40 lines
1.0 KiB
Python
40 lines
1.0 KiB
Python
_base_ = [
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'../_base_/datasets/imagenet_bs32_mocov2.py',
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'../_base_/schedules/imagenet_sgd_coslr_200e.py',
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'../_base_/default_runtime.py',
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]
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# model settings
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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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data_preprocessor=dict(
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mean=(123.675, 116.28, 103.53),
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std=(58.395, 57.12, 57.375),
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to_rgb=True),
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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], # 0: conv-1, x: stage-x
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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(
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type='ContrastiveHead',
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loss=dict(type='mmcls.CrossEntropyLoss'),
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temperature=0.2))
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# only keeps the latest 3 checkpoints
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default_hooks = dict(checkpoint=dict(max_keep_ckpts=3))
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# NOTE: `auto_scale_lr` is for automatically scaling LR
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# based on the actual training batch size.
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auto_scale_lr = dict(base_batch_size=256)
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