FAPM / lavis /models /blip2_models /blip2_t5_instruct.py
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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 file in the repo root or https://opensource.org/licenses/BSD-3-Clause
"""
import logging
import string
import random
import copy
import torch
import torch.nn as nn
from torch.cuda.amp import autocast as autocast
from transformers import T5TokenizerFast
from lavis.common.registry import registry
from lavis.models.blip2_models.blip2 import Blip2Base, disabled_train
from lavis.models.blip2_models.modeling_t5 import T5Config, T5ForConditionalGeneration
from transformers.modeling_outputs import BaseModelOutput
@registry.register_model("blip2_t5_instruct")
class Blip2T5Instruct(Blip2Base):
"""
BLIP2 T5 model.
Supported model types:
- flant5xl
- flant5xxl
Usage:
>>> from lavis.models import load_model
>>> model = load_model("blip2_t5_instruct", "flant5xl")
"""
PRETRAINED_MODEL_CONFIG_DICT = {
"flant5xl": "configs/models/blip2/blip2_instruct_flant5xl.yaml",
"flant5xxl": "configs/models/blip2/blip2_instruct_flant5xxl.yaml",
}
def __init__(
self,
vit_model="eva_clip_g",
img_size=224,
drop_path_rate=0,
use_grad_checkpoint=False,
vit_precision="fp16",
freeze_vit=True,
num_query_token=32,
t5_model="google/flan-t5-xl",
prompt="",
max_txt_len=128,
max_output_txt_len=256,
apply_lemmatizer=False,
num_few_shot_examples=0,
few_shot_prob=0,
qformer_text_input=True,
):
"""
apply_lemmatizer: when set to True, postprocess predict_answers() result with lemmas.
"""
super().__init__()
self.tokenizer = self.init_tokenizer(truncation_side="left")
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
logging.info("freeze vision encoder")
self.Qformer, self.query_tokens = self.init_Qformer(
num_query_token, self.visual_encoder.num_features
)
if not qformer_text_input:
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
else:
self.Qformer.resize_token_embeddings(len(self.tokenizer))
self.Qformer.cls = None
self.t5_tokenizer = T5TokenizerFast.from_pretrained(t5_model, truncation_side='left')
self.t5_output_tokenizer = T5TokenizerFast.from_pretrained(t5_model, truncation_side='right')
t5_config = T5Config.from_pretrained(t5_model)
t5_config.dense_act_fn = "gelu"
self.t5_model = T5ForConditionalGeneration.from_pretrained(
t5_model, config=t5_config
)
for name, param in self.t5_model.named_parameters():
param.requires_grad = False
param.data = param.data.bfloat16()
self.t5_proj = nn.Linear(
self.Qformer.config.hidden_size, self.t5_model.config.hidden_size
)
self.max_txt_len = max_txt_len
self.max_output_txt_len = max_output_txt_len
self.prompt = prompt
self._apply_lemmatizer = apply_lemmatizer
self._lemmatizer = None
self.num_few_shot_examples = num_few_shot_examples
self.few_shot_prob = few_shot_prob
self.qformer_text_input = qformer_text_input
def forward(self, samples):
# print('-----------------')
# print(samples["text_input"])
# print(samples["text_output"])
# print('-----------------')
image = samples["image"]
with self.maybe_autocast():
image_embeds = self.ln_vision(self.visual_encoder(image))
image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(image.device)
query_tokens = self.query_tokens.expand(image_embeds.shape[0], -1, -1)
if self.qformer_text_input:
text_Qformer = self.tokenizer(
samples["text_input"],
padding='longest',
truncation=True,
max_length=self.max_txt_len,
return_tensors="pt",
).to(image.device)
query_atts = torch.ones(query_tokens.size()[:-1], dtype=torch.long).to(image.device)
Qformer_atts = torch.cat([query_atts,text_Qformer.attention_mask],dim=1)
query_output = self.Qformer.bert(
text_Qformer.input_ids,
attention_mask=Qformer_atts,
query_embeds=query_tokens,
encoder_hidden_states=image_embeds,
encoder_attention_mask=image_atts,
return_dict=True,
)
else:
query_output = self.Qformer.bert(
query_embeds=query_tokens,
encoder_hidden_states=image_embeds,
encoder_attention_mask=image_atts,
return_dict=True,
)
inputs_t5 = self.t5_proj(query_output.last_hidden_state[:,:query_tokens.size(1),:])
atts_t5 = torch.ones(inputs_t5.size()[:-1], dtype=torch.long).to(image.device)
fs_embeds, fs_atts = None, None
if self.few_shot_prob > 0 and "few_shot_samples" in samples.keys():
fs_embeds, fs_atts = self.prepare_few_shot_embeds(samples['few_shot_samples'])
with self.maybe_autocast(dtype=torch.bfloat16):
input_tokens = self.t5_tokenizer(
samples["text_input"],
padding="longest",
truncation=True,
max_length=self.max_txt_len,
return_tensors="pt",
).to(image.device)
output_tokens = self.t5_output_tokenizer(
samples["text_output"],
padding="longest",
truncation=True,
max_length=self.max_output_txt_len,
return_tensors="pt",
).to(image.device)
encoder_atts = torch.cat([atts_t5, input_tokens.attention_mask], dim=1)
targets = output_tokens.input_ids.masked_fill(
output_tokens.input_ids == self.t5_tokenizer.pad_token_id, -100
)
inputs_embeds = self.t5_model.encoder.embed_tokens(input_tokens.input_ids)
inputs_embeds = torch.cat([inputs_t5, inputs_embeds], dim=1)
if fs_embeds is not None:
inputs_embeds = torch.cat([fs_embeds, inputs_embeds], dim=1)
encoder_atts = torch.cat([fs_atts, encoder_atts], dim=1)
outputs = self.t5_model(
inputs_embeds=inputs_embeds,
attention_mask=encoder_atts,
decoder_attention_mask=output_tokens.attention_mask,
return_dict=True,
labels=targets,
)
loss = outputs.loss
return {"loss": loss}
def prepare_few_shot_embeds(self, samples):
this_n_fs = random.choices(
list(range(self.num_few_shot_examples + 1)),
weights=[1 - self.few_shot_prob] + [self.few_shot_prob / self.num_few_shot_examples] * self.num_few_shot_examples
)[0]
if this_n_fs == 0:
return None, None
images = []
text_input = []
for sample in samples:
for n in range(this_n_fs):
images.append(sample['image'][n])
text_input.append(sample['text_input'][n])
images = torch.stack(images, dim=0)
image = images
with self.maybe_autocast():
image_embeds = self.ln_vision(self.visual_encoder(image))
image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(
image.device
)
query_tokens = self.query_tokens.expand(image_embeds.shape[0], -1, -1)
if self.qformer_text_input:
text_Qformer = self.tokenizer(
text_input,
padding='longest',
truncation=True,
max_length=self.max_txt_len,
return_tensors="pt",
).to(image.device)
query_atts = torch.ones(query_tokens.size()[:-1], dtype=torch.long).to(image.device)
Qformer_atts = torch.cat([query_atts,text_Qformer.attention_mask],dim=1)
query_output = self.Qformer.bert(
text_Qformer.input_ids,
attention_mask = Qformer_atts,
query_embeds=query_tokens,
encoder_hidden_states=image_embeds,
encoder_attention_mask=image_atts,
return_dict=True,
)
else:
query_output = self.Qformer.bert(
query_embeds=query_tokens,
encoder_hidden_states=image_embeds,
encoder_attention_mask=image_atts,
return_dict=True,
)
inputs_t5 = self.t5_proj(query_output.last_hidden_state[:,:query_tokens.size(1),:])
atts_t5 = torch.ones(inputs_t5.size()[:-1], dtype=torch.long).to(image.device)
with self.maybe_autocast(dtype=torch.bfloat16):
input_tokens = self.t5_tokenizer(
text_input,
padding="longest",
truncation=True,
max_length=self.max_txt_len,
return_tensors="pt",
).to(image.device)
encoder_atts = torch.cat([atts_t5, input_tokens.attention_mask], dim=1)
inputs_embeds = self.t5_model.encoder.embed_tokens(input_tokens.input_ids)
inputs_embeds = torch.cat([inputs_t5, inputs_embeds], dim=1)
if this_n_fs > 1:
encoder_atts = encoder_atts.reshape(encoder_atts.size(0) // this_n_fs, encoder_atts.size(1) * this_n_fs)
inputs_embeds = inputs_embeds.reshape(inputs_embeds.size(0) // this_n_fs, inputs_embeds.size(1) * this_n_fs, inputs_embeds.size(2))
return inputs_embeds, encoder_atts
@torch.no_grad()
def generate(
self,
samples,
use_nucleus_sampling=False,
num_beams=5,
max_length=256,
min_length=1,
top_p=0.9,
repetition_penalty=1.5,
length_penalty=1.0,
num_captions=1,
temperature=1,
):
if "prompt" in samples.keys():
prompt = samples["prompt"]
else:
prompt = self.prompt
image = samples["image"]
bs = image.size(0)
if isinstance(prompt, str):
prompt = [prompt] * bs
else:
assert len(prompt) == bs, "The number of prompts must be equal to the batch size."
# For TextCaps
if "ocr_tokens" in samples.keys() and "{}" in prompt[0]:
prompt = [p.format(', '.join(samples['ocr_tokens'][i][:30])) for i, p in enumerate(prompt)]
query_tokens = self.query_tokens.expand(bs, -1, -1)
if self.qformer_text_input:
# remove ocr tokens in q_former (for eval textvqa)
# qformer_prompt = prompt
# qformer_prompt = ['Question: ' + qp.split(' Question: ')[1] for qp in qformer_prompt]
text_Qformer = self.tokenizer(
prompt,
padding='longest',
truncation=True,
max_length=self.max_txt_len,
return_tensors="pt",
).to(image.device)
query_atts = torch.ones(query_tokens.size()[:-1], dtype=torch.long).to(image.device)
Qformer_atts = torch.cat([query_atts,text_Qformer.attention_mask],dim=1)
# For video data
if image.dim() == 5:
inputs_t5, atts_t5 = [], []
for j in range(image.size(2)):
this_frame = image[:,:,j,:,:]
with self.maybe_autocast():
frame_embeds = self.ln_vision(self.visual_encoder(this_frame))
frame_atts = torch.ones(frame_embeds.size()[:-1], dtype=torch.long).to(image.device)
if self.qformer_text_input:
frame_query_output = self.Qformer.bert(
text_Qformer.input_ids,
attention_mask = Qformer_atts,
query_embeds=query_tokens,
encoder_hidden_states=frame_embeds,
encoder_attention_mask=frame_atts,
return_dict=True,
)
else:
frame_query_output = self.Qformer.bert(
query_embeds=query_tokens,
encoder_hidden_states=frame_embeds,
encoder_attention_mask=frame_atts,
return_dict=True,
)
frame_inputs_t5 = self.t5_proj(frame_query_output.last_hidden_state[:,:query_tokens.size(1),:])
frame_atts_t5 = torch.ones(frame_inputs_t5.size()[:-1], dtype=torch.long).to(image.device)
inputs_t5.append(frame_inputs_t5)
atts_t5.append(frame_atts_t5)
inputs_t5 = torch.cat(inputs_t5, dim=1)
atts_t5 = torch.cat(atts_t5, dim=1)
else:
with self.maybe_autocast():
image_embeds = self.ln_vision(self.visual_encoder(image))
image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(image.device)
if self.qformer_text_input:
query_output = self.Qformer.bert(
text_Qformer.input_ids,
attention_mask=Qformer_atts,
query_embeds=query_tokens,
encoder_hidden_states=image_embeds,
encoder_attention_mask=image_atts,
return_dict=True,
)
else:
query_output = self.Qformer.bert(
query_embeds=query_tokens,
encoder_hidden_states=image_embeds,
encoder_attention_mask=image_atts,
return_dict=True,
)
inputs_t5 = self.t5_proj(query_output.last_hidden_state[:,:query_tokens.size(1),:])
atts_t5 = torch.ones(inputs_t5.size()[:-1], dtype=torch.long).to(image.device)
input_tokens = self.t5_tokenizer(
prompt,
padding="longest",
return_tensors="pt"
).to(image.device)
encoder_atts = torch.cat([atts_t5, input_tokens.attention_mask], dim=1)
with self.maybe_autocast(dtype=torch.bfloat16):
inputs_embeds = self.t5_model.encoder.embed_tokens(input_tokens.input_ids)
inputs_embeds = torch.cat([inputs_t5, inputs_embeds], dim=1)
outputs = self.t5_model.generate(
inputs_embeds=inputs_embeds,
attention_mask=encoder_atts,
do_sample=use_nucleus_sampling,
top_p=top_p,
temperature=temperature,
num_beams=num_beams,
max_new_tokens=max_length,
min_length=min_length,
repetition_penalty=repetition_penalty,
length_penalty=length_penalty,
num_return_sequences=num_captions,
)
output_text = self.t5_tokenizer.batch_decode(
outputs, skip_special_tokens=True
)
return output_text
def predict_answers(
self,
samples,
num_beams=5,
inference_method="generate",
max_len=10,
min_len=1,
num_ans_candidates=128,
answer_list=None,
prompt="",
length_penalty=-1,
**kwargs
):
if isinstance(samples["text_input"], str):
samples["text_input"] = [samples["text_input"]]
if prompt:
if prompt.count("{}") == 2:
if 'ocr_tokens' in samples:
text_input = [
prompt.format(', '.join(samples['ocr_tokens'][i][:30]), samples["text_input"][i])
for i in range(len(samples["text_input"]))]
elif 'choices' in samples:
text_input = []
for i in range(len(samples["text_input"])):
this_choices = [f"({string.ascii_lowercase[j]}) {ch}" for j, ch in enumerate(samples["choices"][i])]
this_choices = " ".join(this_choices)
text_input.append(prompt.format(samples["text_input"][i], this_choices))
else:
text_input = [prompt.format(question) for question in samples["text_input"]]
else:
text_input = samples["text_input"]
samples["prompt"] = text_input
output_text = self.generate(
samples,
num_beams=num_beams,
max_length=max_len,
min_length=min_len,
length_penalty=length_penalty
)
if self._apply_lemmatizer or ("apply_lemmatizer" in samples.keys() and samples["apply_lemmatizer"]):
output_text = self._lemmatize(output_text)
return output_text
def predict_class(
self,
samples,
candidates,
n_segments=1,
):
# If candidates is a list of lists, each sample has its candidates, then we need to iterate one by one
if type(candidates[0]) == list:
results = []
for i in range(samples["image"].size(0)):
this_sample = {
"image": samples["image"][i].unsqueeze(0),
"prompt": samples["prompt"],
}
if "text_input" in samples.keys():
this_sample["text_input"] = [samples["text_input"][i]]
if 'context' in samples.keys():
this_sample['context'] = [samples["context"][i]]
if 'history' in samples.keys():
this_sample['history'] = [samples["history"][i]]
if 'caption' in samples.keys():
this_sample['caption'] = [samples["caption"][i]]
this_result = self._predict_class(this_sample, candidates[i], n_segments)
results.append(this_result)
try:
results = torch.cat(results, dim=0)
except:
results = [res.tolist()[0] for res in results]
return results
return self._predict_class(samples, candidates, n_segments)
def _predict_class(
self,
samples,
candidates,
n_segments=1,
):
"""
Args:
samples (dict): A dictionary containing the following keys:
- image (torch.Tensor): A tensor of shape (batch_size, 3, H, W)
- prompt: the instruction
candidates:
(list): A list of candidate class names;
n_segments:
(int): Split the candidates into n_segments and predict one by one. This is useful when the number of candidates is too large.
Returns:
output_class: predicted class index
"""
image = samples["image"]
prompt = samples["prompt"]
bs = image.size(0)
if isinstance(prompt, str):
prompt = [prompt] * bs
else:
assert len(prompt) == bs, "The number of prompts must be equal to the batch size."
if "text_input" in samples.keys():
if type(samples["text_input"][0]) == list:
prompt = [prompt[i].format(*samples["text_input"][i]) for i in range(len(prompt))]
else:
prompt = [prompt[i].format(samples["text_input"][i]) for i in range(len(prompt))]
# scienceqa
if 'context' in samples.keys() and samples['context'] != '':
prompt = [f'context: {samples["context"][i]}. {prompt[i]}' for i in range(len(prompt))]
# visual dialog
if 'history' in samples.keys() and samples['history'][0] != '':
prompt = [f'dialog history: {samples["history"][i]}\n{prompt[i]}' for i in range(len(prompt))]
if 'caption' in samples.keys() and samples['caption'][0] != '':
prompt = [f'This image has the caption "{samples["caption"][i]}". {prompt[i]}' for i in range(len(prompt))]
query_tokens = self.query_tokens.expand(bs, -1, -1)
if self.qformer_text_input:
text_Qformer = self.tokenizer(
prompt,
padding='longest',
truncation=True,
max_length=self.max_txt_len,
return_tensors="pt"
).to(image.device)
query_atts = torch.ones(query_tokens.size()[:-1], dtype=torch.long).to(image.device)
Qformer_atts = torch.cat([query_atts,text_Qformer.attention_mask], dim=1)
if image.dim() == 5:
inputs_t5, atts_t5 = [], []
for j in range(image.size(2)):
this_frame = image[:,:,j,:,:]
with self.maybe_autocast():
frame_embeds = self.ln_vision(self.visual_encoder(this_frame))
frame_atts = torch.ones(frame_embeds.size()[:-1], dtype=torch.long).to(image.device)
if self.qformer_text_input:
frame_query_output = self.Qformer.bert(
text_Qformer.input_ids,
attention_mask=Qformer_atts,
query_embeds=query_tokens,
encoder_hidden_states=frame_embeds,
encoder_attention_mask=frame_atts,
return_dict=True,
)
else:
frame_query_output = self.Qformer.bert(
query_embeds=query_tokens,
encoder_hidden_states=frame_embeds,
encoder_attention_mask=frame_atts,
return_dict=True,
)
frame_inputs_t5 = self.t5_proj(frame_query_output.last_hidden_state[:,:query_tokens.size(1),:])
frame_atts_t5 = torch.ones(frame_inputs_t5.size()[:-1], dtype=torch.long).to(image.device)
inputs_t5.append(frame_inputs_t5)
atts_t5.append(frame_atts_t5)
inputs_t5 = torch.cat(inputs_t5, dim=1)
atts_t5 = torch.cat(atts_t5, dim=1)
else:
with self.maybe_autocast():
image_embeds = self.ln_vision(self.visual_encoder(image))
image_atts = torch.ones(image_embeds.size()[:-1], dtype=torch.long).to(image.device)
if self.qformer_text_input:
query_output = self.Qformer.bert(
text_Qformer.input_ids,
attention_mask=Qformer_atts,
query_embeds=query_tokens,
encoder_hidden_states=image_embeds,
encoder_attention_mask=image_atts,
return_dict=True,
)
else:
query_output = self.Qformer.bert(
query_embeds=query_tokens,
encoder_hidden_states=image_embeds,
encoder_attention_mask=image_atts,
return_dict=True,
)
inputs_t5 = self.t5_proj(query_output.last_hidden_state[:,:query_tokens.size(1),:])
atts_t5 = torch.ones(inputs_t5.size()[:-1], dtype=torch.long).to(image.device)
input_tokens = self.t5_tokenizer(
prompt, padding="longest", return_tensors="pt"
).to(image.device)
output_tokens = self.t5_tokenizer(
candidates, padding="longest", return_tensors="pt"
).to(image.device)
encoder_atts = torch.cat([atts_t5, input_tokens.attention_mask], dim=1)
n_cands = len(candidates)
with self.maybe_autocast(dtype=torch.bfloat16):
inputs_embeds = self.t5_model.encoder.embed_tokens(input_tokens.input_ids)
inputs_embeds = torch.cat([inputs_t5, inputs_embeds], dim=1)
encoder_outputs = self.t5_model.encoder(
inputs_embeds=inputs_embeds,
attention_mask=encoder_atts,
)
all_losses = []
for n in range(n_segments):
seg_len = n_cands // n_segments
if n == (n_segments - 1):
seg_len = n_cands - seg_len * (n_segments - 1)
# this_encoder_outputs = copy.deepcopy(encoder_outputs)
this_encoder_outputs = BaseModelOutput(
last_hidden_state=encoder_outputs[0].clone(),
)
this_encoder_outputs['last_hidden_state'] = this_encoder_outputs[0].repeat_interleave(seg_len, dim=0)
this_encoder_atts = encoder_atts.repeat_interleave(seg_len, dim=0)
start_i = n * (n_cands // n_segments)
end_i = start_i + seg_len
this_output_tokens_ids = output_tokens.input_ids[start_i:end_i].repeat(bs, 1)
this_output_tokens_atts = output_tokens.attention_mask[start_i:end_i].repeat(bs, 1)
this_targets = this_output_tokens_ids.masked_fill(this_output_tokens_ids == self.t5_tokenizer.pad_token_id, -100)
outputs = self.t5_model(
encoder_outputs=this_encoder_outputs,
attention_mask=this_encoder_atts,
decoder_attention_mask=this_output_tokens_atts,
return_dict=True,
labels=this_targets,
reduction="none",
)
loss = outputs.loss
loss = loss.reshape(bs, seg_len)
# output_class_ranks = torch.argsort(loss, dim=-1)
all_losses.append(loss)
all_losses = torch.cat(all_losses, dim=-1)
output_class_ranks = torch.argsort(all_losses, dim=-1)
# encoder_outputs['last_hidden_state'] = encoder_outputs[0].repeat_interleave(n_cands, dim=0)
# encoder_atts = encoder_atts.repeat_interleave(n_cands, dim=0)
# output_tokens.input_ids = output_tokens.input_ids.repeat(bs, 1)
# output_tokens.attention_mask = output_tokens.attention_mask.repeat(bs, 1)
# # compute the LM loss for each candidate (sum logprob across all tokens) and select the highest
# targets = output_tokens.input_ids.masked_fill(output_tokens.input_ids == self.t5_tokenizer.pad_token_id, -100)
# outputs = self.t5_model(
# encoder_outputs=encoder_outputs,
# attention_mask=encoder_atts,
# decoder_attention_mask=output_tokens.attention_mask,
# return_dict=True,
# labels=targets,
# reduction="none",
# )
# loss = outputs.loss
# loss = loss.reshape(bs, n_cands)
# output_class_ranks = torch.argsort(loss, dim=-1) # (bs, num_candidates)
return output_class_ranks
def _lemmatize(self, answers):
def apply(answer):
doc = self.lemmatizer(answer)
words = []
for token in doc:
if token.pos_ in ["NOUN", "VERB"]:
words.append(token.lemma_)
else:
words.append(token.text)
answer = " ".join(words)
return answer
return [apply(answer) for answer in answers]
@property
def lemmatizer(self):
if self._lemmatizer is None:
try:
import spacy
self._lemmatizer = spacy.load("en_core_web_sm")
except ImportError:
logging.error(
"""
Please install spacy and en_core_web_sm model to apply lemmatization.
python -m spacy download en_core_web_sm
OR
import spacy.cli
spacy.cli.download("en_core_web_sm")
"""
)
exit(1)
return self._lemmatizer
@classmethod
def from_config(cls, cfg):
vit_model = cfg.get("vit_model", "eva_clip_g")
img_size = cfg.get("image_size")
num_query_token = cfg.get("num_query_token")
t5_model = cfg.get("t5_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)
prompt = cfg.get("prompt", "")
max_txt_len = cfg.get("max_txt_len", 128)
max_output_txt_len = cfg.get("max_output_txt_len", 256)
apply_lemmatizer = cfg.get("apply_lemmatizer", False)
num_few_shot_examples = cfg.get("num_few_shot_examples", 0)
few_shot_prob = cfg.get("few_shot_prob", 0.0)
qformer_text_input = cfg.get("qformer_text_input", True)
model = cls(
vit_model=vit_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,
num_query_token=num_query_token,
t5_model=t5_model,
prompt=prompt,
max_txt_len=max_txt_len,
max_output_txt_len=max_output_txt_len,
apply_lemmatizer=apply_lemmatizer,
num_few_shot_examples=num_few_shot_examples,
few_shot_prob=few_shot_prob,
qformer_text_input=qformer_text_input,
)
# if qformer_text_input:
# # Hard-coded to load from BLIP-2 stage-1 pre-trained model (not ideal)
# model.load_from_pretrained(
# url_or_filename="https://storage.googleapis.com/sfr-vision-language-research/LAVIS/models/BLIP2/blip2_pretrained.pth"
# )
model.load_checkpoint_from_config(cfg)
return model