2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.patch_embed.proj.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.patch_embed.proj.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.patch_embed.proj.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.patch_embed.proj.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.patch_embed.norm.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.patch_embed.norm.weight: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.patch_embed.norm.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.patch_embed.norm.bias: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.norm1.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.norm1.weight: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.norm1.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.norm1.bias: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.attn.relative_position_bias_table: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.attn.relative_position_bias_table: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.attn.qkv.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.attn.qkv.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.attn.qkv.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.attn.qkv.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.attn.proj.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.attn.proj.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.attn.proj.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.attn.proj.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.norm2.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.norm2.weight: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.norm2.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.norm2.bias: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.mlp.fc1.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.mlp.fc1.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.mlp.fc1.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.mlp.fc1.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.mlp.fc2.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.mlp.fc2.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.mlp.fc2.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.0.mlp.fc2.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.norm1.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.norm1.weight: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.norm1.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.norm1.bias: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.attn.relative_position_bias_table: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.attn.relative_position_bias_table: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.attn.qkv.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.attn.qkv.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.attn.qkv.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.attn.qkv.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.attn.proj.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.attn.proj.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.attn.proj.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.attn.proj.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.norm2.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.norm2.weight: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.norm2.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.norm2.bias: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.mlp.fc1.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.mlp.fc1.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.mlp.fc1.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.mlp.fc1.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.mlp.fc2.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.mlp.fc2.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.mlp.fc2.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.0.blocks.1.mlp.fc2.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.0.downsample.reduction.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.0.downsample.reduction.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.0.downsample.norm.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.0.downsample.norm.weight: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.0.downsample.norm.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.0.downsample.norm.bias: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.norm1.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.norm1.weight: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.norm1.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.norm1.bias: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.attn.relative_position_bias_table: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.attn.relative_position_bias_table: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.attn.qkv.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.attn.qkv.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.attn.qkv.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.attn.qkv.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.attn.proj.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.attn.proj.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.attn.proj.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.attn.proj.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.norm2.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.norm2.weight: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.norm2.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.norm2.bias: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.mlp.fc1.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.mlp.fc1.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.mlp.fc1.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.mlp.fc1.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.mlp.fc2.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.mlp.fc2.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.mlp.fc2.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.0.mlp.fc2.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.norm1.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.norm1.weight: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.norm1.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.norm1.bias: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.attn.relative_position_bias_table: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.attn.relative_position_bias_table: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.attn.qkv.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.attn.qkv.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.attn.qkv.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.attn.qkv.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.attn.proj.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.attn.proj.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.attn.proj.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.attn.proj.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.norm2.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.norm2.weight: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.norm2.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.norm2.bias: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.mlp.fc1.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.mlp.fc1.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.mlp.fc1.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.mlp.fc1.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.mlp.fc2.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.mlp.fc2.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.mlp.fc2.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.1.blocks.1.mlp.fc2.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.1.downsample.reduction.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.1.downsample.reduction.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.1.downsample.norm.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.1.downsample.norm.weight: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.1.downsample.norm.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.1.downsample.norm.bias: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.norm1.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.norm1.weight: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.norm1.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.norm1.bias: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.attn.relative_position_bias_table: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.attn.relative_position_bias_table: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.attn.qkv.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.attn.qkv.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.attn.qkv.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.attn.qkv.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.attn.proj.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.attn.proj.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.attn.proj.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.attn.proj.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.norm2.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.norm2.weight: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.norm2.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.norm2.bias: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.mlp.fc1.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.mlp.fc1.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.mlp.fc1.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.mlp.fc1.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.mlp.fc2.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.mlp.fc2.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.mlp.fc2.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.0.mlp.fc2.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.norm1.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.norm1.weight: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.norm1.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.norm1.bias: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.attn.relative_position_bias_table: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.attn.relative_position_bias_table: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.attn.qkv.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.attn.qkv.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.attn.qkv.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.attn.qkv.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.attn.proj.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.attn.proj.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.attn.proj.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.attn.proj.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.norm2.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.norm2.weight: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.norm2.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.norm2.bias: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.mlp.fc1.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.mlp.fc1.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.mlp.fc1.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.mlp.fc1.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.mlp.fc2.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.mlp.fc2.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.mlp.fc2.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.1.mlp.fc2.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.norm1.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.norm1.weight: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.norm1.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.norm1.bias: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.attn.relative_position_bias_table: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.attn.relative_position_bias_table: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.attn.qkv.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.attn.qkv.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.attn.qkv.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.attn.qkv.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.attn.proj.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.attn.proj.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.attn.proj.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.attn.proj.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.norm2.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.norm2.weight: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.norm2.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.norm2.bias: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.mlp.fc1.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.mlp.fc1.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.mlp.fc1.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.mlp.fc1.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.mlp.fc2.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.mlp.fc2.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.mlp.fc2.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.2.mlp.fc2.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.norm1.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.norm1.weight: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.norm1.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.norm1.bias: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.attn.relative_position_bias_table: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.attn.relative_position_bias_table: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.attn.qkv.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.attn.qkv.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.attn.qkv.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.attn.qkv.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.attn.proj.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.attn.proj.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.attn.proj.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.attn.proj.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.norm2.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.norm2.weight: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.norm2.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.norm2.bias: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.mlp.fc1.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.mlp.fc1.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.mlp.fc1.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.mlp.fc1.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.mlp.fc2.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.mlp.fc2.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.mlp.fc2.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.3.mlp.fc2.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.norm1.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.norm1.weight: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.norm1.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.norm1.bias: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.attn.relative_position_bias_table: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.attn.relative_position_bias_table: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.attn.qkv.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.attn.qkv.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.attn.qkv.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.attn.qkv.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.attn.proj.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.attn.proj.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.attn.proj.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.attn.proj.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.norm2.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.norm2.weight: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.norm2.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.norm2.bias: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.mlp.fc1.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.mlp.fc1.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.mlp.fc1.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.mlp.fc1.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.mlp.fc2.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.mlp.fc2.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.mlp.fc2.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.4.mlp.fc2.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.norm1.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.norm1.weight: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.norm1.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.norm1.bias: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.attn.relative_position_bias_table: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.attn.relative_position_bias_table: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.attn.qkv.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.attn.qkv.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.attn.qkv.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.attn.qkv.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.attn.proj.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.attn.proj.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.attn.proj.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.attn.proj.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.norm2.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.norm2.weight: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.norm2.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.norm2.bias: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.mlp.fc1.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.mlp.fc1.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.mlp.fc1.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.mlp.fc1.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.mlp.fc2.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.mlp.fc2.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.mlp.fc2.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.5.mlp.fc2.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.6.norm1.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.6.norm1.weight: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.6.norm1.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.6.norm1.bias: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.6.attn.relative_position_bias_table: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.6.attn.relative_position_bias_table: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.6.attn.qkv.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.6.attn.qkv.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.6.attn.qkv.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.6.attn.qkv.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.6.attn.proj.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.6.attn.proj.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.6.attn.proj.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.6.attn.proj.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.6.norm2.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.6.norm2.weight: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.6.norm2.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.6.norm2.bias: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.6.mlp.fc1.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.6.mlp.fc1.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.6.mlp.fc1.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.6.mlp.fc1.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.6.mlp.fc2.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.6.mlp.fc2.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.6.mlp.fc2.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.6.mlp.fc2.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.7.norm1.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.7.norm1.weight: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.7.norm1.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.7.norm1.bias: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.7.attn.relative_position_bias_table: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.7.attn.relative_position_bias_table: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.7.attn.qkv.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.7.attn.qkv.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.7.attn.qkv.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.7.attn.qkv.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.7.attn.proj.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.7.attn.proj.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.7.attn.proj.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.7.attn.proj.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.7.norm2.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.7.norm2.weight: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.7.norm2.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.7.norm2.bias: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.7.mlp.fc1.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.7.mlp.fc1.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.7.mlp.fc1.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.7.mlp.fc1.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.7.mlp.fc2.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.7.mlp.fc2.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.7.mlp.fc2.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.7.mlp.fc2.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.8.norm1.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.8.norm1.weight: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.8.norm1.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.8.norm1.bias: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.8.attn.relative_position_bias_table: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.8.attn.relative_position_bias_table: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.8.attn.qkv.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.8.attn.qkv.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.8.attn.qkv.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.8.attn.qkv.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.8.attn.proj.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.8.attn.proj.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.8.attn.proj.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.8.attn.proj.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.8.norm2.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.8.norm2.weight: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.8.norm2.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.8.norm2.bias: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.8.mlp.fc1.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.8.mlp.fc1.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.8.mlp.fc1.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.8.mlp.fc1.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.8.mlp.fc2.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.8.mlp.fc2.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.8.mlp.fc2.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.8.mlp.fc2.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.9.norm1.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.9.norm1.weight: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.9.norm1.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.9.norm1.bias: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.9.attn.relative_position_bias_table: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.9.attn.relative_position_bias_table: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.9.attn.qkv.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.9.attn.qkv.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.9.attn.qkv.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.9.attn.qkv.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.9.attn.proj.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.9.attn.proj.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.9.attn.proj.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.9.attn.proj.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.9.norm2.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.9.norm2.weight: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.9.norm2.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.9.norm2.bias: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.9.mlp.fc1.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.9.mlp.fc1.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.9.mlp.fc1.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.9.mlp.fc1.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.9.mlp.fc2.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.9.mlp.fc2.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.9.mlp.fc2.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.9.mlp.fc2.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.10.norm1.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.10.norm1.weight: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.10.norm1.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.10.norm1.bias: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.10.attn.relative_position_bias_table: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.10.attn.relative_position_bias_table: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.10.attn.qkv.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.10.attn.qkv.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.10.attn.qkv.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.10.attn.qkv.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.10.attn.proj.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.10.attn.proj.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.10.attn.proj.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.10.attn.proj.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.10.norm2.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.10.norm2.weight: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.10.norm2.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.10.norm2.bias: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.10.mlp.fc1.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.10.mlp.fc1.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.10.mlp.fc1.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.10.mlp.fc1.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.10.mlp.fc2.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.10.mlp.fc2.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.10.mlp.fc2.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.10.mlp.fc2.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.11.norm1.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.11.norm1.weight: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.11.norm1.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.11.norm1.bias: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.11.attn.relative_position_bias_table: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.11.attn.relative_position_bias_table: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.11.attn.qkv.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.11.attn.qkv.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.11.attn.qkv.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.11.attn.qkv.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.11.attn.proj.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.11.attn.proj.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.11.attn.proj.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.11.attn.proj.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.11.norm2.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.11.norm2.weight: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.11.norm2.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.11.norm2.bias: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.11.mlp.fc1.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.11.mlp.fc1.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.11.mlp.fc1.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.11.mlp.fc1.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.11.mlp.fc2.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.11.mlp.fc2.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.11.mlp.fc2.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.11.mlp.fc2.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.12.norm1.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.12.norm1.weight: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.12.norm1.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.12.norm1.bias: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.12.attn.relative_position_bias_table: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.12.attn.relative_position_bias_table: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.12.attn.qkv.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.12.attn.qkv.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.12.attn.qkv.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.12.attn.qkv.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.12.attn.proj.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.12.attn.proj.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.12.attn.proj.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.12.attn.proj.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.12.norm2.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.12.norm2.weight: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.12.norm2.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.12.norm2.bias: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.12.mlp.fc1.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.12.mlp.fc1.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.12.mlp.fc1.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.12.mlp.fc1.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.12.mlp.fc2.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.12.mlp.fc2.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.12.mlp.fc2.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.12.mlp.fc2.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.13.norm1.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.13.norm1.weight: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.13.norm1.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.13.norm1.bias: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.13.attn.relative_position_bias_table: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.13.attn.relative_position_bias_table: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.13.attn.qkv.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.13.attn.qkv.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.13.attn.qkv.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.13.attn.qkv.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.13.attn.proj.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.13.attn.proj.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.13.attn.proj.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.13.attn.proj.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.13.norm2.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.13.norm2.weight: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.13.norm2.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.13.norm2.bias: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.13.mlp.fc1.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.13.mlp.fc1.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.13.mlp.fc1.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.13.mlp.fc1.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.13.mlp.fc2.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.13.mlp.fc2.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.13.mlp.fc2.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.13.mlp.fc2.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.14.norm1.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.14.norm1.weight: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.14.norm1.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.14.norm1.bias: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.14.attn.relative_position_bias_table: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.14.attn.relative_position_bias_table: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.14.attn.qkv.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.14.attn.qkv.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.14.attn.qkv.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.14.attn.qkv.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.14.attn.proj.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.14.attn.proj.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.14.attn.proj.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.14.attn.proj.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.14.norm2.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.14.norm2.weight: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.14.norm2.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.14.norm2.bias: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.14.mlp.fc1.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.14.mlp.fc1.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.14.mlp.fc1.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.14.mlp.fc1.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.14.mlp.fc2.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.14.mlp.fc2.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.14.mlp.fc2.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.14.mlp.fc2.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.15.norm1.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.15.norm1.weight: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.15.norm1.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.15.norm1.bias: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.15.attn.relative_position_bias_table: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.15.attn.relative_position_bias_table: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.15.attn.qkv.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.15.attn.qkv.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.15.attn.qkv.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.15.attn.qkv.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.15.attn.proj.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.15.attn.proj.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.15.attn.proj.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.15.attn.proj.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.15.norm2.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.15.norm2.weight: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.15.norm2.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.15.norm2.bias: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.15.mlp.fc1.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.15.mlp.fc1.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.15.mlp.fc1.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.15.mlp.fc1.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.15.mlp.fc2.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.15.mlp.fc2.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.15.mlp.fc2.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.15.mlp.fc2.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.16.norm1.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.16.norm1.weight: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.16.norm1.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.16.norm1.bias: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.16.attn.relative_position_bias_table: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.16.attn.relative_position_bias_table: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.16.attn.qkv.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.16.attn.qkv.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.16.attn.qkv.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.16.attn.qkv.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.16.attn.proj.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.16.attn.proj.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.16.attn.proj.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.16.attn.proj.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.16.norm2.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.16.norm2.weight: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.16.norm2.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.16.norm2.bias: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.16.mlp.fc1.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.16.mlp.fc1.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.16.mlp.fc1.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.16.mlp.fc1.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.16.mlp.fc2.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.16.mlp.fc2.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.16.mlp.fc2.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.16.mlp.fc2.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.17.norm1.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.17.norm1.weight: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.17.norm1.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.17.norm1.bias: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.17.attn.relative_position_bias_table: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.17.attn.relative_position_bias_table: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.17.attn.qkv.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.17.attn.qkv.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.17.attn.qkv.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.17.attn.qkv.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.17.attn.proj.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.17.attn.proj.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.17.attn.proj.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.17.attn.proj.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.17.norm2.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.17.norm2.weight: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.17.norm2.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.17.norm2.bias: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.17.mlp.fc1.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.17.mlp.fc1.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.17.mlp.fc1.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.17.mlp.fc1.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.17.mlp.fc2.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.17.mlp.fc2.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.17.mlp.fc2.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.blocks.17.mlp.fc2.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.downsample.reduction.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.downsample.reduction.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.downsample.norm.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.downsample.norm.weight: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.downsample.norm.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.2.downsample.norm.bias: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.norm1.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.norm1.weight: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.norm1.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.norm1.bias: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.attn.relative_position_bias_table: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.attn.relative_position_bias_table: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.attn.qkv.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.attn.qkv.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.attn.qkv.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.attn.qkv.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.attn.proj.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.attn.proj.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.attn.proj.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.attn.proj.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.norm2.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.norm2.weight: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.norm2.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.norm2.bias: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.mlp.fc1.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.mlp.fc1.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.mlp.fc1.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.mlp.fc1.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.mlp.fc2.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.mlp.fc2.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.mlp.fc2.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.0.mlp.fc2.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.norm1.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.norm1.weight: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.norm1.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.norm1.bias: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.attn.relative_position_bias_table: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.attn.relative_position_bias_table: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.attn.qkv.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.attn.qkv.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.attn.qkv.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.attn.qkv.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.attn.proj.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.attn.proj.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.attn.proj.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.attn.proj.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.norm2.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.norm2.weight: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.norm2.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.norm2.bias: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.mlp.fc1.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.mlp.fc1.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.mlp.fc1.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.mlp.fc1.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.mlp.fc2.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.mlp.fc2.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.mlp.fc2.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.layers.3.blocks.1.mlp.fc2.bias: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.norm.weight: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.norm.weight: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.norm.bias: lr = 0.0001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- backbone.norm.bias: weight_decay = 0.0
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- cls_head.fc_cls.weight: lr = 0.001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- cls_head.fc_cls.weight: weight_decay = 0.05
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- cls_head.fc_cls.bias: lr = 0.001
2022/08/06 02:11:04 - mmengine - INFO - paramwise_options -- cls_head.fc_cls.bias: weight_decay = 0.05
2022/08/06 02:11:05 - mmengine - INFO - load model from: https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window7_224_22k.pth
2022/08/06 02:23:23 - mmengine - INFO - _IncompatibleKeys(missing_keys=['layers.0.blocks.0.attn.relative_position_index', 'layers.0.blocks.1.attn.relative_position_index', 'layers.1.blocks.0.attn.relative_position_index', 'layers.1.blocks.1.attn.relative_position_index', 'layers.2.blocks.0.attn.relative_position_index', 'layers.2.blocks.1.attn.relative_position_index', 'layers.2.blocks.2.attn.relative_position_index', 'layers.2.blocks.3.attn.relative_position_index', 'layers.2.blocks.4.attn.relative_position_index', 'layers.2.blocks.5.attn.relative_position_index', 'layers.2.blocks.6.attn.relative_position_index', 'layers.2.blocks.7.attn.relative_position_index', 'layers.2.blocks.8.attn.relative_position_index', 'layers.2.blocks.9.attn.relative_position_index', 'layers.2.blocks.10.attn.relative_position_index', 'layers.2.blocks.11.attn.relative_position_index', 'layers.2.blocks.12.attn.relative_position_index', 'layers.2.blocks.13.attn.relative_position_index', 'layers.2.blocks.14.attn.relative_position_index', 'layers.2.blocks.15.attn.relative_position_index', 'layers.2.blocks.16.attn.relative_position_index', 'layers.2.blocks.17.attn.relative_position_index', 'layers.3.blocks.0.attn.relative_position_index', 'layers.3.blocks.1.attn.relative_position_index'], unexpected_keys=['head.weight', 'head.bias'])
2022/08/06 02:23:23 - mmengine - INFO - => loaded successfully 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window7_224_22k.pth'
2022/08/06 02:23:23 - mmengine - INFO - Checkpoints will be saved to /mnt/lustre/daiwenxun/X/swin/mmaction2/work_dirs/swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb by HardDiskBackend.
2022/08/06 02:26:40 - mmengine - INFO - Epoch(train) [1][100/3757]  lr: 1.0949e-05  eta: 2 days, 13:23:16  time: 0.9998  data_time: 0.0128  memory: 68881  top1_acc: 0.0000  top5_acc: 0.0000  loss_cls: 5.8663  loss: 5.8663
2022/08/06 02:28:20 - mmengine - INFO - Epoch(train) [1][200/3757]  lr: 1.1907e-05  eta: 1 day, 22:19:54  time: 0.9983  data_time: 0.0132  memory: 68881  top1_acc: 0.1250  top5_acc: 0.2500  loss_cls: 5.3300  loss: 5.3300
2022/08/06 02:30:00 - mmengine - INFO - Epoch(train) [1][300/3757]  lr: 1.2865e-05  eta: 1 day, 17:17:14  time: 1.0090  data_time: 0.0144  memory: 68881  top1_acc: 0.1250  top5_acc: 0.3750  loss_cls: 4.5981  loss: 4.5981
2022/08/06 02:31:40 - mmengine - INFO - Epoch(train) [1][400/3757]  lr: 1.3824e-05  eta: 1 day, 14:45:45  time: 0.9975  data_time: 0.0145  memory: 68881  top1_acc: 0.2500  top5_acc: 0.3750  loss_cls: 4.0073  loss: 4.0073
2022/08/06 02:33:21 - mmengine - INFO - Epoch(train) [1][500/3757]  lr: 1.4782e-05  eta: 1 day, 13:14:28  time: 1.0070  data_time: 0.0141  memory: 68881  top1_acc: 0.6250  top5_acc: 0.6250  loss_cls: 3.3129  loss: 3.3129
2022/08/06 02:35:02 - mmengine - INFO - Epoch(train) [1][600/3757]  lr: 1.5740e-05  eta: 1 day, 12:15:24  time: 1.0131  data_time: 0.0145  memory: 68881  top1_acc: 0.0000  top5_acc: 0.5000  loss_cls: 3.3259  loss: 3.3259
2022/08/06 02:36:43 - mmengine - INFO - Epoch(train) [1][700/3757]  lr: 1.6699e-05  eta: 1 day, 11:32:02  time: 1.0075  data_time: 0.0145  memory: 68881  top1_acc: 0.1250  top5_acc: 0.7500  loss_cls: 3.0491  loss: 3.0491
2022/08/06 02:38:23 - mmengine - INFO - Epoch(train) [1][800/3757]  lr: 1.7657e-05  eta: 1 day, 10:58:38  time: 1.0101  data_time: 0.0155  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 2.6604  loss: 2.6604
2022/08/06 02:40:04 - mmengine - INFO - Epoch(train) [1][900/3757]  lr: 1.8615e-05  eta: 1 day, 10:32:56  time: 1.0143  data_time: 0.0162  memory: 68881  top1_acc: 0.2500  top5_acc: 0.7500  loss_cls: 2.4728  loss: 2.4728
2022/08/06 02:41:45 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 02:41:45 - mmengine - INFO - Epoch(train) [1][1000/3757]  lr: 1.9574e-05  eta: 1 day, 10:11:31  time: 0.9970  data_time: 0.0150  memory: 68881  top1_acc: 0.6250  top5_acc: 0.7500  loss_cls: 2.6162  loss: 2.6162
2022/08/06 02:43:25 - mmengine - INFO - Epoch(train) [1][1100/3757]  lr: 2.0532e-05  eta: 1 day, 9:52:54  time: 1.0013  data_time: 0.0157  memory: 68881  top1_acc: 0.3750  top5_acc: 0.8750  loss_cls: 2.7815  loss: 2.7815
2022/08/06 02:45:06 - mmengine - INFO - Epoch(train) [1][1200/3757]  lr: 2.1490e-05  eta: 1 day, 9:36:52  time: 1.0041  data_time: 0.0154  memory: 68881  top1_acc: 0.5000  top5_acc: 0.8750  loss_cls: 2.2182  loss: 2.2182
2022/08/06 02:46:46 - mmengine - INFO - Epoch(train) [1][1300/3757]  lr: 2.2448e-05  eta: 1 day, 9:22:59  time: 1.0026  data_time: 0.0143  memory: 68881  top1_acc: 0.3750  top5_acc: 0.5000  loss_cls: 2.3406  loss: 2.3406
2022/08/06 02:48:26 - mmengine - INFO - Epoch(train) [1][1400/3757]  lr: 2.3407e-05  eta: 1 day, 9:11:09  time: 1.0083  data_time: 0.0165  memory: 68881  top1_acc: 0.2500  top5_acc: 1.0000  loss_cls: 2.2842  loss: 2.2842
2022/08/06 02:50:07 - mmengine - INFO - Epoch(train) [1][1500/3757]  lr: 2.4365e-05  eta: 1 day, 9:01:39  time: 1.0079  data_time: 0.0161  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 1.8763  loss: 1.8763
2022/08/06 02:51:48 - mmengine - INFO - Epoch(train) [1][1600/3757]  lr: 2.5323e-05  eta: 1 day, 8:53:11  time: 1.0204  data_time: 0.0171  memory: 68881  top1_acc: 0.5000  top5_acc: 0.5000  loss_cls: 2.3732  loss: 2.3732
2022/08/06 02:53:28 - mmengine - INFO - Epoch(train) [1][1700/3757]  lr: 2.6282e-05  eta: 1 day, 8:44:21  time: 0.9982  data_time: 0.0164  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 1.9952  loss: 1.9952
2022/08/06 02:55:08 - mmengine - INFO - Epoch(train) [1][1800/3757]  lr: 2.7240e-05  eta: 1 day, 8:36:23  time: 1.0023  data_time: 0.0166  memory: 68881  top1_acc: 0.3750  top5_acc: 0.8750  loss_cls: 2.0258  loss: 2.0258
2022/08/06 02:56:49 - mmengine - INFO - Epoch(train) [1][1900/3757]  lr: 2.8198e-05  eta: 1 day, 8:30:01  time: 1.0163  data_time: 0.0185  memory: 68881  top1_acc: 0.5000  top5_acc: 0.6250  loss_cls: 2.2357  loss: 2.2357
2022/08/06 02:58:30 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 02:58:30 - mmengine - INFO - Epoch(train) [1][2000/3757]  lr: 2.9157e-05  eta: 1 day, 8:23:49  time: 1.0068  data_time: 0.0167  memory: 68881  top1_acc: 0.6250  top5_acc: 0.6250  loss_cls: 2.4627  loss: 2.4627
2022/08/06 03:00:11 - mmengine - INFO - Epoch(train) [1][2100/3757]  lr: 3.0115e-05  eta: 1 day, 8:17:58  time: 1.0016  data_time: 0.0171  memory: 68881  top1_acc: 0.2500  top5_acc: 0.5000  loss_cls: 2.3111  loss: 2.3111
2022/08/06 03:01:51 - mmengine - INFO - Epoch(train) [1][2200/3757]  lr: 3.1073e-05  eta: 1 day, 8:12:16  time: 1.0065  data_time: 0.0168  memory: 68881  top1_acc: 0.2500  top5_acc: 0.3750  loss_cls: 1.9772  loss: 1.9772
2022/08/06 03:03:32 - mmengine - INFO - Epoch(train) [1][2300/3757]  lr: 3.2032e-05  eta: 1 day, 8:07:20  time: 1.0255  data_time: 0.0173  memory: 68881  top1_acc: 0.3750  top5_acc: 0.6250  loss_cls: 2.2252  loss: 2.2252
2022/08/06 03:05:13 - mmengine - INFO - Epoch(train) [1][2400/3757]  lr: 3.2990e-05  eta: 1 day, 8:02:18  time: 1.0062  data_time: 0.0166  memory: 68881  top1_acc: 0.6250  top5_acc: 1.0000  loss_cls: 2.0467  loss: 2.0467
2022/08/06 03:06:54 - mmengine - INFO - Epoch(train) [1][2500/3757]  lr: 3.3948e-05  eta: 1 day, 7:57:50  time: 0.9998  data_time: 0.0166  memory: 68881  top1_acc: 0.5000  top5_acc: 0.8750  loss_cls: 2.3655  loss: 2.3655
2022/08/06 03:08:34 - mmengine - INFO - Epoch(train) [1][2600/3757]  lr: 3.4907e-05  eta: 1 day, 7:53:08  time: 0.9989  data_time: 0.0166  memory: 68881  top1_acc: 0.5000  top5_acc: 0.8750  loss_cls: 1.9385  loss: 1.9385
2022/08/06 03:10:14 - mmengine - INFO - Epoch(train) [1][2700/3757]  lr: 3.5865e-05  eta: 1 day, 7:48:45  time: 1.0056  data_time: 0.0173  memory: 68881  top1_acc: 0.5000  top5_acc: 0.6250  loss_cls: 2.0930  loss: 2.0930
2022/08/06 03:11:55 - mmengine - INFO - Epoch(train) [1][2800/3757]  lr: 3.6823e-05  eta: 1 day, 7:44:34  time: 1.0056  data_time: 0.0165  memory: 68881  top1_acc: 0.2500  top5_acc: 0.3750  loss_cls: 2.1513  loss: 2.1513
2022/08/06 03:13:35 - mmengine - INFO - Epoch(train) [1][2900/3757]  lr: 3.7782e-05  eta: 1 day, 7:40:43  time: 0.9978  data_time: 0.0165  memory: 68881  top1_acc: 0.5000  top5_acc: 0.7500  loss_cls: 2.3779  loss: 2.3779
2022/08/06 03:15:16 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 03:15:16 - mmengine - INFO - Epoch(train) [1][3000/3757]  lr: 3.8740e-05  eta: 1 day, 7:37:01  time: 1.0090  data_time: 0.0165  memory: 68881  top1_acc: 0.5000  top5_acc: 0.5000  loss_cls: 2.4052  loss: 2.4052
2022/08/06 03:16:57 - mmengine - INFO - Epoch(train) [1][3100/3757]  lr: 3.9698e-05  eta: 1 day, 7:33:33  time: 1.0101  data_time: 0.0175  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 2.0537  loss: 2.0537
2022/08/06 03:18:38 - mmengine - INFO - Epoch(train) [1][3200/3757]  lr: 4.0656e-05  eta: 1 day, 7:30:21  time: 1.0196  data_time: 0.0185  memory: 68881  top1_acc: 0.6250  top5_acc: 0.7500  loss_cls: 1.8730  loss: 1.8730
2022/08/06 03:20:18 - mmengine - INFO - Epoch(train) [1][3300/3757]  lr: 4.1615e-05  eta: 1 day, 7:26:59  time: 0.9996  data_time: 0.0163  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.8340  loss: 1.8340
2022/08/06 03:21:59 - mmengine - INFO - Epoch(train) [1][3400/3757]  lr: 4.2573e-05  eta: 1 day, 7:23:50  time: 1.0059  data_time: 0.0177  memory: 68881  top1_acc: 0.5000  top5_acc: 0.6250  loss_cls: 2.1125  loss: 2.1125
2022/08/06 03:23:40 - mmengine - INFO - Epoch(train) [1][3500/3757]  lr: 4.3531e-05  eta: 1 day, 7:20:57  time: 1.0069  data_time: 0.0182  memory: 68881  top1_acc: 0.6250  top5_acc: 1.0000  loss_cls: 1.7125  loss: 1.7125
2022/08/06 03:25:21 - mmengine - INFO - Epoch(train) [1][3600/3757]  lr: 4.4490e-05  eta: 1 day, 7:17:48  time: 1.0014  data_time: 0.0171  memory: 68881  top1_acc: 0.5000  top5_acc: 1.0000  loss_cls: 1.9474  loss: 1.9474
2022/08/06 03:27:01 - mmengine - INFO - Epoch(train) [1][3700/3757]  lr: 4.5448e-05  eta: 1 day, 7:14:46  time: 1.0167  data_time: 0.0190  memory: 68881  top1_acc: 0.3750  top5_acc: 0.6250  loss_cls: 2.2580  loss: 2.2580
2022/08/06 03:27:58 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 03:27:58 - mmengine - INFO - Epoch(train) [1][3757/3757]  lr: 4.5994e-05  eta: 1 day, 7:13:34  time: 0.9969  data_time: 0.0175  memory: 68881  top1_acc: 0.4286  top5_acc: 0.7143  loss_cls: 2.0627  loss: 2.0627
2022/08/06 03:29:42 - mmengine - INFO - Epoch(train) [2][100/3757]  lr: 4.6824e-05  eta: 1 day, 7:03:20  time: 1.0041  data_time: 0.0180  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.8811  loss: 1.8811
2022/08/06 03:31:22 - mmengine - INFO - Epoch(train) [2][200/3757]  lr: 4.7780e-05  eta: 1 day, 7:00:25  time: 0.9997  data_time: 0.0172  memory: 68881  top1_acc: 0.6250  top5_acc: 0.6250  loss_cls: 1.8336  loss: 1.8336
2022/08/06 03:32:05 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 03:33:02 - mmengine - INFO - Epoch(train) [2][300/3757]  lr: 4.8735e-05  eta: 1 day, 6:57:38  time: 1.0025  data_time: 0.0175  memory: 68881  top1_acc: 0.5000  top5_acc: 0.8750  loss_cls: 1.7692  loss: 1.7692
2022/08/06 03:34:42 - mmengine - INFO - Epoch(train) [2][400/3757]  lr: 4.9691e-05  eta: 1 day, 6:54:56  time: 1.0031  data_time: 0.0170  memory: 68881  top1_acc: 0.6250  top5_acc: 0.7500  loss_cls: 2.0601  loss: 2.0601
2022/08/06 03:36:23 - mmengine - INFO - Epoch(train) [2][500/3757]  lr: 5.0647e-05  eta: 1 day, 6:52:18  time: 1.0026  data_time: 0.0168  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 1.8528  loss: 1.8528
2022/08/06 03:38:04 - mmengine - INFO - Epoch(train) [2][600/3757]  lr: 5.1602e-05  eta: 1 day, 6:50:05  time: 1.0052  data_time: 0.0178  memory: 68881  top1_acc: 0.3750  top5_acc: 0.3750  loss_cls: 2.0687  loss: 2.0687
2022/08/06 03:39:45 - mmengine - INFO - Epoch(train) [2][700/3757]  lr: 5.2558e-05  eta: 1 day, 6:47:41  time: 1.0033  data_time: 0.0181  memory: 68881  top1_acc: 0.3750  top5_acc: 0.7500  loss_cls: 2.1310  loss: 2.1310
2022/08/06 03:41:25 - mmengine - INFO - Epoch(train) [2][800/3757]  lr: 5.3514e-05  eta: 1 day, 6:45:14  time: 1.0001  data_time: 0.0166  memory: 68881  top1_acc: 0.5000  top5_acc: 1.0000  loss_cls: 1.9178  loss: 1.9178
2022/08/06 03:43:06 - mmengine - INFO - Epoch(train) [2][900/3757]  lr: 5.4469e-05  eta: 1 day, 6:42:54  time: 1.0031  data_time: 0.0170  memory: 68881  top1_acc: 0.6250  top5_acc: 0.7500  loss_cls: 2.2912  loss: 2.2912
2022/08/06 03:44:46 - mmengine - INFO - Epoch(train) [2][1000/3757]  lr: 5.5425e-05  eta: 1 day, 6:40:26  time: 1.0046  data_time: 0.0172  memory: 68881  top1_acc: 0.5000  top5_acc: 0.8750  loss_cls: 1.8062  loss: 1.8062
2022/08/06 03:46:27 - mmengine - INFO - Epoch(train) [2][1100/3757]  lr: 5.6381e-05  eta: 1 day, 6:38:08  time: 1.0096  data_time: 0.0176  memory: 68881  top1_acc: 0.3750  top5_acc: 0.7500  loss_cls: 1.8679  loss: 1.8679
2022/08/06 03:48:08 - mmengine - INFO - Epoch(train) [2][1200/3757]  lr: 5.7337e-05  eta: 1 day, 6:35:53  time: 1.0136  data_time: 0.0185  memory: 68881  top1_acc: 0.6250  top5_acc: 1.0000  loss_cls: 1.7781  loss: 1.7781
2022/08/06 03:48:51 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 03:49:49 - mmengine - INFO - Epoch(train) [2][1300/3757]  lr: 5.8292e-05  eta: 1 day, 6:33:41  time: 1.0189  data_time: 0.0185  memory: 68881  top1_acc: 0.5000  top5_acc: 0.7500  loss_cls: 1.8723  loss: 1.8723
2022/08/06 03:51:30 - mmengine - INFO - Epoch(train) [2][1400/3757]  lr: 5.9248e-05  eta: 1 day, 6:31:39  time: 1.0066  data_time: 0.0182  memory: 68881  top1_acc: 0.6250  top5_acc: 1.0000  loss_cls: 1.9346  loss: 1.9346
2022/08/06 03:53:11 - mmengine - INFO - Epoch(train) [2][1500/3757]  lr: 6.0204e-05  eta: 1 day, 6:29:36  time: 1.0034  data_time: 0.0187  memory: 68881  top1_acc: 0.5000  top5_acc: 0.8750  loss_cls: 1.9142  loss: 1.9142
2022/08/06 03:54:52 - mmengine - INFO - Epoch(train) [2][1600/3757]  lr: 6.1159e-05  eta: 1 day, 6:27:22  time: 1.0029  data_time: 0.0181  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.8101  loss: 1.8101
2022/08/06 03:56:32 - mmengine - INFO - Epoch(train) [2][1700/3757]  lr: 6.2115e-05  eta: 1 day, 6:25:10  time: 1.0023  data_time: 0.0175  memory: 68881  top1_acc: 0.3750  top5_acc: 0.7500  loss_cls: 1.6043  loss: 1.6043
2022/08/06 03:58:12 - mmengine - INFO - Epoch(train) [2][1800/3757]  lr: 6.3071e-05  eta: 1 day, 6:22:51  time: 0.9999  data_time: 0.0176  memory: 68881  top1_acc: 0.5000  top5_acc: 0.7500  loss_cls: 1.9680  loss: 1.9680
2022/08/06 03:59:53 - mmengine - INFO - Epoch(train) [2][1900/3757]  lr: 6.4026e-05  eta: 1 day, 6:20:46  time: 1.0260  data_time: 0.0168  memory: 68881  top1_acc: 0.5000  top5_acc: 0.6250  loss_cls: 1.9575  loss: 1.9575
2022/08/06 04:01:34 - mmengine - INFO - Epoch(train) [2][2000/3757]  lr: 6.4982e-05  eta: 1 day, 6:18:37  time: 1.0033  data_time: 0.0175  memory: 68881  top1_acc: 0.5000  top5_acc: 0.8750  loss_cls: 1.9611  loss: 1.9611
2022/08/06 04:03:14 - mmengine - INFO - Epoch(train) [2][2100/3757]  lr: 6.5938e-05  eta: 1 day, 6:16:27  time: 1.0002  data_time: 0.0170  memory: 68881  top1_acc: 0.5000  top5_acc: 0.8750  loss_cls: 1.6893  loss: 1.6893
2022/08/06 04:04:55 - mmengine - INFO - Epoch(train) [2][2200/3757]  lr: 6.6893e-05  eta: 1 day, 6:14:21  time: 1.0094  data_time: 0.0180  memory: 68881  top1_acc: 0.3750  top5_acc: 1.0000  loss_cls: 1.8047  loss: 1.8047
2022/08/06 04:05:38 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 04:06:36 - mmengine - INFO - Epoch(train) [2][2300/3757]  lr: 6.7849e-05  eta: 1 day, 6:12:14  time: 1.0080  data_time: 0.0178  memory: 68881  top1_acc: 0.6250  top5_acc: 0.7500  loss_cls: 2.0397  loss: 2.0397
2022/08/06 04:08:17 - mmengine - INFO - Epoch(train) [2][2400/3757]  lr: 6.8805e-05  eta: 1 day, 6:10:14  time: 1.0018  data_time: 0.0181  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 2.0993  loss: 2.0993
2022/08/06 04:09:57 - mmengine - INFO - Epoch(train) [2][2500/3757]  lr: 6.9760e-05  eta: 1 day, 6:08:00  time: 0.9982  data_time: 0.0176  memory: 68881  top1_acc: 0.5000  top5_acc: 0.8750  loss_cls: 2.1717  loss: 2.1717
2022/08/06 04:11:37 - mmengine - INFO - Epoch(train) [2][2600/3757]  lr: 7.0716e-05  eta: 1 day, 6:05:56  time: 1.0093  data_time: 0.0172  memory: 68881  top1_acc: 0.6250  top5_acc: 0.7500  loss_cls: 1.7099  loss: 1.7099
2022/08/06 04:13:19 - mmengine - INFO - Epoch(train) [2][2700/3757]  lr: 7.1672e-05  eta: 1 day, 6:04:15  time: 1.0057  data_time: 0.0182  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 1.8569  loss: 1.8569
2022/08/06 04:15:00 - mmengine - INFO - Epoch(train) [2][2800/3757]  lr: 7.2628e-05  eta: 1 day, 6:02:20  time: 1.0057  data_time: 0.0178  memory: 68881  top1_acc: 0.6250  top5_acc: 1.0000  loss_cls: 1.8105  loss: 1.8105
2022/08/06 04:16:41 - mmengine - INFO - Epoch(train) [2][2900/3757]  lr: 7.3583e-05  eta: 1 day, 6:00:25  time: 0.9995  data_time: 0.0176  memory: 68881  top1_acc: 0.5000  top5_acc: 0.5000  loss_cls: 1.9765  loss: 1.9765
2022/08/06 04:18:23 - mmengine - INFO - Epoch(train) [2][3000/3757]  lr: 7.4539e-05  eta: 1 day, 5:58:40  time: 1.0151  data_time: 0.0176  memory: 68881  top1_acc: 0.6250  top5_acc: 0.7500  loss_cls: 2.2751  loss: 2.2751
2022/08/06 04:20:04 - mmengine - INFO - Epoch(train) [2][3100/3757]  lr: 7.5495e-05  eta: 1 day, 5:56:49  time: 1.0156  data_time: 0.0178  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 2.3337  loss: 2.3337
2022/08/06 04:21:46 - mmengine - INFO - Epoch(train) [2][3200/3757]  lr: 7.6450e-05  eta: 1 day, 5:55:03  time: 1.0183  data_time: 0.0191  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 1.8945  loss: 1.8945
2022/08/06 04:22:29 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 04:23:26 - mmengine - INFO - Epoch(train) [2][3300/3757]  lr: 7.7406e-05  eta: 1 day, 5:53:02  time: 1.0010  data_time: 0.0174  memory: 68881  top1_acc: 0.5000  top5_acc: 1.0000  loss_cls: 2.4138  loss: 2.4138
2022/08/06 04:25:07 - mmengine - INFO - Epoch(train) [2][3400/3757]  lr: 7.8362e-05  eta: 1 day, 5:51:08  time: 1.0105  data_time: 0.0176  memory: 68881  top1_acc: 0.5000  top5_acc: 0.8750  loss_cls: 1.7322  loss: 1.7322
2022/08/06 04:26:48 - mmengine - INFO - Epoch(train) [2][3500/3757]  lr: 7.9317e-05  eta: 1 day, 5:49:17  time: 1.0072  data_time: 0.0181  memory: 68881  top1_acc: 0.6250  top5_acc: 0.7500  loss_cls: 1.9951  loss: 1.9951
2022/08/06 04:28:29 - mmengine - INFO - Epoch(train) [2][3600/3757]  lr: 8.0273e-05  eta: 1 day, 5:47:22  time: 1.0012  data_time: 0.0181  memory: 68881  top1_acc: 0.6250  top5_acc: 0.6250  loss_cls: 1.6853  loss: 1.6853
2022/08/06 04:30:10 - mmengine - INFO - Epoch(train) [2][3700/3757]  lr: 8.1229e-05  eta: 1 day, 5:45:18  time: 1.0012  data_time: 0.0178  memory: 68881  top1_acc: 0.5000  top5_acc: 0.6250  loss_cls: 1.9133  loss: 1.9133
2022/08/06 04:31:07 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 04:31:07 - mmengine - INFO - Epoch(train) [2][3757/3757]  lr: 8.1773e-05  eta: 1 day, 5:44:34  time: 1.0105  data_time: 0.0174  memory: 68881  top1_acc: 0.5714  top5_acc: 0.8571  loss_cls: 1.9406  loss: 1.9406
2022/08/06 04:32:50 - mmengine - INFO - Epoch(train) [3][100/3757]  lr: 8.2050e-05  eta: 1 day, 5:38:48  time: 1.0066  data_time: 0.0187  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 1.5344  loss: 1.5344
2022/08/06 04:34:31 - mmengine - INFO - Epoch(train) [3][200/3757]  lr: 8.2998e-05  eta: 1 day, 5:37:00  time: 1.0075  data_time: 0.0179  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.7358  loss: 1.7358
2022/08/06 04:36:14 - mmengine - INFO - Epoch(train) [3][300/3757]  lr: 8.3946e-05  eta: 1 day, 5:35:31  time: 1.0178  data_time: 0.0169  memory: 68881  top1_acc: 0.5000  top5_acc: 0.8750  loss_cls: 1.6834  loss: 1.6834
2022/08/06 04:37:55 - mmengine - INFO - Epoch(train) [3][400/3757]  lr: 8.4894e-05  eta: 1 day, 5:33:53  time: 1.0031  data_time: 0.0193  memory: 68881  top1_acc: 0.6250  top5_acc: 0.7500  loss_cls: 1.8200  loss: 1.8200
2022/08/06 04:39:22 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 04:39:36 - mmengine - INFO - Epoch(train) [3][500/3757]  lr: 8.5841e-05  eta: 1 day, 5:31:58  time: 1.0036  data_time: 0.0175  memory: 68881  top1_acc: 0.3750  top5_acc: 0.6250  loss_cls: 1.9140  loss: 1.9140
2022/08/06 04:41:17 - mmengine - INFO - Epoch(train) [3][600/3757]  lr: 8.6789e-05  eta: 1 day, 5:30:07  time: 1.0033  data_time: 0.0186  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 1.4405  loss: 1.4405
2022/08/06 04:42:57 - mmengine - INFO - Epoch(train) [3][700/3757]  lr: 8.7737e-05  eta: 1 day, 5:28:11  time: 0.9981  data_time: 0.0149  memory: 68881  top1_acc: 0.6250  top5_acc: 1.0000  loss_cls: 1.8870  loss: 1.8870
2022/08/06 04:44:38 - mmengine - INFO - Epoch(train) [3][800/3757]  lr: 8.8685e-05  eta: 1 day, 5:26:17  time: 1.0103  data_time: 0.0169  memory: 68881  top1_acc: 0.7500  top5_acc: 0.7500  loss_cls: 1.5465  loss: 1.5465
2022/08/06 04:46:18 - mmengine - INFO - Epoch(train) [3][900/3757]  lr: 8.9633e-05  eta: 1 day, 5:24:19  time: 0.9964  data_time: 0.0145  memory: 68881  top1_acc: 0.6250  top5_acc: 0.6250  loss_cls: 1.7478  loss: 1.7478
2022/08/06 04:47:58 - mmengine - INFO - Epoch(train) [3][1000/3757]  lr: 9.0581e-05  eta: 1 day, 5:22:22  time: 1.0032  data_time: 0.0164  memory: 68881  top1_acc: 0.5000  top5_acc: 0.6250  loss_cls: 1.8614  loss: 1.8614
2022/08/06 04:49:38 - mmengine - INFO - Epoch(train) [3][1100/3757]  lr: 9.1528e-05  eta: 1 day, 5:20:25  time: 1.0012  data_time: 0.0159  memory: 68881  top1_acc: 0.3750  top5_acc: 0.7500  loss_cls: 2.1732  loss: 2.1732
2022/08/06 04:51:19 - mmengine - INFO - Epoch(train) [3][1200/3757]  lr: 9.2476e-05  eta: 1 day, 5:18:28  time: 1.0042  data_time: 0.0166  memory: 68881  top1_acc: 0.7500  top5_acc: 0.7500  loss_cls: 2.1445  loss: 2.1445
2022/08/06 04:52:59 - mmengine - INFO - Epoch(train) [3][1300/3757]  lr: 9.3424e-05  eta: 1 day, 5:16:33  time: 1.0035  data_time: 0.0173  memory: 68881  top1_acc: 0.5000  top5_acc: 0.8750  loss_cls: 1.8873  loss: 1.8873
2022/08/06 04:54:40 - mmengine - INFO - Epoch(train) [3][1400/3757]  lr: 9.4372e-05  eta: 1 day, 5:14:53  time: 1.0021  data_time: 0.0175  memory: 68881  top1_acc: 0.6250  top5_acc: 0.6250  loss_cls: 1.9810  loss: 1.9810
2022/08/06 04:56:07 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 04:56:22 - mmengine - INFO - Epoch(train) [3][1500/3757]  lr: 9.5320e-05  eta: 1 day, 5:13:07  time: 1.0118  data_time: 0.0169  memory: 68881  top1_acc: 0.2500  top5_acc: 0.7500  loss_cls: 2.0190  loss: 2.0190
2022/08/06 04:58:02 - mmengine - INFO - Epoch(train) [3][1600/3757]  lr: 9.6268e-05  eta: 1 day, 5:11:11  time: 1.0013  data_time: 0.0172  memory: 68881  top1_acc: 0.5000  top5_acc: 0.7500  loss_cls: 1.9081  loss: 1.9081
2022/08/06 04:59:42 - mmengine - INFO - Epoch(train) [3][1700/3757]  lr: 9.7215e-05  eta: 1 day, 5:09:21  time: 1.0177  data_time: 0.0173  memory: 68881  top1_acc: 0.5000  top5_acc: 0.8750  loss_cls: 1.6622  loss: 1.6622
2022/08/06 05:01:23 - mmengine - INFO - Epoch(train) [3][1800/3757]  lr: 9.8163e-05  eta: 1 day, 5:07:27  time: 1.0020  data_time: 0.0175  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.8213  loss: 1.8213
2022/08/06 05:03:03 - mmengine - INFO - Epoch(train) [3][1900/3757]  lr: 9.8912e-05  eta: 1 day, 5:05:37  time: 1.0006  data_time: 0.0173  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.7412  loss: 1.7412
2022/08/06 05:04:43 - mmengine - INFO - Epoch(train) [3][2000/3757]  lr: 9.8912e-05  eta: 1 day, 5:03:42  time: 1.0023  data_time: 0.0167  memory: 68881  top1_acc: 0.6250  top5_acc: 0.7500  loss_cls: 1.8147  loss: 1.8147
2022/08/06 05:06:24 - mmengine - INFO - Epoch(train) [3][2100/3757]  lr: 9.8912e-05  eta: 1 day, 5:01:55  time: 1.0062  data_time: 0.0169  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 2.0096  loss: 2.0096
2022/08/06 05:08:06 - mmengine - INFO - Epoch(train) [3][2200/3757]  lr: 9.8912e-05  eta: 1 day, 5:00:21  time: 1.0042  data_time: 0.0181  memory: 68881  top1_acc: 0.7500  top5_acc: 0.7500  loss_cls: 1.5359  loss: 1.5359
2022/08/06 05:09:47 - mmengine - INFO - Epoch(train) [3][2300/3757]  lr: 9.8912e-05  eta: 1 day, 4:58:31  time: 1.0013  data_time: 0.0172  memory: 68881  top1_acc: 0.5000  top5_acc: 0.8750  loss_cls: 1.8207  loss: 1.8207
2022/08/06 05:11:28 - mmengine - INFO - Epoch(train) [3][2400/3757]  lr: 9.8912e-05  eta: 1 day, 4:56:43  time: 1.0037  data_time: 0.0182  memory: 68881  top1_acc: 0.3750  top5_acc: 0.8750  loss_cls: 1.8577  loss: 1.8577
2022/08/06 05:12:55 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 05:13:09 - mmengine - INFO - Epoch(train) [3][2500/3757]  lr: 9.8912e-05  eta: 1 day, 4:54:59  time: 1.0028  data_time: 0.0185  memory: 68881  top1_acc: 0.5000  top5_acc: 0.8750  loss_cls: 1.8509  loss: 1.8509
2022/08/06 05:14:50 - mmengine - INFO - Epoch(train) [3][2600/3757]  lr: 9.8912e-05  eta: 1 day, 4:53:15  time: 1.0092  data_time: 0.0171  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 1.6406  loss: 1.6406
2022/08/06 05:16:31 - mmengine - INFO - Epoch(train) [3][2700/3757]  lr: 9.8912e-05  eta: 1 day, 4:51:32  time: 1.0022  data_time: 0.0178  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 1.7201  loss: 1.7201
2022/08/06 05:18:12 - mmengine - INFO - Epoch(train) [3][2800/3757]  lr: 9.8912e-05  eta: 1 day, 4:49:49  time: 1.0032  data_time: 0.0177  memory: 68881  top1_acc: 0.5000  top5_acc: 0.6250  loss_cls: 1.9452  loss: 1.9452
2022/08/06 05:19:53 - mmengine - INFO - Epoch(train) [3][2900/3757]  lr: 9.8912e-05  eta: 1 day, 4:48:05  time: 1.0041  data_time: 0.0184  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.9680  loss: 1.9680
2022/08/06 05:21:34 - mmengine - INFO - Epoch(train) [3][3000/3757]  lr: 9.8912e-05  eta: 1 day, 4:46:16  time: 1.0030  data_time: 0.0171  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.8242  loss: 1.8242
2022/08/06 05:23:15 - mmengine - INFO - Epoch(train) [3][3100/3757]  lr: 9.8912e-05  eta: 1 day, 4:44:28  time: 1.0022  data_time: 0.0166  memory: 68881  top1_acc: 0.1250  top5_acc: 0.5000  loss_cls: 1.9668  loss: 1.9668
2022/08/06 05:24:55 - mmengine - INFO - Epoch(train) [3][3200/3757]  lr: 9.8912e-05  eta: 1 day, 4:42:38  time: 1.0058  data_time: 0.0171  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 1.7223  loss: 1.7223
2022/08/06 05:26:36 - mmengine - INFO - Epoch(train) [3][3300/3757]  lr: 9.8912e-05  eta: 1 day, 4:40:54  time: 1.0012  data_time: 0.0172  memory: 68881  top1_acc: 0.3750  top5_acc: 0.7500  loss_cls: 1.8288  loss: 1.8288
2022/08/06 05:28:17 - mmengine - INFO - Epoch(train) [3][3400/3757]  lr: 9.8912e-05  eta: 1 day, 4:39:10  time: 1.0010  data_time: 0.0174  memory: 68881  top1_acc: 0.3750  top5_acc: 0.6250  loss_cls: 2.0459  loss: 2.0459
2022/08/06 05:29:44 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 05:29:58 - mmengine - INFO - Epoch(train) [3][3500/3757]  lr: 9.8912e-05  eta: 1 day, 4:37:26  time: 1.0160  data_time: 0.0179  memory: 68881  top1_acc: 0.5000  top5_acc: 0.6250  loss_cls: 1.8764  loss: 1.8764
2022/08/06 05:31:38 - mmengine - INFO - Epoch(train) [3][3600/3757]  lr: 9.8912e-05  eta: 1 day, 4:35:36  time: 1.0034  data_time: 0.0169  memory: 68881  top1_acc: 0.3750  top5_acc: 0.8750  loss_cls: 1.9867  loss: 1.9867
2022/08/06 05:33:20 - mmengine - INFO - Epoch(train) [3][3700/3757]  lr: 9.8912e-05  eta: 1 day, 4:33:53  time: 1.0024  data_time: 0.0170  memory: 68881  top1_acc: 0.5000  top5_acc: 0.7500  loss_cls: 1.7180  loss: 1.7180
2022/08/06 05:34:17 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 05:34:17 - mmengine - INFO - Epoch(train) [3][3757/3757]  lr: 9.8912e-05  eta: 1 day, 4:33:09  time: 1.0193  data_time: 0.0179  memory: 68881  top1_acc: 0.4286  top5_acc: 0.5714  loss_cls: 1.8081  loss: 1.8081
2022/08/06 05:34:17 - mmengine - INFO - Saving checkpoint at 3 epochs
2022/08/06 05:36:30 - mmengine - INFO - Epoch(val) [3][100/310]    eta: 0:01:29  time: 0.4239  data_time: 0.0097  memory: 14218  
2022/08/06 05:37:11 - mmengine - INFO - Epoch(val) [3][200/310]    eta: 0:00:45  time: 0.4147  data_time: 0.0100  memory: 14218  
2022/08/06 05:37:52 - mmengine - INFO - Epoch(val) [3][300/310]    eta: 0:00:04  time: 0.4068  data_time: 0.0098  memory: 14218  
2022/08/06 05:37:57 - mmengine - INFO - Epoch(val) [3][310/310]  acc/top1: 0.6789  acc/top5: 0.8845  acc/mean1: 0.6787
2022/08/06 05:38:03 - mmengine - INFO - The best checkpoint with 0.6789 acc/top1 at 4 epoch is saved to best_acc/top1_epoch_4.pth.
2022/08/06 05:39:45 - mmengine - INFO - Epoch(train) [4][100/3757]  lr: 9.7558e-05  eta: 1 day, 4:28:37  time: 1.0133  data_time: 0.0159  memory: 68881  top1_acc: 0.2500  top5_acc: 0.3750  loss_cls: 1.8886  loss: 1.8886
2022/08/06 05:41:26 - mmengine - INFO - Epoch(train) [4][200/3757]  lr: 9.7558e-05  eta: 1 day, 4:26:52  time: 1.0041  data_time: 0.0174  memory: 68881  top1_acc: 0.7500  top5_acc: 0.7500  loss_cls: 1.6328  loss: 1.6328
2022/08/06 05:43:06 - mmengine - INFO - Epoch(train) [4][300/3757]  lr: 9.7558e-05  eta: 1 day, 4:25:04  time: 0.9987  data_time: 0.0164  memory: 68881  top1_acc: 0.7500  top5_acc: 0.7500  loss_cls: 1.8401  loss: 1.8401
2022/08/06 05:44:46 - mmengine - INFO - Epoch(train) [4][400/3757]  lr: 9.7558e-05  eta: 1 day, 4:23:16  time: 1.0085  data_time: 0.0161  memory: 68881  top1_acc: 0.5000  top5_acc: 0.7500  loss_cls: 1.6299  loss: 1.6299
2022/08/06 05:46:27 - mmengine - INFO - Epoch(train) [4][500/3757]  lr: 9.7558e-05  eta: 1 day, 4:21:34  time: 1.0016  data_time: 0.0169  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 2.0089  loss: 2.0089
2022/08/06 05:48:08 - mmengine - INFO - Epoch(train) [4][600/3757]  lr: 9.7558e-05  eta: 1 day, 4:19:46  time: 1.0067  data_time: 0.0167  memory: 68881  top1_acc: 0.3750  top5_acc: 0.6250  loss_cls: 1.8225  loss: 1.8225
2022/08/06 05:49:48 - mmengine - INFO - Epoch(train) [4][700/3757]  lr: 9.7558e-05  eta: 1 day, 4:17:59  time: 1.0008  data_time: 0.0162  memory: 68881  top1_acc: 0.3750  top5_acc: 0.7500  loss_cls: 1.9531  loss: 1.9531
2022/08/06 05:50:17 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 05:51:28 - mmengine - INFO - Epoch(train) [4][800/3757]  lr: 9.7558e-05  eta: 1 day, 4:16:10  time: 1.0006  data_time: 0.0158  memory: 68881  top1_acc: 0.7500  top5_acc: 0.7500  loss_cls: 1.7544  loss: 1.7544
2022/08/06 05:53:08 - mmengine - INFO - Epoch(train) [4][900/3757]  lr: 9.7558e-05  eta: 1 day, 4:14:20  time: 1.0002  data_time: 0.0172  memory: 68881  top1_acc: 0.6250  top5_acc: 0.7500  loss_cls: 1.9080  loss: 1.9080
2022/08/06 05:54:49 - mmengine - INFO - Epoch(train) [4][1000/3757]  lr: 9.7558e-05  eta: 1 day, 4:12:33  time: 1.0014  data_time: 0.0172  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 1.6048  loss: 1.6048
2022/08/06 05:56:29 - mmengine - INFO - Epoch(train) [4][1100/3757]  lr: 9.7558e-05  eta: 1 day, 4:10:43  time: 0.9988  data_time: 0.0157  memory: 68881  top1_acc: 0.5000  top5_acc: 0.6250  loss_cls: 1.6770  loss: 1.6770
2022/08/06 05:58:09 - mmengine - INFO - Epoch(train) [4][1200/3757]  lr: 9.7558e-05  eta: 1 day, 4:08:54  time: 1.0002  data_time: 0.0167  memory: 68881  top1_acc: 0.5000  top5_acc: 0.7500  loss_cls: 1.8792  loss: 1.8792
2022/08/06 05:59:49 - mmengine - INFO - Epoch(train) [4][1300/3757]  lr: 9.7558e-05  eta: 1 day, 4:07:06  time: 1.0037  data_time: 0.0173  memory: 68881  top1_acc: 0.5000  top5_acc: 0.6250  loss_cls: 1.8737  loss: 1.8737
2022/08/06 06:01:31 - mmengine - INFO - Epoch(train) [4][1400/3757]  lr: 9.7558e-05  eta: 1 day, 4:05:33  time: 1.0012  data_time: 0.0173  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.7519  loss: 1.7519
2022/08/06 06:03:12 - mmengine - INFO - Epoch(train) [4][1500/3757]  lr: 9.7558e-05  eta: 1 day, 4:03:49  time: 1.0178  data_time: 0.0172  memory: 68881  top1_acc: 0.5000  top5_acc: 0.5000  loss_cls: 2.0928  loss: 2.0928
2022/08/06 06:04:53 - mmengine - INFO - Epoch(train) [4][1600/3757]  lr: 9.7558e-05  eta: 1 day, 4:02:04  time: 1.0007  data_time: 0.0177  memory: 68881  top1_acc: 0.3750  top5_acc: 0.5000  loss_cls: 1.8968  loss: 1.8968
2022/08/06 06:06:33 - mmengine - INFO - Epoch(train) [4][1700/3757]  lr: 9.7558e-05  eta: 1 day, 4:00:16  time: 0.9989  data_time: 0.0166  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.8443  loss: 1.8443
2022/08/06 06:07:02 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 06:08:13 - mmengine - INFO - Epoch(train) [4][1800/3757]  lr: 9.7558e-05  eta: 1 day, 3:58:28  time: 1.0037  data_time: 0.0170  memory: 68881  top1_acc: 0.5000  top5_acc: 0.7500  loss_cls: 1.6760  loss: 1.6760
2022/08/06 06:09:53 - mmengine - INFO - Epoch(train) [4][1900/3757]  lr: 9.7558e-05  eta: 1 day, 3:56:41  time: 1.0019  data_time: 0.0171  memory: 68881  top1_acc: 0.6250  top5_acc: 0.6250  loss_cls: 1.7761  loss: 1.7761
2022/08/06 06:11:34 - mmengine - INFO - Epoch(train) [4][2000/3757]  lr: 9.7558e-05  eta: 1 day, 3:54:58  time: 1.0115  data_time: 0.0178  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.7298  loss: 1.7298
2022/08/06 06:13:14 - mmengine - INFO - Epoch(train) [4][2100/3757]  lr: 9.7558e-05  eta: 1 day, 3:53:11  time: 1.0031  data_time: 0.0161  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 1.6850  loss: 1.6850
2022/08/06 06:14:54 - mmengine - INFO - Epoch(train) [4][2200/3757]  lr: 9.7558e-05  eta: 1 day, 3:51:23  time: 0.9996  data_time: 0.0168  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 1.6097  loss: 1.6097
2022/08/06 06:16:35 - mmengine - INFO - Epoch(train) [4][2300/3757]  lr: 9.7558e-05  eta: 1 day, 3:49:36  time: 1.0009  data_time: 0.0179  memory: 68881  top1_acc: 0.3750  top5_acc: 0.7500  loss_cls: 1.3817  loss: 1.3817
2022/08/06 06:18:15 - mmengine - INFO - Epoch(train) [4][2400/3757]  lr: 9.7558e-05  eta: 1 day, 3:47:49  time: 1.0002  data_time: 0.0176  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.6287  loss: 1.6287
2022/08/06 06:19:55 - mmengine - INFO - Epoch(train) [4][2500/3757]  lr: 9.7558e-05  eta: 1 day, 3:46:01  time: 1.0029  data_time: 0.0177  memory: 68881  top1_acc: 0.2500  top5_acc: 0.6250  loss_cls: 1.6973  loss: 1.6973
2022/08/06 06:21:35 - mmengine - INFO - Epoch(train) [4][2600/3757]  lr: 9.7558e-05  eta: 1 day, 3:44:14  time: 1.0017  data_time: 0.0178  memory: 68881  top1_acc: 0.6250  top5_acc: 1.0000  loss_cls: 1.9920  loss: 1.9920
2022/08/06 06:23:15 - mmengine - INFO - Epoch(train) [4][2700/3757]  lr: 9.7558e-05  eta: 1 day, 3:42:26  time: 0.9997  data_time: 0.0171  memory: 68881  top1_acc: 0.5000  top5_acc: 1.0000  loss_cls: 1.4827  loss: 1.4827
2022/08/06 06:23:45 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 06:24:56 - mmengine - INFO - Epoch(train) [4][2800/3757]  lr: 9.7558e-05  eta: 1 day, 3:40:46  time: 0.9996  data_time: 0.0157  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.9641  loss: 1.9641
2022/08/06 06:26:37 - mmengine - INFO - Epoch(train) [4][2900/3757]  lr: 9.7558e-05  eta: 1 day, 3:38:59  time: 0.9992  data_time: 0.0168  memory: 68881  top1_acc: 0.5000  top5_acc: 0.7500  loss_cls: 1.6790  loss: 1.6790
2022/08/06 06:28:17 - mmengine - INFO - Epoch(train) [4][3000/3757]  lr: 9.7558e-05  eta: 1 day, 3:37:12  time: 0.9992  data_time: 0.0162  memory: 68881  top1_acc: 0.5000  top5_acc: 0.6250  loss_cls: 1.6783  loss: 1.6783
2022/08/06 06:29:57 - mmengine - INFO - Epoch(train) [4][3100/3757]  lr: 9.7558e-05  eta: 1 day, 3:35:26  time: 0.9996  data_time: 0.0178  memory: 68881  top1_acc: 0.3750  top5_acc: 0.6250  loss_cls: 2.1835  loss: 2.1835
2022/08/06 06:31:37 - mmengine - INFO - Epoch(train) [4][3200/3757]  lr: 9.7558e-05  eta: 1 day, 3:33:39  time: 1.0064  data_time: 0.0167  memory: 68881  top1_acc: 0.5000  top5_acc: 0.6250  loss_cls: 1.4625  loss: 1.4625
2022/08/06 06:33:17 - mmengine - INFO - Epoch(train) [4][3300/3757]  lr: 9.7558e-05  eta: 1 day, 3:31:52  time: 0.9993  data_time: 0.0167  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.8883  loss: 1.8883
2022/08/06 06:34:58 - mmengine - INFO - Epoch(train) [4][3400/3757]  lr: 9.7558e-05  eta: 1 day, 3:30:07  time: 1.0066  data_time: 0.0177  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 1.5574  loss: 1.5574
2022/08/06 06:36:38 - mmengine - INFO - Epoch(train) [4][3500/3757]  lr: 9.7558e-05  eta: 1 day, 3:28:25  time: 1.0221  data_time: 0.0183  memory: 68881  top1_acc: 0.6250  top5_acc: 0.7500  loss_cls: 1.5988  loss: 1.5988
2022/08/06 06:38:19 - mmengine - INFO - Epoch(train) [4][3600/3757]  lr: 9.7558e-05  eta: 1 day, 3:26:42  time: 0.9999  data_time: 0.0164  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 1.6675  loss: 1.6675
2022/08/06 06:39:59 - mmengine - INFO - Epoch(train) [4][3700/3757]  lr: 9.7558e-05  eta: 1 day, 3:24:56  time: 1.0012  data_time: 0.0175  memory: 68881  top1_acc: 0.6250  top5_acc: 0.7500  loss_cls: 1.8124  loss: 1.8124
2022/08/06 06:40:28 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 06:40:56 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 06:40:56 - mmengine - INFO - Epoch(train) [4][3757/3757]  lr: 9.7558e-05  eta: 1 day, 3:24:13  time: 1.0000  data_time: 0.0163  memory: 68881  top1_acc: 0.5714  top5_acc: 0.8571  loss_cls: 1.6310  loss: 1.6310
2022/08/06 06:42:40 - mmengine - INFO - Epoch(train) [5][100/3757]  lr: 9.5682e-05  eta: 1 day, 3:20:38  time: 1.0024  data_time: 0.0169  memory: 68881  top1_acc: 0.5000  top5_acc: 0.7500  loss_cls: 1.5025  loss: 1.5025
2022/08/06 06:44:20 - mmengine - INFO - Epoch(train) [5][200/3757]  lr: 9.5682e-05  eta: 1 day, 3:18:56  time: 1.0013  data_time: 0.0176  memory: 68881  top1_acc: 0.5000  top5_acc: 0.6250  loss_cls: 1.9597  loss: 1.9597
2022/08/06 06:46:01 - mmengine - INFO - Epoch(train) [5][300/3757]  lr: 9.5682e-05  eta: 1 day, 3:17:11  time: 1.0041  data_time: 0.0189  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.5284  loss: 1.5284
2022/08/06 06:47:41 - mmengine - INFO - Epoch(train) [5][400/3757]  lr: 9.5682e-05  eta: 1 day, 3:15:29  time: 1.0076  data_time: 0.0171  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.8833  loss: 1.8833
2022/08/06 06:49:22 - mmengine - INFO - Epoch(train) [5][500/3757]  lr: 9.5682e-05  eta: 1 day, 3:13:46  time: 1.0001  data_time: 0.0167  memory: 68881  top1_acc: 0.2500  top5_acc: 0.7500  loss_cls: 1.9093  loss: 1.9093
2022/08/06 06:51:02 - mmengine - INFO - Epoch(train) [5][600/3757]  lr: 9.5682e-05  eta: 1 day, 3:12:01  time: 1.0017  data_time: 0.0172  memory: 68881  top1_acc: 0.5000  top5_acc: 0.6250  loss_cls: 1.3997  loss: 1.3997
2022/08/06 06:52:43 - mmengine - INFO - Epoch(train) [5][700/3757]  lr: 9.5682e-05  eta: 1 day, 3:10:19  time: 1.0042  data_time: 0.0176  memory: 68881  top1_acc: 0.2500  top5_acc: 0.6250  loss_cls: 1.7452  loss: 1.7452
2022/08/06 06:54:24 - mmengine - INFO - Epoch(train) [5][800/3757]  lr: 9.5682e-05  eta: 1 day, 3:08:39  time: 1.0019  data_time: 0.0170  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 1.8534  loss: 1.8534
2022/08/06 06:56:05 - mmengine - INFO - Epoch(train) [5][900/3757]  lr: 9.5682e-05  eta: 1 day, 3:06:58  time: 1.0023  data_time: 0.0183  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 1.7377  loss: 1.7377
2022/08/06 06:57:17 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 06:57:45 - mmengine - INFO - Epoch(train) [5][1000/3757]  lr: 9.5682e-05  eta: 1 day, 3:05:15  time: 1.0094  data_time: 0.0177  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.8282  loss: 1.8282
2022/08/06 06:59:25 - mmengine - INFO - Epoch(train) [5][1100/3757]  lr: 9.5682e-05  eta: 1 day, 3:03:30  time: 1.0015  data_time: 0.0175  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 1.8793  loss: 1.8793
2022/08/06 07:01:05 - mmengine - INFO - Epoch(train) [5][1200/3757]  lr: 9.5682e-05  eta: 1 day, 3:01:45  time: 1.0008  data_time: 0.0175  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.5811  loss: 1.5811
2022/08/06 07:02:46 - mmengine - INFO - Epoch(train) [5][1300/3757]  lr: 9.5682e-05  eta: 1 day, 3:00:04  time: 1.0036  data_time: 0.0177  memory: 68881  top1_acc: 0.6250  top5_acc: 0.7500  loss_cls: 1.7204  loss: 1.7204
2022/08/06 07:04:27 - mmengine - INFO - Epoch(train) [5][1400/3757]  lr: 9.5682e-05  eta: 1 day, 2:58:21  time: 1.0000  data_time: 0.0173  memory: 68881  top1_acc: 0.3750  top5_acc: 0.7500  loss_cls: 1.7592  loss: 1.7592
2022/08/06 07:06:07 - mmengine - INFO - Epoch(train) [5][1500/3757]  lr: 9.5682e-05  eta: 1 day, 2:56:36  time: 1.0006  data_time: 0.0170  memory: 68881  top1_acc: 0.5000  top5_acc: 0.6250  loss_cls: 1.8178  loss: 1.8178
2022/08/06 07:07:47 - mmengine - INFO - Epoch(train) [5][1600/3757]  lr: 9.5682e-05  eta: 1 day, 2:54:51  time: 1.0015  data_time: 0.0180  memory: 68881  top1_acc: 0.7500  top5_acc: 0.7500  loss_cls: 1.5173  loss: 1.5173
2022/08/06 07:09:27 - mmengine - INFO - Epoch(train) [5][1700/3757]  lr: 9.5682e-05  eta: 1 day, 2:53:06  time: 1.0005  data_time: 0.0174  memory: 68881  top1_acc: 0.5000  top5_acc: 0.7500  loss_cls: 1.3721  loss: 1.3721
2022/08/06 07:11:07 - mmengine - INFO - Epoch(train) [5][1800/3757]  lr: 9.5682e-05  eta: 1 day, 2:51:22  time: 1.0035  data_time: 0.0176  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 1.7902  loss: 1.7902
2022/08/06 07:12:49 - mmengine - INFO - Epoch(train) [5][1900/3757]  lr: 9.5682e-05  eta: 1 day, 2:49:42  time: 1.0165  data_time: 0.0187  memory: 68881  top1_acc: 0.3750  top5_acc: 0.7500  loss_cls: 1.8093  loss: 1.8093
2022/08/06 07:14:01 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 07:14:29 - mmengine - INFO - Epoch(train) [5][2000/3757]  lr: 9.5682e-05  eta: 1 day, 2:48:01  time: 1.0182  data_time: 0.0177  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.5303  loss: 1.5303
2022/08/06 07:16:09 - mmengine - INFO - Epoch(train) [5][2100/3757]  lr: 9.5682e-05  eta: 1 day, 2:46:16  time: 1.0035  data_time: 0.0177  memory: 68881  top1_acc: 0.7500  top5_acc: 0.7500  loss_cls: 2.2044  loss: 2.2044
2022/08/06 07:17:50 - mmengine - INFO - Epoch(train) [5][2200/3757]  lr: 9.5682e-05  eta: 1 day, 2:44:32  time: 1.0038  data_time: 0.0185  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 1.8645  loss: 1.8645
2022/08/06 07:19:30 - mmengine - INFO - Epoch(train) [5][2300/3757]  lr: 9.5682e-05  eta: 1 day, 2:42:49  time: 1.0015  data_time: 0.0173  memory: 68881  top1_acc: 0.3750  top5_acc: 0.5000  loss_cls: 1.5106  loss: 1.5106
2022/08/06 07:21:11 - mmengine - INFO - Epoch(train) [5][2400/3757]  lr: 9.5682e-05  eta: 1 day, 2:41:07  time: 1.0152  data_time: 0.0183  memory: 68881  top1_acc: 0.3750  top5_acc: 0.8750  loss_cls: 1.7355  loss: 1.7355
2022/08/06 07:22:51 - mmengine - INFO - Epoch(train) [5][2500/3757]  lr: 9.5682e-05  eta: 1 day, 2:39:23  time: 1.0063  data_time: 0.0170  memory: 68881  top1_acc: 0.3750  top5_acc: 0.8750  loss_cls: 1.7398  loss: 1.7398
2022/08/06 07:24:31 - mmengine - INFO - Epoch(train) [5][2600/3757]  lr: 9.5682e-05  eta: 1 day, 2:37:40  time: 1.0052  data_time: 0.0180  memory: 68881  top1_acc: 0.1250  top5_acc: 0.3750  loss_cls: 1.9755  loss: 1.9755
2022/08/06 07:26:13 - mmengine - INFO - Epoch(train) [5][2700/3757]  lr: 9.5682e-05  eta: 1 day, 2:36:01  time: 1.0058  data_time: 0.0167  memory: 68881  top1_acc: 0.2500  top5_acc: 0.7500  loss_cls: 1.8260  loss: 1.8260
2022/08/06 07:27:53 - mmengine - INFO - Epoch(train) [5][2800/3757]  lr: 9.5682e-05  eta: 1 day, 2:34:19  time: 1.0126  data_time: 0.0174  memory: 68881  top1_acc: 0.5000  top5_acc: 0.8750  loss_cls: 1.6528  loss: 1.6528
2022/08/06 07:29:34 - mmengine - INFO - Epoch(train) [5][2900/3757]  lr: 9.5682e-05  eta: 1 day, 2:32:38  time: 1.0054  data_time: 0.0171  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 1.5745  loss: 1.5745
2022/08/06 07:30:46 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 07:31:14 - mmengine - INFO - Epoch(train) [5][3000/3757]  lr: 9.5682e-05  eta: 1 day, 2:30:54  time: 0.9991  data_time: 0.0179  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.7015  loss: 1.7015
2022/08/06 07:32:55 - mmengine - INFO - Epoch(train) [5][3100/3757]  lr: 9.5682e-05  eta: 1 day, 2:29:13  time: 1.0250  data_time: 0.0189  memory: 68881  top1_acc: 0.3750  top5_acc: 0.8750  loss_cls: 1.7225  loss: 1.7225
2022/08/06 07:34:36 - mmengine - INFO - Epoch(train) [5][3200/3757]  lr: 9.5682e-05  eta: 1 day, 2:27:35  time: 1.0006  data_time: 0.0174  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.6947  loss: 1.6947
2022/08/06 07:36:17 - mmengine - INFO - Epoch(train) [5][3300/3757]  lr: 9.5682e-05  eta: 1 day, 2:25:55  time: 1.0002  data_time: 0.0168  memory: 68881  top1_acc: 0.5000  top5_acc: 0.7500  loss_cls: 1.7196  loss: 1.7196
2022/08/06 07:37:58 - mmengine - INFO - Epoch(train) [5][3400/3757]  lr: 9.5682e-05  eta: 1 day, 2:24:13  time: 1.0026  data_time: 0.0175  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.6465  loss: 1.6465
2022/08/06 07:39:39 - mmengine - INFO - Epoch(train) [5][3500/3757]  lr: 9.5682e-05  eta: 1 day, 2:22:32  time: 1.0013  data_time: 0.0174  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 1.7978  loss: 1.7978
2022/08/06 07:41:20 - mmengine - INFO - Epoch(train) [5][3600/3757]  lr: 9.5682e-05  eta: 1 day, 2:20:53  time: 1.0037  data_time: 0.0170  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.9277  loss: 1.9277
2022/08/06 07:43:01 - mmengine - INFO - Epoch(train) [5][3700/3757]  lr: 9.5682e-05  eta: 1 day, 2:19:11  time: 1.0031  data_time: 0.0180  memory: 68881  top1_acc: 0.5000  top5_acc: 0.8750  loss_cls: 1.6455  loss: 1.6455
2022/08/06 07:43:58 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 07:43:58 - mmengine - INFO - Epoch(train) [5][3757/3757]  lr: 9.5682e-05  eta: 1 day, 2:18:32  time: 1.0006  data_time: 0.0178  memory: 68881  top1_acc: 0.2857  top5_acc: 0.4286  loss_cls: 1.8457  loss: 1.8457
2022/08/06 07:45:41 - mmengine - INFO - Epoch(train) [6][100/3757]  lr: 9.3306e-05  eta: 1 day, 2:15:20  time: 1.0041  data_time: 0.0187  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.7226  loss: 1.7226
2022/08/06 07:47:22 - mmengine - INFO - Epoch(train) [6][200/3757]  lr: 9.3306e-05  eta: 1 day, 2:13:40  time: 1.0047  data_time: 0.0165  memory: 68881  top1_acc: 0.2500  top5_acc: 0.5000  loss_cls: 1.7224  loss: 1.7224
2022/08/06 07:47:37 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 07:49:03 - mmengine - INFO - Epoch(train) [6][300/3757]  lr: 9.3306e-05  eta: 1 day, 2:11:59  time: 1.0043  data_time: 0.0170  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.4720  loss: 1.4720
2022/08/06 07:50:43 - mmengine - INFO - Epoch(train) [6][400/3757]  lr: 9.3306e-05  eta: 1 day, 2:10:16  time: 0.9994  data_time: 0.0168  memory: 68881  top1_acc: 0.5000  top5_acc: 0.8750  loss_cls: 1.3848  loss: 1.3848
2022/08/06 07:52:24 - mmengine - INFO - Epoch(train) [6][500/3757]  lr: 9.3306e-05  eta: 1 day, 2:08:35  time: 1.0131  data_time: 0.0179  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.8426  loss: 1.8426
2022/08/06 07:54:04 - mmengine - INFO - Epoch(train) [6][600/3757]  lr: 9.3306e-05  eta: 1 day, 2:06:52  time: 1.0056  data_time: 0.0184  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.6765  loss: 1.6765
2022/08/06 07:55:45 - mmengine - INFO - Epoch(train) [6][700/3757]  lr: 9.3306e-05  eta: 1 day, 2:05:10  time: 1.0003  data_time: 0.0180  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 1.2818  loss: 1.2818
2022/08/06 07:57:26 - mmengine - INFO - Epoch(train) [6][800/3757]  lr: 9.3306e-05  eta: 1 day, 2:03:30  time: 1.0041  data_time: 0.0186  memory: 68881  top1_acc: 0.5000  top5_acc: 0.7500  loss_cls: 1.3559  loss: 1.3559
2022/08/06 07:59:06 - mmengine - INFO - Epoch(train) [6][900/3757]  lr: 9.3306e-05  eta: 1 day, 2:01:48  time: 1.0052  data_time: 0.0173  memory: 68881  top1_acc: 0.3750  top5_acc: 0.7500  loss_cls: 1.6667  loss: 1.6667
2022/08/06 08:00:46 - mmengine - INFO - Epoch(train) [6][1000/3757]  lr: 9.3306e-05  eta: 1 day, 2:00:04  time: 0.9998  data_time: 0.0178  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 1.6482  loss: 1.6482
2022/08/06 08:02:26 - mmengine - INFO - Epoch(train) [6][1100/3757]  lr: 9.3306e-05  eta: 1 day, 1:58:21  time: 1.0023  data_time: 0.0174  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.4912  loss: 1.4912
2022/08/06 08:04:08 - mmengine - INFO - Epoch(train) [6][1200/3757]  lr: 9.3306e-05  eta: 1 day, 1:56:44  time: 1.0160  data_time: 0.0181  memory: 68881  top1_acc: 0.3750  top5_acc: 0.6250  loss_cls: 1.7895  loss: 1.7895
2022/08/06 08:04:23 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 08:05:48 - mmengine - INFO - Epoch(train) [6][1300/3757]  lr: 9.3306e-05  eta: 1 day, 1:55:02  time: 1.0063  data_time: 0.0183  memory: 68881  top1_acc: 0.1250  top5_acc: 0.3750  loss_cls: 1.4647  loss: 1.4647
2022/08/06 08:07:29 - mmengine - INFO - Epoch(train) [6][1400/3757]  lr: 9.3306e-05  eta: 1 day, 1:53:20  time: 1.0048  data_time: 0.0170  memory: 68881  top1_acc: 0.5000  top5_acc: 0.8750  loss_cls: 1.5397  loss: 1.5397
2022/08/06 08:09:09 - mmengine - INFO - Epoch(train) [6][1500/3757]  lr: 9.3306e-05  eta: 1 day, 1:51:37  time: 1.0006  data_time: 0.0166  memory: 68881  top1_acc: 0.6250  top5_acc: 0.6250  loss_cls: 1.9530  loss: 1.9530
2022/08/06 08:10:49 - mmengine - INFO - Epoch(train) [6][1600/3757]  lr: 9.3306e-05  eta: 1 day, 1:49:53  time: 1.0002  data_time: 0.0190  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.8719  loss: 1.8719
2022/08/06 08:12:29 - mmengine - INFO - Epoch(train) [6][1700/3757]  lr: 9.3306e-05  eta: 1 day, 1:48:10  time: 1.0032  data_time: 0.0178  memory: 68881  top1_acc: 0.5000  top5_acc: 0.7500  loss_cls: 1.9574  loss: 1.9574
2022/08/06 08:14:10 - mmengine - INFO - Epoch(train) [6][1800/3757]  lr: 9.3306e-05  eta: 1 day, 1:46:30  time: 1.0021  data_time: 0.0177  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.7077  loss: 1.7077
2022/08/06 08:15:51 - mmengine - INFO - Epoch(train) [6][1900/3757]  lr: 9.3306e-05  eta: 1 day, 1:44:48  time: 1.0122  data_time: 0.0182  memory: 68881  top1_acc: 0.3750  top5_acc: 0.8750  loss_cls: 1.5551  loss: 1.5551
2022/08/06 08:17:31 - mmengine - INFO - Epoch(train) [6][2000/3757]  lr: 9.3306e-05  eta: 1 day, 1:43:07  time: 1.0222  data_time: 0.0176  memory: 68881  top1_acc: 0.7500  top5_acc: 0.7500  loss_cls: 1.3093  loss: 1.3093
2022/08/06 08:19:12 - mmengine - INFO - Epoch(train) [6][2100/3757]  lr: 9.3306e-05  eta: 1 day, 1:41:26  time: 1.0012  data_time: 0.0172  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 1.6503  loss: 1.6503
2022/08/06 08:20:52 - mmengine - INFO - Epoch(train) [6][2200/3757]  lr: 9.3306e-05  eta: 1 day, 1:39:43  time: 1.0003  data_time: 0.0171  memory: 68881  top1_acc: 0.6250  top5_acc: 0.7500  loss_cls: 1.7266  loss: 1.7266
2022/08/06 08:21:07 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 08:22:32 - mmengine - INFO - Epoch(train) [6][2300/3757]  lr: 9.3306e-05  eta: 1 day, 1:37:59  time: 0.9994  data_time: 0.0171  memory: 68881  top1_acc: 0.5000  top5_acc: 0.7500  loss_cls: 1.4675  loss: 1.4675
2022/08/06 08:24:12 - mmengine - INFO - Epoch(train) [6][2400/3757]  lr: 9.3306e-05  eta: 1 day, 1:36:17  time: 1.0030  data_time: 0.0188  memory: 68881  top1_acc: 0.6250  top5_acc: 1.0000  loss_cls: 1.5111  loss: 1.5111
2022/08/06 08:25:53 - mmengine - INFO - Epoch(train) [6][2500/3757]  lr: 9.3306e-05  eta: 1 day, 1:34:36  time: 1.0031  data_time: 0.0171  memory: 68881  top1_acc: 0.5000  top5_acc: 0.8750  loss_cls: 1.4485  loss: 1.4485
2022/08/06 08:27:34 - mmengine - INFO - Epoch(train) [6][2600/3757]  lr: 9.3306e-05  eta: 1 day, 1:32:54  time: 1.0101  data_time: 0.0171  memory: 68881  top1_acc: 0.7500  top5_acc: 0.7500  loss_cls: 1.7699  loss: 1.7699
2022/08/06 08:29:14 - mmengine - INFO - Epoch(train) [6][2700/3757]  lr: 9.3306e-05  eta: 1 day, 1:31:11  time: 0.9997  data_time: 0.0176  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.5730  loss: 1.5730
2022/08/06 08:30:54 - mmengine - INFO - Epoch(train) [6][2800/3757]  lr: 9.3306e-05  eta: 1 day, 1:29:28  time: 0.9993  data_time: 0.0175  memory: 68881  top1_acc: 0.7500  top5_acc: 0.7500  loss_cls: 1.6160  loss: 1.6160
2022/08/06 08:32:34 - mmengine - INFO - Epoch(train) [6][2900/3757]  lr: 9.3306e-05  eta: 1 day, 1:27:45  time: 1.0054  data_time: 0.0180  memory: 68881  top1_acc: 0.7500  top5_acc: 0.7500  loss_cls: 1.6055  loss: 1.6055
2022/08/06 08:34:16 - mmengine - INFO - Epoch(train) [6][3000/3757]  lr: 9.3306e-05  eta: 1 day, 1:26:08  time: 1.0174  data_time: 0.0179  memory: 68881  top1_acc: 0.3750  top5_acc: 0.8750  loss_cls: 1.7068  loss: 1.7068
2022/08/06 08:35:56 - mmengine - INFO - Epoch(train) [6][3100/3757]  lr: 9.3306e-05  eta: 1 day, 1:24:27  time: 1.0038  data_time: 0.0173  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 1.4307  loss: 1.4307
2022/08/06 08:37:37 - mmengine - INFO - Epoch(train) [6][3200/3757]  lr: 9.3306e-05  eta: 1 day, 1:22:47  time: 1.0036  data_time: 0.0183  memory: 68881  top1_acc: 0.3750  top5_acc: 0.8750  loss_cls: 1.5045  loss: 1.5045
2022/08/06 08:37:53 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 08:39:18 - mmengine - INFO - Epoch(train) [6][3300/3757]  lr: 9.3306e-05  eta: 1 day, 1:21:08  time: 1.0053  data_time: 0.0174  memory: 68881  top1_acc: 0.5000  top5_acc: 0.8750  loss_cls: 1.2500  loss: 1.2500
2022/08/06 08:40:59 - mmengine - INFO - Epoch(train) [6][3400/3757]  lr: 9.3306e-05  eta: 1 day, 1:19:27  time: 1.0018  data_time: 0.0175  memory: 68881  top1_acc: 0.5000  top5_acc: 0.7500  loss_cls: 1.5926  loss: 1.5926
2022/08/06 08:42:39 - mmengine - INFO - Epoch(train) [6][3500/3757]  lr: 9.3306e-05  eta: 1 day, 1:17:44  time: 0.9992  data_time: 0.0169  memory: 68881  top1_acc: 0.5000  top5_acc: 0.7500  loss_cls: 1.8463  loss: 1.8463
2022/08/06 08:44:19 - mmengine - INFO - Epoch(train) [6][3600/3757]  lr: 9.3306e-05  eta: 1 day, 1:16:01  time: 1.0084  data_time: 0.0174  memory: 68881  top1_acc: 0.2500  top5_acc: 0.8750  loss_cls: 1.5646  loss: 1.5646
2022/08/06 08:46:00 - mmengine - INFO - Epoch(train) [6][3700/3757]  lr: 9.3306e-05  eta: 1 day, 1:14:22  time: 1.0118  data_time: 0.0177  memory: 68881  top1_acc: 0.5000  top5_acc: 0.7500  loss_cls: 1.7215  loss: 1.7215
2022/08/06 08:46:58 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 08:46:58 - mmengine - INFO - Epoch(train) [6][3757/3757]  lr: 9.3306e-05  eta: 1 day, 1:13:41  time: 0.9945  data_time: 0.0171  memory: 68881  top1_acc: 0.7143  top5_acc: 0.8571  loss_cls: 1.8040  loss: 1.8040
2022/08/06 08:46:58 - mmengine - INFO - Saving checkpoint at 6 epochs
2022/08/06 08:47:54 - mmengine - INFO - Epoch(val) [6][100/310]    eta: 0:01:26  time: 0.4141  data_time: 0.0098  memory: 14218  
2022/08/06 08:48:35 - mmengine - INFO - Epoch(val) [6][200/310]    eta: 0:00:46  time: 0.4226  data_time: 0.0112  memory: 14218  
2022/08/06 08:49:15 - mmengine - INFO - Epoch(val) [6][300/310]    eta: 0:00:04  time: 0.4075  data_time: 0.0101  memory: 14218  
2022/08/06 08:49:20 - mmengine - INFO - Epoch(val) [6][310/310]  acc/top1: 0.7160  acc/top5: 0.9011  acc/mean1: 0.7159
2022/08/06 08:49:20 - mmengine - INFO - The previous best checkpoint /mnt/lustre/daiwenxun/X/swin/mmaction2/work_dirs/swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb/best_acc/top1_epoch_4.pth is removed
2022/08/06 08:49:26 - mmengine - INFO - The best checkpoint with 0.7160 acc/top1 at 7 epoch is saved to best_acc/top1_epoch_7.pth.
2022/08/06 08:51:08 - mmengine - INFO - Epoch(train) [7][100/3757]  lr: 9.0455e-05  eta: 1 day, 1:10:37  time: 1.0031  data_time: 0.0170  memory: 68881  top1_acc: 0.3750  top5_acc: 0.6250  loss_cls: 1.5061  loss: 1.5061
2022/08/06 08:52:48 - mmengine - INFO - Epoch(train) [7][200/3757]  lr: 9.0455e-05  eta: 1 day, 1:08:55  time: 1.0063  data_time: 0.0185  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 1.4235  loss: 1.4235
2022/08/06 08:54:29 - mmengine - INFO - Epoch(train) [7][300/3757]  lr: 9.0455e-05  eta: 1 day, 1:07:16  time: 1.0191  data_time: 0.0176  memory: 68881  top1_acc: 0.3750  top5_acc: 0.6250  loss_cls: 1.5303  loss: 1.5303
2022/08/06 08:56:10 - mmengine - INFO - Epoch(train) [7][400/3757]  lr: 9.0455e-05  eta: 1 day, 1:05:35  time: 1.0059  data_time: 0.0171  memory: 68881  top1_acc: 0.6250  top5_acc: 1.0000  loss_cls: 1.6170  loss: 1.6170
2022/08/06 08:57:08 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 08:57:50 - mmengine - INFO - Epoch(train) [7][500/3757]  lr: 9.0455e-05  eta: 1 day, 1:03:55  time: 1.0043  data_time: 0.0170  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.2720  loss: 1.2720
2022/08/06 08:59:32 - mmengine - INFO - Epoch(train) [7][600/3757]  lr: 9.0455e-05  eta: 1 day, 1:02:17  time: 1.0256  data_time: 0.0182  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.4305  loss: 1.4305
2022/08/06 09:01:12 - mmengine - INFO - Epoch(train) [7][700/3757]  lr: 9.0455e-05  eta: 1 day, 1:00:35  time: 1.0019  data_time: 0.0179  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.5239  loss: 1.5239
2022/08/06 09:02:53 - mmengine - INFO - Epoch(train) [7][800/3757]  lr: 9.0455e-05  eta: 1 day, 0:58:55  time: 1.0019  data_time: 0.0175  memory: 68881  top1_acc: 0.3750  top5_acc: 0.6250  loss_cls: 1.5505  loss: 1.5505
2022/08/06 09:04:33 - mmengine - INFO - Epoch(train) [7][900/3757]  lr: 9.0455e-05  eta: 1 day, 0:57:13  time: 1.0014  data_time: 0.0171  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 1.4906  loss: 1.4906
2022/08/06 09:06:14 - mmengine - INFO - Epoch(train) [7][1000/3757]  lr: 9.0455e-05  eta: 1 day, 0:55:32  time: 1.0017  data_time: 0.0191  memory: 68881  top1_acc: 0.6250  top5_acc: 1.0000  loss_cls: 1.4322  loss: 1.4322
2022/08/06 09:07:54 - mmengine - INFO - Epoch(train) [7][1100/3757]  lr: 9.0455e-05  eta: 1 day, 0:53:51  time: 1.0022  data_time: 0.0176  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 1.5635  loss: 1.5635
2022/08/06 09:09:34 - mmengine - INFO - Epoch(train) [7][1200/3757]  lr: 9.0455e-05  eta: 1 day, 0:52:09  time: 1.0019  data_time: 0.0183  memory: 68881  top1_acc: 0.6250  top5_acc: 0.7500  loss_cls: 1.4673  loss: 1.4673
2022/08/06 09:11:15 - mmengine - INFO - Epoch(train) [7][1300/3757]  lr: 9.0455e-05  eta: 1 day, 0:50:26  time: 1.0022  data_time: 0.0186  memory: 68881  top1_acc: 0.5000  top5_acc: 0.7500  loss_cls: 1.7738  loss: 1.7738
2022/08/06 09:12:55 - mmengine - INFO - Epoch(train) [7][1400/3757]  lr: 9.0455e-05  eta: 1 day, 0:48:46  time: 1.0095  data_time: 0.0171  memory: 68881  top1_acc: 0.6250  top5_acc: 1.0000  loss_cls: 1.5586  loss: 1.5586
2022/08/06 09:13:54 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 09:14:36 - mmengine - INFO - Epoch(train) [7][1500/3757]  lr: 9.0455e-05  eta: 1 day, 0:47:07  time: 0.9995  data_time: 0.0176  memory: 68881  top1_acc: 0.5000  top5_acc: 0.8750  loss_cls: 1.5267  loss: 1.5267
2022/08/06 09:16:16 - mmengine - INFO - Epoch(train) [7][1600/3757]  lr: 9.0455e-05  eta: 1 day, 0:45:25  time: 1.0027  data_time: 0.0176  memory: 68881  top1_acc: 0.3750  top5_acc: 0.6250  loss_cls: 1.4860  loss: 1.4860
2022/08/06 09:17:57 - mmengine - INFO - Epoch(train) [7][1700/3757]  lr: 9.0455e-05  eta: 1 day, 0:43:44  time: 1.0010  data_time: 0.0175  memory: 68881  top1_acc: 0.7500  top5_acc: 0.7500  loss_cls: 1.6243  loss: 1.6243
2022/08/06 09:19:38 - mmengine - INFO - Epoch(train) [7][1800/3757]  lr: 9.0455e-05  eta: 1 day, 0:42:04  time: 1.0050  data_time: 0.0173  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 1.4200  loss: 1.4200
2022/08/06 09:21:19 - mmengine - INFO - Epoch(train) [7][1900/3757]  lr: 9.0455e-05  eta: 1 day, 0:40:25  time: 1.0009  data_time: 0.0180  memory: 68881  top1_acc: 0.5000  top5_acc: 0.8750  loss_cls: 1.7756  loss: 1.7756
2022/08/06 09:23:00 - mmengine - INFO - Epoch(train) [7][2000/3757]  lr: 9.0455e-05  eta: 1 day, 0:38:44  time: 1.0043  data_time: 0.0175  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 1.4375  loss: 1.4375
2022/08/06 09:24:41 - mmengine - INFO - Epoch(train) [7][2100/3757]  lr: 9.0455e-05  eta: 1 day, 0:37:06  time: 1.0025  data_time: 0.0174  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 1.3819  loss: 1.3819
2022/08/06 09:26:22 - mmengine - INFO - Epoch(train) [7][2200/3757]  lr: 9.0455e-05  eta: 1 day, 0:35:26  time: 1.0064  data_time: 0.0181  memory: 68881  top1_acc: 0.6250  top5_acc: 0.7500  loss_cls: 1.6912  loss: 1.6912
2022/08/06 09:28:02 - mmengine - INFO - Epoch(train) [7][2300/3757]  lr: 9.0455e-05  eta: 1 day, 0:33:45  time: 1.0021  data_time: 0.0166  memory: 68881  top1_acc: 0.3750  top5_acc: 0.7500  loss_cls: 1.5733  loss: 1.5733
2022/08/06 09:29:43 - mmengine - INFO - Epoch(train) [7][2400/3757]  lr: 9.0455e-05  eta: 1 day, 0:32:05  time: 1.0024  data_time: 0.0177  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 1.3552  loss: 1.3552
2022/08/06 09:30:42 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 09:31:24 - mmengine - INFO - Epoch(train) [7][2500/3757]  lr: 9.0455e-05  eta: 1 day, 0:30:25  time: 1.0174  data_time: 0.0175  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.7418  loss: 1.7418
2022/08/06 09:33:05 - mmengine - INFO - Epoch(train) [7][2600/3757]  lr: 9.0455e-05  eta: 1 day, 0:28:46  time: 1.0113  data_time: 0.0178  memory: 68881  top1_acc: 0.5000  top5_acc: 0.7500  loss_cls: 1.5614  loss: 1.5614
2022/08/06 09:34:46 - mmengine - INFO - Epoch(train) [7][2700/3757]  lr: 9.0455e-05  eta: 1 day, 0:27:05  time: 1.0008  data_time: 0.0173  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.7251  loss: 1.7251
2022/08/06 09:36:27 - mmengine - INFO - Epoch(train) [7][2800/3757]  lr: 9.0455e-05  eta: 1 day, 0:25:25  time: 1.0173  data_time: 0.0175  memory: 68881  top1_acc: 0.5000  top5_acc: 0.8750  loss_cls: 1.3438  loss: 1.3438
2022/08/06 09:38:08 - mmengine - INFO - Epoch(train) [7][2900/3757]  lr: 9.0455e-05  eta: 1 day, 0:23:49  time: 1.0287  data_time: 0.0186  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.4417  loss: 1.4417
2022/08/06 09:39:50 - mmengine - INFO - Epoch(train) [7][3000/3757]  lr: 9.0455e-05  eta: 1 day, 0:22:10  time: 0.9999  data_time: 0.0169  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 1.2876  loss: 1.2876
2022/08/06 09:41:30 - mmengine - INFO - Epoch(train) [7][3100/3757]  lr: 9.0455e-05  eta: 1 day, 0:20:28  time: 1.0029  data_time: 0.0177  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 1.3267  loss: 1.3267
2022/08/06 09:43:10 - mmengine - INFO - Epoch(train) [7][3200/3757]  lr: 9.0455e-05  eta: 1 day, 0:18:46  time: 0.9998  data_time: 0.0178  memory: 68881  top1_acc: 0.6250  top5_acc: 0.7500  loss_cls: 1.6319  loss: 1.6319
2022/08/06 09:44:50 - mmengine - INFO - Epoch(train) [7][3300/3757]  lr: 9.0455e-05  eta: 1 day, 0:17:03  time: 0.9978  data_time: 0.0168  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 1.4309  loss: 1.4309
2022/08/06 09:46:31 - mmengine - INFO - Epoch(train) [7][3400/3757]  lr: 9.0455e-05  eta: 1 day, 0:15:22  time: 1.0270  data_time: 0.0170  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.7628  loss: 1.7628
2022/08/06 09:47:29 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 09:48:12 - mmengine - INFO - Epoch(train) [7][3500/3757]  lr: 9.0455e-05  eta: 1 day, 0:13:42  time: 1.0060  data_time: 0.0172  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 1.7791  loss: 1.7791
2022/08/06 09:49:52 - mmengine - INFO - Epoch(train) [7][3600/3757]  lr: 9.0455e-05  eta: 1 day, 0:12:02  time: 1.0017  data_time: 0.0186  memory: 68881  top1_acc: 0.3750  top5_acc: 0.6250  loss_cls: 1.7034  loss: 1.7034
2022/08/06 09:51:32 - mmengine - INFO - Epoch(train) [7][3700/3757]  lr: 9.0455e-05  eta: 1 day, 0:10:20  time: 1.0018  data_time: 0.0167  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 1.3118  loss: 1.3118
2022/08/06 09:52:30 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 09:52:30 - mmengine - INFO - Epoch(train) [7][3757/3757]  lr: 9.0455e-05  eta: 1 day, 0:09:40  time: 1.0115  data_time: 0.0195  memory: 68881  top1_acc: 0.5714  top5_acc: 0.8571  loss_cls: 1.5160  loss: 1.5160
2022/08/06 09:54:14 - mmengine - INFO - Epoch(train) [8][100/3757]  lr: 8.7161e-05  eta: 1 day, 0:06:56  time: 1.0001  data_time: 0.0174  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.5353  loss: 1.5353
2022/08/06 09:55:54 - mmengine - INFO - Epoch(train) [8][200/3757]  lr: 8.7161e-05  eta: 1 day, 0:05:15  time: 1.0024  data_time: 0.0164  memory: 68881  top1_acc: 0.6250  top5_acc: 0.6250  loss_cls: 1.3884  loss: 1.3884
2022/08/06 09:57:35 - mmengine - INFO - Epoch(train) [8][300/3757]  lr: 8.7161e-05  eta: 1 day, 0:03:35  time: 1.0029  data_time: 0.0178  memory: 68881  top1_acc: 0.5000  top5_acc: 0.7500  loss_cls: 1.3425  loss: 1.3425
2022/08/06 09:59:16 - mmengine - INFO - Epoch(train) [8][400/3757]  lr: 8.7161e-05  eta: 1 day, 0:01:56  time: 1.0060  data_time: 0.0174  memory: 68881  top1_acc: 0.5000  top5_acc: 0.8750  loss_cls: 1.8247  loss: 1.8247
2022/08/06 10:00:57 - mmengine - INFO - Epoch(train) [8][500/3757]  lr: 8.7161e-05  eta: 1 day, 0:00:17  time: 1.0012  data_time: 0.0186  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.5584  loss: 1.5584
2022/08/06 10:02:38 - mmengine - INFO - Epoch(train) [8][600/3757]  lr: 8.7161e-05  eta: 23:58:35  time: 1.0041  data_time: 0.0173  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 1.8005  loss: 1.8005
2022/08/06 10:04:18 - mmengine - INFO - Epoch(train) [8][700/3757]  lr: 8.7161e-05  eta: 23:56:53  time: 1.0011  data_time: 0.0176  memory: 68881  top1_acc: 0.3750  top5_acc: 0.7500  loss_cls: 1.5907  loss: 1.5907
2022/08/06 10:04:19 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 10:05:58 - mmengine - INFO - Epoch(train) [8][800/3757]  lr: 8.7161e-05  eta: 23:55:13  time: 1.0032  data_time: 0.0177  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 1.7702  loss: 1.7702
2022/08/06 10:07:39 - mmengine - INFO - Epoch(train) [8][900/3757]  lr: 8.7161e-05  eta: 23:53:32  time: 1.0033  data_time: 0.0172  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.6403  loss: 1.6403
2022/08/06 10:09:19 - mmengine - INFO - Epoch(train) [8][1000/3757]  lr: 8.7161e-05  eta: 23:51:51  time: 1.0023  data_time: 0.0175  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 1.5243  loss: 1.5243
2022/08/06 10:11:00 - mmengine - INFO - Epoch(train) [8][1100/3757]  lr: 8.7161e-05  eta: 23:50:10  time: 1.0040  data_time: 0.0181  memory: 68881  top1_acc: 0.5000  top5_acc: 0.7500  loss_cls: 1.6417  loss: 1.6417
2022/08/06 10:12:41 - mmengine - INFO - Epoch(train) [8][1200/3757]  lr: 8.7161e-05  eta: 23:48:30  time: 1.0022  data_time: 0.0178  memory: 68881  top1_acc: 0.3750  top5_acc: 0.8750  loss_cls: 1.4390  loss: 1.4390
2022/08/06 10:14:22 - mmengine - INFO - Epoch(train) [8][1300/3757]  lr: 8.7161e-05  eta: 23:46:51  time: 1.0101  data_time: 0.0167  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 1.8506  loss: 1.8506
2022/08/06 10:16:03 - mmengine - INFO - Epoch(train) [8][1400/3757]  lr: 8.7161e-05  eta: 23:45:11  time: 1.0133  data_time: 0.0176  memory: 68881  top1_acc: 0.6250  top5_acc: 0.6250  loss_cls: 1.2689  loss: 1.2689
2022/08/06 10:17:44 - mmengine - INFO - Epoch(train) [8][1500/3757]  lr: 8.7161e-05  eta: 23:43:34  time: 1.0109  data_time: 0.0171  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 1.4026  loss: 1.4026
2022/08/06 10:19:25 - mmengine - INFO - Epoch(train) [8][1600/3757]  lr: 8.7161e-05  eta: 23:41:53  time: 1.0129  data_time: 0.0172  memory: 68881  top1_acc: 0.5000  top5_acc: 0.7500  loss_cls: 1.5251  loss: 1.5251
2022/08/06 10:21:06 - mmengine - INFO - Epoch(train) [8][1700/3757]  lr: 8.7161e-05  eta: 23:40:15  time: 1.0123  data_time: 0.0177  memory: 68881  top1_acc: 0.5000  top5_acc: 0.7500  loss_cls: 1.4681  loss: 1.4681
2022/08/06 10:21:07 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 10:22:47 - mmengine - INFO - Epoch(train) [8][1800/3757]  lr: 8.7161e-05  eta: 23:38:36  time: 1.0167  data_time: 0.0180  memory: 68881  top1_acc: 0.5000  top5_acc: 0.8750  loss_cls: 1.7691  loss: 1.7691
2022/08/06 10:24:28 - mmengine - INFO - Epoch(train) [8][1900/3757]  lr: 8.7161e-05  eta: 23:36:55  time: 1.0096  data_time: 0.0177  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 1.3724  loss: 1.3724
2022/08/06 10:26:09 - mmengine - INFO - Epoch(train) [8][2000/3757]  lr: 8.7161e-05  eta: 23:35:15  time: 1.0073  data_time: 0.0183  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 1.7092  loss: 1.7092
2022/08/06 10:27:49 - mmengine - INFO - Epoch(train) [8][2100/3757]  lr: 8.7161e-05  eta: 23:33:34  time: 1.0028  data_time: 0.0182  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.1746  loss: 1.1746
2022/08/06 10:29:30 - mmengine - INFO - Epoch(train) [8][2200/3757]  lr: 8.7161e-05  eta: 23:31:55  time: 1.0025  data_time: 0.0175  memory: 68881  top1_acc: 0.6250  top5_acc: 0.7500  loss_cls: 1.3721  loss: 1.3721
2022/08/06 10:31:11 - mmengine - INFO - Epoch(train) [8][2300/3757]  lr: 8.7161e-05  eta: 23:30:14  time: 0.9996  data_time: 0.0171  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.3587  loss: 1.3587
2022/08/06 10:32:51 - mmengine - INFO - Epoch(train) [8][2400/3757]  lr: 8.7161e-05  eta: 23:28:33  time: 1.0003  data_time: 0.0171  memory: 68881  top1_acc: 0.6250  top5_acc: 0.7500  loss_cls: 1.5884  loss: 1.5884
2022/08/06 10:34:32 - mmengine - INFO - Epoch(train) [8][2500/3757]  lr: 8.7161e-05  eta: 23:26:52  time: 1.0062  data_time: 0.0180  memory: 68881  top1_acc: 0.5000  top5_acc: 0.7500  loss_cls: 1.5629  loss: 1.5629
2022/08/06 10:36:12 - mmengine - INFO - Epoch(train) [8][2600/3757]  lr: 8.7161e-05  eta: 23:25:11  time: 1.0010  data_time: 0.0173  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 1.3209  loss: 1.3209
2022/08/06 10:37:52 - mmengine - INFO - Epoch(train) [8][2700/3757]  lr: 8.7161e-05  eta: 23:23:29  time: 1.0019  data_time: 0.0173  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.3538  loss: 1.3538
2022/08/06 10:37:53 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 10:39:32 - mmengine - INFO - Epoch(train) [8][2800/3757]  lr: 8.7161e-05  eta: 23:21:47  time: 1.0020  data_time: 0.0177  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 1.4867  loss: 1.4867
2022/08/06 10:41:13 - mmengine - INFO - Epoch(train) [8][2900/3757]  lr: 8.7161e-05  eta: 23:20:07  time: 1.0130  data_time: 0.0191  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.4141  loss: 1.4141
2022/08/06 10:42:53 - mmengine - INFO - Epoch(train) [8][3000/3757]  lr: 8.7161e-05  eta: 23:18:25  time: 1.0028  data_time: 0.0177  memory: 68881  top1_acc: 0.6250  top5_acc: 0.7500  loss_cls: 1.2725  loss: 1.2725
2022/08/06 10:44:34 - mmengine - INFO - Epoch(train) [8][3100/3757]  lr: 8.7161e-05  eta: 23:16:43  time: 1.0074  data_time: 0.0176  memory: 68881  top1_acc: 0.3750  top5_acc: 0.7500  loss_cls: 1.3524  loss: 1.3524
2022/08/06 10:46:14 - mmengine - INFO - Epoch(train) [8][3200/3757]  lr: 8.7161e-05  eta: 23:15:03  time: 1.0014  data_time: 0.0179  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 1.3019  loss: 1.3019
2022/08/06 10:47:55 - mmengine - INFO - Epoch(train) [8][3300/3757]  lr: 8.7161e-05  eta: 23:13:22  time: 1.0174  data_time: 0.0183  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 1.4859  loss: 1.4859
2022/08/06 10:49:35 - mmengine - INFO - Epoch(train) [8][3400/3757]  lr: 8.7161e-05  eta: 23:11:41  time: 1.0039  data_time: 0.0175  memory: 68881  top1_acc: 0.3750  top5_acc: 0.6250  loss_cls: 1.8004  loss: 1.8004
2022/08/06 10:51:15 - mmengine - INFO - Epoch(train) [8][3500/3757]  lr: 8.7161e-05  eta: 23:09:59  time: 1.0006  data_time: 0.0172  memory: 68881  top1_acc: 0.7500  top5_acc: 0.7500  loss_cls: 1.3039  loss: 1.3039
2022/08/06 10:52:56 - mmengine - INFO - Epoch(train) [8][3600/3757]  lr: 8.7161e-05  eta: 23:08:18  time: 1.0019  data_time: 0.0174  memory: 68881  top1_acc: 0.6250  top5_acc: 1.0000  loss_cls: 1.5181  loss: 1.5181
2022/08/06 10:54:36 - mmengine - INFO - Epoch(train) [8][3700/3757]  lr: 8.7161e-05  eta: 23:06:38  time: 1.0016  data_time: 0.0178  memory: 68881  top1_acc: 0.5000  top5_acc: 0.8750  loss_cls: 1.4941  loss: 1.4941
2022/08/06 10:54:37 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 10:55:34 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 10:55:34 - mmengine - INFO - Epoch(train) [8][3757/3757]  lr: 8.7161e-05  eta: 23:05:58  time: 0.9987  data_time: 0.0180  memory: 68881  top1_acc: 0.8571  top5_acc: 0.8571  loss_cls: 1.5219  loss: 1.5219
2022/08/06 10:57:18 - mmengine - INFO - Epoch(train) [9][100/3757]  lr: 8.3461e-05  eta: 23:03:22  time: 1.0034  data_time: 0.0171  memory: 68881  top1_acc: 0.5000  top5_acc: 0.6250  loss_cls: 1.5702  loss: 1.5702
2022/08/06 10:59:04 - mmengine - INFO - Epoch(train) [9][200/3757]  lr: 8.3461e-05  eta: 23:01:58  time: 1.2925  data_time: 0.0180  memory: 68881  top1_acc: 0.7500  top5_acc: 0.7500  loss_cls: 1.5779  loss: 1.5779
2022/08/06 11:00:45 - mmengine - INFO - Epoch(train) [9][300/3757]  lr: 8.3461e-05  eta: 23:00:19  time: 1.0011  data_time: 0.0173  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.6565  loss: 1.6565
2022/08/06 11:02:26 - mmengine - INFO - Epoch(train) [9][400/3757]  lr: 8.3461e-05  eta: 22:58:39  time: 1.0030  data_time: 0.0172  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.6436  loss: 1.6436
2022/08/06 11:04:08 - mmengine - INFO - Epoch(train) [9][500/3757]  lr: 8.3461e-05  eta: 22:57:01  time: 1.0378  data_time: 0.0174  memory: 68881  top1_acc: 0.6250  top5_acc: 1.0000  loss_cls: 1.4909  loss: 1.4909
2022/08/06 11:05:49 - mmengine - INFO - Epoch(train) [9][600/3757]  lr: 8.3461e-05  eta: 22:55:21  time: 1.0031  data_time: 0.0175  memory: 68881  top1_acc: 0.5000  top5_acc: 0.7500  loss_cls: 1.2499  loss: 1.2499
2022/08/06 11:07:30 - mmengine - INFO - Epoch(train) [9][700/3757]  lr: 8.3461e-05  eta: 22:53:43  time: 1.0302  data_time: 0.0191  memory: 68881  top1_acc: 0.5000  top5_acc: 0.8750  loss_cls: 1.4600  loss: 1.4600
2022/08/06 11:09:11 - mmengine - INFO - Epoch(train) [9][800/3757]  lr: 8.3461e-05  eta: 22:52:03  time: 1.0035  data_time: 0.0178  memory: 68881  top1_acc: 0.5000  top5_acc: 0.8750  loss_cls: 1.6035  loss: 1.6035
2022/08/06 11:10:51 - mmengine - INFO - Epoch(train) [9][900/3757]  lr: 8.3461e-05  eta: 22:50:22  time: 1.0024  data_time: 0.0175  memory: 68881  top1_acc: 0.2500  top5_acc: 0.6250  loss_cls: 1.4828  loss: 1.4828
2022/08/06 11:11:35 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 11:12:32 - mmengine - INFO - Epoch(train) [9][1000/3757]  lr: 8.3461e-05  eta: 22:48:41  time: 1.0019  data_time: 0.0173  memory: 68881  top1_acc: 0.5000  top5_acc: 0.6250  loss_cls: 1.3534  loss: 1.3534
2022/08/06 11:14:12 - mmengine - INFO - Epoch(train) [9][1100/3757]  lr: 8.3461e-05  eta: 22:46:59  time: 1.0026  data_time: 0.0183  memory: 68881  top1_acc: 0.7500  top5_acc: 0.7500  loss_cls: 1.4075  loss: 1.4075
2022/08/06 11:15:52 - mmengine - INFO - Epoch(train) [9][1200/3757]  lr: 8.3461e-05  eta: 22:45:18  time: 1.0003  data_time: 0.0178  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.7318  loss: 1.7318
2022/08/06 11:17:33 - mmengine - INFO - Epoch(train) [9][1300/3757]  lr: 8.3461e-05  eta: 22:43:37  time: 1.0013  data_time: 0.0167  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 1.2636  loss: 1.2636
2022/08/06 11:19:13 - mmengine - INFO - Epoch(train) [9][1400/3757]  lr: 8.3461e-05  eta: 22:41:56  time: 1.0051  data_time: 0.0177  memory: 68881  top1_acc: 0.5000  top5_acc: 0.8750  loss_cls: 1.3662  loss: 1.3662
2022/08/06 11:20:53 - mmengine - INFO - Epoch(train) [9][1500/3757]  lr: 8.3461e-05  eta: 22:40:15  time: 0.9990  data_time: 0.0167  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.4703  loss: 1.4703
2022/08/06 11:22:34 - mmengine - INFO - Epoch(train) [9][1600/3757]  lr: 8.3461e-05  eta: 22:38:35  time: 1.0069  data_time: 0.0178  memory: 68881  top1_acc: 0.6250  top5_acc: 0.7500  loss_cls: 1.6382  loss: 1.6382
2022/08/06 11:24:14 - mmengine - INFO - Epoch(train) [9][1700/3757]  lr: 8.3461e-05  eta: 22:36:54  time: 1.0036  data_time: 0.0169  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 1.4123  loss: 1.4123
2022/08/06 11:25:55 - mmengine - INFO - Epoch(train) [9][1800/3757]  lr: 8.3461e-05  eta: 22:35:14  time: 0.9997  data_time: 0.0174  memory: 68881  top1_acc: 0.6250  top5_acc: 0.7500  loss_cls: 1.5642  loss: 1.5642
2022/08/06 11:27:36 - mmengine - INFO - Epoch(train) [9][1900/3757]  lr: 8.3461e-05  eta: 22:33:33  time: 1.0007  data_time: 0.0164  memory: 68881  top1_acc: 0.3750  top5_acc: 0.7500  loss_cls: 1.6174  loss: 1.6174
2022/08/06 11:28:20 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 11:29:16 - mmengine - INFO - Epoch(train) [9][2000/3757]  lr: 8.3461e-05  eta: 22:31:52  time: 1.0137  data_time: 0.0180  memory: 68881  top1_acc: 0.3750  top5_acc: 0.8750  loss_cls: 1.3724  loss: 1.3724
2022/08/06 11:30:56 - mmengine - INFO - Epoch(train) [9][2100/3757]  lr: 8.3461e-05  eta: 22:30:10  time: 1.0037  data_time: 0.0175  memory: 68881  top1_acc: 0.3750  top5_acc: 0.8750  loss_cls: 1.5264  loss: 1.5264
2022/08/06 11:32:37 - mmengine - INFO - Epoch(train) [9][2200/3757]  lr: 8.3461e-05  eta: 22:28:29  time: 1.0108  data_time: 0.0164  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.6455  loss: 1.6455
2022/08/06 11:34:17 - mmengine - INFO - Epoch(train) [9][2300/3757]  lr: 8.3461e-05  eta: 22:26:49  time: 1.0014  data_time: 0.0170  memory: 68881  top1_acc: 0.5000  top5_acc: 0.8750  loss_cls: 1.2953  loss: 1.2953
2022/08/06 11:35:57 - mmengine - INFO - Epoch(train) [9][2400/3757]  lr: 8.3461e-05  eta: 22:25:07  time: 1.0028  data_time: 0.0178  memory: 68881  top1_acc: 0.5000  top5_acc: 0.8750  loss_cls: 1.4904  loss: 1.4904
2022/08/06 11:37:38 - mmengine - INFO - Epoch(train) [9][2500/3757]  lr: 8.3461e-05  eta: 22:23:28  time: 1.0157  data_time: 0.0185  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.5565  loss: 1.5565
2022/08/06 11:39:19 - mmengine - INFO - Epoch(train) [9][2600/3757]  lr: 8.3461e-05  eta: 22:21:48  time: 1.0009  data_time: 0.0170  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.2346  loss: 1.2346
2022/08/06 11:41:00 - mmengine - INFO - Epoch(train) [9][2700/3757]  lr: 8.3461e-05  eta: 22:20:07  time: 1.0002  data_time: 0.0171  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.4377  loss: 1.4377
2022/08/06 11:42:40 - mmengine - INFO - Epoch(train) [9][2800/3757]  lr: 8.3461e-05  eta: 22:18:26  time: 0.9994  data_time: 0.0176  memory: 68881  top1_acc: 0.6250  top5_acc: 1.0000  loss_cls: 1.3222  loss: 1.3222
2022/08/06 11:44:21 - mmengine - INFO - Epoch(train) [9][2900/3757]  lr: 8.3461e-05  eta: 22:16:45  time: 1.0009  data_time: 0.0170  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 1.4862  loss: 1.4862
2022/08/06 11:45:05 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 11:46:02 - mmengine - INFO - Epoch(train) [9][3000/3757]  lr: 8.3461e-05  eta: 22:15:06  time: 1.0097  data_time: 0.0173  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.4505  loss: 1.4505
2022/08/06 11:47:42 - mmengine - INFO - Epoch(train) [9][3100/3757]  lr: 8.3461e-05  eta: 22:13:25  time: 1.0064  data_time: 0.0172  memory: 68881  top1_acc: 0.5000  top5_acc: 0.8750  loss_cls: 1.7165  loss: 1.7165
2022/08/06 11:49:23 - mmengine - INFO - Epoch(train) [9][3200/3757]  lr: 8.3461e-05  eta: 22:11:44  time: 1.0013  data_time: 0.0175  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.4881  loss: 1.4881
2022/08/06 11:51:04 - mmengine - INFO - Epoch(train) [9][3300/3757]  lr: 8.3461e-05  eta: 22:10:05  time: 1.0137  data_time: 0.0178  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.1394  loss: 1.1394
2022/08/06 11:52:45 - mmengine - INFO - Epoch(train) [9][3400/3757]  lr: 8.3461e-05  eta: 22:08:27  time: 1.0111  data_time: 0.0171  memory: 68881  top1_acc: 0.5000  top5_acc: 0.7500  loss_cls: 1.4737  loss: 1.4737
2022/08/06 11:54:26 - mmengine - INFO - Epoch(train) [9][3500/3757]  lr: 8.3461e-05  eta: 22:06:46  time: 1.0036  data_time: 0.0171  memory: 68881  top1_acc: 0.5000  top5_acc: 0.7500  loss_cls: 1.2500  loss: 1.2500
2022/08/06 11:56:06 - mmengine - INFO - Epoch(train) [9][3600/3757]  lr: 8.3461e-05  eta: 22:05:05  time: 1.0019  data_time: 0.0173  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.2769  loss: 1.2769
2022/08/06 11:57:47 - mmengine - INFO - Epoch(train) [9][3700/3757]  lr: 8.3461e-05  eta: 22:03:25  time: 1.0103  data_time: 0.0166  memory: 68881  top1_acc: 0.3750  top5_acc: 1.0000  loss_cls: 1.4064  loss: 1.4064
2022/08/06 11:58:45 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 11:58:45 - mmengine - INFO - Epoch(train) [9][3757/3757]  lr: 8.3461e-05  eta: 22:02:46  time: 0.9982  data_time: 0.0183  memory: 68881  top1_acc: 0.1429  top5_acc: 0.4286  loss_cls: 1.6108  loss: 1.6108
2022/08/06 11:58:45 - mmengine - INFO - Saving checkpoint at 9 epochs
2022/08/06 11:59:43 - mmengine - INFO - Epoch(val) [9][100/310]    eta: 0:01:27  time: 0.4153  data_time: 0.0121  memory: 14218  
2022/08/06 12:00:26 - mmengine - INFO - Epoch(val) [9][200/310]    eta: 0:00:44  time: 0.4060  data_time: 0.0101  memory: 14218  
2022/08/06 12:01:06 - mmengine - INFO - Epoch(val) [9][300/310]    eta: 0:00:04  time: 0.4039  data_time: 0.0092  memory: 14218  
2022/08/06 12:01:12 - mmengine - INFO - Epoch(val) [9][310/310]  acc/top1: 0.7369  acc/top5: 0.9126  acc/mean1: 0.7367
2022/08/06 12:01:12 - mmengine - INFO - The previous best checkpoint /mnt/lustre/daiwenxun/X/swin/mmaction2/work_dirs/swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb/best_acc/top1_epoch_7.pth is removed
2022/08/06 12:01:18 - mmengine - INFO - The best checkpoint with 0.7369 acc/top1 at 10 epoch is saved to best_acc/top1_epoch_10.pth.
2022/08/06 12:02:59 - mmengine - INFO - Epoch(train) [10][100/3757]  lr: 7.9393e-05  eta: 22:00:10  time: 0.9989  data_time: 0.0175  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 1.2274  loss: 1.2274
2022/08/06 12:04:26 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 12:04:40 - mmengine - INFO - Epoch(train) [10][200/3757]  lr: 7.9393e-05  eta: 21:58:29  time: 1.0001  data_time: 0.0170  memory: 68881  top1_acc: 0.6250  top5_acc: 0.7500  loss_cls: 1.5572  loss: 1.5572
2022/08/06 12:06:20 - mmengine - INFO - Epoch(train) [10][300/3757]  lr: 7.9393e-05  eta: 21:56:47  time: 1.0037  data_time: 0.0178  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.3139  loss: 1.3139
2022/08/06 12:08:00 - mmengine - INFO - Epoch(train) [10][400/3757]  lr: 7.9393e-05  eta: 21:55:07  time: 1.0105  data_time: 0.0172  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 1.3464  loss: 1.3464
2022/08/06 12:09:41 - mmengine - INFO - Epoch(train) [10][500/3757]  lr: 7.9393e-05  eta: 21:53:27  time: 1.0072  data_time: 0.0175  memory: 68881  top1_acc: 0.5000  top5_acc: 0.6250  loss_cls: 1.4170  loss: 1.4170
2022/08/06 12:11:21 - mmengine - INFO - Epoch(train) [10][600/3757]  lr: 7.9393e-05  eta: 21:51:46  time: 1.0038  data_time: 0.0182  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 1.2401  loss: 1.2401
2022/08/06 12:13:01 - mmengine - INFO - Epoch(train) [10][700/3757]  lr: 7.9393e-05  eta: 21:50:04  time: 1.0009  data_time: 0.0167  memory: 68881  top1_acc: 0.3750  top5_acc: 0.5000  loss_cls: 1.2805  loss: 1.2805
2022/08/06 12:14:42 - mmengine - INFO - Epoch(train) [10][800/3757]  lr: 7.9393e-05  eta: 21:48:23  time: 1.0012  data_time: 0.0175  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 1.4386  loss: 1.4386
2022/08/06 12:16:22 - mmengine - INFO - Epoch(train) [10][900/3757]  lr: 7.9393e-05  eta: 21:46:42  time: 1.0003  data_time: 0.0169  memory: 68881  top1_acc: 0.3750  top5_acc: 0.8750  loss_cls: 1.6160  loss: 1.6160
2022/08/06 12:18:03 - mmengine - INFO - Epoch(train) [10][1000/3757]  lr: 7.9393e-05  eta: 21:45:02  time: 1.0014  data_time: 0.0173  memory: 68881  top1_acc: 0.5000  top5_acc: 0.8750  loss_cls: 1.3648  loss: 1.3648
2022/08/06 12:19:43 - mmengine - INFO - Epoch(train) [10][1100/3757]  lr: 7.9393e-05  eta: 21:43:21  time: 0.9989  data_time: 0.0171  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.3017  loss: 1.3017
2022/08/06 12:21:10 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 12:21:23 - mmengine - INFO - Epoch(train) [10][1200/3757]  lr: 7.9393e-05  eta: 21:41:40  time: 1.0007  data_time: 0.0178  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.1933  loss: 1.1933
2022/08/06 12:23:04 - mmengine - INFO - Epoch(train) [10][1300/3757]  lr: 7.9393e-05  eta: 21:40:00  time: 1.0150  data_time: 0.0171  memory: 68881  top1_acc: 0.2500  top5_acc: 0.7500  loss_cls: 1.3275  loss: 1.3275
2022/08/06 12:24:44 - mmengine - INFO - Epoch(train) [10][1400/3757]  lr: 7.9393e-05  eta: 21:38:19  time: 0.9998  data_time: 0.0172  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.4798  loss: 1.4798
2022/08/06 12:26:24 - mmengine - INFO - Epoch(train) [10][1500/3757]  lr: 7.9393e-05  eta: 21:36:37  time: 1.0017  data_time: 0.0177  memory: 68881  top1_acc: 0.7500  top5_acc: 0.7500  loss_cls: 1.4542  loss: 1.4542
2022/08/06 12:28:05 - mmengine - INFO - Epoch(train) [10][1600/3757]  lr: 7.9393e-05  eta: 21:34:57  time: 1.0012  data_time: 0.0175  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.5516  loss: 1.5516
2022/08/06 12:29:45 - mmengine - INFO - Epoch(train) [10][1700/3757]  lr: 7.9393e-05  eta: 21:33:15  time: 1.0004  data_time: 0.0177  memory: 68881  top1_acc: 0.3750  top5_acc: 0.8750  loss_cls: 1.4969  loss: 1.4969
2022/08/06 12:31:25 - mmengine - INFO - Epoch(train) [10][1800/3757]  lr: 7.9393e-05  eta: 21:31:34  time: 1.0005  data_time: 0.0172  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.3672  loss: 1.3672
2022/08/06 12:33:05 - mmengine - INFO - Epoch(train) [10][1900/3757]  lr: 7.9393e-05  eta: 21:29:53  time: 1.0004  data_time: 0.0174  memory: 68881  top1_acc: 0.5000  top5_acc: 0.7500  loss_cls: 1.3803  loss: 1.3803
2022/08/06 12:34:46 - mmengine - INFO - Epoch(train) [10][2000/3757]  lr: 7.9393e-05  eta: 21:28:12  time: 1.0116  data_time: 0.0170  memory: 68881  top1_acc: 0.3750  top5_acc: 1.0000  loss_cls: 1.2719  loss: 1.2719
2022/08/06 12:36:27 - mmengine - INFO - Epoch(train) [10][2100/3757]  lr: 7.9393e-05  eta: 21:26:32  time: 1.0007  data_time: 0.0170  memory: 68881  top1_acc: 0.3750  top5_acc: 0.8750  loss_cls: 1.4244  loss: 1.4244
2022/08/06 12:37:54 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 12:38:07 - mmengine - INFO - Epoch(train) [10][2200/3757]  lr: 7.9393e-05  eta: 21:24:52  time: 1.0139  data_time: 0.0163  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 1.6246  loss: 1.6246
2022/08/06 12:39:48 - mmengine - INFO - Epoch(train) [10][2300/3757]  lr: 7.9393e-05  eta: 21:23:11  time: 1.0012  data_time: 0.0180  memory: 68881  top1_acc: 0.5000  top5_acc: 0.8750  loss_cls: 1.2600  loss: 1.2600
2022/08/06 12:41:28 - mmengine - INFO - Epoch(train) [10][2400/3757]  lr: 7.9393e-05  eta: 21:21:30  time: 1.0008  data_time: 0.0174  memory: 68881  top1_acc: 0.6250  top5_acc: 0.7500  loss_cls: 1.3033  loss: 1.3033
2022/08/06 12:43:09 - mmengine - INFO - Epoch(train) [10][2500/3757]  lr: 7.9393e-05  eta: 21:19:51  time: 1.0028  data_time: 0.0191  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 1.1911  loss: 1.1911
2022/08/06 12:44:49 - mmengine - INFO - Epoch(train) [10][2600/3757]  lr: 7.9393e-05  eta: 21:18:10  time: 1.0028  data_time: 0.0180  memory: 68881  top1_acc: 0.2500  top5_acc: 0.7500  loss_cls: 1.5844  loss: 1.5844
2022/08/06 12:46:30 - mmengine - INFO - Epoch(train) [10][2700/3757]  lr: 7.9393e-05  eta: 21:16:30  time: 1.0149  data_time: 0.0172  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 1.3128  loss: 1.3128
2022/08/06 12:48:10 - mmengine - INFO - Epoch(train) [10][2800/3757]  lr: 7.9393e-05  eta: 21:14:49  time: 1.0057  data_time: 0.0177  memory: 68881  top1_acc: 0.5000  top5_acc: 0.8750  loss_cls: 1.4574  loss: 1.4574
2022/08/06 12:49:51 - mmengine - INFO - Epoch(train) [10][2900/3757]  lr: 7.9393e-05  eta: 21:13:08  time: 1.0093  data_time: 0.0179  memory: 68881  top1_acc: 0.5000  top5_acc: 0.8750  loss_cls: 1.4404  loss: 1.4404
2022/08/06 12:51:32 - mmengine - INFO - Epoch(train) [10][3000/3757]  lr: 7.9393e-05  eta: 21:11:28  time: 1.0012  data_time: 0.0178  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.5171  loss: 1.5171
2022/08/06 12:53:12 - mmengine - INFO - Epoch(train) [10][3100/3757]  lr: 7.9393e-05  eta: 21:09:47  time: 1.0011  data_time: 0.0180  memory: 68881  top1_acc: 0.6250  top5_acc: 0.6250  loss_cls: 1.7383  loss: 1.7383
2022/08/06 12:54:39 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 12:54:52 - mmengine - INFO - Epoch(train) [10][3200/3757]  lr: 7.9393e-05  eta: 21:08:07  time: 1.0067  data_time: 0.0171  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.4300  loss: 1.4300
2022/08/06 12:56:32 - mmengine - INFO - Epoch(train) [10][3300/3757]  lr: 7.9393e-05  eta: 21:06:25  time: 0.9997  data_time: 0.0177  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.3913  loss: 1.3913
2022/08/06 12:58:13 - mmengine - INFO - Epoch(train) [10][3400/3757]  lr: 7.9393e-05  eta: 21:04:44  time: 0.9994  data_time: 0.0174  memory: 68881  top1_acc: 0.5000  top5_acc: 0.7500  loss_cls: 1.3617  loss: 1.3617
2022/08/06 12:59:53 - mmengine - INFO - Epoch(train) [10][3500/3757]  lr: 7.9393e-05  eta: 21:03:03  time: 1.0001  data_time: 0.0174  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.4488  loss: 1.4488
2022/08/06 13:01:33 - mmengine - INFO - Epoch(train) [10][3600/3757]  lr: 7.9393e-05  eta: 21:01:21  time: 0.9984  data_time: 0.0168  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.4985  loss: 1.4985
2022/08/06 13:03:14 - mmengine - INFO - Epoch(train) [10][3700/3757]  lr: 7.9393e-05  eta: 20:59:42  time: 1.0205  data_time: 0.0192  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.3317  loss: 1.3317
2022/08/06 13:04:12 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 13:04:12 - mmengine - INFO - Epoch(train) [10][3757/3757]  lr: 7.9393e-05  eta: 20:59:02  time: 1.0299  data_time: 0.0208  memory: 68881  top1_acc: 0.7143  top5_acc: 0.7143  loss_cls: 1.8595  loss: 1.8595
2022/08/06 13:05:55 - mmengine - INFO - Epoch(train) [11][100/3757]  lr: 7.5004e-05  eta: 20:56:35  time: 1.0018  data_time: 0.0182  memory: 68881  top1_acc: 0.3750  top5_acc: 0.5000  loss_cls: 1.2073  loss: 1.2073
2022/08/06 13:07:36 - mmengine - INFO - Epoch(train) [11][200/3757]  lr: 7.5004e-05  eta: 20:54:55  time: 1.0101  data_time: 0.0175  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 1.4158  loss: 1.4158
2022/08/06 13:09:16 - mmengine - INFO - Epoch(train) [11][300/3757]  lr: 7.5004e-05  eta: 20:53:14  time: 1.0017  data_time: 0.0170  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.3240  loss: 1.3240
2022/08/06 13:10:56 - mmengine - INFO - Epoch(train) [11][400/3757]  lr: 7.5004e-05  eta: 20:51:33  time: 1.0033  data_time: 0.0173  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 1.0905  loss: 1.0905
2022/08/06 13:11:26 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 13:12:37 - mmengine - INFO - Epoch(train) [11][500/3757]  lr: 7.5004e-05  eta: 20:49:52  time: 1.0040  data_time: 0.0178  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 1.1800  loss: 1.1800
2022/08/06 13:14:17 - mmengine - INFO - Epoch(train) [11][600/3757]  lr: 7.5004e-05  eta: 20:48:11  time: 1.0019  data_time: 0.0166  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 1.2322  loss: 1.2322
2022/08/06 13:15:57 - mmengine - INFO - Epoch(train) [11][700/3757]  lr: 7.5004e-05  eta: 20:46:30  time: 1.0034  data_time: 0.0177  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.5603  loss: 1.5603
2022/08/06 13:17:37 - mmengine - INFO - Epoch(train) [11][800/3757]  lr: 7.5004e-05  eta: 20:44:49  time: 1.0029  data_time: 0.0177  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 1.4153  loss: 1.4153
2022/08/06 13:19:17 - mmengine - INFO - Epoch(train) [11][900/3757]  lr: 7.5004e-05  eta: 20:43:08  time: 1.0010  data_time: 0.0181  memory: 68881  top1_acc: 0.3750  top5_acc: 0.6250  loss_cls: 1.3956  loss: 1.3956
2022/08/06 13:20:57 - mmengine - INFO - Epoch(train) [11][1000/3757]  lr: 7.5004e-05  eta: 20:41:27  time: 1.0030  data_time: 0.0174  memory: 68881  top1_acc: 0.5000  top5_acc: 0.7500  loss_cls: 1.4883  loss: 1.4883
2022/08/06 13:22:38 - mmengine - INFO - Epoch(train) [11][1100/3757]  lr: 7.5004e-05  eta: 20:39:47  time: 0.9993  data_time: 0.0165  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.4588  loss: 1.4588
2022/08/06 13:24:18 - mmengine - INFO - Epoch(train) [11][1200/3757]  lr: 7.5004e-05  eta: 20:38:06  time: 1.0168  data_time: 0.0177  memory: 68881  top1_acc: 0.5000  top5_acc: 0.5000  loss_cls: 1.4641  loss: 1.4641
2022/08/06 13:25:59 - mmengine - INFO - Epoch(train) [11][1300/3757]  lr: 7.5004e-05  eta: 20:36:26  time: 0.9997  data_time: 0.0167  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.5492  loss: 1.5492
2022/08/06 13:27:39 - mmengine - INFO - Epoch(train) [11][1400/3757]  lr: 7.5004e-05  eta: 20:34:45  time: 1.0007  data_time: 0.0177  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 1.0885  loss: 1.0885
2022/08/06 13:28:09 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 13:29:20 - mmengine - INFO - Epoch(train) [11][1500/3757]  lr: 7.5004e-05  eta: 20:33:05  time: 1.0210  data_time: 0.0180  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 1.0535  loss: 1.0535
2022/08/06 13:31:00 - mmengine - INFO - Epoch(train) [11][1600/3757]  lr: 7.5004e-05  eta: 20:31:24  time: 1.0003  data_time: 0.0169  memory: 68881  top1_acc: 0.6250  top5_acc: 1.0000  loss_cls: 1.6438  loss: 1.6438
2022/08/06 13:32:41 - mmengine - INFO - Epoch(train) [11][1700/3757]  lr: 7.5004e-05  eta: 20:29:43  time: 1.0006  data_time: 0.0174  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.1375  loss: 1.1375
2022/08/06 13:34:21 - mmengine - INFO - Epoch(train) [11][1800/3757]  lr: 7.5004e-05  eta: 20:28:02  time: 1.0010  data_time: 0.0182  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 1.1569  loss: 1.1569
2022/08/06 13:36:01 - mmengine - INFO - Epoch(train) [11][1900/3757]  lr: 7.5004e-05  eta: 20:26:21  time: 1.0009  data_time: 0.0173  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.6721  loss: 1.6721
2022/08/06 13:37:41 - mmengine - INFO - Epoch(train) [11][2000/3757]  lr: 7.5004e-05  eta: 20:24:40  time: 1.0016  data_time: 0.0173  memory: 68881  top1_acc: 0.5000  top5_acc: 0.8750  loss_cls: 1.4871  loss: 1.4871
2022/08/06 13:39:22 - mmengine - INFO - Epoch(train) [11][2100/3757]  lr: 7.5004e-05  eta: 20:23:01  time: 1.0013  data_time: 0.0168  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 1.2050  loss: 1.2050
2022/08/06 13:41:03 - mmengine - INFO - Epoch(train) [11][2200/3757]  lr: 7.5004e-05  eta: 20:21:20  time: 1.0046  data_time: 0.0184  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.2983  loss: 1.2983
2022/08/06 13:42:44 - mmengine - INFO - Epoch(train) [11][2300/3757]  lr: 7.5004e-05  eta: 20:19:41  time: 1.0139  data_time: 0.0175  memory: 68881  top1_acc: 0.3750  top5_acc: 0.7500  loss_cls: 1.4324  loss: 1.4324
2022/08/06 13:44:24 - mmengine - INFO - Epoch(train) [11][2400/3757]  lr: 7.5004e-05  eta: 20:18:00  time: 1.0019  data_time: 0.0172  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 1.2088  loss: 1.2088
2022/08/06 13:44:54 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 13:46:04 - mmengine - INFO - Epoch(train) [11][2500/3757]  lr: 7.5004e-05  eta: 20:16:19  time: 1.0009  data_time: 0.0176  memory: 68881  top1_acc: 0.6250  top5_acc: 1.0000  loss_cls: 1.3135  loss: 1.3135
2022/08/06 13:47:44 - mmengine - INFO - Epoch(train) [11][2600/3757]  lr: 7.5004e-05  eta: 20:14:38  time: 1.0004  data_time: 0.0164  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.1853  loss: 1.1853
2022/08/06 13:49:25 - mmengine - INFO - Epoch(train) [11][2700/3757]  lr: 7.5004e-05  eta: 20:12:57  time: 0.9976  data_time: 0.0173  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.3540  loss: 1.3540
2022/08/06 13:51:05 - mmengine - INFO - Epoch(train) [11][2800/3757]  lr: 7.5004e-05  eta: 20:11:17  time: 1.0080  data_time: 0.0167  memory: 68881  top1_acc: 0.3750  top5_acc: 1.0000  loss_cls: 1.3951  loss: 1.3951
2022/08/06 13:52:45 - mmengine - INFO - Epoch(train) [11][2900/3757]  lr: 7.5004e-05  eta: 20:09:36  time: 1.0000  data_time: 0.0170  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 1.3618  loss: 1.3618
2022/08/06 13:54:27 - mmengine - INFO - Epoch(train) [11][3000/3757]  lr: 7.5004e-05  eta: 20:07:57  time: 1.0019  data_time: 0.0175  memory: 68881  top1_acc: 0.5000  top5_acc: 0.7500  loss_cls: 1.5787  loss: 1.5787
2022/08/06 13:56:07 - mmengine - INFO - Epoch(train) [11][3100/3757]  lr: 7.5004e-05  eta: 20:06:17  time: 1.0038  data_time: 0.0178  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 1.5201  loss: 1.5201
2022/08/06 13:57:48 - mmengine - INFO - Epoch(train) [11][3200/3757]  lr: 7.5004e-05  eta: 20:04:36  time: 1.0019  data_time: 0.0171  memory: 68881  top1_acc: 0.5000  top5_acc: 0.6250  loss_cls: 1.4416  loss: 1.4416
2022/08/06 13:59:28 - mmengine - INFO - Epoch(train) [11][3300/3757]  lr: 7.5004e-05  eta: 20:02:56  time: 1.0132  data_time: 0.0190  memory: 68881  top1_acc: 0.3750  top5_acc: 0.6250  loss_cls: 1.3614  loss: 1.3614
2022/08/06 14:01:09 - mmengine - INFO - Epoch(train) [11][3400/3757]  lr: 7.5004e-05  eta: 20:01:15  time: 1.0111  data_time: 0.0163  memory: 68881  top1_acc: 0.5000  top5_acc: 0.6250  loss_cls: 1.3986  loss: 1.3986
2022/08/06 14:01:39 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 14:02:50 - mmengine - INFO - Epoch(train) [11][3500/3757]  lr: 7.5004e-05  eta: 19:59:35  time: 1.0017  data_time: 0.0174  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.2793  loss: 1.2793
2022/08/06 14:04:30 - mmengine - INFO - Epoch(train) [11][3600/3757]  lr: 7.5004e-05  eta: 19:57:55  time: 1.0042  data_time: 0.0176  memory: 68881  top1_acc: 0.5000  top5_acc: 0.8750  loss_cls: 1.4672  loss: 1.4672
2022/08/06 14:06:11 - mmengine - INFO - Epoch(train) [11][3700/3757]  lr: 7.5004e-05  eta: 19:56:15  time: 1.0071  data_time: 0.0175  memory: 68881  top1_acc: 0.3750  top5_acc: 0.7500  loss_cls: 1.3375  loss: 1.3375
2022/08/06 14:07:09 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 14:07:09 - mmengine - INFO - Epoch(train) [11][3757/3757]  lr: 7.5004e-05  eta: 19:55:35  time: 1.0160  data_time: 0.0190  memory: 68881  top1_acc: 0.5714  top5_acc: 1.0000  loss_cls: 1.4819  loss: 1.4819
2022/08/06 14:08:51 - mmengine - INFO - Epoch(train) [12][100/3757]  lr: 7.0340e-05  eta: 19:53:12  time: 1.0022  data_time: 0.0175  memory: 68881  top1_acc: 0.3750  top5_acc: 0.7500  loss_cls: 1.4052  loss: 1.4052
2022/08/06 14:10:32 - mmengine - INFO - Epoch(train) [12][200/3757]  lr: 7.0340e-05  eta: 19:51:31  time: 1.0014  data_time: 0.0167  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.1236  loss: 1.1236
2022/08/06 14:12:12 - mmengine - INFO - Epoch(train) [12][300/3757]  lr: 7.0340e-05  eta: 19:49:51  time: 1.0114  data_time: 0.0171  memory: 68881  top1_acc: 0.7500  top5_acc: 0.7500  loss_cls: 1.1276  loss: 1.1276
2022/08/06 14:13:53 - mmengine - INFO - Epoch(train) [12][400/3757]  lr: 7.0340e-05  eta: 19:48:10  time: 1.0006  data_time: 0.0174  memory: 68881  top1_acc: 0.5000  top5_acc: 0.7500  loss_cls: 1.1275  loss: 1.1275
2022/08/06 14:15:33 - mmengine - INFO - Epoch(train) [12][500/3757]  lr: 7.0340e-05  eta: 19:46:30  time: 1.0116  data_time: 0.0179  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 1.1958  loss: 1.1958
2022/08/06 14:17:14 - mmengine - INFO - Epoch(train) [12][600/3757]  lr: 7.0340e-05  eta: 19:44:50  time: 1.0038  data_time: 0.0170  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 1.2679  loss: 1.2679
2022/08/06 14:18:27 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 14:18:54 - mmengine - INFO - Epoch(train) [12][700/3757]  lr: 7.0340e-05  eta: 19:43:09  time: 1.0010  data_time: 0.0175  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.1890  loss: 1.1890
2022/08/06 14:20:34 - mmengine - INFO - Epoch(train) [12][800/3757]  lr: 7.0340e-05  eta: 19:41:29  time: 1.0018  data_time: 0.0171  memory: 68881  top1_acc: 0.6250  top5_acc: 0.7500  loss_cls: 1.3255  loss: 1.3255
2022/08/06 14:22:15 - mmengine - INFO - Epoch(train) [12][900/3757]  lr: 7.0340e-05  eta: 19:39:48  time: 1.0047  data_time: 0.0174  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.4098  loss: 1.4098
2022/08/06 14:23:55 - mmengine - INFO - Epoch(train) [12][1000/3757]  lr: 7.0340e-05  eta: 19:38:07  time: 1.0045  data_time: 0.0173  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 1.5204  loss: 1.5204
2022/08/06 14:25:35 - mmengine - INFO - Epoch(train) [12][1100/3757]  lr: 7.0340e-05  eta: 19:36:26  time: 1.0008  data_time: 0.0183  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 1.2189  loss: 1.2189
2022/08/06 14:27:15 - mmengine - INFO - Epoch(train) [12][1200/3757]  lr: 7.0340e-05  eta: 19:34:46  time: 1.0025  data_time: 0.0182  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 1.2599  loss: 1.2599
2022/08/06 14:28:56 - mmengine - INFO - Epoch(train) [12][1300/3757]  lr: 7.0340e-05  eta: 19:33:06  time: 1.0003  data_time: 0.0178  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 1.2819  loss: 1.2819
2022/08/06 14:30:37 - mmengine - INFO - Epoch(train) [12][1400/3757]  lr: 7.0340e-05  eta: 19:31:25  time: 1.0025  data_time: 0.0189  memory: 68881  top1_acc: 0.5000  top5_acc: 0.8750  loss_cls: 1.3650  loss: 1.3650
2022/08/06 14:32:17 - mmengine - INFO - Epoch(train) [12][1500/3757]  lr: 7.0340e-05  eta: 19:29:45  time: 1.0011  data_time: 0.0183  memory: 68881  top1_acc: 0.5000  top5_acc: 0.7500  loss_cls: 1.4046  loss: 1.4046
2022/08/06 14:33:58 - mmengine - INFO - Epoch(train) [12][1600/3757]  lr: 7.0340e-05  eta: 19:28:05  time: 1.0030  data_time: 0.0182  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.3976  loss: 1.3976
2022/08/06 14:35:11 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 14:35:38 - mmengine - INFO - Epoch(train) [12][1700/3757]  lr: 7.0340e-05  eta: 19:26:24  time: 1.0002  data_time: 0.0172  memory: 68881  top1_acc: 0.5000  top5_acc: 0.6250  loss_cls: 1.4083  loss: 1.4083
2022/08/06 14:37:19 - mmengine - INFO - Epoch(train) [12][1800/3757]  lr: 7.0340e-05  eta: 19:24:44  time: 1.0159  data_time: 0.0175  memory: 68881  top1_acc: 0.6250  top5_acc: 1.0000  loss_cls: 1.1921  loss: 1.1921
2022/08/06 14:38:59 - mmengine - INFO - Epoch(train) [12][1900/3757]  lr: 7.0340e-05  eta: 19:23:03  time: 1.0026  data_time: 0.0178  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 1.1364  loss: 1.1364
2022/08/06 14:40:40 - mmengine - INFO - Epoch(train) [12][2000/3757]  lr: 7.0340e-05  eta: 19:21:23  time: 1.0006  data_time: 0.0174  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 1.3410  loss: 1.3410
2022/08/06 14:42:20 - mmengine - INFO - Epoch(train) [12][2100/3757]  lr: 7.0340e-05  eta: 19:19:42  time: 1.0024  data_time: 0.0178  memory: 68881  top1_acc: 0.6250  top5_acc: 0.7500  loss_cls: 1.4001  loss: 1.4001
2022/08/06 14:44:00 - mmengine - INFO - Epoch(train) [12][2200/3757]  lr: 7.0340e-05  eta: 19:18:02  time: 1.0011  data_time: 0.0171  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 1.2476  loss: 1.2476
2022/08/06 14:45:41 - mmengine - INFO - Epoch(train) [12][2300/3757]  lr: 7.0340e-05  eta: 19:16:21  time: 1.0041  data_time: 0.0179  memory: 68881  top1_acc: 0.2500  top5_acc: 0.5000  loss_cls: 1.5482  loss: 1.5482
2022/08/06 14:47:21 - mmengine - INFO - Epoch(train) [12][2400/3757]  lr: 7.0340e-05  eta: 19:14:41  time: 1.0021  data_time: 0.0177  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.1993  loss: 1.1993
2022/08/06 14:49:01 - mmengine - INFO - Epoch(train) [12][2500/3757]  lr: 7.0340e-05  eta: 19:13:00  time: 1.0033  data_time: 0.0166  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.2305  loss: 1.2305
2022/08/06 14:50:41 - mmengine - INFO - Epoch(train) [12][2600/3757]  lr: 7.0340e-05  eta: 19:11:19  time: 0.9999  data_time: 0.0172  memory: 68881  top1_acc: 0.3750  top5_acc: 0.7500  loss_cls: 1.2450  loss: 1.2450
2022/08/06 14:51:55 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 14:52:22 - mmengine - INFO - Epoch(train) [12][2700/3757]  lr: 7.0340e-05  eta: 19:09:38  time: 0.9991  data_time: 0.0173  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 1.3218  loss: 1.3218
2022/08/06 14:54:02 - mmengine - INFO - Epoch(train) [12][2800/3757]  lr: 7.0340e-05  eta: 19:07:58  time: 0.9997  data_time: 0.0172  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.6168  loss: 1.6168
2022/08/06 14:55:42 - mmengine - INFO - Epoch(train) [12][2900/3757]  lr: 7.0340e-05  eta: 19:06:17  time: 0.9992  data_time: 0.0168  memory: 68881  top1_acc: 0.3750  top5_acc: 0.7500  loss_cls: 1.3633  loss: 1.3633
2022/08/06 14:57:22 - mmengine - INFO - Epoch(train) [12][3000/3757]  lr: 7.0340e-05  eta: 19:04:36  time: 0.9989  data_time: 0.0169  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.2370  loss: 1.2370
2022/08/06 14:59:03 - mmengine - INFO - Epoch(train) [12][3100/3757]  lr: 7.0340e-05  eta: 19:02:56  time: 1.0126  data_time: 0.0183  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 1.7438  loss: 1.7438
2022/08/06 15:00:43 - mmengine - INFO - Epoch(train) [12][3200/3757]  lr: 7.0340e-05  eta: 19:01:15  time: 1.0024  data_time: 0.0174  memory: 68881  top1_acc: 0.5000  top5_acc: 0.7500  loss_cls: 1.3021  loss: 1.3021
2022/08/06 15:02:24 - mmengine - INFO - Epoch(train) [12][3300/3757]  lr: 7.0340e-05  eta: 18:59:35  time: 0.9999  data_time: 0.0171  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.4104  loss: 1.4104
2022/08/06 15:04:04 - mmengine - INFO - Epoch(train) [12][3400/3757]  lr: 7.0340e-05  eta: 18:57:54  time: 0.9995  data_time: 0.0168  memory: 68881  top1_acc: 0.2500  top5_acc: 0.5000  loss_cls: 1.7804  loss: 1.7804
2022/08/06 15:05:44 - mmengine - INFO - Epoch(train) [12][3500/3757]  lr: 7.0340e-05  eta: 18:56:13  time: 0.9990  data_time: 0.0167  memory: 68881  top1_acc: 0.5000  top5_acc: 0.7500  loss_cls: 1.3627  loss: 1.3627
2022/08/06 15:07:24 - mmengine - INFO - Epoch(train) [12][3600/3757]  lr: 7.0340e-05  eta: 18:54:32  time: 1.0020  data_time: 0.0175  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.4936  loss: 1.4936
2022/08/06 15:08:37 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 15:09:05 - mmengine - INFO - Epoch(train) [12][3700/3757]  lr: 7.0340e-05  eta: 18:52:52  time: 1.0095  data_time: 0.0180  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 1.1792  loss: 1.1792
2022/08/06 15:10:02 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 15:10:02 - mmengine - INFO - Epoch(train) [12][3757/3757]  lr: 7.0340e-05  eta: 18:52:12  time: 1.0193  data_time: 0.0197  memory: 68881  top1_acc: 0.5714  top5_acc: 0.8571  loss_cls: 1.3710  loss: 1.3710
2022/08/06 15:10:02 - mmengine - INFO - Saving checkpoint at 12 epochs
2022/08/06 15:11:01 - mmengine - INFO - Epoch(val) [12][100/310]    eta: 0:01:27  time: 0.4170  data_time: 0.0102  memory: 14218  
2022/08/06 15:11:42 - mmengine - INFO - Epoch(val) [12][200/310]    eta: 0:00:45  time: 0.4091  data_time: 0.0101  memory: 14218  
2022/08/06 15:12:23 - mmengine - INFO - Epoch(val) [12][300/310]    eta: 0:00:04  time: 0.4064  data_time: 0.0114  memory: 14218  
2022/08/06 15:12:28 - mmengine - INFO - Epoch(val) [12][310/310]  acc/top1: 0.7468  acc/top5: 0.9188  acc/mean1: 0.7466
2022/08/06 15:12:28 - mmengine - INFO - The previous best checkpoint /mnt/lustre/daiwenxun/X/swin/mmaction2/work_dirs/swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb/best_acc/top1_epoch_10.pth is removed
2022/08/06 15:12:34 - mmengine - INFO - The best checkpoint with 0.7468 acc/top1 at 13 epoch is saved to best_acc/top1_epoch_13.pth.
2022/08/06 15:14:15 - mmengine - INFO - Epoch(train) [13][100/3757]  lr: 6.5454e-05  eta: 18:49:50  time: 0.9989  data_time: 0.0175  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 1.0228  loss: 1.0228
2022/08/06 15:15:55 - mmengine - INFO - Epoch(train) [13][200/3757]  lr: 6.5454e-05  eta: 18:48:09  time: 0.9994  data_time: 0.0170  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 1.1923  loss: 1.1923
2022/08/06 15:17:36 - mmengine - INFO - Epoch(train) [13][300/3757]  lr: 6.5454e-05  eta: 18:46:28  time: 1.0030  data_time: 0.0163  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.2132  loss: 1.2132
2022/08/06 15:19:16 - mmengine - INFO - Epoch(train) [13][400/3757]  lr: 6.5454e-05  eta: 18:44:48  time: 1.0022  data_time: 0.0172  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 1.2855  loss: 1.2855
2022/08/06 15:20:56 - mmengine - INFO - Epoch(train) [13][500/3757]  lr: 6.5454e-05  eta: 18:43:07  time: 1.0000  data_time: 0.0177  memory: 68881  top1_acc: 0.3750  top5_acc: 0.8750  loss_cls: 1.3666  loss: 1.3666
2022/08/06 15:22:36 - mmengine - INFO - Epoch(train) [13][600/3757]  lr: 6.5454e-05  eta: 18:41:26  time: 0.9993  data_time: 0.0168  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.2661  loss: 1.2661
2022/08/06 15:24:17 - mmengine - INFO - Epoch(train) [13][700/3757]  lr: 6.5454e-05  eta: 18:39:46  time: 1.0014  data_time: 0.0176  memory: 68881  top1_acc: 0.3750  top5_acc: 0.6250  loss_cls: 1.4109  loss: 1.4109
2022/08/06 15:25:57 - mmengine - INFO - Epoch(train) [13][800/3757]  lr: 6.5454e-05  eta: 18:38:05  time: 1.0013  data_time: 0.0173  memory: 68881  top1_acc: 0.6250  top5_acc: 0.7500  loss_cls: 1.3302  loss: 1.3302
2022/08/06 15:27:38 - mmengine - INFO - Epoch(train) [13][900/3757]  lr: 6.5454e-05  eta: 18:36:25  time: 1.0025  data_time: 0.0173  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.2029  loss: 1.2029
2022/08/06 15:27:54 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 15:29:18 - mmengine - INFO - Epoch(train) [13][1000/3757]  lr: 6.5454e-05  eta: 18:34:45  time: 1.0063  data_time: 0.0170  memory: 68881  top1_acc: 0.7500  top5_acc: 0.7500  loss_cls: 1.1587  loss: 1.1587
2022/08/06 15:30:59 - mmengine - INFO - Epoch(train) [13][1100/3757]  lr: 6.5454e-05  eta: 18:33:05  time: 1.0059  data_time: 0.0167  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 1.5023  loss: 1.5023
2022/08/06 15:32:39 - mmengine - INFO - Epoch(train) [13][1200/3757]  lr: 6.5454e-05  eta: 18:31:24  time: 0.9996  data_time: 0.0164  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 1.1470  loss: 1.1470
2022/08/06 15:34:19 - mmengine - INFO - Epoch(train) [13][1300/3757]  lr: 6.5454e-05  eta: 18:29:44  time: 1.0013  data_time: 0.0164  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.9431  loss: 0.9431
2022/08/06 15:36:00 - mmengine - INFO - Epoch(train) [13][1400/3757]  lr: 6.5454e-05  eta: 18:28:03  time: 1.0028  data_time: 0.0172  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 1.4242  loss: 1.4242
2022/08/06 15:37:40 - mmengine - INFO - Epoch(train) [13][1500/3757]  lr: 6.5454e-05  eta: 18:26:22  time: 0.9999  data_time: 0.0170  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.3885  loss: 1.3885
2022/08/06 15:39:20 - mmengine - INFO - Epoch(train) [13][1600/3757]  lr: 6.5454e-05  eta: 18:24:41  time: 1.0007  data_time: 0.0174  memory: 68881  top1_acc: 0.6250  top5_acc: 0.7500  loss_cls: 1.2596  loss: 1.2596
2022/08/06 15:41:00 - mmengine - INFO - Epoch(train) [13][1700/3757]  lr: 6.5454e-05  eta: 18:23:01  time: 0.9995  data_time: 0.0166  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 0.8128  loss: 0.8128
2022/08/06 15:42:40 - mmengine - INFO - Epoch(train) [13][1800/3757]  lr: 6.5454e-05  eta: 18:21:20  time: 1.0050  data_time: 0.0173  memory: 68881  top1_acc: 0.2500  top5_acc: 0.5000  loss_cls: 1.5871  loss: 1.5871
2022/08/06 15:44:21 - mmengine - INFO - Epoch(train) [13][1900/3757]  lr: 6.5454e-05  eta: 18:19:40  time: 0.9999  data_time: 0.0171  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 1.2343  loss: 1.2343
2022/08/06 15:44:37 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 15:46:01 - mmengine - INFO - Epoch(train) [13][2000/3757]  lr: 6.5454e-05  eta: 18:18:00  time: 1.0083  data_time: 0.0176  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.3650  loss: 1.3650
2022/08/06 15:47:41 - mmengine - INFO - Epoch(train) [13][2100/3757]  lr: 6.5454e-05  eta: 18:16:19  time: 1.0016  data_time: 0.0171  memory: 68881  top1_acc: 0.7500  top5_acc: 0.7500  loss_cls: 1.4608  loss: 1.4608
2022/08/06 15:49:22 - mmengine - INFO - Epoch(train) [13][2200/3757]  lr: 6.5454e-05  eta: 18:14:38  time: 1.0021  data_time: 0.0170  memory: 68881  top1_acc: 0.5000  top5_acc: 0.7500  loss_cls: 1.5766  loss: 1.5766
2022/08/06 15:51:02 - mmengine - INFO - Epoch(train) [13][2300/3757]  lr: 6.5454e-05  eta: 18:12:58  time: 1.0011  data_time: 0.0173  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.3170  loss: 1.3170
2022/08/06 15:52:43 - mmengine - INFO - Epoch(train) [13][2400/3757]  lr: 6.5454e-05  eta: 18:11:18  time: 1.0022  data_time: 0.0172  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.3373  loss: 1.3373
2022/08/06 15:54:24 - mmengine - INFO - Epoch(train) [13][2500/3757]  lr: 6.5454e-05  eta: 18:09:38  time: 1.0139  data_time: 0.0179  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 1.3734  loss: 1.3734
2022/08/06 15:56:04 - mmengine - INFO - Epoch(train) [13][2600/3757]  lr: 6.5454e-05  eta: 18:07:58  time: 1.0002  data_time: 0.0173  memory: 68881  top1_acc: 0.6250  top5_acc: 1.0000  loss_cls: 1.3481  loss: 1.3481
2022/08/06 15:57:45 - mmengine - INFO - Epoch(train) [13][2700/3757]  lr: 6.5454e-05  eta: 18:06:18  time: 1.0004  data_time: 0.0173  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 1.2122  loss: 1.2122
2022/08/06 15:59:25 - mmengine - INFO - Epoch(train) [13][2800/3757]  lr: 6.5454e-05  eta: 18:04:37  time: 1.0012  data_time: 0.0173  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.4681  loss: 1.4681
2022/08/06 16:01:05 - mmengine - INFO - Epoch(train) [13][2900/3757]  lr: 6.5454e-05  eta: 18:02:56  time: 1.0003  data_time: 0.0163  memory: 68881  top1_acc: 0.5000  top5_acc: 0.8750  loss_cls: 1.0693  loss: 1.0693
2022/08/06 16:01:21 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 16:02:45 - mmengine - INFO - Epoch(train) [13][3000/3757]  lr: 6.5454e-05  eta: 18:01:15  time: 0.9998  data_time: 0.0168  memory: 68881  top1_acc: 0.5000  top5_acc: 0.7500  loss_cls: 1.6228  loss: 1.6228
2022/08/06 16:04:25 - mmengine - INFO - Epoch(train) [13][3100/3757]  lr: 6.5454e-05  eta: 17:59:35  time: 1.0012  data_time: 0.0174  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.3080  loss: 1.3080
2022/08/06 16:06:05 - mmengine - INFO - Epoch(train) [13][3200/3757]  lr: 6.5454e-05  eta: 17:57:54  time: 1.0004  data_time: 0.0171  memory: 68881  top1_acc: 0.5000  top5_acc: 0.8750  loss_cls: 1.1102  loss: 1.1102
2022/08/06 16:07:46 - mmengine - INFO - Epoch(train) [13][3300/3757]  lr: 6.5454e-05  eta: 17:56:13  time: 1.0033  data_time: 0.0172  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 1.1942  loss: 1.1942
2022/08/06 16:09:26 - mmengine - INFO - Epoch(train) [13][3400/3757]  lr: 6.5454e-05  eta: 17:54:33  time: 1.0009  data_time: 0.0174  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 1.1155  loss: 1.1155
2022/08/06 16:11:07 - mmengine - INFO - Epoch(train) [13][3500/3757]  lr: 6.5454e-05  eta: 17:52:53  time: 1.0026  data_time: 0.0171  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.5146  loss: 1.5146
2022/08/06 16:12:47 - mmengine - INFO - Epoch(train) [13][3600/3757]  lr: 6.5454e-05  eta: 17:51:13  time: 1.0065  data_time: 0.0172  memory: 68881  top1_acc: 0.6250  top5_acc: 1.0000  loss_cls: 1.2193  loss: 1.2193
2022/08/06 16:14:29 - mmengine - INFO - Epoch(train) [13][3700/3757]  lr: 6.5454e-05  eta: 17:49:34  time: 1.0157  data_time: 0.0174  memory: 68881  top1_acc: 0.3750  top5_acc: 0.7500  loss_cls: 1.2824  loss: 1.2824
2022/08/06 16:15:26 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 16:15:27 - mmengine - INFO - Epoch(train) [13][3757/3757]  lr: 6.5454e-05  eta: 17:48:54  time: 1.0227  data_time: 0.0204  memory: 68881  top1_acc: 0.8571  top5_acc: 1.0000  loss_cls: 1.3968  loss: 1.3968
2022/08/06 16:17:09 - mmengine - INFO - Epoch(train) [14][100/3757]  lr: 6.0398e-05  eta: 17:46:37  time: 1.0035  data_time: 0.0170  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 1.1366  loss: 1.1366
2022/08/06 16:18:09 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 16:18:50 - mmengine - INFO - Epoch(train) [14][200/3757]  lr: 6.0398e-05  eta: 17:44:58  time: 1.0170  data_time: 0.0187  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 1.0857  loss: 1.0857
2022/08/06 16:20:31 - mmengine - INFO - Epoch(train) [14][300/3757]  lr: 6.0398e-05  eta: 17:43:18  time: 1.0051  data_time: 0.0173  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.0994  loss: 1.0994
2022/08/06 16:22:11 - mmengine - INFO - Epoch(train) [14][400/3757]  lr: 6.0398e-05  eta: 17:41:37  time: 0.9996  data_time: 0.0169  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.9707  loss: 0.9707
2022/08/06 16:23:52 - mmengine - INFO - Epoch(train) [14][500/3757]  lr: 6.0398e-05  eta: 17:39:57  time: 1.0014  data_time: 0.0177  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 1.3533  loss: 1.3533
2022/08/06 16:25:33 - mmengine - INFO - Epoch(train) [14][600/3757]  lr: 6.0398e-05  eta: 17:38:17  time: 1.0010  data_time: 0.0175  memory: 68881  top1_acc: 0.5000  top5_acc: 0.8750  loss_cls: 1.2518  loss: 1.2518
2022/08/06 16:27:13 - mmengine - INFO - Epoch(train) [14][700/3757]  lr: 6.0398e-05  eta: 17:36:37  time: 1.0085  data_time: 0.0170  memory: 68881  top1_acc: 0.6250  top5_acc: 0.7500  loss_cls: 1.1824  loss: 1.1824
2022/08/06 16:28:54 - mmengine - INFO - Epoch(train) [14][800/3757]  lr: 6.0398e-05  eta: 17:34:57  time: 1.0025  data_time: 0.0167  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.2940  loss: 1.2940
2022/08/06 16:30:35 - mmengine - INFO - Epoch(train) [14][900/3757]  lr: 6.0398e-05  eta: 17:33:18  time: 1.0079  data_time: 0.0175  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 1.3599  loss: 1.3599
2022/08/06 16:32:15 - mmengine - INFO - Epoch(train) [14][1000/3757]  lr: 6.0398e-05  eta: 17:31:37  time: 1.0029  data_time: 0.0196  memory: 68881  top1_acc: 0.6250  top5_acc: 0.7500  loss_cls: 1.1524  loss: 1.1524
2022/08/06 16:33:56 - mmengine - INFO - Epoch(train) [14][1100/3757]  lr: 6.0398e-05  eta: 17:29:58  time: 1.0039  data_time: 0.0171  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.2955  loss: 1.2955
2022/08/06 16:34:56 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 16:35:38 - mmengine - INFO - Epoch(train) [14][1200/3757]  lr: 6.0398e-05  eta: 17:28:19  time: 1.0146  data_time: 0.0167  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.9701  loss: 0.9701
2022/08/06 16:37:18 - mmengine - INFO - Epoch(train) [14][1300/3757]  lr: 6.0398e-05  eta: 17:26:39  time: 1.0020  data_time: 0.0159  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 1.1284  loss: 1.1284
2022/08/06 16:38:59 - mmengine - INFO - Epoch(train) [14][1400/3757]  lr: 6.0398e-05  eta: 17:24:59  time: 1.0154  data_time: 0.0170  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 0.9435  loss: 0.9435
2022/08/06 16:40:40 - mmengine - INFO - Epoch(train) [14][1500/3757]  lr: 6.0398e-05  eta: 17:23:19  time: 1.0019  data_time: 0.0172  memory: 68881  top1_acc: 0.6250  top5_acc: 0.7500  loss_cls: 1.0884  loss: 1.0884
2022/08/06 16:42:20 - mmengine - INFO - Epoch(train) [14][1600/3757]  lr: 6.0398e-05  eta: 17:21:38  time: 1.0017  data_time: 0.0179  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 1.3534  loss: 1.3534
2022/08/06 16:44:01 - mmengine - INFO - Epoch(train) [14][1700/3757]  lr: 6.0398e-05  eta: 17:19:59  time: 1.0005  data_time: 0.0181  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 1.3358  loss: 1.3358
2022/08/06 16:45:42 - mmengine - INFO - Epoch(train) [14][1800/3757]  lr: 6.0398e-05  eta: 17:18:18  time: 1.0058  data_time: 0.0178  memory: 68881  top1_acc: 0.5000  top5_acc: 0.8750  loss_cls: 1.3861  loss: 1.3861
2022/08/06 16:47:22 - mmengine - INFO - Epoch(train) [14][1900/3757]  lr: 6.0398e-05  eta: 17:16:38  time: 1.0041  data_time: 0.0189  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 1.0908  loss: 1.0908
2022/08/06 16:49:04 - mmengine - INFO - Epoch(train) [14][2000/3757]  lr: 6.0398e-05  eta: 17:15:00  time: 1.0107  data_time: 0.0251  memory: 68881  top1_acc: 0.7500  top5_acc: 0.7500  loss_cls: 1.4476  loss: 1.4476
2022/08/06 16:50:45 - mmengine - INFO - Epoch(train) [14][2100/3757]  lr: 6.0398e-05  eta: 17:13:20  time: 1.0013  data_time: 0.0189  memory: 68881  top1_acc: 0.5000  top5_acc: 1.0000  loss_cls: 1.0612  loss: 1.0612
2022/08/06 16:51:44 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 16:52:25 - mmengine - INFO - Epoch(train) [14][2200/3757]  lr: 6.0398e-05  eta: 17:11:40  time: 1.0062  data_time: 0.0180  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.2691  loss: 1.2691
2022/08/06 16:54:06 - mmengine - INFO - Epoch(train) [14][2300/3757]  lr: 6.0398e-05  eta: 17:09:59  time: 1.0042  data_time: 0.0182  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.5068  loss: 1.5068
2022/08/06 16:55:46 - mmengine - INFO - Epoch(train) [14][2400/3757]  lr: 6.0398e-05  eta: 17:08:19  time: 1.0041  data_time: 0.0192  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.4308  loss: 1.4308
2022/08/06 16:57:27 - mmengine - INFO - Epoch(train) [14][2500/3757]  lr: 6.0398e-05  eta: 17:06:39  time: 1.0156  data_time: 0.0173  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 1.1860  loss: 1.1860
2022/08/06 16:59:09 - mmengine - INFO - Epoch(train) [14][2600/3757]  lr: 6.0398e-05  eta: 17:05:00  time: 1.0279  data_time: 0.0187  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 1.2285  loss: 1.2285
2022/08/06 17:00:50 - mmengine - INFO - Epoch(train) [14][2700/3757]  lr: 6.0398e-05  eta: 17:03:21  time: 1.0129  data_time: 0.0187  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 1.2624  loss: 1.2624
2022/08/06 17:02:31 - mmengine - INFO - Epoch(train) [14][2800/3757]  lr: 6.0398e-05  eta: 17:01:41  time: 1.0082  data_time: 0.0182  memory: 68881  top1_acc: 0.6250  top5_acc: 1.0000  loss_cls: 1.1864  loss: 1.1864
2022/08/06 17:04:12 - mmengine - INFO - Epoch(train) [14][2900/3757]  lr: 6.0398e-05  eta: 17:00:02  time: 1.0025  data_time: 0.0179  memory: 68881  top1_acc: 0.6250  top5_acc: 1.0000  loss_cls: 0.9816  loss: 0.9816
2022/08/06 17:05:53 - mmengine - INFO - Epoch(train) [14][3000/3757]  lr: 6.0398e-05  eta: 16:58:22  time: 1.0003  data_time: 0.0172  memory: 68881  top1_acc: 0.7500  top5_acc: 0.7500  loss_cls: 1.3009  loss: 1.3009
2022/08/06 17:07:35 - mmengine - INFO - Epoch(train) [14][3100/3757]  lr: 6.0398e-05  eta: 16:56:43  time: 1.0252  data_time: 0.0180  memory: 68881  top1_acc: 0.6250  top5_acc: 0.7500  loss_cls: 1.0505  loss: 1.0505
2022/08/06 17:08:34 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 17:09:15 - mmengine - INFO - Epoch(train) [14][3200/3757]  lr: 6.0398e-05  eta: 16:55:03  time: 1.0055  data_time: 0.0173  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 1.2530  loss: 1.2530
2022/08/06 17:10:56 - mmengine - INFO - Epoch(train) [14][3300/3757]  lr: 6.0398e-05  eta: 16:53:22  time: 1.0052  data_time: 0.0197  memory: 68881  top1_acc: 0.7500  top5_acc: 0.7500  loss_cls: 1.1461  loss: 1.1461
2022/08/06 17:12:37 - mmengine - INFO - Epoch(train) [14][3400/3757]  lr: 6.0398e-05  eta: 16:51:43  time: 1.0155  data_time: 0.0174  memory: 68881  top1_acc: 0.5000  top5_acc: 0.6250  loss_cls: 1.3634  loss: 1.3634
2022/08/06 17:14:18 - mmengine - INFO - Epoch(train) [14][3500/3757]  lr: 6.0398e-05  eta: 16:50:03  time: 1.0049  data_time: 0.0197  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.1596  loss: 1.1596
2022/08/06 17:15:58 - mmengine - INFO - Epoch(train) [14][3600/3757]  lr: 6.0398e-05  eta: 16:48:23  time: 1.0029  data_time: 0.0170  memory: 68881  top1_acc: 0.1250  top5_acc: 0.6250  loss_cls: 1.2917  loss: 1.2917
2022/08/06 17:17:41 - mmengine - INFO - Epoch(train) [14][3700/3757]  lr: 6.0398e-05  eta: 16:46:45  time: 1.0384  data_time: 0.0193  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.2217  loss: 1.2217
2022/08/06 17:18:40 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 17:18:40 - mmengine - INFO - Epoch(train) [14][3757/3757]  lr: 6.0398e-05  eta: 16:46:06  time: 1.0297  data_time: 0.0180  memory: 68881  top1_acc: 0.7143  top5_acc: 0.8571  loss_cls: 1.1517  loss: 1.1517
2022/08/06 17:20:26 - mmengine - INFO - Epoch(train) [15][100/3757]  lr: 5.5229e-05  eta: 16:43:56  time: 1.0312  data_time: 0.0233  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 0.9174  loss: 0.9174
2022/08/06 17:22:09 - mmengine - INFO - Epoch(train) [15][200/3757]  lr: 5.5229e-05  eta: 16:42:19  time: 1.0152  data_time: 0.0178  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 1.3070  loss: 1.3070
2022/08/06 17:23:50 - mmengine - INFO - Epoch(train) [15][300/3757]  lr: 5.5229e-05  eta: 16:40:39  time: 1.0200  data_time: 0.0178  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 1.2622  loss: 1.2622
2022/08/06 17:25:32 - mmengine - INFO - Epoch(train) [15][400/3757]  lr: 5.5229e-05  eta: 16:39:01  time: 1.0551  data_time: 0.0175  memory: 68881  top1_acc: 0.3750  top5_acc: 0.6250  loss_cls: 1.1977  loss: 1.1977
2022/08/06 17:25:34 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 17:27:14 - mmengine - INFO - Epoch(train) [15][500/3757]  lr: 5.5229e-05  eta: 16:37:22  time: 1.0060  data_time: 0.0176  memory: 68881  top1_acc: 0.5000  top5_acc: 0.7500  loss_cls: 1.5170  loss: 1.5170
2022/08/06 17:28:55 - mmengine - INFO - Epoch(train) [15][600/3757]  lr: 5.5229e-05  eta: 16:35:42  time: 1.0085  data_time: 0.0166  memory: 68881  top1_acc: 0.7500  top5_acc: 0.7500  loss_cls: 1.0970  loss: 1.0970
2022/08/06 17:30:36 - mmengine - INFO - Epoch(train) [15][700/3757]  lr: 5.5229e-05  eta: 16:34:02  time: 1.0016  data_time: 0.0177  memory: 68881  top1_acc: 0.5000  top5_acc: 1.0000  loss_cls: 1.2321  loss: 1.2321
2022/08/06 17:32:17 - mmengine - INFO - Epoch(train) [15][800/3757]  lr: 5.5229e-05  eta: 16:32:22  time: 1.0088  data_time: 0.0185  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 1.2554  loss: 1.2554
2022/08/06 17:33:59 - mmengine - INFO - Epoch(train) [15][900/3757]  lr: 5.5229e-05  eta: 16:30:44  time: 1.0190  data_time: 0.0193  memory: 68881  top1_acc: 0.6250  top5_acc: 0.7500  loss_cls: 1.0239  loss: 1.0239
2022/08/06 17:35:40 - mmengine - INFO - Epoch(train) [15][1000/3757]  lr: 5.5229e-05  eta: 16:29:04  time: 1.0024  data_time: 0.0179  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 1.3402  loss: 1.3402
2022/08/06 17:37:22 - mmengine - INFO - Epoch(train) [15][1100/3757]  lr: 5.5229e-05  eta: 16:27:25  time: 1.0045  data_time: 0.0182  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 1.1876  loss: 1.1876
2022/08/06 17:39:03 - mmengine - INFO - Epoch(train) [15][1200/3757]  lr: 5.5229e-05  eta: 16:25:45  time: 1.0182  data_time: 0.0176  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 0.9886  loss: 0.9886
2022/08/06 17:40:44 - mmengine - INFO - Epoch(train) [15][1300/3757]  lr: 5.5229e-05  eta: 16:24:05  time: 1.0006  data_time: 0.0177  memory: 68881  top1_acc: 0.6250  top5_acc: 0.7500  loss_cls: 1.0094  loss: 1.0094
2022/08/06 17:42:25 - mmengine - INFO - Epoch(train) [15][1400/3757]  lr: 5.5229e-05  eta: 16:22:26  time: 1.0194  data_time: 0.0176  memory: 68881  top1_acc: 0.5000  top5_acc: 1.0000  loss_cls: 0.9730  loss: 0.9730
2022/08/06 17:42:27 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 17:44:06 - mmengine - INFO - Epoch(train) [15][1500/3757]  lr: 5.5229e-05  eta: 16:20:46  time: 1.0025  data_time: 0.0172  memory: 68881  top1_acc: 0.5000  top5_acc: 0.7500  loss_cls: 1.1226  loss: 1.1226
2022/08/06 17:45:46 - mmengine - INFO - Epoch(train) [15][1600/3757]  lr: 5.5229e-05  eta: 16:19:06  time: 1.0012  data_time: 0.0174  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 1.3314  loss: 1.3314
2022/08/06 17:47:27 - mmengine - INFO - Epoch(train) [15][1700/3757]  lr: 5.5229e-05  eta: 16:17:25  time: 1.0014  data_time: 0.0174  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.2413  loss: 1.2413
2022/08/06 17:49:07 - mmengine - INFO - Epoch(train) [15][1800/3757]  lr: 5.5229e-05  eta: 16:15:44  time: 1.0009  data_time: 0.0166  memory: 68881  top1_acc: 0.5000  top5_acc: 0.7500  loss_cls: 1.1416  loss: 1.1416
2022/08/06 17:50:47 - mmengine - INFO - Epoch(train) [15][1900/3757]  lr: 5.5229e-05  eta: 16:14:04  time: 0.9993  data_time: 0.0167  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 1.2140  loss: 1.2140
2022/08/06 17:52:27 - mmengine - INFO - Epoch(train) [15][2000/3757]  lr: 5.5229e-05  eta: 16:12:23  time: 1.0033  data_time: 0.0166  memory: 68881  top1_acc: 0.6250  top5_acc: 1.0000  loss_cls: 1.0677  loss: 1.0677
2022/08/06 17:54:07 - mmengine - INFO - Epoch(train) [15][2100/3757]  lr: 5.5229e-05  eta: 16:10:42  time: 0.9993  data_time: 0.0166  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 1.3039  loss: 1.3039
2022/08/06 17:55:47 - mmengine - INFO - Epoch(train) [15][2200/3757]  lr: 5.5229e-05  eta: 16:09:02  time: 1.0012  data_time: 0.0166  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.4415  loss: 1.4415
2022/08/06 17:57:27 - mmengine - INFO - Epoch(train) [15][2300/3757]  lr: 5.5229e-05  eta: 16:07:21  time: 0.9989  data_time: 0.0170  memory: 68881  top1_acc: 0.7500  top5_acc: 0.7500  loss_cls: 1.1981  loss: 1.1981
2022/08/06 17:59:08 - mmengine - INFO - Epoch(train) [15][2400/3757]  lr: 5.5229e-05  eta: 16:05:40  time: 1.0008  data_time: 0.0172  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 1.3853  loss: 1.3853
2022/08/06 17:59:10 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 18:00:49 - mmengine - INFO - Epoch(train) [15][2500/3757]  lr: 5.5229e-05  eta: 16:04:01  time: 1.0017  data_time: 0.0167  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.2477  loss: 1.2477
2022/08/06 18:02:29 - mmengine - INFO - Epoch(train) [15][2600/3757]  lr: 5.5229e-05  eta: 16:02:21  time: 1.0003  data_time: 0.0175  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 1.3115  loss: 1.3115
2022/08/06 18:04:10 - mmengine - INFO - Epoch(train) [15][2700/3757]  lr: 5.5229e-05  eta: 16:00:40  time: 0.9997  data_time: 0.0165  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 1.1361  loss: 1.1361
2022/08/06 18:05:50 - mmengine - INFO - Epoch(train) [15][2800/3757]  lr: 5.5229e-05  eta: 15:58:59  time: 1.0039  data_time: 0.0177  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 1.1828  loss: 1.1828
2022/08/06 18:07:30 - mmengine - INFO - Epoch(train) [15][2900/3757]  lr: 5.5229e-05  eta: 15:57:19  time: 1.0002  data_time: 0.0166  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 1.1864  loss: 1.1864
2022/08/06 18:09:11 - mmengine - INFO - Epoch(train) [15][3000/3757]  lr: 5.5229e-05  eta: 15:55:38  time: 1.0028  data_time: 0.0169  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 1.2201  loss: 1.2201
2022/08/06 18:10:51 - mmengine - INFO - Epoch(train) [15][3100/3757]  lr: 5.5229e-05  eta: 15:53:58  time: 1.0028  data_time: 0.0178  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.9069  loss: 0.9069
2022/08/06 18:12:31 - mmengine - INFO - Epoch(train) [15][3200/3757]  lr: 5.5229e-05  eta: 15:52:18  time: 1.0005  data_time: 0.0167  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 1.3907  loss: 1.3907
2022/08/06 18:14:12 - mmengine - INFO - Epoch(train) [15][3300/3757]  lr: 5.5229e-05  eta: 15:50:37  time: 1.0051  data_time: 0.0180  memory: 68881  top1_acc: 0.5000  top5_acc: 0.5000  loss_cls: 1.0045  loss: 1.0045
2022/08/06 18:15:52 - mmengine - INFO - Epoch(train) [15][3400/3757]  lr: 5.5229e-05  eta: 15:48:56  time: 1.0008  data_time: 0.0171  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 0.9839  loss: 0.9839
2022/08/06 18:15:54 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 18:17:32 - mmengine - INFO - Epoch(train) [15][3500/3757]  lr: 5.5229e-05  eta: 15:47:16  time: 0.9998  data_time: 0.0168  memory: 68881  top1_acc: 0.2500  top5_acc: 0.8750  loss_cls: 1.3272  loss: 1.3272
2022/08/06 18:19:13 - mmengine - INFO - Epoch(train) [15][3600/3757]  lr: 5.5229e-05  eta: 15:45:36  time: 1.0137  data_time: 0.0166  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 1.2079  loss: 1.2079
2022/08/06 18:20:53 - mmengine - INFO - Epoch(train) [15][3700/3757]  lr: 5.5229e-05  eta: 15:43:55  time: 1.0016  data_time: 0.0171  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.1264  loss: 1.1264
2022/08/06 18:21:50 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 18:21:50 - mmengine - INFO - Epoch(train) [15][3757/3757]  lr: 5.5229e-05  eta: 15:43:15  time: 0.9952  data_time: 0.0181  memory: 68881  top1_acc: 0.7143  top5_acc: 0.8571  loss_cls: 1.4337  loss: 1.4337
2022/08/06 18:21:50 - mmengine - INFO - Saving checkpoint at 15 epochs
2022/08/06 18:22:49 - mmengine - INFO - Epoch(val) [15][100/310]    eta: 0:01:28  time: 0.4221  data_time: 0.0148  memory: 14218  
2022/08/06 18:23:30 - mmengine - INFO - Epoch(val) [15][200/310]    eta: 0:00:45  time: 0.4152  data_time: 0.0105  memory: 14218  
2022/08/06 18:24:11 - mmengine - INFO - Epoch(val) [15][300/310]    eta: 0:00:04  time: 0.4077  data_time: 0.0105  memory: 14218  
2022/08/06 18:24:16 - mmengine - INFO - Epoch(val) [15][310/310]  acc/top1: 0.7593  acc/top5: 0.9224  acc/mean1: 0.7592
2022/08/06 18:24:16 - mmengine - INFO - The previous best checkpoint /mnt/lustre/daiwenxun/X/swin/mmaction2/work_dirs/swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb/best_acc/top1_epoch_13.pth is removed
2022/08/06 18:24:22 - mmengine - INFO - The best checkpoint with 0.7593 acc/top1 at 16 epoch is saved to best_acc/top1_epoch_16.pth.
2022/08/06 18:26:03 - mmengine - INFO - Epoch(train) [16][100/3757]  lr: 5.0002e-05  eta: 15:41:02  time: 1.0042  data_time: 0.0171  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 1.2860  loss: 1.2860
2022/08/06 18:27:43 - mmengine - INFO - Epoch(train) [16][200/3757]  lr: 5.0002e-05  eta: 15:39:21  time: 1.0002  data_time: 0.0168  memory: 68881  top1_acc: 0.3750  top5_acc: 0.7500  loss_cls: 1.2794  loss: 1.2794
2022/08/06 18:29:24 - mmengine - INFO - Epoch(train) [16][300/3757]  lr: 5.0002e-05  eta: 15:37:40  time: 1.0007  data_time: 0.0173  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 1.3377  loss: 1.3377
2022/08/06 18:31:04 - mmengine - INFO - Epoch(train) [16][400/3757]  lr: 5.0002e-05  eta: 15:36:00  time: 1.0022  data_time: 0.0178  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.1426  loss: 1.1426
2022/08/06 18:32:45 - mmengine - INFO - Epoch(train) [16][500/3757]  lr: 5.0002e-05  eta: 15:34:20  time: 1.0008  data_time: 0.0171  memory: 68881  top1_acc: 0.5000  top5_acc: 0.7500  loss_cls: 1.0846  loss: 1.0846
2022/08/06 18:34:25 - mmengine - INFO - Epoch(train) [16][600/3757]  lr: 5.0002e-05  eta: 15:32:40  time: 0.9996  data_time: 0.0167  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 1.0783  loss: 1.0783
2022/08/06 18:35:10 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 18:36:06 - mmengine - INFO - Epoch(train) [16][700/3757]  lr: 5.0002e-05  eta: 15:31:00  time: 1.0008  data_time: 0.0173  memory: 68881  top1_acc: 0.7500  top5_acc: 0.7500  loss_cls: 1.0627  loss: 1.0627
2022/08/06 18:37:46 - mmengine - INFO - Epoch(train) [16][800/3757]  lr: 5.0002e-05  eta: 15:29:19  time: 1.0009  data_time: 0.0172  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.0435  loss: 1.0435
2022/08/06 18:39:26 - mmengine - INFO - Epoch(train) [16][900/3757]  lr: 5.0002e-05  eta: 15:27:38  time: 1.0017  data_time: 0.0174  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 0.9442  loss: 0.9442
2022/08/06 18:41:07 - mmengine - INFO - Epoch(train) [16][1000/3757]  lr: 5.0002e-05  eta: 15:25:59  time: 1.0016  data_time: 0.0183  memory: 68881  top1_acc: 0.5000  top5_acc: 0.8750  loss_cls: 1.1341  loss: 1.1341
2022/08/06 18:42:47 - mmengine - INFO - Epoch(train) [16][1100/3757]  lr: 5.0002e-05  eta: 15:24:18  time: 1.0034  data_time: 0.0175  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.1534  loss: 1.1534
2022/08/06 18:44:28 - mmengine - INFO - Epoch(train) [16][1200/3757]  lr: 5.0002e-05  eta: 15:22:38  time: 1.0028  data_time: 0.0179  memory: 68881  top1_acc: 0.7500  top5_acc: 0.7500  loss_cls: 1.4435  loss: 1.4435
2022/08/06 18:46:09 - mmengine - INFO - Epoch(train) [16][1300/3757]  lr: 5.0002e-05  eta: 15:20:58  time: 1.0005  data_time: 0.0168  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 0.8882  loss: 0.8882
2022/08/06 18:47:49 - mmengine - INFO - Epoch(train) [16][1400/3757]  lr: 5.0002e-05  eta: 15:19:17  time: 1.0018  data_time: 0.0176  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 1.2586  loss: 1.2586
2022/08/06 18:49:29 - mmengine - INFO - Epoch(train) [16][1500/3757]  lr: 5.0002e-05  eta: 15:17:37  time: 1.0094  data_time: 0.0176  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.2472  loss: 1.2472
2022/08/06 18:51:10 - mmengine - INFO - Epoch(train) [16][1600/3757]  lr: 5.0002e-05  eta: 15:15:57  time: 1.0017  data_time: 0.0173  memory: 68881  top1_acc: 0.5000  top5_acc: 0.6250  loss_cls: 1.3565  loss: 1.3565
2022/08/06 18:51:55 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 18:52:51 - mmengine - INFO - Epoch(train) [16][1700/3757]  lr: 5.0002e-05  eta: 15:14:17  time: 1.0048  data_time: 0.0174  memory: 68881  top1_acc: 0.6250  top5_acc: 1.0000  loss_cls: 1.1608  loss: 1.1608
2022/08/06 18:54:31 - mmengine - INFO - Epoch(train) [16][1800/3757]  lr: 5.0002e-05  eta: 15:12:37  time: 1.0022  data_time: 0.0176  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.1139  loss: 1.1139
2022/08/06 18:56:12 - mmengine - INFO - Epoch(train) [16][1900/3757]  lr: 5.0002e-05  eta: 15:10:56  time: 1.0025  data_time: 0.0166  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 1.2462  loss: 1.2462
2022/08/06 18:57:52 - mmengine - INFO - Epoch(train) [16][2000/3757]  lr: 5.0002e-05  eta: 15:09:16  time: 0.9988  data_time: 0.0179  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 1.0798  loss: 1.0798
2022/08/06 18:59:32 - mmengine - INFO - Epoch(train) [16][2100/3757]  lr: 5.0002e-05  eta: 15:07:35  time: 1.0019  data_time: 0.0166  memory: 68881  top1_acc: 0.3750  top5_acc: 0.3750  loss_cls: 1.1888  loss: 1.1888
2022/08/06 19:01:13 - mmengine - INFO - Epoch(train) [16][2200/3757]  lr: 5.0002e-05  eta: 15:05:55  time: 1.0079  data_time: 0.0176  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.2439  loss: 1.2439
2022/08/06 19:02:53 - mmengine - INFO - Epoch(train) [16][2300/3757]  lr: 5.0002e-05  eta: 15:04:15  time: 1.0027  data_time: 0.0167  memory: 68881  top1_acc: 0.5000  top5_acc: 1.0000  loss_cls: 1.1004  loss: 1.1004
2022/08/06 19:04:34 - mmengine - INFO - Epoch(train) [16][2400/3757]  lr: 5.0002e-05  eta: 15:02:35  time: 1.0103  data_time: 0.0178  memory: 68881  top1_acc: 0.6250  top5_acc: 0.7500  loss_cls: 1.0583  loss: 1.0583
2022/08/06 19:06:14 - mmengine - INFO - Epoch(train) [16][2500/3757]  lr: 5.0002e-05  eta: 15:00:54  time: 1.0011  data_time: 0.0170  memory: 68881  top1_acc: 0.6250  top5_acc: 0.6250  loss_cls: 1.1963  loss: 1.1963
2022/08/06 19:07:55 - mmengine - INFO - Epoch(train) [16][2600/3757]  lr: 5.0002e-05  eta: 14:59:15  time: 1.0132  data_time: 0.0171  memory: 68881  top1_acc: 0.5000  top5_acc: 0.7500  loss_cls: 1.2509  loss: 1.2509
2022/08/06 19:08:42 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 19:09:37 - mmengine - INFO - Epoch(train) [16][2700/3757]  lr: 5.0002e-05  eta: 14:57:35  time: 1.0051  data_time: 0.0173  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 1.1390  loss: 1.1390
2022/08/06 19:11:18 - mmengine - INFO - Epoch(train) [16][2800/3757]  lr: 5.0002e-05  eta: 14:55:56  time: 1.0005  data_time: 0.0168  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.3006  loss: 1.3006
2022/08/06 19:12:59 - mmengine - INFO - Epoch(train) [16][2900/3757]  lr: 5.0002e-05  eta: 14:54:15  time: 1.0015  data_time: 0.0167  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 1.1655  loss: 1.1655
2022/08/06 19:14:40 - mmengine - INFO - Epoch(train) [16][3000/3757]  lr: 5.0002e-05  eta: 14:52:35  time: 1.0125  data_time: 0.0171  memory: 68881  top1_acc: 0.5000  top5_acc: 0.8750  loss_cls: 1.4553  loss: 1.4553
2022/08/06 19:16:20 - mmengine - INFO - Epoch(train) [16][3100/3757]  lr: 5.0002e-05  eta: 14:50:56  time: 1.0135  data_time: 0.0172  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 1.1986  loss: 1.1986
2022/08/06 19:18:01 - mmengine - INFO - Epoch(train) [16][3200/3757]  lr: 5.0002e-05  eta: 14:49:15  time: 1.0059  data_time: 0.0169  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 1.3628  loss: 1.3628
2022/08/06 19:19:42 - mmengine - INFO - Epoch(train) [16][3300/3757]  lr: 5.0002e-05  eta: 14:47:35  time: 1.0031  data_time: 0.0177  memory: 68881  top1_acc: 0.6250  top5_acc: 1.0000  loss_cls: 1.1007  loss: 1.1007
2022/08/06 19:21:23 - mmengine - INFO - Epoch(train) [16][3400/3757]  lr: 5.0002e-05  eta: 14:45:56  time: 1.0197  data_time: 0.0190  memory: 68881  top1_acc: 0.3750  top5_acc: 0.7500  loss_cls: 1.1044  loss: 1.1044
2022/08/06 19:23:04 - mmengine - INFO - Epoch(train) [16][3500/3757]  lr: 5.0002e-05  eta: 14:44:16  time: 1.0034  data_time: 0.0169  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 1.2223  loss: 1.2223
2022/08/06 19:24:45 - mmengine - INFO - Epoch(train) [16][3600/3757]  lr: 5.0002e-05  eta: 14:42:36  time: 1.0186  data_time: 0.0177  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 0.9057  loss: 0.9057
2022/08/06 19:25:31 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 19:26:26 - mmengine - INFO - Epoch(train) [16][3700/3757]  lr: 5.0002e-05  eta: 14:40:56  time: 1.0003  data_time: 0.0170  memory: 68881  top1_acc: 0.5000  top5_acc: 0.5000  loss_cls: 1.5488  loss: 1.5488
2022/08/06 19:27:23 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 19:27:23 - mmengine - INFO - Epoch(train) [16][3757/3757]  lr: 5.0002e-05  eta: 14:40:15  time: 0.9926  data_time: 0.0163  memory: 68881  top1_acc: 0.7143  top5_acc: 0.8571  loss_cls: 0.8979  loss: 0.8979
2022/08/06 19:29:05 - mmengine - INFO - Epoch(train) [17][100/3757]  lr: 4.4776e-05  eta: 14:38:05  time: 1.0009  data_time: 0.0169  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 1.0851  loss: 1.0851
2022/08/06 19:30:46 - mmengine - INFO - Epoch(train) [17][200/3757]  lr: 4.4776e-05  eta: 14:36:25  time: 0.9996  data_time: 0.0166  memory: 68881  top1_acc: 0.3750  top5_acc: 0.8750  loss_cls: 1.2386  loss: 1.2386
2022/08/06 19:32:26 - mmengine - INFO - Epoch(train) [17][300/3757]  lr: 4.4776e-05  eta: 14:34:44  time: 1.0006  data_time: 0.0165  memory: 68881  top1_acc: 0.5000  top5_acc: 0.7500  loss_cls: 1.1240  loss: 1.1240
2022/08/06 19:34:07 - mmengine - INFO - Epoch(train) [17][400/3757]  lr: 4.4776e-05  eta: 14:33:04  time: 1.0168  data_time: 0.0184  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 0.9167  loss: 0.9167
2022/08/06 19:35:48 - mmengine - INFO - Epoch(train) [17][500/3757]  lr: 4.4776e-05  eta: 14:31:24  time: 1.0045  data_time: 0.0170  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 1.1392  loss: 1.1392
2022/08/06 19:37:29 - mmengine - INFO - Epoch(train) [17][600/3757]  lr: 4.4776e-05  eta: 14:29:45  time: 1.0102  data_time: 0.0180  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 1.1611  loss: 1.1611
2022/08/06 19:39:09 - mmengine - INFO - Epoch(train) [17][700/3757]  lr: 4.4776e-05  eta: 14:28:04  time: 1.0026  data_time: 0.0174  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 1.0930  loss: 1.0930
2022/08/06 19:40:50 - mmengine - INFO - Epoch(train) [17][800/3757]  lr: 4.4776e-05  eta: 14:26:24  time: 1.0046  data_time: 0.0176  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.2322  loss: 1.2322
2022/08/06 19:42:18 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 19:42:30 - mmengine - INFO - Epoch(train) [17][900/3757]  lr: 4.4776e-05  eta: 14:24:44  time: 1.0035  data_time: 0.0177  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.9437  loss: 0.9437
2022/08/06 19:44:11 - mmengine - INFO - Epoch(train) [17][1000/3757]  lr: 4.4776e-05  eta: 14:23:04  time: 1.0025  data_time: 0.0180  memory: 68881  top1_acc: 0.5000  top5_acc: 0.7500  loss_cls: 1.2612  loss: 1.2612
2022/08/06 19:45:51 - mmengine - INFO - Epoch(train) [17][1100/3757]  lr: 4.4776e-05  eta: 14:21:23  time: 1.0012  data_time: 0.0180  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 1.0554  loss: 1.0554
2022/08/06 19:47:32 - mmengine - INFO - Epoch(train) [17][1200/3757]  lr: 4.4776e-05  eta: 14:19:43  time: 0.9996  data_time: 0.0168  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.0952  loss: 1.0952
2022/08/06 19:49:12 - mmengine - INFO - Epoch(train) [17][1300/3757]  lr: 4.4776e-05  eta: 14:18:02  time: 1.0027  data_time: 0.0176  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.0610  loss: 1.0610
2022/08/06 19:50:53 - mmengine - INFO - Epoch(train) [17][1400/3757]  lr: 4.4776e-05  eta: 14:16:23  time: 1.0030  data_time: 0.0180  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 1.1559  loss: 1.1559
2022/08/06 19:52:33 - mmengine - INFO - Epoch(train) [17][1500/3757]  lr: 4.4776e-05  eta: 14:14:42  time: 1.0085  data_time: 0.0169  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 1.1100  loss: 1.1100
2022/08/06 19:54:14 - mmengine - INFO - Epoch(train) [17][1600/3757]  lr: 4.4776e-05  eta: 14:13:02  time: 1.0005  data_time: 0.0170  memory: 68881  top1_acc: 0.5000  top5_acc: 0.8750  loss_cls: 1.3591  loss: 1.3591
2022/08/06 19:55:54 - mmengine - INFO - Epoch(train) [17][1700/3757]  lr: 4.4776e-05  eta: 14:11:21  time: 1.0003  data_time: 0.0166  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.2022  loss: 1.2022
2022/08/06 19:57:34 - mmengine - INFO - Epoch(train) [17][1800/3757]  lr: 4.4776e-05  eta: 14:09:41  time: 1.0005  data_time: 0.0175  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.7846  loss: 0.7846
2022/08/06 19:59:02 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 19:59:14 - mmengine - INFO - Epoch(train) [17][1900/3757]  lr: 4.4776e-05  eta: 14:08:00  time: 1.0002  data_time: 0.0174  memory: 68881  top1_acc: 0.7500  top5_acc: 0.7500  loss_cls: 1.0343  loss: 1.0343
2022/08/06 20:00:54 - mmengine - INFO - Epoch(train) [17][2000/3757]  lr: 4.4776e-05  eta: 14:06:20  time: 0.9999  data_time: 0.0172  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 0.9919  loss: 0.9919
2022/08/06 20:02:34 - mmengine - INFO - Epoch(train) [17][2100/3757]  lr: 4.4776e-05  eta: 14:04:39  time: 0.9997  data_time: 0.0186  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.1855  loss: 1.1855
2022/08/06 20:04:15 - mmengine - INFO - Epoch(train) [17][2200/3757]  lr: 4.4776e-05  eta: 14:02:59  time: 1.0005  data_time: 0.0174  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 0.9059  loss: 0.9059
2022/08/06 20:05:55 - mmengine - INFO - Epoch(train) [17][2300/3757]  lr: 4.4776e-05  eta: 14:01:18  time: 1.0007  data_time: 0.0178  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.1319  loss: 1.1319
2022/08/06 20:07:35 - mmengine - INFO - Epoch(train) [17][2400/3757]  lr: 4.4776e-05  eta: 13:59:38  time: 1.0025  data_time: 0.0174  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.0163  loss: 1.0163
2022/08/06 20:09:16 - mmengine - INFO - Epoch(train) [17][2500/3757]  lr: 4.4776e-05  eta: 13:57:57  time: 1.0003  data_time: 0.0176  memory: 68881  top1_acc: 0.5000  top5_acc: 0.8750  loss_cls: 1.2737  loss: 1.2737
2022/08/06 20:10:56 - mmengine - INFO - Epoch(train) [17][2600/3757]  lr: 4.4776e-05  eta: 13:56:17  time: 1.0005  data_time: 0.0168  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 1.0648  loss: 1.0648
2022/08/06 20:12:36 - mmengine - INFO - Epoch(train) [17][2700/3757]  lr: 4.4776e-05  eta: 13:54:37  time: 1.0104  data_time: 0.0167  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.1212  loss: 1.1212
2022/08/06 20:14:18 - mmengine - INFO - Epoch(train) [17][2800/3757]  lr: 4.4776e-05  eta: 13:52:57  time: 1.0123  data_time: 0.0176  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.1249  loss: 1.1249
2022/08/06 20:15:46 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 20:15:58 - mmengine - INFO - Epoch(train) [17][2900/3757]  lr: 4.4776e-05  eta: 13:51:17  time: 1.0057  data_time: 0.0186  memory: 68881  top1_acc: 0.6250  top5_acc: 1.0000  loss_cls: 1.2459  loss: 1.2459
2022/08/06 20:17:39 - mmengine - INFO - Epoch(train) [17][3000/3757]  lr: 4.4776e-05  eta: 13:49:37  time: 1.0001  data_time: 0.0172  memory: 68881  top1_acc: 0.7500  top5_acc: 0.7500  loss_cls: 1.1563  loss: 1.1563
2022/08/06 20:19:20 - mmengine - INFO - Epoch(train) [17][3100/3757]  lr: 4.4776e-05  eta: 13:47:57  time: 1.0065  data_time: 0.0179  memory: 68881  top1_acc: 0.6250  top5_acc: 1.0000  loss_cls: 1.0297  loss: 1.0297
2022/08/06 20:21:00 - mmengine - INFO - Epoch(train) [17][3200/3757]  lr: 4.4776e-05  eta: 13:46:16  time: 0.9988  data_time: 0.0164  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.2833  loss: 1.2833
2022/08/06 20:22:40 - mmengine - INFO - Epoch(train) [17][3300/3757]  lr: 4.4776e-05  eta: 13:44:36  time: 1.0004  data_time: 0.0167  memory: 68881  top1_acc: 0.7500  top5_acc: 0.7500  loss_cls: 1.0564  loss: 1.0564
2022/08/06 20:24:20 - mmengine - INFO - Epoch(train) [17][3400/3757]  lr: 4.4776e-05  eta: 13:42:55  time: 1.0006  data_time: 0.0172  memory: 68881  top1_acc: 0.2500  top5_acc: 0.8750  loss_cls: 1.2363  loss: 1.2363
2022/08/06 20:26:01 - mmengine - INFO - Epoch(train) [17][3500/3757]  lr: 4.4776e-05  eta: 13:41:15  time: 1.0045  data_time: 0.0171  memory: 68881  top1_acc: 0.7500  top5_acc: 0.7500  loss_cls: 1.0352  loss: 1.0352
2022/08/06 20:27:41 - mmengine - INFO - Epoch(train) [17][3600/3757]  lr: 4.4776e-05  eta: 13:39:34  time: 1.0017  data_time: 0.0174  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 1.2459  loss: 1.2459
2022/08/06 20:29:22 - mmengine - INFO - Epoch(train) [17][3700/3757]  lr: 4.4776e-05  eta: 13:37:54  time: 1.0009  data_time: 0.0177  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 0.9646  loss: 0.9646
2022/08/06 20:30:19 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 20:30:19 - mmengine - INFO - Epoch(train) [17][3757/3757]  lr: 4.4776e-05  eta: 13:37:14  time: 0.9950  data_time: 0.0173  memory: 68881  top1_acc: 0.5714  top5_acc: 0.8571  loss_cls: 0.9749  loss: 0.9749
2022/08/06 20:32:02 - mmengine - INFO - Epoch(train) [18][100/3757]  lr: 3.9606e-05  eta: 13:35:06  time: 1.0046  data_time: 0.0168  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.8255  loss: 0.8255
2022/08/06 20:32:33 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 20:33:43 - mmengine - INFO - Epoch(train) [18][200/3757]  lr: 3.9606e-05  eta: 13:33:26  time: 1.0150  data_time: 0.0166  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 1.0311  loss: 1.0311
2022/08/06 20:35:23 - mmengine - INFO - Epoch(train) [18][300/3757]  lr: 3.9606e-05  eta: 13:31:46  time: 0.9984  data_time: 0.0168  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 1.2039  loss: 1.2039
2022/08/06 20:37:04 - mmengine - INFO - Epoch(train) [18][400/3757]  lr: 3.9606e-05  eta: 13:30:05  time: 1.0062  data_time: 0.0172  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 1.1273  loss: 1.1273
2022/08/06 20:38:45 - mmengine - INFO - Epoch(train) [18][500/3757]  lr: 3.9606e-05  eta: 13:28:26  time: 1.0249  data_time: 0.0192  memory: 68881  top1_acc: 0.6250  top5_acc: 0.7500  loss_cls: 1.0631  loss: 1.0631
2022/08/06 20:40:28 - mmengine - INFO - Epoch(train) [18][600/3757]  lr: 3.9606e-05  eta: 13:26:47  time: 1.0316  data_time: 0.0164  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.2099  loss: 1.2099
2022/08/06 20:42:09 - mmengine - INFO - Epoch(train) [18][700/3757]  lr: 3.9606e-05  eta: 13:25:07  time: 1.0094  data_time: 0.0196  memory: 68881  top1_acc: 0.7500  top5_acc: 0.7500  loss_cls: 1.1274  loss: 1.1274
2022/08/06 20:43:51 - mmengine - INFO - Epoch(train) [18][800/3757]  lr: 3.9606e-05  eta: 13:23:28  time: 1.0267  data_time: 0.0222  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.2243  loss: 1.2243
2022/08/06 20:45:32 - mmengine - INFO - Epoch(train) [18][900/3757]  lr: 3.9606e-05  eta: 13:21:49  time: 1.0065  data_time: 0.0186  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 1.2332  loss: 1.2332
2022/08/06 20:47:13 - mmengine - INFO - Epoch(train) [18][1000/3757]  lr: 3.9606e-05  eta: 13:20:09  time: 1.0032  data_time: 0.0183  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 1.0424  loss: 1.0424
2022/08/06 20:48:56 - mmengine - INFO - Epoch(train) [18][1100/3757]  lr: 3.9606e-05  eta: 13:18:30  time: 1.0232  data_time: 0.0179  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.3189  loss: 1.3189
2022/08/06 20:49:27 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 20:50:37 - mmengine - INFO - Epoch(train) [18][1200/3757]  lr: 3.9606e-05  eta: 13:16:50  time: 1.0256  data_time: 0.0196  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 1.0578  loss: 1.0578
2022/08/06 20:52:19 - mmengine - INFO - Epoch(train) [18][1300/3757]  lr: 3.9606e-05  eta: 13:15:11  time: 1.0085  data_time: 0.0166  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.1213  loss: 1.1213
2022/08/06 20:54:00 - mmengine - INFO - Epoch(train) [18][1400/3757]  lr: 3.9606e-05  eta: 13:13:31  time: 1.0046  data_time: 0.0171  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 1.2681  loss: 1.2681
2022/08/06 20:55:40 - mmengine - INFO - Epoch(train) [18][1500/3757]  lr: 3.9606e-05  eta: 13:11:51  time: 0.9998  data_time: 0.0176  memory: 68881  top1_acc: 0.3750  top5_acc: 0.8750  loss_cls: 0.9202  loss: 0.9202
2022/08/06 20:57:21 - mmengine - INFO - Epoch(train) [18][1600/3757]  lr: 3.9606e-05  eta: 13:10:10  time: 1.0101  data_time: 0.0192  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.0871  loss: 1.0871
2022/08/06 20:59:02 - mmengine - INFO - Epoch(train) [18][1700/3757]  lr: 3.9606e-05  eta: 13:08:31  time: 1.0144  data_time: 0.0201  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 0.9945  loss: 0.9945
2022/08/06 21:00:43 - mmengine - INFO - Epoch(train) [18][1800/3757]  lr: 3.9606e-05  eta: 13:06:50  time: 1.0035  data_time: 0.0176  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 1.1088  loss: 1.1088
2022/08/06 21:02:23 - mmengine - INFO - Epoch(train) [18][1900/3757]  lr: 3.9606e-05  eta: 13:05:10  time: 1.0025  data_time: 0.0173  memory: 68881  top1_acc: 0.6250  top5_acc: 1.0000  loss_cls: 1.2122  loss: 1.2122
2022/08/06 21:04:04 - mmengine - INFO - Epoch(train) [18][2000/3757]  lr: 3.9606e-05  eta: 13:03:30  time: 1.0005  data_time: 0.0170  memory: 68881  top1_acc: 0.6250  top5_acc: 0.6250  loss_cls: 1.1233  loss: 1.1233
2022/08/06 21:05:45 - mmengine - INFO - Epoch(train) [18][2100/3757]  lr: 3.9606e-05  eta: 13:01:50  time: 1.0253  data_time: 0.0171  memory: 68881  top1_acc: 0.6250  top5_acc: 1.0000  loss_cls: 0.7925  loss: 0.7925
2022/08/06 21:06:16 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 21:07:26 - mmengine - INFO - Epoch(train) [18][2200/3757]  lr: 3.9606e-05  eta: 13:00:10  time: 1.0022  data_time: 0.0174  memory: 68881  top1_acc: 0.3750  top5_acc: 0.6250  loss_cls: 1.2567  loss: 1.2567
2022/08/06 21:09:14 - mmengine - INFO - Epoch(train) [18][2300/3757]  lr: 3.9606e-05  eta: 12:58:35  time: 1.0308  data_time: 0.0189  memory: 68881  top1_acc: 0.7500  top5_acc: 0.7500  loss_cls: 0.8885  loss: 0.8885
2022/08/06 21:10:56 - mmengine - INFO - Epoch(train) [18][2400/3757]  lr: 3.9606e-05  eta: 12:56:56  time: 0.9995  data_time: 0.0174  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 1.3774  loss: 1.3774
2022/08/06 21:12:36 - mmengine - INFO - Epoch(train) [18][2500/3757]  lr: 3.9606e-05  eta: 12:55:15  time: 1.0027  data_time: 0.0185  memory: 68881  top1_acc: 0.5000  top5_acc: 1.0000  loss_cls: 0.9500  loss: 0.9500
2022/08/06 21:14:16 - mmengine - INFO - Epoch(train) [18][2600/3757]  lr: 3.9606e-05  eta: 12:53:35  time: 1.0005  data_time: 0.0172  memory: 68881  top1_acc: 0.6250  top5_acc: 0.7500  loss_cls: 1.0525  loss: 1.0525
2022/08/06 21:15:57 - mmengine - INFO - Epoch(train) [18][2700/3757]  lr: 3.9606e-05  eta: 12:51:55  time: 1.0162  data_time: 0.0165  memory: 68881  top1_acc: 0.5000  top5_acc: 0.6250  loss_cls: 0.9910  loss: 0.9910
2022/08/06 21:17:38 - mmengine - INFO - Epoch(train) [18][2800/3757]  lr: 3.9606e-05  eta: 12:50:15  time: 1.0011  data_time: 0.0185  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.1489  loss: 1.1489
2022/08/06 21:19:20 - mmengine - INFO - Epoch(train) [18][2900/3757]  lr: 3.9606e-05  eta: 12:48:35  time: 1.0191  data_time: 0.0174  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.1202  loss: 1.1202
2022/08/06 21:21:02 - mmengine - INFO - Epoch(train) [18][3000/3757]  lr: 3.9606e-05  eta: 12:46:56  time: 1.0191  data_time: 0.0256  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 1.2420  loss: 1.2420
2022/08/06 21:22:44 - mmengine - INFO - Epoch(train) [18][3100/3757]  lr: 3.9606e-05  eta: 12:45:17  time: 1.0042  data_time: 0.0177  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 1.0474  loss: 1.0474
2022/08/06 21:23:15 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 21:24:24 - mmengine - INFO - Epoch(train) [18][3200/3757]  lr: 3.9606e-05  eta: 12:43:37  time: 1.0050  data_time: 0.0176  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 1.1286  loss: 1.1286
2022/08/06 21:26:05 - mmengine - INFO - Epoch(train) [18][3300/3757]  lr: 3.9606e-05  eta: 12:41:56  time: 1.0031  data_time: 0.0174  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.3162  loss: 1.3162
2022/08/06 21:27:45 - mmengine - INFO - Epoch(train) [18][3400/3757]  lr: 3.9606e-05  eta: 12:40:16  time: 1.0012  data_time: 0.0173  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.2860  loss: 1.2860
2022/08/06 21:29:26 - mmengine - INFO - Epoch(train) [18][3500/3757]  lr: 3.9606e-05  eta: 12:38:36  time: 1.0035  data_time: 0.0177  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.1041  loss: 1.1041
2022/08/06 21:31:08 - mmengine - INFO - Epoch(train) [18][3600/3757]  lr: 3.9606e-05  eta: 12:36:56  time: 1.0000  data_time: 0.0171  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 1.0269  loss: 1.0269
2022/08/06 21:32:48 - mmengine - INFO - Epoch(train) [18][3700/3757]  lr: 3.9606e-05  eta: 12:35:16  time: 1.0036  data_time: 0.0182  memory: 68881  top1_acc: 0.5000  top5_acc: 0.7500  loss_cls: 1.3966  loss: 1.3966
2022/08/06 21:33:46 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 21:33:46 - mmengine - INFO - Epoch(train) [18][3757/3757]  lr: 3.9606e-05  eta: 12:34:36  time: 0.9962  data_time: 0.0180  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 1.1786  loss: 1.1786
2022/08/06 21:33:46 - mmengine - INFO - Saving checkpoint at 18 epochs
2022/08/06 21:34:44 - mmengine - INFO - Epoch(val) [18][100/310]    eta: 0:01:25  time: 0.4055  data_time: 0.0103  memory: 14218  
2022/08/06 21:35:25 - mmengine - INFO - Epoch(val) [18][200/310]    eta: 0:00:44  time: 0.4050  data_time: 0.0095  memory: 14218  
2022/08/06 21:36:06 - mmengine - INFO - Epoch(val) [18][300/310]    eta: 0:00:04  time: 0.4194  data_time: 0.0101  memory: 14218  
2022/08/06 21:36:11 - mmengine - INFO - Epoch(val) [18][310/310]  acc/top1: 0.7706  acc/top5: 0.9276  acc/mean1: 0.7704
2022/08/06 21:36:11 - mmengine - INFO - The previous best checkpoint /mnt/lustre/daiwenxun/X/swin/mmaction2/work_dirs/swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb/best_acc/top1_epoch_16.pth is removed
2022/08/06 21:36:18 - mmengine - INFO - The best checkpoint with 0.7706 acc/top1 at 19 epoch is saved to best_acc/top1_epoch_19.pth.
2022/08/06 21:37:59 - mmengine - INFO - Epoch(train) [19][100/3757]  lr: 3.4551e-05  eta: 12:32:28  time: 1.0206  data_time: 0.0176  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 1.2583  loss: 1.2583
2022/08/06 21:39:39 - mmengine - INFO - Epoch(train) [19][200/3757]  lr: 3.4551e-05  eta: 12:30:47  time: 1.0001  data_time: 0.0171  memory: 68881  top1_acc: 0.7500  top5_acc: 0.7500  loss_cls: 1.0055  loss: 1.0055
2022/08/06 21:41:20 - mmengine - INFO - Epoch(train) [19][300/3757]  lr: 3.4551e-05  eta: 12:29:07  time: 1.0012  data_time: 0.0173  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 0.8903  loss: 0.8903
2022/08/06 21:42:34 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 21:43:01 - mmengine - INFO - Epoch(train) [19][400/3757]  lr: 3.4551e-05  eta: 12:27:27  time: 1.0239  data_time: 0.0177  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.0350  loss: 1.0350
2022/08/06 21:44:41 - mmengine - INFO - Epoch(train) [19][500/3757]  lr: 3.4551e-05  eta: 12:25:46  time: 1.0014  data_time: 0.0172  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.8926  loss: 0.8926
2022/08/06 21:46:21 - mmengine - INFO - Epoch(train) [19][600/3757]  lr: 3.4551e-05  eta: 12:24:06  time: 1.0014  data_time: 0.0166  memory: 68881  top1_acc: 0.5000  top5_acc: 0.7500  loss_cls: 1.0030  loss: 1.0030
2022/08/06 21:48:01 - mmengine - INFO - Epoch(train) [19][700/3757]  lr: 3.4551e-05  eta: 12:22:25  time: 1.0008  data_time: 0.0179  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 1.0560  loss: 1.0560
2022/08/06 21:49:41 - mmengine - INFO - Epoch(train) [19][800/3757]  lr: 3.4551e-05  eta: 12:20:45  time: 1.0003  data_time: 0.0173  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 1.2768  loss: 1.2768
2022/08/06 21:51:22 - mmengine - INFO - Epoch(train) [19][900/3757]  lr: 3.4551e-05  eta: 12:19:04  time: 1.0026  data_time: 0.0174  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 0.9433  loss: 0.9433
2022/08/06 21:53:02 - mmengine - INFO - Epoch(train) [19][1000/3757]  lr: 3.4551e-05  eta: 12:17:24  time: 1.0091  data_time: 0.0194  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 0.8111  loss: 0.8111
2022/08/06 21:54:56 - mmengine - INFO - Epoch(train) [19][1100/3757]  lr: 3.4551e-05  eta: 12:15:53  time: 1.1297  data_time: 0.0177  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 1.0863  loss: 1.0863
2022/08/06 21:56:52 - mmengine - INFO - Epoch(train) [19][1200/3757]  lr: 3.4551e-05  eta: 12:14:22  time: 1.2269  data_time: 0.0269  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 1.1426  loss: 1.1426
2022/08/06 21:58:34 - mmengine - INFO - Epoch(train) [19][1300/3757]  lr: 3.4551e-05  eta: 12:12:43  time: 1.0023  data_time: 0.0174  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.1871  loss: 1.1871
2022/08/06 21:59:49 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 22:00:15 - mmengine - INFO - Epoch(train) [19][1400/3757]  lr: 3.4551e-05  eta: 12:11:02  time: 1.0016  data_time: 0.0170  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 0.8065  loss: 0.8065
2022/08/06 22:01:55 - mmengine - INFO - Epoch(train) [19][1500/3757]  lr: 3.4551e-05  eta: 12:09:22  time: 0.9994  data_time: 0.0169  memory: 68881  top1_acc: 0.6250  top5_acc: 0.6250  loss_cls: 1.1005  loss: 1.1005
2022/08/06 22:03:36 - mmengine - INFO - Epoch(train) [19][1600/3757]  lr: 3.4551e-05  eta: 12:07:42  time: 1.0001  data_time: 0.0167  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 0.9766  loss: 0.9766
2022/08/06 22:05:16 - mmengine - INFO - Epoch(train) [19][1700/3757]  lr: 3.4551e-05  eta: 12:06:01  time: 0.9997  data_time: 0.0166  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 1.1650  loss: 1.1650
2022/08/06 22:06:56 - mmengine - INFO - Epoch(train) [19][1800/3757]  lr: 3.4551e-05  eta: 12:04:20  time: 1.0005  data_time: 0.0175  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 0.9678  loss: 0.9678
2022/08/06 22:08:36 - mmengine - INFO - Epoch(train) [19][1900/3757]  lr: 3.4551e-05  eta: 12:02:40  time: 1.0022  data_time: 0.0180  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 1.0881  loss: 1.0881
2022/08/06 22:10:17 - mmengine - INFO - Epoch(train) [19][2000/3757]  lr: 3.4551e-05  eta: 12:01:00  time: 0.9996  data_time: 0.0170  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 0.9106  loss: 0.9106
2022/08/06 22:11:57 - mmengine - INFO - Epoch(train) [19][2100/3757]  lr: 3.4551e-05  eta: 11:59:19  time: 1.0014  data_time: 0.0175  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.0051  loss: 1.0051
2022/08/06 22:13:37 - mmengine - INFO - Epoch(train) [19][2200/3757]  lr: 3.4551e-05  eta: 11:57:38  time: 1.0002  data_time: 0.0173  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.1048  loss: 1.1048
2022/08/06 22:15:18 - mmengine - INFO - Epoch(train) [19][2300/3757]  lr: 3.4551e-05  eta: 11:55:58  time: 1.0067  data_time: 0.0170  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.2083  loss: 1.2083
2022/08/06 22:16:32 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 22:16:58 - mmengine - INFO - Epoch(train) [19][2400/3757]  lr: 3.4551e-05  eta: 11:54:18  time: 1.0024  data_time: 0.0185  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 1.1900  loss: 1.1900
2022/08/06 22:18:38 - mmengine - INFO - Epoch(train) [19][2500/3757]  lr: 3.4551e-05  eta: 11:52:37  time: 1.0004  data_time: 0.0167  memory: 68881  top1_acc: 0.5000  top5_acc: 0.8750  loss_cls: 1.0204  loss: 1.0204
2022/08/06 22:20:18 - mmengine - INFO - Epoch(train) [19][2600/3757]  lr: 3.4551e-05  eta: 11:50:57  time: 0.9996  data_time: 0.0161  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 0.8323  loss: 0.8323
2022/08/06 22:21:58 - mmengine - INFO - Epoch(train) [19][2700/3757]  lr: 3.4551e-05  eta: 11:49:16  time: 0.9995  data_time: 0.0171  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.8842  loss: 0.8842
2022/08/06 22:23:39 - mmengine - INFO - Epoch(train) [19][2800/3757]  lr: 3.4551e-05  eta: 11:47:35  time: 1.0006  data_time: 0.0171  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 1.0827  loss: 1.0827
2022/08/06 22:25:19 - mmengine - INFO - Epoch(train) [19][2900/3757]  lr: 3.4551e-05  eta: 11:45:55  time: 0.9994  data_time: 0.0168  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.0801  loss: 1.0801
2022/08/06 22:26:59 - mmengine - INFO - Epoch(train) [19][3000/3757]  lr: 3.4551e-05  eta: 11:44:14  time: 1.0017  data_time: 0.0170  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 0.7444  loss: 0.7444
2022/08/06 22:28:39 - mmengine - INFO - Epoch(train) [19][3100/3757]  lr: 3.4551e-05  eta: 11:42:34  time: 1.0029  data_time: 0.0172  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.4484  loss: 1.4484
2022/08/06 22:30:20 - mmengine - INFO - Epoch(train) [19][3200/3757]  lr: 3.4551e-05  eta: 11:40:54  time: 1.0110  data_time: 0.0172  memory: 68881  top1_acc: 0.6250  top5_acc: 1.0000  loss_cls: 1.1787  loss: 1.1787
2022/08/06 22:32:00 - mmengine - INFO - Epoch(train) [19][3300/3757]  lr: 3.4551e-05  eta: 11:39:13  time: 1.0034  data_time: 0.0168  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 0.9192  loss: 0.9192
2022/08/06 22:33:15 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 22:33:41 - mmengine - INFO - Epoch(train) [19][3400/3757]  lr: 3.4551e-05  eta: 11:37:33  time: 1.0018  data_time: 0.0184  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 1.1171  loss: 1.1171
2022/08/06 22:35:21 - mmengine - INFO - Epoch(train) [19][3500/3757]  lr: 3.4551e-05  eta: 11:35:52  time: 1.0017  data_time: 0.0173  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 0.9487  loss: 0.9487
2022/08/06 22:37:02 - mmengine - INFO - Epoch(train) [19][3600/3757]  lr: 3.4551e-05  eta: 11:34:12  time: 1.0067  data_time: 0.0178  memory: 68881  top1_acc: 0.6250  top5_acc: 0.7500  loss_cls: 1.2511  loss: 1.2511
2022/08/06 22:38:42 - mmengine - INFO - Epoch(train) [19][3700/3757]  lr: 3.4551e-05  eta: 11:32:31  time: 1.0015  data_time: 0.0173  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 1.1802  loss: 1.1802
2022/08/06 22:39:39 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 22:39:39 - mmengine - INFO - Epoch(train) [19][3757/3757]  lr: 3.4551e-05  eta: 11:31:51  time: 0.9920  data_time: 0.0173  memory: 68881  top1_acc: 0.8571  top5_acc: 1.0000  loss_cls: 1.0499  loss: 1.0499
2022/08/06 22:41:22 - mmengine - INFO - Epoch(train) [20][100/3757]  lr: 2.9665e-05  eta: 11:29:45  time: 1.0113  data_time: 0.0170  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 0.9211  loss: 0.9211
2022/08/06 22:43:03 - mmengine - INFO - Epoch(train) [20][200/3757]  lr: 2.9665e-05  eta: 11:28:05  time: 1.0034  data_time: 0.0178  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 0.9748  loss: 0.9748
2022/08/06 22:44:43 - mmengine - INFO - Epoch(train) [20][300/3757]  lr: 2.9665e-05  eta: 11:26:25  time: 1.0133  data_time: 0.0173  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.9459  loss: 0.9459
2022/08/06 22:46:24 - mmengine - INFO - Epoch(train) [20][400/3757]  lr: 2.9665e-05  eta: 11:24:45  time: 0.9992  data_time: 0.0172  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 1.1374  loss: 1.1374
2022/08/06 22:48:05 - mmengine - INFO - Epoch(train) [20][500/3757]  lr: 2.9665e-05  eta: 11:23:04  time: 1.0074  data_time: 0.0170  memory: 68881  top1_acc: 0.6250  top5_acc: 0.7500  loss_cls: 0.9101  loss: 0.9101
2022/08/06 22:49:45 - mmengine - INFO - Epoch(train) [20][600/3757]  lr: 2.9665e-05  eta: 11:21:24  time: 1.0023  data_time: 0.0177  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 1.1622  loss: 1.1622
2022/08/06 22:50:02 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 22:51:25 - mmengine - INFO - Epoch(train) [20][700/3757]  lr: 2.9665e-05  eta: 11:19:44  time: 1.0012  data_time: 0.0164  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 0.9419  loss: 0.9419
2022/08/06 22:53:07 - mmengine - INFO - Epoch(train) [20][800/3757]  lr: 2.9665e-05  eta: 11:18:04  time: 1.0075  data_time: 0.0172  memory: 68881  top1_acc: 0.3750  top5_acc: 0.8750  loss_cls: 1.0864  loss: 1.0864
2022/08/06 22:54:48 - mmengine - INFO - Epoch(train) [20][900/3757]  lr: 2.9665e-05  eta: 11:16:24  time: 1.0028  data_time: 0.0174  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 0.8957  loss: 0.8957
2022/08/06 22:56:28 - mmengine - INFO - Epoch(train) [20][1000/3757]  lr: 2.9665e-05  eta: 11:14:43  time: 1.0019  data_time: 0.0174  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 0.9724  loss: 0.9724
2022/08/06 22:58:08 - mmengine - INFO - Epoch(train) [20][1100/3757]  lr: 2.9665e-05  eta: 11:13:03  time: 1.0037  data_time: 0.0177  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 0.9288  loss: 0.9288
2022/08/06 22:59:49 - mmengine - INFO - Epoch(train) [20][1200/3757]  lr: 2.9665e-05  eta: 11:11:22  time: 1.0126  data_time: 0.0180  memory: 68881  top1_acc: 0.5000  top5_acc: 1.0000  loss_cls: 0.9961  loss: 0.9961
2022/08/06 23:01:29 - mmengine - INFO - Epoch(train) [20][1300/3757]  lr: 2.9665e-05  eta: 11:09:42  time: 0.9992  data_time: 0.0169  memory: 68881  top1_acc: 0.7500  top5_acc: 0.7500  loss_cls: 1.2485  loss: 1.2485
2022/08/06 23:03:09 - mmengine - INFO - Epoch(train) [20][1400/3757]  lr: 2.9665e-05  eta: 11:08:01  time: 1.0006  data_time: 0.0169  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 0.7808  loss: 0.7808
2022/08/06 23:04:49 - mmengine - INFO - Epoch(train) [20][1500/3757]  lr: 2.9665e-05  eta: 11:06:21  time: 1.0014  data_time: 0.0174  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 0.9065  loss: 0.9065
2022/08/06 23:06:30 - mmengine - INFO - Epoch(train) [20][1600/3757]  lr: 2.9665e-05  eta: 11:04:40  time: 1.0023  data_time: 0.0180  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 1.1653  loss: 1.1653
2022/08/06 23:06:47 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 23:08:10 - mmengine - INFO - Epoch(train) [20][1700/3757]  lr: 2.9665e-05  eta: 11:03:00  time: 0.9996  data_time: 0.0172  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.9841  loss: 0.9841
2022/08/06 23:09:50 - mmengine - INFO - Epoch(train) [20][1800/3757]  lr: 2.9665e-05  eta: 11:01:19  time: 0.9995  data_time: 0.0172  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.9399  loss: 0.9399
2022/08/06 23:11:30 - mmengine - INFO - Epoch(train) [20][1900/3757]  lr: 2.9665e-05  eta: 10:59:39  time: 0.9992  data_time: 0.0165  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 0.9706  loss: 0.9706
2022/08/06 23:13:10 - mmengine - INFO - Epoch(train) [20][2000/3757]  lr: 2.9665e-05  eta: 10:57:58  time: 1.0064  data_time: 0.0169  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.0934  loss: 1.0934
2022/08/06 23:14:51 - mmengine - INFO - Epoch(train) [20][2100/3757]  lr: 2.9665e-05  eta: 10:56:18  time: 1.0025  data_time: 0.0177  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 0.9434  loss: 0.9434
2022/08/06 23:16:32 - mmengine - INFO - Epoch(train) [20][2200/3757]  lr: 2.9665e-05  eta: 10:54:38  time: 1.0022  data_time: 0.0169  memory: 68881  top1_acc: 0.6250  top5_acc: 0.7500  loss_cls: 1.0107  loss: 1.0107
2022/08/06 23:18:12 - mmengine - INFO - Epoch(train) [20][2300/3757]  lr: 2.9665e-05  eta: 10:52:58  time: 1.0010  data_time: 0.0178  memory: 68881  top1_acc: 0.7500  top5_acc: 0.7500  loss_cls: 1.1427  loss: 1.1427
2022/08/06 23:19:52 - mmengine - INFO - Epoch(train) [20][2400/3757]  lr: 2.9665e-05  eta: 10:51:17  time: 1.0014  data_time: 0.0179  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 0.8539  loss: 0.8539
2022/08/06 23:21:34 - mmengine - INFO - Epoch(train) [20][2500/3757]  lr: 2.9665e-05  eta: 10:49:37  time: 1.0044  data_time: 0.0176  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 1.3518  loss: 1.3518
2022/08/06 23:23:15 - mmengine - INFO - Epoch(train) [20][2600/3757]  lr: 2.9665e-05  eta: 10:47:57  time: 1.0027  data_time: 0.0174  memory: 68881  top1_acc: 0.3750  top5_acc: 0.6250  loss_cls: 0.8933  loss: 0.8933
2022/08/06 23:23:32 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 23:24:56 - mmengine - INFO - Epoch(train) [20][2700/3757]  lr: 2.9665e-05  eta: 10:46:17  time: 1.0002  data_time: 0.0173  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.9921  loss: 0.9921
2022/08/06 23:26:36 - mmengine - INFO - Epoch(train) [20][2800/3757]  lr: 2.9665e-05  eta: 10:44:36  time: 1.0022  data_time: 0.0179  memory: 68881  top1_acc: 0.6250  top5_acc: 1.0000  loss_cls: 1.1185  loss: 1.1185
2022/08/06 23:28:16 - mmengine - INFO - Epoch(train) [20][2900/3757]  lr: 2.9665e-05  eta: 10:42:56  time: 1.0009  data_time: 0.0177  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.0462  loss: 1.0462
2022/08/06 23:29:56 - mmengine - INFO - Epoch(train) [20][3000/3757]  lr: 2.9665e-05  eta: 10:41:15  time: 1.0002  data_time: 0.0169  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 1.0568  loss: 1.0568
2022/08/06 23:31:36 - mmengine - INFO - Epoch(train) [20][3100/3757]  lr: 2.9665e-05  eta: 10:39:35  time: 1.0001  data_time: 0.0170  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.8108  loss: 0.8108
2022/08/06 23:33:17 - mmengine - INFO - Epoch(train) [20][3200/3757]  lr: 2.9665e-05  eta: 10:37:54  time: 1.0018  data_time: 0.0170  memory: 68881  top1_acc: 0.5000  top5_acc: 1.0000  loss_cls: 0.9441  loss: 0.9441
2022/08/06 23:34:57 - mmengine - INFO - Epoch(train) [20][3300/3757]  lr: 2.9665e-05  eta: 10:36:14  time: 1.0011  data_time: 0.0181  memory: 68881  top1_acc: 0.6250  top5_acc: 0.7500  loss_cls: 1.4333  loss: 1.4333
2022/08/06 23:36:37 - mmengine - INFO - Epoch(train) [20][3400/3757]  lr: 2.9665e-05  eta: 10:34:33  time: 1.0003  data_time: 0.0176  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 0.9270  loss: 0.9270
2022/08/06 23:38:17 - mmengine - INFO - Epoch(train) [20][3500/3757]  lr: 2.9665e-05  eta: 10:32:53  time: 1.0010  data_time: 0.0168  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.0742  loss: 1.0742
2022/08/06 23:39:57 - mmengine - INFO - Epoch(train) [20][3600/3757]  lr: 2.9665e-05  eta: 10:31:12  time: 1.0054  data_time: 0.0174  memory: 68881  top1_acc: 0.6250  top5_acc: 0.7500  loss_cls: 0.9581  loss: 0.9581
2022/08/06 23:40:15 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 23:41:38 - mmengine - INFO - Epoch(train) [20][3700/3757]  lr: 2.9665e-05  eta: 10:29:32  time: 1.0026  data_time: 0.0174  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 1.1190  loss: 1.1190
2022/08/06 23:42:35 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 23:42:35 - mmengine - INFO - Epoch(train) [20][3757/3757]  lr: 2.9665e-05  eta: 10:28:52  time: 0.9978  data_time: 0.0176  memory: 68881  top1_acc: 0.5714  top5_acc: 0.8571  loss_cls: 1.1192  loss: 1.1192
2022/08/06 23:44:18 - mmengine - INFO - Epoch(train) [21][100/3757]  lr: 2.5001e-05  eta: 10:26:47  time: 1.0006  data_time: 0.0174  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 0.8811  loss: 0.8811
2022/08/06 23:45:58 - mmengine - INFO - Epoch(train) [21][200/3757]  lr: 2.5001e-05  eta: 10:25:07  time: 1.0072  data_time: 0.0172  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.0838  loss: 1.0838
2022/08/06 23:47:39 - mmengine - INFO - Epoch(train) [21][300/3757]  lr: 2.5001e-05  eta: 10:23:26  time: 1.0015  data_time: 0.0178  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.8905  loss: 0.8905
2022/08/06 23:49:19 - mmengine - INFO - Epoch(train) [21][400/3757]  lr: 2.5001e-05  eta: 10:21:46  time: 1.0054  data_time: 0.0178  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.9379  loss: 0.9379
2022/08/06 23:50:59 - mmengine - INFO - Epoch(train) [21][500/3757]  lr: 2.5001e-05  eta: 10:20:05  time: 0.9988  data_time: 0.0165  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.8664  loss: 0.8664
2022/08/06 23:52:39 - mmengine - INFO - Epoch(train) [21][600/3757]  lr: 2.5001e-05  eta: 10:18:25  time: 1.0014  data_time: 0.0174  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.1834  loss: 1.1834
2022/08/06 23:54:19 - mmengine - INFO - Epoch(train) [21][700/3757]  lr: 2.5001e-05  eta: 10:16:44  time: 0.9998  data_time: 0.0167  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.0247  loss: 1.0247
2022/08/06 23:55:59 - mmengine - INFO - Epoch(train) [21][800/3757]  lr: 2.5001e-05  eta: 10:15:04  time: 0.9991  data_time: 0.0171  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 0.8620  loss: 0.8620
2022/08/06 23:57:00 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/06 23:57:40 - mmengine - INFO - Epoch(train) [21][900/3757]  lr: 2.5001e-05  eta: 10:13:23  time: 1.0009  data_time: 0.0177  memory: 68881  top1_acc: 0.6250  top5_acc: 1.0000  loss_cls: 1.0703  loss: 1.0703
2022/08/06 23:59:20 - mmengine - INFO - Epoch(train) [21][1000/3757]  lr: 2.5001e-05  eta: 10:11:43  time: 1.0014  data_time: 0.0177  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 0.9803  loss: 0.9803
2022/08/07 00:01:02 - mmengine - INFO - Epoch(train) [21][1100/3757]  lr: 2.5001e-05  eta: 10:10:04  time: 1.0040  data_time: 0.0169  memory: 68881  top1_acc: 0.5000  top5_acc: 0.7500  loss_cls: 0.9221  loss: 0.9221
2022/08/07 00:02:42 - mmengine - INFO - Epoch(train) [21][1200/3757]  lr: 2.5001e-05  eta: 10:08:23  time: 1.0010  data_time: 0.0169  memory: 68881  top1_acc: 0.6250  top5_acc: 1.0000  loss_cls: 0.9572  loss: 0.9572
2022/08/07 00:04:22 - mmengine - INFO - Epoch(train) [21][1300/3757]  lr: 2.5001e-05  eta: 10:06:43  time: 1.0001  data_time: 0.0174  memory: 68881  top1_acc: 0.6250  top5_acc: 0.7500  loss_cls: 0.9536  loss: 0.9536
2022/08/07 00:06:02 - mmengine - INFO - Epoch(train) [21][1400/3757]  lr: 2.5001e-05  eta: 10:05:02  time: 1.0024  data_time: 0.0170  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.9827  loss: 0.9827
2022/08/07 00:07:43 - mmengine - INFO - Epoch(train) [21][1500/3757]  lr: 2.5001e-05  eta: 10:03:22  time: 1.0024  data_time: 0.0174  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.9023  loss: 0.9023
2022/08/07 00:09:23 - mmengine - INFO - Epoch(train) [21][1600/3757]  lr: 2.5001e-05  eta: 10:01:41  time: 1.0016  data_time: 0.0172  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 0.8065  loss: 0.8065
2022/08/07 00:11:03 - mmengine - INFO - Epoch(train) [21][1700/3757]  lr: 2.5001e-05  eta: 10:00:01  time: 1.0011  data_time: 0.0173  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 0.8169  loss: 0.8169
2022/08/07 00:12:44 - mmengine - INFO - Epoch(train) [21][1800/3757]  lr: 2.5001e-05  eta: 9:58:20  time: 1.0014  data_time: 0.0173  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 1.0416  loss: 1.0416
2022/08/07 00:13:44 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/07 00:14:24 - mmengine - INFO - Epoch(train) [21][1900/3757]  lr: 2.5001e-05  eta: 9:56:40  time: 1.0019  data_time: 0.0178  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 0.9263  loss: 0.9263
2022/08/07 00:16:05 - mmengine - INFO - Epoch(train) [21][2000/3757]  lr: 2.5001e-05  eta: 9:55:00  time: 1.0052  data_time: 0.0164  memory: 68881  top1_acc: 0.6250  top5_acc: 1.0000  loss_cls: 1.0395  loss: 1.0395
2022/08/07 00:17:46 - mmengine - INFO - Epoch(train) [21][2100/3757]  lr: 2.5001e-05  eta: 9:53:20  time: 1.0037  data_time: 0.0165  memory: 68881  top1_acc: 0.7500  top5_acc: 0.7500  loss_cls: 1.1926  loss: 1.1926
2022/08/07 00:19:26 - mmengine - INFO - Epoch(train) [21][2200/3757]  lr: 2.5001e-05  eta: 9:51:40  time: 1.0060  data_time: 0.0174  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 1.0232  loss: 1.0232
2022/08/07 00:21:08 - mmengine - INFO - Epoch(train) [21][2300/3757]  lr: 2.5001e-05  eta: 9:50:00  time: 1.0019  data_time: 0.0175  memory: 68881  top1_acc: 0.5000  top5_acc: 0.8750  loss_cls: 0.9192  loss: 0.9192
2022/08/07 00:22:48 - mmengine - INFO - Epoch(train) [21][2400/3757]  lr: 2.5001e-05  eta: 9:48:19  time: 1.0025  data_time: 0.0174  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 1.0611  loss: 1.0611
2022/08/07 00:24:28 - mmengine - INFO - Epoch(train) [21][2500/3757]  lr: 2.5001e-05  eta: 9:46:39  time: 1.0002  data_time: 0.0166  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 0.9194  loss: 0.9194
2022/08/07 00:26:09 - mmengine - INFO - Epoch(train) [21][2600/3757]  lr: 2.5001e-05  eta: 9:44:58  time: 1.0030  data_time: 0.0160  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 0.9424  loss: 0.9424
2022/08/07 00:27:49 - mmengine - INFO - Epoch(train) [21][2700/3757]  lr: 2.5001e-05  eta: 9:43:18  time: 1.0018  data_time: 0.0179  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 1.1931  loss: 1.1931
2022/08/07 00:29:29 - mmengine - INFO - Epoch(train) [21][2800/3757]  lr: 2.5001e-05  eta: 9:41:37  time: 1.0028  data_time: 0.0166  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 0.9482  loss: 0.9482
2022/08/07 00:30:29 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/07 00:31:09 - mmengine - INFO - Epoch(train) [21][2900/3757]  lr: 2.5001e-05  eta: 9:39:57  time: 1.0028  data_time: 0.0167  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.0063  loss: 1.0063
2022/08/07 00:32:50 - mmengine - INFO - Epoch(train) [21][3000/3757]  lr: 2.5001e-05  eta: 9:38:17  time: 1.0032  data_time: 0.0159  memory: 68881  top1_acc: 0.3750  top5_acc: 0.8750  loss_cls: 0.8191  loss: 0.8191
2022/08/07 00:34:30 - mmengine - INFO - Epoch(train) [21][3100/3757]  lr: 2.5001e-05  eta: 9:36:36  time: 1.0033  data_time: 0.0162  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 0.8838  loss: 0.8838
2022/08/07 00:36:10 - mmengine - INFO - Epoch(train) [21][3200/3757]  lr: 2.5001e-05  eta: 9:34:56  time: 1.0008  data_time: 0.0166  memory: 68881  top1_acc: 0.7500  top5_acc: 0.7500  loss_cls: 0.8986  loss: 0.8986
2022/08/07 00:37:50 - mmengine - INFO - Epoch(train) [21][3300/3757]  lr: 2.5001e-05  eta: 9:33:15  time: 1.0009  data_time: 0.0165  memory: 68881  top1_acc: 0.6250  top5_acc: 1.0000  loss_cls: 0.8681  loss: 0.8681
2022/08/07 00:39:31 - mmengine - INFO - Epoch(train) [21][3400/3757]  lr: 2.5001e-05  eta: 9:31:35  time: 1.0009  data_time: 0.0165  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.7818  loss: 0.7818
2022/08/07 00:41:11 - mmengine - INFO - Epoch(train) [21][3500/3757]  lr: 2.5001e-05  eta: 9:29:54  time: 0.9980  data_time: 0.0168  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 1.0781  loss: 1.0781
2022/08/07 00:42:51 - mmengine - INFO - Epoch(train) [21][3600/3757]  lr: 2.5001e-05  eta: 9:28:14  time: 1.0043  data_time: 0.0177  memory: 68881  top1_acc: 0.5000  top5_acc: 0.8750  loss_cls: 1.0927  loss: 1.0927
2022/08/07 00:44:31 - mmengine - INFO - Epoch(train) [21][3700/3757]  lr: 2.5001e-05  eta: 9:26:34  time: 1.0012  data_time: 0.0170  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 1.0620  loss: 1.0620
2022/08/07 00:45:28 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/07 00:45:28 - mmengine - INFO - Epoch(train) [21][3757/3757]  lr: 2.5001e-05  eta: 9:25:53  time: 0.9908  data_time: 0.0165  memory: 68881  top1_acc: 0.7143  top5_acc: 1.0000  loss_cls: 1.0941  loss: 1.0941
2022/08/07 00:45:28 - mmengine - INFO - Saving checkpoint at 21 epochs
2022/08/07 00:46:27 - mmengine - INFO - Epoch(val) [21][100/310]    eta: 0:01:25  time: 0.4079  data_time: 0.0113  memory: 14218  
2022/08/07 00:47:08 - mmengine - INFO - Epoch(val) [21][200/310]    eta: 0:00:45  time: 0.4152  data_time: 0.0108  memory: 14218  
2022/08/07 00:47:48 - mmengine - INFO - Epoch(val) [21][300/310]    eta: 0:00:04  time: 0.4022  data_time: 0.0091  memory: 14218  
2022/08/07 00:47:54 - mmengine - INFO - Epoch(val) [21][310/310]  acc/top1: 0.7774  acc/top5: 0.9300  acc/mean1: 0.7773
2022/08/07 00:47:54 - mmengine - INFO - The previous best checkpoint /mnt/lustre/daiwenxun/X/swin/mmaction2/work_dirs/swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb/best_acc/top1_epoch_19.pth is removed
2022/08/07 00:48:01 - mmengine - INFO - The best checkpoint with 0.7774 acc/top1 at 22 epoch is saved to best_acc/top1_epoch_22.pth.
2022/08/07 00:49:42 - mmengine - INFO - Epoch(train) [22][100/3757]  lr: 2.0612e-05  eta: 9:23:49  time: 1.0000  data_time: 0.0165  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.0204  loss: 1.0204
2022/08/07 00:49:45 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/07 00:51:22 - mmengine - INFO - Epoch(train) [22][200/3757]  lr: 2.0612e-05  eta: 9:22:09  time: 1.0006  data_time: 0.0164  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 0.9509  loss: 0.9509
2022/08/07 00:53:03 - mmengine - INFO - Epoch(train) [22][300/3757]  lr: 2.0612e-05  eta: 9:20:28  time: 1.0003  data_time: 0.0161  memory: 68881  top1_acc: 0.6250  top5_acc: 0.7500  loss_cls: 0.8070  loss: 0.8070
2022/08/07 00:54:43 - mmengine - INFO - Epoch(train) [22][400/3757]  lr: 2.0612e-05  eta: 9:18:48  time: 1.0013  data_time: 0.0174  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 0.8267  loss: 0.8267
2022/08/07 00:56:23 - mmengine - INFO - Epoch(train) [22][500/3757]  lr: 2.0612e-05  eta: 9:17:08  time: 1.0024  data_time: 0.0164  memory: 68881  top1_acc: 0.7500  top5_acc: 0.7500  loss_cls: 1.1613  loss: 1.1613
2022/08/07 00:58:03 - mmengine - INFO - Epoch(train) [22][600/3757]  lr: 2.0612e-05  eta: 9:15:27  time: 1.0000  data_time: 0.0172  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 1.0831  loss: 1.0831
2022/08/07 00:59:44 - mmengine - INFO - Epoch(train) [22][700/3757]  lr: 2.0612e-05  eta: 9:13:47  time: 1.0034  data_time: 0.0164  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 1.0012  loss: 1.0012
2022/08/07 01:01:24 - mmengine - INFO - Epoch(train) [22][800/3757]  lr: 2.0612e-05  eta: 9:12:06  time: 0.9999  data_time: 0.0170  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 1.0033  loss: 1.0033
2022/08/07 01:03:04 - mmengine - INFO - Epoch(train) [22][900/3757]  lr: 2.0612e-05  eta: 9:10:26  time: 1.0041  data_time: 0.0169  memory: 68881  top1_acc: 0.7500  top5_acc: 0.7500  loss_cls: 0.7267  loss: 0.7267
2022/08/07 01:04:44 - mmengine - INFO - Epoch(train) [22][1000/3757]  lr: 2.0612e-05  eta: 9:08:45  time: 1.0002  data_time: 0.0168  memory: 68881  top1_acc: 0.7500  top5_acc: 0.7500  loss_cls: 0.9760  loss: 0.9760
2022/08/07 01:06:24 - mmengine - INFO - Epoch(train) [22][1100/3757]  lr: 2.0612e-05  eta: 9:07:05  time: 1.0009  data_time: 0.0168  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 0.9499  loss: 0.9499
2022/08/07 01:06:27 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/07 01:08:04 - mmengine - INFO - Epoch(train) [22][1200/3757]  lr: 2.0612e-05  eta: 9:05:25  time: 1.0000  data_time: 0.0176  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 0.9999  loss: 0.9999
2022/08/07 01:09:45 - mmengine - INFO - Epoch(train) [22][1300/3757]  lr: 2.0612e-05  eta: 9:03:44  time: 1.0114  data_time: 0.0173  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 0.8684  loss: 0.8684
2022/08/07 01:11:25 - mmengine - INFO - Epoch(train) [22][1400/3757]  lr: 2.0612e-05  eta: 9:02:04  time: 1.0009  data_time: 0.0160  memory: 68881  top1_acc: 0.6250  top5_acc: 1.0000  loss_cls: 1.1366  loss: 1.1366
2022/08/07 01:13:05 - mmengine - INFO - Epoch(train) [22][1500/3757]  lr: 2.0612e-05  eta: 9:00:23  time: 0.9985  data_time: 0.0164  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 0.8454  loss: 0.8454
2022/08/07 01:14:45 - mmengine - INFO - Epoch(train) [22][1600/3757]  lr: 2.0612e-05  eta: 8:58:43  time: 0.9994  data_time: 0.0172  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 0.8302  loss: 0.8302
2022/08/07 01:16:25 - mmengine - INFO - Epoch(train) [22][1700/3757]  lr: 2.0612e-05  eta: 8:57:02  time: 0.9994  data_time: 0.0172  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 0.9987  loss: 0.9987
2022/08/07 01:18:05 - mmengine - INFO - Epoch(train) [22][1800/3757]  lr: 2.0612e-05  eta: 8:55:22  time: 1.0008  data_time: 0.0166  memory: 68881  top1_acc: 0.5000  top5_acc: 0.6250  loss_cls: 1.3461  loss: 1.3461
2022/08/07 01:19:45 - mmengine - INFO - Epoch(train) [22][1900/3757]  lr: 2.0612e-05  eta: 8:53:42  time: 1.0005  data_time: 0.0175  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.1896  loss: 1.1896
2022/08/07 01:21:26 - mmengine - INFO - Epoch(train) [22][2000/3757]  lr: 2.0612e-05  eta: 8:52:01  time: 1.0003  data_time: 0.0169  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 0.9044  loss: 0.9044
2022/08/07 01:23:06 - mmengine - INFO - Epoch(train) [22][2100/3757]  lr: 2.0612e-05  eta: 8:50:21  time: 1.0008  data_time: 0.0162  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 0.9866  loss: 0.9866
2022/08/07 01:23:09 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/07 01:24:46 - mmengine - INFO - Epoch(train) [22][2200/3757]  lr: 2.0612e-05  eta: 8:48:40  time: 1.0045  data_time: 0.0174  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 0.8144  loss: 0.8144
2022/08/07 01:26:26 - mmengine - INFO - Epoch(train) [22][2300/3757]  lr: 2.0612e-05  eta: 8:47:00  time: 1.0050  data_time: 0.0171  memory: 68881  top1_acc: 0.7500  top5_acc: 0.7500  loss_cls: 1.1282  loss: 1.1282
2022/08/07 01:28:06 - mmengine - INFO - Epoch(train) [22][2400/3757]  lr: 2.0612e-05  eta: 8:45:19  time: 1.0005  data_time: 0.0168  memory: 68881  top1_acc: 0.6250  top5_acc: 0.6250  loss_cls: 0.8691  loss: 0.8691
2022/08/07 01:29:46 - mmengine - INFO - Epoch(train) [22][2500/3757]  lr: 2.0612e-05  eta: 8:43:39  time: 1.0002  data_time: 0.0170  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 1.2134  loss: 1.2134
2022/08/07 01:31:26 - mmengine - INFO - Epoch(train) [22][2600/3757]  lr: 2.0612e-05  eta: 8:41:59  time: 1.0023  data_time: 0.0167  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 0.9083  loss: 0.9083
2022/08/07 01:33:07 - mmengine - INFO - Epoch(train) [22][2700/3757]  lr: 2.0612e-05  eta: 8:40:18  time: 1.0009  data_time: 0.0173  memory: 68881  top1_acc: 0.6250  top5_acc: 1.0000  loss_cls: 1.0124  loss: 1.0124
2022/08/07 01:34:47 - mmengine - INFO - Epoch(train) [22][2800/3757]  lr: 2.0612e-05  eta: 8:38:38  time: 1.0000  data_time: 0.0173  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 0.8220  loss: 0.8220
2022/08/07 01:36:27 - mmengine - INFO - Epoch(train) [22][2900/3757]  lr: 2.0612e-05  eta: 8:36:57  time: 0.9993  data_time: 0.0162  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 0.8262  loss: 0.8262
2022/08/07 01:38:07 - mmengine - INFO - Epoch(train) [22][3000/3757]  lr: 2.0612e-05  eta: 8:35:17  time: 1.0012  data_time: 0.0174  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 1.1067  loss: 1.1067
2022/08/07 01:39:47 - mmengine - INFO - Epoch(train) [22][3100/3757]  lr: 2.0612e-05  eta: 8:33:36  time: 1.0010  data_time: 0.0184  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 0.9493  loss: 0.9493
2022/08/07 01:39:50 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/07 01:41:27 - mmengine - INFO - Epoch(train) [22][3200/3757]  lr: 2.0612e-05  eta: 8:31:56  time: 0.9991  data_time: 0.0165  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 0.9923  loss: 0.9923
2022/08/07 01:43:08 - mmengine - INFO - Epoch(train) [22][3300/3757]  lr: 2.0612e-05  eta: 8:30:16  time: 1.0000  data_time: 0.0172  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 0.8102  loss: 0.8102
2022/08/07 01:44:48 - mmengine - INFO - Epoch(train) [22][3400/3757]  lr: 2.0612e-05  eta: 8:28:35  time: 1.0003  data_time: 0.0172  memory: 68881  top1_acc: 0.5000  top5_acc: 0.8750  loss_cls: 1.2327  loss: 1.2327
2022/08/07 01:46:28 - mmengine - INFO - Epoch(train) [22][3500/3757]  lr: 2.0612e-05  eta: 8:26:55  time: 1.0020  data_time: 0.0165  memory: 68881  top1_acc: 0.6250  top5_acc: 1.0000  loss_cls: 1.0877  loss: 1.0877
2022/08/07 01:48:08 - mmengine - INFO - Epoch(train) [22][3600/3757]  lr: 2.0612e-05  eta: 8:25:14  time: 1.0006  data_time: 0.0170  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 0.8424  loss: 0.8424
2022/08/07 01:49:48 - mmengine - INFO - Epoch(train) [22][3700/3757]  lr: 2.0612e-05  eta: 8:23:34  time: 1.0001  data_time: 0.0171  memory: 68881  top1_acc: 0.7500  top5_acc: 0.7500  loss_cls: 0.9425  loss: 0.9425
2022/08/07 01:50:45 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/07 01:50:45 - mmengine - INFO - Epoch(train) [22][3757/3757]  lr: 2.0612e-05  eta: 8:22:54  time: 0.9933  data_time: 0.0171  memory: 68881  top1_acc: 0.8571  top5_acc: 0.8571  loss_cls: 1.1326  loss: 1.1326
2022/08/07 01:52:29 - mmengine - INFO - Epoch(train) [23][100/3757]  lr: 1.6544e-05  eta: 8:20:51  time: 1.0001  data_time: 0.0172  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 1.1168  loss: 1.1168
2022/08/07 01:54:09 - mmengine - INFO - Epoch(train) [23][200/3757]  lr: 1.6544e-05  eta: 8:19:11  time: 1.0021  data_time: 0.0174  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 0.9404  loss: 0.9404
2022/08/07 01:55:49 - mmengine - INFO - Epoch(train) [23][300/3757]  lr: 1.6544e-05  eta: 8:17:31  time: 1.0013  data_time: 0.0174  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 0.9466  loss: 0.9466
2022/08/07 01:56:35 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/07 01:57:29 - mmengine - INFO - Epoch(train) [23][400/3757]  lr: 1.6544e-05  eta: 8:15:50  time: 1.0014  data_time: 0.0164  memory: 68881  top1_acc: 0.5000  top5_acc: 0.7500  loss_cls: 1.1523  loss: 1.1523
2022/08/07 01:59:09 - mmengine - INFO - Epoch(train) [23][500/3757]  lr: 1.6544e-05  eta: 8:14:10  time: 1.0011  data_time: 0.0171  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 1.1149  loss: 1.1149
2022/08/07 02:00:50 - mmengine - INFO - Epoch(train) [23][600/3757]  lr: 1.6544e-05  eta: 8:12:30  time: 1.0113  data_time: 0.0169  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.9514  loss: 0.9514
2022/08/07 02:02:30 - mmengine - INFO - Epoch(train) [23][700/3757]  lr: 1.6544e-05  eta: 8:10:49  time: 1.0009  data_time: 0.0171  memory: 68881  top1_acc: 0.6250  top5_acc: 1.0000  loss_cls: 0.9388  loss: 0.9388
2022/08/07 02:04:11 - mmengine - INFO - Epoch(train) [23][800/3757]  lr: 1.6544e-05  eta: 8:09:09  time: 1.0029  data_time: 0.0174  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.0024  loss: 1.0024
2022/08/07 02:05:51 - mmengine - INFO - Epoch(train) [23][900/3757]  lr: 1.6544e-05  eta: 8:07:29  time: 1.0015  data_time: 0.0172  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 0.8988  loss: 0.8988
2022/08/07 02:07:31 - mmengine - INFO - Epoch(train) [23][1000/3757]  lr: 1.6544e-05  eta: 8:05:48  time: 1.0004  data_time: 0.0163  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 0.9247  loss: 0.9247
2022/08/07 02:09:11 - mmengine - INFO - Epoch(train) [23][1100/3757]  lr: 1.6544e-05  eta: 8:04:08  time: 1.0024  data_time: 0.0170  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 1.0997  loss: 1.0997
2022/08/07 02:10:51 - mmengine - INFO - Epoch(train) [23][1200/3757]  lr: 1.6544e-05  eta: 8:02:27  time: 1.0010  data_time: 0.0164  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 0.9561  loss: 0.9561
2022/08/07 02:12:32 - mmengine - INFO - Epoch(train) [23][1300/3757]  lr: 1.6544e-05  eta: 8:00:47  time: 0.9989  data_time: 0.0163  memory: 68881  top1_acc: 0.6250  top5_acc: 1.0000  loss_cls: 1.0659  loss: 1.0659
2022/08/07 02:13:18 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/07 02:14:12 - mmengine - INFO - Epoch(train) [23][1400/3757]  lr: 1.6544e-05  eta: 7:59:07  time: 1.0005  data_time: 0.0171  memory: 68881  top1_acc: 0.7500  top5_acc: 0.7500  loss_cls: 1.1235  loss: 1.1235
2022/08/07 02:15:52 - mmengine - INFO - Epoch(train) [23][1500/3757]  lr: 1.6544e-05  eta: 7:57:26  time: 1.0010  data_time: 0.0168  memory: 68881  top1_acc: 0.6250  top5_acc: 0.7500  loss_cls: 0.8556  loss: 0.8556
2022/08/07 02:17:32 - mmengine - INFO - Epoch(train) [23][1600/3757]  lr: 1.6544e-05  eta: 7:55:46  time: 0.9998  data_time: 0.0166  memory: 68881  top1_acc: 0.5000  top5_acc: 0.8750  loss_cls: 1.2732  loss: 1.2732
2022/08/07 02:19:13 - mmengine - INFO - Epoch(train) [23][1700/3757]  lr: 1.6544e-05  eta: 7:54:06  time: 1.0008  data_time: 0.0167  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.8835  loss: 0.8835
2022/08/07 02:20:53 - mmengine - INFO - Epoch(train) [23][1800/3757]  lr: 1.6544e-05  eta: 7:52:25  time: 1.0006  data_time: 0.0176  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 0.9072  loss: 0.9072
2022/08/07 02:22:33 - mmengine - INFO - Epoch(train) [23][1900/3757]  lr: 1.6544e-05  eta: 7:50:45  time: 1.0020  data_time: 0.0166  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.2040  loss: 1.2040
2022/08/07 02:24:13 - mmengine - INFO - Epoch(train) [23][2000/3757]  lr: 1.6544e-05  eta: 7:49:05  time: 1.0003  data_time: 0.0171  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 1.0721  loss: 1.0721
2022/08/07 02:25:53 - mmengine - INFO - Epoch(train) [23][2100/3757]  lr: 1.6544e-05  eta: 7:47:24  time: 1.0019  data_time: 0.0170  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 1.0523  loss: 1.0523
2022/08/07 02:27:34 - mmengine - INFO - Epoch(train) [23][2200/3757]  lr: 1.6544e-05  eta: 7:45:44  time: 1.0009  data_time: 0.0174  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 0.8456  loss: 0.8456
2022/08/07 02:29:14 - mmengine - INFO - Epoch(train) [23][2300/3757]  lr: 1.6544e-05  eta: 7:44:04  time: 1.0013  data_time: 0.0182  memory: 68881  top1_acc: 0.7500  top5_acc: 0.7500  loss_cls: 0.6493  loss: 0.6493
2022/08/07 02:30:00 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/07 02:30:54 - mmengine - INFO - Epoch(train) [23][2400/3757]  lr: 1.6544e-05  eta: 7:42:23  time: 1.0022  data_time: 0.0205  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 0.9676  loss: 0.9676
2022/08/07 02:32:35 - mmengine - INFO - Epoch(train) [23][2500/3757]  lr: 1.6544e-05  eta: 7:40:43  time: 1.0022  data_time: 0.0161  memory: 68881  top1_acc: 0.5000  top5_acc: 0.8750  loss_cls: 0.9399  loss: 0.9399
2022/08/07 02:34:15 - mmengine - INFO - Epoch(train) [23][2600/3757]  lr: 1.6544e-05  eta: 7:39:03  time: 1.0012  data_time: 0.0179  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 0.9425  loss: 0.9425
2022/08/07 02:35:55 - mmengine - INFO - Epoch(train) [23][2700/3757]  lr: 1.6544e-05  eta: 7:37:22  time: 1.0005  data_time: 0.0173  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.1738  loss: 1.1738
2022/08/07 02:37:35 - mmengine - INFO - Epoch(train) [23][2800/3757]  lr: 1.6544e-05  eta: 7:35:42  time: 1.0036  data_time: 0.0174  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 0.9339  loss: 0.9339
2022/08/07 02:39:16 - mmengine - INFO - Epoch(train) [23][2900/3757]  lr: 1.6544e-05  eta: 7:34:01  time: 0.9999  data_time: 0.0166  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 0.8452  loss: 0.8452
2022/08/07 02:40:56 - mmengine - INFO - Epoch(train) [23][3000/3757]  lr: 1.6544e-05  eta: 7:32:21  time: 1.0061  data_time: 0.0179  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.0270  loss: 1.0270
2022/08/07 02:42:36 - mmengine - INFO - Epoch(train) [23][3100/3757]  lr: 1.6544e-05  eta: 7:30:41  time: 0.9994  data_time: 0.0169  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 0.8934  loss: 0.8934
2022/08/07 02:44:17 - mmengine - INFO - Epoch(train) [23][3200/3757]  lr: 1.6544e-05  eta: 7:29:01  time: 0.9987  data_time: 0.0166  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 1.0422  loss: 1.0422
2022/08/07 02:45:57 - mmengine - INFO - Epoch(train) [23][3300/3757]  lr: 1.6544e-05  eta: 7:27:20  time: 0.9992  data_time: 0.0165  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 0.8069  loss: 0.8069
2022/08/07 02:46:43 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/07 02:47:37 - mmengine - INFO - Epoch(train) [23][3400/3757]  lr: 1.6544e-05  eta: 7:25:40  time: 0.9993  data_time: 0.0166  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 0.7785  loss: 0.7785
2022/08/07 02:49:17 - mmengine - INFO - Epoch(train) [23][3500/3757]  lr: 1.6544e-05  eta: 7:23:59  time: 1.0013  data_time: 0.0163  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.0078  loss: 1.0078
2022/08/07 02:50:57 - mmengine - INFO - Epoch(train) [23][3600/3757]  lr: 1.6544e-05  eta: 7:22:19  time: 1.0019  data_time: 0.0172  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 0.9378  loss: 0.9378
2022/08/07 02:52:37 - mmengine - INFO - Epoch(train) [23][3700/3757]  lr: 1.6544e-05  eta: 7:20:39  time: 0.9993  data_time: 0.0169  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.0844  loss: 1.0844
2022/08/07 02:53:34 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/07 02:53:34 - mmengine - INFO - Epoch(train) [23][3757/3757]  lr: 1.6544e-05  eta: 7:19:58  time: 0.9930  data_time: 0.0160  memory: 68881  top1_acc: 0.5714  top5_acc: 0.7143  loss_cls: 0.8714  loss: 0.8714
2022/08/07 02:55:17 - mmengine - INFO - Epoch(train) [24][100/3757]  lr: 1.2843e-05  eta: 7:17:57  time: 1.0017  data_time: 0.0168  memory: 68881  top1_acc: 0.7500  top5_acc: 0.7500  loss_cls: 0.7222  loss: 0.7222
2022/08/07 02:56:57 - mmengine - INFO - Epoch(train) [24][200/3757]  lr: 1.2843e-05  eta: 7:16:16  time: 1.0011  data_time: 0.0171  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.8714  loss: 0.8714
2022/08/07 02:58:37 - mmengine - INFO - Epoch(train) [24][300/3757]  lr: 1.2843e-05  eta: 7:14:36  time: 1.0012  data_time: 0.0173  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.8205  loss: 0.8205
2022/08/07 03:00:17 - mmengine - INFO - Epoch(train) [24][400/3757]  lr: 1.2843e-05  eta: 7:12:55  time: 1.0015  data_time: 0.0173  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 0.9096  loss: 0.9096
2022/08/07 03:01:57 - mmengine - INFO - Epoch(train) [24][500/3757]  lr: 1.2843e-05  eta: 7:11:15  time: 0.9998  data_time: 0.0172  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.7086  loss: 0.7086
2022/08/07 03:03:26 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/07 03:03:37 - mmengine - INFO - Epoch(train) [24][600/3757]  lr: 1.2843e-05  eta: 7:09:35  time: 1.0004  data_time: 0.0173  memory: 68881  top1_acc: 0.7500  top5_acc: 0.7500  loss_cls: 1.0037  loss: 1.0037
2022/08/07 03:05:17 - mmengine - INFO - Epoch(train) [24][700/3757]  lr: 1.2843e-05  eta: 7:07:54  time: 1.0020  data_time: 0.0170  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.7184  loss: 0.7184
2022/08/07 03:06:58 - mmengine - INFO - Epoch(train) [24][800/3757]  lr: 1.2843e-05  eta: 7:06:14  time: 1.0007  data_time: 0.0169  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 0.9456  loss: 0.9456
2022/08/07 03:08:38 - mmengine - INFO - Epoch(train) [24][900/3757]  lr: 1.2843e-05  eta: 7:04:34  time: 1.0020  data_time: 0.0167  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 0.8744  loss: 0.8744
2022/08/07 03:10:18 - mmengine - INFO - Epoch(train) [24][1000/3757]  lr: 1.2843e-05  eta: 7:02:53  time: 1.0024  data_time: 0.0176  memory: 68881  top1_acc: 0.6250  top5_acc: 0.7500  loss_cls: 0.8709  loss: 0.8709
2022/08/07 03:11:58 - mmengine - INFO - Epoch(train) [24][1100/3757]  lr: 1.2843e-05  eta: 7:01:13  time: 0.9992  data_time: 0.0172  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 0.7588  loss: 0.7588
2022/08/07 03:13:38 - mmengine - INFO - Epoch(train) [24][1200/3757]  lr: 1.2843e-05  eta: 6:59:33  time: 1.0016  data_time: 0.0174  memory: 68881  top1_acc: 0.6250  top5_acc: 0.7500  loss_cls: 0.7350  loss: 0.7350
2022/08/07 03:15:18 - mmengine - INFO - Epoch(train) [24][1300/3757]  lr: 1.2843e-05  eta: 6:57:52  time: 1.0020  data_time: 0.0170  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.7960  loss: 0.7960
2022/08/07 03:16:59 - mmengine - INFO - Epoch(train) [24][1400/3757]  lr: 1.2843e-05  eta: 6:56:12  time: 1.0018  data_time: 0.0176  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 0.9586  loss: 0.9586
2022/08/07 03:18:39 - mmengine - INFO - Epoch(train) [24][1500/3757]  lr: 1.2843e-05  eta: 6:54:32  time: 1.0000  data_time: 0.0167  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 0.9400  loss: 0.9400
2022/08/07 03:20:08 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/07 03:20:19 - mmengine - INFO - Epoch(train) [24][1600/3757]  lr: 1.2843e-05  eta: 6:52:51  time: 0.9999  data_time: 0.0166  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 0.9479  loss: 0.9479
2022/08/07 03:21:59 - mmengine - INFO - Epoch(train) [24][1700/3757]  lr: 1.2843e-05  eta: 6:51:11  time: 1.0007  data_time: 0.0173  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 1.0056  loss: 1.0056
2022/08/07 03:23:39 - mmengine - INFO - Epoch(train) [24][1800/3757]  lr: 1.2843e-05  eta: 6:49:31  time: 1.0015  data_time: 0.0166  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 0.9354  loss: 0.9354
2022/08/07 03:25:19 - mmengine - INFO - Epoch(train) [24][1900/3757]  lr: 1.2843e-05  eta: 6:47:50  time: 1.0005  data_time: 0.0172  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.7778  loss: 0.7778
2022/08/07 03:27:00 - mmengine - INFO - Epoch(train) [24][2000/3757]  lr: 1.2843e-05  eta: 6:46:10  time: 1.0001  data_time: 0.0169  memory: 68881  top1_acc: 0.5000  top5_acc: 0.8750  loss_cls: 0.8308  loss: 0.8308
2022/08/07 03:28:40 - mmengine - INFO - Epoch(train) [24][2100/3757]  lr: 1.2843e-05  eta: 6:44:30  time: 1.0020  data_time: 0.0162  memory: 68881  top1_acc: 0.5000  top5_acc: 0.5000  loss_cls: 1.0431  loss: 1.0431
2022/08/07 03:30:20 - mmengine - INFO - Epoch(train) [24][2200/3757]  lr: 1.2843e-05  eta: 6:42:49  time: 0.9999  data_time: 0.0167  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 0.8515  loss: 0.8515
2022/08/07 03:32:00 - mmengine - INFO - Epoch(train) [24][2300/3757]  lr: 1.2843e-05  eta: 6:41:09  time: 1.0000  data_time: 0.0175  memory: 68881  top1_acc: 0.6250  top5_acc: 1.0000  loss_cls: 0.9029  loss: 0.9029
2022/08/07 03:33:40 - mmengine - INFO - Epoch(train) [24][2400/3757]  lr: 1.2843e-05  eta: 6:39:29  time: 1.0009  data_time: 0.0169  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 0.7180  loss: 0.7180
2022/08/07 03:35:20 - mmengine - INFO - Epoch(train) [24][2500/3757]  lr: 1.2843e-05  eta: 6:37:48  time: 1.0023  data_time: 0.0168  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 0.8487  loss: 0.8487
2022/08/07 03:36:50 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/07 03:37:01 - mmengine - INFO - Epoch(train) [24][2600/3757]  lr: 1.2843e-05  eta: 6:36:08  time: 1.0028  data_time: 0.0174  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 0.9905  loss: 0.9905
2022/08/07 03:38:41 - mmengine - INFO - Epoch(train) [24][2700/3757]  lr: 1.2843e-05  eta: 6:34:28  time: 1.0000  data_time: 0.0163  memory: 68881  top1_acc: 0.2500  top5_acc: 1.0000  loss_cls: 0.8970  loss: 0.8970
2022/08/07 03:40:21 - mmengine - INFO - Epoch(train) [24][2800/3757]  lr: 1.2843e-05  eta: 6:32:47  time: 0.9991  data_time: 0.0164  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.8229  loss: 0.8229
2022/08/07 03:42:01 - mmengine - INFO - Epoch(train) [24][2900/3757]  lr: 1.2843e-05  eta: 6:31:07  time: 0.9998  data_time: 0.0166  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 0.8779  loss: 0.8779
2022/08/07 03:43:41 - mmengine - INFO - Epoch(train) [24][3000/3757]  lr: 1.2843e-05  eta: 6:29:27  time: 0.9997  data_time: 0.0161  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.9249  loss: 0.9249
2022/08/07 03:45:21 - mmengine - INFO - Epoch(train) [24][3100/3757]  lr: 1.2843e-05  eta: 6:27:46  time: 0.9991  data_time: 0.0167  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 1.0688  loss: 1.0688
2022/08/07 03:47:01 - mmengine - INFO - Epoch(train) [24][3200/3757]  lr: 1.2843e-05  eta: 6:26:06  time: 0.9991  data_time: 0.0161  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.8426  loss: 0.8426
2022/08/07 03:48:42 - mmengine - INFO - Epoch(train) [24][3300/3757]  lr: 1.2843e-05  eta: 6:24:26  time: 1.0045  data_time: 0.0167  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.9794  loss: 0.9794
2022/08/07 03:50:22 - mmengine - INFO - Epoch(train) [24][3400/3757]  lr: 1.2843e-05  eta: 6:22:45  time: 1.0003  data_time: 0.0166  memory: 68881  top1_acc: 0.7500  top5_acc: 0.7500  loss_cls: 0.8345  loss: 0.8345
2022/08/07 03:52:02 - mmengine - INFO - Epoch(train) [24][3500/3757]  lr: 1.2843e-05  eta: 6:21:05  time: 0.9991  data_time: 0.0168  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 0.7735  loss: 0.7735
2022/08/07 03:53:31 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/07 03:53:42 - mmengine - INFO - Epoch(train) [24][3600/3757]  lr: 1.2843e-05  eta: 6:19:25  time: 1.0005  data_time: 0.0172  memory: 68881  top1_acc: 0.7500  top5_acc: 0.7500  loss_cls: 0.8243  loss: 0.8243
2022/08/07 03:55:22 - mmengine - INFO - Epoch(train) [24][3700/3757]  lr: 1.2843e-05  eta: 6:17:44  time: 1.0048  data_time: 0.0163  memory: 68881  top1_acc: 0.5000  top5_acc: 0.7500  loss_cls: 0.7864  loss: 0.7864
2022/08/07 03:56:19 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/07 03:56:19 - mmengine - INFO - Epoch(train) [24][3757/3757]  lr: 1.2843e-05  eta: 6:17:04  time: 0.9915  data_time: 0.0169  memory: 68881  top1_acc: 0.7143  top5_acc: 0.8571  loss_cls: 0.8090  loss: 0.8090
2022/08/07 03:56:19 - mmengine - INFO - Saving checkpoint at 24 epochs
2022/08/07 03:57:17 - mmengine - INFO - Epoch(val) [24][100/310]    eta: 0:01:25  time: 0.4053  data_time: 0.0099  memory: 14218  
2022/08/07 03:57:58 - mmengine - INFO - Epoch(val) [24][200/310]    eta: 0:00:44  time: 0.4087  data_time: 0.0106  memory: 14218  
2022/08/07 03:58:38 - mmengine - INFO - Epoch(val) [24][300/310]    eta: 0:00:04  time: 0.4032  data_time: 0.0094  memory: 14218  
2022/08/07 03:58:43 - mmengine - INFO - Epoch(val) [24][310/310]  acc/top1: 0.7857  acc/top5: 0.9338  acc/mean1: 0.7855
2022/08/07 03:58:44 - mmengine - INFO - The previous best checkpoint /mnt/lustre/daiwenxun/X/swin/mmaction2/work_dirs/swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb/best_acc/top1_epoch_22.pth is removed
2022/08/07 03:58:50 - mmengine - INFO - The best checkpoint with 0.7857 acc/top1 at 25 epoch is saved to best_acc/top1_epoch_25.pth.
2022/08/07 04:00:31 - mmengine - INFO - Epoch(train) [25][100/3757]  lr: 9.5496e-06  eta: 6:15:03  time: 1.0025  data_time: 0.0175  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 1.1640  loss: 1.1640
2022/08/07 04:02:11 - mmengine - INFO - Epoch(train) [25][200/3757]  lr: 9.5496e-06  eta: 6:13:23  time: 1.0054  data_time: 0.0170  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 0.7690  loss: 0.7690
2022/08/07 04:03:51 - mmengine - INFO - Epoch(train) [25][300/3757]  lr: 9.5496e-06  eta: 6:11:42  time: 0.9994  data_time: 0.0172  memory: 68881  top1_acc: 0.7500  top5_acc: 0.7500  loss_cls: 0.9798  loss: 0.9798
2022/08/07 04:05:31 - mmengine - INFO - Epoch(train) [25][400/3757]  lr: 9.5496e-06  eta: 6:10:02  time: 1.0052  data_time: 0.0173  memory: 68881  top1_acc: 0.7500  top5_acc: 0.7500  loss_cls: 0.9849  loss: 0.9849
2022/08/07 04:07:11 - mmengine - INFO - Epoch(train) [25][500/3757]  lr: 9.5496e-06  eta: 6:08:22  time: 1.0026  data_time: 0.0165  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.9146  loss: 0.9146
2022/08/07 04:08:52 - mmengine - INFO - Epoch(train) [25][600/3757]  lr: 9.5496e-06  eta: 6:06:41  time: 0.9992  data_time: 0.0161  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 0.8323  loss: 0.8323
2022/08/07 04:10:32 - mmengine - INFO - Epoch(train) [25][700/3757]  lr: 9.5496e-06  eta: 6:05:01  time: 1.0038  data_time: 0.0163  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 1.0502  loss: 1.0502
2022/08/07 04:12:12 - mmengine - INFO - Epoch(train) [25][800/3757]  lr: 9.5496e-06  eta: 6:03:21  time: 1.0020  data_time: 0.0169  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.0595  loss: 1.0595
2022/08/07 04:12:44 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/07 04:13:52 - mmengine - INFO - Epoch(train) [25][900/3757]  lr: 9.5496e-06  eta: 6:01:40  time: 1.0002  data_time: 0.0164  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 0.8195  loss: 0.8195
2022/08/07 04:15:32 - mmengine - INFO - Epoch(train) [25][1000/3757]  lr: 9.5496e-06  eta: 6:00:00  time: 1.0039  data_time: 0.0187  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.0082  loss: 1.0082
2022/08/07 04:17:12 - mmengine - INFO - Epoch(train) [25][1100/3757]  lr: 9.5496e-06  eta: 5:58:20  time: 1.0024  data_time: 0.0167  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.0052  loss: 1.0052
2022/08/07 04:18:53 - mmengine - INFO - Epoch(train) [25][1200/3757]  lr: 9.5496e-06  eta: 5:56:39  time: 1.0054  data_time: 0.0168  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 1.1312  loss: 1.1312
2022/08/07 04:20:33 - mmengine - INFO - Epoch(train) [25][1300/3757]  lr: 9.5496e-06  eta: 5:54:59  time: 1.0015  data_time: 0.0179  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 0.7033  loss: 0.7033
2022/08/07 04:22:13 - mmengine - INFO - Epoch(train) [25][1400/3757]  lr: 9.5496e-06  eta: 5:53:19  time: 1.0027  data_time: 0.0187  memory: 68881  top1_acc: 0.6250  top5_acc: 1.0000  loss_cls: 0.9670  loss: 0.9670
2022/08/07 04:23:53 - mmengine - INFO - Epoch(train) [25][1500/3757]  lr: 9.5496e-06  eta: 5:51:39  time: 1.0016  data_time: 0.0184  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 0.6169  loss: 0.6169
2022/08/07 04:25:33 - mmengine - INFO - Epoch(train) [25][1600/3757]  lr: 9.5496e-06  eta: 5:49:58  time: 1.0015  data_time: 0.0175  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 0.6914  loss: 0.6914
2022/08/07 04:27:14 - mmengine - INFO - Epoch(train) [25][1700/3757]  lr: 9.5496e-06  eta: 5:48:18  time: 0.9993  data_time: 0.0169  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.0003  loss: 1.0003
2022/08/07 04:28:54 - mmengine - INFO - Epoch(train) [25][1800/3757]  lr: 9.5496e-06  eta: 5:46:38  time: 1.0017  data_time: 0.0172  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.7338  loss: 0.7338
2022/08/07 04:29:27 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/07 04:30:35 - mmengine - INFO - Epoch(train) [25][1900/3757]  lr: 9.5496e-06  eta: 5:44:58  time: 1.0037  data_time: 0.0173  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 0.9813  loss: 0.9813
2022/08/07 04:32:15 - mmengine - INFO - Epoch(train) [25][2000/3757]  lr: 9.5496e-06  eta: 5:43:17  time: 1.0051  data_time: 0.0176  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.2109  loss: 1.2109
2022/08/07 04:33:56 - mmengine - INFO - Epoch(train) [25][2100/3757]  lr: 9.5496e-06  eta: 5:41:37  time: 1.0015  data_time: 0.0174  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 0.8830  loss: 0.8830
2022/08/07 04:35:36 - mmengine - INFO - Epoch(train) [25][2200/3757]  lr: 9.5496e-06  eta: 5:39:57  time: 1.0002  data_time: 0.0171  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.0079  loss: 1.0079
2022/08/07 04:37:16 - mmengine - INFO - Epoch(train) [25][2300/3757]  lr: 9.5496e-06  eta: 5:38:16  time: 1.0014  data_time: 0.0166  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 0.8287  loss: 0.8287
2022/08/07 04:38:56 - mmengine - INFO - Epoch(train) [25][2400/3757]  lr: 9.5496e-06  eta: 5:36:36  time: 1.0027  data_time: 0.0169  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 0.8361  loss: 0.8361
2022/08/07 04:40:36 - mmengine - INFO - Epoch(train) [25][2500/3757]  lr: 9.5496e-06  eta: 5:34:56  time: 1.0008  data_time: 0.0166  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.0042  loss: 1.0042
2022/08/07 04:42:17 - mmengine - INFO - Epoch(train) [25][2600/3757]  lr: 9.5496e-06  eta: 5:33:16  time: 0.9993  data_time: 0.0172  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 0.8127  loss: 0.8127
2022/08/07 04:43:57 - mmengine - INFO - Epoch(train) [25][2700/3757]  lr: 9.5496e-06  eta: 5:31:35  time: 1.0004  data_time: 0.0166  memory: 68881  top1_acc: 0.7500  top5_acc: 0.7500  loss_cls: 0.7898  loss: 0.7898
2022/08/07 04:45:37 - mmengine - INFO - Epoch(train) [25][2800/3757]  lr: 9.5496e-06  eta: 5:29:55  time: 0.9998  data_time: 0.0165  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 0.7636  loss: 0.7636
2022/08/07 04:46:09 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/07 04:47:17 - mmengine - INFO - Epoch(train) [25][2900/3757]  lr: 9.5496e-06  eta: 5:28:15  time: 0.9998  data_time: 0.0167  memory: 68881  top1_acc: 0.5000  top5_acc: 0.6250  loss_cls: 0.9017  loss: 0.9017
2022/08/07 04:48:57 - mmengine - INFO - Epoch(train) [25][3000/3757]  lr: 9.5496e-06  eta: 5:26:34  time: 1.0018  data_time: 0.0173  memory: 68881  top1_acc: 0.6250  top5_acc: 0.6250  loss_cls: 0.9759  loss: 0.9759
2022/08/07 04:50:38 - mmengine - INFO - Epoch(train) [25][3100/3757]  lr: 9.5496e-06  eta: 5:24:54  time: 1.0035  data_time: 0.0170  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.8774  loss: 0.8774
2022/08/07 04:52:18 - mmengine - INFO - Epoch(train) [25][3200/3757]  lr: 9.5496e-06  eta: 5:23:14  time: 1.0053  data_time: 0.0184  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 0.8899  loss: 0.8899
2022/08/07 04:53:58 - mmengine - INFO - Epoch(train) [25][3300/3757]  lr: 9.5496e-06  eta: 5:21:34  time: 1.0022  data_time: 0.0167  memory: 68881  top1_acc: 0.6250  top5_acc: 0.7500  loss_cls: 0.7149  loss: 0.7149
2022/08/07 04:55:38 - mmengine - INFO - Epoch(train) [25][3400/3757]  lr: 9.5496e-06  eta: 5:19:53  time: 1.0008  data_time: 0.0170  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 0.6426  loss: 0.6426
2022/08/07 04:57:18 - mmengine - INFO - Epoch(train) [25][3500/3757]  lr: 9.5496e-06  eta: 5:18:13  time: 1.0034  data_time: 0.0179  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 1.0429  loss: 1.0429
2022/08/07 04:58:58 - mmengine - INFO - Epoch(train) [25][3600/3757]  lr: 9.5496e-06  eta: 5:16:33  time: 0.9999  data_time: 0.0165  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.9163  loss: 0.9163
2022/08/07 05:00:39 - mmengine - INFO - Epoch(train) [25][3700/3757]  lr: 9.5496e-06  eta: 5:14:52  time: 0.9999  data_time: 0.0168  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 0.8743  loss: 0.8743
2022/08/07 05:01:35 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/07 05:01:35 - mmengine - INFO - Epoch(train) [25][3757/3757]  lr: 9.5496e-06  eta: 5:14:12  time: 0.9909  data_time: 0.0163  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 0.7074  loss: 0.7074
2022/08/07 05:02:53 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/07 05:03:18 - mmengine - INFO - Epoch(train) [26][100/3757]  lr: 6.6991e-06  eta: 5:12:12  time: 1.0007  data_time: 0.0175  memory: 68881  top1_acc: 0.6250  top5_acc: 0.6250  loss_cls: 1.0528  loss: 1.0528
2022/08/07 05:04:58 - mmengine - INFO - Epoch(train) [26][200/3757]  lr: 6.6991e-06  eta: 5:10:32  time: 1.0008  data_time: 0.0160  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.9035  loss: 0.9035
2022/08/07 05:06:38 - mmengine - INFO - Epoch(train) [26][300/3757]  lr: 6.6991e-06  eta: 5:08:51  time: 1.0000  data_time: 0.0166  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 0.8861  loss: 0.8861
2022/08/07 05:08:18 - mmengine - INFO - Epoch(train) [26][400/3757]  lr: 6.6991e-06  eta: 5:07:11  time: 1.0008  data_time: 0.0171  memory: 68881  top1_acc: 0.5000  top5_acc: 1.0000  loss_cls: 0.5435  loss: 0.5435
2022/08/07 05:09:58 - mmengine - INFO - Epoch(train) [26][500/3757]  lr: 6.6991e-06  eta: 5:05:31  time: 1.0008  data_time: 0.0169  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 0.8189  loss: 0.8189
2022/08/07 05:11:39 - mmengine - INFO - Epoch(train) [26][600/3757]  lr: 6.6991e-06  eta: 5:03:51  time: 1.0023  data_time: 0.0171  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 1.0296  loss: 1.0296
2022/08/07 05:13:19 - mmengine - INFO - Epoch(train) [26][700/3757]  lr: 6.6991e-06  eta: 5:02:10  time: 0.9997  data_time: 0.0163  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 0.9140  loss: 0.9140
2022/08/07 05:14:59 - mmengine - INFO - Epoch(train) [26][800/3757]  lr: 6.6991e-06  eta: 5:00:30  time: 1.0027  data_time: 0.0167  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 0.7279  loss: 0.7279
2022/08/07 05:16:39 - mmengine - INFO - Epoch(train) [26][900/3757]  lr: 6.6991e-06  eta: 4:58:50  time: 1.0023  data_time: 0.0170  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 0.8964  loss: 0.8964
2022/08/07 05:18:19 - mmengine - INFO - Epoch(train) [26][1000/3757]  lr: 6.6991e-06  eta: 4:57:09  time: 1.0028  data_time: 0.0170  memory: 68881  top1_acc: 0.7500  top5_acc: 0.7500  loss_cls: 0.6547  loss: 0.6547
2022/08/07 05:19:34 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/07 05:19:59 - mmengine - INFO - Epoch(train) [26][1100/3757]  lr: 6.6991e-06  eta: 4:55:29  time: 1.0015  data_time: 0.0163  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 0.6911  loss: 0.6911
2022/08/07 05:21:40 - mmengine - INFO - Epoch(train) [26][1200/3757]  lr: 6.6991e-06  eta: 4:53:49  time: 1.0002  data_time: 0.0172  memory: 68881  top1_acc: 0.6250  top5_acc: 1.0000  loss_cls: 0.8915  loss: 0.8915
2022/08/07 05:23:20 - mmengine - INFO - Epoch(train) [26][1300/3757]  lr: 6.6991e-06  eta: 4:52:09  time: 1.0017  data_time: 0.0165  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 0.7328  loss: 0.7328
2022/08/07 05:25:00 - mmengine - INFO - Epoch(train) [26][1400/3757]  lr: 6.6991e-06  eta: 4:50:28  time: 1.0033  data_time: 0.0178  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 0.8902  loss: 0.8902
2022/08/07 05:26:40 - mmengine - INFO - Epoch(train) [26][1500/3757]  lr: 6.6991e-06  eta: 4:48:48  time: 1.0002  data_time: 0.0172  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 1.0979  loss: 1.0979
2022/08/07 05:28:20 - mmengine - INFO - Epoch(train) [26][1600/3757]  lr: 6.6991e-06  eta: 4:47:08  time: 1.0008  data_time: 0.0181  memory: 68881  top1_acc: 0.6250  top5_acc: 1.0000  loss_cls: 1.1334  loss: 1.1334
2022/08/07 05:30:00 - mmengine - INFO - Epoch(train) [26][1700/3757]  lr: 6.6991e-06  eta: 4:45:27  time: 1.0014  data_time: 0.0171  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 0.8351  loss: 0.8351
2022/08/07 05:31:40 - mmengine - INFO - Epoch(train) [26][1800/3757]  lr: 6.6991e-06  eta: 4:43:47  time: 1.0008  data_time: 0.0168  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 0.5950  loss: 0.5950
2022/08/07 05:33:21 - mmengine - INFO - Epoch(train) [26][1900/3757]  lr: 6.6991e-06  eta: 4:42:07  time: 1.0016  data_time: 0.0163  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 0.7841  loss: 0.7841
2022/08/07 05:35:01 - mmengine - INFO - Epoch(train) [26][2000/3757]  lr: 6.6991e-06  eta: 4:40:27  time: 1.0011  data_time: 0.0166  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 0.7791  loss: 0.7791
2022/08/07 05:36:16 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/07 05:36:41 - mmengine - INFO - Epoch(train) [26][2100/3757]  lr: 6.6991e-06  eta: 4:38:46  time: 0.9997  data_time: 0.0169  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 0.9977  loss: 0.9977
2022/08/07 05:38:21 - mmengine - INFO - Epoch(train) [26][2200/3757]  lr: 6.6991e-06  eta: 4:37:06  time: 1.0003  data_time: 0.0177  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 0.8846  loss: 0.8846
2022/08/07 05:40:01 - mmengine - INFO - Epoch(train) [26][2300/3757]  lr: 6.6991e-06  eta: 4:35:26  time: 1.0022  data_time: 0.0165  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 0.8962  loss: 0.8962
2022/08/07 05:41:41 - mmengine - INFO - Epoch(train) [26][2400/3757]  lr: 6.6991e-06  eta: 4:33:46  time: 1.0023  data_time: 0.0165  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 0.9077  loss: 0.9077
2022/08/07 05:43:22 - mmengine - INFO - Epoch(train) [26][2500/3757]  lr: 6.6991e-06  eta: 4:32:05  time: 1.0057  data_time: 0.0165  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 0.8390  loss: 0.8390
2022/08/07 05:45:02 - mmengine - INFO - Epoch(train) [26][2600/3757]  lr: 6.6991e-06  eta: 4:30:25  time: 1.0011  data_time: 0.0169  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.8484  loss: 0.8484
2022/08/07 05:46:42 - mmengine - INFO - Epoch(train) [26][2700/3757]  lr: 6.6991e-06  eta: 4:28:45  time: 1.0026  data_time: 0.0169  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 0.8019  loss: 0.8019
2022/08/07 05:48:22 - mmengine - INFO - Epoch(train) [26][2800/3757]  lr: 6.6991e-06  eta: 4:27:04  time: 1.0012  data_time: 0.0170  memory: 68881  top1_acc: 0.5000  top5_acc: 0.8750  loss_cls: 0.9561  loss: 0.9561
2022/08/07 05:50:02 - mmengine - INFO - Epoch(train) [26][2900/3757]  lr: 6.6991e-06  eta: 4:25:24  time: 1.0002  data_time: 0.0166  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 0.7828  loss: 0.7828
2022/08/07 05:51:42 - mmengine - INFO - Epoch(train) [26][3000/3757]  lr: 6.6991e-06  eta: 4:23:44  time: 1.0030  data_time: 0.0170  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.8360  loss: 0.8360
2022/08/07 05:52:58 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/07 05:53:23 - mmengine - INFO - Epoch(train) [26][3100/3757]  lr: 6.6991e-06  eta: 4:22:04  time: 1.0036  data_time: 0.0174  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 0.9111  loss: 0.9111
2022/08/07 05:55:03 - mmengine - INFO - Epoch(train) [26][3200/3757]  lr: 6.6991e-06  eta: 4:20:23  time: 0.9988  data_time: 0.0168  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 0.8890  loss: 0.8890
2022/08/07 05:56:43 - mmengine - INFO - Epoch(train) [26][3300/3757]  lr: 6.6991e-06  eta: 4:18:43  time: 1.0013  data_time: 0.0172  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 0.7974  loss: 0.7974
2022/08/07 05:58:23 - mmengine - INFO - Epoch(train) [26][3400/3757]  lr: 6.6991e-06  eta: 4:17:03  time: 1.0010  data_time: 0.0165  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 0.7781  loss: 0.7781
2022/08/07 06:00:05 - mmengine - INFO - Epoch(train) [26][3500/3757]  lr: 6.6991e-06  eta: 4:15:23  time: 1.0881  data_time: 0.0195  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 1.0270  loss: 1.0270
2022/08/07 06:01:45 - mmengine - INFO - Epoch(train) [26][3600/3757]  lr: 6.6991e-06  eta: 4:13:43  time: 1.0045  data_time: 0.0165  memory: 68881  top1_acc: 0.6250  top5_acc: 0.7500  loss_cls: 0.8025  loss: 0.8025
2022/08/07 06:03:26 - mmengine - INFO - Epoch(train) [26][3700/3757]  lr: 6.6991e-06  eta: 4:12:02  time: 1.0007  data_time: 0.0172  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.6779  loss: 0.6779
2022/08/07 06:04:22 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/07 06:04:22 - mmengine - INFO - Epoch(train) [26][3757/3757]  lr: 6.6991e-06  eta: 4:11:22  time: 0.9927  data_time: 0.0162  memory: 68881  top1_acc: 0.7143  top5_acc: 1.0000  loss_cls: 0.9943  loss: 0.9943
2022/08/07 06:06:05 - mmengine - INFO - Epoch(train) [27][100/3757]  lr: 4.3229e-06  eta: 4:09:23  time: 1.0031  data_time: 0.0163  memory: 68881  top1_acc: 0.5000  top5_acc: 0.8750  loss_cls: 0.8415  loss: 0.8415
2022/08/07 06:07:45 - mmengine - INFO - Epoch(train) [27][200/3757]  lr: 4.3229e-06  eta: 4:07:42  time: 1.0003  data_time: 0.0166  memory: 68881  top1_acc: 0.6250  top5_acc: 0.7500  loss_cls: 0.6724  loss: 0.6724
2022/08/07 06:09:25 - mmengine - INFO - Epoch(train) [27][300/3757]  lr: 4.3229e-06  eta: 4:06:02  time: 1.0005  data_time: 0.0169  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 0.6793  loss: 0.6793
2022/08/07 06:09:43 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/07 06:11:05 - mmengine - INFO - Epoch(train) [27][400/3757]  lr: 4.3229e-06  eta: 4:04:22  time: 0.9995  data_time: 0.0162  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 0.6716  loss: 0.6716
2022/08/07 06:12:45 - mmengine - INFO - Epoch(train) [27][500/3757]  lr: 4.3229e-06  eta: 4:02:42  time: 1.0033  data_time: 0.0169  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 0.8535  loss: 0.8535
2022/08/07 06:14:26 - mmengine - INFO - Epoch(train) [27][600/3757]  lr: 4.3229e-06  eta: 4:01:01  time: 1.0013  data_time: 0.0164  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.7256  loss: 0.7256
2022/08/07 06:16:06 - mmengine - INFO - Epoch(train) [27][700/3757]  lr: 4.3229e-06  eta: 3:59:21  time: 1.0013  data_time: 0.0164  memory: 68881  top1_acc: 0.7500  top5_acc: 0.7500  loss_cls: 0.8820  loss: 0.8820
2022/08/07 06:17:46 - mmengine - INFO - Epoch(train) [27][800/3757]  lr: 4.3229e-06  eta: 3:57:41  time: 0.9999  data_time: 0.0166  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 0.7128  loss: 0.7128
2022/08/07 06:19:26 - mmengine - INFO - Epoch(train) [27][900/3757]  lr: 4.3229e-06  eta: 3:56:01  time: 0.9999  data_time: 0.0171  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 0.9103  loss: 0.9103
2022/08/07 06:21:06 - mmengine - INFO - Epoch(train) [27][1000/3757]  lr: 4.3229e-06  eta: 3:54:20  time: 1.0028  data_time: 0.0181  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.8362  loss: 0.8362
2022/08/07 06:22:46 - mmengine - INFO - Epoch(train) [27][1100/3757]  lr: 4.3229e-06  eta: 3:52:40  time: 1.0013  data_time: 0.0172  memory: 68881  top1_acc: 0.5000  top5_acc: 0.8750  loss_cls: 0.9247  loss: 0.9247
2022/08/07 06:24:27 - mmengine - INFO - Epoch(train) [27][1200/3757]  lr: 4.3229e-06  eta: 3:51:00  time: 1.0013  data_time: 0.0169  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 0.8851  loss: 0.8851
2022/08/07 06:26:07 - mmengine - INFO - Epoch(train) [27][1300/3757]  lr: 4.3229e-06  eta: 3:49:20  time: 0.9992  data_time: 0.0164  memory: 68881  top1_acc: 0.5000  top5_acc: 0.6250  loss_cls: 0.9220  loss: 0.9220
2022/08/07 06:26:25 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/07 06:27:47 - mmengine - INFO - Epoch(train) [27][1400/3757]  lr: 4.3229e-06  eta: 3:47:39  time: 1.0001  data_time: 0.0174  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 0.7869  loss: 0.7869
2022/08/07 06:29:27 - mmengine - INFO - Epoch(train) [27][1500/3757]  lr: 4.3229e-06  eta: 3:45:59  time: 0.9988  data_time: 0.0162  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.6984  loss: 0.6984
2022/08/07 06:31:07 - mmengine - INFO - Epoch(train) [27][1600/3757]  lr: 4.3229e-06  eta: 3:44:19  time: 1.0002  data_time: 0.0169  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 0.9467  loss: 0.9467
2022/08/07 06:32:47 - mmengine - INFO - Epoch(train) [27][1700/3757]  lr: 4.3229e-06  eta: 3:42:39  time: 1.0005  data_time: 0.0175  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.0228  loss: 1.0228
2022/08/07 06:34:28 - mmengine - INFO - Epoch(train) [27][1800/3757]  lr: 4.3229e-06  eta: 3:40:58  time: 1.0016  data_time: 0.0172  memory: 68881  top1_acc: 0.6250  top5_acc: 0.7500  loss_cls: 0.7697  loss: 0.7697
2022/08/07 06:36:08 - mmengine - INFO - Epoch(train) [27][1900/3757]  lr: 4.3229e-06  eta: 3:39:18  time: 1.0023  data_time: 0.0165  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.0175  loss: 1.0175
2022/08/07 06:37:48 - mmengine - INFO - Epoch(train) [27][2000/3757]  lr: 4.3229e-06  eta: 3:37:38  time: 1.0024  data_time: 0.0162  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 0.9150  loss: 0.9150
2022/08/07 06:39:29 - mmengine - INFO - Epoch(train) [27][2100/3757]  lr: 4.3229e-06  eta: 3:35:58  time: 1.0015  data_time: 0.0171  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 0.7937  loss: 0.7937
2022/08/07 06:41:09 - mmengine - INFO - Epoch(train) [27][2200/3757]  lr: 4.3229e-06  eta: 3:34:18  time: 0.9998  data_time: 0.0166  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 0.7557  loss: 0.7557
2022/08/07 06:42:49 - mmengine - INFO - Epoch(train) [27][2300/3757]  lr: 4.3229e-06  eta: 3:32:37  time: 1.0004  data_time: 0.0163  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 0.7505  loss: 0.7505
2022/08/07 06:43:07 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/07 06:44:29 - mmengine - INFO - Epoch(train) [27][2400/3757]  lr: 4.3229e-06  eta: 3:30:57  time: 1.0001  data_time: 0.0162  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 0.6376  loss: 0.6376
2022/08/07 06:46:10 - mmengine - INFO - Epoch(train) [27][2500/3757]  lr: 4.3229e-06  eta: 3:29:17  time: 1.0003  data_time: 0.0165  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.7643  loss: 0.7643
2022/08/07 06:47:50 - mmengine - INFO - Epoch(train) [27][2600/3757]  lr: 4.3229e-06  eta: 3:27:37  time: 1.0017  data_time: 0.0174  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.0018  loss: 1.0018
2022/08/07 06:49:30 - mmengine - INFO - Epoch(train) [27][2700/3757]  lr: 4.3229e-06  eta: 3:25:56  time: 1.0004  data_time: 0.0169  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 0.6995  loss: 0.6995
2022/08/07 06:51:10 - mmengine - INFO - Epoch(train) [27][2800/3757]  lr: 4.3229e-06  eta: 3:24:16  time: 1.0011  data_time: 0.0164  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 0.8237  loss: 0.8237
2022/08/07 06:52:50 - mmengine - INFO - Epoch(train) [27][2900/3757]  lr: 4.3229e-06  eta: 3:22:36  time: 0.9992  data_time: 0.0171  memory: 68881  top1_acc: 0.3750  top5_acc: 0.6250  loss_cls: 1.1322  loss: 1.1322
2022/08/07 06:54:30 - mmengine - INFO - Epoch(train) [27][3000/3757]  lr: 4.3229e-06  eta: 3:20:56  time: 1.0024  data_time: 0.0181  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 0.6915  loss: 0.6915
2022/08/07 06:56:11 - mmengine - INFO - Epoch(train) [27][3100/3757]  lr: 4.3229e-06  eta: 3:19:15  time: 1.0010  data_time: 0.0172  memory: 68881  top1_acc: 0.7500  top5_acc: 0.7500  loss_cls: 0.9332  loss: 0.9332
2022/08/07 06:57:51 - mmengine - INFO - Epoch(train) [27][3200/3757]  lr: 4.3229e-06  eta: 3:17:35  time: 1.0004  data_time: 0.0168  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 0.8886  loss: 0.8886
2022/08/07 06:59:31 - mmengine - INFO - Epoch(train) [27][3300/3757]  lr: 4.3229e-06  eta: 3:15:55  time: 0.9997  data_time: 0.0168  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 0.9385  loss: 0.9385
2022/08/07 06:59:49 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/07 07:01:11 - mmengine - INFO - Epoch(train) [27][3400/3757]  lr: 4.3229e-06  eta: 3:14:15  time: 1.0009  data_time: 0.0167  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 0.6939  loss: 0.6939
2022/08/07 07:02:51 - mmengine - INFO - Epoch(train) [27][3500/3757]  lr: 4.3229e-06  eta: 3:12:34  time: 1.0010  data_time: 0.0172  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 0.7259  loss: 0.7259
2022/08/07 07:04:31 - mmengine - INFO - Epoch(train) [27][3600/3757]  lr: 4.3229e-06  eta: 3:10:54  time: 0.9995  data_time: 0.0165  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 0.8141  loss: 0.8141
2022/08/07 07:06:12 - mmengine - INFO - Epoch(train) [27][3700/3757]  lr: 4.3229e-06  eta: 3:09:14  time: 1.0012  data_time: 0.0167  memory: 68881  top1_acc: 0.5000  top5_acc: 1.0000  loss_cls: 1.0110  loss: 1.0110
2022/08/07 07:07:09 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/07 07:07:09 - mmengine - INFO - Epoch(train) [27][3757/3757]  lr: 4.3229e-06  eta: 3:08:34  time: 0.9940  data_time: 0.0172  memory: 68881  top1_acc: 0.7143  top5_acc: 0.7143  loss_cls: 0.9403  loss: 0.9403
2022/08/07 07:07:09 - mmengine - INFO - Saving checkpoint at 27 epochs
2022/08/07 07:08:06 - mmengine - INFO - Epoch(val) [27][100/310]    eta: 0:01:25  time: 0.4064  data_time: 0.0102  memory: 14218  
2022/08/07 07:08:47 - mmengine - INFO - Epoch(val) [27][200/310]    eta: 0:00:44  time: 0.4068  data_time: 0.0098  memory: 14218  
2022/08/07 07:09:28 - mmengine - INFO - Epoch(val) [27][300/310]    eta: 0:00:04  time: 0.4032  data_time: 0.0092  memory: 14218  
2022/08/07 07:09:33 - mmengine - INFO - Epoch(val) [27][310/310]  acc/top1: 0.7877  acc/top5: 0.9338  acc/mean1: 0.7876
2022/08/07 07:09:33 - mmengine - INFO - The previous best checkpoint /mnt/lustre/daiwenxun/X/swin/mmaction2/work_dirs/swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb/best_acc/top1_epoch_25.pth is removed
2022/08/07 07:09:39 - mmengine - INFO - The best checkpoint with 0.7877 acc/top1 at 28 epoch is saved to best_acc/top1_epoch_28.pth.
2022/08/07 07:11:21 - mmengine - INFO - Epoch(train) [28][100/3757]  lr: 2.4473e-06  eta: 3:06:35  time: 0.9984  data_time: 0.0166  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 0.9896  loss: 0.9896
2022/08/07 07:13:01 - mmengine - INFO - Epoch(train) [28][200/3757]  lr: 2.4473e-06  eta: 3:04:55  time: 1.0012  data_time: 0.0172  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 0.7743  loss: 0.7743
2022/08/07 07:14:41 - mmengine - INFO - Epoch(train) [28][300/3757]  lr: 2.4473e-06  eta: 3:03:14  time: 0.9991  data_time: 0.0169  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 0.7563  loss: 0.7563
2022/08/07 07:16:21 - mmengine - INFO - Epoch(train) [28][400/3757]  lr: 2.4473e-06  eta: 3:01:34  time: 1.0020  data_time: 0.0161  memory: 68881  top1_acc: 0.5000  top5_acc: 0.6250  loss_cls: 0.9024  loss: 0.9024
2022/08/07 07:18:01 - mmengine - INFO - Epoch(train) [28][500/3757]  lr: 2.4473e-06  eta: 2:59:54  time: 1.0002  data_time: 0.0166  memory: 68881  top1_acc: 0.6250  top5_acc: 0.7500  loss_cls: 0.8805  loss: 0.8805
2022/08/07 07:19:02 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/07 07:19:41 - mmengine - INFO - Epoch(train) [28][600/3757]  lr: 2.4473e-06  eta: 2:58:14  time: 0.9997  data_time: 0.0167  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 0.8564  loss: 0.8564
2022/08/07 07:21:22 - mmengine - INFO - Epoch(train) [28][700/3757]  lr: 2.4473e-06  eta: 2:56:33  time: 0.9995  data_time: 0.0169  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 0.9444  loss: 0.9444
2022/08/07 07:23:02 - mmengine - INFO - Epoch(train) [28][800/3757]  lr: 2.4473e-06  eta: 2:54:53  time: 0.9991  data_time: 0.0161  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 0.8073  loss: 0.8073
2022/08/07 07:24:42 - mmengine - INFO - Epoch(train) [28][900/3757]  lr: 2.4473e-06  eta: 2:53:13  time: 1.0011  data_time: 0.0177  memory: 68881  top1_acc: 0.5000  top5_acc: 1.0000  loss_cls: 0.8950  loss: 0.8950
2022/08/07 07:26:22 - mmengine - INFO - Epoch(train) [28][1000/3757]  lr: 2.4473e-06  eta: 2:51:33  time: 0.9998  data_time: 0.0169  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 0.7117  loss: 0.7117
2022/08/07 07:28:02 - mmengine - INFO - Epoch(train) [28][1100/3757]  lr: 2.4473e-06  eta: 2:49:52  time: 1.0012  data_time: 0.0169  memory: 68881  top1_acc: 0.7500  top5_acc: 0.7500  loss_cls: 0.7622  loss: 0.7622
2022/08/07 07:29:42 - mmengine - INFO - Epoch(train) [28][1200/3757]  lr: 2.4473e-06  eta: 2:48:12  time: 0.9997  data_time: 0.0171  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.9363  loss: 0.9363
2022/08/07 07:31:23 - mmengine - INFO - Epoch(train) [28][1300/3757]  lr: 2.4473e-06  eta: 2:46:32  time: 1.0077  data_time: 0.0165  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 0.9208  loss: 0.9208
2022/08/07 07:33:03 - mmengine - INFO - Epoch(train) [28][1400/3757]  lr: 2.4473e-06  eta: 2:44:52  time: 1.0030  data_time: 0.0166  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 0.8747  loss: 0.8747
2022/08/07 07:34:43 - mmengine - INFO - Epoch(train) [28][1500/3757]  lr: 2.4473e-06  eta: 2:43:12  time: 1.0013  data_time: 0.0167  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 0.9779  loss: 0.9779
2022/08/07 07:35:44 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/07 07:36:23 - mmengine - INFO - Epoch(train) [28][1600/3757]  lr: 2.4473e-06  eta: 2:41:31  time: 1.0012  data_time: 0.0169  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 0.8861  loss: 0.8861
2022/08/07 07:38:04 - mmengine - INFO - Epoch(train) [28][1700/3757]  lr: 2.4473e-06  eta: 2:39:51  time: 1.0025  data_time: 0.0167  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 0.8266  loss: 0.8266
2022/08/07 07:39:44 - mmengine - INFO - Epoch(train) [28][1800/3757]  lr: 2.4473e-06  eta: 2:38:11  time: 0.9997  data_time: 0.0168  memory: 68881  top1_acc: 0.3750  top5_acc: 0.7500  loss_cls: 0.8831  loss: 0.8831
2022/08/07 07:41:24 - mmengine - INFO - Epoch(train) [28][1900/3757]  lr: 2.4473e-06  eta: 2:36:31  time: 1.0018  data_time: 0.0170  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.0342  loss: 1.0342
2022/08/07 07:43:04 - mmengine - INFO - Epoch(train) [28][2000/3757]  lr: 2.4473e-06  eta: 2:34:51  time: 1.0014  data_time: 0.0174  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 0.7769  loss: 0.7769
2022/08/07 07:44:44 - mmengine - INFO - Epoch(train) [28][2100/3757]  lr: 2.4473e-06  eta: 2:33:10  time: 1.0012  data_time: 0.0169  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 0.5628  loss: 0.5628
2022/08/07 07:46:24 - mmengine - INFO - Epoch(train) [28][2200/3757]  lr: 2.4473e-06  eta: 2:31:30  time: 1.0030  data_time: 0.0165  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.8218  loss: 0.8218
2022/08/07 07:48:05 - mmengine - INFO - Epoch(train) [28][2300/3757]  lr: 2.4473e-06  eta: 2:29:50  time: 1.0013  data_time: 0.0177  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.0349  loss: 1.0349
2022/08/07 07:49:45 - mmengine - INFO - Epoch(train) [28][2400/3757]  lr: 2.4473e-06  eta: 2:28:10  time: 1.0008  data_time: 0.0170  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 0.8791  loss: 0.8791
2022/08/07 07:51:25 - mmengine - INFO - Epoch(train) [28][2500/3757]  lr: 2.4473e-06  eta: 2:26:29  time: 1.0034  data_time: 0.0168  memory: 68881  top1_acc: 0.6250  top5_acc: 0.7500  loss_cls: 0.8441  loss: 0.8441
2022/08/07 07:52:26 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/07 07:53:05 - mmengine - INFO - Epoch(train) [28][2600/3757]  lr: 2.4473e-06  eta: 2:24:49  time: 0.9992  data_time: 0.0174  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 0.7436  loss: 0.7436
2022/08/07 07:54:45 - mmengine - INFO - Epoch(train) [28][2700/3757]  lr: 2.4473e-06  eta: 2:23:09  time: 1.0030  data_time: 0.0166  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 0.7275  loss: 0.7275
2022/08/07 07:56:25 - mmengine - INFO - Epoch(train) [28][2800/3757]  lr: 2.4473e-06  eta: 2:21:29  time: 1.0002  data_time: 0.0167  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 0.5980  loss: 0.5980
2022/08/07 07:58:06 - mmengine - INFO - Epoch(train) [28][2900/3757]  lr: 2.4473e-06  eta: 2:19:48  time: 1.0014  data_time: 0.0172  memory: 68881  top1_acc: 0.6250  top5_acc: 0.6250  loss_cls: 1.1109  loss: 1.1109
2022/08/07 07:59:46 - mmengine - INFO - Epoch(train) [28][3000/3757]  lr: 2.4473e-06  eta: 2:18:08  time: 1.0004  data_time: 0.0169  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.8888  loss: 0.8888
2022/08/07 08:01:26 - mmengine - INFO - Epoch(train) [28][3100/3757]  lr: 2.4473e-06  eta: 2:16:28  time: 1.0004  data_time: 0.0167  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 0.8365  loss: 0.8365
2022/08/07 08:03:06 - mmengine - INFO - Epoch(train) [28][3200/3757]  lr: 2.4473e-06  eta: 2:14:48  time: 1.0047  data_time: 0.0177  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 0.7515  loss: 0.7515
2022/08/07 08:04:46 - mmengine - INFO - Epoch(train) [28][3300/3757]  lr: 2.4473e-06  eta: 2:13:08  time: 1.0016  data_time: 0.0170  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 0.8362  loss: 0.8362
2022/08/07 08:06:26 - mmengine - INFO - Epoch(train) [28][3400/3757]  lr: 2.4473e-06  eta: 2:11:27  time: 1.0009  data_time: 0.0175  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.0187  loss: 1.0187
2022/08/07 08:08:06 - mmengine - INFO - Epoch(train) [28][3500/3757]  lr: 2.4473e-06  eta: 2:09:47  time: 1.0012  data_time: 0.0171  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.7349  loss: 0.7349
2022/08/07 08:09:08 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/07 08:09:47 - mmengine - INFO - Epoch(train) [28][3600/3757]  lr: 2.4473e-06  eta: 2:08:07  time: 1.0016  data_time: 0.0172  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 0.6295  loss: 0.6295
2022/08/07 08:11:27 - mmengine - INFO - Epoch(train) [28][3700/3757]  lr: 2.4473e-06  eta: 2:06:27  time: 1.0007  data_time: 0.0171  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.9519  loss: 0.9519
2022/08/07 08:12:24 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/07 08:12:24 - mmengine - INFO - Epoch(train) [28][3757/3757]  lr: 2.4473e-06  eta: 2:05:47  time: 0.9933  data_time: 0.0169  memory: 68881  top1_acc: 0.8571  top5_acc: 0.8571  loss_cls: 0.6294  loss: 0.6294
2022/08/07 08:14:06 - mmengine - INFO - Epoch(train) [29][100/3757]  lr: 1.0927e-06  eta: 2:03:48  time: 1.0008  data_time: 0.0166  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 0.8164  loss: 0.8164
2022/08/07 08:15:46 - mmengine - INFO - Epoch(train) [29][200/3757]  lr: 1.0927e-06  eta: 2:02:08  time: 1.0029  data_time: 0.0168  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.5725  loss: 0.5725
2022/08/07 08:17:26 - mmengine - INFO - Epoch(train) [29][300/3757]  lr: 1.0927e-06  eta: 2:00:28  time: 1.0002  data_time: 0.0172  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.8348  loss: 0.8348
2022/08/07 08:19:07 - mmengine - INFO - Epoch(train) [29][400/3757]  lr: 1.0927e-06  eta: 1:58:48  time: 1.0013  data_time: 0.0171  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.9564  loss: 0.9564
2022/08/07 08:20:47 - mmengine - INFO - Epoch(train) [29][500/3757]  lr: 1.0927e-06  eta: 1:57:08  time: 1.0008  data_time: 0.0163  memory: 68881  top1_acc: 0.7500  top5_acc: 0.7500  loss_cls: 0.8343  loss: 0.8343
2022/08/07 08:22:27 - mmengine - INFO - Epoch(train) [29][600/3757]  lr: 1.0927e-06  eta: 1:55:27  time: 1.0006  data_time: 0.0170  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.9718  loss: 0.9718
2022/08/07 08:24:07 - mmengine - INFO - Epoch(train) [29][700/3757]  lr: 1.0927e-06  eta: 1:53:47  time: 1.0003  data_time: 0.0171  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 0.7977  loss: 0.7977
2022/08/07 08:25:47 - mmengine - INFO - Epoch(train) [29][800/3757]  lr: 1.0927e-06  eta: 1:52:07  time: 1.0008  data_time: 0.0167  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 0.7073  loss: 0.7073
2022/08/07 08:25:51 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/07 08:27:27 - mmengine - INFO - Epoch(train) [29][900/3757]  lr: 1.0927e-06  eta: 1:50:27  time: 1.0010  data_time: 0.0171  memory: 68881  top1_acc: 0.3750  top5_acc: 0.5000  loss_cls: 0.8321  loss: 0.8321
2022/08/07 08:29:08 - mmengine - INFO - Epoch(train) [29][1000/3757]  lr: 1.0927e-06  eta: 1:48:46  time: 1.0030  data_time: 0.0169  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 1.0468  loss: 1.0468
2022/08/07 08:30:48 - mmengine - INFO - Epoch(train) [29][1100/3757]  lr: 1.0927e-06  eta: 1:47:06  time: 1.0033  data_time: 0.0194  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.1695  loss: 1.1695
2022/08/07 08:32:28 - mmengine - INFO - Epoch(train) [29][1200/3757]  lr: 1.0927e-06  eta: 1:45:26  time: 0.9994  data_time: 0.0168  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 0.6844  loss: 0.6844
2022/08/07 08:34:09 - mmengine - INFO - Epoch(train) [29][1300/3757]  lr: 1.0927e-06  eta: 1:43:46  time: 1.0014  data_time: 0.0185  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 0.8006  loss: 0.8006
2022/08/07 08:35:49 - mmengine - INFO - Epoch(train) [29][1400/3757]  lr: 1.0927e-06  eta: 1:42:06  time: 1.0092  data_time: 0.0173  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 0.6549  loss: 0.6549
2022/08/07 08:37:29 - mmengine - INFO - Epoch(train) [29][1500/3757]  lr: 1.0927e-06  eta: 1:40:26  time: 1.0009  data_time: 0.0172  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.7949  loss: 0.7949
2022/08/07 08:39:09 - mmengine - INFO - Epoch(train) [29][1600/3757]  lr: 1.0927e-06  eta: 1:38:45  time: 1.0022  data_time: 0.0173  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 0.8534  loss: 0.8534
2022/08/07 08:40:50 - mmengine - INFO - Epoch(train) [29][1700/3757]  lr: 1.0927e-06  eta: 1:37:05  time: 1.0026  data_time: 0.0174  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 1.0658  loss: 1.0658
2022/08/07 08:42:30 - mmengine - INFO - Epoch(train) [29][1800/3757]  lr: 1.0927e-06  eta: 1:35:25  time: 1.0023  data_time: 0.0168  memory: 68881  top1_acc: 0.6250  top5_acc: 0.7500  loss_cls: 0.8200  loss: 0.8200
2022/08/07 08:42:34 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/07 08:44:10 - mmengine - INFO - Epoch(train) [29][1900/3757]  lr: 1.0927e-06  eta: 1:33:45  time: 1.0034  data_time: 0.0169  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.7240  loss: 0.7240
2022/08/07 08:45:50 - mmengine - INFO - Epoch(train) [29][2000/3757]  lr: 1.0927e-06  eta: 1:32:05  time: 0.9998  data_time: 0.0164  memory: 68881  top1_acc: 0.7500  top5_acc: 0.7500  loss_cls: 0.9450  loss: 0.9450
2022/08/07 08:47:30 - mmengine - INFO - Epoch(train) [29][2100/3757]  lr: 1.0927e-06  eta: 1:30:24  time: 1.0023  data_time: 0.0164  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 0.7198  loss: 0.7198
2022/08/07 08:49:11 - mmengine - INFO - Epoch(train) [29][2200/3757]  lr: 1.0927e-06  eta: 1:28:44  time: 1.0005  data_time: 0.0167  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.8767  loss: 0.8767
2022/08/07 08:50:51 - mmengine - INFO - Epoch(train) [29][2300/3757]  lr: 1.0927e-06  eta: 1:27:04  time: 0.9996  data_time: 0.0169  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 0.7834  loss: 0.7834
2022/08/07 08:52:31 - mmengine - INFO - Epoch(train) [29][2400/3757]  lr: 1.0927e-06  eta: 1:25:24  time: 0.9989  data_time: 0.0167  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 0.7236  loss: 0.7236
2022/08/07 08:54:11 - mmengine - INFO - Epoch(train) [29][2500/3757]  lr: 1.0927e-06  eta: 1:23:43  time: 1.0018  data_time: 0.0166  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 0.6776  loss: 0.6776
2022/08/07 08:55:51 - mmengine - INFO - Epoch(train) [29][2600/3757]  lr: 1.0927e-06  eta: 1:22:03  time: 1.0034  data_time: 0.0182  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 0.7347  loss: 0.7347
2022/08/07 08:57:31 - mmengine - INFO - Epoch(train) [29][2700/3757]  lr: 1.0927e-06  eta: 1:20:23  time: 1.0019  data_time: 0.0174  memory: 68881  top1_acc: 0.7500  top5_acc: 0.7500  loss_cls: 0.8848  loss: 0.8848
2022/08/07 08:59:11 - mmengine - INFO - Epoch(train) [29][2800/3757]  lr: 1.0927e-06  eta: 1:18:43  time: 1.0028  data_time: 0.0175  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.7989  loss: 0.7989
2022/08/07 08:59:16 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/07 09:00:52 - mmengine - INFO - Epoch(train) [29][2900/3757]  lr: 1.0927e-06  eta: 1:17:03  time: 1.0020  data_time: 0.0170  memory: 68881  top1_acc: 0.6250  top5_acc: 1.0000  loss_cls: 0.8676  loss: 0.8676
2022/08/07 09:02:32 - mmengine - INFO - Epoch(train) [29][3000/3757]  lr: 1.0927e-06  eta: 1:15:22  time: 1.0027  data_time: 0.0168  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.7674  loss: 0.7674
2022/08/07 09:04:12 - mmengine - INFO - Epoch(train) [29][3100/3757]  lr: 1.0927e-06  eta: 1:13:42  time: 1.0003  data_time: 0.0163  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 0.7538  loss: 0.7538
2022/08/07 09:05:52 - mmengine - INFO - Epoch(train) [29][3200/3757]  lr: 1.0927e-06  eta: 1:12:02  time: 1.0006  data_time: 0.0163  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 0.7672  loss: 0.7672
2022/08/07 09:07:32 - mmengine - INFO - Epoch(train) [29][3300/3757]  lr: 1.0927e-06  eta: 1:10:22  time: 1.0006  data_time: 0.0171  memory: 68881  top1_acc: 0.6250  top5_acc: 1.0000  loss_cls: 0.9220  loss: 0.9220
2022/08/07 09:09:12 - mmengine - INFO - Epoch(train) [29][3400/3757]  lr: 1.0927e-06  eta: 1:08:42  time: 1.0012  data_time: 0.0166  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 0.7847  loss: 0.7847
2022/08/07 09:10:53 - mmengine - INFO - Epoch(train) [29][3500/3757]  lr: 1.0927e-06  eta: 1:07:01  time: 0.9994  data_time: 0.0165  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.8732  loss: 0.8732
2022/08/07 09:12:33 - mmengine - INFO - Epoch(train) [29][3600/3757]  lr: 1.0927e-06  eta: 1:05:21  time: 0.9995  data_time: 0.0159  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.7783  loss: 0.7783
2022/08/07 09:14:13 - mmengine - INFO - Epoch(train) [29][3700/3757]  lr: 1.0927e-06  eta: 1:03:41  time: 1.0029  data_time: 0.0177  memory: 68881  top1_acc: 0.6250  top5_acc: 0.7500  loss_cls: 0.8116  loss: 0.8116
2022/08/07 09:15:10 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/07 09:15:10 - mmengine - INFO - Epoch(train) [29][3757/3757]  lr: 1.0927e-06  eta: 1:03:01  time: 0.9916  data_time: 0.0161  memory: 68881  top1_acc: 0.8571  top5_acc: 1.0000  loss_cls: 0.7997  loss: 0.7997
2022/08/07 09:15:59 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/07 09:16:52 - mmengine - INFO - Epoch(train) [30][100/3757]  lr: 2.7392e-07  eta: 1:01:03  time: 1.0036  data_time: 0.0164  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 0.6277  loss: 0.6277
2022/08/07 09:18:32 - mmengine - INFO - Epoch(train) [30][200/3757]  lr: 2.7392e-07  eta: 0:59:23  time: 1.0028  data_time: 0.0164  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 0.5768  loss: 0.5768
2022/08/07 09:20:12 - mmengine - INFO - Epoch(train) [30][300/3757]  lr: 2.7392e-07  eta: 0:57:43  time: 1.0004  data_time: 0.0164  memory: 68881  top1_acc: 0.6250  top5_acc: 0.7500  loss_cls: 1.0186  loss: 1.0186
2022/08/07 09:21:53 - mmengine - INFO - Epoch(train) [30][400/3757]  lr: 2.7392e-07  eta: 0:56:03  time: 1.0023  data_time: 0.0166  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 0.8673  loss: 0.8673
2022/08/07 09:23:33 - mmengine - INFO - Epoch(train) [30][500/3757]  lr: 2.7392e-07  eta: 0:54:23  time: 0.9992  data_time: 0.0160  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.7696  loss: 0.7696
2022/08/07 09:25:13 - mmengine - INFO - Epoch(train) [30][600/3757]  lr: 2.7392e-07  eta: 0:52:42  time: 1.0027  data_time: 0.0175  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 0.6040  loss: 0.6040
2022/08/07 09:26:53 - mmengine - INFO - Epoch(train) [30][700/3757]  lr: 2.7392e-07  eta: 0:51:02  time: 0.9999  data_time: 0.0165  memory: 68881  top1_acc: 0.6250  top5_acc: 0.8750  loss_cls: 0.7981  loss: 0.7981
2022/08/07 09:28:33 - mmengine - INFO - Epoch(train) [30][800/3757]  lr: 2.7392e-07  eta: 0:49:22  time: 1.0044  data_time: 0.0167  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 0.6315  loss: 0.6315
2022/08/07 09:30:13 - mmengine - INFO - Epoch(train) [30][900/3757]  lr: 2.7392e-07  eta: 0:47:42  time: 0.9999  data_time: 0.0181  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.8154  loss: 0.8154
2022/08/07 09:31:54 - mmengine - INFO - Epoch(train) [30][1000/3757]  lr: 2.7392e-07  eta: 0:46:02  time: 1.0010  data_time: 0.0166  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 0.7488  loss: 0.7488
2022/08/07 09:32:41 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/07 09:33:34 - mmengine - INFO - Epoch(train) [30][1100/3757]  lr: 2.7392e-07  eta: 0:44:21  time: 1.0033  data_time: 0.0167  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 0.9636  loss: 0.9636
2022/08/07 09:35:14 - mmengine - INFO - Epoch(train) [30][1200/3757]  lr: 2.7392e-07  eta: 0:42:41  time: 0.9989  data_time: 0.0158  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 0.7107  loss: 0.7107
2022/08/07 09:36:54 - mmengine - INFO - Epoch(train) [30][1300/3757]  lr: 2.7392e-07  eta: 0:41:01  time: 1.0008  data_time: 0.0166  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 0.7119  loss: 0.7119
2022/08/07 09:38:34 - mmengine - INFO - Epoch(train) [30][1400/3757]  lr: 2.7392e-07  eta: 0:39:21  time: 1.0020  data_time: 0.0173  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.7775  loss: 0.7775
2022/08/07 09:40:14 - mmengine - INFO - Epoch(train) [30][1500/3757]  lr: 2.7392e-07  eta: 0:37:41  time: 0.9992  data_time: 0.0164  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 1.0291  loss: 1.0291
2022/08/07 09:41:54 - mmengine - INFO - Epoch(train) [30][1600/3757]  lr: 2.7392e-07  eta: 0:36:00  time: 0.9994  data_time: 0.0165  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 1.0600  loss: 1.0600
2022/08/07 09:43:35 - mmengine - INFO - Epoch(train) [30][1700/3757]  lr: 2.7392e-07  eta: 0:34:20  time: 1.0019  data_time: 0.0169  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 0.8131  loss: 0.8131
2022/08/07 09:45:15 - mmengine - INFO - Epoch(train) [30][1800/3757]  lr: 2.7392e-07  eta: 0:32:40  time: 1.0026  data_time: 0.0171  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.8331  loss: 0.8331
2022/08/07 09:46:55 - mmengine - INFO - Epoch(train) [30][1900/3757]  lr: 2.7392e-07  eta: 0:31:00  time: 1.0004  data_time: 0.0163  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 0.8444  loss: 0.8444
2022/08/07 09:48:35 - mmengine - INFO - Epoch(train) [30][2000/3757]  lr: 2.7392e-07  eta: 0:29:20  time: 1.0008  data_time: 0.0169  memory: 68881  top1_acc: 0.7500  top5_acc: 0.7500  loss_cls: 0.9297  loss: 0.9297
2022/08/07 09:49:22 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/07 09:50:15 - mmengine - INFO - Epoch(train) [30][2100/3757]  lr: 2.7392e-07  eta: 0:27:40  time: 1.0017  data_time: 0.0164  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 0.7912  loss: 0.7912
2022/08/07 09:51:56 - mmengine - INFO - Epoch(train) [30][2200/3757]  lr: 2.7392e-07  eta: 0:25:59  time: 0.9991  data_time: 0.0172  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 0.7433  loss: 0.7433
2022/08/07 09:53:36 - mmengine - INFO - Epoch(train) [30][2300/3757]  lr: 2.7392e-07  eta: 0:24:19  time: 1.0002  data_time: 0.0172  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 0.7648  loss: 0.7648
2022/08/07 09:55:16 - mmengine - INFO - Epoch(train) [30][2400/3757]  lr: 2.7392e-07  eta: 0:22:39  time: 1.0008  data_time: 0.0166  memory: 68881  top1_acc: 0.8750  top5_acc: 0.8750  loss_cls: 0.6379  loss: 0.6379
2022/08/07 09:56:56 - mmengine - INFO - Epoch(train) [30][2500/3757]  lr: 2.7392e-07  eta: 0:20:59  time: 1.0012  data_time: 0.0174  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 0.6105  loss: 0.6105
2022/08/07 09:58:36 - mmengine - INFO - Epoch(train) [30][2600/3757]  lr: 2.7392e-07  eta: 0:19:19  time: 1.0005  data_time: 0.0169  memory: 68881  top1_acc: 0.7500  top5_acc: 0.7500  loss_cls: 0.9959  loss: 0.9959
2022/08/07 10:00:16 - mmengine - INFO - Epoch(train) [30][2700/3757]  lr: 2.7392e-07  eta: 0:17:38  time: 1.0003  data_time: 0.0177  memory: 68881  top1_acc: 0.6250  top5_acc: 0.6250  loss_cls: 0.6366  loss: 0.6366
2022/08/07 10:01:56 - mmengine - INFO - Epoch(train) [30][2800/3757]  lr: 2.7392e-07  eta: 0:15:58  time: 0.9998  data_time: 0.0167  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.8499  loss: 0.8499
2022/08/07 10:03:37 - mmengine - INFO - Epoch(train) [30][2900/3757]  lr: 2.7392e-07  eta: 0:14:18  time: 1.0014  data_time: 0.0170  memory: 68881  top1_acc: 0.7500  top5_acc: 0.8750  loss_cls: 0.8613  loss: 0.8613
2022/08/07 10:05:17 - mmengine - INFO - Epoch(train) [30][3000/3757]  lr: 2.7392e-07  eta: 0:12:38  time: 1.0007  data_time: 0.0162  memory: 68881  top1_acc: 0.7500  top5_acc: 1.0000  loss_cls: 0.7917  loss: 0.7917
2022/08/07 10:06:04 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/07 10:06:57 - mmengine - INFO - Epoch(train) [30][3100/3757]  lr: 2.7392e-07  eta: 0:10:58  time: 1.0004  data_time: 0.0169  memory: 68881  top1_acc: 0.6250  top5_acc: 0.7500  loss_cls: 0.9185  loss: 0.9185
2022/08/07 10:08:37 - mmengine - INFO - Epoch(train) [30][3200/3757]  lr: 2.7392e-07  eta: 0:09:18  time: 1.0027  data_time: 0.0169  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.7520  loss: 0.7520
2022/08/07 10:10:17 - mmengine - INFO - Epoch(train) [30][3300/3757]  lr: 2.7392e-07  eta: 0:07:37  time: 1.0044  data_time: 0.0167  memory: 68881  top1_acc: 0.8750  top5_acc: 1.0000  loss_cls: 0.5435  loss: 0.5435
2022/08/07 10:11:58 - mmengine - INFO - Epoch(train) [30][3400/3757]  lr: 2.7392e-07  eta: 0:05:57  time: 1.0060  data_time: 0.0167  memory: 68881  top1_acc: 0.6250  top5_acc: 0.7500  loss_cls: 0.7089  loss: 0.7089
2022/08/07 10:13:38 - mmengine - INFO - Epoch(train) [30][3500/3757]  lr: 2.7392e-07  eta: 0:04:17  time: 0.9997  data_time: 0.0169  memory: 68881  top1_acc: 0.3750  top5_acc: 0.7500  loss_cls: 0.8818  loss: 0.8818
2022/08/07 10:15:18 - mmengine - INFO - Epoch(train) [30][3600/3757]  lr: 2.7392e-07  eta: 0:02:37  time: 1.0017  data_time: 0.0163  memory: 68881  top1_acc: 0.6250  top5_acc: 0.7500  loss_cls: 0.8580  loss: 0.8580
2022/08/07 10:16:58 - mmengine - INFO - Epoch(train) [30][3700/3757]  lr: 2.7392e-07  eta: 0:00:57  time: 0.9999  data_time: 0.0175  memory: 68881  top1_acc: 1.0000  top5_acc: 1.0000  loss_cls: 0.8314  loss: 0.8314
2022/08/07 10:17:56 - mmengine - INFO - Exp name: swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb_20220806_021038
2022/08/07 10:17:56 - mmengine - INFO - Epoch(train) [30][3757/3757]  lr: 2.7392e-07  eta: 0:00:17  time: 1.0073  data_time: 0.0170  memory: 68881  top1_acc: 0.7143  top5_acc: 1.0000  loss_cls: 0.5557  loss: 0.5557
2022/08/07 10:17:56 - mmengine - INFO - Saving checkpoint at 30 epochs
2022/08/07 10:18:53 - mmengine - INFO - Epoch(val) [30][100/310]    eta: 0:01:25  time: 0.4057  data_time: 0.0098  memory: 14218  
2022/08/07 10:19:34 - mmengine - INFO - Epoch(val) [30][200/310]    eta: 0:00:45  time: 0.4128  data_time: 0.0108  memory: 14218  
2022/08/07 10:20:15 - mmengine - INFO - Epoch(val) [30][300/310]    eta: 0:00:04  time: 0.4099  data_time: 0.0090  memory: 14218  
2022/08/07 10:20:20 - mmengine - INFO - Epoch(val) [30][310/310]  acc/top1: 0.7904  acc/top5: 0.9344  acc/mean1: 0.7902
2022/08/07 10:20:20 - mmengine - INFO - The previous best checkpoint /mnt/lustre/daiwenxun/X/swin/mmaction2/work_dirs/swin_large_imagenet_22k_pretrained_32x2x1_30e_8xb8_kinetics400_rgb/best_acc/top1_epoch_28.pth is removed
2022/08/07 10:20:27 - mmengine - INFO - The best checkpoint with 0.7904 acc/top1 at 31 epoch is saved to best_acc/top1_epoch_31.pth.