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"""
Copyright (c) 2023, salesforce.com, inc.
All rights reserved.
SPDX-License-Identifier: BSD-3-Clause
For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause
"""
import logging
import torch
import torch.nn as nn
from .dist_utils import download_cached_file
from .Qformer import BertConfig, BertLMHeadModel
from .eva_vit import create_eva_vit_g
from transformers import BertTokenizer
class Blip2Base(nn.Module):
@classmethod
def init_tokenizer(cls):
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
tokenizer.add_special_tokens({"bos_token": "[DEC]"})
return tokenizer
@property
def device(self):
return list(self.parameters())[0].device
@classmethod
def init_Qformer(cls, num_query_token, vision_width, cross_attention_freq=2):
encoder_config = BertConfig.from_pretrained("bert-base-uncased")
encoder_config.encoder_width = vision_width
# insert cross-attention layer every other block
encoder_config.add_cross_attention = True
encoder_config.cross_attention_freq = cross_attention_freq
encoder_config.query_length = num_query_token
encoder_config.is_decoder = True
Qformer = BertLMHeadModel(config=encoder_config)
query_tokens = nn.Parameter(
torch.zeros(1, num_query_token, encoder_config.hidden_size)
)
query_tokens.data.normal_(mean=0.0, std=encoder_config.initializer_range)
return Qformer, query_tokens
@classmethod
def init_vision_encoder(
cls, model_name, img_size, drop_path_rate, use_grad_checkpoint, precision
):
assert model_name == "eva_clip_g", "vit model must be eva_clip_g for current version of MiniGPT-4"
visual_encoder = create_eva_vit_g(
img_size, drop_path_rate, use_grad_checkpoint, precision
)
ln_vision = LayerNorm(visual_encoder.num_features)
return visual_encoder, ln_vision
def load_from_pretrained(self, url_or_filename):
cached_file = download_cached_file(
url_or_filename, check_hash=False, progress=True
)
checkpoint = torch.load(cached_file, map_location="cpu")
state_dict = checkpoint["model"]
msg = self.load_state_dict(state_dict, strict=False)
# logging.info("Missing keys {}".format(msg.missing_keys))
logging.info("load checkpoint from %s" % url_or_filename)
return msg
class LayerNorm(nn.LayerNorm):
"""Subclass torch's LayerNorm to handle fp16."""
def forward(self, x: torch.Tensor):
orig_type = x.dtype
ret = super().forward(x.type(torch.float32))
return ret.type(orig_type)
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