dim=1))
|
|
# ret_data['crossattn'].append(torch.cat(words, dim=1))
|
|
# ret_data['crossattn'] = torch.cat(ret_data['crossattn'], dim=0)
|
|
# ret_data['y'] = torch.cat(ret_data['y'], dim=0)
|
|
return ret_data
|
|
|
|
if not self.use_grad:
|
|
with torch.no_grad():
|
|
return fn()
|
|
else:
|
|
return fn()
|
|
|
|
@staticmethod
|
|
def get_config_template():
|
|
return dict_to_yaml('BACKBONE',
|
|
__class__.__name__,
|
|
ClipEncoder.para_dict,
|
|
set_name=True)
|