from __future__ import print_function
import os.path as osp
from torch.utils.data import DataLoader
from reid.dataset import get_sequence
from reid.data import seqtransforms as T
from reid.data import SeqTrainPreprocessor
from reid.data import SeqTestPreprocessor
from reid.data import RandomPairSampler


def get_data(dataset_name, split_id, data_dir, batch_size, seq_len, seq_srd,
    workers):
    root = osp.join(data_dir, dataset_name)
    dataset = get_sequence(dataset_name, root, split_id=split_id, seq_len=
        seq_len, seq_srd=seq_srd, num_val=1, download=True)
    train_set = dataset.trainval
    num_classes = dataset.num_trainval_ids
    normalizer = T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224,
        0.225])
    train_processor = SeqTrainPreprocessor(train_set, dataset, seq_len,
        transform=T.Compose([T.RectScale(256, 128), T.RandomHorizontalFlip(
        ), T.RandomSizedEarser(), T.ToTensor(), normalizer]))
    query_processor = SeqTestPreprocessor(dataset.query, dataset, seq_len,
        transform=T.Compose([T.RectScale(256, 128), T.ToTensor(), normalizer]))
    gallery_processor = SeqTestPreprocessor(dataset.gallery, dataset,
        seq_len, transform=T.Compose([T.RectScale(256, 128), T.ToTensor(),
        normalizer]))
    train_loader = DataLoader(train_processor, batch_size=batch_size,
        num_workers=workers, sampler=RandomPairSampler(train_set),
        pin_memory=True, drop_last=True)
    query_loader = DataLoader(query_processor, batch_size=8, num_workers=
        workers, shuffle=False, pin_memory=True, drop_last=False)
    gallery_loader = DataLoader(gallery_processor, batch_size=8,
        num_workers=workers, shuffle=False, pin_memory=True, drop_last=False)
    return dataset, num_classes, train_loader, query_loader, gallery_loader

6b2112414cf939db1796dd2e1eb185b135bd6834
<mask token>

<mask token>


def get_data(dataset_name, split_id, data_dir, batch_size, seq_len, seq_srd,
    workers):
    root = osp.join(data_dir, dataset_name)
    dataset = get_sequence(dataset_name, root, split_id=split_id, seq_len=
        seq_len, seq_srd=seq_srd, num_val=1, download=True)
    train_set = dataset.trainval
    num_classes = dataset.num_trainval_ids
    normalizer = T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224,
        0.225])
    train_processor = SeqTrainPreprocessor(train_set, dataset, seq_len,
        transform=T.Compose([T.RectScale(256, 128), T.RandomHorizontalFlip(
        ), T.RandomSizedEarser(), T.ToTensor(), normalizer]))
    query_processor = SeqTestPreprocessor(dataset.query, dataset, seq_len,
        transform=T.Compose([T.RectScale(256, 128), T.ToTensor(), normalizer]))
    gallery_processor = SeqTestPreprocessor(dataset.gallery, dataset,
        seq_len, transform=T.Compose([T.RectScale(256, 128), T.ToTensor(),
        normalizer]))
    train_loader = DataLoader(train_processor, batch_size=batch_size,
        num_workers=workers, sampler=RandomPairSampler(train_set),
        pin_memory=True, drop_last=True)
    query_loader = DataLoader(query_processor, batch_size=8, num_workers=
        workers, shuffle=False, pin_memory=True, drop_last=False)
    gallery_loader = DataLoader(gallery_processor, batch_size=8,
        num_workers=workers, shuffle=False, pin_memory=True, drop_last=False)
    return dataset, num_classes, train_loader, query_loader, gallery_loader

from __future__ import print_function
import os.path as osp
from torch.utils.data import DataLoader
from reid.dataset import get_sequence
from reid.data import seqtransforms as T
from reid.data import SeqTrainPreprocessor
from reid.data import SeqTestPreprocessor
from reid.data import RandomPairSampler


def get_data(dataset_name, split_id, data_dir, batch_size, seq_len, seq_srd,
    workers):
    root = osp.join(data_dir, dataset_name)
    dataset = get_sequence(dataset_name, root, split_id=split_id, seq_len=
        seq_len, seq_srd=seq_srd, num_val=1, download=True)
    train_set = dataset.trainval
    num_classes = dataset.num_trainval_ids
    normalizer = T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224,
        0.225])
    train_processor = SeqTrainPreprocessor(train_set, dataset, seq_len,
        transform=T.Compose([T.RectScale(256, 128), T.RandomHorizontalFlip(
        ), T.RandomSizedEarser(), T.ToTensor(), normalizer]))
    query_processor = SeqTestPreprocessor(dataset.query, dataset, seq_len,
        transform=T.Compose([T.RectScale(256, 128), T.ToTensor(), normalizer]))
    gallery_processor = SeqTestPreprocessor(dataset.gallery, dataset,
        seq_len, transform=T.Compose([T.RectScale(256, 128), T.ToTensor(),
        normalizer]))
    train_loader = DataLoader(train_processor, batch_size=batch_size,
        num_workers=workers, sampler=RandomPairSampler(train_set),
        pin_memory=True, drop_last=True)
    query_loader = DataLoader(query_processor, batch_size=8, num_workers=
        workers, shuffle=False, pin_memory=True, drop_last=False)
    gallery_loader = DataLoader(gallery_processor, batch_size=8,
        num_workers=workers, shuffle=False, pin_memory=True, drop_last=False)
    return dataset, num_classes, train_loader, query_loader, gallery_loader
