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from transformers import PretrainedConfig, PreTrainedModel, AutoModel, AutoConfig
class EasyDict(dict):
def __init__(self, d=None, **kwargs):
if d is None:
d = {}
if kwargs:
d.update(**kwargs)
for k, v in d.items():
setattr(self, k, v)
# Class attributes
for k in self.__class__.__dict__.keys():
if not (k.startswith("__") and k.endswith("__")) and not k in ("update", "pop"):
setattr(self, k, getattr(self, k))
def __setattr__(self, name, value):
if isinstance(value, (list, tuple)):
value = [self.__class__(x) if isinstance(x, dict) else x for x in value]
elif isinstance(value, dict) and not isinstance(value, self.__class__):
value = self.__class__(value)
super(EasyDict, self).__setattr__(name, value)
super(EasyDict, self).__setitem__(name, value)
__setitem__ = __setattr__
def update(self, e=None, **f):
d = e or dict()
d.update(f)
for k in d:
setattr(self, k, d[k])
def pop(self, k, d=None):
if hasattr(self, k):
delattr(self, k)
return super(EasyDict, self).pop(k, d)
class InternVideo2Config(PretrainedConfig):
model_type = "internvideo2"
def __init__(self,
tokenizer=None,
train_file=None,
test_file=None,
test_types=None,
num_workers=6,
best_key=None,
num_frames=8,
num_frames_test=8,
batch_size=64,
batch_size_test=4,
max_txt_l=32,
inputs=None,
text_enc="bert_large",
model=None,
criterion=None,
optimizer=None,
scheduler=None,
evaluate=False,
deep_fusion=False,
evaluation=None,
use_half_precision=False,
use_bf16=True,
gradient_checkpointing=True,
use_flash_sdp=False,
use_mem_efficient_sdp=False,
compile_model=False,
wandb=None,
dist_url="env://",
device="cuda",
mode="pt",
output_dir=None,
resume=False,
debug=False,
log_freq=100,
seed=42,
save_latest=True,
auto_resume=False,
jump_evaluate=False,
pretrained_path="",
save_ckpt_iter=None,
delete_ds_optim_states=True,
deepspeed=None,
**kwargs):
super().__init__(**kwargs)
self.tokenizer = tokenizer
# Data configuration
self.train_file = train_file or "available_corpus[\"pretrain_example_data_1B\"]"
self.test_file = EasyDict(test_file or {
"msrvtt_1k_test": "available_corpus[\"msrvtt_1k_test\"]",
"didemo_ret_test": "available_corpus[\"didemo_ret_test\"]"
})
self.test_types = test_types or ["msrvtt_1k_test", "didemo_ret_test"]
self.num_workers = num_workers
self.best_key = best_key or ["msrvtt_1k_test_match", "t2v_r1"]
# Input configuration
self.num_frames = num_frames
self.num_frames_test = num_frames_test
self.batch_size = batch_size
self.batch_size_test = batch_size_test
self.max_txt_l = max_txt_l
self.inputs = EasyDict(inputs or {
"image_res": 224,
"video_input": EasyDict({
"num_frames": num_frames,
"sample_type": "rand",
"num_frames_test": num_frames_test,
"sample_type_test": "middle",
"random_aug": False
}),
"max_txt_l": EasyDict({"image": max_txt_l, "video": max_txt_l}),
"batch_size": EasyDict({"image": batch_size, "video": batch_size}),
"batch_size_test": EasyDict({"image": batch_size_test, "video": batch_size_test})
})
# Model configuration
self.text_enc = text_enc
self.model = EasyDict(model or {
"model_cls": "InternVideo2_Stage2",
"vision_encoder": EasyDict({
"name": "pretrain_internvideo2_1b_patch14_224",
"img_size": 224,
"num_frames": num_frames,
"tubelet_size": 1,
"patch_size": 14,
"d_model": 1408,
"clip_embed_dim": 768,
"clip_teacher_embed_dim": 3200,
"clip_teacher_final_dim": 768,
"clip_norm_type": "l2",
"clip_return_layer": 6,
"clip_student_return_interval": 1,
"pretrained": None,
"use_checkpoint": False,
"checkpoint_num": 40,
"use_flash_attn": True,
"use_fused_rmsnorm": True,
"use_fused_mlp": True,
"clip_teacher": None,
"clip_input_resolution": 224,
"clip_teacher_return_interval": 1,
"video_mask_type": "random",
"video_mask_ratio": 0.8,
"image_mask_type": "random",
"image_mask_ratio": 0.5,
"sep_image_video_pos_embed": True,
"keep_temporal": False,
"only_mask": True
}),
"text_encoder": text_enc,
"multimodal": EasyDict({"enable": True}),
"embed_dim": 512,
"temp": 0.07,
"find_unused_parameters": False
})
# Criterion configuration
self.criterion = EasyDict(criterion or {
"loss_weight": EasyDict({
"vtc": 1.0,
"mlm": 1.0,
"vtm": 1.0,
"mvm": 0.0,
"uta": 0.0
}),
"vtm_hard_neg": True,
"mlm_masking_prob": 0.5,
"distill_final_features": True,
"clip_loss_ratio": [1.0, 1.0]
})
# Optimizer configuration
self.optimizer = EasyDict(optimizer or {
"opt": "adamW",
"lr": 5e-5,
"opt_betas": [0.9, 0.98],
"weight_decay": 0.05,
"max_grad_norm": 3.0,
"different_lr": EasyDict({"enable": False, "module_names": [], "lr": 1e-3})
})
# Scheduler configuration
self.scheduler = EasyDict(scheduler or {
"sched": "cosine",
"epochs": 10,
"min_lr_multi": 0.01,
"warmup_epochs": 1
})
# Evaluation configuration
self.evaluate = evaluate
self.deep_fusion = deep_fusion
self.evaluation = EasyDict(evaluation or {
"eval_frame_ensemble": "concat",
"eval_x_only": False,
"k_test": 128,
"eval_offload": True
})
# Miscellaneous
self.use_half_precision = use_half_precision
self.use_bf16 = use_bf16
self.gradient_checkpointing = gradient_checkpointing
self.use_flash_sdp = use_flash_sdp
self.use_mem_efficient_sdp = use_mem_efficient_sdp
self.compile_model = compile_model
self.wandb = EasyDict(wandb or {
"enable": False,
"entity": "opengvlab",
"project": "InternVideo2-Stage2"
})
self.dist_url = dist_url
self.device = device
self.mode = mode
self.output_dir = output_dir
self.resume = resume
self.debug = debug
self.log_freq = log_freq
self.seed = seed
self.save_latest = save_latest
self.auto_resume = auto_resume
self.jump_evaluate = jump_evaluate
self.pretrained_path = pretrained_path
self.save_ckpt_iter = save_ckpt_iter
self.delete_ds_optim_states = delete_ds_optim_states
self.deepspeed = EasyDict(deepspeed or {
"enable": True,
"stage": 1
})
def set_num_frames(self, num_frames):
# print('Here ', num_frames)
self.num_frames = num_frames
self.inputs.video_input.num_frames = num_frames
self.model.vision_encoder.num_frames = num_frames |