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import logging
import os
import os.path as osp
from os.path import join
import io
from copy import deepcopy
import torch
from torch.utils.data import ConcatDataset, DataLoader
from models_viclip.backbones.clip.clip_vision import interpolate_pos_embed_vit
from models_viclip.backbones.bert.tokenization_bert import BertTokenizer
#from utils_viclip.optimizer import create_optimizer
#from utils_viclip.scheduler import create_scheduler
#from utils_viclip.distributed import get_world_size
#import deepspeed
#
#logger = logging.getLogger(__name__)
def get_media_types(datasources):
"""get the media types for for all the dataloaders.
Args:
datasources (List): List of dataloaders or datasets.
Returns: List. The media_types.
"""
if isinstance(datasources[0], DataLoader):
datasets = [dataloader.dataset for dataloader in datasources]
else:
datasets = datasources
media_types = [
dataset.datasets[0].media_type
if isinstance(dataset, ConcatDataset)
else dataset.media_type
for dataset in datasets
]
return media_types
def setup_model(
config, model_cls, has_decoder=False, pretrain=False, find_unused_parameters=False, num_steps_per_epoch=-1,
num_classes=101):
#logger.info("Creating model")
config = copy.deepcopy(config)
# tokenizer = BertTokenizer.from_pretrained(config.model.text_encoder.pretrained)
#tokenizer = BertTokenizer.from_pretrained("bert-base-uncased", local_files_only=True)
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
#model = model_cls(config=config, tokenizer=tokenizer, is_pretrain=pretrain,num_classes=num_classes)
model = model_cls(config=config, tokenizer=tokenizer, is_pretrain=pretrain,num_classes=num_classes)
#model = model.to(torch.device(config.device))
#model_without_ddp = model
# if hasattr(config, "deepspeed") and config.deepspeed.enable:
# optimizer_params = create_optimizer(config.optimizer, model, return_group=True)
# scheduler = None
# scaler = None
# else:
# if config.distributed:
# model = torch.nn.parallel.DistributedDataParallel(
# model,
# device_ids=[config.gpu],
# find_unused_parameters=find_unused_parameters, # `False` for image-only task
# )
# optimizer = create_optimizer(config.optimizer, model)
# scaler = torch.cuda.amp.GradScaler(enabled=config.fp16) # This is never used actually
# scheduler = create_scheduler(config.scheduler, optimizer)
# start_epoch = 0
# global_step = 0
# auto resume the latest checkpoint
#if config.get("auto_resume", False):
#logger.info("Auto resuming")
#model_latest = join(config.output_dir, "ckpt_latest.pth")
#model_best = join(config.output_dir, "ckpt_best.pth")
#large_num = -1
#for p in os.listdir(config.output_dir):
# if 'ckpt' in p:
# num = p.split('_')[1].split('.')[0]
# if str.isnumeric(num):
# if int(num) > large_num:
# large_num = int(num)
#if large_num != -1:
# model_latest = join(config.output_dir, f"ckpt_{large_num:02d}.pth")
#if osp.exists(model_latest):
# config.pretrained_path = model_latest
# config.resume = True
#elif osp.exists(model_best):
# config.pretrained_path = model_best
# config.resume = True
#else:
#logger.info(f"Not found checkpoint in {config.output_dir}")
#if config.pretrained_path.strip() and (osp.isfile(config.pretrained_path) or "s3://" in config.pretrained_path):
#logger.info(f"Loading checkpoint from {config.pretrained_path}")
#checkpoint = torch.load(config.pretrained_path, map_location="cpu")
#try:
# state_dict = checkpoint["model"]
#except: # This is a deepspeed stage 1 model
# state_dict = checkpoint["module"]
#if config.resume:
# optimizer.load_state_dict(checkpoint["optimizer"])
# scheduler.load_state_dict(checkpoint["scheduler"])
# scaler.load_state_dict(checkpoint["scaler"])
# start_epoch = checkpoint["epoch"] + 1
# global_step = checkpoint["global_step"]
#elif config.evaluate or (not pretrain): # downstream init from pretrained ckpt
# is_blip_model = "VindLU_BLIP" in config.model.get("model_cls", "")
# # interpolate positional embeddings.
# if "vit" in config.model.vision_encoder.name:
# state_dict = interpolate_pos_embed_vit(state_dict, model_without_ddp)
# else:
# raise ValueError(
# f" vision encoder: {config.model.vision_encoder.name} not implelented"
# )
# if not config.evaluate or config.get("zero_shot", False): # finetuning from a pretarined weights.
# for key in list(state_dict.keys()):
# if "bert" in key and not is_blip_model:
# encoder_key = key.replace("bert.", "")
# state_dict[encoder_key] = state_dict[key]
# if not has_decoder:
# del state_dict[key]
# # init text decoder as multimodal encoder (last 6 layers of model.text_encoder)
# # only for generation tasks like VQA
# if has_decoder and "text_encoder" in key and not is_blip_model:
# if "layer" in key:
# encoder_keys = key.split(".")
# layer_num = int(encoder_keys[4])
# if layer_num < config.model.text_encoder.fusion_layer:
# del state_dict[key]
# continue
# else:
# decoder_layer_num = layer_num - config.model.text_encoder.fusion_layer
# encoder_keys[4] = str(decoder_layer_num)
# encoder_key = ".".join(encoder_keys)
# else:
# encoder_key = key
# decoder_key = encoder_key.replace("text_encoder", "text_decoder")
# state_dict[decoder_key] = state_dict[key]
# del state_dict[key]
#if hasattr(config, "wiseft") and config.wiseft.enable:
# #logger.info(f"Wiseft with coefficient {config.wiseft.coef}")
# missing_keys_in_pretrained = [k for k in model_without_ddp.state_dict().keys() if k not in state_dict]
# missing_keys_in_model = [k for k in state_dict.keys() if k not in model_without_ddp.state_dict()]
# mismatch_keys = [k for k in state_dict.keys() if k in model_without_ddp.state_dict() \
# and state_dict[k].shape != model_without_ddp.state_dict()[k].shape]
# common_keys = [k for k in state_dict.keys() if k in model_without_ddp.state_dict()]
#logger.info(f"Missing keys in pretrained: {missing_keys_in_pretrained}")
#logger.info(f"Missing keys in model: {missing_keys_in_model}")
#logger.info(f"Mismatch keys: {mismatch_keys}")
#logger.info(f"Keys to exclude: {config.wiseft.keys_to_exclude}")
#for k in common_keys:
# if k in config.wiseft.keys_to_exclude:
# continue
# state_dict[k] = config.wiseft.coef * state_dict[k] + (1 - config.wiseft.coef) * model_without_ddp.state_dict()[k].cpu()
#msg = model_without_ddp.load_state_dict(state_dict, strict=False)
#print(msg)
#logger.info(msg)
#logger.info(f"Loaded checkpoint from {config.pretrained_path}")
#else:
#logger.warning("No pretrained checkpoint provided, training from scratch")
#if scaler is None:
# model = model_without_ddp
# model, optimizer, _, _ = deepspeed.initialize(
# args=config, model=model, model_parameters=optimizer_params, dist_init_required=not config.distributed,
# lr_scheduler=lambda opt: create_scheduler(config.scheduler, opt)
# )
#
#if config.resume and osp.isdir(config.pretrained_path):
# output_dir, tag = os.path.split(config.pretrained_path)
# model.load_checkpoint(output_dir, tag=tag)
# global_step = model.global_steps
# assert num_steps_per_epoch > 0, "Please provide num_steps_per_epoch"
# start_epoch = global_step // num_steps_per_epoch
#return (
# model,
# model_without_ddp,
# optimizer,
# scheduler,
# scaler,
# tokenizer,
# start_epoch,
# global_step,
#)
return model
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