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import time
from dataclasses import dataclass
from multiprocessing import Value
import torch
from torch.utils.data import DataLoader
from torch.utils.data.distributed import DistributedSampler
from data.base_datasets import VAT_dataset
from data.new_loadvat import get_wds_dataset
from open_clip import get_tokenizer
from open_clip.factory import HF_HUB_PREFIX
class SharedEpoch:
def __init__(self, epoch: int = 0):
self.shared_epoch = Value('i', epoch)
def set_value(self, epoch):
self.shared_epoch.value = epoch
def get_value(self):
return self.shared_epoch.value
@dataclass
class DataInfo:
dataloader: DataLoader
sampler: DistributedSampler = None
shared_epoch: SharedEpoch = None
def set_epoch(self, epoch):
if self.shared_epoch is not None:
self.shared_epoch.set_value(epoch)
if self.sampler is not None and isinstance(self.sampler, DistributedSampler):
self.sampler.set_epoch(epoch)
def get_VAT_dataset(args):
dataset = VAT_dataset(args)
num_samples = len(dataset)
sampler = DistributedSampler(dataset) if args.distributed else None
shuffle = sampler is None
dataloader = DataLoader(
dataset,
batch_size=args.batch_size,
# prefetch_factor=2,
# persistent_workers=True,
shuffle=shuffle,
num_workers=args.workers,
pin_memory=True,
sampler=sampler,
drop_last=True,
)
dataloader.num_samples = num_samples
dataloader.num_batches = len(dataloader)
return DataInfo(dataloader, sampler)
def get_data(args, epoch=0):
data = {}
if args.do_train:
if args.train_data.endswith(".json"):
data[f"{args.clip_type}_pt"] = get_VAT_dataset(args)
elif args.train_data.endswith(".tar"):
data[f"{args.clip_type}_pt"] = get_wds_dataset(args, is_train=True, epoch=epoch)
else:
raise NameError
if args.do_eval:
temp_batch_size = args.batch_size
args.batch_size = 8 if args.val_vl_ret_data else 16
data_root = "/apdcephfs_cq3/share_1311970/downstream_datasets/VideoTextRetrieval/vtRetdata"
if args.val_vl_ret_data:
data["vl_ret"] = []
for val_vl_ret_data in args.val_vl_ret_data:
if val_vl_ret_data == "msrvtt":
args.train_csv = os.path.join(f'{data_root}/MSRVTT/MSRVTT_train.9k.csv')
args.val_csv = os.path.join(f'{data_root}/MSRVTT/MSRVTT_JSFUSION_test.csv')
args.data_path = os.path.join(f'{data_root}/MSRVTT/MSRVTT_data.json')
args.features_path = os.path.join(f'{data_root}/MSRVTT/MSRVTT_Videos')
elif val_vl_ret_data == "msvd":
args.data_path = os.path.join(f'{data_root}/MSVD')
args.features_path = os.path.join(f'{data_root}/MSVD/MSVD_Videos')
elif val_vl_ret_data == "activity":
args.data_path = os.path.join(f'{data_root}/ActivityNet')
args.features_path = os.path.join(f'{data_root}/ActivityNet/Videos/Activity_Videos')
elif val_vl_ret_data == "didemo":
args.data_path = os.path.join(f'{data_root}/Didemo')
args.features_path = os.path.join(f'{data_root}/Didemo/videos')
else:
raise NameError
args.batch_size_val = args.batch_size if args.batch_size_val == 0 else args.batch_size_val
args.max_frames = args.num_frames
args.num_thread_reader = args.workers
args.slice_framepos = 2 # "0: cut from head frames; 1: cut from tail frames; 2: extract frames uniformly."
from vl_ret.data_dataloaders import DATALOADER_DICT
tokenizer = get_tokenizer(HF_HUB_PREFIX + args.model, cache_dir=args.cache_dir)
test_dataloader, test_length = None, 0
if DATALOADER_DICT[val_vl_ret_data]["test"] is not None:
test_dataloader, test_length = DATALOADER_DICT[val_vl_ret_data]["test"](args, tokenizer)
if DATALOADER_DICT[val_vl_ret_data]["val"] is not None:
val_dataloader, val_length = DATALOADER_DICT[val_vl_ret_data]["val"](args, tokenizer, subset="val")
else:
val_dataloader, val_length = test_dataloader, test_length
## report validation results if the ["test"] is None
if test_dataloader is None:
test_dataloader, test_length = val_dataloader, val_length
data["vl_ret"].append({val_vl_ret_data: test_dataloader})
if args.val_v_cls_data:
data["v_cls"] = []
temp_val_v_cls_data = args.val_v_cls_data
for val_v_cls_data in temp_val_v_cls_data:
from v_cls import get_video_cls_dataloader
args.val_v_cls_data = val_v_cls_data
if args.val_v_cls_data == 'Kinetics-400':
args.video_data_path = "/apdcephfs_cq3/share_1311970/downstream_datasets/VideoCls/new_k400/Kinetics-400/raw/Kinetics-400"
args.nb_classes = 400
elif args.val_v_cls_data == 'Kinetics-600':
args.video_data_path = "/apdcephfs_cq3/share_1311970/downstream_datasets/VideoCls/new_k600/Kinetics600/raw/Kinetics600"
args.nb_classes = 600
args.data_root = args.video_data_path
args.data_set = val_v_cls_data
args.dist_eval = True
args.sampling_rate = 8
args.num_sample = 1
args.test_num_segment = 5
args.test_num_crop = 3
args.num_workers = args.workers
data['v_cls'].append({val_v_cls_data: get_video_cls_dataloader(args)})
args.val_v_cls_data = temp_val_v_cls_data
if args.val_a_cls_data:
temp_audio_mean, temp_audio_std = args.audio_mean, args.audio_std
args.audio_mean, args.audio_std = -4.2677393, 4.5689974
data["a_cls"] = []
data_root = "/apdcephfs_cq3/share_1311970/downstream_datasets/Audio"
temp_val_a_cls_data = args.val_a_cls_data
for val_a_cls_data in temp_val_a_cls_data:
from a_cls.datasets import get_audio_dataset
args.val_a_cls_data = val_a_cls_data
args.audio_data_path = os.path.join(data_root, f'{val_a_cls_data.lower()}/test')
data['a_cls'].append({val_a_cls_data: get_audio_dataset(args)})
args.val_a_cls_data = temp_val_a_cls_data
args.audio_mean, args.audio_mean = temp_audio_mean, temp_audio_std
if args.val_al_ret_data:
temp_audio_mean, temp_audio_std = args.audio_mean, args.audio_std
args.audio_mean, args.audio_std = -4.2677393, 4.5689974
data["al_ret"] = []
data_root = "/apdcephfs_cq3/share_1311970/downstream_datasets/Audio"
temp_val_al_ret_data = args.val_al_ret_data
for val_al_ret_data in temp_val_al_ret_data:
from al_ret.datasets import get_audio_dataset
args.val_al_ret_data = val_al_ret_data
if val_al_ret_data.lower() != 'msrvtt':
args.audio_data_path = os.path.join(data_root, val_al_ret_data.lower())
data['al_ret'].append({val_al_ret_data: get_audio_dataset(args)})
elif val_al_ret_data.lower() == 'msrvtt':
args.train_csv = os.path.join(f'/apdcephfs_cq3/share_1311970/downstream_datasets/VideoTextRetrieval/vtRetdata/MSRVTT/MSRVTT_train.9k.csv')
args.val_csv = os.path.join(f'/apdcephfs_cq3/share_1311970/downstream_datasets/VideoTextRetrieval/Audio/MSRVTT/MSRVTT_AUDIO_test.csv')
args.data_path = os.path.join(f'/apdcephfs_cq3/share_1311970/downstream_datasets/VideoTextRetrieval/vtRetdata/MSRVTT/MSRVTT_data.json')
args.features_path = os.path.join(f'/apdcephfs_cq3/share_1311970/downstream_datasets/VideoTextRetrieval/Audio/MSRVTT/videos/all')
args.num_thread_reader = args.workers
from al_ret.data_dataloaders import DATALOADER_DICT
args.batch_size_val = args.batch_size if args.batch_size_val == 0 else args.batch_size_val
tokenizer = get_tokenizer(HF_HUB_PREFIX + args.model, cache_dir=args.cache_dir)
test_dataloader, test_length = None, 0
if DATALOADER_DICT[val_al_ret_data.lower()]["test"] is not None:
test_dataloader, test_length = DATALOADER_DICT[val_al_ret_data.lower()]["test"](args, tokenizer)
if DATALOADER_DICT[val_al_ret_data.lower()]["val"] is not None:
val_dataloader, val_length = DATALOADER_DICT[val_al_ret_data.lower()]["val"](args, tokenizer, subset="val")
else:
val_dataloader, val_length = test_dataloader, test_length
## report validation results if the ["test"] is None
if test_dataloader is None:
test_dataloader, test_length = val_dataloader, val_length
data['al_ret'].append({val_al_ret_data: test_dataloader})
args.val_al_ret_data = temp_val_al_ret_data
args.audio_mean, args.audio_mean = temp_audio_mean, temp_audio_std
if args.val_a_cls_data:
temp_audio_mean, temp_audio_std = args.audio_mean, args.audio_std
args.audio_mean, args.audio_std = -4.2677393, 4.5689974
data["a_cls"] = []
data_root = "/apdcephfs_cq3/share_1311970/downstream_datasets/Audio"
temp_val_a_cls_data = args.val_a_cls_data
for val_a_cls_data in temp_val_a_cls_data:
from a_cls.datasets import get_audio_dataset
args.val_a_cls_data = val_a_cls_data
args.audio_data_path = os.path.join(data_root, f'{val_a_cls_data.lower()}/test')
data['a_cls'].append({val_a_cls_data: get_audio_dataset(args)})
args.val_a_cls_data = temp_val_a_cls_data
args.audio_mean, args.audio_mean = temp_audio_mean, temp_audio_std
if args.imagenet_val is not None:
from i_cls.datasets import get_imagenet
data['i_cls'] = {}
data['i_cls']["imagenet-val"] = get_imagenet(args, "val")
if args.imagenet_v2 is not None:
from i_cls.datasets import get_imagenet
if data.get('i_cls', None) is None:
data['i_cls'] = {}
data['i_cls']["imagenet-v2"] = get_imagenet(args, "v2")
if args.val_d_cls_data:
data["d_cls"] = []
data_root = "/apdcephfs_cq3/share_1311970/downstream_datasets/Depth"
temp_val_d_cls_data = args.val_d_cls_data
for val_d_cls_data in temp_val_d_cls_data:
from d_cls.datasets import get_depth_dataset
args.val_d_cls_data = val_d_cls_data
args.depth_data_path = os.path.join(data_root, f'{val_d_cls_data.lower()}/data/val')
data['d_cls'].append({val_d_cls_data: get_depth_dataset(args)})
args.val_d_cls_data = temp_val_d_cls_data
if args.val_t_cls_data:
data["t_cls"] = []
data_root = "/apdcephfs_cq3/share_1311970/downstream_datasets/Thermal"
temp_val_t_cls_data = args.val_t_cls_data
for val_t_cls_data in temp_val_t_cls_data:
from t_cls.datasets import get_thermal_dataset
args.val_t_cls_data = val_t_cls_data
args.thermal_data_path = os.path.join(data_root, f'{val_t_cls_data.lower()}/val')
data['t_cls'].append({val_t_cls_data: get_thermal_dataset(args)})
args.val_t_cls_data = temp_val_t_cls_data
args.batch_size = temp_batch_size
return data
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