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""" Conversion functions for 3rd part state-dicts and non-torch native checkpoint formats.
"""
from typing import Union
import torch
import numpy as np
from .model import CLIP, CustomTextCLIP
from .transformer import TextTransformer, Transformer
@torch.no_grad()
def load_big_vision_weights(model: CustomTextCLIP, checkpoint_path: str):
""" Load weights from .npz checkpoints for official Google big_vision image-text models
Currently the SigLIP source models are supported and a CustomTextCLIP destination model
w/ timm image encoder.
"""
from timm.layers import resample_patch_embed, resample_abs_pos_embed
def _n2p(w, t=True):
if w.ndim == 4 and w.shape[0] == w.shape[1] == w.shape[2] == 1:
w = w.flatten()
if t:
if w.ndim == 4:
w = w.transpose([3, 2, 0, 1])
elif w.ndim == 3:
w = w.transpose([2, 0, 1])
elif w.ndim == 2:
w = w.transpose([1, 0])
return torch.from_numpy(w)
w = np.load(checkpoint_path)
interpolation = 'bilinear'
antialias = False
def _convert_timm_img(module, prefix):
embed_conv_w = _n2p(w[f'{prefix}embedding/kernel'])
if embed_conv_w.shape[-2:] != module.patch_embed.proj.weight.shape[-2:]:
embed_conv_w = resample_patch_embed(
embed_conv_w,
module.patch_embed.proj.weight.shape[-2:],
interpolation=interpolation,
antialias=antialias,
verbose=True,
)
module.patch_embed.proj.weight.copy_(embed_conv_w)
module.patch_embed.proj.bias.copy_(_n2p(w[f'{prefix}embedding/bias']))
if module.cls_token is not None:
module.cls_token.copy_(_n2p(w[f'{prefix}cls'], t=False))
pos_embed_w = _n2p(w[f'{prefix}pos_embedding'], t=False)
if pos_embed_w.shape != module.pos_embed.shape:
assert False, f'{pos_embed_w.shape}, {module.pos_embed.shape}'
num_prefix_tokens = 0 if getattr(module, 'no_embed_class', False) else getattr(module, 'num_prefix_tokens', 1)
pos_embed_w = resample_abs_pos_embed( # resize pos embedding when different size from pretrained weights
pos_embed_w,
new_size=module.patch_embed.grid_size,
num_prefix_tokens=num_prefix_tokens,
interpolation=interpolation,
antialias=antialias,
verbose=True,
)
module.pos_embed.copy_(pos_embed_w)
mha_sub, b_sub, ln1_sub = (0, 0, 1)
for i, block in enumerate(module.blocks.children()):
block_prefix = f'{prefix}Transformer/encoderblock_{i}/'
mha_prefix = block_prefix + f'MultiHeadDotProductAttention_{mha_sub}/'
block.norm1.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/scale']))
block.norm1.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/bias']))
block.attn.qkv.weight.copy_(torch.cat([
_n2p(w[f'{mha_prefix}{n}/kernel'], t=False).flatten(1).T for n in ('query', 'key', 'value')]))
block.attn.qkv.bias.copy_(torch.cat([
_n2p(w[f'{mha_prefix}{n}/bias'], t=False).reshape(-1) for n in ('query', 'key', 'value')]))
block.attn.proj.weight.copy_(_n2p(w[f'{mha_prefix}out/kernel']).flatten(1))
block.attn.proj.bias.copy_(_n2p(w[f'{mha_prefix}out/bias']))
for r in range(2):
getattr(block.mlp, f'fc{r + 1}').weight.copy_(_n2p(w[f'{block_prefix}MlpBlock_{b_sub}/Dense_{r}/kernel']))
getattr(block.mlp, f'fc{r + 1}').bias.copy_(_n2p(w[f'{block_prefix}MlpBlock_{b_sub}/Dense_{r}/bias']))
block.norm2.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_{ln1_sub}/scale']))
block.norm2.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_{ln1_sub}/bias']))
module.norm.weight.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/scale']))
module.norm.bias.copy_(_n2p(w[f'{prefix}Transformer/encoder_norm/bias']))
if module.attn_pool is not None:
block_prefix = f'{prefix}MAPHead_0/'
mha_prefix = block_prefix + f'MultiHeadDotProductAttention_0/'
module.attn_pool.latent.copy_(_n2p(w[f'{block_prefix}probe'], t=False))
module.attn_pool.q.weight.copy_(_n2p(w[f'{mha_prefix}query/kernel'], t=False).flatten(1).T)
module.attn_pool.q.bias.copy_(_n2p(w[f'{mha_prefix}query/bias'], t=False).reshape(-1))
module.attn_pool.kv.weight.copy_(torch.cat([
_n2p(w[f'{mha_prefix}{n}/kernel'], t=False).flatten(1).T for n in ('key', 'value')]))
module.attn_pool.kv.bias.copy_(torch.cat([
_n2p(w[f'{mha_prefix}{n}/bias'], t=False).reshape(-1) for n in ('key', 'value')]))
module.attn_pool.proj.weight.copy_(_n2p(w[f'{mha_prefix}out/kernel']).flatten(1))
module.attn_pool.proj.bias.copy_(_n2p(w[f'{mha_prefix}out/bias']))
module.attn_pool.norm.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/scale']))
module.attn_pool.norm.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/bias']))
for r in range(2):
getattr(module.attn_pool.mlp, f'fc{r + 1}').weight.copy_(_n2p(w[f'{block_prefix}MlpBlock_0/Dense_{r}/kernel']))
getattr(module.attn_pool.mlp, f'fc{r + 1}').bias.copy_(_n2p(w[f'{block_prefix}MlpBlock_0/Dense_{r}/bias']))
def _convert_openclip_transformer(module: Transformer, prefix):
for i, block in enumerate(module.resblocks.children()):
block_prefix = f'{prefix}encoderblock_{i}/'
mha_prefix = block_prefix + f'MultiHeadDotProductAttention_0/'
block.ln_1.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/scale']))
block.ln_1.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_0/bias']))
block.attn.in_proj_weight.copy_(torch.cat([
_n2p(w[f'{mha_prefix}{n}/kernel'], t=False).flatten(1).T for n in ('query', 'key', 'value')]))
block.attn.in_proj_bias.copy_(torch.cat([
_n2p(w[f'{mha_prefix}{n}/bias'], t=False).reshape(-1) for n in ('query', 'key', 'value')]))
block.attn.out_proj.weight.copy_(_n2p(w[f'{mha_prefix}out/kernel']).flatten(1))
block.attn.out_proj.bias.copy_(_n2p(w[f'{mha_prefix}out/bias']))
block.ln_2.weight.copy_(_n2p(w[f'{block_prefix}LayerNorm_1/scale']))
block.ln_2.bias.copy_(_n2p(w[f'{block_prefix}LayerNorm_1/bias']))
block.mlp.c_fc.weight.copy_(_n2p(w[f'{block_prefix}MlpBlock_0/Dense_0/kernel']))
block.mlp.c_fc.bias.copy_(_n2p(w[f'{block_prefix}MlpBlock_0/Dense_0/bias']))
block.mlp.c_proj.weight.copy_(_n2p(w[f'{block_prefix}MlpBlock_0/Dense_1/kernel']))
block.mlp.c_proj.bias.copy_(_n2p(w[f'{block_prefix}MlpBlock_0/Dense_1/bias']))
def _convert_openclip_txt(module: TextTransformer, prefix):
module.token_embedding.weight.copy_(_n2p(w[f'{prefix}Embed_0/embedding'], t=False))
pos_embed_w = _n2p(w[f'{prefix}pos_embedding'], t=False).squeeze(0)
module.positional_embedding.copy_(pos_embed_w)
_convert_openclip_transformer(module.transformer, prefix=prefix + 'Encoder_0/')
module.ln_final.weight.copy_(_n2p(w[f'{prefix}Encoder_0/encoder_norm/scale']))
module.ln_final.bias.copy_(_n2p(w[f'{prefix}Encoder_0/encoder_norm/bias']))
module.text_projection.weight.copy_(_n2p(w[f'{prefix}head/kernel']))
module.text_projection.bias.copy_(_n2p(w[f'{prefix}head/bias']))
_convert_timm_img(model.visual.trunk, 'params/img/')
_convert_openclip_txt(model.text, 'params/txt/')
model.logit_bias.copy_(_n2p(w['params/b'])[0])
model.logit_scale.copy_(_n2p(w['params/t'])[0])
@torch.no_grad()
def convert_mobile_clip_state_dict(model: CustomTextCLIP, state_dict, fastvit = True):
def _convert_timm_img(state_dict):
if fastvit:
from timm.models.fastvit import checkpoint_filter_fn
else:
from timm.models.vision_transformer_hybrid import checkpoint_filter_fn
timm_state_dict = checkpoint_filter_fn(state_dict, model.visual.trunk)
timm_state_dict = {'visual.trunk.' + k: v for k, v in timm_state_dict.items()}
return timm_state_dict
def _convert_openclip_txt(state_dict, prefix='text_encoder.'):
text_dict = {}
for k, v in state_dict.items():
if not k.startswith(prefix):
continue
k = k.replace(prefix, '')
k = k.replace('projection_layer', 'text_projection')
k = k.replace('embedding_layer', 'token_embedding')
if k.startswith('positional_embedding.pos_embed.pos_embed'):
k = k.replace('positional_embedding.pos_embed.pos_embed', 'positional_embedding')
v = v.squeeze()
k = k.replace('final_layer_norm', 'ln_final')
k = k.replace('pre_norm_mha.0', 'ln_1')
k = k.replace('pre_norm_mha.1', 'attn')
k = k.replace('pre_norm_ffn.0', 'ln_2')
k = k.replace('pre_norm_ffn.1', 'mlp.c_fc')
k = k.replace('pre_norm_ffn.4', 'mlp.c_proj')
k = k.replace('qkv_proj.weight', 'in_proj_weight')
k = k.replace('qkv_proj.bias', 'in_proj_bias')
k = k.replace('transformer.', 'transformer.resblocks.')
text_dict['text.' + k] = v
return text_dict
image_dict = _convert_timm_img(state_dict)
text_dict = _convert_openclip_txt(state_dict)
out_dict = {**image_dict, **text_dict}
out_dict['logit_scale'] = state_dict['logit_scale']
return out_dict
def convert_state_dict(model: Union[CustomTextCLIP, CLIP], state_dict):
if 'image_encoder.model.patch_embed.0.rbr_conv.0.conv.weight' in state_dict:
# Apple MobileCLIP s1 & s2 state_dicts (s0 and b not currently supported)
state_dict = convert_mobile_clip_state_dict(model, state_dict)
if 'image_encoder.model.patch_emb.0.block.conv.weight' in state_dict:
# convert b model
state_dict = convert_mobile_clip_state_dict(model, state_dict, fastvit=False)
return state_dict
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