HaoxingChen
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d57e54e
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Parent(s):
41293de
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.gitattributes
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@@ -34,3 +34,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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simclr_256_e800/20221227_183052.log.json filter=lfs diff=lfs merge=lfs -text
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simclr_256_e800/train_20221227_183052.log filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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simclr_256_e800/20221227_183052.log.json filter=lfs diff=lfs merge=lfs -text
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simclr_256_e800/train_20221227_183052.log filter=lfs diff=lfs merge=lfs -text
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moco_256_e800_t15a05A02/20230110_110029.log.json filter=lfs diff=lfs merge=lfs -text
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moco_256_e800_t15a05A02/train_20230110_110029.log filter=lfs diff=lfs merge=lfs -text
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moco_256_e800_t15a05A02/20230110_110029.log.json
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version https://git-lfs.github.com/spec/v1
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oid sha256:79b782fbe32038e45e83b350a21bdd5a53269a85cd9d5d12779511aaf1ed1fc9
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size 10654175
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moco_256_e800_t15a05A02/latest.pth
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version https://git-lfs.github.com/spec/v1
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oid sha256:98dc9dda7cb9440c91a24ca22a46d47f1460435b9b3dfefb9ad0b3863d2dccf6
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size 482065006
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moco_256_e800_t15a05A02/mocov2_macl_resnet50_256-coslr-800e.py
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@@ -0,0 +1,107 @@
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model = dict(
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type='MoCo',
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queue_len=65536,
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feat_dim=128,
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momentum=0.999,
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backbone=dict(
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type='ResNet',
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depth=50,
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in_channels=3,
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out_indices=[4],
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norm_cfg=dict(type='BN')),
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neck=dict(
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type='MoCoV2Neck',
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in_channels=2048,
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hid_channels=2048,
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out_channels=128,
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with_avg_pool=True),
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head=dict(type='MaclaHead', temperature=0.15))
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data_source = 'ImageNet'
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dataset_type = 'MultiViewDataset'
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img_norm_cfg = dict(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
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train_pipeline = [
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dict(type='RandomResizedCrop', size=224, scale=(0.2, 1.0)),
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dict(
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type='RandomAppliedTrans',
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transforms=[
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dict(
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type='ColorJitter',
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brightness=0.4,
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contrast=0.4,
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saturation=0.4,
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hue=0.1)
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],
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p=0.8),
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dict(type='RandomGrayscale', p=0.2),
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dict(type='GaussianBlur', sigma_min=0.1, sigma_max=2.0, p=0.5),
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dict(type='RandomHorizontalFlip'),
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dict(type='ToTensor'),
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dict(
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type='Normalize',
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mean=[0.485, 0.456, 0.406],
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std=[0.229, 0.224, 0.225])
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]
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prefetch = False
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data = dict(
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samples_per_gpu=128,
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workers_per_gpu=8,
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drop_last=True,
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train=dict(
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type='MultiViewDataset',
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data_source=dict(
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type='ImageNet',
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data_prefix='./data/train',
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ann_file='./data/train.txt'),
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num_views=[2],
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pipelines=[[{
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'type': 'RandomResizedCrop',
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'size': 224,
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'scale': (0.2, 1.0)
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}, {
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'type':
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'RandomAppliedTrans',
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'transforms': [{
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'type': 'ColorJitter',
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'brightness': 0.4,
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'contrast': 0.4,
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'saturation': 0.4,
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'hue': 0.1
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}],
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'p':
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0.8
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}, {
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'type': 'RandomGrayscale',
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'p': 0.2
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}, {
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'type': 'GaussianBlur',
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'sigma_min': 0.1,
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'sigma_max': 2.0,
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'p': 0.5
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}, {
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'type': 'RandomHorizontalFlip'
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}, {
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'type': 'ToTensor'
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}, {
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'type': 'Normalize',
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'mean': [0.485, 0.456, 0.406],
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'std': [0.229, 0.224, 0.225]
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}]],
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prefetch=False))
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optimizer = dict(type='SGD', lr=0.03, weight_decay=0.0001, momentum=0.9)
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optimizer_config = dict()
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lr_config = dict(policy='CosineAnnealing', min_lr=0.0)
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runner = dict(type='EpochBasedRunner', max_epochs=800)
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checkpoint_config = dict(interval=10, max_keep_ckpts=3)
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log_config = dict(interval=50, hooks=[dict(type='TextLoggerHook')])
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dist_params = dict(backend='nccl')
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cudnn_benchmark = True
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log_level = 'INFO'
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load_from = None
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resume_from = None
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workflow = [('train', 1)]
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persistent_workers = True
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opencv_num_threads = 0
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mp_start_method = 'fork'
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work_dir = 'trained/pretrain/moco_256_e200/'
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auto_resume = False
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gpu_ids = range(0, 2)
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moco_256_e800_t15a05A02/train_20230110_110029.log
ADDED
@@ -0,0 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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oid sha256:6e79687631bf36d820417177bbdff855af97129b4bfa3315bb222a4ebd6d689f
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size 13153567
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