Feature Extraction
Transformers
Safetensors
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C-RADIO / open_clip_adaptor.py
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# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
from argparse import Namespace
import torch
from torch import nn
import torch.nn.functional as F
from .adaptor_registry import adaptor_registry, dict_t, state_t
from .adaptor_generic import GenericAdaptor
class OpenCLIP_RADIO(GenericAdaptor):
def __init__(self, main_config: Namespace, adaptor_config: dict_t, state: state_t):
super().__init__(main_config, adaptor_config, state)
import open_clip
self.oc_model = open_clip.create_model_from_pretrained(
model_name=adaptor_config['model'],
pretrained=adaptor_config['pretrained'],
return_transform=False,
)
# Unload these parameters
self.oc_model.visual = None
self.tokenizer = open_clip.get_tokenizer(model_name=adaptor_config['model'])
def encode_text(self, text, normalize: bool = False):
return self.oc_model.encode_text(text, normalize=normalize)
@adaptor_registry.register_adaptor("open_clip")
def create_open_clip_adaptor(main_config: Namespace, adaptor_config: dict_t, state: state_t):
return OpenCLIP_RADIO(main_config, adaptor_config, state)