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vitlens.py
executable file
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/
vitlens.py
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import os
import logging
import torch
import torch.nn as nn
import torch.nn.functional as F
import einops
import fsspec
from functools import partial
from open_clip.factory import tri_create_model_and_transforms
from open_clip.constants import PROJECT_DIR
from mm_vit_lens.model_cfg import fetch_model_cfg
def load_ckpt(file_path, map_location=None):
of = fsspec.open(file_path, "rb")
with of as f:
out = torch.load(f, map_location=map_location)
return out
class ViTLens(nn.Module):
def __init__(
self,
model_var="vitlensL",
modality_loaded=["image", "text", "pc", "depth", "audio", "tactile", "eeg"],
load_from_ckpt=os.path.join(PROJECT_DIR, "model_release"),
):
super().__init__()
self.model_var = model_var
self.modality_loaded = modality_loaded
self.processors = dict()
self.vitlens = nn.ModuleDict()
self.init_processors_and_model(load_from_ckpt=load_from_ckpt)
def _init_modality_module(self, modality, load_from_pt_flag=False):
cfg = fetch_model_cfg(modality=modality, model_option=self.model_var)
model, _, image_process_val = tri_create_model_and_transforms(
cfg.model,
cfg.pretrained,
precision=cfg.precision,
device=cfg.device,
jit=False,
force_quick_gelu=cfg.force_quick_gelu,
force_custom_text=cfg.force_custom_text,
force_patch_dropout=False,
force_image_size=cfg.force_image_size,
pretrained_image=cfg.pretrained_image,
load_ckpt_strict=False,
image_mean=None,
image_std=None,
aug_cfg=None,
output_dict=True,
cache_dir=cfg.cache_dir,
args=cfg,
)
if modality == "image":
self.vitlens.add_module("image", model.image)
self.processors["image"].set_image_transform(image_process_val)
elif modality == "text":
if hasattr(model, "text"):
self.vitlens.add_module("text", model.text)
else:
self.vitlens.add_module("text", nn.ModuleDict())
self.vitlens.text.add_module("transformer", model.transformer)
self.vitlens.text.context_length = model.context_length
self.vitlens.text.vocab_size = model.vocab_size
self.vitlens.text.add_module("token_embedding", model.token_embedding)
self.vitlens.text.positional_embedding = model.positional_embedding
self.vitlens.text.add_module("ln_final", model.ln_final)
self.vitlens.text.text_projection = model.text_projection
self.vitlens.text.register_buffer(
"attn_mask", model.attn_mask, persistent=False
)
def encode_text(module, text):
cast_dtype = module.transformer.get_cast_dtype()
x = module.token_embedding(text).to(
cast_dtype
) # [batch_size, n_ctx, d_model]
x = x + module.positional_embedding.to(cast_dtype)
x = x.permute(1, 0, 2) # NLD -> LND
x = module.transformer(x, attn_mask=module.attn_mask)
x = x.permute(1, 0, 2) # LND -> NLD
x = module.ln_final(x) # [batch_size, n_ctx, transformer.width]
# 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)]
@ module.text_projection
)
return x
self.vitlens.text.forward = partial(encode_text, self.vitlens.text)
else: # for modalities except image and text
self.vitlens.add_module(modality, model.visual)
if load_from_pt_flag: # load from modality specific pt weights
self.load_modality_from_pt_ckpt(
modality=modality, pt_ckpt_path=cfg.ckpt_pth
)
del model
def init_processors_and_model(self, load_from_ckpt=None):
# init processors
from mm_vit_lens.data_processors import get_vitlens_processors_cls
processors = get_vitlens_processors_cls()[self.model_var]()
for m in self.modality_loaded:
self.processors[m] = processors[m]
# init model
for m in self.modality_loaded:
self._init_modality_module(m)
if load_from_ckpt:
ckpt_path = os.path.join(load_from_ckpt, f"{self.model_var}.pt")
if not os.path.exists(ckpt_path):
print(f"Downloading ViT-Lens weights to {ckpt_path} ...")
os.makedirs(load_from_ckpt, exist_ok=True)
torch.hub.download_url_to_file(
f"https://huggingface.co/TencentARC/ViT-Lens/resolve/main/{self.model_var}.pt",
ckpt_path,
progress=True,
)
ckpt = load_ckpt(ckpt_path)
msg = self.load_state_dict(ckpt["state_dict"], strict=False)
logging.info(msg)
def load_modality_from_pt_ckpt(self, modality, pt_ckpt_path):
"""
Load weights from pre-trained ckpt of each modality encoder
Use this function to load your own checkpoint
"""
checkpoint = load_ckpt(pt_ckpt_path, map_location="cpu")
sd = checkpoint["state_dict"]
# deal with distributed ckpt during pt
if next(iter(sd.items()))[0].startswith("module."):
sd = {k[len("module.") :]: v for k, v in sd.items()}
# only load `visual` tower
sd = {k[len("visual.") :]: v for k, v in sd.items() if k.startswith("visual.")}
msg = self.vitlens[modality].load_state_dict(sd, strict=False)
print(f"[Load ViT-Lens from `{modality}` Pretrained ckpt] : {msg}.")
def export_checkpoint(self, save_path="model_release/vitlens.pt"):
model_dict = dict(
model_var=self.model_var,
modality_loaded=self.modality_loaded,
state_dict=self.state_dict(),
)
torch.save(model_dict, save_path)
@property
def device(self):
return list(self.parameters())[0].device
def reduce_list(self, modality):
return modality in [
"audio",
]
def encode(self, input_dict, normalize=True):
output_dict = dict()
for m in input_dict:
x = self.processors[m](input_dict[m], device=self.device)
if self.reduce_list(m):
B, S = x.shape[:2]
x = einops.rearrange(x, "B S ... -> (B S) ...")
features = self.vitlens[m](x)
if self.reduce_list(m):
features = einops.rearrange(features, "(B S) ... -> B S ...", B=B, S=S)
features = features.mean(dim=1)
features = F.normalize(features, dim=-1) if normalize else features
output_dict[m] = features
return output_dict