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import logging
import random
import torch
from torch.cuda.amp import autocast as autocast
import torch.nn as nn
from video_llama.common.registry import registry
from video_llama.models.blip2 import Blip2Base, disabled_train
from video_llama.models.modeling_llama import LlamaForCausalLM
# from video_llama.models.Qformer import BertEncoder
from transformers import LlamaTokenizer,BertConfig
# from transformers.models.bert.modeling_bert import BertEncoder
import einops
import copy
from video_llama.models.Qformer import BertConfig, BertLMHeadModel
from video_llama.models.ImageBind.models.imagebind_model import ImageBindModel,ModalityType
from video_llama.models.ImageBind.models import imagebind_model
# from flamingo_pytorch import PerceiverResampler
@registry.register_model("video_llama")
class VideoLLAMA(Blip2Base):
"""
BLIP2 GPT-LLAMA model.
"""
PRETRAINED_MODEL_CONFIG_DICT = {
"pretrain_vicuna": "configs/models/video_llama.yaml",
"pretrain_llama_v2": "configs/models/video_llama.yaml",
}
@classmethod
def init_video_Qformer(cls, num_query_token, vision_width,num_hidden_layers =2):
encoder_config = BertConfig.from_pretrained("bert-base-uncased")
encoder_config.num_hidden_layers = num_hidden_layers
encoder_config.encoder_width = vision_width
# insert cross-attention layer every other block
encoder_config.add_cross_attention = True
encoder_config.cross_attention_freq = 1
encoder_config.query_length = num_query_token
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
def __init__(
self,
vit_model="eva_clip_g",
q_former_model="https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained_flant5xxl.pth",
img_size=224,
drop_path_rate=0,
use_grad_checkpoint=False,
vit_precision="fp16",
freeze_vit=True,
freeze_qformer=True,
num_query_token=32,
llama_model="",
prompt_path="",
prompt_template="",
max_txt_len=32,
end_sym='\n',
low_resource=False, # use 8 bit and put vit in cpu
device_8bit=0, # the device of 8bit model should be set when loading and cannot be changed anymore.
frozen_llama_proj=True,
frozen_video_Qformer=True,
frozen_audio_Qformer=True,
llama_proj_model='',
fusion_header_type= "seqTransf",
max_frame_pos= 32,
fusion_head_layers = 2,
num_video_query_token = 32,
num_audio_query_token = 8,
imagebind_ckpt_path = '/mnt/workspace/ckpt',
equip_audio_branch = True
):
super().__init__()
self.tokenizer = self.init_tokenizer()
self.low_resource = low_resource
print('Loading VIT')
self.visual_encoder, self.ln_vision = self.init_vision_encoder(
vit_model, img_size, drop_path_rate, use_grad_checkpoint, vit_precision
)
if freeze_vit:
for name, param in self.visual_encoder.named_parameters():
param.requires_grad = False
self.visual_encoder = self.visual_encoder.eval()
self.visual_encoder.train = disabled_train
for name, param in self.ln_vision.named_parameters():
param.requires_grad = False
self.ln_vision = self.ln_vision.eval()
self.ln_vision.train = disabled_train
logging.info("freeze vision encoder")
print('Loading VIT Done')
print('Loading Q-Former')
self.Qformer, self.query_tokens = self.init_Qformer(
num_query_token, self.visual_encoder.num_features
)
self.Qformer.cls = None
self.Qformer.bert.embeddings.word_embeddings = None
self.Qformer.bert.embeddings.position_embeddings = None
for layer in self.Qformer.bert.encoder.layer:
layer.output = None
layer.intermediate = None
self.load_from_pretrained(url_or_filename=q_former_model)
if freeze_qformer:
for name, param in self.Qformer.named_parameters():
param.requires_grad = False
self.Qformer = self.Qformer.eval()
self.Qformer.train = disabled_train
self.query_tokens.requires_grad = False
logging.info("freeze Qformer")
logging.info('Loading Q-Former Done')
logging.info('Loading LLAMA Tokenizer')
self.llama_tokenizer = LlamaTokenizer.from_pretrained(llama_model, use_fast=False)
if self.llama_tokenizer.pad_token is None:
self.llama_tokenizer.pad_token = self.llama_tokenizer.unk_token
DEFAULT_IMAGE_PATCH_TOKEN = '<ImageHere>'
DEFAULT_AUDIO_PATCH_TOKEN = '<AudioHere>'
self.llama_tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True)
self.llama_tokenizer.add_tokens([DEFAULT_AUDIO_PATCH_TOKEN], special_tokens=True)
self.IMAGE_PATCH_TOKEN_ID = self.llama_tokenizer.get_vocab()[DEFAULT_IMAGE_PATCH_TOKEN]
self.AUDIO_PATCH_TOKEN_ID = self.llama_tokenizer.get_vocab()[DEFAULT_AUDIO_PATCH_TOKEN]
logging.info('Loading LLAMA Model')
if self.low_resource:
self.llama_model = LlamaForCausalLM.from_pretrained(
llama_model,
torch_dtype=torch.bfloat16,
load_in_8bit=True,
device_map={'': device_8bit}
)
else:
self.llama_model = LlamaForCausalLM.from_pretrained(
llama_model,
torch_dtype=torch.bfloat16,
)
for name, param in self.llama_model.named_parameters():
param.requires_grad = False
logging.info('Loading LLAMA Done')
logging.info('Loading LLAMA proj')
self.llama_proj = nn.Linear(
self.Qformer.config.hidden_size, self.llama_model.config.hidden_size
)
if llama_proj_model:
print("load llama proj weight: {}".format(llama_proj_model))
llama_proj_weight = torch.load(llama_proj_model, map_location="cpu")
msg = self.load_state_dict(llama_proj_weight['model'], strict=False)
if frozen_llama_proj:
# todo frozen llama_proj
for name, param in self.llama_proj.named_parameters():
param.requires_grad = False
logging.info('LLAMA proj is frozen')
else:
for name, param in self.llama_proj.named_parameters():
param.requires_grad = True
logging.info('LLAMA proj is not frozen')
logging.info('Loading llama_proj Done')
self.max_txt_len = max_txt_len
self.end_sym = end_sym
if prompt_path:
with open(prompt_path, 'r') as f:
raw_prompts = f.read().splitlines()
filted_prompts = [raw_prompt for raw_prompt in raw_prompts if "<ImageHere>" in raw_prompt]
self.prompt_list = [prompt_template.format(p) for p in filted_prompts]
print('Load {} training prompts'.format(len(self.prompt_list)))
print('Prompt Example \n{}'.format(random.choice(self.prompt_list)))
else:
self.prompt_list = []
self.video_frame_position_embedding = nn.Embedding(max_frame_pos, self.Qformer.config.hidden_size)
self.num_video_query_token = num_video_query_token
self.video_Qformer,self.video_query_tokens = self.init_video_Qformer(num_query_token = num_video_query_token,\
vision_width=self.Qformer.config.hidden_size, num_hidden_layers =2)
self.video_Qformer.cls = None
self.video_Qformer.bert.embeddings.word_embeddings = None
self.video_Qformer.bert.embeddings.position_embeddings = None
for layer in self.video_Qformer.bert.encoder.layer:
layer.output = None
layer.intermediate = None
if frozen_video_Qformer:
# todo frozen llama_proj
for name, param in self.video_Qformer.named_parameters():
param.requires_grad = False
for name, param in self.video_frame_position_embedding.named_parameters():
param.requires_grad = False
self.video_query_tokens.requires_grad = False
logging.info('video_Qformer is frozen')
else:
for name, param in self.video_Qformer.named_parameters():
param.requires_grad = True
for name, param in self.video_frame_position_embedding.named_parameters():
param.requires_grad = True
self.video_query_tokens.requires_grad = True
logging.info('video_Qformer is not frozen')
if frozen_video_Qformer and (not frozen_audio_Qformer):
self.train_flag = 1 # 只训练audio_Qformer
elif not(frozen_video_Qformer) and frozen_audio_Qformer:
self.train_flag = 0 # 训练video_Qformer
elif not(frozen_video_Qformer) and not(frozen_audio_Qformer):
self.train_flag = 2 # video_Qformer and AL trained
else:
self.train_flag = 3
if equip_audio_branch:
print (f'Initializing audio encoder from {imagebind_ckpt_path} ...')
self.audio_encoder,self.audio_hidden_size = \
imagebind_model.imagebind_huge()
self.audio_encoder.load_state_dict(torch.load("{}/imagebind_huge.pth".format(imagebind_ckpt_path)))
# free vision encoder
for name, param in self.audio_encoder.named_parameters():
param.requires_grad = False
self.audio_encoder.eval()
print ('audio encoder initialized.')
self.num_audio_query_token = num_audio_query_token
self.audio_Qformer,self.audio_query_tokens = self.init_video_Qformer(num_query_token = self.num_audio_query_token,\
vision_width=self.audio_hidden_size, num_hidden_layers =2)
self.audio_Qformer.cls = None
self.audio_Qformer.bert.embeddings.word_embeddings = None
self.audio_Qformer.bert.embeddings.position_embeddings = None
for layer in self.audio_Qformer.bert.encoder.layer:
layer.output = None
layer.intermediate = None
self.audio_llama_proj = nn.Linear(
self.audio_Qformer.config.hidden_size, self.llama_model.config.hidden_size
)
self.audio_position_embedding = nn.Embedding(8, self.audio_hidden_size)
if frozen_audio_Qformer:
# todo frozen llama_proj
for name, param in self.audio_Qformer.named_parameters():
param.requires_grad = False
self.audio_query_tokens.requires_grad = False
for name, param in self.audio_llama_proj.named_parameters():
param.requires_grad = False
for name, param in self.audio_position_embedding.named_parameters():
param.requires_grad = False
logging.info('audio_Qformer and audio-LLAMA proj is frozen')
else:
for name, param in self.audio_Qformer.named_parameters():
param.requires_grad = True
self.audio_query_tokens.requires_grad = True
for name, param in self.audio_llama_proj.named_parameters():
param.requires_grad = True
for name, param in self.audio_position_embedding.named_parameters():
param.requires_grad = True
logging.info('audio_Qformer is not frozen')
# self.audio_hidden_size
def vit_to_cpu(self):
self.ln_vision.to("cpu")
self.ln_vision.float()
self.visual_encoder.to("cpu")
self.visual_encoder.float()
def encode_videoQformer_visual(self, image):
device = image.device
# input shape b,c,t,h,w
batch_size,_,time_length,_,_ = image.size()
image = einops.rearrange(image, 'b c t h w -> (b t) c h w')
with self.maybe_autocast():
# embed image features with blip2, out: (b t) q h
image_embeds = self.ln_vision(self.visual_encoder(image)).to(device)
image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(device)
query_tokens = self.query_tokens.expand(image_embeds.shape[0], -1, -1)
query_output = self.Qformer.bert(
query_embeds=query_tokens,
encoder_hidden_states=image_embeds,
encoder_attention_mask=image_atts,
return_dict=True,
)
# add frame_pos embedding
position_ids = torch.arange(time_length, dtype=torch.long, device=query_tokens.device)
position_ids = position_ids.unsqueeze(0).expand(batch_size, -1)
frame_position_embeddings = self.video_frame_position_embedding(position_ids)
q_hidden_state = query_output.last_hidden_state
frame_position_embeddings = frame_position_embeddings.unsqueeze(-2)
frame_hidden_state = einops.rearrange(q_hidden_state, '(b t) q h -> b t q h',b=batch_size,t=time_length)
frame_hidden_state = frame_position_embeddings + frame_hidden_state
# frame attention
frame_hidden_state = einops.rearrange(frame_hidden_state, 'b t q h -> b (t q) h',b=batch_size,t=time_length)
frame_atts = torch.ones(frame_hidden_state.size()[:-1], dtype=torch.long).to(device)
video_query_tokens = self.video_query_tokens.expand(frame_hidden_state.shape[0], -1, -1)
video_query_output = self.video_Qformer.bert(
query_embeds=video_query_tokens,
encoder_hidden_states=frame_hidden_state,
encoder_attention_mask=frame_atts,
return_dict=True,
)
video_hidden = video_query_output.last_hidden_state
inputs_llama = self.llama_proj(video_hidden)
atts_llama = torch.ones(inputs_llama.size()[:-1], dtype=torch.long).to(image_embeds.device)
return inputs_llama, atts_llama
def prompt_wrap(self, img_embeds, atts_img, prompt):
if prompt:
batch_size = img_embeds.shape[0]
# print(prompt)
p_before, p_after = prompt.split('<ImageHere>')
p_before_tokens = self.llama_tokenizer(
p_before, return_tensors="pt", add_special_tokens=False).to(img_embeds.device)
p_after_tokens = self.llama_tokenizer(
p_after, return_tensors="pt", add_special_tokens=False).to(img_embeds.device)
p_before_embeds = self.llama_model.model.embed_tokens(p_before_tokens.input_ids).expand(batch_size, -1, -1)
p_after_embeds = self.llama_model.model.embed_tokens(p_after_tokens.input_ids).expand(batch_size, -1, -1)
wrapped_img_embeds = torch.cat([p_before_embeds, img_embeds, p_after_embeds], dim=1)
wrapped_atts_img = atts_img[:, :1].expand(-1, wrapped_img_embeds.shape[1])
return wrapped_img_embeds, wrapped_atts_img
else:
return img_embeds, atts_img
# input audio shape [b t c h w]
def encode_audioQformer(self, audio,modality_type=ModalityType.AUDIO):
device = audio.device
with self.maybe_autocast():
audio_feature, audio_imagebind_finalout = self.audio_encoder.get_audio_feature(audio,modality_type=modality_type)
batch_size,time_length = audio.size()[:2]
position_ids = torch.arange(time_length, dtype=torch.long, device=device)
position_ids = position_ids.unsqueeze(0).expand(batch_size, -1)
audio_position_embeddings = self.audio_position_embedding(position_ids)
audio_imagebind_finalout = audio_imagebind_finalout + audio_position_embeddings
audio_query_tokens = self.audio_query_tokens.expand(audio_imagebind_finalout.shape[0], -1, -1)
frame_atts = torch.ones(audio_imagebind_finalout.size()[:-1], dtype=torch.long).to(device)
audio_query_output = self.audio_Qformer.bert(
query_embeds=audio_query_tokens, #[32,768]
encoder_hidden_states=audio_imagebind_finalout,
encoder_attention_mask=frame_atts,
return_dict=True,
)
audio_hidden = audio_query_output.last_hidden_state
inputs_llama = self.audio_llama_proj(audio_hidden)
atts_llama = torch.ones(inputs_llama.size()[:-1], dtype=torch.long).to(device)
return inputs_llama, atts_llama
def encode_videoQformer_audiovideo(self, image, audio):
device = image.device
# input shape b,c,t,h,w
batch_size,_,time_length,_,_ = image.size()
image = einops.rearrange(image, 'b c t h w -> (b t) c h w')
with self.maybe_autocast():
# embed image features with blip2, out: (b t) q h
image_embeds = self.ln_vision(self.visual_encoder(image)).to(device)
image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(device)
query_tokens = self.query_tokens.expand(image_embeds.shape[0], -1, -1)
query_output = self.Qformer.bert(
query_embeds=query_tokens,
encoder_hidden_states=image_embeds,
encoder_attention_mask=image_atts,
return_dict=True,
)
# add frame_pos embedding
position_ids = torch.arange(time_length, dtype=torch.long, device=query_tokens.device)
position_ids = position_ids.unsqueeze(0).expand(batch_size, -1)
frame_position_embeddings = self.video_frame_position_embedding(position_ids)
q_hidden_state = query_output.last_hidden_state
frame_position_embeddings = frame_position_embeddings.unsqueeze(-2)
frame_hidden_state = einops.rearrange(q_hidden_state, '(b t) q h -> b t q h',b=batch_size,t=time_length)
frame_hidden_state = frame_position_embeddings + frame_hidden_state
# encode audio
audio_feature, audio_imagebind_finalout = self.audio_encoder.get_audio_feature(audio,modality_type=ModalityType.AUDIO) # [batch,8*1,768] 8*32, 768
audio_frame_position_embeddings = frame_position_embeddings.squeeze(-2)
audio_feature = audio_feature + audio_frame_position_embeddings
# frame attention a
frame_hidden_state = einops.rearrange(frame_hidden_state, 'b t q h -> b (t q) h',b=batch_size,t=time_length)
frame_hidden_state = torch.cat([frame_hidden_state,audio_feature],dim = 1)
video_query_tokens = self.video_query_tokens.expand(frame_hidden_state.shape[0], -1, -1)
frame_atts = torch.ones(frame_hidden_state.size()[:-1], dtype=torch.long).to(device)
video_query_output = self.video_Qformer.bert(
query_embeds=video_query_tokens, #[32,768]
encoder_hidden_states=frame_hidden_state,
encoder_attention_mask=frame_atts,
return_dict=True,
)
video_hidden = video_query_output.last_hidden_state
inputs_llama = self.llama_proj(video_hidden)
atts_llama = torch.ones(inputs_llama.size()[:-1], dtype=torch.long).to(image_embeds.device)
return inputs_llama, atts_llama
def forward(self, samples):
if 'conv_type' in samples.keys() and samples['conv_type']=='multi':
im_patch_token_id = self.IMAGE_PATCH_TOKEN_ID
image = samples["images"]
input_ids = samples['input_ids']
if len(image.size())==4:
time = 1
image = einops.repeat(image, 'b c h w -> b c t h w',t = time)
if self.train_flag == 0:
num_patch_tokens = self.num_video_query_token
img_embeds, atts_img = self.encode_videoQformer_visual(image)
elif self.train_flag == 1:
num_patch_tokens = self.num_audio_query_token
image = einops.rearrange(image, 'b c t h w -> b t c h w')
img_embeds, atts_img = self.encode_audioQformer(image, modality_type=ModalityType.VISION)
temp_input_ids = copy.deepcopy(input_ids)
temp_input_ids[temp_input_ids == im_patch_token_id] = 0
temp_input_embedding = self.llama_model.model.embed_tokens(temp_input_ids)
new_input_embeds=[]
cur_image_idx = 0
for cur_input_ids, cur_input_embeds in zip(input_ids, temp_input_embedding):
cur_image_features = img_embeds[cur_image_idx]
if (cur_input_ids == im_patch_token_id).sum() != num_patch_tokens:
raise ValueError("The number of image patch tokens should be the same as the number of image patches.")
masked_indices = torch.where(cur_input_ids == im_patch_token_id)[0]
mask_index_start = masked_indices[0]
if (masked_indices != torch.arange(mask_index_start, mask_index_start+num_patch_tokens, device=masked_indices.device, dtype=masked_indices.dtype)).any():
raise ValueError("The image patch tokens should be consecutive.")
cur_new_input_embeds = torch.cat((cur_input_embeds[:mask_index_start], cur_image_features, cur_input_embeds[mask_index_start+num_patch_tokens:]), dim=0)
new_input_embeds.append(cur_new_input_embeds)
cur_image_idx+=1
inputs_embeds = torch.stack(new_input_embeds, dim=0)
targets = samples['labels']
attention_mask = samples['attention_mask']
with self.maybe_autocast():
outputs = self.llama_model(
inputs_embeds=inputs_embeds,
attention_mask=attention_mask,
return_dict=True,
labels=targets,
)
loss = outputs.loss
return {"loss": loss}
else:
image = samples["image"]
if len(image.size()) != 5:
time = 1
image = einops.repeat(image, 'b c h w -> b c t h w',t = time)
if self.train_flag == 1:
image = einops.rearrange(image, 'b c t h w -> b t c h w')
img_embeds, atts_img = self.encode_audioQformer(image, modality_type=ModalityType.VISION)
else:
img_embeds, atts_img = self.encode_videoQformer_visual(image)
if self.prompt_list:
prompt = random.choice(self.prompt_list)
img_embeds, atts_img = self.prompt_wrap(img_embeds, atts_img, prompt)
self.llama_tokenizer.padding_side = "right"
text = [t + self.end_sym for t in samples["text_input"]]
to_regress_tokens = self.llama_tokenizer(
text,
return_tensors="pt",
padding="longest",
truncation=True,
max_length=self.max_txt_len,
add_special_tokens=False
).to(image.device)
targets = to_regress_tokens.input_ids.masked_fill(
to_regress_tokens.input_ids == self.llama_tokenizer.pad_token_id, -100
)
empty_targets = (
torch.ones([atts_img.shape[0], atts_img.shape[1]+1],
dtype=torch.long).to(image.device).fill_(-100) # plus one for bos
)
targets = torch.cat([empty_targets, targets], dim=1)
batch_size = img_embeds.shape[0]
bos = torch.ones([batch_size, 1],
dtype=to_regress_tokens.input_ids.dtype,
device=to_regress_tokens.input_ids.device) * self.llama_tokenizer.bos_token_id
bos_embeds = self.llama_model.model.embed_tokens(bos)
atts_bos = atts_img[:, :1]
to_regress_embeds = self.llama_model.model.embed_tokens(to_regress_tokens.input_ids)
inputs_embeds = torch.cat([bos_embeds, img_embeds, to_regress_embeds], dim=1)
attention_mask = torch.cat([atts_bos, atts_img, to_regress_tokens.attention_mask], dim=1)
with self.maybe_autocast():
outputs = self.llama_model(
inputs_embeds=inputs_embeds,
attention_mask=attention_mask,
return_dict=True,
labels=targets,
)
loss = outputs.loss
return {"loss": loss}
@classmethod
def from_config(cls, cfg):
vit_model = cfg.get("vit_model", "eva_clip_g")
q_former_model = cfg.get("q_former_model", "https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained_flant5xxl.pth")
img_size = cfg.get("image_size")
num_query_token = cfg.get("num_query_token")
llama_model = cfg.get("llama_model")
drop_path_rate = cfg.get("drop_path_rate", 0)
use_grad_checkpoint = cfg.get("use_grad_checkpoint", False)
vit_precision = cfg.get("vit_precision", "fp16")
freeze_vit = cfg.get("freeze_vit", True)
freeze_qformer = cfg.get("freeze_qformer", True)
low_resource = cfg.get("low_resource", False)
device_8bit = cfg.get("device_8bit", 0)
prompt_path = cfg.get("prompt_path", "")
prompt_template = cfg.get("prompt_template", "")
max_txt_len = cfg.get("max_txt_len", 32)
end_sym = cfg.get("end_sym", '\n')
frozen_llama_proj = cfg.get("frozen_llama_proj", True)
frozen_video_Qformer = cfg.get("frozen_video_Qformer", True)
frozen_audio_Qformer = cfg.get("frozen_audio_Qformer", True)
llama_proj_model = cfg.get("llama_proj_model", '')
fusion_header_type = cfg.get("fusion_header_type", 'seqTransf')
max_frame_pos = cfg.get("max_frame_pos", 32)
fusion_head_layers = cfg.get("fusion_head_layers", 2)
num_video_query_token = cfg.get("num_video_query_token", 32)
equip_audio_branch= cfg.get("equip_audio_branch", True)
num_audio_query_token = cfg.get("num_audio_query_token", 8)
imagebind_ckpt_path = cfg.get("imagebind_ckpt_path", '/mnt/workspace/ckpt')
model = cls(
vit_model=vit_model,
q_former_model=q_former_model,
img_size=img_size,
drop_path_rate=drop_path_rate,
use_grad_checkpoint=use_grad_checkpoint,
vit_precision=vit_precision,
freeze_vit=freeze_vit,
freeze_qformer=freeze_qformer,
num_query_token=num_query_token,
llama_model=llama_model,
prompt_path=prompt_path,
prompt_template=prompt_template,
max_txt_len=max_txt_len,
end_sym=end_sym,
low_resource=low_resource,
device_8bit=device_8bit,
fusion_header_type=fusion_header_type,
max_frame_pos=max_frame_pos,
fusion_head_layers=fusion_head_layers,
frozen_llama_proj=frozen_llama_proj,
frozen_video_Qformer=frozen_video_Qformer,
frozen_audio_Qformer=frozen_audio_Qformer,
num_video_query_token=num_video_query_token,
num_audio_query_token = num_audio_query_token,
imagebind_ckpt_path = imagebind_ckpt_path,
equip_audio_branch = equip_audio_branch,
llama_proj_model = llama_proj_model
)
ckpt_path = cfg.get("ckpt", "") # load weights of MiniGPT-4
if ckpt_path:
print("Load first Checkpoint: {}".format(ckpt_path))
ckpt = torch.load(ckpt_path, map_location="cpu")
msg = model.load_state_dict(ckpt['model'], strict=False)
ckpt_path_2 = cfg.get("ckpt_2", "")
if ckpt_path_2:
print("Load second Checkpoint: {}".format(ckpt_path_2))
ckpt = torch.load(ckpt_path_2, map_location="cpu")
msg = model.load_state_dict(ckpt['model'], strict=False)
return model
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